To analyse fundus autofluorescence (AF) changes in retinal reattachment following primary scleral buckling (SB) surgery for rhegmatogenous retinal detachment (RRD).  Prospective noninterventional chart review study. AF images were reviewed for peripheral and central changes and compared to clinical and OCT findings.  A total of 73 eyes from 69 patients were included, four presenting with bilateral RRD. Mean age was 55 ± 12 years, male/female ratio 40/29, fovea-on/-off RRD 43/30, and mean follow-up time 376 ± 270 days, with a mean of 5 ± 3 postoperative visits. Preoperatively, RRD was seen as a hypofluorescent area with a hyperfluorescent leading edge. Immediately postoperatively, three types of cryopexy could be differentiated, gradually transforming to scleral hyperfluorescence. Buckle tightening produced alternating hyper-/hypofluorescent streaks, and demarcation lines showed a persistent rugged hyperfluorescent signal. Choroidal detachment led to transient hypofluorescence, whereas vortex vein compression induced persistent hypofluorescence. Peripheral retinal folds were hyperfluorescent and the drainage site was hypofluorescent. AF was highly sensitive in detecting even small amounts of hyperfluorescent persistent subretinal fluid (SRF) that showed a slow resolution during follow-up. A granular “salt-and-pepper-” like pattern in the central macula was seen in 80% of eyes with fovea-off RRD and alternating streaks in 10%. Findings from OCT imaging correlated well with AF regarding SRF, macular oedema, retinal pigment epithelial detachment, and presence of a subretinal scar, but only moderately in epiretinal membrane formation and choroidal folds.  AF is a useful, noninvasive, adjuvant tool in the long-term follow-up after SB surgery. In rhegmatogenous retinal detachment (RRD), the separation of the neural retina from the retinal pigment epithelium (RPE) leads to loss of the metabolic supply and cell death of the photoreceptors . The preThe metabolic stress induced on the retina by the detachment and the following surgical intervention can be observed by mapping fundus fluorophores . These nIt has been previously published that AF can offer valuable insight in the immediate postoperative period after SB surgery . Little The study was a noninterventional prospective chart review of patients undergoing primary scleral buckling surgery for RRD by one of the authors (P.S.) at the Department of Ophthalmology, Oslo University Hospital, Norway. The study was approved by the institutional data protection officer, and informed consent was obtained from all patients.All patients were operated upon with a 2.5 mm encircling band, a 6 or 7 mm silicone segmental buckle, transscleral cryopexy, and the choice of diathermy drainage and air/SF6 gas tamponade. The exclusion criteria included previous vitreoretinal surgery, dense media opacities that obscured the view of the fundus, and inability to attend follow-up visits.The follow-up period at the hospital was individually planned for each patient according to clinical indication. At all visits, every patient underwent a comprehensive ophthalmological examination, including best-corrected visual acuity (BCVA) by Snellen decimal units, applanation tonometry, slit-lamp biomicroscopy, and indirect ophthalmoscopy. The clinical examination was complemented with colour and AF fundus imaging and spectral-domain optical coherence tomography . Upon indication, a subgroup of 13 patients additionally underwent ultra-widefield fundus fluorescein angiography (FFA) to evaluate the retinal circulation.The ultra-widefield AF images were obtained using an Optomap P200Tx fundus camera  from both eyes during follow-up visits. A 532 nm (green) laser was used to capture ultra-widefield AF images, while composite colour images were obtained with the combined 532 nm and 633 nm laser during the same consultation. All AF images were subsequently transferred to ImageJ open-source software  for image analysis.The AF images were analysed for the presence of qualitative and quantitative changes. The qualitative analysis focused on a predefined set of descriptions for the effect of cryopexy, indentation of the SB and the encircling element, and macular changes. The cryopexy scar was classified as type 1 , type 2 , type 3 , or scleral hyperfluorescence. The presence of subretinal fluid (SRF), alternating hyper-/hypofluorescent streaks on the segmental buckle and the encircling band, peripheral retinal folds, and choroidal detachment-induced hypofluorescence were documented as previously published . In the The subsequent quantitative image analysis was performed by transferring the uncompressed images to ImageJ. In the periphery, the cryopexy area was manually marked, and the pixel signal intensity analysed recording the area, signal intensity , and perimeter of the lesion. In the macula, we analysed the signal intensity in a circular area manually centred at the umbo with a 500-pixel diameter. In the vast majority of patients, this area diameter included the whole macula extending just within the temporal vascular arcades. A corresponding analysis was also performed for AF changes occurring only in the fovea by utilizing a smaller circle with a 150-pixel diameter centred manually at the umbo. The data recorded in both instances aimed at characterizing AF signal intensity .All OCT images were qualitatively evaluated for the presence of persistent SRF postoperatively, cystoid macular oedema (CMO), epiretinal membrane formation (ERM), choroidal folds, focal irregularities of the ellipsoid zone (EZ), and the presence of a subretinal scar. These results were then compared to the clinical and AF findings after surgery.The 488 nm blue laser of the Optomap P200Tx was used for image acquisition and ImageJ software for image analysis. The qualitative analysis focused on the presence of CMO, peripapillary dye leakage, peripheral demarcation line(s), alternating hyper-/hypofluorescent streaks on the buckle, visible sclerochoroidal drainage site, and peripheral vascular leakage (PVL), qualitatively classified on a 0–4 scale. In addition, qualitative analysis of the cryopexy lesions was carried out following manual marking, recording the area (in pixels), perimeter, and signal intensity.The statistical analysis used to check for possible correlation between the AF, clinical, OCT, and FFA imaging findings was performed using R statistical software (R Core Team (2019), Vienna, Austria). A mixed-model analysis using AF imaging variables in the macula  or the periphery as outcome, clinical data , OCT, and FFA findings as predictors was performed.A total of 73 eyes from 69 patients were included in the present study. The preoperative and the demographic features are listed in (1) Preoperative. The RRD was apparent preoperatively in AF images as a hypofluorescent area with a hyperfluorescent posterior border at the leading edge of the detachment. Large retinal breaks were easily identified as hyperfluorescent areas due to window defect. Small breaks were difficult to identify in AF imaging.(2) Postoperative.  Periphery: postoperatively, we observed peripheral alternating hyper-hypofluorescent radial streaks on the tightened encircling band in 44 eyes (60%) and corresponding changes on the segmental buckle in 27 eyes (37%). No significant change in the number or signal intensity of these streaks was seen during follow-up.  There was obvious peripheral hyperfluorescence from persistent SRF in a total of 19 eyes (26%) after surgery . Of thes  A peripheral hyperfluorescent retinal fold was evident in four eyes (5.5%) postoperatively. In two eyes, the retinal fold appeared straight after successful reattachment, and the hyperfluorescent signal in AF imaging remained throughout the follow-up period. One eye had a retinal fold that disappeared 3 months postoperatively, and another eye developed several folds after PPV due to redetachment. The AF findings correlated very well with ophthalmoscopy.  Eleven cases (15%) had one or several peripheral demarcation lines that showed a rugged hyperfluorescence and were readily visible in AF imaging throughout the follow-up with no observable change. The sclerochoroidal drainage site was ophthalmoscopically evident in 45 eyes (62%) as a hypofluorescent spot, sometimes accompanied by a thin hyperfluorescent halo .  Eight eyes (11%) developed choroidal detachment that produced a hypofluorescent signal during the immediate postoperative period. There was significant discrepancy in the duration of the hypofluorescent signal, with five cases disappearing within the first postoperative month, two cases disappearing within three months, and one case lasting for almost 9 months with complete normalization thereafter. Four eyes (5.5%) showed postoperative hypofluorescence from a swollen vortex vein. This finding was consistent during the whole follow-up period showing no change in size or signal intensity.  When analysing the cryopexy effect in the immediate postoperative period, seven eyes (9.5%) were found to have type 1, 20 eyes (27.5%) type 2, and the remaining 46 eyes (63%) type 3 cryopexy effect. All eyes with type 1 and 2 cryopexy effect changed over time to type 3, which in turn gradually changed to scleral hyperfluorescence following the development of chorioretinal atrophy in the treated area, corresponding well with ophthalmoscopic findings and colour fundus images .  Macula. The qualitative analysis of the postoperative AF images  revealedR2 = 35.9%; Fisher's test for regression P value = 0.00024) between the mean AF pixel value in the macular area and the size of the retinal detachment  and the number of operations , respectively. The significant predictor was time . The residuals were Gaussian . No statistically significant correlation was found between the AF signal in the fovea or the periphery and other clinical variables.  The mixed-model analysis  revealedThe OCT examination revealed EZ irregularities in five eyes (6.8%), showing good correspondence with AF findings in four of five eyes (80%) . An ERM A total of 13 patients included in this study underwent FFA imaging upon indication. Ten patients were subjected to one angiogram, one underwent two examinations, and another patient underwent four angiograms during follow-up. The patient receiving two FFA had preoperatively a peripheral retinal haemangioma in the detached retina that was attributed to chronic retinal detachment. The haemangioma disappeared after focal transscleral cryotherapy during the SB procedure. The patient that was subjected to four angiograms due to the development of progressive retinal ischemia with neovascularization in both eyes during follow-up after SB for RRD, combined with neurosensory hearing loss and neurological symptoms, was diagnosed with possible Susac's syndrome and was treated accordingly with panretinal photocoagulation, systemic steroids, and immunosuppressant therapy, with a good outcome as previously reported .P=0.03) was found between the size of the peripheral cryopexy lesion in FFA and AF imaging. Five eyes (38.5%) showed no or minimal PVL, and four eyes significant widespread PVL. Interestingly, AF was not sensitive in detecting PVL  .To our knowledge, this is the first study to use ultra-widefield fundus AF to analyse metabolic stress patterns and functional recovery of the retina and the RPE in long-term follow-up after scleral buckling surgery.The anatomical success of the surgery relies on a correctly placed buckle with the proper indentation and retinopexy. The ensuing metabolic alterations induced by the buckle have been so far studied by demanding methods, not integrated in everyday clinical practice  , 13. PosThe improvement in BCVA and the refractive changes observed after surgery are in accordance with previously published results . The posPreoperative AF findings confirmed previous published findings . The posEven small quantities of SRF postoperatively were easily identified in AF imaging as a strongly hyperfluorescent area, a highly specific finding, with a distinct pattern of reduction over time in both the periphery and the macula. This can be helpful in differentiating persistent SRF from other entities that may indicate the need for a new intervention, such as shallow redetachment in the periphery that has a characteristic pattern in the leading