Incidence and Predictors of Breakthrough and Severe Breakthrough Infections of SARS-CoV-2 After Primary Series Vaccination in Adults: A Population-Based Survey of 22 575 Participants.

BACKGROUND: Breakthrough infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are well documented. The current study estimates breakthrough incidence across pandemic waves, and evaluates predictors of breakthrough and severe breakthrough infections (defined as those requiring hospitalization). METHODS: In total, 89 762 participants underwent longitudinal antibody surveillance. Incidence rates were calculated using total person-days contributed. Bias-corrected and age-adjusted logistic regression determined multivariable predictors of breakthrough and severe breakthrough infection, respectively. RESULTS: The incidence was 0.45 (95% confidence interval [CI], .38-.50) during pre-Delta, 2.80 (95% CI, 2.25-3.14) during Delta, and 11.2 (95% CI, 8.80-12.95) during Omicron, per 10 000 person-days. Factors associated with elevated odds of breakthrough included Hispanic ethnicity (vs non-Hispanic white, OR = 1.243; 95% CI, 1.073-1.441), larger household size (OR = 1.251 [95% CI, 1.048-1.494] for 3-5 vs 1 and OR = 1.726 [95% CI, 1.317-2.262] for more than 5 vs 1 person), rural versus urban living (OR = 1.383; 95% CI, 1.122-1.704), receiving Pfizer or Johnson & Johnson versus Moderna, and multiple comorbidities. Of the 1700 breakthrough infections, 1665 reported on severity; 112 (6.73%) were severe. Higher body mass index, Hispanic ethnicity, vaccine type, asthma, and hypertension predicted severe breakthroughs. CONCLUSIONS: Breakthrough infection was 4-25 times more common during the Omicron-dominant wave versus earlier waves. Higher burden of severe breakthrough infections was identified in subgroups.

Antibody Duration After Infection From SARS-CoV-2 in the Texas Coronavirus Antibody Response Survey.

Understanding the duration of antibodies to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus that causes COVID-19 is important to controlling the current pandemic. Participants from the Texas Coronavirus Antibody Response Survey (Texas CARES) with at least 1 nucleocapsid protein antibody test were selected for a longitudinal analysis of antibody duration. A linear mixed model was fit to data from participants (n = 4553) with 1 to 3 antibody tests over 11 months (1 October 2020 to 16 September 2021), and models fit showed that expected antibody response after COVID-19 infection robustly increases for 100 days postinfection, and predicts individuals may remain antibody positive from natural infection beyond 500 days depending on age, body mass index, smoking or vaping use, and disease severity (hospitalized or not; symptomatic or not).

Long-term immune response to SARS-CoV-2 infection and vaccination in children and adolescents.

BACKGROUND: This analysis examined the durability of antibodies present after SARS-CoV-2 infection and vaccination in children and adolescents. METHODS: Data were collected over 4 time points between October 2020-November 2022 as part of a prospective population-based cohort aged 5-to-19 years (N = 810). Results of the (1) Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test); and (2) qualitative and semi-quantitative detection of antibodies to the SARS CoV-2 spike protein receptor binding domain (Roche S-test); and (3) self-reported antigen/PCR COVID-19 test results, vaccination and symptom status were analyzed. RESULTS: N antibody levels reached a median of 84.10 U/ml (IQR: 20.2, 157.7) cutoff index (COI) ~ 6 months post-infection and increased slightly to a median of 85.25 (IQR: 28.0, 143.0) COI at 12 months post-infection. Peak S antibody levels were reached at a median of 2500 U/mL ~6 months post-vaccination and remained for ~12 months (mean 11.6 months, SD 1.20). CONCLUSIONS: This analysis provides evidence of robust durability of nucleocapsid and spike antibodies in a large pediatric sample up to 12 months post-infection/vaccination. This information can inform pediatric SARS-CoV-2 vaccination schedules. IMPACT: This study provided evidence of robust durability of both nucleocapsid and spike antibodies in a large pediatric sample up to 12 months after infection. Little is known about the long-term durability of natural and vaccine-induced SARS-CoV-2 antibodies in the pediatric population. Here, we determined the durability of anti-SARS-CoV-2 spike (S-test) and nucleocapsid protein (N-test) in children/adolescents after SARS-CoV-2 infection and/or vaccination lasts at least up to 12 months. This information can inform future SARS-CoV-2 vaccination schedules in this age group.

