Contrast-induced pulmonary edema is a rare but life-threatening condition often missed in heart failure patients. We present a case of a 65-year-old female with a past medical history of coronary artery disease, diastolic heart failure, and chronic kidney disease who presented with chest pain. She received low osmolar intravenous (IV) contrast for cardiac catheterization. Within 24 hours of receiving the contrast, the patient developed respiratory distress, which was found to be secondary to pulmonary edema. Pulmonary edema was considered to be related to cardiogenic at first; however, the patient’s physical examination was normal, with no jugular venous distention (JVD). A transthoracic echocardiogram showed a central venous pressure of 3 mmHg. The patient's respiratory condition improved after receiving an IV diuretic. Chart review showed that the patient had a similar presentation in the past, which was also thought to be related to heart failure leading to recurrent exposure to contrast. Non-cardiogenic pulmonary edema should be considered in the differential diagnosis of pulmonary edema in heart failure patients receiving contrast. Contrast media is used in non-invasive and invasive imaging modalities. Contrast-induced nephropathy and anaphylactic reaction are common complications of contrast media . Acute sThe clinical presentation of non-cardiogenic pulmonary edema is similar to cardiogenic pulmonary edema, which leads to missing the diagnosis of non-cardiogenic pulmonary edema and significant morbidity and mortality. Our case highlights the importance of considering contrast-induced pulmonary edema in the differential diagnosis of pulmonary edema after using contrast, even if the patient has a history of heart failure.A 65-year-old female presented to the emergency room with typical anginal chest pain, relieved with nitroglycerin. She denied dyspnea, lower extremity edema, orthopnea, paroxysmal nocturnal dyspnea, and weight gain. She was euvolemic on physical examination with clear breath sound, no JVD, and lower extremity edema. The electrocardiogram showed normal sinus rhythm. Transthoracic echocardiogram showed a left ventricular ejection fraction of 55-60% with grade II diastolic dysfunction and central venous pressure of 3 mmHg. Cardiac positron tomography myocardial perfusion imaging with lexiscan showed a significant reversible defect in the basal to apical, anterior wall, anteroseptal wall, and basal to apical inferolateral wall suggestive of ischemia due to occlusion of the left anterior descending artery and left circumflex artery Figure .The patient underwent a coronary angiogram with iodinated contrast, which showed a patent left internal mammary artery to the left anterior descending artery and saphenous vein graft to the obtuse marginal artery (OM1) and right coronary artery with left ventricular end-diastolic pressure of 16 mmHg. Chest pain was considered secondary to coronary vasospasm, and the patient was discharged home on medical management. The same day, the patient returned and was admitted with severe dyspnea requiring bilevel-positive airway pressure ventilation.Past medical historyPast medical history was notable for coronary artery disease status post coronary artery bypass in 2010, diastolic heart failure, stage 3 chronic kidney disease, diabetes mellitus, hyperlipidemia, and peripheral vascular disease.Differential diagnosisDifferential diagnoses of shortness of breath include cardiogenic pulmonary edema and non-cardiogenic pulmonary edema. Despite being euvolemic on the physical examination with normal central venous pressure, dyspnea was considered cardiogenic given her history of diastolic heart failure.InvestigationsCreatinine on the day of the coronary angiogram was 1.2 mg/dl (baseline creatinine around 1.2-1.6 mg/dl). Brain natriuretic peptide on admission was 1898 pg/ml, which increased to 9289 pg/ml on readmission. Chest X-ray before cardiac catheterization was unremarkable Figure .ManagementThe patient was treated with IV diuretics and showed significant improvement. Because of her diastolic heart failure history, pulmonary edema was considered to be cardiogenic. Further chart review revealed the patient had a similar presentation the year before when she was evaluated for chest pain and found to have a patent graft. She returned the same day with shortness of breath and had rales but was otherwise euvolemic on the examination. Chest X-ray showed significant pulmonary edema, which was considered to be cardiogenic. The patient’s breathing improved after one dose of IV furosemide 80 mg. Iodinated contrast was added to her allergies with a side effect of probable non-cardiogenic pulmonary edema. The patient was advised to inform healthcare providers of this allergy before undergoing imaging requiring contrast.Follow-upThe patient had no further episodes of chest pain or hospitalizations related to non-cardiogenic pulmonary edema.Contrast-induced pulmonary edema is a rare and fulminant complication of intravenous (IV) contrast ,2. A fewOur patient developed acute respiratory failure after receiving iodinated contrast during left heart catheterization. Although the patient appeared to be euvolemic on physical examination and echocardiogram, elevated brain natriuretic peptide (BNP) and a history of heart failure led to the diagnosis of cardiogenic pulmonary edema. The patient was treated with intravenous diuretics and discharged home. Since pulmonary edema was considered cardiogenic, the patient was exposed to intravenous contrast again without premedication with steroids and antihistamines, which could have prevented recurrent respiratory failure.Besides premedication with steroids and antihistamines, patients with non-cardiogenic pulmonary edema must be treated with intravenous diuretics. In a case report by Nagao et al., respiratory failure after IV contrast was treated with antihistamine and steroids with no improvement until the patient received a diuretic .In previous case reports of non-cardiogenic pulmonary edema following IV contrast, patients had no history of heart disease and their BNP levels were normal . HoweverAlthough our patient had a sudden significant increase in brain natriuretic peptide after receiving contrast compatible with cardiac overload, it is not apparent if the cardiac overload is secondary to contrast media or heart failure. Due to the lack of right heart catheterization in our patient, we cannot say if pulmonary edema was non-cardiogenic. However, sudden onset of respiratory distress after receiving IV contrast and euvolemia on physical examination and normal central venous pressure on an echocardiogram is more suggestive of contrast-induced pulmonary edema than heart failure.Although previous case reports have shown that osmolality and viscosity play a significant role in non-cardiogenic pulmonary edema, our patient developed pulmonary edema despite receiving low osmolar contrast. Pincet et al. published a case report in which a 55-year-old patient developed noncardiogenic pulmonary edema after receiving low osmolar contrast for an abdominal CT scan .Contrast-induced pulmonary edema is related to increased alveolar-capillary membrane permeability due to endothelial injury. The endothelial injury could be connected to direct chemotoxic, osmotic, immunologic, or allergic effects on the vascular endothelium ,8.Considering contrast-induced pulmonary edema as cardiogenic in heart failure can lead to misclassifying a patient as having refractory heart failure due to multiple hospitalizations. The misclassifying of patients can lead to unnecessary procedures and treatments. It can also lead to multiple hospitalizations due to recurrent exposure to contrast.Contrast-induced non-cardiogenic pulmonary edema remained undiagnosed due to premature closure, not considering all differential diagnoses and anchoring to cardiogenic pulmonary edema in the setting of heart failure. Identifying non-cardiogenic pulmonary edema in heart failure patients who recently received contrast can be challenging. Providers should avoid using contrast or premedication with steroids or antihistamines before procedures requiring contrast to prevent associated morbidity and mortality.


ACS Central Science, Fieschi, Bernardi,and co-workers1 reveal key design principlesand validation strategies to guide the development of high aviditybinding therapies. Importantly, their results provide a general frameworkto design therapeutics that use minimal binding ligands  to achieve high affinity binding to target lectins.In this work, the authors perform systematic and rigorous thermodynamicanalysis of dendrimeric glycomimetic ligand interactions with thetetrameric C-type lectin receptor DC-SIGN. DC-SIGN is a receptor implicatedin a number of biological recognition processes, including SARS-COV-2viral entry. Common to the general design of multivalent probes andinhibitors, these glycomimetic ligands present an array of multiplelow affinity (synthetic mannoside) binding ligands to engage the targetbiomolecule , with high avidity. The fundamental goalof this study was to understand how and why relatively subtle changesin the spacing and valency of mannoside ligands on the glycomimeticcan significantly affect its avidity for DC-SIGN. Understanding themolecular origins of binding avidity is highly complex given the interplayof multiple contributing factors, namely, chelation binding, receptorclustering, and statistical rebinding events/multiple binding modes interaction with a binding cleft on thetarget protein of interest. In addition to achieving high target bindingaffinity, these therapies can benefit from impressively high selectivityfor the target of interest, whose copy number on a cell  or number of binding sites complements the binding ligand arrangement on the avidity therapeutic.To successfully develop such a therapeutic, several lower affinitybinding ligands must be carefully arranged spatially, to maximizethe probability of making one or several contacts with the target.In this issue of ng modes1. Collec2 Here, the concept of avidity entropy was used to account for theobserved high (sub-nanomolar) binding affinity between a multivalentcarbohydrate inhibitor and pentameric protein toxin, whose interactionis favored by the potential for multiple statistical rebinding events.In thiswork, the authors perform structure/function analysis ona series of multivalent glycomimetic scaffolds that differ in thenumber  and spacing of mannoside binding ligands. Theirexperimental design enabled assessment of the relative contributionsof chelation binding, statistical rebinding events, and receptor (DC-SIGN)flexibility on binding avidity. It was observed that the greatestgains in binding affinity were achieved when (1) the glycomimeticsufficiently preorganized binding ligands for chelation binding and(2) binding ligands were arranged to increase statistical rebindingevents .In the current study,alogue 1 1. Notabl valency 1. The en1 synergizes with emergingefforts in translational chemical biology to design avidity bindingprobes and molecular therapeutics. One major current focus is to targetsingle oligomeric receptors with multiple binding sites to block pathogenicinfection. Such therapeutic modalities are especially useful whenthe goal is to inhibit an intrinsically high avidity interaction itself. A recent key example targets HIV-1 envelope spikeproteins using bivalent DNA-Fab conjugates that can simultaneouslybind two of three binding sites on the spike trimer protein and blockHIV-1 infection.4 Notably, the results of thesestudies also reveal the importance of a rigid linker to preorganizethe binding ligands for efficient chelation binding. In this work,the binding ligands are strategically tethered to this rigid linkerthrough a short flexible strand, analogous to the short PEG-like chainsconnecting mannoside binding ligands in the current glycomimetic study.This feature provides the necessary trade off in enthalpy/entropycompensation since it is very difficult to preorganize the two bindingligands perfectly for chelation binding. Too short or long a rigidlinker will drastically destabilize bivalent binding enthalpically.Too flexible a linker, however, can incur significant entropic costdepending on the number of rotatable bonds in the linker that becomefrozen post binding. The findings of the current study1 also suggest the HIV-1 targeting conjugate and other aviditytargeting conjugates for viral spike proteins may benefit from additionaldegenerate binding ligands and alternative topologies to increasestatistical rebinding events.The current study6 or targeting a diseasedcell that overexpresses a key protein of interest.8 Lakeet al. reported the development of multivalent tumor targeting chimerasthat efficiently recruit serum antibodies to the surface of tumorcells, with the goal of inducing antitumor immune responses.8 These chimeras consisted of multiple tumor antigenand serum antibody binding ligands decorating a polymer backbone,and were observed to bind tumor cells with significantly longer residencetimes, compared to monovalent chimera analogues (A second major current focus inthe development of avidity bindingprobes and molecular therapeutics aims to target multiple receptorson a cell surface simultaneously. This is often done with the goalof selectively clustering and activating biological receptors to affecta specific cellular response,nalogues 2. These Considering the potential impactof DC-SIGN flexibility on glycomimeticavidity, it would be interesting to consider the role of membranefluidity and the ability of surface antigens to transverse the plasmamembrane in two dimensions on binding avidity in these cell targetingapplications.1 This can potentially avoid the extensive medicinalchemistry required to generate highly potent small molecule bindingligands. In practice, however, this is highly nontrivial and requirescareful design and biophysical experimentation as illustrated in thecurrent body of work.1 As described above,multivalent ligands can benefit from multiple binding modes; however,this same binding event necessarily orders linker regions connectingthe ligands, introducing conformational entropic cost. Future effortsto overcome entropic cost might benefit from creative solutions tocouple high avidity therapeutic binding to the simultaneous “un-freezing”of constrained molecular elements. Inspiration for these designs mightbe found in nature, where proteins are known to interact with concomitantconformation changes in distal domains toward a more flexible disorderedstate, helping to offset the entropic cost of protein–proteinbinding.9The development of avidity targeting therapeuticsis likely toaccelerate in future drug discovery programs at both academic andindustrial levels. High avidity binding inhibitors can in principlebe efficiently developed using highly accessible lower affinity bindingligands like the simple disaccharides used in the current study.


