The methamphetamine epidemic continues to worsen each year and has contributed to more overdose deaths than opioids. Methamphetamine was listed in the top ten lethal drugs in 2021 in the United States. The drug has been shown to cause health problems such as addiction and neurological and behavioral changes. One possible solution to address this crisis is through vaccinations. Vaccinations consist of injecting a controlled substance with the goal of creating compound-specific antibodies. Although still early in development, vaccinations have been found to improve withdrawal symptoms and decrease drug-seeking behavior with minimal health side effects in rodent studies. This paper provides an overview of the clinical presentation and neurobiology of methamphetamine addiction and drug-seeking behaviors. The responses and adverse effects of conjugate vaccines IXTv-100 with adjuvant glucopyranosyl lipid A administered in oil-water stable emulsion and tetanus-toxoid conjugated to succinyl-methamphetamine adsorbed on aluminum hydroxide combined with adjuvant E6020 are examined. Preprints.org server on May 2, 2023.This article was previously posted to the Methamphetamine was synthesized in 1893 and is a derivative of amphetamine. Over the years, drug applications were found for depression, narcolepsy, weight loss, and as an over-the-counter nasal decongestant . The stiMethamphetamine use disorder is a global concern, impacting users’ organ systems, particularly the neurological and cardiovascular systems . ApproxiThere are limited efficacious pharmacotherapeutic options available to combat this highly addictive substance . ImmunopThe advantage of using vaccines include expectations of high patient adherence rates, and given treatment consists of a few injections over a brief period of time compared to taking pharmaceutical treatments daily or on a weekly basis to produce active immunization, reduction of adverse effects, reduction of drug reinforcement, and prevention of overdoses and relapses during recovery .Herein, we reviewed the literature on the clinical presentation of methamphetamine misuse, the neuropsychiatric drug-seeking behavior, the responses of anti-methamphetamine vaccines IXTv-100 with adjuvant glucopyranosyl lipid A administered in oil-water stable emulsion (GLA-SE) and tetanus-toxoid conjugated to succinyl-methamphetamine (TT-SMA) adsorbed on aluminum hydroxide  combined with adjuvant E6020, and the adverse effects in vaccinated rodents.MethodsLiterature reviews on vaccine treatments for drug misuse, as well as the effects of methamphetamine, were conducted using a variety of sources and credible databases such as PubMed and Google Scholar using search terms: “methamphetamine,” “methamphetamine addiction,” “neurological methamphetamine effects,” “clinical methamphetamine effects,” and “methamphetamine vaccine.” Research articles were deemed valid and credible for involvement as they were published in peer-reviewed journals and sourced from respectable databases. The search was limited to articles published in English with no restrictions on the publication dates on the neurobiology of methamphetamine misuse and the clinical presentation. Articles that evaluated preclinical investigations on the reduction of methamphetamine misuse with the use of conjugate vaccines targeting toll-like receptor 4 ranged from 2013 to 2022.Clinical presentation of methamphetamine addictionMethamphetamine has a high lipid solubility that leads to rapid transfer of the drug across the blood-brain barrier -18. MethToxic high doses of methamphetamine have a severe effect on the cardiovascular system such as vasoconstriction, pulmonary hypertension, atherosclerotic plaque formation, arrhythmias, and heart failure ,28. A coMethamphetamine affects the sympathetic and parasympathetic nervous system that regulates the GI ,27,28. AWith methamphetamine addiction, physiological and psychological changes take place. An investigation looked at the effects of face asymmetry with methamphetamine addiction and found chronic misuse increased poor performance on face recognition and changes to the individual's face . AnotherThe social impacts of misusing methamphetamine have been examined in children under 18. If misused when young, school attendance decreased in children. The Ontario Student Drug Use Survey 2005 found that in grades 7-12, there were 2.2% of students admitted to using methamphetamine . This stAddiction and methamphetamine misuse have behavioral effects on the individual's social well-being. Not only is there an increase in irritability, but other behavioral patterns are seen. These behaviors are noted in different age groups as well as overall addiction to the drug. Common behavior changes due to addiction and withdrawal include aggression that leads to violent and criminal behaviors . The incThe neuropsychiatric substrates of drug-seeking behaviorThe brain plays a key role in drug-seeking behavior as it is the control center for positive and negative feedback. Sections of the brain work together to stimulate drug-seeking behaviors. While the brain exhibits neuroplasticity and other mechanisms to restore itself, certain brain transformations can increase drug-seeking behaviors, while other areas of the brain suffer irreversible damage . Nearly Some people may be more vulnerable to the effects of drugs due to genetic predisposition, environmental factors, and lifestyle. Genetic makeup contributes to a person’s vulnerability to drug addiction, including environmental impact on gene function and expression . EnvironMesolimbic Dopamine SystemThe mesolimbic dopamine (DA) system is also known as the reward center. It is responsible for euphoric feelings due to the release of neurotransmitters such as norepinephrine and dopamine in the brain . The DA Additionally, prolonged drug usage leads to decreases in dopamine transporters . DopaminBasal GangliaThe basal ganglia are part of the nigrostriatal dopamine pathway that works together with the DA system to produce euphoric effects and increased addiction behaviors. The basal ganglia are responsible for coordinated movements and motivating behavior. It is essential to encourage prolonged drug usage. There are two sub-regions in the basal ganglia that particularly contribute to drug-seeking behaviors. The first region in the basal ganglia is the nucleus accumbens, which is involved in the euphoric experience and positive feedback ,40. The Extended AmygdalaThe extended amygdala consists of the bed nucleus of the stria terminalis, sublenticular substantia innominata, and the central medial amygdala and controls feelings of stress, anxiety, and unease . It is rPrefrontal CortexThe prefrontal cortex (PFC) is located in the frontal lobe of the brain and is responsible for executive functions such as planning, solving problems, and making decisions. It plays a role in executing compulsive activities as well as impulse control . With prWhat PET Imaging Has Shown UsPositron emission tomography (PET) is most widely used to image the brain in adults with addiction . This teNeuroimaging also revealed changes to the extended amygdala. Prolonged use of drugs increased activation of the amygdala and decreased control of the PFC over the amygdala . This deLastly, drug abuse can harm the neurodevelopment of fetuses. Fetal brain structures, in particular the frontal cortex and subcortical areas, were found to be highly susceptible to functional and structural change due to intrauterine drug exposure .Treatment vaccine response in rodentsCurrently, the US FDA does not have any approved medications for the treatment of methamphetamine addiction . AddressIXTv-100 With Adjuvant GLA-SE Rodent-Related StudiesPrior studies were conducted to produce methamphetamine antibodies to reduce related addictive behavior with the administration of the vaccine IXTv-100 in rodents . IXTv-10The mice were intravenously self-injecting 0.06 mg/kg/infusion of methamphetamine through an indwelling jugular catheter . After tThe IXT-v100 GLA-SE vaccine reduced meth-self administration by 50% compared to the rodents' baseline . GLA-SE IXT-v100-related vaccines resulted in decreased neurotransmitter release . MethampTT-SMA With Alum and Adjuvant E6020 Rodent StudiesWhile KLH is an efficient carrier protein, the large size of the protein makes it difficult to control the quality of the haptenation process ,14,15,54A prior study investigated the effects of TT-SMA with alum and adjuvant E6020. The aim of the study was to determine the efficacy of the vaccine by measuring the levels and affinity of anti-methamphetamine antibodies produced as well as the ability to attenuate methamphetamine-induced locomotor responses and the assessment of methamphetamine levels in the brain. Methamphetamine hydrochloride was dissolved in sterile saline based on concentrations of 0.5mg/ml . The micWhile adjuvant E6020 was used in vaccines to increase immunization of antigen-specific antibodies, there was a reduction in drug hunger and locomotor-induced responses . More reActive Immunity on the Reward SystemThe effects of methamphetamine misuse on the reward system and on the active immune system are not fully understood in humans . The druThe goal of active immunization is to create an immunological response against the drug and create an immunological memory with reexposure to the vaccine through booster injection . The immRodent studies show further research is needed to create conjugate vaccines ,6. IncreRodent research has found that methamphetamine vaccines can generate a sufficient level of antibodies . HoweverAdverse Effects Past research in rodents showed active immunization using conjugated vaccines can reduce the physiological and behavioral effects of methamphetamine -15,58,68Methamphetamine use increased rectal temperature in rodents at 27ᵒC and decreased body temperature at 23ᵒC in unvaccinated control groups ,55. In vBehavior was measured using wheel activity. Given methamphetamine is a stimulant, there was a significant increase in induced physical activity . In bothA group of unvaccinated rodents had reduced food intake compared to the rodents given a dose of the IXTv-100 vaccine after high levels of methamphetamine administration ,12,57,59Prior preclinical studies using rodents suggest vaccine administration is safe as there were no adverse effects ,6,12,63.Strategies utilizing either active immunization have shown potential . Not onlThere are currently no clinical trials given a high drop-out rate . ResultsThese studies showed that a significant decrease in drug self-administration can be achieved via higher levels of a drug-specific antibody to block the drug from entering the brain’s reward center. A sufficient antibody response is required for the immunotherapeutic strategy to be efficacious. Generating methamphetamine-specific antibodies to bind and form complexes with drug molecules in the peripheral bloodstream through the immunotherapeutic strategy resulted in the reduced entrance to the blood-brain barrier and decreased drug effect on the CNS. Effective processing and leukocyte recognition of antibody-drug complexes produced long-lasting antibody responses. As a result, the likelihood of methamphetamine misuse, relapse, and overdose can be potentially reduced. Methamphetamine-conjugated vaccines such as IXTv-100 GLA-SE and TT-SMA with adjuvant E6020 were found to reduce side effects such as hyperlocomotion, impaired food responses, and promotion of thermoregulation.More clinical trials evaluating the immunological response of vaccines on methamphetamine misuse are needed. The immunological response of conjugate vaccines depends on the hapten design, the chemical position of a linker between the target antigen and carrier protein, and the modification and selection of carrier proteins and adjuvants. The methamphetamine overdose epidemic poses a challenge to public health and places a costly burden on the healthcare system. Given the minimal development of adverse effects and the efficacy of antibodies to reduce drug effects, reducing methamphetamine abuse through vaccinations in humans may be a feasible and promising option in the future.


