Adolescent alcohol use is associated with differences in the diversity and composition of the oral microbiome.

BACKGROUND: Adolescence is a sensitive stage of oral microbial development that often coincides with the initiation and escalation of alcohol use. Thus, adolescents may be particularly susceptible to alcohol-induced alterations in the oral microbiome, though minimal research has been done in this area. Understanding the connection between the oral microbiome and alcohol use during adolescence is important to understand fully the biological consequences of alcohol use to mitigate potential adverse outcomes. METHODS: Saliva samples were collected from adolescents aged 17-19 who used alcohol heavily (n = 21, 52.4% female) and those who did not use alcohol or any other substances (n = 18, 44.4% female). We utilized 16S rRNA sequencing to examine differences in microbial diversity and composition between the groups. RESULTS: For alpha diversity, evenness was significantly lower in the drinking group than the control group as indicated by Pielou's evenness, Shannon, and Simpson indices. There were no statistically significant findings for beta diversity. Differential abundance analyses revealed higher abundances of Rothia and Corynebacterium in the alcohol-using group using both centered-log-ratio and relative abundance normalization. These genera are known for their high capacity to convert alcohol into acetaldehyde, a toxic metabolite reported to play a role in the neurobiological effects of alcohol. An unclassified Clostridia UCG-014, Streptobacillus, Comamonas, unclassified Lachnospiraceae, and Parvimonas were also identified as significantly different between groups when using only one of the normalization techniques. CONCLUSIONS: This is the first study designed specifically to compare the oral microbiome of adolescents who use alcohol with that of control participants. Our findings reveal distinct alcohol-related differences in microbial composition and taxon abundance, emphasizing the importance of understanding the impact on the oral microbiome of alcohol use during adolescence. Because the oral microbiome is malleable, this study provides foundational work for future prevention and intervention studies.

County Mammogram Uptake can be Predicted from Social Determinants of Health, but Patterns Do Not Hold for Individual Patients.

Purpose: The objective of this study is to describe patterns in barriers to breast cancer screening uptake with the end goal of improving screening adherence and decreasing the burden of mortality due to breast cancer. This study looks at social determinants of health and their association to screening and mortality. It also investigates the extent that models trained on county data are generalizable to individuals. Methods: County level screening uptake and age adjusted mortality due to breast cancer are combined with the Centers for Disease Controls Social Vulnerability Index (SVI) to train a model predicting screening uptake rates. Patterns learned are then applied to de-identified electronic medical records from individual patients to make predictions on mammogram screening follow through. Results: Accurate predictions can be made about a county's breast cancer screening uptake with the SVI. However, the association between increased screening, and decreased age adjusted mortality, doesn't hold in areas with a high proportion of minority residents. It is also shown that patterns learned from county SVI data have little discriminative power at the patient level. Conclusion: This study demonstrates that social determinants in the SVI can explain much of the variance in county breast cancer screening rates. However, these same patterns fail to discriminate which patients will have timely follow through of a mammogram screening test. This study also concludes that the core association between increased screening and decreased age adjusted mortality does not hold in high proportion minority areas. Objective: The objective of this study is to describe patterns in social determinants of health and their association with female breast cancer screening uptake, age adjusted breast cancer mortality rate and the extent that models trained on county data are generalizable to individuals.

SpaceANOVA: Spatial Co-occurrence Analysis of Cell Types in Multiplex Imaging Data Using Point Process and Functional ANOVA.

Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or the tumor microenvironment. Exploring the potential variations in the spatial co-occurrence or colocalization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process and functional analysis of variance. Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered due to data-collection complexities. We demonstrate the superior statistical power and robustness of the method in comparison with existing approaches through realistic simulation studies. Furthermore, we apply the method to three real data sets on different diseases collected using different imaging platforms. In particular, one of these data sets reveals novel insights into the spatial characteristics of various types of colorectal adenoma.

The adolescent and young adult microbiome and its association with substance use: a scoping review.

