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Background Pneumonia due to typical bacterial, atypical bacterial and viral pathogens can be difficult to clinically differentiate. Host response-based diagnostics are emerging as a complementary diagnostic strategy to pathogen detection.Methods We used murine models of typical bacterial, atypical bacterial and viral pneumonia to develop diagnostic signatures and understand the host’s response to these types of infections. Mice were intranasally inoculated with Streptococcus pneumoniae, Mycoplasma pneumoniae, influenza or saline as a control. Peripheral blood gene expression analysis was performed at multiple time points. Differentially expressed genes were used to perform gene set enrichment analysis and generate diagnostic signatures. These murine-derived signatures were externally validated in silico using human gene expression data. The response to S. pneumoniae was the most rapid and robust.Results Mice infected with M. pneumoniae had a delayed response more similar to influenza-infected animals. Diagnostic signatures for the three types of infection had 0.94–1.00 area under the receiver operator curve (auROC). Validation in five human gene expression datasets revealed auROC of 0.82–0.96.Discussion This study identified discrete host responses to typical bacterial, atypical bacterial and viral aetiologies of pneumonia in mice. These signatures validated well in humans, highlighting the conserved nature of the host response to these pathogen classes.

The image quality received for clinical evaluation is often suboptimal. The goal is to develop an image quality analysis tool to assess patient- and primary care physician–derived images using deep learning model. Dataset included patient- and primary care physician–derived images from August 21, 2018 to June 30, 2022 with 4 unique quality labels. VGG16 model was fine tuned with input data, and optimal threshold was determined by Youden’s index. Ordinal labels were transformed to binary labels using a majority vote because model distinguishes between 2 categories (good vs bad). At a threshold of 0.587, area under the curve for the test set was 0.885 (95% confidence interval = 0.838–0.933); sensitivity, specificity, positive predictive value, and negative predictive value were 0.829, 0.784, 0.906, and 0.645, respectively. Independent validation of 300 additional images (from patients and primary care physicians) demonstrated area under the curve of 0.864 (95% confidence interval = 0.818–0.909) and area under the curve of 0.902 (95% confidence interval = 0.85–0.95), respectively. The sensitivity, specificity, positive predictive value, and negative predictive value for the 300 images were 0.827, 0.800, 0.959, and 0.450, respectively. We demonstrate a practical approach improving the image quality for clinical workflow. Although users may have to capture additional images, this is offset by the improved workload and efficiency for clinical teams.

Abstract Diagnostic limitations challenge management of clinically indistinguishable acute infectious illness globally. Gene expression classification models show great promise distinguishing causes of fever. We generated transcriptional data for a 294-participant (USA, Sri Lanka) discovery cohort with adjudicated viral or bacterial infections of diverse etiology or non-infectious disease mimics. We then derived and cross-validated gene expression classifiers including: 1) a single model to distinguish bacterial vs. viral (Global Fever-Bacterial/Viral [GF-B/V]) and 2) a two-model system to discriminate bacterial and viral in the context of noninfection (Global Fever-Bacterial/Viral/Non-infectious [GF-B/V/N]). We then translated to a multiplex RT-PCR assay and independent validation involved 101 participants (USA, Sri Lanka, Australia, Cambodia, Tanzania). The GF-B/V model discriminated bacterial from viral infection in the discovery cohort an area under the receiver operator curve (AUROC) of 0.93. Validation in an independent cohort demonstrated the GF-B/V model had an AUROC of 0.84 (95% CI 0.76–0.90) with overall accuracy of 81.6% (95% CI 72.7–88.5). Performance did not vary with age, demographics, or site. Host transcriptional response diagnostics distinguish bacterial and viral illness across global sites with diverse endemic pathogens.

Las lagunas de oxidación de Zaragoza son un sistema de tratamiento de agua residual de las Empresas Municipales de Cartago E.S.P. con un fragmento de Bosque Seco Tropical, donde se han implementado estrategias de manejo para mejorar la calidad del agua y del entorno terrestre. Se realizó un estudio del estado actual de la riqueza y abundancia de mamíferos no voladores para establecer una línea base de monitoreo a largo plazo. El muestreo se realizó durante 6 días intermitentes en mayo de 2023 empleando recorridos libres de observación, rastreo y cámaras trampa. Se registraron 25 individuos pertenecientes a ocho especies de los órdenes Rodentia, Carnivora y Didelphimorphia, de las cuales tres son introducidas. Si bien ninguna está en condición de amenaza nacional o internacional, se resalta el registro del yaguarundí (Herpailurus yagouaroundi) teniendo en cuenta que se trata de un depredador tope, poco común, que habita en bajas densidades poblacionales. De acuerdo con la literatura, el Gurre Dasypus novemcinctus tiene potencial como especie indicadora del estado del ecosistema. Se sugiere esta especie como posible objeto de monitoreo para el estudio a largo plazo.