A novel approach for propensity score matching and stratification for multiple treatments: Application to an electronic health record-derived study.

Currently, methods for conducting multiple treatment propensity scoring in the presence of high-dimensional covariate spaces that result from "big data" are lacking-the most prominent method relies on inverse probability treatment weighting (IPTW). However, IPTW only utilizes one element of the generalized propensity score (GPS) vector, which can lead to a loss of information and inadequate covariate balance in the presence of multiple treatments. This limitation motivates the development of a novel propensity score method that uses the entire GPS vector to establish a scalar balancing score that, when adjusted for, achieves covariate balance in the presence of potentially high-dimensional covariates. Specifically, the generalized propensity score cumulative distribution function (GPS-CDF) method is introduced. A one-parameter power function fits the CDF of the GPS vector and a resulting scalar balancing score is used for matching and/or stratification. Simulation results show superior performance of the new method compared to IPTW both in achieving covariate balance and estimating average treatment effects in the presence of multiple treatments. The proposed approach is applied to a study derived from electronic medical records to determine the causal relationship between three different vasopressors and mortality in patients with non-traumatic aneurysmal subarachnoid hemorrhage. Results suggest that the GPS-CDF method performs well when applied to large observational studies with multiple treatments that have large covariate spaces.

Vasopressor treatment and mortality following nontraumatic subarachnoid hemorrhage: a nationwide electronic health record analysis.

OBJECTIVE: Subarachnoid hemorrhage (SAH) is a devastating cerebrovascular condition, not only due to the effect of initial hemorrhage, but also due to the complication of delayed cerebral ischemia (DCI). While hypertension facilitated by vasopressors is often initiated to prevent DCI, which vasopressor is most effective in improving outcomes is not known. The objective of this study was to determine associations between initial vasopressor choice and mortality in patients with nontraumatic SAH. METHODS: The authors conducted a retrospective cohort study using a large, national electronic medical record data set from 2000-2014 to identify patients with a new diagnosis of nontraumatic SAH (based on ICD-9 codes) who were treated with the vasopressors dopamine, phenylephrine, or norepinephrine. The relationship between the initial choice of vasopressor therapy and the primary outcome, which was defined as in-hospital death or discharge to hospice care, was examined. RESULTS: In total, 2634 patients were identified with nontraumatic SAH who were treated with a vasopressor. In this cohort, the average age was 56.5 years, 63.9% were female, and 36.5% of patients developed the primary outcome. The incidence of the primary outcome was higher in those initially treated with either norepinephrine (47.6%) or dopamine (50.6%) than with phenylephrine (24.5%). After adjusting for possible confounders using propensity score methods, the adjusted OR of the primary outcome was higher with dopamine (OR 2.19, 95% CI 1.70-2.81) and norepinephrine (OR 2.24, 95% CI 1.80-2.80) compared with phenylephrine. Sensitivity analyses using different variable selection procedures, causal inference models, and machine-learning methods confirmed the main findings. CONCLUSIONS: In patients with nontraumatic SAH, phenylephrine was significantly associated with reduced mortality in SAH patients compared to dopamine or norepinephrine. Prospective randomized clinical studies are warranted to confirm this finding.

FLEXc: protein flexibility prediction using context-based statistics, predicted structural features, and sequence information.

BACKGROUND: The fluctuation of atoms around their average positions in protein structures provides important information regarding protein dynamics. This flexibility of protein structures is associated with various biological processes. Predicting flexibility of residues from protein sequences is significant for analyzing the dynamic properties of proteins which will be helpful in predicting their functions. RESULTS: In this paper, an approach of improving the accuracy of protein flexibility prediction is introduced. A neural network method for predicting flexibility in 3 states is implemented. The method incorporates sequence and evolutionary information, context-based scores, predicted secondary structures and solvent accessibility, and amino acid properties. Context-based statistical scores are derived, using the mean-field potentials approach, for describing the different preferences of protein residues in flexibility states taking into consideration their amino acid context. The 7-fold cross validated accuracy reached 61 % when context-based scores and predicted structural states are incorporated in the training process of the flexibility predictor. CONCLUSIONS: Incorporating context-based statistical scores with predicted structural states are important features to improve the performance of predicting protein flexibility, as shown by our computational results. Our prediction method is implemented as web service called "FLEXc" and available online at: http://hpcr.cs.odu.edu/flexc .