Sickness presenteeism among healthcare workers (HCW) risks nosocomial infection, but its prevalence among HCW with COVID-19 is unknown. Contemporaneous interviews revealed a sickness presenteeism prevalence of 49.8% among 255 HCW with symptomatic COVID-19. Presenteeism prevalence did not differ among HCW with and without specific COVID-19 symptoms or direct patient care. Studies on laboratory-confirmed influenza in HCW revealed a presenteeism prevalence of 14% to 68%. The risks posed by presenteeism are higher with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) because it is more transmissible and virulent than influenza, yet HCWs with coronavirus disease 2019 (COVID-19) still demonstrate presenteeism. Despite numerous reports of nosocomial outbreaks of COVID-19 linked to symptomatic staff, there have been no systematic studies on the prevalence of presenteeism in HCWs with COVID-19. We sought to determine the prevalence and factors contributing to presenteeism with COVID-19.Sickness presenteeism (working while sick) in healthcare workers (HCWs) likely contributes to nosocomial transmission of respiratory viruses.This observational cohort study included all HCWs at the Veterans’ Affairs Boston Healthcare System who tested positive for SARS-CoV-2 infection by polymerase chain reaction (PCR) assay between December 1, 2020, and September 30, 2021. The VABHS provides outpatient, tertiary inpatient, and long-term care on 3 main campuses and 5 outpatient clinics in eastern Massachusetts. HCWs included all employees working at VABHS, including those who did and did not provide direct patient care. Employees who tested positive outside VA Boston were also required to report results and were included in the study.During the observation period, all HCWs were required to perform a daily self-review of COVID-19 symptoms and to stay home or leave work if symptomatic. Free, onsite SARS-CoV-2 PCR testing was available for all HCWs and was required for those with COVID-19 symptoms, those with community exposure, and those who were part of contact tracing. Mandatory surveillance testing was conducted weekly to biweekly for all HCW working on long-term care units and the acute spinal cord injury unit.At the time of COVID-19 diagnosis, all HCWs completed a structured health interview under the direction of occupational health and infection prevention personnel . This interview captured the onset and type of symptoms if present and the number of days working on campus while symptomatic. Presenteeism was defined as working at least part of a day while newly symptomatic with COVID-19. Those without presenteeism tested positive prior to the start of their shift and did not work with symptoms.To explore HCW rationales for presenteeism, we distributed a survey between October 21, 2021, and November 21, 2021, to all HCWs who reported COVID-19 symptoms during the study period . This survey was anonymous and confidential. This study follows STROBE reporting guidelines.2 analysis. Statistical significance was defined as P < .05.The VABHS Institutional Review Board granted this project an exemption waiver because it was deemed quality improvement. Differences in proportions were determined using χDuring the study period, 327 of ∼4,000 HCWs at VABHS tested positive. All underwent the structured interview by occupational health, and 255 HCWs (78.0%) were symptomatic. Among those symptomatic with COVID-19, 127 HCWs (49.8%) reported presenteeism at the time of diagnosis . Rates of working with specific COVID-19 symptoms were similar in HCWs with and without direct patient-care responsibilities . HCW with presenteeism were 3 times more likely to be identified by mandatory surveillance than those without  completed the follow-up survey. This respondent subgroup did not differ significantly from the overall cohort with respect to age, sex, race, and direct patient contact Table . 53.4% oS1).Among all respondents, concerns over workload burden for coworkers and personal responsibility were endorsed more frequently  than limits on paid leave or perceived expectations to work while sick  (Supplementary Table  Somewhat surprisingly, rates of presenteeism did not differ between HCWs with and without direct patient care, suggesting that the perception of risk of transmission from HCW to patient alone did not modify choices about working while sick.COVID-19 presenteeism poses risk to both HCW and patients; the prevalence of COVID-19 presenteeism in this study was 49.8%. To our knowledge, this is the only systematic estimate of the prevalence of presenteeism due to COVID-19. Many studies have demonstrated the consequences of presenteeism, including COVID-19 clusters among HCWs and HCW transmission to patients.Our systematic interview data demonstrate that just over half of HCWs with presenteeism experienced relatively nonspecific symptoms. The follow-up survey suggests that for this subgroup, these nonspecific symptoms were frequently attributed to noninfectious causes. However, nearly half of HCWs with presenteeism had relatively specific symptoms of COVID-19. The HCW perception that “they knew how to take precautions at work to avoid getting others sick” may have led to decreased perceived risk. This sense of controllable risk may partly account for the lack of difference in the rates of presenteeism among HCWs with specific versus nonspecific symptoms of COVID-19. Indeed, a previous report indicated that most HCWs would work with “minor” symptoms of influenza-like illness. The fact that many HCWs were detected through mandatory surveillance testing, despite the wide availability of free testing for symptomatic HCWs, speaks to the many factors that promote presenteeism in healthcare settings. Targeted surveillance during community surges or in high-risk settings could be utilized to mitigate risks related to noncompliance with self-screening or misinterpretation of symptoms among HCWs.The high rate of presenteeism among HCWs mirrors high rates of COVID-19 symptom misrepresentation among a sample of the US population. Financial concerns related to missing work were endorsed infrequently, perhaps because paid leave was available for HCWs with COVID-19 throughout the study period. New strategies are needed to help HCWs with COVID-19 reconcile their duties to do no harm and to provide or support care.Consistent with reports of presenteeism prior to the COVID-19 pandemic, concerns about patient care responsibilities and burdening coworkers were expressed frequently.A strength of our study is the contemporaneous recording of symptoms across all symptomatic HCWs with COVID-19. One limitation of this study is the restriction of the analysis to a single, multicampus healthcare system. Our data did not allow us to differentiate HCWs who worked a small part of a day from those who worked all day with symptoms; hence, our estimate of 49.8% presenteeism should be considered a maximum. Although the survey response rate was relatively low, respondents were demographically and symptomatically representative of the entire cohort, and the survey data were used to augment the primary objective of the study—estimating the prevalence of COVID-19 presenteeism across the continuum of care settings.


It is of interest to document data on the molecular dynamics simulation analysis of alpha-cobratoxin docked with phytochemical compounds. This can be used as effective drug candidates against the snake and scorpion venom. It should be noted experimentalverification is needed to further validate the current data. Snake and scorpion venoms contain toxins that impact the prey's circulatory, nerve, muscular, and a variety of other systems . The volThe toxin that is present in both snake bite and scorpion venom was alpha-cobratoxin. The PDB structure for this toxin was retrieved from the Protein Database with the PDB ID 6ZFM . The proMolecular docking was performed by using Gold suite 5.3.0 using the standard operating procedures . The higMolecular dynamic simulations were performed to check the stability of the bonds that have been made during docking protocol. To understand the stability of the docked complexes generated from docking protocol, 50 ns of Molecular Dynamics (MD) simulationwas performed by using software known as SCHRODINGER , 28. TheAll the selected molecules were analyzed through the Molinspiration server for Lipinski’s Rule of Five. This rule explains the different drugs properties like absorption, metabolism and drug secretion in the human body. Compounds are evaluated on the basisof this rule as it includes different values like Molecular weight fewer than 500 Daltons, H-bound donor less than five, H-bound acceptors less than ten and log P-values must be <5 . Swiss-AVirtual screening of compounds: After successful virtual screening of 500 compounds top 5 inhibitors were chosen for further study against the target protein alpha-cobratoxin 2D visualization of those compounds were mentioned in the Figure 1(see PDF).The peptide that was removed from the PDB structure of alpha-Cobratoxin gave information about the binding pocket of the alpha-Cobratoxin. The binding pocket residue constituted Tyr-2, Met-3, Trp-4, Asp-5, Gly-6, Pro-7, Ile-32, Arg-33, Gly-34, Lys-35,Arg-36 Val-37, Asp-38, and Leu-39 . These rThen the energy minimized structure and toxin_inhibitors database was undergone through molecular docking protocol by utilizing GOLD suite software. The GA algorithm was selected for gold fitness score, and the 50 iterations were performed. The resultantdocked complexes were observed for the interacting patterns and hydrogen bond interactions with respect to their respective gold fitness score (Table 1 - see PDF)During interaction analysis, all ligands bound within the binding domain, validating the inhibitory domain of toxin. Leu-39 observed strong hydrogen bonds with all inhibitors, stating the importance of this residue in this interaction, thus inhibition.Edunol complex with a gold fitness score of 50 showed the maximum interacting pattern with three hydrogen bonds and two hydrophobic interactions. This pattern showed that inhibitor2 represents induced-fit properties within the binding domain. First and fourth thio)acetate) complex (gold fitness score 47) (gold fitness score 42 and 47) showed three hydrogen bonds and one hydrophobic interaction. Neostigmine complex exhibited two hydrogen bonds and one hydrophobicinteraction with a gold fitness score of 43. The change in interaction pattern is due to a change in the cationic nature of the ligand, which forms inter-polar interaction within the ligand. Elaidamide complex was declared as the least active compound with agold fitness score of 27 and only one interaction. 3d visualization of the docked complexes was indicated in the Figure 2(see PDF).A correlation plot was made to find the relation of the interaction score (gold score) and the inhibitor's respective molecular weight. The correlation plot for the complexes was made with a gold fitness score on the y-axis and molecular weight on thex-axis. The graphs represent the interaction that the inhibitors have shown is due to their molecular weight. The R-value of the correlation plot was 0.77, which shows a strong positive correlation as well . A ligand shows its interactionfor two reasons: its activity and its transport property. The transport property is the ability of a ligand to diffuse in the membrane for its way towards the target. As the cell membrane is hydopobic in nature, it allows hydrophobic molecules to pass through.The more the compound's molecular weight, the more it will carry hydrophobic properties, thus interacting easily and efficiently with the target. Here the Edunolcomplex with a gold fitness score of 50 showed highly active behavior. Vidalenolone,Neostigmine,andEthyl,2-((4-chlorobenzoyl)thio)acetatecomplexes make a group of similar behavior and interacting pattern (as mentioned earlier). Elaidamide, however, showed an outlier or least active behavior due to the polar nature of the structure.For this purpose, SCHRODINGER software was utilized. The system was built by TIP3P water molecules. The ions were added as sodium or chloride. After taking the system towards equilibrium, the MD simulations were run for 100nsec at the specific temperatureand pressure. After the simulations, the trajectory was analyzed by CαRMSD, declaring the stability of complexes and interaction during the simulation time (Table 2 - see PDF). During the simulation, it was observed that some bonds remained consistentthroughout the simulation, like Leu-39, Val-37, which were stable in the docking studies as well. The purpose of the MD simulation is to check the compound's high stability to some extent. Some bonds disappeared with the evolution of time.As observed from Table 2(see PDF), most residues remain stable over time. To observe the simulation trajectory, the RMSD plot was observed . The RMSD plot gave the snapshot of the simulation with time .Here thUtilizing the molinspration service, a drug scanning was done to evaluate the drug-like qualities of the top Compounds. The Lipinski five-rule is now accepted as the norm. This rule emphasises key aspects of drug metabolism, interactions, and excretion inthe human body, including pharmacokinetics. Lipinski's five conditions were all met by some compounds, and they also had drug-like characteristics including molecular weight (Table 4 - see PDF). Several pharmacokinetic factors were assessed using ADME andAdmetSAR. Pharmacokinetic traits can be used to assess the ADME and toxic effects of the top candidate agents. The ADMET characteristics of derived phytochemicals for both targets are shown in Table 5(see PDF). Many drugs do not utilise this pathway in theirdevelopment due to cytotoxicity and poor pharmacokinetic qualities. To find active lead components during the early stages of drug discovery, high-performance and rapid ADMET profiling research is prioritized. By analysing their ADMET profiles, promisingcompounds' drug-likeness was also confirmed using the ADMET Lab (Table 4 - see PDF).Computer aided compound library is available for developments in chemo informatics compounds . Hence, Data shows that phytochemicals thio)acetate and elaidamide) binds to Alpha-Cobra toxin involved in snake and scorpion. This can be used as effective drug candidates against the snake and scorpionvenom. It should be noted experimental verification is needed to further validate the current data.