Plant viruses, as obligate intracellular parasites, rely exclusively on host machinery to complete their life cycle. Whether a virus is pathogenic or not depends on the balance between the mechanisms used by both plants and viruses during the intense encounter. Antiviral defence mechanisms in plants can be of two types, i.e., natural resistance and engineered resistance. Innate immunity, RNA silencing, translational repression, autophagy-mediated degradation, and resistance to virus movement are the possible natural defence mechanisms against viruses in plants, whereas engineered resistance includes pathogen-derived resistance along with gene editing technologies. The incorporation of various resistance genes through breeding programmes, along with gene editing tools such as CRISPR/Cas technologies, holds great promise in developing virus-resistant plants. In this review, different resistance mechanisms against viruses in plants along with reported resistance genes in major vegetable crops are discussed. Plant viruses cause significant yield loss in the cultivation of many agricultural and horticultural crops around the world. Conventional methods of virus control have been used for a long time, including crop rotation, other types of cultivation, removal of infected plant debris, and chemical control of insect vectors. Since these methods have very little impact in the mitigation of virus diseases, numerous inventive methods to manage viral infections in plants have been developed as a result of a growing understanding of the molecular genetics of plant viruses as well as of the hosts’ defence mechanisms. The primary defence mechanisms against viruses in plants include innate immunity, RNA silencing, translational repression, dominant resistance genes, and autophagy-mediated degradation . The risThe virus infection cycle within the plants can be narrated as described by Calil and Fontes . VirusesThis infection process is targeted by several host defence systems which help the host to counter the virus attack. There are several points in the infection cycle which can be targeted by host defences such as the uncoating of the virus capsid, translation events, encapsidation, and cell-to-cell movement .This can be divided into two types: Natural resistance to plant viruses and Engineered resistance to plant viruses.Possible natural resistance mechanisms against viruses include innate antiviral immunity, translation repression, small RNA-mediated antiviral defence, dominant viral resistance genes, resistance to virus movement, autophagy, and cross protection.1)). PTI is the initial mechanism by which plants detect the microbes at the cell membrane through detection of conserved PAMPs by extracellular pattern recognition receptors (PRRs) [Arabidopsis [Arabidopsis showed PTI-like responses due to the coat proteins of tobacco mosaic virus and potato virus X, respectively [There are two layers of plant immune responses against microbial pathogens, i.e., PAMP (pathogen-associated molecular pattern) triggered immunity (PTI) and Effector-triggered immunity (ETI) (s (PRRs) . PRRs bes (PRRs) . Activats (PRRs) . Althougs (PRRs) . Howeverbidopsis . In anotectively ,9.Pathogens in response to PTI introduce particular proteins called effectors in plant cells to weaken PTI-mediated defence. In response to these effectors, host plants depend on specific intracellular receptors known as R gene proteins which cause direct or indirect identification of the pathogen effector molecules. These R gene products can block the effectors and activate the effector-triggered immunity (ETI) . R genes2)). The most thoroughly studied RIP with antiviral activity is the pokeweed antiviral protein (PAP) from Phytolacca americana. PAP slows down the spread of various plant viruses, including the cauliflower mosaic virus, potato virus X, and cucumber mosaic virus [Arabidopsis thaliana and Chenopodium foetidum [The initiation of translation is a critical step in protein synthesis that demands an array of eukaryotic initiation factors (eIFs). The translation of eukaryotic mRNA hinges on the association between the translation eukaryotic initiation factor 4E (eIF4E) and their 5′ m7 G cap structure, and the 3′ polyA tail’s interaction with the polyA-binding protein (PABP) also heightens the process. Although translation factors are not typically encoded by viruses, they have developed several techniques to hijack translation factors from their hosts, resulting in the promotion of viral RNA translation while compromising the translation of endogenous mRNAs. Mutated isoforms of the translation IFs, i.e., eIF4E and eIF4G have been found linked to many plant recessive resistance genes. These mutations typically prevent host factors from interacting with viral RNAs or proteins to suppress viral protein translation, an endogenous antiviral mechanism that has emerged recently . Recessiic virus . In the ic virus . Additiofoetidum .3b))Small RNAs (sRNA) are essential for the epigenetic and post-transcriptional control of gene expression in plants throughout growth, developmental, and biotic/abiotic stress responses. Recent research has highlighted the role of two distinct families of small RNAs, known as small interfering RNA (siRNA) and micro RNA (miRNA) with respect to biotic stress responses in plants. By regulating the gene expression of the modulators of host defence pathways, these sRNAs activate antiviral defence during virus infection. RNA silencing, commonly referred to as RNA interference (RNAi), is a conserved evolutionary method for regulating endogenous expressions of genes and preventing the entry of foreign nucleic acids such as viruses and transposons . The pla3a)). Recently, the roles of various miRNAs, such as miR168, miR528, miR319, and miR444 in rice antiviral immunity have been identified. By modifying jasmonic acid (JA) signalling, MiR319 has been reported to confer antiviral resistance to rice against the rice-ragged stunt virus and to wheat against the rice black-streaked dwarf virus. [Unlike siRNAs, miRNAs are endogenously produced non-coding short RNAs by RNA polymerase II from ssRNA precursors with a hairpin structure. . Along wf virus. .Arabidopsis, and it particularly provides resistance to a number of potyviruses, including TEV, lettuce mosaic virus (LMV), and plum pox virus by inhibiting their long-distance movement [Arabidopsis called Jacaline-type lectin required for potexvirus resistance 1 (JAX1) imparts broad spectrum resistance to potexviruses at the initial infection stage by suppressing the activity of viral RNA-dependent RNA polymerase (RdRp) [Musa paradisiaca, attaches to the TMV CP and inhibits virus infection in plants [A few dominant resistance genes that function independently of the traditional innate immune signalling pathway have also been discovered in the past ten years. These dominant resistance genes express proteins that cannot be incorporated into the so-called plant innate immunity because they differ structurally from conventional R proteins. The majority of these dominant resistance genes inhibit the action of viral proteins by interacting with them in a direct manner. These dominant resistance gene products are referred to as atypical dominant viral resistance proteins (ADVRPs). The majority of ADVRPs that have been identified so far are from a protein family called lectin. Restricted TEV movement (RTM) 1, a member of the lectin-protein family is found in movement . A lectie (RdRp) . BanLec-n plants .cum1 and cum2 mutations in Arabidopsis cause reduced multiplication of CMV, thus rendering its movement in adjacent cells [Pvr11 and Sbm1 were found to be mutated at a locus expressing eIF4E in pepper and pea, respectively [RTM1, RTM2, and RTM3 prevent systemic tobacco etch virus (TEV) movement between plant cells. These genes interact with the virus CP and are expressed in phloem sieve elements [Ny-1 gene confers potato virus Y (PVY) resistance in potatoes [For plant viruses to infect the entire plant system, they must spread from the initially infected cells to nearby ones. This must occur after the virus multiplication has begun within the cytoplasm and/or nucleus of a plant cell in a susceptible host. Host resistance to viral infection is evident when the virus only appears to affect one or a small number of cells but is unable to move past this initial focus of infection. At this point, resistance can be brought on by active host defence systems that swiftly thwart virus spread or by a breakdown in the connections between plant and viral components necessary for cell-to-cell movement. It is known that a variety of host gene alterations hinder the cell-to-cell movement of plant viruses. The nt cells . Pvr11 aectively ,31. eIF4elements . Similarelements . By indupotatoes .ATGs) get activated and initiate the formation of autophagosome, which ultimately fuse with the vacuole leading to the vacuolar degradation of autophagosome contents which also harbour viral particles (4)). As TMV aggregates at infection sites in Beclin-1 or ATG7 silenced plants, it has been hypothesised that autophagy contributes to plant defence against viruses. It is interesting to note that induction of autophagy extends beyond the TMV infection sites to uninfected neighbouring tissues, where it prevents cell death [ATG8 specifically interacts with the beta-satellite (βC1) of the CLCuMuV [ATG6, also known as Beclin1) to prevent viral replication [Arabidopsis. [Proteins and defective organelles are transferred to vacuoles or lysosomes for destruction through autophagy, which is an evolutionarily conserved intracellular degradation mechanism . Autophall death . Several CLCuMuV . Not onllication . Additioidopsis. .Cross protection has been utilised to manage viral diseases together with protein and RNA-mediated resistance. By infecting the host plant with a mild strain of the virus, the resistance is conferred effectively. The infection caused by attenuated strain makes the affected plant immune to further infections by a viral strain that is closely related to the inoculated virus. After the attenuated