A Bayesian Survival Analysis on Long COVID and non Long COVID patients: A Cohort Study Using National COVID Cohort Collaborative (N3C) Data.

Since the outbreak of COVID-19 pandemic in 2020, numerous researches and studies have focused on the long-term effects of COVID infection. The Centers for Disease Control (CDC) implemented an additional code into the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) for reporting 'Post COVID-19 condition, unspecified (U09.9)' effective on October 1st 2021, representing that Long COVID is a real illness with potential chronic conditions. The National COVID Cohort Collaborative (N3C) provides researchers with abundant electronic health records (EHR) data by aggregating and harmonizing EHR data across different clinical organizations in the United States, making it convenient to build up a survival analysis on Long COVID patients and non Long COVID patients among large amounts of COVID positive patients.

Fast clustering and cell-type annotation of scATAC data using pre-trained embeddings.

Data from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) are now widely available. One major computational challenge is dealing with high dimensionality and inherent sparsity, which is typically addressed by producing lower dimensional representations of single cells for downstream clustering tasks. Current approaches produce such individual cell embeddings directly through a one-step learning process. Here, we propose an alternative approach by building embedding models pre-trained on reference data. We argue that this provides a more flexible analysis workflow that also has computational performance advantages through transfer learning. We implemented our approach in scEmbed, an unsupervised machine-learning framework that learns low-dimensional embeddings of genomic regulatory regions to represent and analyze scATAC-seq data. scEmbed performs well in terms of clustering ability and has the key advantage of learning patterns of region co-occurrence that can be transferred to other, unseen datasets. Moreover, models pre-trained on reference data can be exploited to build fast and accurate cell-type annotation systems without the need for other data modalities. scEmbed is implemented in Python and it is available to download from GitHub. We also make our pre-trained models available on huggingface for public use. scEmbed is open source and available at https://github.com/databio/geniml. Pre-trained models from this work can be obtained on huggingface: https://huggingface.co/databio.

Machine-learning-based integrative -'omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction.

Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED.

Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries.

BACKGROUND: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. METHODS: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. RESULTS: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. CONCLUSIONS: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Influence of temporary emigration on wood turtle (Glyptemys insculpta) detectability, with implications for abundance estimation.

Reliable population estimates are important for making informed management decisions about wildlife species. Standardized survey protocols have been developed for monitoring population trends of the wood turtle (Glyptemys insculpta), a semi-aquatic freshwater turtle species of conservation concern throughout its distribution in east-central North America. The protocols use repeated active search surveys of defined areas, allowing for estimation of survey-specific detection probability (p) and site-specific abundance. These protocols assume population closure within the survey area during the survey period, which is unlikely to be met as wood turtles are a highly mobile species. Additionally, current protocols use a single-pass design that does not allow for separation of availability (pa) and detectability (pd). If there are systematic influences on pa or pd that are not accounted for in the survey design or data analysis, then resulting abundance estimates could be biased. The objectives of this study were to determine if pa is a random process and if pa and pd are influenced by demographic characteristics. We modified the wood turtle survey protocol used in the upper Midwest to include a double-pass design, allowing us to estimate pa and pd using a robust design capture-recapture model. The modified protocol was implemented at 14 wood turtle monitoring sites in Minnesota and Wisconsin between 2017 and 2022. Our results indicated that pa was non-random and that pd increased with turtle carapace length. Our study suggests that model assumptions for current wood turtle population models may be violated, likely resulting in an overestimation of abundance. We discuss possible protocol and modeling modifications that could result in more accurate wood turtle abundance estimates.

Joint Representation Learning for Retrieval and Annotation of Genomic Interval Sets.

As available genomic interval data increase in scale, we require fast systems to search them. A common approach is simple string matching to compare a search term to metadata, but this is limited by incomplete or inaccurate annotations. An alternative is to compare data directly through genomic region overlap analysis, but this approach leads to challenges like sparsity, high dimensionality, and computational expense. We require novel methods to quickly and flexibly query large, messy genomic interval databases. Here, we develop a genomic interval search system using representation learning. We train numerical embeddings for a collection of region sets simultaneously with their metadata labels, capturing similarity between region sets and their metadata in a low-dimensional space. Using these learned co-embeddings, we develop a system that solves three related information retrieval tasks using embedding distance computations: retrieving region sets related to a user query string, suggesting new labels for database region sets, and retrieving database region sets similar to a query region set. We evaluate these use cases and show that jointly learned representations of region sets and metadata are a promising approach for fast, flexible, and accurate genomic region information retrieval.