AIMS: The microbiome is a critical factor in health throughout human development. The aims of this scoping review are to (i) elucidate the differences between the youth (post-natal day 21-65 for rodents, 2-7 years for non-human primates, and 10-25 years for humans) microbiome with other life stages and (ii) identify youth-specific microbial changes associated with substance use. METHODS: Peer-reviewed studies published up to May 2023 were identified in PubMed and SCOPUS and included gut and oral microbiome studies from rodents, non-human primates, and humans (N = 1733). Twenty-six articles were determined eligible based on inclusion criteria (aim 1: n = 19, aim 2: n = 7). RESULTS: The adolescent and young adult oral and gut microbiomes are distinct compared to other life stages, within both non-human and human models. While there is limited research in this area, the microbiome appears to be vulnerable to substance use exposure earlier in life, including substances commonly initiated and escalated during adolescence and young adulthood (i.e. alcohol, cannabis, and tobacco). CONCLUSIONS: Studies across the lifespan indicate that adolescence and young adulthood are distinct periods of development, where the microbiome is sensitive to exposures, including substance use. There is a need for more studies focused on the adolescent and young adult microbiome and substance use, as well as focused on the oral microbiome during this developmental period. Understanding the gut and oral microbiome during adolescence and young adulthood may provide insight into the pathophysiology of substance use disorders.

Application of Spatial Analysis for Electronic Health Records: Characterizing Patient Phenotypes and Emerging Trends.

Background: Electronic health records (EHR) commonly contain patient addresses that provide valuable data for geocoding and spatial analysis, enabling more comprehensive descriptions of individual patients for clinical purposes. Despite the widespread use of EHR in clinical decision support and interventions, no systematic review has examined the extent to which spatial analysis is used to characterize patient phenotypes. Objective: This study reviews advanced spatial analyses that employed individual-level health data from EHR within the US to characterize patient phenotypes. Methods: We systematically evaluated English-language peer-reviewed articles from PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar databases from inception to August 20, 2023, without imposing constraints on time, study design, or specific health domains. Results: Only 49 articles met the eligibility criteria. These articles utilized diverse spatial methods, with a predominant focus on clustering techniques, while spatiotemporal analysis (frequentist and Bayesian) and modeling were relatively underexplored. A noteworthy surge (n = 42, 85.7%) in publications was observed post-2017. The publications investigated a variety of adult and pediatric clinical areas, including infectious disease, endocrinology, and cardiology, using phenotypes defined over a range of data domains, such as demographics, diagnoses, and visits. The primary health outcomes investigated were asthma, hypertension, and diabetes. Notably, patient phenotypes involving genomics, imaging, and notes were rarely utilized. Conclusions: This review underscores the growing interest in spatial analysis of EHR-derived data and highlights knowledge gaps in clinical health, phenotype domains, and spatial methodologies. Additionally, this review proposes guidelines for harnessing the potential of spatial analysis to enhance the context of individual patients for future clinical decision support.

Enabling the clinical application of artificial intelligence in genomics: a perspective of the AMIA Genomics and Translational Bioinformatics Workgroup.

OBJECTIVE: Given the importance AI in genomics and its potential impact on human health, the American Medical Informatics Association-Genomics and Translational Biomedical Informatics (GenTBI) Workgroup developed this assessment of factors that can further enable the clinical application of AI in this space. PROCESS: A list of relevant factors was developed through GenTBI workgroup discussions in multiple in-person and online meetings, along with review of pertinent publications. This list was then summarized and reviewed to achieve consensus among the group members. CONCLUSIONS: Substantial informatics research and development are needed to fully realize the clinical potential of such technologies. The development of larger datasets is crucial to emulating the success AI is achieving in other domains. It is important that AI methods do not exacerbate existing socio-economic, racial, and ethnic disparities. Genomic data standards are critical to effectively scale such technologies across institutions. With so much uncertainty, complexity and novelty in genomics and medicine, and with an evolving regulatory environment, the current focus should be on using these technologies in an interface with clinicians that emphasizes the value each brings to clinical decision-making.