strain has been inoculated, it may trigger strain or sequence-specific resistance against the challenger virus. In a number of experiments, the effect of coat protein on cross-protection reactions has been examined. When systemically expressed using PVX as a virus vector, various TMV CP mutants with altered CP aggregation demonstrated that the CP mutants with high assembly capacity offered effective cross protection against TMV infections. TMV cross protection and CP-mediated resistance may therefore depend on CP’s capacity to prevent the challenging virus from uncoating its capsid . NumerouEngineered resistance against plant viruses can be divided into two parts: pathogen-derived resistance (PDR) and gene editing technologies.The majority of the functions played by plant viruses during the replication cycle are well-characterised since they have short genomes and few genes. Viruses are suitable targets for developing artificial resistance based on the concept of pathogen-derived resistance (PDR). Sanford and Johnston were the ones who originally introduced this idea . The mosThese are further divided into Engineering ZFN or TALEN-based resistance and CRISPR/Cas technology.Arabidopsis [Rep genes tobacco curly shoot virus (TbCSV) and tomato yellow leaf curl China virus (TYLCCNV) in tobacco plants, which showed a considerable suppression of viral genome replication [It only became possible a decade ago to manipulate the genetic material in many cellular organisms, thanks to a novel technique called genome editing. The first-generation genome editing technologies are zinc finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) . TALENs bidopsis . This prbidopsis . The BSClication .A promising tool for plant genome modification is the CRISPR /CRISPR associated 9 (CRISPR/Cas9) system. The Cas9 nuclease and a single guide RNA (sgRNA) with a 20-nucleotide-long spacer sequence which guides the Cas protein to the DNA or RNA target make up the CRISPR/Cas machinery. Recently, various research has revealed the generation of virus-resistant plants with CRISPR/Cas9 technique having resistance durability . By disrStreptococcus pyrogenes  after it targeted the viral dsDNA for cleavage [Arabidopsis. It was reported that Arabidopsis transgenic plants involve the expression of several sgRNAs that target the CaMV CP coding sequences. Moreover, it was found that siRNAs with 21–24 nucleotides (nt) were produced from sgRNAs, most of which were mapped to 3’ end of the sgRNA backbone region and less frequently to the spacer region, which binds to the CaMV coat protein-coding sequences. This finding proved CRISPR/Cas9 effectiveness against double-stranded DNA viruses as well [DNA is the target molecule for the CRISPR/Cas9 technology derived from yrogenes A. In ordn plants ,60,61. Tcleavage . A similcleavage . CRISPR/ as well .Francisella novicida, a pathogenic bacterium, whose FnCas9 orthologue targets RNA has been exploited to neutralise various human and/or plant viruses. Numerous Cas protein variants, including the Cas9 from Francisella novicida (FnCas9) and Cas13a from Leptotrichia shahii (LshCas13a) or Leptotrichia wadei (LwaCas13a), have been reported to target RNA in vivo, which shows promising results against RNA viruses .Avr) gene products of the pathogen [Avr determinants is dependent on precise molecular interactions, virus resistance genes appear to have more specificity than one might anticipate [It was found that around 80% of the host-virus combinations have monogenic resistance, with the remaining 20% being either oligo- or polygenic . Genes tpathogen . Membersticipate . While aticipate . As viruticipate . In the Despite great progress over the past decade, many questions remain unanswered, such as the molecular mechanisms controlling incompatible virus-plant interactions, the existence of PAMPs from viruses and their associated PRRs, and the mechanisms controlling the mixed infection-assisted resistance loss in host plants. Increased understanding of the natural resistance mechanisms of plants against viruses has been attained through in-depth investigations on virus/plant interactions employing innovative technologies. Numerous antiviral defence strategies have also been identified by utilising the growing understanding of the molecular interactions between viruses and their host plants. The use of well characterised natural resistance genes that can be inserted by marker-assisted breeding techniques is very promising along with genome editing tools, especially CRISPR/Cas-mediated technologies which stand out for their simplicity, robustness, and versatility in developing virus-resistant plants.A promising approach to building genetic resistance to viruses in plants is to use host genes with antiviral activity. However, viruses are rapidly evolving and have a remarkable ability for mutation. Although significant progress has been made over the past few years in the understanding of the structure and function of the plant-virus resistance genes and in the deployment of these genes in various fields and horticultural crops, more work still needs to be carried out. Viral disease threats are emerging, particularly in developing countries, due to the expansion of monoculturing of specific crops for which the resistance resources are still unknown. Interpretation of the techniques which are significantly promising in virus resistance, and the different pathways by which these mechanisms can be deployed in resistance breeding are definitely going to be a centre of attraction for research in the coming future.


The purpose of this study was to report the rare presentation of bilateral acute posterior multifocal placoid pigment epitheliopathy (APMPPE) and unilateral papillitis treated successfully with corticosteroid therapy. The methods used in this study include fundus photography and fluorescein angiography.A 40-year-old female presented to the emergency room with decreased vision, headache, and photophobia with fundus examination findings of bilateral creamy placoid lesions in the posterior pole and unilateral papillitis, macular edema, and disc hemorrhages. Fluorescein angiography demonstrated early hypofluorescence corresponding to the placoid lesions followed by late, irregular hyperfluorescent staining. Optical coherence tomography revealed peripapillary and macular edema of the left eye. The patient was treated with two retrobulbar corticosteroid injections and a course of oral prednisone with improvement in fundus findings and visual acuity at follow-up examination six weeks from the presentation. The presence of optic nerve and macular edema in APMPPE suggests severe chorioretinal inflammation for which systemic and local corticosteroids are a reasonable treatment option. Acute posterior multifocal placoid pigment epitheliopathy (APMPPE) is a self-limited inflammatory uveitis characterized by cream-colored placoid lesions localized at the retinal pigment epithelium (RPE), which causes acute central vision loss . APMPPE Though the pathogenesis of APMPPE is still under debate, an inflammatory or autoimmune process is thought to play a role. APMPPE often follows a flu-like illness and has been associated with a variety of systemic vasculitic, infectious, and inflammatory processes ,3. It caThere have been few reports in the literature that describe atypical features of APMPEE and fewer that report APMPPE presenting after COVID-19 vaccination . Here, wA 40-year-old female presented to the emergency department with a three-day history of decreased vision of the left eye with associated photophobia and headaches. Past medical history was significant for type 2 diabetes mellitus, hyperlipidemia, and hypothyroidism. The patient had obtained the Pfizer-BioNTech COVID-19 booster vaccine 14 days prior to presentation. Best-corrected visual acuity (BCVA) was 20/20 in the right eye and count fingers at 6 feet in the left eye. Intraocular pressures were within normal limits. Pupils were round and reactive, without afferent pupillary defect bilaterally. Confrontational visual fields were full in the right eye and showed temporal visual field cut of the left eye. Slit-lamp examination of the right eye revealed trace cell without flare in the anterior chamber, 2+ nuclear sclerotic cataracts, and 1+ vitreous cells. Slit lamp examination of the left eye revealed trace cell with flare in anterior chamber, 2+ nuclear sclerotic cataracts, and clear vitreous. On dilated fundus examination, multiple non-coalescing creamy-colored placoid lesions were noted throughout the posterior pole of both eyes Figures , 1B. ExaAcute posterior multifocal placoid pigment epitheliopathy (APMPPE) is an inflammatory disease involving the retina and choroid . ChoroidIn this case, the severity of the left-sided papillitis coupled with the presence of significant macular edema prompted treatment with corticosteroids. Our patient’s visual acuity improved from counting fingers OS at initial presentation to 20/40 OS after a series of triamcinolone and dexamethasone injections into the left retrobulbar space after three weeks of follow-up. Local and systemic steroids have previously been successfully employed in cases of foveal-involving APMPPE in the active phase, and may even play a role in non-foveal APMPPE ,13-15. IAPMPPE has been shown to occur as a post-viral syndrome and in association with systemic inflammatory or immune-mediated conditions  . It has This case presents an atypical presentation of bilateral APMPPE with unilateral papillitis, macular edema, and disc hemorrhages, which is suggestive of a more severe chorioretinal vasculitis. The patient was treated successfully with topical, retrobulbar, and oral corticosteroids. Although it is not possible to predict whether our patient’s vision would have still recovered without intervention, we determined corticosteroid therapy to have a high benefit and low-risk profile given the level of optic nerve and macular involvement. This case highlights the importance of close monitoring and low threshold for corticosteroid treatment in cases of severe foveal-involving APMPPE.