edge of the detachment  or CMO iAutofluorescence imaging with AF was also very sensitive in revealing demarcation lines, a finding that persisted during the follow-up. Intriguingly, in the small subgroup of patients who underwent FFA, AF was more sensitive in depicting demarcation lines compared to the angiogram. Hyperfluorescent peripheral retinal folds in AF also corresponded well with clinical findings and remained unchanged during follow-up as long as the folds were clinically visible. Further, we found that postoperative hypofluorescence can be differentiated due to choroidal detachment or vortex vein stasis. Choroidal detachment was found to lead to a marked hypofluorescent signal that normalized over time (though with a significant timespan between patients) correlating well with ophthalmoscopic findings, whereas the hypofluorescent signal over congested vortex vein(s) remained unchanged during the follow-up.While scleral buckling surgery has proven to be an effective method for treating RRD, anatomical failure due to PVR remains a concern, with the amount of cryopexy used strongly implicated in PVR development , 21. HerPeripheral vascular leakage (PVL) is an important angiographic finding in cases of uveitis, indicating peripheral vascular inflammation and leakage . To dateThe known anatomical success rate of conventional buckling surgery for RRD is good , 4, but Finally, only half of the eyes with ERM on OCT showed corresponding findings in AF, whereas in cases of CMO, persistent SRF, and subretinal scar the correspondence was good between these two imaging techniques. We hypothesize that these differences are due to the fact that AF photographs a dynamic process, depicting the metabolic stress at RPE-photoreceptor complex, whereas OCT displays the microstructure of the whole retina and choroid. We lack at present a prediction biomarker of the final visual acuity after retinal reattachment.Limitations of the present study include the fact that fundus AF is a dynamic process and image quantification was performed manually due to lack of an automated system. The sample size was relatively small and with a variable follow-up period. Studies with a larger population size and combination with microperimetry testing to test point-to-point correspondence of the AF changes in the macula to light sensitivity can be helpful in delineating how AF changes correlate to functional outcomes after reattachment.In conclusion, ultra-widefield fundus AF proves to be a useful noninvasive adjuvant tool in the examination and long-term follow-up of scleral buckling surgery. It offers valuable insight into the pathophysiological mechanisms of retinal repair and can serve as a potential biomarker after surgical treatment for RRD.


Moreover, the anti-prostate cancer efficacy of many nutrients, medicinal plant extracts and Chinese medical formulations were presented, and the future prospects for the application of these compounds and extracts were discussed. Although the failure of conventional chemotherapy as well as involved serious side effects makes natural products ideal candidates for the treatment of prostate cancer, more investigations of preclinical and even clinical studies are necessary to make use of these medical substances reasonably. Therefore, the elucidation of structure-activity relationship and precise mechanism of action, identification of novel potential molecular targets, and optimization of drug combination are essential in natural medicine research and development.Prostate cancer is the second most common malignant cancer in males. It involves a complex process driven by diverse molecular pathways that closely related to the survival, apoptosis, metabolic and metastatic characteristics of aggressive cancer. Prostate cancer can be categorized into androgen dependent prostate cancer and castration-resistant prostate cancer and cure remains elusive due to the developed resistance of the disease. Natural compounds represent an extraordinary resource of structural scaffolds with high diversity that can offer promising chemical agents for making prostate cancer less devastating and curable. Herein, those natural compounds of different origins and structures with potential cytotoxicity and/or  So a systematic summary and knowledge of future prospects are necessary to facilitate further chemical and pharmacological studies for anti-prostate cancer agents.It is nowadays evident that prostate cancer (PCa) is recognized as the second most common cancer and the fifth cause of cancer death in males Initiallyin vivo and in vitro. In addition, we also provided possible novel targets for screening natural compounds (or extracts or Chinese herb preparations) with anti-prostate cancer activity and discuss the future prospects for the application of these compounds and extracts and the novel available approaches and technological improvements that should be explored to treat prostate cancer.Herein, we reviewed the detailed molecular causes of prostate cancer and critically summarized the natural compounds (or extracts and Chinese herb preparations) that have been reported to inhibit prostate cancer cells proliferation/tumor growth, induce prostate cancer cells apoptosis or exhibit effects on specific signaling pathways involved in prostate cancer Androgens, principally testosterone (T) and dihydrotestosterone (DHT), are synthesized from cholesterol as the initial 27-carbon substrate via multiple enzymatic steps . As a meThe development and maintenance of the prostate is inseparable from androgen acting through the AR. Since Huggins and Hodges first demonstrated the responsiveness of prostate cancer to androgen deprivation, it has been clear that prostate cancer is dependent on androgen and AR activation for growth and survival . From thHistorically, there are much debate about the mechanisms of castration resistance, which are mainly summarized as the following by recent studies: canonical AR signaling, relying on AR nuclear translocation and AR-DNA binding, and non-nuclear AR signaling which requires neither AR nuclear translocation nor AR-DNA binding.de novo route; 3) alterations in cofactors of the AR pathway .The potential mechanisms of canonical AR signaling that lead to CRPC can be categorized into three parts. 1) common alterations in AR, which can lead to AR increase its sensitivity to very low levels of androgens or constitutive activation of AR signaling; 2) AR activation by androgens converted from adrenalandrogens or synthesized intratumorally via the  pathway .AR, including AR amplification or overexpression, AR mutations and truncated AR lacking ligand-binding domains (ARΔLBD). Thus, these changes in AR increase its sensitivity to very low levels of androgens or lead to constitutive activation of AR signaling. Strictly speaking, this mechanism of prostate cancers is not androgen-independent and the responses still depend on AR and androgen.One possible mechanism by which the prostate cancer becomes resistant to androgen deprivation therapy is alterations in Despite low circulating androgens in the CRPC patients, one potential mechanism that would allow tumor cell proliferation is by promoting the expression of the AR itself, which increases ligand-occupied receptor content . Plenty Numerous studies provide the simplest explanation of how increased androgen receptor expression leads to resistance to anti-androgen therapy. According to one study, a three-to-five-fold increase in receptor levels could compensate for low ligand levels and restore androgen receptor signaling in xenotransplantation models. . AlthougAR mutations in pre-treated tumors is 5–30%  instead of a 19-carbon androgen  than in primary prostate cancer (4%)  . In addicer (4%) . GATA2, cer (4%) . Gupta ecer (4%) . Moreovecer (4%) .Trafficking from the nucleus into the cytoplasm, AR may have unexpected consequences because AR has known functions in the cytoplasm, which is called non-genomic signaling . One of The PI3K/AKT pathway is one of the most frequently activated signal transduction pathways in human cancer, including prostate cancer. Alterations of the PI3K/AKT pathway, including altered expression, mutation, and copy number alterations, have been reported in 42% of primary prostate tumors and 100% of metastatic tumors . AndrogePreclinical studies have confirmed that non receptor tyrosine kinase c-Src and Src family kinase (SFK) regulate a complex signal network, driving the development of castration-resistance prostate cancer and bone metastasis. . After tRecently, a large number of studies have shown that the activation of SRC is an important mediator of AR signaling. . AR can The MAPKs signaling cascade play important roles in regulating diverse biological functions including cell proliferation, motility and survival, which are essential to prostate carcinogenesis . Studies of DHT-responsiveness in prostate cancer cells show that DHT treatment induces phosphorylation of ERK-1/2 within 1–2 min and peak levels of phosphorylation within 5–10 min . ActivatJanus kinases (JAK) signal transducers and activator of transcription (STAT) pathway play an important role in differentiation, hematopoiesis, immune function and cell growth . Recentl2+signaling is also involved in prostate cancer progression . Increased calcium intake from dairy products has been considered as a risk factor for prostate cancer  which directly regulates cell signal transduction and the Ca2+ channels . When Ca2+ is released from intracellular Ca2+ pool, Ca2+ dissociates from a luminal EF hand motif of STIM1. As a consequence, STIM1 proteins aggregate and recruit Orai1 Ca2+channels, which then mediate SOCE  in primary prostate cancer  is the substrate-binding subunit of a cullin-3 (CUL3)-based E3 ubiquitin ligase complex, which mediates the ubiquitylation and degradation of many target proteins. SPOP binds to the substrates through its N-terminal meprin and traf homology (MATH) domain, whereas it interacts with cullin-3 via BTB domain on its C terminal . Recent e cancer . Interesradation . Recent radation . Patholoradation . Recentlhibitors . Similarpoptosis . Furtherutations . Taken tTMPRSS2-ERG fusion gene arising from genetic rearrangement  has also been a central focus in prostate cancer, which leads to aberrant expression of the ETS transcription factor ERG .The ctor ERG . TMPRSS2ountries . Previoupromoter . Howevere cancer . ERG ovee cancer . Collectin vivo and in vitro are categorized and presented according to their source of isolation  and the structural scaffolds. Besides the effects on prostate cancer cells growth or apoptosis, special emphasis was given to the mechanism of action of a compound interfering specific signaling pathways involved in prostate cancer.Natural compounds that have been found to inhibit prostate cancer cells proliferation/tumor growth, promote prostate cancer cells apoptosis, or modulate specific signaling pathways involved in prostate cancer in vivo or in vitro. Detailed information about the compounds origin, activity and mechanism of action is listed in in vivo but also significantly inhibited the cell growth of PC-3 and DU145 with a notable reduction of lung metastasis and decrease of circulating tumor cells in the peripheral blood . So most components involved in above-mentioned pathways represent potential targets for screening natural compounds and/or extracts with anti-prostate cancer activity. And natural compounds or extracts that could function as modulators of canonical AR or non-nuclear AR signalling pathways thus can be regarded as promising candidates for anti-prostate interventions.In our review, we provided a comprehensive overview of the molecular basis of the incidence and development of prostate cancer, especially for castration-resistant prostate cancer (CRPC), which mainly including canonical AR signaling , and non-nuclear AR signaling  is a much needed task that requires not only the great progress in understanding the genetic basis of prostate cancer, but also the significant technological improvements in tracking of bioactive natural compounds and structural characterization, which will facilitate the identification of novel natural compounds with significant anti-prostate cancer properties for drug development and therefore can be translated into significant health benefits for humans.