Predicting Mammogram Screening Follow Through with Electronic Health Record and Geographically Linked Data.

Cancer is the second leading cause of death in the United States, and breast cancer is the fourth leading cause of cancer-related death, with 42,275 women dying of breast cancer in the United States in 2020. Screening is a key strategy for reducing mortality from breast cancer and is recommended by various national guidelines. This study applies machine learning classification methods to the task of predicting which patients will fail to complete a mammogram screening after having one ordered, as well as understanding the underlying features that influence predictions. The results show that a small group of patients can be identified that are very unlikely to complete mammogram screening, enabling care managers to focus resources. SIGNIFICANCE: The motivation behind this study is to create an automated system that can identify a small group of individuals that are at elevated risk for not following through completing a mammogram screening. This will enable interventions to boost screening to be focused on patients least likely to complete screening.

Mammogram Uptake from Social Determinants of Health Can Be Lost in Translation to Individual Patients.

Purpose: The objective of this study is to describe patterns in barriers to breast cancer screening uptake with the end goal of improving screening adherence and decreasing the burden of mortality due to breast cancer. This study looks at social determinants of health and their association to screening and mortality. It also investigates the extent that models trained on county data are generalizable to individuals. Methods: County level screening uptake and age adjusted mortality due to breast cancer are combined with the Centers for Disease Controls Social Vulnerability Index (SVI) to train a model predicting screening uptake rates. Patterns learned are then applied to de-identified electronic medical records from individual patients to make predictions on mammogram screening follow through. Results: Accurate predictions can be made about a county's breast cancer screening uptake with the SVI. However, the association between increased screening, and decreased age adjusted mortality, doesn't hold in areas with a high proportion of minority residents. It is also shown that patterns learned from county SVI data have little discriminative power at the patient level. Conclusion: This study demonstrates that social determinants in the SVI can explain much of the variance in county breast cancer screening rates. However, these same patterns fail to discriminate which patients will have timely follow through of a mammogram screening test. This study also concludes that the core association between increased screening and decreased age adjusted mortality does not hold in high proportion minority areas.

The hidden microbiome of hospital infection surveillance testing: biomarkers of health outcomes in MRSA and VRE colonization.

Background: Hospital-acquired infections present a major concern for healthcare systems in the U.S. and worldwide. Drug-resistant infections result in increased costs and prolonged hospital stays. Methicillin-resistant Staphylococcus aureus (MRSA) and vancomycin-resistant Enterococcus (VRE) are responsible for many drug-resistant infections in the U.S. We undertook two parallel studies aimed to investigate the differences in the microbial communities of individuals colonized with MRSA (or VRE) as compared to their respective non-colonized counterparts matched for age, sex, race, ethnicity, unit of admission, and diagnostic-related group, when available. Results: The VRE study showed considerably more Enterococcus genus communities in the VRE colonized samples. Our findings for both MRSA and VRE studies suggest a strong association between 16S rRNA gene alpha diversity, beta diversity, and colonization status. When we assessed the colonized microbial communities in isolation, the differences disappeared, suggesting that the colonized microbial communities drove the change. Isolating Staphylococcus, we saw significant differences expressed across colonization in specific sequence variants. Conclusions: The differences seen in the microbial communities from MRSA (or VRE) colonized samples as compared to non-colonized match-pairs are driven by the isolated communities of the Staphylococcus (or Enterococcus) genus, the removal of which results in the disappearance of any differences in the diversity observed across the match-pairs.

SpaceANOVA: Spatial co-occurrence analysis of cell types in multiplex imaging data using point process and functional ANOVA.