Pathological Markers of Alzheimer's Disease and Related Dementia in the Rhesus Macaque Amygdala.

Rhesus macaques develop amyloid-ß (Aß) plaques during old age, but it is unclear how extensively they express other pathological hallmarks of dementia. Here we used immunohistochemistry to examine expression of phosphorylated tau (pTau) protein and cytoplasmic inclusions of TAR DNA binding protein 43 kDa (TDP-43) within the amygdala of young and old males, and also in old surgically-menopausal females that were maintained on regular or obesogenic diets. Only one animal, a 23-year-old female, showed pTau expression and none showed TDP-43 inclusions. What genetic and/or environmental factors protect macaques from expressing more severe human neuro-pathologies remains an interesting unresolved question.

Signal Variability Comparative Analysis of Healthy Early- and Late-Pubertal Children during Cardiopulmonary Exercise Testing.

PURPOSE: The kinetics of physiological responses to exercise have traditionally been characterized by estimating exponential equation parameters using iterative best-fit techniques of heart rate (HR) and gas exchange (respiratory rate, oxygen uptake (V̇O 2 ), carbon dioxide output, and ventilation). In this study, we present a novel approach to characterizing the maturation of physiological responses to exercise in children by accounting for response uncertainty and variability. METHODS: Thirty-seven early-pubertal (17 females, 20 males) and 44 late-pubertal (25 females, 19 males) participants performed three multiple brief exercise bouts (MBEB). MBEB consisted of ten 2-min bouts of cycle ergometry at constant work rate interspersed by 1-min rest. Exercise intensity was categorized as low, moderate, or high, corresponding to 40%, 60%, and 80% of peak work rate, and performed in random order on 3 separate days. We evaluated sample entropy (SampEn), approximate entropy, detrended fluctuation analysis, and average absolute local variability of HR and gas exchange. RESULTS: SampEn of HR and gas-exchange responses to MBEB was greater in early- compared with late-pubertal participants (e.g., V̇O 2 early-pubertal vs late-pubertal, 1.70 ± 0.023 vs 1.41 ± 0.027; P = 2.97 × 10 -14 ), and decreased as MBEB intensity increased (e.g., 0.37 ± 0.01 HR for low-intensity compared with 0.21 ± 0.014 for high intensity, P = 3.56 × 10 -17 ). Females tended to have higher SampEn than males (e.g., 1.61 ± 0.025 V̇O 2 for females vs 1.46 ± 0.031 for males, P = 1.28 × 10 -4 ). Average absolute local variability was higher in younger participants for both gas exchange and HR (e.g., early-pubertal vs late-pubertal V̇O 2 , 17.48 % ± 0.56% vs 10.24 % ± 0.34%; P = 1.18 × 10 -21 ). CONCLUSIONS: The greater entropy in signal response to a known, quantifiable exercise perturbation in the younger children might represent maturation-dependent, enhanced competition among physiological controlling mechanisms that originate at the autonomic, subconscious, and cognitive levels.

Modeling meibum secretion: Alternatives for obstructive Meibomian Gland Dysfunction (MGD).