We tested the predictive discrimination validity of each DRAOR item and then subscale scores in univariate and multivariate models . DRAOR scores were associated with general recidivism outcomes and effect sizes were generally similar or stronger compared to models with people convicted of nonsexual crime (n = 2854). DRAOR Acute scores were consistently and incrementally related to general recidivism outcomes beyond other scores. In practice, case managers should remain aware that people convicted of sexual crime are at risk for nonsexual recidivism outcomes and assess problematic functioning broadly alongside problems in sexual domains. Clinically, interconnection among domains potentially provides multiple avenues for effective intervention.There are clinical practice and operational reasons why it may be appropriate to primarily focus on general risk factors when supervising people convicted of sexual crime in the community. General risk domains may be particularly relevant when supervision officers engage in frequent reassessment of acute dynamic risk factors. We tested the ability of a case management tool, the Dynamic Risk Assessment for Offender Re-entry, to discriminate community based, short-term general  recidivism versus nonrecidivism among people convicted of sexual crime ( People convicted of sexual crime represent a relatively small proportion of the correctional population, but the serious interpersonal violation of sexual offending generates strong correctional effort toward reducing sexual recidivism. For example, in New Zealand (the context of the present research), 4.6% of people supervised in the community were convicted of a sexual crime . Yet, beInternationally, several community management strategies  are substantially long-term, often applied equally among people with different degrees of risk for recidivism, and have limited, unknown, and/or unlikely efficacy for reducing sexual recidivism . An alteThis approach is consistent with the risk-need-responsivity (RNR) model  that tasd = .71) and any recidivism (Cohen’s d = .64) with medium-to-large effects . Most results were from four samples assessing pre- and post-program Violence Risk Scale-Sex Offense , we examined the relationship between risk scores and short-term, imminent recidivism (prior to the next assessment). However, the total follow-up timeframe (1 year) was too short to observe a meaningful base rate of sexual recidivism, so our validation focused on whether DRAOR scores discriminate imminent general (any outcome) recidivism versus non-recidivism.People convicted of sexual crime are more likely to recidivate with nonsexual than sexual recidivism . Criminac = .73 for any arrest, c = .77 for any revocation; c = .65; Still, if a corrections agency wants to address general recidivism risk among people convicted of sexual crimes or wants to standardize core assessment procedures, they can rely on general risk tools that predict general recidivism in all sub-populations, including people convicted of sexual crime. Examples include the Level of Service (LS) family of tools, i.e., Level of Service Inventory-Revised LSI-R; , Level orejection of supervision, hostility, change in social supports, emotional collapse, substance use, and victim access) rather than sex-specific risk factors  among its list of faster changing acute dynamic risk factors. This balance toward general risk factors is consistent with other sexual recidivism risk tools. Still, scoring context and manual guidance may be critical. For example, it may be meaningfully different for prediction if raters score the simple presence of substance use on a general risk tool, but only consider current substance use to be problematic if similar use previously occurred in the context of prior sexual offending. Similarly, general hostility may not have the same predictive effects as the more specific consideration of hostility toward women among people convicted of sexual crime. Or, it may matter if raters consider only potential child victims when scoring victim access rather than considering potential victims of family violence, stalking, fraud, etc. Despite broad conceptual similarities across tools, predictive effects may depend on definition specificity as it applies to the specific sub-population or may depend on concordance between items and outcome behavior.The LS family of tools and PCRA are more comprehensive, longer-term dynamic risk tools, whereas, in the context of short-term imminent risk in the community, there has not yet been similar tests of the relationship between general risk tools and general recidivism among people convicted of sexual crime. However, Acute-2007  assessesStill, Acute-2007 assesses general risk well enough for scores to be related to general recidivism and, importantly, Two DRAOR subscales (Stable and Acute) generally demonstrate conceptual concordance with SOTIPS and Acute-2007, respectively, by assessing similar general risk factors yet omitting all sex-specific factors. Several research teams have validated the predictive discrimination of DRAOR scores in New Zealand, including within the general population of people on parole , high-riHowever, there are limited evaluations of DRAOR for assessing risk for recidivism among people convicted of sexual crimes and neither used reassessment to test imminent prediction. In one unpublished thesis, Our evaluation of DRAOR among people convicted of sexual crimes had several aims. First, following research on the LS family of tools and PCRA, and using stronger analytic techniques than prior evaluations using DRAOR with samples of people convicted of sexual crime, we examined whether each individual general risk and strength item on DRAOR discriminated general recidivism. Item-level analysis is relevant because, although many DRAOR scale items are similar to items on other tools, it may be critical that the DRAOR manual is not specifically designed for people convicted of sexual crimes. We repeated models to explore all recidivism outcomes (including breaches of parole) and only criminal recidivism outcomes (excluding breaches because breaches may be less serious and more foreseen by supervision officers recording DRAOR scores). Second, following several findings  that preThird, following N = 3694; April 2010 to March 2012). After excluding those with missing risk assessments (DRAOR or static risk scores), the final dataset represented 92.5% of the parole population in New Zealand during this time (N = 3416). Prior research using this dataset concluded that DRAOR scores (1) demonstrate measurement invariance across time , (2) were associated with future general and violent recidivism within the whole sample , (3) were more strongly related to general and violent recidivism when using updated scores compared to initial or averaged scores , and (4) are best conceptualized as assessing risk and promotive factors .New Zealand Department of Corrections provided a dataset containing information about all people supervised on parole over a 2-year period  and was 19.2 weeks  among people convicted of a sexual index offense.New Zealand Department of Corrections provided a database of official record recidivism events. We used two definitions of recidivism: (1) any recidivism, including either the first violation of conditions of supervision or the first new criminal charge after release from incarceration, and (2) criminal recidivism, excluding violations of conditions of supervision and including only new criminal charges. Because we restricted our analyses to the first 12 months following release from incarceration, the mean follow-up time among people convicted of a nonsexual index offense was 17.5 weeks , slight or possible problem (1), or definite problem (2), for possible scores between 0–12 and 0–14, respectively, and the six DRAOR Protect items as not an asset (0), slight or possible asset (1), or definite asset (2), for possible scores between 0–12. As a field-implemented tool, there is no information on interrater reliability in this sample. However, training to score DRAOR was centralized and standardized for all staff within New Zealand Department of Corrections, with consistent oversight from staff supervisors to address quality assurance.y DRAOR;  is a 19-New Zealand Department of Corrections provided a database with participants’ date of birth, gender, ethnicity, marital status, and index offense. Index offense categories indicated whether the victim was under the age of 16 or older, except for 27 sexual index offenses that did not specify victim age.We obtained ethics approval for our use of the data from institutional review boards at the following institutions: Carleton University (#11-113) and Swinburne University of Technology (#2017/230). In New Zealand, supervision officers score all 19 DRAOR items for each client at every reporting session or “quality contact” . Policy dictates supervision officers fully reconsider each DRAOR Acute item at reassessment but keep a “watching brief” on Stable and Protect items. For each client, the rate of reassessment depends upon the frequency of contact (determined by static risk and the amount of time since release from incarceration); this typically varies between twice weekly to fortnightly.n = 597 release events among 562 people with sexual index offenses and n = 3051 release events among 2854 people with nonsexual index offenses). Across all participants, we observed 91,945 weeks of follow-up time in the community.During the time period recorded in the dataset, some people experienced re-incarceration and subsequent re-release. Because predictor information updated with each new release, we retained each release event for analysis see , resultiFor most of the sample, parole supervision orders and DRAOR assessments ceased within 12 months following release from incarceration; records of DRAOR scores extended beyond 12 months for only 16.5% of the sample. To maintain consistent interpretation of the outcome , we truncated the follow-up at 12 months. We organized all assessments to be prospective predictors of subsequent recidivism, removing any assessments that occurred on the same day as a recidivism event to avoid the possibility that supervision officers recorded scores retrospectively after a recidivism outcome.For both groups of people supervised on parole  and for each of the three DRAOR subscale domains , we calculated descriptive information  recorded at the first assessment at the time of release from incarceration. For 28.9%, this assessment occurred on the date of release; the remaining occurred within days or up to 4 weeks shortly prior to release or within the first days up to 4 weeks after release. We also calculated mean static risk scores (RoC*RoI) and demographic statistics. Further, we calculated descriptive information for two subgroups of people convicted for sexual offenses, i.e., people with identified victims under the age of 16 and people with victims 16 years and older. We used chi-square and analysis of variance (ANOVA) analyses to compare differences in demographics and assessment scores across people with (1) nonsexual offenses, (2) sexual offenses with identified victims under the age of 16, and (3) sexual offenses with victims 16 years and older.Next, to evaluate whether each of the 19 DRAOR items was associated with recidivism outcome (defined broadly to include breaches of conditions of supervision orders) among people convicted for sexual offenses, we estimated Cox regression survival analysis models, entering each item score as the predictor and recidivism as the outcome. We estimated 19 models using all assessments within the