Motivation: Multiplex imaging platforms have enabled the identification of the spatial organization of different types of cells in complex tissue or tumor microenvironment (TME). Exploring the potential variations in the spatial co-occurrence or co-localization of different cell types across distinct tissue or disease classes can provide significant pathological insights, paving the way for intervention strategies. However, the existing methods in this context either rely on stringent statistical assumptions or suffer from a lack of generalizability. Results: We present a highly powerful method to study differential spatial co-occurrence of cell types across multiple tissue or disease groups, based on the theories of the Poisson point process (PPP) and functional analysis of variance (FANOVA). Notably, the method accommodates multiple images per subject and addresses the problem of missing tissue regions, commonly encountered in such a context due to the complex nature of the data-collection procedure. We demonstrate the superior statistical power and robustness of the method in comparison to existing approaches through realistic simulation studies. Furthermore, we apply the method to three real datasets on different diseases collected using different imaging platforms. In particular, one of these datasets reveals novel insights into the spatial characteristics of various types of precursor lesions associated with colorectal cancer. Availability: The associated R package can be found here, https://github.com/sealx017/SpaceANOVA.

Longitudinal gut microbiome analyses and blooms of pathogenic strains during lupus disease flares.

OBJECTIVE: Whereas genetic susceptibility for systemic lupus erythematosus (SLE) has been well explored, the triggers for clinical disease flares remain elusive. To investigate relationships between microbiota community resilience and disease activity, we performed the first longitudinal analyses of lupus gut-microbiota communities. METHODS: In an observational study, taxononomic analyses, including multivariate analysis of ß-diversity, assessed time-dependent alterations in faecal communities from patients and healthy controls. From gut blooms, strains were isolated, with genomes and associated glycans analysed. RESULTS: Multivariate analyses documented that, unlike healthy controls, significant temporal community-wide ecological microbiota instability was common in SLE patients, and transient intestinal growth spikes of several pathogenic species were documented. Expansions of only the anaerobic commensal, Ruminococcus (blautia) gnavus (RG) occurred at times of high-disease activity, and were detected in almost half of patients during lupus nephritis (LN) disease flares. Whole genome sequence analysis of RG strains isolated during these flares documented 34 genes postulated to aid adaptation and expansion within a host with an inflammatory condition. Yet, the most specific feature of strains found during lupus flares was the common expression of a novel type of cell membrane-associated lipoglycan. These lipoglycans share conserved structural features documented by mass spectroscopy, and highly immunogenic repetitive antigenic-determinants, recognised by high-level serum IgG2 antibodies, that spontaneously arose, concurrent with RG blooms and lupus flares. CONCLUSIONS: Our findings rationalise how blooms of the RG pathobiont may be common drivers of clinical flares of often remitting-relapsing lupus disease, and highlight the potential pathogenic properties of specific strains isolated from active LN patients.

Prolonged Antibiotic Exposure during Adolescence Dysregulates Liver Metabolism and Promotes Adiposity in Mice.

Antibiotic administration during early life has been shown to have lasting effects on the gut microbiota, which have been linked to sustained alterations in liver metabolism and adiposity. Recent investigations have discerned that the gut microbiota continues to develop toward an adult-like profile during adolescence. However, the impact of antibiotic exposure during adolescence on metabolism and adiposity is unclear. Herein, a retrospective analysis of Medicaid claims data was performed, which indicated that tetracycline class antibiotics are commonly prescribed for the systemic treatment of adolescent acne. The purpose of this was to discern the impact of a prolonged tetracycline antibiotic exposure during adolescence on the gut microbiota, liver metabolism, and adiposity. Male C57BL/6T specific pathogen-free mice were administered a tetracycline antibiotic during the pubertal/postpubertal adolescent growth phase. Groups were euthanized at different time points to assess immediate and sustained antibiotic treatment effects. Antibiotic exposure during adolescence caused lasting genera-level shifts in the intestinal bacteriome and persistent dysregulation of metabolic pathways in the liver. Dysregulated hepatic metabolism was linked to sustained disruption of the intestinal farnesoid X receptor-fibroblast growth factor 15 axis, a gut-liver endocrine axis that supports metabolic homeostasis. Antibiotic exposure during adolescence increased subcutaneous, visceral, and marrow adiposity, which intriguingly manifested following antibiotic therapy. This preclinical work highlights that prolonged antibiotic courses for the clinical treatment of adolescent acne may have unintended deleterious effects on liver metabolism and adiposity.