PURPOSE: While changes in meibum quality are correlated with severity of meibomian gland dysfunction (MGD) and dry eye disease, little is known regarding the mechanics of meibum secretion. The purpose of this study was to develop a finite element model of meibum secretion and evaluate the effect of various factors that might impact meibum delivery to the ocular surface. METHODS: A finite element analysis in COMSOL 6.0 was used to simulate the flow of meibum within the gland's terminal excretory duct. Historical normal human meibum rheology data taken over the meibum melting range from fluid (35-40 °C) to solid (25-30 °C) were then used to calculate the minimum yield stress and plastic viscosity of meibum. The effects of meibum melting state, eyelid pressure and terminal duct diameter on meibum flow rates were then systematically investigated. RESULTS: The melting state of meibum from liquid to solid was associated with an increase in the minimum yield stress and plastic viscosity that caused an exponential decrease in meibum flow. Modeling also established that there was a linear correlation between meibum flow rate and eyelid pressure needed to express meibum and the 4th power of the terminal duct radius. CONCLUSIONS: Our results suggest that changes in the melting state of meibum from fluid to solid, as well as changes in the radius of the terminal excretory duct and the force exerted by the eyelid can lead to dramatic decreases in the flow of meibum. Together these findings suggest alternative mechanisms for meibomian gland obstruction.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

BACKGROUND: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. METHODS: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS: Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS: This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

A review of the epithelial and stromal effects of corneal collagen crosslinking.

Induced corneal collagen crosslinking and mechanical stiffening via ultraviolet-A photoactivation of riboflavin (UVA CXL) is now a common treatment for corneal ectasia and Keratoconus. Some effects of the procedure such as induced mechanical stiffening, corneal flattening, and cellular toxicity are well-known, but others remain more controversial. Authors report a variety of contradictory effects, and provide evidence based on individual results and observations. A full understanding of the effects of and mechanisms behind this procedure are essential to predicting its outcome. A growing interest in modifications to the standard UVA CXL protocol, such as transepithelial or accelerated UVA CXL, makes analyzing the literature as a whole more urgent. This review presents an analysis of both the agreed-upon and contradictory results reported and the various methods used to obtain them.

The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning.

Introduction: Technical burdens and time-intensive review processes limit the practical utility of video capsule endoscopy (VCE). Artificial intelligence (AI) is poised to address these limitations, but the intersection of AI and VCE reveals challenges that must first be overcome. We identified five challenges to address. Challenge #1: VCE data are stochastic and contains significant artifact. Challenge #2: VCE interpretation is cost-intensive. Challenge #3: VCE data are inherently imbalanced. Challenge #4: Existing VCE AIMLT are computationally cumbersome. Challenge #5: Clinicians are hesitant to accept AIMLT that cannot explain their process. Methods: An anatomic landmark detection model was used to test the application of convolutional neural networks (CNNs) to the task of classifying VCE data. We also created a tool that assists in expert annotation of VCE data. We then created more elaborate models using different approaches including a multi-frame approach, a CNN based on graph representation, and a few-shot approach based on meta-learning. Results: When used on full-length VCE footage, CNNs accurately identified anatomic landmarks (99.1%), with gradient weighted-class activation mapping showing the parts of each frame that the CNN used to make its decision. The graph CNN with weakly supervised learning (accuracy 89.9%, sensitivity of 91.1%), the few-shot model (accuracy 90.8%, precision 91.4%, sensitivity 90.9%), and the multi-frame model (accuracy 97.5%, precision 91.5%, sensitivity 94.8%) performed well. Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models. Our goal of producing high performance using lightweight models that aim to improve clinician confidence was achieved.

Silencing of the Vasa gene by RNA Interference Affects Embryonic Development and Reproductive Output in the Sea Louse Caligus rogercresseyi.

The sea louse Caligus rogercresseyi is a major ectoparasitic copepod that causes significant economic losses in the salmon farming industry. Despite recent advancements, the mechanisms underlying germline and embryo development in this species remain poorly understood. The Vasa gene encodes a highly conserved DEAD box helicase that is required for germ cell formation and function in many species. In this study, the Vasa gene was characterized in C. rogercresseyi, and its expression and function were analyzed. Phylogenetic analysis showed that the Cr-Vasa gene product formed clusters in clades with Vasa proteins from closely related species of crustaceans. Cr-Vasa gene expression patterns were assessed by qPCR, and the results showed a significantly higher relative expression level in adult females compared to copepodid, chalimus, and adult male stages. Tissue-specific localization of Cr-Vasa mRNA in C. rogercresseyi was determined using chromogenic in situ hybridization, and strong positive signal was observed in male testes, but also in the intestine and cuticle, while in females, it was observed in the ovaries, oocytes, cuticle, intestine, and egg strings. RNAi-mediated gene silencing of Cr-Vasa impacted embryonic development and reproductive output in adult female lice. Females from the dsVasa-treated group displayed unusual phenotypes, including shorter egg strings with numerous extra-embryonic inclusions, irregularly shaped abnormal embryos, and aborted egg strings. This study provides insights into the role of the Vasa gene in C. rogercresseyi embryonic development and reproductive output, which may have implications for the control of this parasitic copepod in the salmon farming industry.