follow-up period as a predictor that varied with time. This analytic approach retains the temporal structure of all assessments and outcomes for both people with and without recidivism (with release from incarceration as the comparable start point), so, at any one time, our models compared DRAOR scores among people with current recidivism to DRAOR assessments recorded at the same time for people who had not (yet) recidivated. In these time-varying models, the models use each DRAOR assessment as the predictor until it is superseded and replaced in time by a subsequent assessment. For comparison, we repeated these models with the subsample of people convicted for nonsexual offenses.c-index; In Cox regression, hazard ratio quantifies the difference in recidivism rate associated with each 1-point difference on the scale. Lower Akaike information criterion (AIC) and Bayesian information criterion (BIC) values indicate better fitting models, with a 10-point difference considered strong support for the better-fitting model . We repoc-index with an R2-analogue (R2 (XO). Readers can interpret this statistic as the proportion of variance in the timing of recidivism that is explained by the prediction model. We conducted analyses using R version 3  DRAOR Stable subscale scores, (2) DRAOR Acute scores, (3) DRAOR Protect scores, and then a multi-predictor model with (4) RoC*RoI static risk scores and all three DRAOR subscale scores. These were also time-varying models. Finally, to ensure that results did not differ by age of identified victim, we repeated the multi-predictor models on the subsamples of people convicted for sexual offenses against a person under the age of 16 versus against a person aged 16 and older. In tables displaying these 17 Cox regression models, we supplement the analogue  as an adersion 3  using suersion 3 , survAUCersion 3 , and risersion 3  packagesSD = 12.1) DRAOR assessments per person.For each subgroup, During the maximum of 1-year follow-up while under community supervision, the subsample of people convicted of nonsexual offenses had an official total recidivism rate  of 39.7%. Excluding breaches and including only criminal recidivism, the recidivism rate was 15.3%. Comparatively, recidivism was relatively rarer among people convicted of sexual offenses, in terms of the total recidivism rate observed for the full subsample (22.1%), people with victims under the age of 16 (18.8%), or people with victims aged 16 or older (29.0%). Including only criminal events, 6.4% of people released from incarceration for a sexual offense recidivated and only six of these recidivism events were for sexual recidivism.c-index = .54 to .62. With two exceptions , DRAOR items generally discriminated any recidivism outcome equally or slightly stronger among the subsample of people convicted of sexual offenses compared to people convicted with nonsexual offenses.To explore whether each item on DRAOR discriminated recidivism from non-recidivism within both subsamples , we conducted 38 prediction models using Cox regression survival analysis. In c-index = .65 to .78. In each case , the magnitude of the predictive discrimination effect was slightly stronger in the subsample of people convicted of sexual offenses. The predictive discrimination effect was stronger when removing breaches of supervision from the definition of recidivism and examining criminal recidivism events only.After combining DRAOR items into scales  to enter into Cox regression models, each scale discriminated future recidivism from non-recidivism in both subsamples see . Effect Testing multivariate models in which we entered all three DRAOR scales simultaneously after first accounting for static risk , the pattern of results suggested that DRAOR Acute and DRAOR Protect scale scores added incrementally beyond static risk scores, whereas predictive discrimination effects associated with DRAOR Stable scores were no longer statistically significant. Finally, we repeated the multivariate models displayed in The goal of this study was to evaluate whether items and scales within a case management tool designed to predict general recidivism outcomes among the general population of people on community supervision  may be validly used with people on parole for convictions for sexual offenses. Because we were unable to directly compare predictive discrimination validity of DRAOR in our sample to a tool designed specifically for case management with people convicted of sexual offenses , we evaluated whether the association between DRAOR scores and general recidivism may be different among subgroups. Further, because tools designed for rapid reassessment within community corrections (including DRAOR and Acute-2007) are focused on the faster-changing, acute dynamic risk factors that represent core areas of concern within case management, we evaluated whether frequently reassessed DRAOR scores (every 1–2 weeks) demonstrated predictive discrimination of imminent (prior to the next assessment) general recidivism (with and without breaches).Overall, results demonstrated each independent DRAOR item and scale domain scores  showed predictive discrimination validity among the subgroup of people convicted of sexual crime, and, compared to the sample of people convicted for nonsexual crimes, discrimination effects were not reduced, but were generally slightly stronger. Effects were generally medium-to-large. Next, DRAOR Acute emerged as the dynamic scale with the most consistent incremental validity beyond static risk scores, with DRAOR Protect additionally demonstrating incremental validity among people convicted of sexual crimes with victims aged 16 or older, suggesting results among this subgroup were more closely similar to the results observed among people convicted of nonsexual crime compared to those with victims younger than 16. Because DRAOR Protect factors are conceptualized as strengths rather than risk factors, it is not clear from this finding that people with underage victims have specialized risk profiles; however, the definition of strengths or their role for risk of recidivism may differ in this subgroup, notwithstanding this group’s relatively low rate of general recidivism outcomes.c-index = .64   and using sex to cope with life difficulties. These two factors are problems with self-regulation  within the sexual domain. Other sex-specific factors are behavioral markers  of positive attitudes toward specific sexually violating behaviors , i.e., specific types of offense-supportive attitudes. Thus, these sex recidivism-specific risk domains are conceptually nested within general risk domains because they more narrowly assess how risk manifests in a specific area of life functioning.Thus, rather than a dimensional or “ingredient” approach, a different conceptualization of general risk factors in this cohort might focus on item-outcome concordance or the “nesting” of specific risk factors and criminal behaviors into broader risk factors and criminal behaviors. Consider, for example, the sex-specific risk factors Empirically, greater predictor-outcome correspondence is related to stronger associations. Beliefs about behaviors are more strongly associated with their specific behavior targets compared to similar but more adjacent behaviors see ; for exaIn this conceptualization of general risk factors, DRAOR’s general risk items and domains were associated with general recidivism outcomes in the present study because they showed item-outcome congruence. In other words, items that assess general self-regulation problems and antisocial attitudes are conceptually similar to the self-regulation breakdowns and cognitive drivers that occurred at the time of nonsexual general recidivism events. If so, had the focus of this study been on predicting a more specific behavior with greater specificity , the assessment strategy should have assessed self-regulation and attitude patterns related specifically to heroin use. In this conceptualization, the future of risk assessment may require introducing greater conceptual specificity into assessments to better reflect how general risk factors can manifest toward anticipated outcomes in either congruent or contradictory ways, depending on the degree of specificity the assessment uses for individuals’ beliefs or capabilities.The current findings suggest DRAOR Acute items represent useful case management concerns that supervision officers should assess and actively manage when providing services to people convicted of sexual crime in the community. Because general  recidivism is the most likely recidivism outcome, especially in the first year after release from incarceration, case managers should not discount signs of general poor self-regulation or instability among clients convicted of sexual crime, even if these problems manifest in ways that seem (and may be) unrelated to the client’s risk for sexual recidivism. Within their scope of practice, supervision officers should address any dysregulated behaviors or procriminal beliefs  as they monitor change in dynamic risk factors, challenge risk behaviors, and encourage clients to both solidify prior self-management gains and further embed risk-reducing activities into their lives. General risk factors may be more an intervention opportunity than a potential distraction from sex-specific risk factors. DRAOR Protect factors discriminated general recidivism outcomes among the relatively younger, higher-risk cohorts  but not among people convicted for sexual crimes against children. On the surface, this may suggest that case managers should only focus on prosocial strengths among the former two subgroups. However, the lower static risk among people convicted for sexual crimes against children suggests this subgroup is generally and relatively more stable in their life functioning. As such, when strengths are present in these clients, case managers should focus on clients maintaining those strengths against any potential threats to their continued prosocial functioning. For example, people convicted of sexual crime may experience difficulty obtaining or maintaining social support due to stigma  which maOur conclusions about the utility of DRAOR among people convicted of sexual crime is limited by the relatively brief follow-up period. However, case managers completed assessments with enough frequency to estimate the association between DRAOR scores and imminent, short-term outcomes. Future research would benefit from conducting similar analyses with a longer follow-up period and by employing direct comparisons of DRAOR with tools specifically designed for predicting sexual recidivism, such as Stable-2007 and Acute-2007.n = 562), people convicted of sexual crime are more likely to recidivate with nonsexual (n = 118 recidivism events) than sexual crime (n = 6 recidivism events), meaning subsequent releases will list nonsexual index offenses. If many with nonsexual index convictions in this study (n = 2854) had prior convictions for sexual crime, our group comparisons would attenuate any true group differences. We were unable to find New Zealand data that specifies the percentage of individuals with nonsexual convictions who also have histories of sexual crime. However, recidivism data suggest the percentage would be small. For example, if all 118 people in this study with nonsexual recidivism during parole were re-released as part of a new similar study, this group would comprise 4.1% of the comparison group . Similarly, a New Zealand study of 1100 people convicted of sexual crimes released over 3 years in 2001–2003 showed 183 were reimprisoned for nonsexual convictions across 5 years follow-up .