Given the urgency of the antimicrobial resistance crisis, we analyze uptake of open science best practices in AI-driven antibiotic discovery and argue for openness and reproducibility as a means of accelerating preclinical research. Finally, trends in the literature and areas for future inquiry are discussed, as artificially intelligent enhancements to drug discovery at large offer many opportunities for future applications in antibiotic development.By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics, therapies decline in efficacy and must be replaced, distinguishing antibiotics from most other forms of drug development. Together with a slow and expensive antibiotic development pipeline, the proliferation of drug-resistant pathogens drives urgent interest in computational methods that promise to expedite candidate discovery. Strides in artificial intelligence (AI) have encouraged its application to multiple dimensions of computer-aided drug design, with increasing application to antibiotic discovery. This review describes AI-facilitated advances in the discovery of both small molecule antibiotics and antimicrobial peptides. Beyond the essential prediction of antimicrobial activity, emphasis is also given to antimicrobial compound representation, determination of drug-likeness traits, antimicrobial resistance, and  Melo, Maasch and de la Fuente-Nunez review the current practices in use of artificial intelligence in the discovery of antibiotics and antimicrobials. They also provide details about the best-practices that should be engaged with during computational drug discovery, including open science and reproducibility. The Centers for Disease Control and Prevention estimates that 2.8 million infections are caused by antibiotic-resistant bacteria in the United States annually, leading to 35,000 deaths from such untreatable infections2. Current evidence also suggests that the solution may be part of the problem itself: antibiotics have been shown to cause significant damage to the gut microbiome, reducing species diversity and encouraging the evolution and dissemination of AMR genes3. Antibiotics under clinical trial are generally analogs to existing drugs for which AMR mechanisms have already emerged1, further underscoring the need for novel approaches in antibiotic discovery.Antimicrobial resistance (AMR) in clinically significant bacteria is undermining the efficacy of existing antibiotics, incurring concerning levels of global morbidity and mortality4. Between 2014 and 2019, only 14 new antibiotics were developed and approved5. In a survey of nearly 186,000 clinical trials for over 21,000 compounds, the probability of success for new drugs that treat infectious diseases was 25.2%6. For orphan drugs, i.e., those that treat rare infectious diseases, this probability dropped to only 19.1%6. This risk of failure drives corporations to pursue research and development with a higher guarantee of return on investment, opening the way for academia to initiate early stages of antibiotic design and optimization8.Compounding this issue, antibiotic development is a slow, expensive, and failure-prone process that can span over 10 years and cost hundreds of millions of dollars9. It is speculated that 1030–1060 drug-like chemicals exist10, while 20n variants exist per n-length canonical amino acid sequence. Although this immense combinatorial space presents a broad opportunity for computational antibiotic design, an exhaustive search cannot be achieved on a reasonable timescale. These challenges strongly incentivize the development of efficient heuristics and artificially intelligent algorithms for high-throughput antibiotic discovery. A prominent subdomain of computer science, artificial intelligence (AI) concerns the study and development of machines that are capable of learning, problem-solving, or mimicking other displays of reasoning akin to natural intelligence. For the purposes of this review, AI will generally pertain to machine learning (ML), the training of mathematical models to output predictions when presented with previously unseen data. The application of ML to drug discovery, and antibiotic discovery specifically, has been greatly facilitated by the public availability of empirical datasets (Table Accelerated antibiotic discovery will require computer-aided prospection for novel drugs with new mechanisms of action (MOAs)11. Leveraging advances in protein structure prediction and modeling, small-molecule antibiotic targets can be reliably described in atomic detail. Protein structures are then probed for binding sites, allowing large libraries of compounds to be used for automated large-scale docking and binding affinity studies in a process known as virtual screening (VS). This practice is now integral to many drug development pipelines, receiving ample attention from the ML community12. The most challenging step in VS is evaluating binding site affinity, driving the development of ML tools that significantly outperform traditional binding affinity prediction methods15. In recent years, deep learning (DL) has been used to successfully bypass docking and affinity estimation entirely, resulting in the identification of a small-molecule antibiotic active against multiple bacterial pathogens16.The integration of computational tools to expedite drug development has led to key advances for the rational design of bioactive compounds efficacious in animal models, thus demonstrating that computers can yield preclinical antibiotic candidates17 due in part to a relatively low propensity to induce AMR18. Research topics will be introduced by following the logical flow of an ML pipeline, starting with compound representation and progressing through trait prediction and novel compound design. ML innovation in general drug development will be reviewed where it has cross-over utility for antibiotic-specific applications. Trends in the literature and directions for future research will be discussed, including prospects for increasing data availability, computational–experimental collaboration, and innovation in interpretable ML (IML). Additionally, we provide an original analysis of open science practices among cited research and discuss the potential for best practices in open and reproducible ML to expedite antibiotic discovery.In this review, we will focus on the application of AI to the development of two major classes of bioactive compounds: small-molecule antibiotics and antimicrobial peptides (AMPs). The former, studied since the beginning of the twentieth century with the discovery of penicillin and in use for over 70 years, represents the majority of antibiotics in use today. The latter, a class of small proteins usually composed of 5 to 50 amino acids, is receiving increasing attention in research and clinical trials19. Aiming to computationally predict the activity of newly designed molecules, saving time and money by avoiding synthesis and experimentation on inactive compounds, researchers relied on computational representations of drug candidates to predict their properties. As it became apparent, the problem of representing biological or chemical data for use in computational models is in itself an important field of research. Likewise, it is an essential component of the computational drug discovery pipeline  drove over 50 years of research and innovationχ2 statistical tests or mutual information estimation, the search for reduced and information-rich representations has now fully integrated ML tools and principles. The efforts described below highlight how the theoretical and methodological advances made in diverse ML applications and interest areas can be adapted to aid ML-driven antibiotic development.From traditional dimensionality reduction techniques like principal component analysis (PCA) and singular value decomposition to feature selection approaches involving 25. A similar approach was taken to study and predict protein structures26. In an extensive benchmark study of available methods and datasets27, it was found that neural networks can enhance not only the process of describing a drug based on a set of molecular descriptors but also the determination of such molecular descriptors themselves. This work was extended to create a series of antimicrobial compounds that were correctly predicted as active despite being structurally distant from known antibiotics 16.A prominent example is the use of graph convolutional networks to leverage the geometry and connectivity of molecules to naturally translate them into graphs, using neural networks to learn from the chemical structure itself28. Alternatively, RNNs have been combined with reinforcement learning to autonomously create an embedded representation for drugs based on their SMILES representations29.While common in the fields of signal processing and natural language processing (NLP), recurrent neural networks (RNNs) have now been adapted to process simplified molecular-input line-entry system (SMILES) representations, which encode structures of chemical species using simple text strings. In one case, researchers used long short-term memory (LSTM) generative neural networks to learn from SMILES representations of known drugs and then used the trained neural network to generate new compounds30 and multiplicative-LSTM neural network31 have been trained to create embedded representations for peptide sequences. The latter led to an embedded representation that could be used to derive a protein’s secondary structure, thermal stability, deep mutational scanning classification, and even the functional impact of mutations31.RNNs have emerged as a natural embedding approach for AMP sequences, given their ability to parse sequence-based inputs. Based on a one-hot encoding of amino acid residues , both an LSTM-based autoencoder7 and attracting a variety of approaches32  and takes a weighted vote of each model into consideration to achieve the final prediction35. The underlying assumption behind ensemble learning is that errors made by one model will be compensated for by others, and this assumption has been confirmed in applications ranging from proinflammatory peptide identification36 to prediction of drug side effects37.In a recent effort to repurpose previously developed drugs as antibiotics39. Alternatively, deep neural networks have been used to predict antimicrobial properties from simplified residue representations of arbitrary amino acid sequences. In 2009, researchers combined 44 peptide descriptors traditionally used for QSAR studies and used them as inputs for an artificial neural network that predicted peptide activity against P. aeruginosa40. More recently, a 2020 study created a deep convolutional neural network model based on a simplified amino acid vocabulary that translated the natural 20 amino acids into pseudo residue types41. This model predicts antimicrobial activity in small peptides and is available in a web server. Extreme gradient boosting has been used for genome-based prediction of minimum inhibitory concentrations for 20 antibiotics against Klebsiella pneumoniae42 and 15 antibiotics against nontyphoidal Salmonella strains43. Using RNNs44, a combination of input representation and regression models were created to select peptide sequences with antimicrobial activity. Finally, through a variational autoencoder approach, peptide sequences were embedded in a latent space that was subsequently searched for new AMP sequences45.Classical ML techniques such as support vector machines (SVMs) have been applied to describe AMPs and quantify their MOAs9. The ability of AMPs to limit AMR development has been related to their varied MOAs46, which has led researchers to focus on classifying peptides and discovering new MoAs. Specifically, DBSCAN was used