Existing drug-target interaction (DTI) prediction methods generally fail to generalize well to novel (unseen) proteins and drugs. In this study, we propose a protein-specific meta-learning framework ZeroBind with subgraph matching for predicting protein-drug interactions from their structures. During the meta-training process, ZeroBind formulates training a protein-specific model, which is also considered a learning task, and each task uses graph neural networks (GNNs) to learn the protein graph embedding and the molecular graph embedding. Inspired by the fact that molecules bind to a binding pocket in proteins instead of the whole protein, ZeroBind introduces a weakly supervised subgraph information bottleneck (SIB) module to recognize the maximally informative and compressive subgraphs in protein graphs as potential binding pockets. In addition, ZeroBind trains the models of individual proteins as multiple tasks, whose importance is automatically learned with a task adaptive self-attention module to make final predictions. The results show that ZeroBind achieves superior performance on DTI prediction over existing methods, especially for those unseen proteins and drugs, and performs well after fine-tuning for those proteins or drugs with a few known binding partners. Existing drug-target interaction (DTI) prediction methods generally fail to generalize well unseen proteins and drugs. Here the authors report a protein-specific meta-learning framework, ZeroBind, with subgraph matching for predicting protein-drug interactions from their structures. However, the traditional experimental methods for resolving the crystal structures of drug-protein complexes to identify the drug-target interactions are costly and time-consuming5. In order to reduce costs, in silico approaches are gaining more attention. Instead of taking massive candidates into an in vitro search, it is more efficient and less costly to use computational approaches to virtually screen out most candidates prior to an in vitro search. Generally, there are two major groups of in silico approaches, docking simulations and data-driven learning-based methods. Docking simulations utilize the 3D structure of drug molecules and target proteins to identify their potential binding sites, which are also time-consuming7. In contrast, due to the rapid development of machine learning, utilizing the features derived from proteins and drugs to identify their interactions achieves both high accuracy and low cost.Identifying the interactions between drugs and targets (proteins) plays a crucial role in the process of drug discovery11, where interacting pairs of proteins and drugs are extracted from existing databases like BindingDB12, CHEMBL13, PDBbind15, and DrugBank16. Since the nature of potency values is logarithmic, a decrease in kinetic constants from micromolar to nanomolar levels results in an exponential change. Thus, the output values of regression tasks are normally defined as the negative log of kinetic constants, i.e., 18. Machine learning-based methods from molecules and proteins features mainly focus on learning a good representation of molecules and proteins, which are then fed into a classification/regression model to perform the prediction task19.Data-driven learning methods generally formulate drug-target interaction (DTI) prediction as binary classification or regression tasks20. Similarity/distance-based23 and network-based26 methods, which utilize protein-protein similarity, drug-drug similarity and known DTIs, achieve overestimated performance on the transductive test9, where both proteins and ligands in the test set are present in the training set. In addition, there are often few known binding drugs with a newly discovered target protein, making it difficult to work on the inductive test, where both proteins and ligands in the test set are absent in the training set. Currently, most existing methods focus on how to effectively learn representations of molecules and proteins, and then feed the representations into classification or regression models19. The representation learning methods, like DeepPurpose8, take molecular fingerprints and SMILES27 strings as the molecular features and amino acid sequences as protein features. Then they apply a convolutional neural network (CNN)28, recurrent neural network (RNN)29 or Transformer30 model to embed the input features. To take spatial information into consideration, some methods apply CNNs to 3D images derived from structures of proteins and molecule31. GNN32 has gained much attention in recent years due to its capability of handling graphs as non-Euclidean structured data33. Other methods utilize GNNs to embed graphs of proteins and molecules to perform DTI prediction10. DTI prediction also benefits from the rapid development of GNNs since both drug and target protein are naturally graph-structured data. GEFA34 combines pre-trained protein embeddings and a graph-in-graph neural network with an attention mechanism to capture the interactions between drugs and protein residues. Thus, it is reasonable to use GNNs for the representation learning of proteins and drugs.Recently, deep learning has achieved exciting results in DTI prediction by learning from known drug-target interactions. However, these methods cannot generalize well to those unseen proteins and drugs35 that remember the degree ratio of binding annotations in the training set rather than learning the molecular features of interactions9. To generalize to unseen proteins and drugs, AI-Bind leverages network-derived negatives and pre-training to resolve the shortcut learning issue9. Furthermore, proteins may have diverse binding patterns with drugs, which can be captured by training a protein-specific model for individual proteins. Recently, a ligand-based method MetaDTA36 is developed as a few-shot predictor for proteins with a few known binding drugs under the meta-learning framework. However, MetaDTA does not support zero-shot prediction for unseen proteins without known binding drugs in the training set.However, classical pair-input approaches often take drug-protein pairs as training samples by concatenating and feeding their representations into a dense layer to identify the interactions. However, these approaches may fail at inductive tests for unseen proteins and drugs not seen during the model training, which is due to their potential shortcuts37 as the training strategy and each base model makes binding drug predictions of a specific protein, which mainly consists of four modules: (1) a Graph Convolutional Network (GCN) encoder learning the embeddings of the molecule graph and protein graph, (2) a Subgraph Information Bottleneck (SIB) module generating the essential IB-subgraph of the protein graph as a potential binding pocket, (3) a Multilayer Perceptron (MLP) module concatenating the protein IB-subgraph embedding and molecular embedding to perform DTI prediction, and (4) a task adaptive self-attention module to measure the importance of different tasks, where different DTI tasks contribute differently to the meta-learner. The generated task weight is used for weighted average loss and further incorporated into the meta-learning procedure.To address these challenges, we propose a protein-specific zero-shot predictor ZeroBind for drug-target interaction prediction under the meta-learning framework. ZeroBind adopts the meta-learning framework MAML++In summary, our contributions are as follows: (1) We formulate the drug-target interaction prediction for unseen drugs and proteins as a zero-shot learning problem. The general knowledge of DTI prediction learned from existing proteins, drugs, and their interactions with meta-learning strategy have a greater ability for generalization to unseen proteins and drugs than existing methods. (2) We train one DTI task model per protein, where task adaptive self-attention is designed to calculate the contributions of multiple DTI tasks to the protein-specific meta-learner. Thus, each protein-specific model captures individual binding patterns to drugs. (3) We propose model-agnostic IB-subgraph learning to automatically discover compressed subgraphs as potential binding pockets in proteins instead of redundant graph information derived from the whole protein. (4) We conduct extensive experiments on three independent zero-shot test sets and one few-shot test set. Results show that ZeroBind consistently outperforms existing methods. Further validation of real-world SARS-COV-2 drug-target binding prediction demonstrates the reliability of ZeroBind predictions and the subgraphs detected by IB-subgraph learning align well with the known binding pockets in proteins.In this study, ZeroBind formulates the DTI prediction as a meta-learning task and proposes a meta-learning framework to solve the generalization problem of unseen proteins and drugs in DTI prediction. Specifically, a meta-learning task is defined as binding drug predictions of a specific protein, where IB-subgraph learning is leveraged to automatically discover compressed subgraphs as potential binding pockets in proteins and a self-attention mechanism is designed to learn weights for each task of a protein. The flowchart of ZeroBind is illustrated in Fig. 9 as data augmentation to alleviate the annotation imbalance  and the area under the precision-recall curve (AUPRC) on three independent test sets and one few-shot test set. To ensure the effectiveness of performance comparison, we conduct five independent experiments with five random seeds for datasets partitioning and model training, and report the average results along with standard deviation.−6, 8.02 × 10−11, 3.06 × 10−7, respectively. Moreover, the relative improvements of AUPRC are 1.00% on the Transductive test set, 0.96% on the Semi-inductive test set, and 1.21% on the Inductive test set compared to the best baseline method, the corresponding t-test p-value is 8.93 × 10−8, 5.15 × 10−3, 1.56 × 10−5, respectively. In addition, on the three test sets, we observe that the performance of all methods decreases, indicating that a certain external shortcut learning exists for the Transductive test set.The overall performances of all methods in three independent test sets are reported in Fig. Figure We also evaluate the protein-specific DTI prediction performance of ZeroBind with other baseline methods on the inductive test set and semi-inductive test set Fig. . After eWe further evaluate ZeroBind against the baseline methods on the few-shot test set. ZeroBind and other baseline methods are first trained with the training set as the pre-training model, and further fine-tuned on each few-shot fine-tuning set and evaluated on the corresponding few-shot test set. The performance of ZeroBind model and other baselines on the few-shot test set is shown in Fig. 38 to compare the predicted binding pocket against the true binding pocket on PDBbind dataset15. The true binding pocket information containing pocket residues and 3D coordinate locations are downloaded from PDBbind, which contains 14,000 binding residues for 535 proteins. The Jaccard similarity coefficient is used to calculate the degree of intersection between two sets of individual DTIs, and the formula is defined as ZeroBind does not use binding pocket information as ground truth labels for model training, instead, it uses the global DTI labels as a weakly supervised labels. To demonstrate that the IB-subgraph module in ZeroBind is able to detect the binding pocket in the protein, we use the Jaccard similarity coefficientIn addition, we calculate the Jaccard similarity coefficient of predicted binding pockets nodes and the first-order neighbors of the true binding pocket nodes as