Template-based C8-SCORPION: a protein 8-state secondary structure prediction method using structural information and context-based features.

BACKGROUND: Secondary structures prediction of proteins is important to many protein structure modeling applications. Correct prediction of secondary structures can significantly reduce the degrees of freedom in protein tertiary structure modeling and therefore reduces the difficulty of obtaining high resolution 3D models. METHODS: In this work, we investigate a template-based approach to enhance 8-state secondary structure prediction accuracy. We construct structural templates from known protein structures with certain sequence similarity. The structural templates are then incorporated as features with sequence and evolutionary information to train two-stage neural networks. In case of structural templates absence, heuristic structural information is incorporated instead. RESULTS: After applying the template-based 8-state secondary structure prediction method, the 7-fold cross-validated Q8 accuracy is 78.85%. Even templates from structures with only 20%~30% sequence similarity can help improve the 8-state prediction accuracy. More importantly, when good templates are available, the prediction accuracy of less frequent secondary structures, such as 3-10 helices, turns, and bends, are highly improved, which are useful for practical applications. CONCLUSIONS: Our computational results show that the templates containing structural information are effective features to enhance 8-state secondary structure predictions. Our prediction algorithm is implemented on a web server named "C8-SCORPION" available at: http://hpcr.cs.odu.edu/c8scorpion.

Context-based features enhance protein secondary structure prediction accuracy.

We report a new approach of using statistical context-based scores as encoded features to train neural networks to achieve secondary structure prediction accuracy improvement. The context-based scores are pseudo-potentials derived by evaluating statistical, high-order inter-residue interactions, which estimate the favorability of a residue adopting certain secondary structure conformation within its amino acid environment. Encoding these context-based scores as important training and prediction features provides a way to address a long-standing difficulty in neural network-based secondary structure predictions of taking interdependency among secondary structures of neighboring residues into account. Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracy of secondary structure predictions. An overall 7-fold cross-validated Q3 accuracy of 82.74% and Segment Overlap Accuracy (SOV) accuracy of 86.25% are achieved on a set of more than 7987 protein chains with, at most, 25% sequence identity. The Q3 prediction accuracy on benchmarks of CB513, Manesh215, Carugo338, as well as CASP9 protein chains is higher than popularly used secondary structure prediction servers, including Psipred, Profphd, Jpred, Porter (ab initio), and Netsurf. More significant improvement is observed in the SOV accuracy, where more than 4% enhancement is observed, compared to the server with the best SOV accuracy. A Q8 accuracy of >70% (71.5%) is also found in eight-state secondary structure prediction. The majority of the Q3 accuracy improvement is contributed from correctly identifying ß-sheets and α-helices. When the context-based scores are incorporated, there are 15.5% more residues predicted with >90% confidence. These high-confidence predictions usually have a rather high accuracy (averagely ~95%). The three- and eight-state prediction servers (SCORPION) implementing our methods are available online.

Baseline characteristics of SARS-CoV-2 vaccine non-responders in a large population-based sample.

INTRODUCTION: Studies indicate that individuals with chronic conditions and specific baseline characteristics may not mount a robust humoral antibody response to SARS-CoV-2 vaccines. In this paper, we used data from the Texas Coronavirus Antibody REsponse Survey (Texas CARES), a longitudinal state-wide seroprevalence program that has enrolled more than 90,000 participants, to evaluate the role of chronic diseases as the potential risk factors of non-response to SARS-CoV-2 vaccines in a large epidemiologic cohort. METHODS: A participant needed to complete an online survey and a blood draw to test for SARS-CoV-2 circulating plasma antibodies at four-time points spaced at least three months apart. Chronic disease predictors of vaccine non-response are evaluated using logistic regression with non-response as the outcome and each chronic disease + age as the predictors. RESULTS: As of April 24, 2023, 18,240 participants met the inclusion criteria; 0.58% (N = 105) of these are non-responders. Adjusting for age, our results show that participants with self-reported immunocompromised status, kidney disease, cancer, and "other" non-specified comorbidity were 15.43, 5.11, 2.59, and 3.13 times more likely to fail to mount a complete response to a vaccine, respectively. Furthermore, having two or more chronic diseases doubled the prevalence of non-response. CONCLUSION: Consistent with smaller targeted studies, a large epidemiologic cohort bears the same conclusion and demonstrates immunocompromised, cancer, kidney disease, and the number of diseases are associated with vaccine non-response. This study suggests that those individuals, with chronic diseases with the potential to affect their immune system response, may need increased doses or repeated doses of COVID-19 vaccines to develop a protective antibody level.