for cluster-based prediction of AMP activity against Gram-negative bacteria47, with promising candidates being synthesized and tested in vitro48.The variety of techniques utilized thus far correlates with an increasing focus on AMPs, which have been regarded as a major source of new antibiotics to tackle the development of resistance in microbesEscherichia coli49. Since patterns found by generalized linear models can be directly interpreted by analyzing the model weights, one can directly translate the model into actionable information for AMP design.The direct combination of experimental and ML techniques in a closed-loop approach has also benefited the development of new AMPs. Starting from a template with known antimicrobial activity and a series of homologous sequences, it was possible to train a generalized linear model to create new AMPs with 160-fold increased antimicrobial activity against 50. This approach can expand the search space for new drugs by avoiding direct comparisons between molecular descriptors and focusing instead on their effects on pathogens.While most ML-based antibiotic development approaches focus on creating new representations for drug candidates and new models to predict their activity based on molecular descriptors, the phenotypic drug discovery approach focuses not on describing the molecule itself but on its effects on target organisms. For example, a recent study used a random forest model to predict antimicrobial activity based on featurization of cell imaging, avoiding detailed description of the molecules themselves51 and subsequent concepts of drug-likeness and lead-likeness. Prediction of drug-likeness has been refined and increasingly automated over recent decades, with traits of interest including absorption, distribution, metabolism, excretion, and toxicity (ADMET)52. ML-based prediction of binding affinity can also accelerate high-throughput screening and structure-based drug lead optimization by pinpointing candidates with more favorable drug–target interactions, as discussed in recent reviews53.ML can yield a fuller aggregate picture of antibiotic therapeutic potential than simply predicting antimicrobial activity. Attempts to quantitatively distinguish the subsets of chemical space that have therapeutic potential from those which do not have yielded various schemas, including the introduction of the Rule of 5 in 1997Like many ML problems, drug-likeness prediction can be attempted using a wide array of algorithms. While experimental observations often require a specific methodology or well-established gold standard, diverse ML algorithms can often provide comparable performance for a given classification or regression problem. There is often no way to know a priori which algorithm will perform best, although theoretical knowledge can guide decision-making. Therefore, it is important to follow a rigorous model selection process that compares multiple algorithms  across several performance metrics that are salient to the particular use case. In this section, we note the use of diverse algorithms that have been applied to multiple drug-likeness prediction problems.52, incentivizing pre-trial in silico exploration. Host cell toxicity is a critical ADMET endpoint and a significant risk in antibiotic development, motivating the design of predictive tools for mammalian red blood cell toxicity, kidney cell toxicity, and other forms of eukaryotic cell damage. Hemolytic activity, or the ability to burst red blood cells, has been a major focus of therapeutic development given that numerous drugs enter the bloodstream. Prediction of hemolytic activity in AMPs and antimicrobial peptidomimetics has been explored using neural networks54, classification trees55, and gradient boosting classifiers56. Consensus model-based software for hemolytic activity prediction has also been released for general applications in drug development, with an emphasis on small molecules57 and saponins58. A feedforward fully connected neural network has demonstrated comparable performance to prior random forest models for the prediction of drug candidate cytotoxicity59. Deep Taylor Decomposition was used to identify the most significant features in DL-based cytotoxicity classification, with an emphasis on visualization to facilitate interpretability59. Additional antibiotic side effects can also be foreshadowed using ML, as has been done for the seizure-inducing potential of enoxacin, a broad-spectrum fluoroquinolone antibacterial60.Dangerous pharmacokinetic properties and toxicity are leading causes of clinical trial failure61. Protein solubility prediction has used neural network62, gradient boosting machine63, logistic regression classifier64, SVM65, and random forest models66. Degradation via the action of proteolytic enzymes is a significant concern when evaluating the stability of peptide-based antibiotics68. The in silico identification of putative proteolytic cleavage sites can inform AMP lead selection and guide sequence optimization for increased stability. Cleavage site prediction has been explored through the lens of drug development69 and other protein informatics applications using classification and regression mode SVM74, convolutional neural network75, conditional random field classifier76, and logistic regression models77. Similarly, the stability of drug-like chemicals has been modeled using an attention-based graph convolution neural network78 and Naive Bayes classifier79.The development of AMP-based antibiotics must also consider peptide solubility and stability, which are necessary for manufacture and efficacy. Pharmaceutically viable AMPs will be soluble, a trait that can be predicted from amino acid sequence80. Collateral damage to the gut microbiome has been proposed as one additional ADMET endpoint, and consensus model-based software has been released for ML prediction of microbiome damage58. Indeed, disruption to the microbiome is a significant side effect of antibiotics and has been implicated in AMR evolution3. For this particular endpoint, species-specific antimicrobial activity prediction may be the answer: ML can aid in the selection of candidates with high specificity for target pathogens and low activity against known commensals.As outliers to original drug-likeness definitions expand the boundaries of these criteria, new qualitative endpoints and quantitative thresholds have come under consideration9. Likewise, the need to track AMR emergence, mechanisms, and dynamics are raising new applied ML questions unique to computational antibiotic discovery, bacterial genomics, and infectious disease epidemiology. While ML-based AMR prediction may be clinically useful for informing AMR diagnosis and antibiotic prescription82, it may also be experimentally useful in the drug development process. We anticipate that ML approaches to AMR genomics in epidemiology and medicine will increasingly be adapted specifically for drug development purposes, e.g. ML-informed resistance evolution experiments for new lead compounds.Unlike most therapeutics, antibiotics are designed to kill a living target with the capacity for resistance evolution. The near-inevitability of AMR evolution thus adds an additional urgent consideration that is absent from most other drug development niches. Incentives to develop less resistance-prone countermeasures are drawing research to historically underexplored sources of inspiration for novel antibiotic design84. Small-molecule AMR has also been observed to coincide frequently with a collateral sensitivity to AMPs, yet rarely with AMP cross-resistance85. Together with the fact that protein target modifications are a common AMR mechanism, this suggests the large potential for cross-over between ML and traditional protein informatics in AMR research. However, the majority of existing ML models forgo this route in favor of pathogen genetic and genomic inputs. Although model design strategies are expected to diversify, the current state of ML for AMR learns from the bacterial genome rather than drug or molecular target features.Protein space is one such underexplored area that is expected to yield future antibiotics with minimal AMR risk. Antimicrobial host defense peptides, including encrypted AMPs released from precursor proteins through proteolytic cleavage, have notably emerged as reservoirs for low AMR-risk antibiotic templates due in part to a tendency to act on multiple cellular targetsK. pneumoniae42, E. coli88, P. aeruginosa89, Mycobacterium tuberculosis91, and Staphylococcus aureus86. While ML models of AMR may be trained on drug- and bacteria-specific data94, a more agnostic approach has been explored using a neural network to facilitate environmental metagenomic analysis95. However, predictive performance has been observed to vary significantly by antibiotic, target species, genomic data sampling method, and resistance mechanism complexity96, suggesting that AMR prediction may at times require relatively context-specific modeling. A free web server and standalone software have been released for SVM-based prediction of efflux-mediated AMR97. ML-assisted metagenomic analysis has implicated AMR genes associated with antibiotic-induced microbiome perturbations98. A novel combination of protein homology modeling and LASSO penalized logistic regression has been used to investigate the horizontal transfer of antibiotic resistance determinants from gut commensals to bacterial pathogens99.Pathogen genomic data have been used to build ML models of antibiotic susceptibility and resistance phenotypes in clinically relevant bacteria, including 81, IML can enable models to suggest causal factors in AMR at the organismal and population scale. Coupling ML with gene–protein structure mapping to investigate drivers of M. tuberculosis AMR evolution, interactions between genes conferring AMR were hypothesized to manifest as correlations in their weights and signs across the hyperplanes of an SVM ensemble100. An ML-integrated genome-scale model using data from microbial genome-wide association studies has enabled allele-parameterized flux balance analysis to reveal metabolomic insights into M. tuberculosis AMR101. Open-source software using protein orthology-based gene variant mapping has also been developed for interpretable AMR prediction96. Computationally characterizing the molecular signatures and population dynamics of AMR might help indicate which MOAs are overused and which present promising new avenues, even on a regional scale. Using training data from multiple countries, geographic analysis of predicted AMR genes revealed population dynamics that could be supported by national rates of multidrug-resistant tuberculosis and antibiotic prescription trends100.While “black-box” approaches may limit the utility of ML for AMR-risk reduction102. Like classical autoencoders, VAEs are trained to encode inputs to a compressed representation and then to decode an approximate reconstruction, learning the latent variables describing the training data in the process. However, VAEs are directed probabilistic models, learning continuous latent variables through a variational Bayesian approach to generative DL103. This section will note the use of several variations on these common generative architectures as applied to drug discovery.Generative DL can lend itself to computational antibiotic discovery in multiple ways. Here, we will focus on de novo molecular design, which often employs generative adversarial networks (GANs), variational autoencoders (VAEs), or related architectures. Comprised of dueling generative and discriminative models, GANs infer the probability distribution from which training data derive in order to construct novel samples from this distribution. Engaging in a two-player