random. The results are shown in the ablation studies and here we also calculate the Jaccard similarity coefficients of randomly sampled binding residues with true binding pockets and the first-order neighbors of true binding pockets, respectively. The ZeroBindrandom yields an average Jaccard similarity coefficients of 0.013 and 0.072 , which is much smaller than that of ZeroBind, respectively. As shown in Fig. Figure To further demonstrate the effectiveness of the IB-subgraph module, we train a variant ZeroBind for those proteins with known binding pockets using true binding pocket as the node assignment matrix 39.We further visualize the generated IB-subgraph as the potential binding pocket for Serine/threonine-protein kinase N1 protein in Fig. 40, where the PDB IDs for SARS-COV-2 protein structures are given in Supplementary Table 41.To better demonstrate the effectiveness of ZeroBind, we use Auto-Docking simulations to validate its predicted potential drugs targeting the SARS-COV-2 proteins. Auto-Docking simulation is a time-consuming but reliable tool to simulate the drug-target binding process. To respond quickly to public health emergencies, we need to validate ZeroBind on proteins that don’t have many binding molecules. Thus, we apply ZeroBind to predict the interactions between the structures of 10 SARS-CoV-2 viral proteins not in the training set and 10,000 drugs in PubChem42. As demonstrated in9, the average binding affinities are close to −7.5 kcal/mole for three binding pairs of SARS-COV-2 proteins and drugs. Furthermore, we simulate the drug-target binding complex between ORF8 accessory protein and VZBSCWDKCMOJCR-UHFFFAOYSA-N drug in Fig. The average binding affinity of the top-10 predicted pairs by ZeroBind is −7.42 kcal/mole Fig. , which aMAML-: Train the base model of ZeroBind directly without the meta-learning strategy.ZeroBindSIB-: Using all node embeddings of the protein to identify the interaction instead of applying a SIB module to find the IB-graph on the protein graph.ZeroBindattention-: ZeroBind without task adapted attention module to balance the importance of different tasks.ZeroBindGIN: ZeroBind uses GIN instead of GCN as the backbone GNN.ZeroBindrandom: ZeroBind sets the node assignment matrix ZeroBindZ in Eq.  randomlyTo demonstrate the added value of individual modules in ZeroBind, we also conducted an ablation study to evaluate the effectiveness. The details of ZeroBind with different modules are illustrated as follows:MAML-, we observe a significant decrease in the semi-inductive and inductive test sets, demonstrating that the meta-learning training strategy provides the powerful generalization ability to unseen proteins and drugs. We can see that ZeroBindSIB- yields a lower performance without the SIB module to find the IB-subgraph, indicating the embedding of the whole protein is less effective than the subgraph embedding. The potential reason is that redundant graph information exists in the whole protein graph, further indicating that the molecule binds to a binding pocket instead of the whole protein. ZeroBindattention- performs slightly worse than ZeroBind, indicating the necessity of task adaptive self-attention module to balance different DTI tasks for different proteins. We also see a performance decline for ZeroBindsampling-, due to the lack of the value-generated nonbinding annotations by network-based negative samplings that is able to alleviate the potential short-cut learning. For ZeroBindGIN, there is no significant difference in the performance compared with ZeroBind, which indicates that the backbone GNN has a smaller impact on the performance than other modules in ZeroBind. Compared to randomly selected binding pockets, ZeroBindrandom performs worse than ZeroBind, further validating the effectiveness of the SIB module for detecting potential binding pockets. Moreover, we can see that ZeroBindrandom with randomly selected pockets performs worse than ZeroBindSIB- without the IB-subgraph module, indicating that initially inaccurate binding pockets guides the model to not locate true binding pockets, resulting in a wrong DTI prediction.The results are shown in Table The interaction of proteins with drug molecules is an important research topic, especially in the face of proteins and drug molecules not seen in the training set. Considering the information of proteins and molecules simultaneously is an under-explored idea to solve this problem. In this study, we formulate the drug-target interaction (DTI) prediction as a meta-learning task and propose a meta-learning framework called ZeroBind to solve the generalization problem of unseen proteins and drugs in DTI. Specifically, a meta-learning task is defined as binding drug predictions of a specific protein. The results demonstrate that ZeroBind outperforms existing methods in zero-shot and few-shot scenarios. In addition, the subgraphs learned by SIB align well with binding pockets in the protein, where randomly selected residues as binding pockets almost do not overlap with true binding pockets. Furthermore, we validate ZeroBind’s performance in a real-world scenario, where it predicts drug-target bindings for SARS-COV-2.Due to the rapid development of the research on GNNs, proteins and molecules can be encoded in a more natural form than sequences in previous studies. In addition, the meta-learning strategy also provides a more precise way of delineating the protein-specific DTI task space, which is also consistent with the experimental workflow for proteins in real drug experiments. However, ZeroBind also has some limitations, such as the difficulty of meta-learning training, where the training procedure is complex and prone to instability.The IB-subgraph method offers an interpretable ability of the model for understanding representation learning. In ZeroBind, the potential binding pockets are automatically detected with the weakly supervised IB-subgraph method that does not use binding pocket annotations as labels. To the best of our knowledge, still no published methods use IB-subgraph or other subgraph-based methods to identify potential binding pockets in the proteins, existing subgraph-based methods are focused on drug molecules. In the current study, we do not incorporate real binding pocket information into model training, since the binding pocket data is far less than the DTI data, and the protein structures predicted by AlphaFold2 still have no known binding pocket annotations. Of the proteins in the benchmark dataset, only 535 proteins have a part of known binding pockets. Thus, it is difficult to train the SIB module in ZeroBind for all proteins to detect binding pockets using the local binding pocket labels. Instead, we train the SIB module of ZeroBind using the global DTI binding labels, which potentially guides the SIB module to locate the potential binding pockets in proteins. As shown in our experiment, ZeroBind with true binding pockets yields better performance. A future update of ZeroBind is expected to take true binding pocket information into the training process to make a more accurate fitting to the DTI problem, if we can collect more true binding pocket data.43. In future work, we expect to investigate more advanced GNNs in ZeroBind.Furthermore, the base model in ZeroBind is GCN and there have been more advanced neural architectures for protein-molecule binding, such as SE(3)-equivariant GNN used in EquiBind12 is a public database of DTI interactions, which deposits binding affinity data between drugs (drug-like molecules) and target proteins. It currently contains over 2,600,000 experimentally determined binding affinities of protein-drug complexes between over 8000 protein targets and over 1,100,000 small molecules.BindingDB44, 1,500,000 protein-drug pairs were collected. We use the threshold in AI-bind9, which treats kinetic constants To create the training and test datasets for ZeroBind, we apply several filtering and preprocessing steps to create a high-quality benchmark dataset. First, data points are filtered with “single protein” for the “target type” attribute, and kinetic constants To demonstrate the effectiveness of ZeroBind, we construct three independent test sets for model evaluation through protein sequence similarity clustering by cd-hit and molecule scaffold split: (1) Transductive test set, where molecules with the same scaffold and proteins with the same cluster are in the training set, but their interactions do not exist in the training set; (2) Semi-inductive test set, where the proteins in the same cluster are in the training set, but the molecules with the same scaffold are not; (3) Inductive test set, where molecules with the same scaffolds and proteins in the same clusters are not in the training set.We first use cd-hit, a widely used program for clustering protein sequences, to cluster 1603 proteins into 1101 clusters with a similarity threshold 0.4. Through comparing molecule scaffolds, we split the molecules into the training molecule set and test molecule set with a training ratio of 0.95, and ensure that these two sets do not have overlapping scaffolds. As the meta-learning-based framework requires sufficient data, we first divide the clusters that have any protein with the number of associated molecules <20 into the test clusters, and the remaining clusters as the training clusters. Then, we construct the training set using 95% of the proteins in the training clusters and SMILES in the training molecules set. The remaining 5% of the proteins in the training clusters and smiles in the training molecules set are used as the Transductive test set. Finally, the proteins and smiles both in the test clusters and molecule set are further used as the Inductive test set, and the rest data are used as the Semi-inductive test set. During the model training, the cross-validation approach is applied for model optimization.In addition, we construct another few-shot test set with combining the Semi-inductive test set and the Inductive test set to evaluate the few-shot learning power of ZeroBind. Then, we randomly select 5 positive and 5 negative DTIs of each protein as the few-shot fine-tuning set, and the rest DTIs of each protein as the few-shot test set. Proteins that don’t have enough positive or negative DTIs are excluded from the few-shot fine-tuning set and the few-shot test set. The details of the training set and four test sets are given in Table 9 demonstrates that the annotation imbalance causes the network to learn topological shortcuts instead of the binding patterns of drug-target interactions. Similar to AI-Bind9, we use the network-based negative sampling of training datasets as data augmentation to alleviate the annotation imbalance. Specifically, we construct a bipartite drug-target network. The bipartite network is illustrated in Fig. We observe there exists data imbalance in the training set, and most proteins have a high positive ratio. AI-bindhttps://www.rcsb.org)45, and the rest 3D structures of 635 proteins are predicted by AlphaFold246. In addition, we download the true binding pocket information containing pocket residuals and pocket 3D coordinate locations from the PDBbind database, which contains binding pockets for 14,336 DTIs.The 3D structures of 996 proteins in this study are downloaded in PDB format from the RCSB Protein Data Bank  datasets47 to obtain molecular representation. Specifically, atom chemical features consist of atomic number, chirality, atomic formal charge, number of hydrogen atoms attached, radical electrons number, hybridization type, and aromatic. Geometric features consist of atom degree, and a binary value that whether in the ring is used for encoding node