Is the future female for turtles? Climate change and wetland configuration predict sex ratios of a freshwater species.

Climate change and land-use change are leading drivers of biodiversity decline, affecting demographic parameters that are important for population persistence. For example, scientists have speculated for decades that climate change may skew adult sex ratios in taxa that express temperature-dependent sex determination (TSD), but limited evidence exists that this phenomenon is occurring in natural settings. For species that are vulnerable to anthropogenic land-use practices, differential mortality among sexes may also skew sex ratios. We sampled the spotted turtle (Clemmys guttata), a freshwater species with TSD, across a large portion of its geographic range (Florida to Maine), to assess the environmental factors influencing adult sex ratios. We present evidence that suggests recent climate change has potentially skewed the adult sex ratio of spotted turtles, with samples following a pattern of increased proportions of females concomitant with warming trends, but only within the warmer areas sampled. At intermediate temperatures, there was no relationship with climate, while in the cooler areas we found the opposite pattern, with samples becoming more male biased with increasing temperatures. These patterns might be explained in part by variation in relative adaptive capacity via phenotypic plasticity in nest site selection. Our findings also suggest that spotted turtles have a context-dependent and multi-scale relationship with land use. We observed a negative relationship between male proportion and the amount of crop cover (within 300 m) when wetlands were less spatially aggregated. However, when wetlands were aggregated, sex ratios remained consistent. This pattern may reflect sex-specific patterns in movement that render males more vulnerable to mortality from agricultural machinery and other threats. Our findings highlight the complexity of species' responses to both climate change and land use, and emphasize the role that landscape structure can play in shaping wildlife population demographics.

Increased stroke severity and mortality in patients with SARS-CoV-2 infection: An analysis from the N3C database.

BACKGROUND: Studies from early in the COVID-19 pandemic showed that patients with ischemic stroke and concurrent SARS-CoV-2 infection had increased stroke severity. We aimed to test the hypothesis that this association persisted throughout the first year of the pandemic and that a similar increase in stroke severity was present in patients with hemorrhagic stroke. METHODS: Using the National Institute of Health National COVID Cohort Collaborative (N3C) database, we identified a cohort of patients with stroke hospitalized in the United States between March 1, 2020 and February 28, 2021. We propensity score matched patients with concurrent stroke and SARS-COV-2 infection and available NIH Stroke Scale (NIHSS) scores to all other patients with stroke in a 1:3 ratio. Nearest neighbor matching with a caliper of 0.25 was used for most factors and exact matching was used for race/ethnicity and site. We modeled stroke severity as measured by admission NIHSS and the outcomes of death and length of stay. We also explored the temporal relationship between time of SARS-COV-2 diagnosis and incidence of stroke. RESULTS: Our query identified 43,295 patients hospitalized with ischemic stroke (5765 with SARS-COV-2, 37,530 without) and 18,107 patients hospitalized with hemorrhagic stroke (2114 with SARS-COV-2, 15,993 without). Analysis of our propensity matched cohort revealed that stroke patients with concurrent SARS-COV-2 had increased NIHSS (Ischemic stroke: IRR=1.43, 95% CI:1.33-1.52, p<0.001; hemorrhagic stroke: IRR=1.20, 95% CI:1.08-1.33, p<0.001), length of stay (Ischemic stroke: estimate = 1.48, 95% CI: 1.37, 1.61, p<0.001; hemorrhagic stroke: estimate = 1.25, 95% CI: 1.06, 1.47, p=0.007) and higher odds of death (Ischemic stroke: OR 2.19, 95% CI: 1.79-2.68, p<0.001; hemorrhagic stroke: OR 2.19, 95% CI: 1.79-2.68, p<0.001). We observed the highest incidence of stroke diagnosis on the same day as SARS-COV-2 diagnosis with a logarithmic decline in counts. CONCLUSION: This retrospective observational analysis suggests that stroke severity in patients with concurrent SARS-COV-2 was increased throughout the first year of the pandemic.