Summary: To elucidate host response elements that define impending decompensation during SARS-CoV-2 infection, we enrolled subjects hospitalized with COVID-19 who were matched for disease severity and comorbidities at the time of admission. We performed combined single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) on peripheral blood mononuclear cells (PBMCs) at admission and compared subjects who improved from their moderate disease with those who later clinically decompensated and required invasive mechanical ventilation or died. Chromatin accessibility and transcriptomic immune profiles were markedly altered between the two groups, with strong signals in CD4+ T cells, inflammatory T cells, dendritic cells, and NK cells. Multiomic signature scores at admission were tightly associated with future clinical deterioration (auROC 1.0). Epigenetic and transcriptional changes in PBMCs reveal early, broad immune dysregulation before typical clinical signs of decompensation are apparent and thus may act as biomarkers to predict future severity in COVID-19.

Purpose: To develop a machine learning tool capable of differentiating eyes of subjects with normal cognition from those with mild cognitive impairment (MCI) using OCT and OCT angiography (OCTA). Design: Evaluation of a diagnostic technology. Participants: Subjects with normal cognition were compared to subjects with MCI. Methods: A multimodal convolutional neural network (CNN) was built to predict likelihood of MCI from ganglion cell-inner plexiform layer (GC-IPL) thickness maps, OCTA images, and quantitative data including patient characteristics. Main Outcome Measures: Area under the receiver operating characteristic curve (AUC) and summaries of the confusion matrix (sensitivity and specificity) were used as performance metrics for the prediction outputs of the CNN. Results: Images from 236 eyes of 129 cognitively normal subjects and 154 eyes of 80 MCI subjects were used for training, validating, and testing the CNN. When applied to the independent test set using inputs including GC-IPL thickness maps, OCTA images, and quantitative OCT and OCTA data, the AUC value for the CNN was 0.809 (95% confidence interval [CI]: 0.681–0.937). This model achieved a sensitivity of 79% and specificity of 83%. The AUC value for GC-IPL thickness maps alone was 0.681 (95% CI: 0.529–0.832), for OCTA images alone was 0.625 (95% CI: 0.466–0.784) and for both GC-IPL maps and OCTA images was 0.693 (95% CI: 0.543–0.843). Models using quantitative data alone were also tested, with a model using quantitative data derived from images, 0.960 (95% CI: 0.902–1.00), outperforming a model using demographic data alone, 0.580 (95% CI: 0.417–0.742). Conclusions: This novel CNN was able to identify an MCI diagnosis using an independent test set comprised of OCT and OCTA images and quantitative data. The GC-IPL thickness maps provided more useful decision support than the OCTA images. The addition of quantitative data inputs also provided significant decision support to the CNN to identify individuals with MCI. Quantitative imaging metrics provided superior decision support than demographic data. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references.

Background & Aims: Nonalcoholic steatohepatitis (NASH), a leading cause of cirrhosis, strongly associates with the metabolic syndrome, an insulin-resistant proinflammatory state that disrupts energy balance and promotes progressive liver degeneration. We aimed to define the role of Smoothened (Smo), an obligatory component of the Hedgehog signaling pathway, in controlling hepatocyte metabolic homeostasis and, thereby, susceptibility to NASH. Methods: We conditionally deleted Smo in hepatocytes of healthy chow-fed mice and performed metabolic phenotyping, coupled with single-cell RNA sequencing (RNA-seq), to characterize the role of hepatocyte Smo in regulating basal hepatic and systemic metabolic homeostasis. Liver RNA-seq datasets from 2 large human cohorts were also analyzed to define the relationship between Smo and NASH susceptibility in people. Results: Hepatocyte Smo deletion inhibited the Hedgehog pathway and promoted fatty liver, hyperinsulinemia, and insulin resistance. We identified a plausible mechanism whereby inactivation of Smo stimulated the mTORC1-SREBP1c signaling axis, which promoted lipogenesis while inhibiting the hepatic insulin cascade. Transcriptomics of bulk and single Smo-deficient hepatocytes supported suppression of insulin signaling and also revealed molecular abnormalities associated with oxidative stress and mitochondrial dysfunction. Analysis of human bulk RNA-seq data revealed that Smo expression was (1) highest in healthy livers, (2) lower in livers with NASH than in those with simple steatosis, (3) negatively correlated with markers of insulin resistance and liver injury, and (4) declined progressively as fibrosis severity worsened. Conclusions: The Hedgehog pathway controls insulin sensitivity and energy homeostasis in adult livers. Loss of hepatocyte Hedgehog activity induces hepatic and systemic metabolic stress and enhances susceptibility to NASH by promoting hepatic lipoxicity and insulin resistance.