representation. Edge features consist of bond type, bond stereo and a binary value that whether bond is conjugated.After using RDkitDefinition 1: We define a drug as a graph denoted as 48, the two residue nodes are connected with one edge in the protein graph.Definition 2: We define a protein graph as 46 to predict their structures as complementary. The node features of the protein graph are initialized with pre-trained ESM-249 emebddings, a general-purpose protein language model, of the residues to incorporate prior knowledge. The edge features of the protein graph are the Euclidean distance between the pairs of nodes. The whole process of constructing a protein graph is shown in Fig. For proteins without known 3D structures, we use AlphaFold2In this work, we train a DTI task per protein.p is a protein in the protein set P and Mp is the drug set of its corresponding binding molecules and non-binding molecules.Definition 3: We define a DTI task Definition 4: We define the zero-shot DTI predicting task inherited from DTI task, which represents that the meta-model is directly used for prediction without fine-tuning with samples of the protein in meta-learning test set.Definition 5: We define the few-shot DTI predicting task inherited from DTI task, which uses the protein-specific model for prediction after fine-tuning with samples of the protein in meta-testing.The few-shot DTI prediction schedule and zero-shot DTI prediction schedule are shown in Fig. 50. There are two main types of meta-learning framework: (1) Gradient-based methods: The classic gradient-based method MAML51 uses a meta-learner to learn a good initialization by summing up multiple task losses and updating the parameters across tasks. Therefore, MAML could achieve both high accuracy and speed for generalization; (2) Metric-based methods: The prototypical network52 is a classic metric-based meta-learning algorithm that directly trains the vector representation  of each category. Once a good feature extractor is trained, the category of a new sample is determined by its closest prototype in the vector space. In our study, we apply gradient-based methods MAML as the training strategy. Besides, we follow MAML++37 to make some improvements to stabilize the training process of MAML.The meta-learning framework was originally proposed to learn general knowledge across multiple related tasks within a distribution and used this general experience to quickly adapt to additional tasks and improve prediction performanceθ. Given training tasks (proteins) sampled from the task distribution k data samples of each label for several inner steps to learn a task-specific model and then tests on m data samples. After obtaining the loss for each task, MAML applies the gradient descent with the sum or average of the loss across all tasks. Here we refer the training task and test task as the support set and query set, respectively. However, MAML suffers from instability during training and is sensitive to neural network architectures and a large number of hyperparameters. Therefore, we follow MAML++37 training procedure, which leverages multi-step loss optimization (MSL) and learning per-layer per-step learning rates and gradient directions (LSLR) in MAML.In the details of ZeroBind, several techniques including multi-step loss optimization (MSL), learning per-layer per-step learning rates and gradient directions (LSLR), subgraph information bottleneck (SIB), and task adaptive self-attention are applied to improve the performance of ZeroBind. We build the zero-shot learning framework based on MAML. First, we randomly initialize the network parameter Specifically, the multi-step loss optimization calculates the loss of the query set after each inner step, and optimizes the base network with the support set after all inner step optimization is completed. More formally, we optimize the model parameters as follows:The LSLR module sets the learning rate to be learnable parameters for each layer at each inner step. Without adding much computation, different learning rates are automatically learned for each layer at each step, which may help alleviate overfitting.To summarize the meta-learning procedure in ZeroBind Boxes  and 2, tBox 1 ZeroBind training processRequire: While not done do:Sample batch of tasks For all  Sample k examples as support sets of  Sample m examples as query sets of   For i=1 to inner steps do:  Calculate the inner step loss weight            End forEnd forEnd whileBox 2 BaseModel forward processRequire: For i=1 to num steps doFigure Graph neural network-based representation learning for proteins and drugs. After constructing drug and protein graphs for a given protein-drug pair, we first embed the molecule atom features to vector space with randomly initialized parameters. We denote the node embedding of the molecule as For protein graphs, we initialize the node embedding 53 to identify the most interpretable subgraph with the most crucial information associated with the DTI task, where the learned subgraph corresponds to the binding pocket on the protein graph. The SIB module is proposed to recognize a compressed subgraph named IB-subgraph under the information bottleneck (IB) principle. The IB-subgraph could also eliminate the noisy and redundant graph information. In our DTI task, the DTI score is largely determined by the protein pocket features, which are the subgraph information bottleneck of the protein graph or the potential drug binding sites in the protein.Subgraph learning in ZeroBind for potential binding pockets in proteins. Considering that a molecule binds to a binding pocket in the protein instead of the whole protein, after the protein backbone GCN, we apply a model-agnostic SIB moduleThe SIB module contains a subgraph generator for node assignment and a dense layer to guarantee that the generated subgraph matches the IB-subgraph. The subgraph generator is a Multi-layer Perceptron (MLP), which takes the protein node embedding and the molecule embedding as the input and outputs the node assignment matrix To stabilize the training process of the subgraph generation and separate Formally, the subgraph information bottleneck seeks the IB-subgraph by optimizing the following objective function:54 of the KL-divergence is applied to approximate the upper bound of To optimize the objective function, the lower bound of the first term is formulated as the opposite number of classification loss between the ground truth label and predicted label with subgraph embedding. By minimizing the classification loss, the lower bound of To minimize After obtaining the embedding of the generated protein IB-subgraph, we concatenate the molecular embedding and protein IB-subgraph embedding as the input to the MLP classification layer. The DTI classification loss could be formulated as:The overall loss can be formulated as:Here, we summarize the training process, the d by Eq. , represed in Eq. . To maxid in Eq.  and achis of Eq. . After ts of Eq. . By optimize Eq.  and seekIn our task, we aim to find the protein binding pocket under the weakly supervised training process, since the amount of protein binding pockets data is much smaller than the amount of binding data. So we design the IB-subgraph to find the protein binding pockets optimized on the DTI binding labels instead of binding pocket labels in proteins. Considering the protein pockets are determined by both proteins and molecules, we take the concatenation of molecule embedding and protein embedding as input when we generate the node assignment matrix Z in Eq. .In a traditional meta-learning schedule, ule Fig.  to learn36 uses multi-head cross-attention network to capture the relationships between the support and the query ligands instead of different proteins, where it does not use any protein information. In contrast, ZeroBind utilizes a task adaptive self-attention module to learn the importance for different tasks of proteins, and it focuses on the shared binding patterns among different proteins.MetaDTAWe take graph convolutional networks as base graph neural network. In our experiment, the GCN layer number of 11: DeepConv-DTI trains a CNN to learn the embedding of the neighboring residues and an MLP to learn the molecular fingerprint, which is concatenated to make DTI prediction.DeepConv-DTI10: GraphDTA leverages multiple types of GNN models to embed molecular embeddings and a CNN model to embed protein residue features.GraphDTA8: Deeppurpose is a state-of-art deep learning library for DTI prediction with multiple backbone networks. Here we use CNN as the backbone network for learning embeddings of both drugs and proteins.Deeppurpose9: AI-bind uses pre-trained mol2vec and protvec models to initialize the molecule embedding and protein embedding, and makes the DTI predictions by concatenating the molecule embedding and protein embedding.AI-bind55: DrugBAN uses a deep bilinear attention network (BAN) framework with domain adaptation to explicitly learn local pairwise interactions between drugs and targets.DrugBANWe compare ZeroBind with multiple baselines to evaluate its advantages, and calculate the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC) on three independent test sets and one few-shot test set.Further information on research design is available in the SupplementaryInformationPeer Review FileReporting SummarySource data


Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test  to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing. There has additionally been a great deal of work on artificial intelligence (AI) methods to automatically diagnose COVID-19 using CT imaging22, chest X-Rays34 , audio samples36, or blood test results41. The chest X-Ray (CXR) is a low-cost image which can be acquired using portable equipment and operated by personnel with minimal training, making it a very useful diagnostic tool in resource-constrained settings. Many commercial products have been launched for the detection of tuberculosis (TB) on CXR42 in regions where TB is endemic and resources are limited. Furthermore, while many areas of the world do not have access to hospital laboratories, point-of-care (POC) blood testing can be achieved at low cost and with immediate results. Both CXR and POC blood-testing, therefore, suggest themselves as feasible and cost-effective diagnostic tools for COVID-19 in low resource settings. In such regions, there is additionally a severe shortage of medical experts to interpret radiological images or blood test results and AI tools provide a way to bridge this gap. In this study we describe the application of AI for analysis of CXR and POC blood-tests in a COVID-19 screening trial, using different AI systems37. The AI systems were applied to data collected from four sites in low resource settings in Lesotho and South Africa.As the SARS-CoV-2 (coronavirus) pandemic unfolded around the world in 2020, scientists and clinicians scrambled for tools to aid with diagnosing the associated respiratory illness, coronavirus disease 2019 (COVID-19). The gold standard tests—reverse transcription polymerase chain reaction (RT-PCR)—were in short supply worldwide and additionally took approximately 2 days to produce a result. Antigen-based tests were still being developed and were not widely available. At the same time, health services around the world experienced large numbers of people presenting with respiratory symptoms and urgently needed to determine which patients should be isolated and treated for COVID-19. A large body of scientific literature emerged at that time, detailing various routes through which COVID-19 could potentially be diagnosed, particularly using imaging or laboratory markers. These methods included analysis