minimax game, both models are trained to optimize the error rate of the discriminator: while the generator is trained to maximize the likelihood that the discriminator fails to distinguish empirical data from synthetic data, the discriminator is trained to minimize this likelihood104, including inverse design of inorganic matter105 and graph-based neural network models for the NP-hard106 inverse protein folding problem107. Increasingly, generative DL is applied explicitly to drug discovery, whereby synthetic molecular designs are proposed from drug-like chemical spaces. De novo drug candidate design has been attempted with deep reinforcement learning coupling generative and predictive neural networks29, deep generative adversarial autoencoder architecture108, differentiable neural computer architecture with reinforcement learning and adversarial training110, deep neural networks coupled with Monte Carlo tree search111, and an autoencoder–GAN combination for both random and target-biased molecular design112. Given their suitability for sequential data, generative RNNs taking SMILES inputs have drawn attention in drug design114 and have demonstrated relatively broad, uniform, and complete coverage of chemical space116. Experimentally validated membranolytic anticancer peptides have been generated by both an LSTM RNN with transfer learning117 and a counterpropagation artificial neural network optimized by a genetic algorithm118.Generative DL has found diverse chemical and protein engineering applicationsE. coli than ampicillin119. Additional preliminary success in AMP discovery is described in a proof-of-concept study coupling a VAE with experimental validation45. A generative LSTM RNN with transfer learning has demonstrated success in reconstructing molecules known to target S. aureus after pretraining on a large generalized dataset and fine-tuning on a smaller set of target-specific bioactive molecules120. An RNN with unidirectional LSTM cells for de novo AMP design observed 82% of generated peptides to be putative AMPs, while only 65% of random permutations from the amino acid distribution of the training data were predicted to be antimicrobial30.A burgeoning interest in generative DL within chemical engineering, protein engineering, and drug development at large suggests that similar techniques may be increasingly applied to AMP and small-molecule antibiotic design. To date, a GAN has been used to generate an AMP with a significantly lower minimum inhibitory concentration against In this section, we present an argument for increasing openness and reproducibility in ML-based antibiotic discovery. This argument hinges on a two-pronged crisis: (1) the global public health crisis of AMR, slow antibiotic development rates, and emerging infectious diseases and (2) the reproducibility crisis currently plaguing AI. We conclude with an original analysis of open science practices among the publications cited in this review.121, sudden pathogen emergence, unexpected large-scale losses in quality of life and economic security122, and structural inequities that render some populations disproportionately vulnerable123 raise unique questions of urgency and justice in infectious disease control. These questions heighten the need for swift research and development, evoking calls for increased openness under global public health crises124. We argue that similar calls should extend to the global crisis of AMR evolution, and thus to computational antibiotic discovery.Accelerating antibiotic discovery through open information and technology exchange carries both practical and ethical weight. As evidenced by poignant examples from the COVID-19 pandemic, factors such as AMR125 signal a growing concern for transparency, reproducibility, and equitable access to information within the scientific community. Effective 2021, Plan S dictates that publications resulting from public and private grants of participating bodies “must be published in Open-Access Journals, on Open-Access Platforms, or made immediately available through Open-Access Repositories without embargo” . Nevertheless, open-access publishing addresses only one facet of computational openness and reproducibility. With stakes as high as they are in computational antibiotic discovery, we call for a more comprehensive set of open science best practices.The international movement toward open-access publishing represented by groups like cOAlition S126, and some argue that AI generally suffers from a reproducibility crisis, not unlike that of psychology127. Reproducibility challenges common to ML  can also have detrimental interactions with challenges unique to biomedicine 128.An open science regime that ensures computational reproducibility can accelerate ML-based antibiotic discovery through free public access to (1) source code, (2) training and testing data, and (3) published findings. Computational reproducibility facilitates the external validation of published claims while encouraging the dissemination of knowledge and methods. However, standards of openness and reproducibility in biomedical ML are still subject to debate126, an analysis of 400 general AI conference papers revealed that only 6% released code, 54% released pseudocode, and ~30% released test data127. Within ML for the life sciences and medicine specifically, a recent review found that 50% of 300 publications released software, while 64% released data129. A review of 511 studies found that papers applying ML to healthcare data underperformed relative to NLP, computer vision, and general ML on multiple metrics of reproducibility, including code release rates130. A systematic review of 415 studies on ML-based image analysis for COVID-19 diagnosis found that all publications contained serious methodological flaws or failed to report key information needed for reproducibility and substantiation of claims, such that not a single model was of clinical use131.Although releasing source code, training data, and testing data could mitigate reproducibility concerns while increasing the scientific value of AI research132. Exemplifying the conflict between researchers and publishers, the recent 2-year-long negotiation between the University of California system and Elsevier resulted in the largest deal for open-access publishing for scientific articles in North America133. Interestingly, the fields of medical and biological research are among the most accessible, with biology having the largest fraction of immediately free-to-read articles134.Confounding factors such as lack of incentives in academia or misaligned objectives in the private sector may further hinder the adoption of open science practices. While open-access journals continue to grow, many prestigious scientific journals charge premiums over publication fees in order to make articles open access. Authors then face a tough choice between funding their research or paying premiums to make their publications free to all readers. Indeed, a recent study showed that authors of open-access publications in US research institutions tend to have more access to funding and belong to more advanced career stages135 can still provide great advances to the field, finding a balance between open access and closed IP may prove to be a barrier in itself. Guidance may be found in the efforts of related fields to establish community-wide standards for responsible and reproducible ML publications, with the Checklist for Artificial Intelligence in Medical Imaging being a notable example136.Beyond publishing research findings, the release of source code and training and testing data may also raise conflicts regarding intellectual property (IP) and competitiveness in the private sector. Therefore, while industry-funded research for antimicrobial discoveryThis conversation in AI, and in biomedical ML specifically, motivated our analysis of code, data, and software release rates among models cited in this review Fig. . This anhttps://zitniklab.hms.harvard.edu/TDC/) provides free ML datasets to lower barriers to entry and accelerate drug development pipelines. The Open-Access Antimicrobial Screening Program extends the concept of openness to experimental methods by offering free compound screening services (https://www.co-add.org). Such creative counterexamples to the closed research paradigm will ideally become the norm in antibiotic discovery.Moving forward, inspiration can be found in projects taking a broad view of openness and reproducibility in drug discovery. The open-source Therapeutics Data Commons . To explore the extent to which research interest has changed over time, we queried PubMed by year for texts on ML and antibiotics, ML and cancer therapies, ML and cardiovascular drugs, or ML and drugs broadly defined (Fig. 137, querying for these applications provides relevant public health context for infectious disease applications. Further, a blanket query for AI and ML keywords with no additional qualifiers provides the most macroscopic view of research interest in these predictive methodologies, irrespective of application area. Exact Boolean search phrases can be found in Supplementary Table To assess the state of publishing on ML for antibiotic discovery, we measured trends among papers in PubMed, a public database maintained by the United States National Library of Medicine of the National Institutes of Health (ned Fig. . QueryinResults indicate increasing research interest in all areas over the first two decades of the twenty-first century, with the volume of ML literature focused explicitly on antibiotics and cancer drugs lagging behind broader drug development applications by nearly a decade. Surprisingly, publication counts for cardiovascular drugs and ML remain very low. As the general drug query did not double-count observations from the major disease groups explored, these results may suggest that broad applications have received greater research attention than disease group-specific applications. However, similar trends in cancer- and antibiotic-related publication rates suggest that antibiotics might not be disproportionately neglected. The prevalence of general-interest lines of inquiry might be due to the relative recency of ML for drug discovery, whereby the initial establishment phase lays the groundwork for future specialization. To that end, the significantly higher volume of general drug development applications represents a reservoir of research that is expected to have trickle-down impacts on disease group-specific research over time. Further, the proportion of AI and ML publications that feature applications in general drug discovery, antibiotic discovery, and cancer drug discovery have each increased throughout the twenty-first century. Our analysis also marks 2018 as a watershed moment for the use of ML for antibiotic discovery, coinciding with landmark papers in the field published that year together with preceding software developments.138. Increased data sharing from both successful and failed projects in the pharmaceutical industry has also been proposed as a means of accelerating research and development139. Existing data can also be further mined for new purposes, as exemplified by resources like the Drug Repurposing Hub140. While ML increasingly opens up new regions of chemical space to exploration, the repurposing of non-antibiotic pharmaceuticals could also be a promising avenue for antibiotic discovery1 that has already benefited from DL methods16.Over the third decade of the twenty-first century, prospects for ML-facilitated antibiotic discovery will partially hinge on data improvements. As larger data sources become publicly available, new ML questions can be pursued and ongoing questions can be revisited with greater rigor. While expanding public