Red Flag/Blue Flag visualization of a common CNN for text classification.

A shallow convolutional neural network (CNN), TextCNN, has become nearly ubiquitous for classification among clinical and medical text. This research presents a novel eXplainable-AI (X-AI) software, Red Flag/Blue Flag (RFBF), designed for binary classification with TextCNN. RFBF visualizes each convolutional filter's discriminative capability. This is a more informative approach than direct assessment of logit contribution, features that overfit to train set nuances on smaller datasets may indiscriminately activate large logits on validation samples from both classes. RFBF enables model diagnosis, term feature verification, and overfit prevention. We present 3 use cases of (1) filter consistency assessment; (2) predictive performance improvement; and (3) estimation of information leakage between train and holdout sets. The use cases derive from experiments on TextCNN for binary prediction of surgical misadventure outcomes from physician-authored operative notes. Due to TextCNN's prevalence, this X-AI can benefit clinical text research, and hence improve patient outcomes.

Minocycline-induced disruption of the intestinal FXR/FGF15 axis impairs osteogenesis in mice.

Antibiotic-induced shifts in the indigenous gut microbiota influence normal skeletal maturation. Current theory implies that gut microbiota actions on bone occur through a direct gut/bone signaling axis. However, our prior work supports that a gut/liver signaling axis contributes to gut microbiota effects on bone. Our purpose was to investigate the effects of minocycline, a systemic antibiotic treatment for adolescent acne, on pubertal/postpubertal skeletal maturation. Sex-matched specific pathogen-free (SPF) and germ-free (GF) C57BL/6T mice were administered a clinically relevant minocycline dose from age 6-12 weeks. Minocycline caused dysbiotic shifts in the gut bacteriome and impaired skeletal maturation in SPF mice but did not alter the skeletal phenotype in GF mice. Minocycline administration in SPF mice disrupted the intestinal farnesoid X receptor/fibroblast growth factor 15 axis, a gut/liver endocrine axis supporting systemic bile acid homeostasis. Minocycline-treated SPF mice had increased serum conjugated bile acids that were farnesoid X receptor (FXR) antagonists, suppressed osteoblast function, decreased bone mass, and impaired bone microarchitecture and fracture resistance. Stimulating osteoblasts with the serum bile acid profile from minocycline-treated SPF mice recapitulated the suppressed osteogenic phenotype found in vivo, which was mediated through attenuated FXR signaling. This work introduces bile acids as a potentially novel mediator of gut/liver signaling actions contributing to gut microbiota effects on bone.

Not all phenotypes are created equal: covariates of success in e-phenotype specification.

BACKGROUND: Electronic (e)-phenotype specification by noninformaticist investigators remains a challenge. Although validation of each patient returned by e-phenotype could ensure accuracy of cohort representation, this approach is not practical. Understanding the factors leading to successful e-phenotype specification may reveal generalizable strategies leading to better results. MATERIALS AND METHODS: Noninformaticist experts (n = 21) were recruited to produce expert-mediated e-phenotypes using i2b2 assisted by a honest data-broker and a project coordinator. Patient- and visit-sets were reidentified and a random sample of 20 charts matching each e-phenotype was returned to experts for chart-validation. Attributes of the queries and expert characteristics were captured and related to chart-validation rates using generalized linear regression models. RESULTS: E-phenotype validation rates varied according to experts' domains and query characteristics (mean = 61%, range 20-100%). Clinical domains that performed better included infectious, rheumatic, neonatal, and cancers, whereas other domains performed worse (psychiatric, GI, skin, and pulmonary). Match-rate was negatively impacted when specification of temporal constraints was required. In general, the increase in e-phenotype specificity contributed positively to match-rate. DISCUSSIONS AND CONCLUSIONS: Clinical experts and informaticists experience a variety of challenges when building e-phenotypes, including the inability to differentiate clinical events from patient characteristics or appropriately configure temporal constraints; a lack of access to available and quality data; and difficulty in specifying routes of medication administration. Biomedical query mediation by informaticists and honest data-brokers in designing e-phenotypes cannot be overstated. Although tools such as i2b2 may be widely available to noninformaticists, successful utilization depends not on users' confidence, but rather on creating highly specific e-phenotypes.