of chest imaging including computed tomography (CT) or X-RayThe participants included in this work were recruited through two separate trials, TB TRIAGE+ and MistraL as described in more detail below. All data collection was carried out in accordance with relevant guidelines and regulations including the International Council for Harmonisation Good Clinical Practice (South Africa and Lesotho) and the Protection of Public Information Act (South Africa). The study protocols were approved by the Ethikkommission Nordwest- und Zentralschweiz in Switzerland and the National Health Research Ethics Committee of Lesotho (TB TRIAGE+ and MistraL), as well as the Provincial Department of Health of KwaZulu-Natal and the Human Sciences Research Council Research Ethics Committee in South Africa (TB TRIAGE+). All participants provided written informed consent. In case of illiteracy, the adult participant signed with a thumbprint and an independent person signed as a witness.Participants enrolled for COVID-19 screening in either trial had a chest X-Ray acquired as well as a blood test to determine white blood cell (WBC) differential counts (HemoCueR WBC DIFF). This is a point-of-care finger prick blood test which provides counts for the five types of WBC . All participants received an antigen based SARS-CoV-2 rapid diagnostic test (RDT)  and RT-PCR, both using nasopharyngeal swabs. Participants in TB TRIAGE+ additionally had CRP levels measured in a finger prick blood test  in rural populations in Africa. It is testing the diagnostic accuracy, effectiveness and cost-effectiveness of an AI solution, CAD4TB, for CXR image analysis as well as the CRP blood marker as a means of screening and triaging for TB in community settings. At the height of the coronavirus pandemic, detection of COVID-19 was added to the TB TRIAGE+ protocol since the two diseases have overlapping symptoms and it was expected that both would be present in the community. The participants included in this study were recruited from the Butha-Buthe Government District Hospital, Lesotho and the Caluza Clinic, Pietermaritzburg, KwaZulu-Natal, South Africa if they had any of the four cardinal symptoms of TB . A CXR of all enrolled participants was acquired as well as a CRP blood test. During the COVID-19 recruitment period, enrolled participants had an additional WBC differential count blood test as well as a SARS-CoV-2 antigen test and an RT-PCR test .MistraL: The MistraL trial44 was set up specifically in response to the coronavirus pandemic in Lesotho to determine optimal methods of screening for COVID-19. Patients presenting to St Charles Missionary Hospital Seboche or to Government District Hospital of Mokhotlong, as well as initially to the Butha-Buthe Government District Hospital, were screened at the hospital entry point and invited to participate in the MistraL trial if they were experiencing any COVID-19 symptom  or if they reported close contact with a positive case. A CXR image and a POC WBC differential blood test was obtained for all participants as well as a nasopharyngeal swab rapid antigen test and a nasopharyngeal swab RT-PCR test as the reference standard for COVID-19 infection.26  was developed in April 2020 to detect COVID-19 from chest X-Ray images. This system, which uses deep neural networks for prediction, was first pre-trained using CXR data acquired from various settings prior to the coronavirus pandemic. The system was then fine-tuned (re-trained) for the detection of COVID-19 (defined by RT-PCR results) using CXR images from participants presenting at a Dutch emergency department with respiratory symptoms in March and April 2020. Full details of the training data and procedure can be found in26. In testing on a dataset from a different Dutch hospital the system had a performance comparable with six independent radiologists in identifying COVID-19 from the CXR26. The CAD4COVID-XRay system takes a single frontal CXR as input and produces a score in the range 0–100 indicating the likelihood of COVID-19. It additionally outputs a heatmap image to illustrate the regions of abnormality on the CXR as determined by the system.The CAD4COVID-XRay AI software37. This system uses deep neural networks and is designed and trained to be robust to missing data, and while it is trained on participants with up to 28 inputs (27 laboratory results and a CXR score) it is optimized to provide the best possible results on any subset of these inputs available in a given setting. Training and test data for this system came from the same two independent Dutch hospitals that provided training and test sets for CAD4COVID-XRay. Full details of the network architecture, data and training procedure can be found in37. For the purpose of this article we refer to this second AI system as COVID-LAB+ to indicate its purpose to detect COVID-19 using any combination of available laboratory results or CXR score. COVID-LAB+ takes between 1 and 28 numeric values as input—the operator must identify each input as 1 of 27 possible laboratory test results or as a CXR score. It outputs a numeric score (0–100) indicating the likelihood of COVID-19 infection based on analysis of the given inputs. Figure In July of 2020, a second AI system was developed to detect COVID-19 using a combination of data from laboratory testing and CXRNeither CAD4COVID-XRay nor COVID-LAB+ were re-trained for the purpose of this study. There was insufficient data available from our study to do so and there was no evidence to suggest that the systems would not generalise well.To provide a radiological reference for the CXR images in the study a chest radiologist with 8 years of experience was asked to view and rate a subset of the CXR data collected. This subset was chosen to include CXRs from all participants with a positive RT-PCR test result. To balance the data set, each RT-PCR positive participant was then matched with a participant with negative RT-PCR, from the same trial with sex and age matched (within 3 years). In this way the subset of data contained equal numbers of RT-PCR positive and negative participants.26 (or to rate the image as unreadable). The scores assigned have the following definitions: 0.normal, no finding1.abnormal but no lung opacity consistent with pneumonia2.lung opacity consistent with pneumonia (unlikely COVID-19)3.lung opacity consistent with pneumonia (consistent with COVID-19)The radiologist performance for COVID-19 prediction is calculated in terms of sensitivity and specificity using different cut-off thresholds on these scores. For this purpose, the scores 0 and 1 are merged, since both represent a clear opinion that no pneumonia related abnormality is present. Since COVID-19 pneumonia is difficult to distinguish from other-cause pneumonia on CXR a cut-off threshold at scores>1 is expected to produce higher sensitivity, whereas with the cut-off at scores>2 specificity is expected to improve.The radiologist was blinded to the RT-PCR results and images were presented in a randomly shuffled order. For each image the radiologist was asked to provide a score from 0 to 3, using the same radiological rating system that was employed in the CAD4COVID-XRay studyCXR+WBC+CRPCXR+WBCCXR+CRPWBC+CRPWBCCRPIn each case ROC analysis is provided by thresholding on the scores output by the system. Area under the ROC curve is provided for the data from each trial (TB TRIAGE+ and MistraL) separately as well as for the combined datasets. It should be noted that the COVID-LAB+ systems are run on varying datasets, with each dataset being selected to include participants with the required input data parameters available. The sensitivity and specificity of the rapid antigen test used in the MistraL trial is additionally plotted for comparison with the different AI systems.In all cases the RT-PCR result is considered as the reference standard for COVID-19 infection. To determine the most effective screening tools for detection of COVID-19, AI systems using different inputs and combinations thereof are examined as follows: CAD4COVID-XRay: This system requires only a CXR image as input and outputs a likelihood score for COVID-19 (0–100). A receiver operating characteristic (ROC) curve is plotted by applying all possible thresholds to the set of scores, generating sensitivity and specificity points. COVID-LAB+: The COVID-LAB+ system is run using all possible combinations of inputs available. These include:45 was used. For this comparison, only participants where all test results were available were included, to ensure exactly the same test subjects were provided to every system. Results for TB TRIAGE+  and for MistraL  are shown separately. The scores from all tested AI systems were used to determine whether the achieved AUC values are statistically different. The p-values for each comparison are provided and p< 0.05 is considered to represent a statistically significant difference.To make a statistical comparison of the AI system results, de Long’s test44. A publication from the TB TRIAGE+ study is in preparation.Data from a total of 5763 participants was collected across both trials between December 2020 and August 2022. Local guidelines in Lesotho were amended to remove the need for PCR testing in late 2021. In keeping with those guidelines, MistraL participants from 19th November 2021 onwards did not routinely receive RT-PCR testing and are thus excluded from this analysis. After the exclusion of children under 18 years of age, 5394 participants remained with RT-PCR results available for 3738 (69%) of these. A total of 260 positive RT-PCR tests were recorded in adults. For pragmatic and operational reasons, some participants did not receive all tests available in the trial in which they were enrolled. Figures Numbers of participants enrolled, excluded and finally included in our analysis. Figures in brackets indicate the subset which had a positive RT-PCR result.A total of 3231 participants were included with CXR imaging see Fig. , of whicOn the subset of X-ray data from TB TRIAGE+ the CAD4COVID-XRay system achieved an AUC of 0.64, while on that from MistraL the AUC was 0.57 Table .Table 2SThe COVID-LAB+ AI system was used to predict COVID-19 using all possible combinations of X-Ray and blood tests available. ROC analysis of these experiments is illustrated in Fig. Comparing results on the subsets of data from TB TRIAGE+ and MistraL respectively Table  the syst45. Table The set of 1250 participants with all test results needed for AI evaluation  was included for statistical testing. Scores for all AI systems on this subset of participants were used to generate AUC values and determine whether the differences in these AUC values were statistically significantIn this work, AI systems for detection of COVID-19 have been applied to input data collected from participants in resource-constrained settings in Africa during the COVID-19 pandemic. Although many studies have been published on the use of AI systems for COVID-19 detection, to our knowledge this is the first published application of AI for COVID-19 screening in an African setting, and one of few applied outside a hospital setting to participants without severe respiratory symptoms at presentation.26. In fact, there was an overall low incidence of COVID-19 cases (7%) during the study and the majority of these had mild symptoms. This may be attributed to fluctuations in variants circulating and in population immunity which substantially affect disease prevalence and severity. For this reason, CAD4COVID-XRay had a relatively poor performance at identifying RT-PCR+ participants, since they rarely had pneumonia visible in the lungs. This is supported by the radiological reading where the chest