sources of experimental data will be crucial, federated learning across institutions may facilitate empirical dataset expansion without sharing private data, as has been done in other areas of biomedical ML129, suggesting that computational antibiotic discovery might similarly benefit from combined expertise. Increased coupling of in silico model testing with in vitro and in vivo validation—and even additional computational methods, e.g., molecular dynamics simulation141—will help ensure that published models are robust and yield experimentally actionable predictions. Interdisciplinary collaboration might also facilitate increasingly insightful predictions through biologically informed IML. As a response to the prevalent “black-boxing” of ML models’ internal decision-making, IML is an expanding focus in biomedical computation142 that has been used to elucidate antibiotic MOAs143. As firmer terminological and methodological standards alleviate significant confusion surrounding its diverse implementations144, IML is expected to enable greater human interpretability and causal inference in antibiotic discovery than opaque algorithms generally allow. Expanding interpretability for causal biological insights will surely require both computational creativity and biomedical domain knowledge.A recent review observed greater technical correctness among biomedical ML publications featuring collaborations across computer science, biology, and medicine145 may indicate the utility of ML-guided autonomous robotics in future antibiotic discovery. Creative integration of diverse lessons from NLP, computer vision, generative DL, computer-aided drug design, and other flourishing areas in ML research will play important roles in accelerating the urgent task of novel antibiotic discovery.Additional new avenues for ML-facilitated antibiotic discovery are expected to trickle in from algorithmic theory, robotic AI, and adjacent computational domains. While this review has focused on ML rather than embodied AI, recent attempts to deploy intelligent robots in chemical experimentationSupplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1


The ancestor of cetaceans underwent a macroevolutionary transition from land to water early in the Eocene Period >50 million years ago. However, little is known about how diverse retroviruses evolved during this shift from terrestrial to aquatic environments. Did retroviruses transition into water accompanying their hosts? Did retroviruses infect cetaceans through cross-species transmission after cetaceans invaded the aquatic environments? Endogenous retroviruses (ERVs) provide important molecular fossils for tracing the evolution of retroviruses during this macroevolutionary transition. Here, we use a phylogenomic approach to study the origin and evolution of ERVs in cetaceans. We identify a total of 8,724 ERVs within the genomes of 25 cetaceans, and phylogenetic analyses suggest these ERVs cluster into 315 independent lineages, each of which represents one or more independent endogenization events. We find that cetacean ERVs originated through two possible routes. 298 ERV lineages may derive from retrovirus endogenization that occurred before or during the transition from land to water of cetaceans, and most of these cetacean ERVs were reaching evolutionary dead-ends. 17 ERV lineages are likely to arise from independent retrovirus endogenization events that occurred after the split of mysticetes and odontocetes, indicating that diverse retroviruses infected cetaceans through cross-species transmission from non-cetacean mammals after the transition to aquatic life of cetaceans. Both integration time and synteny analyses support the recent or ongoing activity of multiple retroviral lineages in cetaceans, some of which proliferated into hundreds of copies within the host genomes. Although ERVs only recorded a proportion of past retroviral infections, our findings illuminate the complex evolution of retroviruses during one of the most marked macroevolutionary transitions in vertebrate history. The ancestor of cetaceans invaded the aquatic environments >50 million years ago. Recent comparative genomic studies have begun to reveal the molecular mechanisms underlying the morphological and ecological transformation of cetaceans. However, it has been largely obscure how retroviruses evolved during this land-to-water transition. Endogenous retroviruses (ERVs) recorded past retroviral infections. Here, we systematically mined and analyzed ERVs in the genomes of 25 cetaceans, and found that cetacean ERVs clustered into 315 distinct lineages. Among these cetacean ERV lineages, 298 lineages may originate through retrovirus endogenization before or during the colonization of aquatic environments by cetaceans, and 17 lineages may arise from endogenization of retroviruses that infected cetaceans through cross-species transmission from non-cetacean mammals after the transition to aquatic life of cetaceans. Taken together, our study provides a unique snapshot of the evolution of retroviruses during a major macroevolutionary transition. The ancestors of modern cetaceans  underwent a macroevolutionary transition from terrestrial to aquatic environments early in the Eocene >50 million years ago –4. PakicDiverse viruses have been reported to infect cetaceans, including adenoviruses , astroviRetroviruses have been known to infect vertebrates, including cetaceans –27. The Alpharetrovirus, Betaretrovirus, Gammaretrovirus, Deltaretrovirus, Epsilonretrovirus, Lentivirus, and Spumaretrovirus (foamy viruses). Based on their relationship with exogenous retroviruses, ERVs have been grouped into three classes, namely Class I, Class II, and Class III ERVs . Ho. HoKogiaBalaenoptera acutorostrata and H. amphibious (reflecting the divergence between cetaceans and hippopotamuses) as well as the genetic distance of orthologous introns between B. acutorostrata and Orcinus orca (reflecting the divergence between mysticetes and odontocetes). The peak of genetic distance between 5’- and 3’-LTRs of the LTW ERVs overlaps the mean genetic distance of introns between cetacean and hippopotamus and is much greater than the mean genetic distance of introns between mysticetes and odontocetes  on both sides of a provirus are identical at the beginning of virus integration, followed by divergence due to neutral evolution in the host genome. Therefore, the timing of a single ERV integration event can be estimated by measuring the genetic distance between LTR sequences. The genetic distance between 5’- and 3’-LTRs of an ERV increases with its integration time , 38. To ntocetes . These aElephantulus edwardii but not in the mysticetes  are subject to different selection pressure. To explore the proliferation modes for the SHS ERV lineages, we performed selection pressure analyses of retroviral genes for eight lineages with greater than four ERVs in a certain species by estimating dN/dS ratios for internal branches [env gene, and the gag and pol genes of lineage 307 ERVs underwent purifying selection  of ERV lineages as genomic loci (not exogenous retroviruses) appear to have undergone a shift from land to water with their cetacean hosts. The LTW scenario actually includes retroviruses that infected and became endogenized in the terrestrial ancestor of cetaceans (before the conquest of aquatic environment) and retroviruses that infected the semiaquatic or aquatic ancestor of cetaceans before the last common ancestor of mysticetes and odontocetes (during the conquest of aquatic environment), which cannot be clearly distinguished based on the current data. The ERV copy numbers for these ERV lineages are generally low within a single host genome, suggesting the activity of ERVs was not high upon transiting into water. These retrovirus lineages seem to await degradation after evolutionary journey to aquatic environment, namely evolutionary dead-ends.Interestingly, we identified 17 ERVs lineages that are only present in a sub-lineage of cetaceans, either within mysticetes or within odontocetes. Synteny analyses and integration time analyses show that these ERVs derived from recent retroviral integrations. Phylogenetic analyses indicate these retroviral lineages might originate from cross-species transmissions after the colonization of aquatic environments of cetaceans. These ERVs might represent a proportion of retroviruses currently circulating in cetaceans, given not all the retroviruses in cetacean have been endogenized. Many of these cetacean ERVs are closely related to mammal species other than even-toed ungulates. Actually, cetaceans have more interaction with terrestrial and semi-aquatic mammals than intuitively thought; for example, killer wales have been seen feeding on terrestrial mammals and seals –46. IndeThe LTW ERV lineages belong to Class I and III ERVs, but the SHS ERV lineages belong to Class I and II ERVs, reflecting a changed retrovirus spectrum after diving into the aquatic environments. Given that the 17 SHS ERV lineages originated independently, our results suggest at least 17 cross-species transmission events from non-cetacean mammals to cetaceans occurred after cetaceans invaded aquatic environments but during the evolutionary course of the modern cetaceans. However, these ERVs only represent a proportion of retroviruses currently circulating in cetaceans, because not all the retroviruses in cetaceans have been endogenized. It is possible that host-switching from non-cetacean mammals to cetaceans might be more frequently than appreciated.Dozens of viruses, both DNA viruses and RNA viruses, have been described in cetaceans. Due to the under-sampling of viruses in cetaceans and more generally wild mammals, it is difficult to explore how these DNA and RNA viruses originated in cetaceans, as well as how viruses evolved during the evolutionary transition from terrestrial to aquatic environments. In contrast, ERVs provide one of the best models to study these questions. Our study provides novel insights into the complex evolution of retroviruses, and possibly viruses in general, during the macroevolutionary transition of cetaceans.https://www.ncbi.nlm.nih.gov/genome/), including 6 Mysticeti species and 19 Odontoceti species e cut-offB. acutorostrata and H. amphibian and 100 random orthologous introns of Balaenoptera acutorostrata and Orcinus orca were retrieved and aligned using MAFFT 7.450 [To identify complete ERVs, we bidirectionally extended all the cetacean ERV RT sequences and used LTRfinder  and LTRhFT 7.450 .H. amphibious or between mysticetes and odontocetes, we bidirectionally extended 500–1,000 bp sequences flanking the ERV  was used to predict ORFs. The Conserved domain (CD) Search [gag, pol, and env. Premature stop codons were removed. The dN/dS ratio was estimated using the CodeML program in PAML 4.9 [Within a single cetacean species, all the complete ERV sequences for each SHS ERV lineage were retrieved and aligned with the L-INS-i strategy using MAFFT 7.450 . Dataset) Search  and the ) Search  were usePAML 4.9 . The "onS1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.S7 Table(PDF)Click here for additional data file.S8 Table(PDF)Click here for additional data file.S1 FigReference retrovirus sequences are highlighted in red. The 315 cetacean ERV lineages are classified into Class I (in blue and green boxes), Class II (in red box), and Class III (in purple box) ERVs. The support values (ultrafast bootstrap approximation) are shown for selected nodes. The information of the representative cetacean ERVs is available in (PDF)Click here for additional data file.S2 FigThe cetacean ERV sequences are highlighted in purple. The support values are shown for selected nodes. Abbreviations are listed in (PDF)Click here for additional data file.