Comparison of a Cancer Family History Collection and Risk Assessment Tool - ItRunsInMyFamily - with Risk Assessment by Health-Care Professionals.

INTRODUCTION: Primary care providers (PCPs) and oncologists lack time and training to appropriately identify patients at increased risk for hereditary cancer using family health history (FHx) and clinical practice guideline (CPG) criteria. We built a tool, "ItRunsInMyFamily" (ItRuns) that automates FHx collection and risk assessment using CPGs. The purpose of this study was to evaluate ItRuns by measuring the level of concordance in referral patterns for genetic counseling/testing (GC/GT) between the CPGs as applied by the tool and genetic counselors (GCs), in comparison to oncologists and PCPs. The extent to which non-GCs are discordant with CPGs is a gap that health information technology, such as ItRuns, can help close to facilitate the identification of individuals at risk for hereditary cancer. METHODS: We curated 18 FHx cases and surveyed GCs and non-GCs (oncologists and PCPs) to assess concordance with ItRuns CPG criteria for referring patients for GC/GT. Percent agreement was used to describe concordance, and logistic regression to compare providers and the tool's concordance with CPG criteria. RESULTS: GCs had the best overall concordance with the CPGs used in ItRuns at 82.2%, followed by oncologists with 66.0% and PCPs with 60.6%. GCs were significantly more likely to concur with CPGs (OR = 4.04, 95% CI = 3.35-4.89) than non-GCs. All providers had higher concordance with CPGs for FHx cases that met the criteria for genetic counseling/testing than for cases that did not. DISCUSSION/CONCLUSION: The risk assessment provided by ItRuns was highly concordant with that of GC's, particularly for at-risk individuals. The use of such technology-based tools improves efficiency and can lead to greater numbers of at-risk individuals accessing genetic counseling, testing, and mutation-based interventions to improve health.

Antimicrobial-induced oral dysbiosis exacerbates naturally occurring alveolar bone loss.

Periodontitis-mediated alveolar bone loss is caused by dysbiotic shifts in the commensal oral microbiota that upregulate proinflammatory osteoimmune responses. The study purpose was to determine whether antimicrobial-induced disruption of the commensal microbiota has deleterious effects on alveolar bone. We administered an antibiotic cocktail, minocycline, or vehicle-control to sex-matched C57BL/6T mice from age 6- to 12 weeks. Antibiotic cocktail and minocycline had catabolic effects on alveolar bone in specific-pathogen-free (SPF) mice. We then administered minocycline or vehicle-control to male mice reared under SPF and germ-free conditions, and we subjected minocycline-treated SPF mice to chlorhexidine oral antiseptic rinses. Alveolar bone loss was greater in vehicle-treated SPF versus germ-free mice, demonstrating that the commensal microbiota drives naturally occurring alveolar bone loss. Minocycline- versus vehicle-treated germ-free mice had similar alveolar bone loss outcomes, implying that antimicrobial-driven alveolar bone loss is microbiota dependent. Minocycline induced phylum-level shifts in the oral bacteriome and exacerbated naturally occurring alveolar bone loss in SPF mice. Chlorhexidine further disrupted the oral bacteriome and worsened alveolar bone loss in minocycline-treated SPF mice, validating that antimicrobial-induced oral dysbiosis has deleterious effects on alveolar bone. Minocycline enhanced osteoclast size and interface with alveolar bone in SPF mice. Neutrophils and plasmacytoid dendritic cells were upregulated in cervical lymph nodes of minocycline-treated SPF mice. Paralleling the upregulated proinflammatory innate immune cells, minocycline therapy increased TH 1 and TH 17 cells that have known pro-osteoclastic actions in the alveolar bone. This report reveals that antimicrobial perturbation of the commensal microbiota induces a proinflammatory oral dysbiotic state that exacerbates naturally occurring alveolar bone loss.

Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing.

OBJECTIVE: The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing. MATERIALS AND METHODS: To capture COVID-19 information from multiple sources, a new data mart and a new natural language processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a Web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information. RESULTS: The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81% to 92% and to enable pooled testing with a negative predictive value of 90% to 91%, reducing the required tests to about 63%. CONCLUSIONS: SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing.

Immune Responses Vary in Preinvasive Colorectal Lesions by Tumor Location and Histology.

Immune responses vary in colorectal cancers, which strongly influence prognosis. However, little is known about the variance in immune response within preinvasive lesions. The study aims to investigate how the immune contexture differs by clinicopathologic features (location, histology, dysplasia) associated with progression and recurrence in early carcinogenesis. We performed a cross-sectional study using preinvasive lesions from the surgical pathology laboratory at the Medical University of South Carolina. We stained the tissues with immunofluorescence antibodies, then scanned and analyzed expression using automated image analysis software. We stained CD117 as a marker of mast cells, CD4/RORC to indicate Th17 cells, MICA/B as a marker of NK-cell ligands, and also used antibodies directed against cytokines IL6, IL17A, and IFNγ. We used negative binomial regression analysis to compare analyte density counts by location, histology, degree of dysplasia adjusted for age, sex, race, and batch. All immune markers studied (except IL17a) had significantly higher density counts in the proximal colon than distal colon and rectum. Increases in villous histology were associated with significant decreases in immune responses for IL6, IL17a, NK ligand, and mast cells. No differences were observed in lesions with low- and high-grade dysplasia, except in mast cells. The lesions of the proximal colon were rich in immune infiltrate, paralleling the responses observed in normal mucosa and invasive disease. The diminishing immune response with increasing villous histology suggests an immunologically suppressive tumor environment. Our findings highlight the heterogeneity of the immune responses in preinvasive lesions, which may have implications for prevention strategies. PREVENTION RELEVANCE: Our study is focused on immune infiltrate expression in preinvasive colorectal lesions; our results suggest important differences by clinicopathologic features that have implications for immune prevention research.

A phylogenetic approach for weighting genetic sequences.

BACKGROUND: Many important applications in bioinformatics, including sequence alignment and protein family profiling, employ sequence weighting schemes to mitigate the effects of non-independence of homologous sequences and under- or over-representation of certain taxa in a dataset. These schemes aim to assign high weights to sequences that are 'novel' compared to the others in the same dataset, and low weights to sequences that are over-represented. RESULTS: We formalise this principle by rigorously defining the evolutionary 'novelty' of a sequence within an alignment. This results in new sequence weights that we call 'phylogenetic novelty scores'. These scores have various desirable properties, and we showcase their use by considering, as an example application, the inference of character frequencies at an alignment column-important, for example, in protein family profiling. We give computationally efficient algorithms for calculating our scores and, using simulations, show that they are versatile and can improve the accuracy of character frequency estimation compared to existing sequence weighting schemes. CONCLUSIONS: Our phylogenetic novelty scores can be useful when an evolutionarily meaningful system for adjusting for uneven taxon sampling is desired. They have numerous possible applications, including estimation of evolutionary conservation scores and sequence logos, identification of targets in conservation biology, and improving and measuring sequence alignment accuracy.