Dinosolve: a protein disulfide bonding prediction server using context-based features to enhance prediction accuracy.

BACKGROUND: Disulfide bonds play an important role in protein folding and structure stability. Accurately predicting disulfide bonds from protein sequences is important for modeling the structural and functional characteristics of many proteins. METHODS: In this work, we introduce an approach of enhancing disulfide bonding prediction accuracy by taking advantage of context-based features. We firstly derive the first-order and second-order mean-force potentials according to the amino acid environment around the cysteine residues from large number of cysteine samples. The mean-force potentials are integrated as context-based scores to estimate the favorability of a cysteine residue in disulfide bonding state as well as a cysteine pair in disulfide bond connectivity. These context-based scores are then incorporated as features together with other sequence and evolutionary information to train neural networks for disulfide bonding state prediction and connectivity prediction. RESULTS: The 10-fold cross validated accuracy is 90.8% at residue-level and 85.6% at protein-level in classifying an individual cysteine residue as bonded or free, which is around 2% accuracy improvement. The average accuracy for disulfide bonding connectivity prediction is also improved, which yields overall sensitivity of 73.42% and specificity of 91.61%. CONCLUSIONS: Our computational results have shown that the context-based scores are effective features to enhance the prediction accuracies of both disulfide bonding state prediction and connectivity prediction. Our disulfide prediction algorithm is implemented on a web server named "Dinosolve" available at: http://hpcr.cs.odu.edu/dinosolve.

A novel NIH research grant recommender using BERT.

Research grants are important for researchers to sustain a good position in academia. There are many grant opportunities available from different funding agencies. However, finding relevant grant announcements is challenging and time-consuming for researchers. To resolve the problem, we proposed a grant announcements recommendation system for the National Institute of Health (NIH) grants using researchers' publications. We formulated the recommendation as a classification problem and proposed a recommender using state-of-the-art deep learning techniques: i.e. Bidirectional Encoder Representations from Transformers (BERT), to capture intrinsic, non-linear relationship between researchers' publications and grants announcements. Internal and external evaluations were conducted to assess the system's usefulness. During internal evaluations, the grant citations were used to establish grant-publication ground truth, and results were evaluated against Recall@k, Precision@k, Mean reciprocal rank (MRR) and Area under the Receiver Operating Characteristic curve (ROC-AUC). During external evaluations, researchers' publications were clustered using Dirichlet Process Mixture Model (DPMM), recommended grants by our model were then aggregated per cluster through Recency Weight, and finally researchers were invited to provide ratings to recommendations to calculate Precision@k. For comparison, baseline recommenders using Okapi Best Matching (BM25), Term-Frequency Inverse Document Frequency (TF-IDF), doc2vec, and Naïve Bayes (NB) were also developed. Both internal and external evaluations (all metrics) revealed favorable performances of our proposed BERT-based recommender.

Integrating, Harmonizing, and Curating Studies With High-Frequency and Hourly Physiological Data: Proof of Concept from Seven Traumatic Brain Injury Data Sets.