Nairoviridae family and Bunyavirales order, is transmitted to humans via tick bites or contact with the blood of infected animals. It can cause severe symptoms, including hemorrhagic fever, with a mortality rate between 5 to 30%. CCHFV is classified as a high-priority pathogen by the World Health Organization (WHO) due to its high fatality rate and the absence of effective medical countermeasures. CCHFV is endemic in several regions across the world, including Africa, Europe, the Middle East, and Asia, and has the potential for global spread. The emergence of the disease in new areas, as well as the presence of the tick vector in countries without reported cases, emphasizes the need for preventive measures to be taken. In the past, the lack of a suitable animal model susceptible to CCHFV infection has been a major obstacle in the development of vaccines and treatments. However, recent advances in biotechnology and the availability of suitable animal models have significantly expedited the development of vaccines against CCHF. These advancements have not only contributed to an enhanced understanding of the pathogenesis of CCHF but have also facilitated the evaluation of potential vaccine candidates. This review outlines the immune response to CCHFV and animal models utilized for the study of CCHFV and highlights the progress made in CCHFV vaccine studies. Despite remarkable advancements in vaccine development for CCHFV, it remains crucial to prioritize continued research, collaboration, and investment in this field. Crimean–Congo hemorrhagic fever virus (CCHFV), a member of the  Orthonairovirus in the family Nairoviridae and the order Bunyavirales . X. X144]. Plant-based vaccines offer scalable and cost-effective approaches for foreign gene expression in transgenic plants compared to conventional methods. In recent years, there has been considerable growth in producing human vaccine candidates in plants that address different targets . Using pDNA vaccines are a safe, efficient, and cost-effective means of inducing an immune response. Recombinant DNA is used to express antigens in antigen-presenting cells, inducing an immune response similar to viral infections. The delivered DNA can be translated into a desired protein in the cell cytoplasm, and the resulting peptide fragments can be presented by MHC class I and/or II molecules, promoting innate and adaptive immune responses. DNA vaccines have several advantages over other vaccines, including ease of manufacturing, improved safety, and simplified production. While there are some potential drawbacks to this approach, such as autoimmune responses and low immunogenicity, these can be addressed through innovative design ,150. Ove−/− mice were successfully protected from lethal CCHFV challenge through the successful elicitation of both antibody and T cell immune responses . T. T−/− mo strains  158]. I. I−/− moecovered . These fFlavivirus replicons, which are viral self-replicating sub-genomic replicons, are a potent tool for studying viral genome replication, antiviral screening, and creating chimeric vaccines . RecentlAlpha vector systems have emerged as a promising platform for developing prophylactic and therapeutic vaccines against infectious diseases . A vacciThe progress made in mRNA design, nucleic acid delivery technology, and the identification of new antigen targets has elevated mRNA vaccines to an exceptional tool for fighting emerging infectious diseases . During −/− mice vaccinated with either GnGc mRNA-LNP or NP mRNA-LNP  vaccines that encoded either the CCHFV IbAr10200 glycoproteins (GcGn) or NP and tested them in both immunocompromised and immunocompetent mice. Challenge studies demonstrated complete protection for IFNARmRNA-LNP  168]. T. T−/− miRecombinant viral vectors have been extensively studied as a promising vaccine platform due to their ability to express the antigens, stimulate antigen-specific immune responses, and generate potent antibody titers, all without the need for external adjuvants ,170. Mul−/− mice  of the IbAr10200 CCHFV strain. Following intramuscular administration twice, the MVA-GP vaccine has shown the capability to induce NAbs and T cell responses leading to complete protection against intradermal lethal challenge in IFNAR−/− mice  171]. D. D−/− miA recent study found that the ChAdOx2 CCHF vaccine, which employs a recombinant chimpanzee adenovirus to express the entire M segment of CCHFV, produced comparable results to a study using the MVA-GP vaccine. This study has been conducted to examine the immunogenicity and protection provided by the ChAdOx2 CCHF vaccine either alone or combined with the MVA-GP CCHF vaccine. They found strong antibody responses and IFN-γ-mediated cellular immunity upon administration of various vaccine combinations in both immunocompetent BALB/c and immune-deficient A129 mice. The study demonstrated that a single dose of the ChAdOx2 CCHF vaccine or homologous/heterologous prime-boost vaccination regimens resulted in full protection against CCHFV-induced disease in the A129 lethal mouse model  175]..175].The recombinant vesicular stomatitis virus vector vaccine (rVSV) for CCHF is another example that utilizes the GPC antigen. In a STAT-1/mouse model, a replication-competent recombinant rVSV that encoded the CCHFV GPC gene of the IbAr10200 CCHFV strain provided complete protection following a single intraperitoneal immunization. Conversely, a replication-deficient rVSV construct failed to provide protection against a lethal virus challenge administered intraperitoneally .−/− mice against virus challenge. Additionally, a prime-boost strategy was used, which resulted in enhanced protection against the virus and reduced clinical symptoms compared to single-dose vaccination methods (The MVA-NP vaccine candidate, which encodes the S segment ORF of the IbAr10200 CCHFV strain, failed to provide protection after a lethal challenge, even though it was capable of activating both arms of the immune system against CCHFV . In cont methods . Another methods . FurtherBoHV-4 is another viral vector that has been used in the development of a vaccine for CCHFV. BoHV-4 possesses several characteristics that make it an ideal vaccine vector, such as its ability to stably express foreign genes, its replication capability in foreign hosts, and minimal pathogenicity in various hosts. When used in combination with a prime and boost strategy, the BoHV-4 vector encoding the full-length NP of CCHFV provided complete protection after lethal challenge . AdditioCCHF is a severe viral infection that poses a significant threat to public health. The high fatality rate and the absence of a specific treatment or vaccine make it crucial to understand the pathogenesis and immunology of CCHF to develop effective countermeasures. The use of animal models, including interferon-deficient mice and the Cynomolgus macaque, has significantly advanced the study of the disease’s mechanisms and potential treatments. Preclinical vaccine studies for various vaccine platforms have shown promising results. However, the genetic variability of CCHFV makes it challenging to develop a vaccine that can provide broad protection against all strains of the virus. Additionally, most CCHF vaccine studies have primarily employed the prototype IbAr10200 CCHFV strain, isolated in ticks, with uncertain human virulence. To address these challenges, heterologous challenge studies are required to develop more reliable vaccines that can provide broad protection against different strains of the CCHFV. Although preclinical studies show potential, these vaccines have not yet been tested in humans, and it remains to be determined if these findings can be successfully applied in human clinical trials.


Teacher–student learning is a feasible solution on natural image domain, but few works focus on unlabeled aerial images. Inspired by foundation models with the powerful generalization in computer vision field, we propose an expert teacher framework based on foundation image segmentation model called ET-FSM. Our approach provides the performance gains for the student detector by generating high-quality pseudo-labels for unlabeled aerial images. In the ET-FSM, we design the binary detector with expert guidance mechanism to sufficiently leverage the extra knowledge obtained from the foundation image segmentation model, which accurately detects object positions in the complex backgrounds. Also, we present the momentum contrast classification module to distinguish confused object categories in aerial images. To demonstrate the effectiveness of the proposed method, we construct an unlabeled aerial image dataset covering various scenes. The experiments are conducted on diverse types of student detectors. The results show that the proposed approach achieves superior performance compared to existing methods, and allows the student detector to achieve fully supervised performance with much less labeled aerial images. Our dataset and code are available at  Although general object detection has made prominent success since the rise of deep learning, the complex working environment of UAVs and the scarcity of labeled aerial images impair the robustness and generalization of the detector, which limits the advancement and application of aerial image detection4. Current works mainly focus on data augmentation and elaborate network architecture design to improve the detector performance6. Nevertheless, these methods ignore the potential application of unlabeled aerial images available everywhere7.Object detection for aerial images captured by UAVs  has been widely used in numerous practical applications, such as traffic surveillance, disaster relief and smart agriculture9. Typically, this methodology adopts a complex and high-performance teacher model to generate pseudo-labels for unlabeled images, and then these pseudo-labels and ground-truth labels are used to train a lightweight student model10. In the training process, accurate pseudo-labels are critical that can provide the correct supervision information to the student model12. Nevertheless, aerial images usually contain small objects and complex backgrounds, which makes teacher models generate inaccurate predictions13.Recent semi-supervised object detection in natural images has obtained performance gains from a large number of unlabeled images by leveraging the teacher-student learning manner14. Inspired by this, the foundation models in computer vision field are also developing rapidly16. The segment anything model (SAM) released by Meta AI Research is the most representative in semantic segmentation tasks17. The model is trained on over one billion masks, and constructs a data collection loop to continuously enhance zero-shot and few-shot generalization. The unique property can assist the detector to resist noise disturbances in aerial image detection tasks.Large language foundation models can generalize to unseen data distributions by training with abundant text corpora, such as GPT-418 and UAVDT19 datasets. Our dataset and code are available at https://github.com/cq100/ET-FSM.To this end, we design an effective teacher framework based on foundation image segmentation model for object detection in aerial images. The proposed approach can transfer knowledge learned from unlabeled aerial images to the student detector, which makes the student detector achieve superior performance with a small number of labeled aerial images. Specifically, we propose a binary detector with expert guidance mechanism (EGD) to achieve the finer bounding box prediction by incorporating the guidance information provided from the foundation image segmentation model. Also, the momentum contrast classification (MCC) module is designed for object classification, which is able to distinguish confused object categories and boost the feature representation ability. The two key components can be used to generate accurate pseudo-labels for objects in complex aerial images. To prove the validity of the proposed method, we collect 14110 unlabeled aerial images under different scenes and conduct extensive experiments on VisdroneWe present an expert teacher framework based on foundation image segmentation model called ET-FSM, which uses the unlabeled aerial images with high-quality pseudo-labels to enhance the robustness and generalization of the student detector. We also construct an unlabeled aerial image dataset to provide valuable resources for unlabeled data study in aerial image detection.We design the binary detector with expert guidance mechanism (EGD) that treats the extra knowledge provided from the foundation image segmentation model as new image modality information. It is able to resist the background disturbances and accurately locate object positions.We propose the momentum contrast classification (MCC) module. It regards object features of the same class as a cluster, and uses the cluster expectations for object classification, producing higher-quality pseudo category labels