Background: Racial/ethnic minorities generally have a lower knowledge of human papillomavirus (HPV) and the HPV vaccine than non-Hispanic Whites. They are also less likely to have a regular healthcare provider (HCP). Given the role of HCPs in disseminating health information, we evaluated whether racial/ethnic disparities in HPV knowledge are moderated by regular HCP status. Methods: Data from the Health Information National Trends Survey Five (HINTS 5) Cycles One and Two (2017–2018) were analyzed. HPV and HPV vaccine knowledge were compared by regular HCP status across race/ethnicities. Independent partially-adjusted multivariable logistic regression models were used to assess the association between race/ethnicity and knowledge after controlling for sociodemographic characteristics. The resulting adjusted odds ratios were compared to those from fully-adjusted models that included HCP status. Results: After adjusting for regular HCP status, differences in knowledge persisted between racial/ethnic groups. Compared to Whites, Hispanics and Other race/ethnicities had significantly lower odds of having heard of HPV. Blacks, Hispanics, and Other race/ethnicities had significantly lower odds of having heard of the HPV vaccine. Conclusion: Racial/ethnic minorities had significantly lower levels of knowledge despite HCP status. These data suggest the need to address disparities in health information and strengthen provider–patient communication regarding HPV and the HPV vaccine. Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States (U.S.), with nearly 80 million Americans currently infected and 14 million new infections expected each year . Over 45Racial/ethnic minority populations, particularly non-Hispanic Blacks and Hispanics, are disproportionately impacted by HPV and certain HPV-associated cancers. For example, non-Hispanic Black women have a 25% higher incidence of cervical cancer compared to non-Hispanic White women and are almost twice as likely to die from cervical cancer . Among HVaccine and other preventive care uptake is a complex, multilayered phenomenon influenced by factors at the personal, interpersonal, structural, and societal level . KnowledHCPs play an important role in the acceptance and uptake of the HPV vaccine and cervical cancer screening ,19. A hiGiven the role of HCPs in recommending HPV prevention services, we hypothesized that having a regular healthcare provider may attenuate racial/ethnic disparities in knowledge of HPV and the HPV vaccine. Here we examine racial/ethnic differences in knowledge of HPV and the HPV vaccine and assess how they are moderated by having a regular healthcare provider.Cross-sectional data were obtained from the fifth iteration of the National Cancer Institute’s (NCI) Health Information National Trends Survey (HINTS 5), Cycles 1 (2017) and 2 (2018). HINTS is a nationally representative random-digit dial telephone survey of non-institutionalized U.S. adults to monitor data on health-related behaviors, knowledge, and attitudes ,29. DataHave you heard of HPV?” and “Have you ever heard of the cervical cancer vaccine or HPV shot?” Having a regular healthcare provider was assessed through the item, “Is there a doctor/nurse/health professional that you see most often?” Sociodemographic variables were race/ethnicity , gender, age, education, and household income. Census region  and geographic area  were also available in the HINTS data files. Discussion of the HPV vaccine with a healthcare provider for the ad hoc analysis was assessed through the item “Has a doctor ever talked with you about the HPV shot?” This item was only asked to those individuals who reported having an immediate family member between the ages of 9 and 27 years old.Knowledge of HPV and the HPV vaccine were assessed based on the items, “p-values were used for comparisons by race/ethnicity, both overall and stratified by HCP status. Independent, partially-adjusted multivariable logistic regression models were used to examine the association between HPV and HPV vaccine knowledge and awareness in each racial/ethnic group after adjusting for sociodemographic characteristics. A second, fully-adjusted model for each outcome, which included sociodemographic characteristics and regular HCP status, was used to determine whether the latter had an impact on the association. All statistical tests were considered significant if the p-value was less than 0.05. An ad hoc analysis was conducted to examine the receipt of a provider recommendation among individuals with a regular HCP who were themselves or who had a family member who was age-eligible for the HPV vaccine . To account for the complex survey design and provide representative estimates of the U.S. population, data from Cycles 1 and 2 were weighted based on the analysis recommendations given by the HINTS investigators [Descriptive statistics were used to describe within-group frequencies and between-group comparisons. Wald chi-square tests and adjusted tigators ,31. Thistigators ,29. Datatigators ,31. All tigators .Of the 6552 respondents, 4093 were non-Hispanic White, 1057 were non-Hispanic Black, 778 were Hispanic, and 624 were of Other race/ethnicity. The results of the partially-adjusted multivariate logistic regression models (Model 1) comparing odds of HPV and HPV vaccine knowledge while adjusting for sex, age, education, income, census region, and geographic area, are presented in Racial/ethnic differences in knowledge of HPV-associated cancers were mostly non-significant except for cervical and oral cancer . In the In ad hoc analyses examining receipt of a provider recommendation among individuals with a regular HCP who were themselves or who had a family member who was age-eligible for the HPV vaccine, individuals of Other race/ethnicity had lower odds (OR = 0.44 (0.20–0.95)) of receiving a provider recommendation compared to non-Hispanic Whites . Odds ofThe findings of this nationally representative analysis show that racial/ethnic minority groups in the U.S. have significantly lower knowledge of HPV and the HPV vaccine compared to non-Hispanic Whites. Furthermore, certain minority groups also had significantly lower knowledge of the causal link between HPV and cervical cancer (Other races) or oral cancer (non-Hispanic Blacks) compared to non-Hispanic Whites. Given that a regular healthcare provider is more likely to recommend preventative services  and counsel patients during preventative service encounters ,19, we hWe hypothesize that the null effect of regular HCP status on the association between race/ethnicity and knowledge of HPV and the HPV vaccine is due to the general low prevalence of HPV and HPV vaccine discussion between HCPs and patients. While a strong recommendation by a provider is a prominent predictor of HPV vaccine uptake , many HCIn our ad hoc analysis of individuals with a regular healthcare provider who were themselves age-eligible for the HPV vaccine or had an age-eligible family member, we found that the prevalence of HPV vaccine discussion with a provider was not significantly different for non-Hispanic Blacks and Hispanics compared to non-Hispanic Whites. Our ad hoc analysis had a relatively small sample size and was underpowered to detect meaningful differences. However, prior studies have reported significant racial/ethnic disparities in provider HPV vaccine recommendations . While fOur findings suggest that having access to a regular HCP does not mitigate disparities in knowledge of HPV or the HPV vaccine among racial/ethnic minority populations. This informs future opportunities to explore other options that may contribute to disparate knowledge such as the communication strategy used for patient–provider discussions regarding vaccination  especialThere are limitations to this research that should be considered when interpreting these results. As with all survey data, there is room for recall bias, particularly when reporting provider HPV vaccine recommendations. However, we are unaware of any data indicating that recall bias is differential across race/ethnicities. Our ad hoc analysis had a small sample size resulting in its low power to detect differences in HPV vaccine recommendation by regular HCP. Finally, while HCP status is a common marker of healthcare access and utilization , the varIn conclusion, having a regular HCP does not seem to attenuate the significant racial/ethnic disparities in HPV and HPV vaccine knowledge. Further research is needed to understand factors contributing to HPV knowledge differences and the subsequent development of targeted interventions to mitigate racial/ethnic disparities in knowledge and awareness of HPV and the HPV vaccine.