Research in severe traumatic brain injury (TBI) has historically been limited by studies with relatively small sample sizes that result in low power to detect small, yet clinically meaningful outcomes. Data sharing and integration from existing sources hold promise to yield larger more robust sample sizes that improve the potential signal and generalizability of important research questions. However, curation and harmonization of data of different types and of disparate provenance is challenging. We report our approach and experience integrating multiple TBI data sets containing collected physiological data, including both expected and unexpected challenges encountered in the integration process. Our harmonized data set included data on 1536 patients from the Citicoline Brain Injury Treatment Trial (COBRIT), Effect of erythropoietin and transfusion threshold on neurological recovery after traumatic brain injury: a randomized clinical trial (EPO Severe TBI), BEST-TRIP, Progesterone for the Treatment of Traumatic Brain Injury III Clinical Trial (ProTECT III), Transforming Research and Clinical Knowledge in Traumatic brain Injury (TRACK-TBI), Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II (BOOST-2), and Ben Taub General Hospital (BTGH) Research Database studies. We conclude with process recommendations for data acquisition for future prospective studies to aid integration of these data with existing studies. These recommendations include using common data elements whenever possible, a standardized recording system for labeling and timing of high-frequency physiological data, and secondary use of studies in systems such as Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR), to engage investigators who collected the original data.

SARS-CoV-2 Serostatus and COVID-19 Illness Characteristics by Variant Time Period in Non-Hospitalized Children and Adolescents.

OBJECTIVE: To describe COVID-19 illness characteristics, risk factors, and SARS-CoV-2 serostatus by variant time period in a large community-based pediatric sample. DESIGN: Data were collected prospectively over four timepoints between October 2020 and November 2022 from a population-based cohort ages 5 to 19 years old. SETTING: State of Texas, USA. PARTICIPANTS: Participants ages 5 to 19 years were recruited from large pediatric healthcare systems, Federally Qualified Healthcare Centers, urban and rural clinical practices, health insurance providers, and a social media campaign. EXPOSURE: SARS-CoV-2 infection. MAIN OUTCOME(S) AND MEASURE(S): SARS-CoV-2 antibody status was assessed by the Roche Elecsys® Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein (Roche N-test). Self-reported antigen or PCR COVID-19 test results and symptom status were also collected. RESULTS: Over half (57.2%) of the sample (N = 3911) was antibody positive. Symptomatic infection increased over time from 47.09% during the pre-Delta variant time period, to 76.95% during Delta, to 84.73% during Omicron, and to 94.79% during the Omicron BA.2. Those who were not vaccinated were more likely (OR 1.71, 95% CI 1.47, 2.00) to be infected versus those fully vaccinated. CONCLUSIONS: Results show an increase in symptomatic COVID-19 infection among non-hospitalized children with each progressive variant over the past two years. Findings here support the public health guidance that eligible children should remain up to date with COVID-19 vaccinations.

A Recommender for Research Collaborators Using Graph Neural Networks.

As most great discoveries and advancements in science and technology invariably involve the cooperation of a group of researchers, effective collaboration is the key factor. Nevertheless, finding suitable scholars and researchers to work with is challenging and, mostly, time-consuming for many. A recommender who is capable of finding and recommending collaborators would prove helpful. In this work, we utilized a life science and biomedical research database, i.e., MEDLINE, to develop a collaboration recommendation system based on novel graph neural networks, i.e., GraphSAGE and Temporal Graph Network, which can capture intrinsic, complex, and changing dependencies among researchers, including temporal user-user interactions. The baseline methods based on LightGCN and gradient boosting trees were also developed in this work for comparison. Internal automatic evaluations and external evaluations through end-users' ratings were conducted, and the results revealed that our graph neural networks recommender exhibits consistently encouraging results.

ImmuneData: an integrated data discovery system for immunology data repositories.

To meet the increasing demand for data sharing, data reuse and meta-analysis in the immunology research community, we have developed the data discovery system ImmuneData. The system provides integrated access to five immunology data repositories funded by the National Institute of Allergy and Infectious Diseases, Division of Allergy, Immunology and Transplantation, including ImmPort, ImmuneSpace, ITN TrialShare, ImmGen and IEDB. ImmuneData restructures the data repositories' metadata into a uniform schema using domain experts' knowledge and state-of-the-art Natural Language Processing (NLP) technologies. It comes with a user-friendly web interface, accessible at http://www.immunedata.org/, and a Google-like search engine for biological researchers to find and access data easily. The vast quantity of synonyms used in biomedical research increase the likelihood of incomplete search results. Thus, our search engine converts queries submitted by users into ontology terms, which are then expended by NLP technologies to ensure that the search results will include all synonyms for a particular concept. The system also includes an advanced search function to build customized queries to meet higher-level users' needs. ImmuneData ensures the FAIR principle (Findability, Accessibility, Interoperability and Reusability) of the five data repositories to benefit data reuse in the immunology research community. The data pipeline constructing our system can be extended to other data repositories to build a more comprehensive biological data discovery system. DATABASE URL: http://www.immunedata.org/.