PREVALENCE OF RANAVIRUS IN SPOTTED SALAMANDER (AMBYSTOMA MACULATUM) LARVAE FROM CREATED VERNAL POOLS IN WEST VIRGINIA, USA.

Ranavirosis is a disease of high concern for amphibians due to widespread documentation of its lethal and sublethal impacts and its high transmission potential across populations and species. We investigated whether spotted salamander (Ambystoma maculatum) ranavirus prevalence and viral load were associated with habitat characteristics, genetic diversity, corticosterone levels, and body size. In 2015 and 2016, we sampled 34 recently created vernal pools in the Monongahela National Forest, West Virginia, USA. We collected tail clippings from 1,128 spotted salamander larvae and waterborne hormone samples from 436 of those larvae, along with eight environmental characteristics of the pools. Over the 2-yr period, we detected ranavirus in 62% of pools, with prevalence ranging from 0% to 63% (mean, 7.68%). Spotted salamander size was positively correlated with ranavirus presence and viral load; however, we did not find associations between ranavirus prevalence or viral load and habitat characteristics, spotted salamander genetic diversity, relatedness, effective number of breeders, or corticosterone levels. The widespread occurrence of ranavirus in the vernal pools illustrates the potential for rapid natural introduction of the pathogen to created wetlands. Managers could consider monitoring local distributions of ranavirus before creation of new vernal pools to guide strategic placement of the wetlands to minimize occurrence and prevalence of this pathogen.

Evaluating Mechanisms of Short-term Woodland Salamander Response to Forest Management.

Contemporary forest management often requires meeting diverse ecological objectives including maintaining ecosystem function and promoting biodiversity through timber harvesting. Wildlife are essential in this process by providing ecological services that can facilitate forest resiliency in response to timber harvesting. However, the mechanisms driving species' responses remain ambiguous. The goal of this study was to assess mechanisms influencing eastern red-backed salamander (RBS; Plethodon cinereus) response to overstory cover removal. We evaluated two mitigation strategies for the RBS in response to overstory removal. We used a before-after-control-impact design to study how (1) retaining residual trees or (2) eliminating soil compaction affected RBS surface counts and body condition index (BCI) up to two-years post-treatment. Additionally, we assessed how surface counts of RBS were influenced by overstory tree cover. Surface counts of RBS were not strongly influenced by overstory removal when tree residuals were retained. Body condition index increased in treatments where harvest residuals were retained. In treatments where soil compaction was eliminated, surface counts and BCI were inversely related. Finally, surface counts from both mitigation strategies were not strongly influenced by overstory cover. Overall, both mitigation techniques appeared to ameliorate impacts of overstory removal on RBS. These results highlight the importance of understanding mechanisms driving species' responses to forest management. To reduce the perceived negative effects of overstory removal on RBS, incorporating these mitigation measures may contribute to the viability and stability of RBS populations. Incorporating species' life history traits into management strategies could increase continuity of ecological function and integrity through harvesting.

Digitization of Symbol-Denoted Blood Pressure Data From Intraoperative Paper Health Records in a Low-Middle-Income Country Using Deep Image Segmentation and Associated Postoperative Outcomes: A Feasibility Study.