General anesthetics disrupt brain processes involved in consciousness by altering synaptic patterns of excitation and inhibition. In the cerebral cortex and hippocampus, GABAergic inhibition is largely mediated by inhibitory interneurons, a heterogeneous group of specialized neuronal subtypes that form characteristic microcircuits with excitatory neurons. Distinct interneuron subtypes regulate specific excitatory neuron networks during normal behavior, but how these interneuron subtypes are affected by general anesthetics is unclear. This narrative review summarizes current principles of the synaptic architecture of cortical and interneuron subtypes, their contributions to different forms of inhibition, and their roles in distinct neuronal microcircuits. The molecular and cellular targets in these circuits that are sensitive to anesthetics are reviewed in the context of how anesthetics impact interneuron function in a subtype-specific manner. The implications of this functional interneuron diversity for mechanisms of anesthesia are discussed, as are their implications for anesthetic-induced changes in neural plasticity and overall brain function. The mechanistic understanding of general anesthesia has seen substantial recent progress, but there is a conceptual gap between the molecular pharmacology of anesthetic targets and the network level changes in the anesthetized central nervous system (CNS). General anesthetics alter the balance and dynamics of neural excitation and inhibition at the levels of single receptors, synapses, and circuits. Neural inhibition is largely mediated by GABAergic interneurons in the brain, with biochemically and functionally distinct interneuron subtypes responsible for different forms of synaptic and circuit inhibition. Specialized interneurons are adapted for specific neural circuit roles  to stabilize the membrane potential away from the action potential threshold, either by hyperpolarization or shunting inhibition, effectively controlling neurotransmission in target cells. The interneuron function has classically described “local” effects within the same anatomical region as the cell body and dendrites. However, recent findings demonstrate small populations of cortical and hippocampal GABAergic neurons with long-range projections Jinno, . InterneHere we review cortical and hippocampal interneuron diversity in the context of the neurophysiological mechanisms and consequences of general anesthesia. We describe the architecture for interneuron specialization, including their heterogeneous expression of known molecular targets for various anesthetics, and discuss evidence of interneuron subtype-specific anesthetic sensitivities determined by differential expression of these anesthetic targets. We also discuss evidence that changes in interneuron function contribute to the persistent effects of anesthesia, and summarize preclinical evidence for altered interneuron function in both positive and negative changes in brain function following anesthetic exposure. Finally, we propose approaches to elucidate the roles of interneurons in anesthetic mechanisms and discuss the advanced methods that could address current questions.We chose to focus this review on cortical and hippocampal interneurons in part because we have the most advanced understanding of the fundamental inhibitory systems governing excitatory neuron output and network function in these brain regions. However, anesthesia is a composite state with several distinct behavioral endpoints  attributed to different brain regions. For example, loss of consciousness alone involves not only the cortex but also the thalamus, subcortical regions, and the brainstem. It is difficult to compare cortical and hippocampal interneuron systems with those in other brain regions critical for anesthesia in part because the basic neuroanatomy differs. However, interneurons do share common features and embryological origins, such that the overlapping pharmacological effects may be generalizable.In the cerebral cortex and hippocampus GABAergic interneurons represent only ~10–20% of total neurons, however, they exert a disproportionately large impact on functional network outputs. By definition, inhibitory interneurons synthesize and release GABA by one of two glutamic acid decarboxylase isoforms (GAD65 and GAD67). However, many GABAergic interneurons also corelease various neuromodulators and neuropeptides that define them both immunocytochemically and functionally. Interneuron subpopulations include multiple subtypes  with chaBelow we outline the defining characteristics of prominent cortical and hippocampal interneuron subtypes following conventional classification  interneuronsParvalbumin-expressing  response to depolarizing current injection without accommodation (diminished spike height during a train). In contrast, excitatory pyramidal neurons and most other interneurons have lower maximal firing rates and show spike accommodation with AP height rapidly declining during a sustained depolarization. PV+ cells have the highest maximal firing rate of hippocampal neurons (>300 Hz), critical for participation in high-frequency network activity. The AP waveform of PV+ interneurons is also unique compared to excitatory neurons and other interneurons, characterized by a narrow profile  and a large, brief afterhyperpolarization due to high axonal expression of kinetically fast K+ channels  in the cortical electroencephalogram  interneuronsSomatostatin-expressing  and a single prominent axon that projects superficially. Hippocampal SST+ interneurons, named OLM and hilar perforant path-associated (HIPP) neurons, are considered the functional homologs of cortical Martinotti cells and share a similar morphology4.Most cortical SST+ neurons are non-fast firing, show spike adaptation, and have a maximum firing rate far lower than PV+ interneurons. The AP is wider due to differential voltage-gated Na+ and K+ channel expression and kinetics. SST+ interneurons have a higher intrinsic excitability than PV+ interneurons, with a lower AP threshold and high basal activity in the theta bandwidth (3–10 Hz). SST+ interneurons are primarily excited by pyramidal neurons and inhibited by VIP+ interneurons. VIP+/SST+ axo-dendritic synapses express GABAA receptors containing the high-affinity α5 subunit consistent with strong inhibition  and selectively target other interneurons. This circuit role is thought to disinhibit local excitatory cells by creating holes in the “blanket of inhibition”, and/or modulate spatiotemporal patterns of inhibition and tune the excitatory cell input/output firing relationship  which creates the gaseous transmitter nitric oxide (NO) that has both presynaptic and postsynaptic actions . In the basal forebrain associated with overlapping sleep and anesthesia circuits, selective activation of SST+ but not of PV+ interneurons results in increased sensitivity to isoflurane anesthesia and propofol anesthesia concomitant with potentiation of several EEG hallmarks of deep anesthesia including increased δ frequency power  shape the AP waveform. Kv3 channels are activated by depolarized membrane potential during the AP and mediate rapid repolarization, enabling hippocampal PV+ and SST+-OLM interneurons to fire at a higher frequency than pyramidal neurons  channelsTwo pore domain K, including the Twik-related acid-sensitive K+ channel (TASK) subtype, mediate background leak conductance. Many forms of K2P are implicated in anesthesia and natural sleep cycles  participate in subthreshold excitability and rhythmicity; HCN inhibition by general anesthetics contributes to loss of consciousness and amnesia Voltage-gated Na are inhibited by volatile anesthetics (with depressed peak current and enhanced inactivation) resulting in AP depression and reduced presynaptic excitability. The CNS Nav channels have distinct anesthetic sensitivities and cellular expression patterns. Nav1.1 is enriched in interneurons  and is the least sensitive to volatile anesthetics, whereas Nav1.2 is more abundant in SST+ interneurons and is more sensitive to volatile anesthetics ionotropic serotonin receptor  that can be depolarized by 5-HT3AR agonists Voltage-gated Ca subtypes differ in biophysical parameters, cellular expression profiles, and roles for synaptic plasticity  mediated Ca2+ entry controls synaptic vesicle exocytosis and neurotransmitter release. Excitatory synapses express a mixture of P/Q and N-type channels to mediate synchronous glutamate release  utilize slower N-type channels and mediate asynchronous transmission .2+ channels also influence Ca2+-gated second messenger signaling cascades for activity-dependent plasticity. v1.2/1.3 (L-type)Ca channel expression is highest in PV+ and SST+ interneurons. In PV+ interneurons, and L-type Ca2+ channels participate in a novel cell-specific pathway for CREB phosphorylation and activity-dependent transcriptional regulation . Cav3 (T-type) channels have also been associated with distinct forms of plasticity between hippocampal PV+ and SST+ synapses onto pyramidal neurons  receptor subunit expression, which alters the kinetic properties of Ca2+ influx . NMDA receptor subunits also control interneuron development in a subtype-specific manner, so distinct anesthetic sensitivities could result in different consequences from exposure during the critical period of neurodevelopment . Given the complexity of neuronal circuits and the delicate balance between excitation and inhibition, transmitter-specific effects can produce opposite neurophysiologic results. For example, enhanced inhibitory input onto inhibitory interneurons can lead to disinhibition, or indirect excitation by removal of an inhibitory input , and especially compared to pyramidal neurons  currents, ~60% show Ih potentiation by halothane with no effect in the rest. In most interneurons, halothane decreases spontaneous firing rate, but for ~8% of interneurons, halothane depolarizes the resting membrane potential and increases spontaneous firing. Halothane, isoflurane, and sevoflurane all increase spontaneous inhibitory postsynaptic current frequency, consistent with increased presynaptic release probability at unidentified local interneuron-interneuron connections  do beg the question of whether any of these associations are recapitulated elsewhere in the brain. For example, there are PV+ interneurons in the thalamus, although these are not from the same interneuron progenitor pool as cortical and hippocampal PV+ interneurons , and receives research funding unrelated to this study from Instrumentation Laboratory/Werfen . The remaining author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.