radiologist found that 100 of 153 images from RT-PCR+ particpants were completely normal in appearance, and only 18 of the 153 were suggestive of COVID-19 pneumonia. The similarity in performance between CAD4COVID-XRay and the radiologist suggests that the AI system was operating correctly (did not require re-training) but that the information pertaining to COVID-19 (signs of pneumonia) was simply absent from the majority of X-ray images. While many studies have suggested that chest X-ray analysis can identify COVID-19 infection (both with or without AI)34 it is important to clarify that this is only applicable in settings where disease is severe and participants report with acute respiratory symptoms indicative of pneumonia which would result in relevant radiological findings.At the time of commencing data collection (December 2020) there was an expectation that, during the study, waves of COVID-19 would be encountered, where case numbers would be high and disease would often be severe. In this context, a much higher incidence of radiologically evident COVID-19 pneumonia was expected, suggesting AI for chest X-ray (CAD4COVID-XRay) as a suitable predictor of infection. This was the scenario already encountered in many countries around the world, and in which CAD4COVID-XRay had been demonstrated to perform well37. CRP was additionally included in this work since it was collected as part of the TB TRIAGE+ protocol and had also been indicated as a potentially useful marker of COVID-19 prognosis48. Our results indicate that WBC count alone is a strong predictor of COVID-19 infection and all AI systems using it performed significantly better than those without it. No significant difference was found between any of the four systems that incorporated WBC  although the suggested optimal operating point  was achieved by combining CRP with WBC.The COVID-LAB+ system was employed to investigate the potential of combinations of POC tests for prediction of COVID-19. In designing the study, differential WBC was selected as an inexpensive POC blood test which showed promise as a COVID-19 predictor in earlier studies37. This is likely related to the fact that the data in that study (both training and test data) was collected at emergency departments in Dutch hospitals at the beginning of the pandemic, where patients presented with severe respiratory complaints. The variants in circulation at that time, as well as lack of population immunity typically resulted in more severe disease manifestation. Further investigation is required to verify whether severity of illness was the only reason for the disparity in AUC values between this study and the earlier one.The AUC value (0.73) achieved by COVID-LAB+ using the combination of X-Ray + WBC + CRP is substantially lower than that reported using an identical set of input variables in the first publication on the system (AUC = 0.919)49, although there is evidence that antigen based tests are more effective in subjects with high viral load50. In many environments, however, such as healthcare settings, a test with higher sensitivity is desirable, even for cases with milder or earlier stage disease, in order to reduce the possibility of COVID-19 transmission among vulnerable populations. A triage test with high sensitivity could enable accurate selection of participants recommended to receive RT-PCR testing, for example. Future investigation should determine whether antigen-based RDT results could be usefully incorporated as an input to our COVID-LAB+ system, potentially improving specificity without loss of sensitivity. Although the need for tests which can detect SARS-CoV-2 has reduced over the course of time, the benefits of combining different types of inputs  could also be investigated in other applications such as the accurate detection of TB, for example.At the time that this study commenced, antigen-based testing was not commonly used or well validated, although it has since become commonplace in many parts of the world. The performance of antigen RDTs in this study  concurs with many previous works which report relatively low sensitivity values but very high specificity44. No data on participant vaccination status was collected although COVID-19 vaccines were introduced in South Africa and Lesotho on a phased basis starting in 2021. Variations in such factors may have affected the predictive values of the diagnostic tests used51. Furthermore, a substantial number of those recruited in the MistraL study (39%) had no result for RT-PCR as they were recruited after November 2021 when local guidelines indicated antigen-based RDT instead of RT-PCR testing. This may result in imbalances in the data included from the two trials, in terms of circulating variants and prevalence. Finally, due to limited numbers of positive COVID-19 cases and limited scope of this manuscript, we did not perform subgroup analysis using age, HIV-status, clinical severity or other factors. Future work may indicate the role of these factors in diagnosis.Our study had some limitations. Since the data was collected over a period of almost 2 years there were fluctuations in the prevalence of COVID-19, population immunity, and in the circulating variants during that period52 of tools for COVID-19 screening, triage and severity scoring found none that had been validated in low or middle income countries, despite 51 of the identified tools being considered feasible for use in such settings. This study is, to the best of our knowledge, the first to demonstrate that such tools can be successfully deployed in a low-resource setting. Participants were people presenting at local medical facilities and all tests were carried out on the same day, using temporary facilities on-site, without interruption to their standard care. This demonstrates the feasibility of collecting such point-of-care imaging and blood tests in low resource settings where patient follow-up is difficult and therefore cost-effective screening tools with immediate results are of utmost importance. This novel study lays the foundations for future AI development for low resource settings, with the potential, in the future, to detect a variety of conditions based on low-cost point-of-care testing.Diagnostic AI systems in resource-constrained settings are limited, compared to those used elsewhere, in that the input data must generally be acquired at low-cost, at point-of-care and by personnel with minimal training. A review by Hirner et al.


T cells represent a crucial component of the adaptive immune system and mediate anti-tumoral immunity as well as protection against infections, including respiratory viruses such as SARS-CoV-2. Next-generation sequencing of the T-cell receptors (TCRs) can be used to profile the T-cell repertoire. We developed a customized pipeline for Network Analysis of Immune Repertoire (NAIR) with advanced statistical methods to characterize and investigate changes in the landscape of TCR sequences. We first performed network analysis on the TCR sequence data based on sequence similarity. We then quantified the repertoire network by network properties and correlated it with clinical outcomes of interest. In addition, we identified (1) disease-specific/associated clusters and (2) shared clusters across samples based on our customized search algorithms and assessed their relationship with clinical outcomes such as recovery from COVID-19 infection. Furthermore, to identify disease-specific TCRs, we introduced a new metric that incorporates the clonal generation probability and the clonal abundance by using the Bayes factor to filter out the false positives. TCR-seq data from COVID-19 subjects and healthy donors were used to illustrate that the proposed approach to analyzing the network architecture of the immune repertoire can reveal potential disease-specific TCRs responsible for the immune response to infection. The unique T-cell receptor (TCR) on each T cell dictates antigen specificity. Collectively, all of an individual’s TCRs make up the T-cell immune repertoire. Thus, investigating the immune repertoire is paramount to understanding the basis underlying the immune response to infection . BecauseRecently, the AIRR-seq analysis has been applied to COVID-19 subjects to understand how the adaptive immune system is induced by SARS-CoV-2 . A higheUnlike the immune repertoire diversity, which is based on the frequency profiles of individual clones , sequencPublic or shared T-cell clones are T cells that have the exact same CDR3 nucleotide or amino acid sequence between individuals or within an individual across time . Functiogen) evaluates which specific amino acid sequences and sequence motifs are likely to be generated and found in repertoires , a cohort of subjects with active infection and severely symptomatic who had comorbidities (n = 18), all of which required hospitalization and an age-matched healthy donor cohort tested negative for COVID-19 antibodies (n = 39). Up to nine follow-up blood samples were available per subject, some spanning different disease stages in the same subject , totaling 108 samples  was used to identify antigen-specific TCRs . The COV2.3versus COVID-19 samples coming from multiple time points, we applied a generalized linear mixed model to account for the repeated measures, where we focused on the global properties. Specially, for each global property, we used the maximum value within a given sample as the outcome variable, since we usually have many clusters within one sample. We then treated time (the number of weeks since diagnosed with COVID) and sample characteristics, such as COVID active, COVID received, or healthy, as fixed effects, while the subject was considered as a random effect. This approach allowed us to account for multiple samples from the same subject and to compare the maximum property values across different groups. In addition, for each global property, we used heatmaps to display the distribution of all values for each cluster (columns) across the samples (rows), while the dendrogram on the left side shows the hierarchical clustering based on the corresponding property values.The pairwise distance matrix of TCR amino acid sequences for each subject was calculated using Hamming distance (Python module SciPy with pdist function). When Hamming distance is less than or equal to 1, then the edge is equal to 1; otherwise, it is equal to 0. A network cluster was defined as a group of clones with a Hamming distance less than or equal to 1  by fast greedy algorithm (igraph ). Networ2.4p< 0.05) to identify TCR sequences that are potentially associated with COVID-19 based on this 2 × 2 table. To identify clusters of COVID-associated TCRs, we searched for TCRs that were in the same cluster as the COVID-associated TCRs by analyzing all TCRs from shared samples using network analysis (with a Hamming distance of<= 1). The TCR clusters that were found only in COVID-19 samples were defined as COVID-only TCR clusters, while the rest were defined as COVID-associated TCR clusters. Finally, we constructed a network across all COVID-associated TCRs, including their member TCRs in the same cluster, and assigned global membership to the COVID-associated clusters.The disease-associated TCR cluster is characterized as a group comprising TCRs that exhibit, at most, one amino acid difference in their TCR sequences and display a statistically significant difference in their frequency between the disease group and the control group. As illustrated in We proposed a workflow to identify the public clusters  in each sample and then normalized this value by dividing it by the total number of TCRs within the sample. Next, we calculated the correlation coefficient based