Methodology to estimate natural- and vaccine-induced antibodies to SARS-CoV-2 in a large geographic region.

Accurate estimates of natural and/or vaccine-induced antibodies to SARS-CoV-2 are difficult to obtain. Although model-based estimates of seroprevalence have been proposed, they require inputting unknown parameters including viral reproduction number, longevity of immune response, and other dynamic factors. In contrast to a model-based approach, the current study presents a data-driven detailed statistical procedure for estimating total seroprevalence (defined as antibodies from natural infection or from full vaccination) in a region using prospectively collected serological data and state-level vaccination data. Specifically, we conducted a longitudinal statewide serological survey with 88,605 participants 5 years or older with 3 prospective blood draws beginning September 30, 2020. Along with state vaccination data, as of October 31, 2021, the estimated percentage of those 5 years or older with naturally occurring antibodies to SARS-CoV-2 in Texas is 35.0% (95% CI = (33.1%, 36.9%)). This is 3× higher than, state-confirmed COVID-19 cases (11.83%) for all ages. The percentage with naturally occurring or vaccine-induced antibodies (total seroprevalence) is 77.42%. This methodology is integral to pandemic preparedness as accurate estimates of seroprevalence can inform policy-making decisions relevant to SARS-CoV-2.

Comparison of Persistent Symptoms Following SARS-CoV-2 Infection by Antibody Status in Nonhospitalized Children and Adolescents.

BACKGROUND: The prevalence of long-term symptoms of coronavirus disease 2019 (COVID-19) in nonhospitalized pediatric populations in the United States is not well described. The objective of this analysis was to examine the presence of persistent COVID symptoms in children by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody status. METHODS: Data were collected between October 2020 and May 2022 from the Texas Coronavirus Antibody REsponse Survey, a statewide prospective population-based survey among 5-90 years old. Serostatus was assessed by the Roche Elecsys Anti-SARS-CoV-2 Immunoassay for detection of antibodies to the SARS-CoV-2 nucleocapsid protein. Self-reported antigen/polymerase chain reaction COVID-19 test results and persistent COVID symptom status/type/duration were collected simultaneously. Risk ratios for persistent COVID symptoms were calculated versus adults and by age group, antibody status, symptom presence/severity, variant, body mass index and vaccine status. RESULTS: A total of 82 (4.5% of the total sample [n = 1813], 8.0% pre-Delta, 3.4% Delta and beyond) participants reported persistent COVID symptoms (n = 27 [1.5%] 4-12 weeks, n = 58 [3.3%] >12 weeks). Compared with adults, all pediatric age groups had a lower risk for persistent COVID symptoms regardless of length of symptoms reported. Additional increased risk for persistent COVID symptoms >12 weeks included severe symptoms with initial infection, not being vaccinated and having unhealthy weight (body mass index ≥85th percentile for age and sex). CONCLUSIONS: These findings highlight the existence of nonhospitalized youth who may also experience persistent COVID symptoms. Children and adolescents are less likely to experience persistent COVID symptoms than adults and more likely to be symptomatic, experience severe symptoms and have unhealthy weight compared with children/adolescents without persistent COVID symptoms.

Recommender system of scholarly papers using public datasets.

The exponential growth of public datasets in the era of Big Data demands new solutions for making these resources findable and reusable. Therefore, a scholarly recommender system for public datasets is an important tool in the field of information filtering. It will aid scholars in identifying prior and related literature to datasets, saving their time, as well as enhance the datasets reusability. In this work, we developed a scholarly recommendation system that recommends research-papers, from PubMed, relevant to public datasets, from Gene Expression Omnibus (GEO). Different techniques for representing textual data are employed and compared in this work. Our results show that term-frequency based methods (BM25 and TF-IDF) outperformed all others including popular Natural Language Processing embedding models such as doc2vec, ELMo and BERT.