for easily confused objects in the aerial images.The primary contributions of our paper are as follows:21. Existing methods are mainly carried out in terms of model structure and labeled data22.Different from natural images, UAVs usually capture aerial images under varying illumination and uncontrolled outdoor conditions, which requires object detection models with strong robustness23 designed the extra nuisance prediction branch to learn robust features for each domain covering altitude, view and weather. Cascaded zoom-in detector24 was a recent method that reused detectors based on object density in the training and inference stages. This manner brings tremendous computational costs, which is difficult to deploy on embedded UAV platform.Designing elaborate model architectures facilitates better extraction of small object features. Nuisance disentangled feature transform25. Data augmentation expands the dataset by providing a diverse view of the sample. Uniform cropping26 as a popular augmentation approach divided the aerial image into four equal-sized patches, and then merged these patches into the training set. Mask Re-sampling27 generated numerous object chips form dataset, and used masks to determine proper positions for these chips. These methods still extract features from the labeled aerial image dataset without utilizing unlabeled aerial images easily available.Using large-scale labeled aerial image dataset to train detectors can intuitively enhance robustness, but manually labeling objects is time-consuming and labor-intensive29. The latter is a mainstream method that leverages the teacher model trained on ground-truth labels to generate pseudo-labels for unlabeled images, and retrains the student model with all data.In recent years, semi-supervised learning has gradually focused on object detection tasks, which can be divided into consistency-based approach and pseudo labeling approach30 first followed the popular teacher-student learning framework to achieve semi-supervised object detection. Unbiased Teacher31 utilized the threshold to select more reliable pseudo-labels for student models. Soft Teacher32 dynamically adjusted the training loss weights for each pseudo-box, which alleviated the negative effects of incorrect pseudo-labels. Despite significant progress in natural images, these teacher models cannot be directly applied to aerial images. The bounding boxes of small objects in aerial images are particularly sensitive to noise perturbations, which lead to the unreliable prediction from the teacher model. ScaleKD33 was the recently released teacher-student learning scheme specifically for small object detection, and it designed a cross-scale assistant to reduce the adverse effect of teacher model. ZoomInNet34 distilled a standard teacher model by learning cross-scale knowledge of small objects.STACWhen detecting small and confused objects in aerial images, general teacher detectors have poor performance against complex background disturbances. To generate more accurate object bounding boxes and category soft labels, we decouple object localization and classification tasks. The overall architecture of the proposed approach based on SAM is presented in Fig. 35 as a baseline example. The optimization loss L for student detector is calculated as follows:The ET-FSM is responsible for pseudo-label generation, and the student detector is optimized with these pseudo-labels and ground-truth labels. In principle, the student detector is arbitrary. We use Faster R-CNNThe To explore the performance gains of the proposed ET-FSM on the student detector, we construct an unlabeled aerial image dataset containing 14110 visible frames. This available resource is exceptionally valuable in studying unlabeled data and enhancing the detection capabilities of aerial images.Our dataset covers multiple scenes including traffic roads, campuses and parks. The resolution of collected image is 192036. Inspired by the multimodal object detection approach in aerial images37, we consider the extra knowledge from SAM as new modality information to help the detector focus on the relevant object regions. Specifically, we propose a binary detector with expert guidance mechanism (EGD) to generate trustworthy bounding boxes for unlabeled aerial images.The SAM is a foundation image segmentation model, and has the powerful zero-shot generalization ability. When directly applying to aerial images, the segmentation performance of SAM may be unsatisfactory due to sensitivity to environmental perturbationsThe workflow of the designed detector is shown in Fig. In the inference stage, the unlabeled aerial images are employed to generate expert masks. The expert mask concatenates with the original image along the channel dimension as the binary detector input to provide the guidance information. Moreover, our detector only performs binary classification of objects and backgrounds for accurate object location. We adopt the Faster R-CNN as the binary detector in this paper.38, we propose the momentum contrast classification (MCC) module to generate the accurate pseudo category labels. Our module can be combined with most image classifiers. We use the PVTv2 (pyramid vision transformer version2)39 in this paper.Since the disturbances caused by flying altitude, viewing angle, and weather condition are more severe in aerial images, objects of different categories usually have the similar appearances. It is difficult for a general classifier to distinguish multiple confused object categories. Inspired by the momentum contrast learningq is an input encoded sample vector, i, and Q of the queue capacity is reached, the encoded sample vectors for each category are updated through momentum.m is a momentum parameter, and i.The MCC module uses the classifier to encode input samples. Sample features of the same class are regarded as a cluster. The expectation vectors of the clusters are used for contrast classification. By minimizing the contrast classification loss In the training stage, we use the segmented background and object regions from the labeled aerial images. In the inference stage, the MCC module distinguishes objects and backgrounds output from the SAM on the unlabeled aerial images, and determines the specific category scores of objects output from the binary detector. Figure 18 and UAVDT19 datasets, which provide 10209 images and 38327 images with annotations, respectively. We measure the performance gains of the proposed method on the student detector by adding the aerial images with pseudo-labels. To further test robustness, we introduce ten corrupted types for the two testing sets to simulate the UAV-specific perturbations, which is the same setting as in the previous method40.The experiments are conducted on the Visdrone41. The input image size of student detectors is set to 1000 Our method is trained on a NVIDIA Tesla P40 GPU platform with 24GB memory, and the implementation is based on the MMDetection toolbox42 to evaluate the performance gains of our method, including anchor-based Faster R-CNN (FRCNN)35, RetinaNet43 (Retina), and anchor-free FCOS44. Also, we use the advanced standard-scale detector DINO45 and the recently released UAV-specific detector CEASC46 as student models. Table We employ three types of base student detectors based on ResNet50Table 26 and the cascaded zoom-in detector24 increase the AP scores, but the improvement is marginal. Compared to the two methods, our approach achieves better performance. It suggests that sufficiently leveraging unlabeled aerial images through the proposed teacher framework does bring in greater gains. We also compare the advanced Soft Teacher32 in the field of natural images, and ZoomInNet34 and ScaleKD33 in the field of aerial images, which are based on the teacher-student learning framework typically used for semi-supervised methods. It can be observed that the ET-FSM outperforms these comparative methods, and achieves higher AP score increases of 4.4We compare the performance of existing approaches on the clean Visdrone dataset in Table Our ablation experiments are conducted on the clean Visdrone dataset. We evaluate the AP scores of FRCNN baseline and the ET-FSM on different unlabeled image proportions in Fig. Q of the queue capacity. When the Q is 512, the highest macro-average score of 85.4We further investigate the effect of the designed momentum contrast classification (MCC) module on the classification performance in Table Table Figure We propose an expert teacher framework ET-FSM based on foundation image segmentation model to boost the robustness and generalization of student detectors in aerial images. Our approach takes full advantage of the effective knowledge from the powerful foundation image segmentation model to generate accurate pseudo-labels for unlabeled aerial images. Specifically, we design the binary detector with expert guidance mechanism (EGD) and the momentum contrast classification (MCC) module in the ET-FSM to make teacher models predict more accurate location bounding boxes and object category scores. Moreover, we collect an unlabeled aerial image dataset in various real-world scenes, which provides abundant resources for unlabeled aerial image research. The experiment results show that the proposed method brings greater performance gains than advanced methods, and enables the student detector to outperform 100


Protein–ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein–ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket–ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset; the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind’s performance on the severe acute respiratory syndrome coronavirus 2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods. Understanding protein–ligand interactions is an essential procedure in drug development , 2. AlthMolecular docking is a widely used computational approach for predicting protein–ligand binding pose and binding affinity. Depending on whether the protein’s binding site is known, molecular docking can be classified into blind docking and site-specific docking. Blind docking explores the entire protein surface without prior knowledge of the binding site , while sTraditional docking tools predict the protein–ligand binding pose and binding affinity by two main operations: conformational sampling and scoring. Conformational sampling explores the diverse docking poses of the ligand as it interacts with the protein receptor, while the scoring step evaluates how well each conformation fits the receptor and estimates the binding affinity , 16. OveIn recent years, an increasing number of studies have employed deep learning to improve protein–ligand interaction predictions . NumerouIn contrast to binding affinity prediction, only a few deep learning-based methods are specifically designed for protein–ligand binding pose prediction, including DeepDock , EDM-DocIn the first stage, DeepDock, EDM-Dock and TankBind employ graph models to represent molecules. However, graph models face challenges such as over-smoothing and difficulty in capturing long-range dependencies. In contrast, Uni-Mol utilizes atom and pair representations combined with a transformer-based network to model the 3D structures of molecules. Nevertheless, the input pair representation only comprises geometric distances between atom pairs, lacking the incorporation of essential 2D topology information of molecules. Additionally, the pair representation merely receives attention weights from the atom representations for updates, overlooking many-body interactions between pairs during molecular representation learning.Moving to the second stage, both DeepDock and EDM-Dock concatenate the node features of molecular graphs in a pairwise manner, which are then fed into a multilayer perceptron to predict intermolecular distances. However, this simplistic feature concatenation approach fails to fully capture the complexities of intermolecular interactions. Uni-Mol concatenates learned molecular representations to form the pocket–ligand complex representation, which is then considered as a whole molecule to predict atomic distances via a transformer-based network, treating intermolecular and intramolecular interactions indifferently. TankBind initializes the pocket–ligand interaction embedding using node features of molecular graphs and updates it using a trigonometry module that incorporates intra-molecular distance maps. While the trigonometry module incorporates intramolecular geometry constraints, the utilization of intramolecular distance maps does not comprehensively integrate intramolecular interactions.To address the limitations of current methods, we propose GAABind, a geometry-aware attention-based network for the simultaneous prediction of protein–ligand binding pose and binding affinity. The significant role that 3D structures play in governing molecular interactions has motivated us to incorporate geometry-aware attention into our model. This