on the normalized number of TCRs in each TCR cluster across all samples. We clustered the samples by hierarchical clustering based on the normalized frequencies calculated in the previous step.2.5.4pgen) and clonal abundance using Bayes factor to evaluate the significance of identified clones. We then calculated Bayes factor adjusted p-value and false discovery rate (FDR) for each TCR and summarized the proportion of the TCRs with Bayes adjusted FDR< 0.05.We introduced a new metric to evaluate the importance of the clones by incorporating clonal generation probability (pgen) probabilistically annotates sequences, and its modular structure can be used to investigate models of increasing biological complexity for different organisms, which is calculated by OLGA  associated with the COVID-19 samples based on Fisher’s exact test Among the 105 non-COVID-only clusters, we found eight clusters associated with COVID-19 samples  and three associated with HD  based on differential testing  . Downstream differential testing based on TCR clonal abundance was applied to the 3,979 TCR clusters  with at least three position matching. We further compared our findings with the results obtained by GLIPH2   unction) , we usedWe chose fast greedy approach as the clustering approach for network analysis due to the fastest speed. We compared the results of all available methods in igraph . All approaches generated similar results for small-to-moderate-size clusters except Leiden , it uses 50–1,700 Mb memory to perform network analysis (Hamming distance) on a sample with 1,000–50,000 clones by using the current version of the software, which takes 2 s to 35 min, respectively. It takes much longer (up to 25 times longer) if Levenshtein distance is applied.4Due to the heterogeneous nature of T cells across time and different subjects, analyses of AIRR-seq data have been challenging. Network analysis allows us to potentially uncover the biological significance of unique TCRs based on sequence. We developed two different pipelines to identify disease-specific clusters and public clusters, along with novel metrics to evaluate the identified clusters along with downstream analysis. The workflows can be applied to B-cell repertoires directly and can be easily extended to identify the clusters that respond to treatment .COVID-19 epitope-specific TCR clones in MIRA were also found in the European datasets, suggesting that COVID-19 subjects develop a distinct subset of T cells against just a few epitopes . When maSeveral methods for the computational and statistical analysis of large-scale AIRR-seq data have been developed to resolve immune repertoire complexity and to understand the dynamics of adaptive immunity by using network analysis, such as GLIPH2 , ImmunoMOne limitation of our proposed method is that we have used a dataset derived from COVID-19 subjects. SARS-CoV-2, the virus responsible for COVID-19, has relatively fewer epitopes compared with more complex diseases, such as malignancies with high-mutational burden . Thus, ain-silico bioinformatic approach can improve the current workflows by narrowing the number of TCRs that need to be tested.In conclusion, we have developed a bioinformatics pipeline by incorporating the proposed methods and techniques to tackle the complexity of the immunosequencing data in a translational fashion. The associations found in our study need further functional studies to confirm the biological significance and to explore their clinical applications. Validation of TCR antigen specificity traditionally require identification of antigen-specific TCRs with peptide/HLA multimers then expression and functional testing of identified TCRs, which is labor and time intensive. This via The Adaptive Immune Receptor Repertoire (AIRR) Data Commons. Availability and Implementation The code and scripts have been deposited in GitHub .Publicly available datasets were analyzed in this study. This data can be found here: The European data are available in gateway.ireceptor.org; Study ID: IR-Binder-000001 Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.HY, JC and LZ initiated and designed the experiment. HY, ZF and LZ acquired the data. YH, ZF, TH and LZ performed the data analysis. HY and BN developed the software. TH and LZ acquired the funding. All authors contributed to the article and approved the submitted version.


ObjectivesThis study aims to compare the severity of chronic rhinosinusitis (CRS) symptoms pre- and post-COVID-19 infection and estimate the impact of the COVID-19 pandemic on the use of intranasal corticosteroids (ICS) among adult CRS patients.MethodsThis was an observational retrospective cohort study conducted at King Abdulaziz University Hospital, Riyadh, Saudi Arabia, between July 2022 and October 2022. Adult CRS patients with sino-nasal outcomes test-22 (SNOT-22) scores documented prior to March 2020, marking the occurrence of Saudi Arabia's initial reported case of COVID-19, were requested to complete the SNOT-22 questionnaire following COVID-19 infection. A comparison was subsequently made between the two scores obtained.ResultsThe study enrolled a total of 33 patients, with 16 assigned to the control group and 17 with a history of COVID-19 infection. The mean age of the patients was 43 years, and the majority (52%) were males. Statistical analysis did not reveal any statistically significant differences in the total SNOT-22 scores or domain-level scores between the two groups. Furthermore, the use of ICS during the COVID-19 pandemic did not show any significant associations, except for patients with asthma, where 80% of them used ICS during the pandemic (p=0.0073).ConclusionThere was no statistically significant disparity observed in the SNOT-22 scores between patients who tested positive for COVID-19 and those who did not. The use of corticosteroids during the COVID-19 pandemic was found to be more prevalent in this study compared to previous studies conducted before the pandemic, particularly among patients with asthma. The use of ICS during the pandemic was not associated with the presence of polyps, functional endoscopic sinus surgery (FESS), allergic rhinitis, or eczema. Chronic rhinosinusitis (CRS) is a multifaceted inflammatory condition with diverse phenotypes and endotypes . DespiteStudy designThis study employed an observational retrospective cohort design and was conducted at King Abdulaziz University Hospital in Riyadh, Saudi Arabia, from July 2022 to October 2022. The data were obtained from adult patients diagnosed with CRS who had documented SNOT-22 scores recorded before March 2020, which marked the onset of the COVID-19 pandemic in Saudi Arabia.SubjectsThe diagnosis of CRS in this study followed the criteria set forth by the European Position Paper on Rhinosinusitis and Nasal Polyps (EPOS), which defines CRS as the presence of two or more symptoms lasting for at least 12 weeks, with at least one symptom being nasal blockage/obstruction/congestion or nasal discharge , ± facial pain/pressure ± reduction or loss of smell . In addiData collectionAdult CRS patients with SNOT-22 scores documented prior to March 2020 were requested to complete the SNOT-22 questionnaire again following COVID-19 infection. All subjects were identified through electronic health records and subsequently contacted via phone calls and requested to complete the electronic questionnaire. Responses were directly recorded on an electronic data sheet to ensure accuracy and reliability. The study ensured the privacy and confidentiality of all participants, and informed consent was obtained from each participant. The validated Arabic version of SNOT-22, a 22-item chronic rhinosinusitis-specific quality-of-life (QoL) instrument, was utilized . The SNOStatistical analysisThe numerical data are described as mean and standard deviations , while categorical data are described as frequency and percentages. A p-value of <0.05 and 95% confidence interval (CI) were used to report the statistical significance and precision of the results. The t-test for unpaired groups was used to test the difference in the means of the SNOT scores between the cases and the controls. The chi-square test was used to test the associations between the use of ICS during the COVID-19 pandemic and demographic characteristics. The statistical analysis was performed using Stata-17 .In this study, a total of 101 patients with CRS were initially enrolled. However, after applying the pre-determined eligibility criteria, only 33 subjects were deemed suitable for inclusion in the study. This included 17 cases and 16 controls. The average age of the patients was 43 years, and the majority 52%) were male. The two groups were similar in terms of their baseline characteristics, except for the presence of polyps, which was found to be significantly higher in the control group (p=0.009), and functional endoscopic sinus surgery (FESS) (p=0.029). A summary of the baseline characteristics can be found in Table % were maThe study assessed changes in symptom severity in participants with CRS before and after COVID-19 infection, as well as the impact of the pandemic on the use of ICS in adults with CRS. The severity of CRS symptoms was measured using the SNOT-22 questionnaire, which measures health-related QoL in patients with CRS. The results showed no statistically significant difference in SNOT-22 scores before and after COVID-19 infection when the four domains  were evaluated separately. Additionally, there was no significant difference in total SNOT-22 scores, and the mean difference in SNOT-22 scores before and after the COVID-19 pandemic was below the minimal clinically important difference (MCID) for the validated Arabic SNOT-22 questionnaire . This inThe use of ICS, which is the therapeutic standard for CRS, was also evaluated in this study. The EAACI's position paper on the management of CRS during COVID-19 suggests that corticosteroid administration is safe and should be continued as it may decrease virus-induced inflammation in the upper airways, and reduce the duration and severity of respiratory symptoms ,14. HoweThis study had several limitations including the small sample size due to strict eligibility criteria, resulting in the exclusion of a majority of respondents. However, these criteria were implemented to ensure internal validity by minimizing possible confounders. Efforts were made to address the time confounder by including a control group for comparison. It should be noted that a higher number of participants in the control group had nasal polyps and underwent FESS before the pandemic compared to cases, which could potentially introduce biased findings. Nevertheless, despite these limitations, the findings of this study contribute to the existing literature on CRS and COVID-19. Further large-scale studies are warranted to comprehensively evaluate the possible effects of COVID-19 infection on CRS symptoms and outcomes.In conclusion, the results of this study suggest that COVID-19 infection did not have a statistically or clinically significant effect on the severity of CRS symptoms as measured by the SNOT-22 questionnaire. The use of corticosteroids during the COVID-19 pandemic was found to be more prevalent in this study compared to previous studies conducted before the pandemic, particularly among patients with asthma. The use of ICS during the pandemic was not associated with the presence of polyps, FESS, allergic rhinitis, or eczema. Overall, the findings of this study provide valuable insights for clinicians managing CRS patients post-COVID-19 infection. Further large-scale studies are needed to better understand the potential effects of COVID-19 on CRS symptoms and outcomes.