Identifying Individualized Risk Profiles for Radiotherapy-Induced Lymphopenia Among Patients With Esophageal Cancer Using Machine Learning.

PURPOSE: Radiotherapy (RT)-induced lymphopenia (RIL) is commonly associated with adverse clinical outcomes in patients with cancer. Using machine learning techniques, a retrospective study was conducted for patients with esophageal cancer treated with proton and photon therapies to characterize the principal pretreatment clinical and radiation dosimetric risk factors of grade 4 RIL (G4RIL) as well as to establish G4RIL risk profiles. METHODS: A single-institution retrospective data of 746 patients with esophageal cancer treated with photons (n = 500) and protons (n = 246) was reviewed. The primary end point of our study was G4RIL. Clustering techniques were applied to identify patient subpopulations with similar pretreatment clinical and radiation dosimetric characteristics. XGBoost was built on a training set (n = 499) to predict G4RIL risks. Predictive performance was assessed on the remaining n = 247 patients. SHapley Additive exPlanations were used to rank the importance of individual predictors. Counterfactual analyses compared patients' risk profiles assuming that they had switched modalities. RESULTS: Baseline absolute lymphocyte count and volumes of lung and spleen receiving ≥ 15 and ≥ 5 Gy, respectively, were the most important G4RIL risk determinants. The model achieved sensitivitytesting-set 0.798 and specificitytesting-set 0.667 with an area under the receiver operating characteristics curve (AUCtesting-set) of 0.783. The G4RIL risk for an average patient receiving protons increased by 19% had the patient switched to photons. Reductions in G4RIL risk were maximized with proton therapy for patients with older age, lower baseline absolute lymphocyte count, and higher lung and heart dose. CONCLUSION: G4RIL risk varies for individual patients with esophageal cancer and is modulated by radiotherapy dosimetric parameters. The framework for machine learning presented can be applied broadly to study risk determinants of other adverse events, providing the basis for adapting treatment strategies for mitigation.

Strategies to Estimate Prevalence of SARS-CoV-2 Antibodies in a Texas Vulnerable Population: Results From Phase I of the Texas Coronavirus Antibody Response Survey.

Introduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and immunity remains uncertain in populations. The state of Texas ranks 2nd in infection with over 2.71 million cases and has seen a disproportionate rate of death across the state. The Texas CARES project was funded by the state of Texas to estimate the prevalence of SARS-CoV-2 antibody status in children and adults. Identifying strategies to understand natural as well as vaccine induced antibody response to COVID-19 is critical. Materials and Methods: The Texas CARES (Texas Coronavirus Antibody Response Survey) is an ongoing prospective population-based convenience sample from the Texas general population that commenced in October 2020. Volunteer participants are recruited across the state to participate in a 3-time point data collection Texas CARES to assess antibody response over time. We use the Roche Elecsys® Anti-SARS-CoV-2 Immunoassay to determine SARS-CoV-2 antibody status. Results: The crude antibody positivity prevalence in Phase I was 26.1% (80/307). The fully adjusted seroprevalence of the sample was 31.5%. Specifically, 41.1% of males and 21.9% of females were seropositive. For age categories, 33.5% of those 18-34; 24.4% of those 35-44; 33.2% of those 45-54; and 32.8% of those 55+ were seropositive. In this sample, 42.2% (89/211) of those negative for the antibody test reported having had a COVID-19 test. Conclusions: In this survey we enrolled and analyzed data for 307 participants, demonstrating a high survey and antibody test completion rate, and ability to implement a questionnaire and SARS-CoV-2 antibody testing within clinical settings. We were also able to determine our capability to estimate the cross-sectional seroprevalence within Texas's federally qualified community centers (FQHCs). The crude positivity prevalence for SARS-CoV-2 antibodies in this sample was 26.1% indicating potentially high exposure to COVID-19 for clinic employees and patients. Data will also allow us to understand sex, age and chronic illness variation in seroprevalence by natural and vaccine induced. These methods are being used to guide the completion of a large longitudinal survey in the state of Texas with implications for practice and population health.