mechanism extends traditional attention models by integrating geometric information, thereby enhancing the modeling of interatomic interactions and enabling the learning of physically plausible geometric relationships between ligands and proteins. In GAABind, we leverage atom and pair embeddings for molecular representation. The atom embedding is used to represent each atom in the molecule, while the pair embedding denotes the relationships between a pair of atoms. The core of GAABind lies in the Atom–Pair Attentive Encoding Block and the Mutual Interaction Block. The former focuses on learning expressive molecular representations, while the latter effectively models the mutual interactions between pockets and ligands. By utilizing these components, GAABind makes predictions for the pocket–ligand pair distances, ligand pair distances and protein–ligand binding affinity. Leveraging the predicted distances, we employ a simple and efficient back-propagation method to generate the binding poses of ligands. Our experiments on the CASF2016 benchmark dataset demonstrate the superior performance of GAABind compared with other state-of-the-art baseline methods for binding pose prediction. Additionally, GAABind exhibits comparable performance in predicting binding affinity without relying on protein–ligand complex structures as input. Furthermore, we evaluated GAABind’s cross-docking capabilities on the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease, and its exceptional performance in both binding pose and binding affinity prediction reveals its robustness and generalizability.The main contributions of this paper are as follows:We introduce the Atom–Pair Attentive Encoding Block, a novel approach for expressive molecular representation learning. In this block, the atom and pair embeddings are alternately updated and communicate with each other, facilitating the comprehensive encoding of the intramolecular interactions of the input molecules.To effectively model the mutual interaction between pockets and ligands, we present the Mutual Interaction Block. This block involves an iterative process, incorporating stages of pocket/ligand-to-complex, complex self-update and complex-to-ligand information flow, allowing for an effective and dynamic modeling of the docking process.By combining the Atom–Pair Attentive Encoding and Mutual Interaction Block, we propose GAABind, a powerful framework for pocket–ligand binding pose and binding affinity prediction. The experimental results illustrate GAABind’s robust performance in both tasks.The overall architecture of GAABind is illustrated in The Atom–Pair Attentive Encoding Block begins with the Embedding Initialization Layer, where the atom and pair embeddings of each molecule are initialized using the extracted features of the input molecules. Then, the Atom Attention Layer and Pair Attention Layer are alternately employed to update the atom and pair embeddings. Both the Atom–Pair Attentive Encoding Blocks for the binding pocket and ligand share the same architecture but have different weights. For simplicity, we use the ligand as an example to explain the details of this block.To reduce computational burdens, we consider only the heavy atoms in each molecule. The details of the input atomic and bond features can be found in here, et al. [For pair embedding initialization, we adopt spatial positional encoding as proposed by Zhou et al.  to captuwhere We use multi-head self-attention (MHA)  in the Awhere The Pair Attention Layer manipulates the pair embedding to model many-body interactions between pairs. It is crucial to preserve geometric consistency, such as the triangle inequality, in the pair embedding updates as it represents pairwise information about atoms. Inspired by the Evoformer framework used in AlphaFold2 , we haveThe first module is called the Self-Triangular Update Module, which incorporates the ‘Triangular multiplicative update with outgoing edge’ and ‘Triangular multiplicative update with incoming edge’ introduced by AlphaFold2. Specifically, each pair embedding where The second module is called the Pair Axial Attention Module. Similar to AlphaFold2’s ‘starting node attention’ and ‘ending node attention’, each pair embedding where To initialize the complex pair embedding, we take the outer product of the pocket and ligand atom embedding, which can be represented as where In the Mutual Interaction Block, three layers are designed to effectively model intermolecular interactions between the binding pocket and ligand. First, the Complex Triangular Update Layer updates the complex pair embedding by incorporating intramolecular information from the ligand and pocket pair embeddings. Next, the Complex Axial Attention Layer focuses on capturing relationships among complex pairs that share the same atom, which operates similarly to the Pair Axial Attention module mentioned earlier. Therefore, we will not provide a detailed explanation of this layer here. Last, the Complex to Ligand Communication Layer integrates interaction information derived from the complex pair embedding to update the ligand pair embedding. Afterwards, we apply the previously proposed Pair Attention Layer once again to the ligand pair embedding, ensuring the preservation of geometric consistency among ligand pairs.The Complex Triangular Update Layer incorporates the trigonometry-aware architecture proposed by TankBind , enablinwhere To integrate interaction information from the complex into the ligand pair embedding, we employ the Complex To Ligand Communication Layer. This layer captures the ligand molecule’s dynamic conformational adaptations in response to its interactions with the binding pocket. For each pair where In Prediction Block, the obtained pair embeddings are utilized to predict the pair distances within ligand, the pair distances between the pocket and ligand atoms and the binding affinity of the complex. This process can be formally denoted as where The loss function is composed of three parts, namely the smooth L1 loss for the predicted intermolecular distances between the pocket and ligand, intramolecular distances within ligand and binding affinity. It can be formally expressed as follows: where With the predicted interatomic distances and the input Cartesian coordinates of the binding pocket, it becomes feasible to deduce the Cartesian coordinates of the ligand atoms. To achieve this, we follow a similar method used by Uni-Mol  and Tankhere, In our experiments, we utilized the PDBbind database  to trainet al. [The COVID Moonshot initiative reported over 200 protein–ligand complex structures against the SARS-CoV-2 main protease , servinget al. , which cet al.  and wereThe binding pocket is defined as the set of residues that contain atoms within the range of 6Å from any heavy atom in the ligand. For each ligand, we generated 100 apo conformations using the ETKDG  conformeDetails of the training process and model hyperparameters can be found in the We compared the performance of GAABind’s binding pose predictions with several docking tools, including AutoDock Vina , Smina , UCSF DoWe used the ligand Root-Mean-Square Deviation (RMSD) of atomic coordinates to compare the predicted binding poses with the ground truths. The percentage of predictions below predefined RMSD thresholds and percentiles of predictions’ RMSD were employed as evaluation metrics. For the evaluation of binding affinity prediction, we calculated the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Pearson correlation coefficient and Spearman correlation coefficient between the predicted and ground-truth binding affinity.We conducted a comprehensive comparative analysis of the binding pose prediction performance between GAABind and baseline methods using the CASF2016 dataset. As illustrated in As discussed in earlier studies , dockingSince GAABind can accommodate ligands in any unbound conformation to make binding pose predictions, it is important to assess its sensitivity to variations in the initial ligand conformation. In By comprehensively learning the molecular representations and intermolecular interactions of pockets and ligands, GAABind is also capable of predicting the binding affinity simultaneously. To evaluate GAABind’s performance in this regard, we compared it with both complex-based and complex-free baseline methods on the CASF2016 dataset. The evaluation results are presented in We conducted ablation studies to investigate the impact of different components in GAABind. Within the Atom–Pair Attentive Encoding Block, our primary focus was on the Pair Attention Layer due to its crucial role in capturing many-body interactions and modeling spatial correlations between atom pairs. As shown in In the investigation of the Mutual Interaction Block, each component was systematically removed to evaluate its effect on the model’s performance. Specifically, the removal of the Complex Triangular Update Layer led to the average of predictions’ RMSD increasing from 1.447 to 1.788Å. In addition, the fraction of predicted binding poses below 2Å decreased by about 8.1%. Besides, the removal of the Complex Axial Attention Layer had a noticeable effect on binding affinity prediction, with the Pearson and Spearman coefficients dropping by 3.5 and 4.4%, respectively. Furthermore, removing the Complex To Ligand Communication Layer resulted in a more dramatic decline in binding pose prediction performance, with the percentage of predicted binding poses below 2Å dropping by 7.0%. Overall, these experiments revealed the individual contribution of each module, highlighting their essential roles in enhancing the model’s prediction performance.Cross-docking refers to the process of docking a ligand to a protein structure that was either co-crystallized with a different ligand or crystallized in the apo state. To validate the practical value of GAABind, we evaluated its cross-docking performance using the COVID Moonshot dataset. This dataset contains over 200 crystallized structures of different ligands against the SARS-CoV-2 main protease (ARS-CoV-2. We perfThe RMSD distribution of GAABind’s predictions, as well as those of the baseline methods, is depicted in Additionally, we evaluated GAABind’s performance in predicting binding affinity on this dataset. For fair comparison, the compared complex-based baseline methods utilized the cross-docked complex structures as their input. The evaluation results are shown in Accurate prediction of protein–ligand interactions is crucial for drug discovery and development, which allows researchers to optimize ligand chemical structures and improve binding affinity, selectivity and overall drug efficacy. This study introduces GAABind, a deep learning-based model specifically designed for precise binding pose and binding affinity prediction. Our experiments on the CASF2016 benchmark dataset demonstrate the superior performance of GAABind. In terms of binding pose prediction, the 25th, 50th and 75th percentiles of predictions’ RMSD stand at 0.658, 0.964 and 1.545Å, respectively, with 56.49 and 83.51% of predictions falling below 2.0 and 5.0Å. For binding affinity prediction, GAABind achieves an MAE of 1.026, an RMSE of 1.298 and the Pearson and Spearman correlation coefficients of 0.803 and 0.793, respectively. Moreover, we also employed the COVID Moonshot dataset to evaluate GAABind’s performance on the cross-docking task, where GAABind achieves a 76.5% success rate. This underscores GAABind’s practical applicability in real-world scenarios.Given GAABind’s use of a deep learning-based model to predict pocket–ligand interatomic distances, which are then used to generate the binding pose, exploring an end-to-end strategy in future work would be beneficial. Additionally, given the limited availability of pocket–ligand structures, obtaining access to more annotated data holds the potential to further improve the performance of GAABind.In conclusion, our novel deep learning-based model demonstrates remarkable predictive capabilities for pocket–ligand binding pose and binding affinity prediction. Its superior performance makes it a valuable tool in the field of computational drug discovery and molecular design.We propose the Atom–Pair Attentive Encoding Block for molecular representation learning, which comprehensively encodes geometric properties and intramolecular interactions of input molecules.We introduce the Mutual Interaction Block for modeling pocket–ligand interactions, incorporating iterative stages for effective and dynamic docking process modeling.Combining the Atom–Pair Attentive Encoding and Mutual Interaction Block, we present GAABind, a powerful framework for pocket–ligand binding pose and binding affinity prediction.GAABind shows outstanding performance in both binding pose and affinity prediction tasks on CASF2016 datasets. Additionally, it demonstrates superior performance with cross-docking on the SARS-CoV-2 main protease.Supplementary_Information_bbad462Click here for additional data file.