BACKGROUND: In low-middle-income countries (LMICs), perioperative clinical information is almost universally collected on paper health records (PHRs). The lack of accessible digital databases limits LMICs in leveraging data to predict and improve patient outcomes after surgery. In this feasibility study, our aims were to: (1) determine the detection performance and prediction error of the U-Net deep image segmentation approach for digitization of hand-drawn blood pressure symbols from an image of the intraoperative PHRs and (2) evaluate the association between deep image segmentation-derived blood pressure parameters and postoperative mortality and length of stay. METHODS: A smartphone mHealth platform developed by our team was used to capture images of completed intraoperative PHRs. A 2-stage deep image segmentation modeling approach was used to create 2 separate segmentation masks for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Iterative postprocessing was utilized to convert the segmentation mask results into numerical SBP and DBP values. Detection performance and prediction errors were evaluated for the U-Net models by comparison with ground-truth values. Using multivariate regression analysis, we investigated the association of deep image segmentation-derived blood pressure values, total time spent in predefined blood pressure ranges, and postoperative outcomes including in-hospital mortality and length of stay. RESULTS: A total of 350 intraoperative PHRs were imaged following surgery. Overall accuracy was 0.839 and 0.911 for SBP and DBP symbol detections, respectively. The mean error rate and standard deviation for the difference between the actual and predicted blood pressure values were 2.1 ± 4.9 and -0.8 ± 3.9 mm Hg for SBP and DBP, respectively. Using the U-Net model-derived blood pressures, minutes of time where DBP <50 mm Hg (odds ratio [OR], 1.03; CI, 1.01-1.05; P = .003) was associated with an increased in-hospital mortality. In addition, increased cumulative minutes of time with SBP between 80 and 90 mm Hg was significantly associated with a longer length of stay (incidence rate ratio, 1.02 [1.0-1.03]; P < .05), while increased cumulative minutes of time where SBP between 140 and 160 mm Hg was associated with a shorter length of stay (incidence rate ratio, 0.9 [0.96-0.99]; P < .05). CONCLUSIONS: In this study, we report our experience with a deep image segmentation model for digitization of symbol-denoted blood pressure from intraoperative anesthesia PHRs. Our data support further development of this novel approach to digitize PHRs from LMICs, to provide accessible, curated, and reproducible data for both quality improvement- and outcome-based research.

Vital Measurements of Hospitalized COVID-19 Patients as a Predictor of Long COVID: An EHR-based Cohort Study from the RECOVER Program in N3C.

It is shown that various symptoms could remain in the stage of post-acute sequelae of SARS-CoV-2 infection (PASC), otherwise known as Long COVID. A number of COVID patients suffer from heterogeneous symptoms, which severely impact recovery from the pandemic. While scientists are trying to give an unambiguous definition of Long COVID, efforts in prediction of Long COVID could play an important role in understanding the characteristic of this new disease. Vital measurements (e.g. oxygen saturation, heart rate, blood pressure) could reflect body's most basic functions and are measured regularly during hospitalization, so among patients diagnosed COVID positive and hospitalized, we analyze the vital measurements of first 7 days since the hospitalization start date to study the pattern of the vital measurements and predict Long COVID with the information from vital measurements.

Functional Data Analysis for Predicting Pediatric Failure to Complete Ten Brief Exercise Bouts.

Physiological response to physical exercise through analysis of cardiopulmonary measurements has been shown to be predictive of a variety of diseases. Nonetheless, the clinical use of exercise testing remains limited because interpretation of test results requires experience and specialized training. Additionally, until this work no methods have identified which dynamic gas exchange or heart rate responses influence an individual's decision to start or stop physical activity. This research examines the use of advanced machine learning methods to predict completion of a test consisting of multiple exercise bouts by a group of healthy children and adolescents. All participants could complete the ten bouts at low or moderate-intensity work rates, however, when the bout work rates were high-intensity, 50% refused to begin the subsequent exercise bout before all ten bouts had been completed (task failure). We explored machine learning strategies to model the relationship between the physiological time series, the participant's anthropometric variables, and the binary outcome variable indicating whether the participant completed the test. The best performing model, a generalized spectral additive model with functional and scalar covariates, achieved 93.6% classification accuracy and an F1 score of 93.5%. Additionally, functional analysis of variance testing showed that participants in the 'failed' and 'success' groups have significantly different functional means in three signals: heart rate, oxygen uptake rate, and carbon dioxide uptake rate. Overall, these results show the capability of functional data analysis with generalized spectral additive models to identify key differences in the exercise-induced responses of participants in multiple bout exercise testing.