Objective The purpose of this study was to evaluate a novel scoring system, the Encounter Appropriateness Score for You (EASY), to assess provider perceptions of telehealth appropriateness in rheumatology encounters. Methods The EASY scoring system prompts providers to rate their own encounters as follows: in‐person or telehealth acceptable, EASY = 1; in‐person preferred, EASY = 2; or telehealth preferred, EASY = 3. Assessment of the EASY scoring system occurred at a single academic institution from January 1, 2021, to August 31, 2021. Data were collected in three rounds: 1) initial survey (31 providers) assessing EASY responsiveness to five hypothetical scenarios, 2) follow‐up survey (34 providers) exploring EASY responsiveness to 11 scenario modifications, and 3) assessment of EASYs documented in clinic care. Results The initial and follow‐up surveys demonstrated responsiveness of EASYs to different clinical and nonclinical factors. For instance, less than 20% of providers accepted telehealth when starting a biologic for active rheumatoid arthritis, although more than 35% accepted telehealth in the same scenario if the patient lived far away or was well known to the provider. Regarding EASY documentation, 27 providers provided EASYs for 12,381 encounters. According to these scores, telehealth was acceptable or preferred for 29.7% of all encounters, including 21.4% of in‐person encounters. Conversely, 24.4% of telehealth encounters were scored as in‐person preferred. Conclusion EASY is simple, understandable, and responsive to changes in the clinical scenario. We have successfully accumulated 12,381 EASYs that can be studied in future work to better understand telehealth utility and optimize telehealth triage.

Abstract We consider machine-learning-based lesion identification and malignancy prediction from clinical dermatological images, which can be indistinctly acquired via smartphone or dermoscopy capture. Additionally, we do not assume that images contain single lesions, thus the framework supports both focal or wide-field images. Specifically, we propose a two-stage approach in which we first identify all lesions present in the image regardless of sub-type or likelihood of malignancy, then it estimates their likelihood of malignancy, and through aggregation, it also generates an image-level likelihood of malignancy that can be used for high-level screening processes. Further, we consider augmenting the proposed approach with clinical covariates (from electronic health records) and publicly available data (the ISIC dataset). Comprehensive experiments validated on an independent test dataset demonstrate that (1) the proposed approach outperforms alternative model architectures; (2) the model based on images outperforms a pure clinical model by a large margin, and the combination of images and clinical data does not significantly improves over the image-only model; and (3) the proposed framework offers comparable performance in terms of malignancy classification relative to three board certified dermatologists with different levels of experience.

Background Nonalcoholic fatty liver disease (NAFLD) and heart failure with preserved ejection fraction (HFpEF) share common risk factors, including obesity and diabetes. They are also thought to be mechanistically linked. The aim of this study was to define serum metabolites associated with HFpEF in a cohort of patients with biopsy‐proven NAFLD to identify common mechanisms. Methods and Results We performed a retrospective, single‐center study of 89 adult patients with biopsy‐proven NAFLD who had transthoracic echocardiography performed for any indication. Metabolomic analysis was performed on serum using ultrahigh performance liquid and gas chromatography/tandem mass spectrometry. HFpEF was defined as ejection fraction >50% plus at least 1 echocardiographic feature of HFpEF (diastolic dysfunction, abnormal left atrial size) and at least 1 heart failure sign or symptom. We performed generalized linear models to evaluate associations between individual metabolites, NAFLD, and HFpEF. Thirty‐seven out of 89 (41.6%) patients met criteria for HFpEF. A total of 1151 metabolites were detected; 656 were analyzed after exclusion of unnamed metabolites and those with >30% missing values. Fifty‐three metabolites were associated with the presence of HFpEF with unadjusted P value

Abstract SARS-CoV-2 infection triggers profound and variable immune responses in human hosts. Chromatin remodeling has been observed in individuals severely ill or convalescing with COVID-19, but chromatin remodeling early in disease prior to anti-spike protein IgG seroconversion has not been defined. We performed the Assay for Transposase-Accessible Chromatin using sequencing (ATAC-seq) and RNA-seq on peripheral blood mononuclear cells (PBMCs) from outpatients with mild or moderate symptom severity at different stages of clinical illness. Early in the disease course prior to IgG seroconversion, modifications in chromatin accessibility associated with mild or moderate symptoms were already robust and included severity-associated changes in accessibility of genes in interleukin signaling, regulation of cell differentiation and cell morphology. Furthermore, single-cell analyses revealed evolution of the chromatin accessibility landscape and transcription factor motif accessibility for individual PBMC cell types over time. The most extensive remodeling occurred in CD14+ monocytes, where sub-populations with distinct chromatin accessibility profiles were observed prior to seroconversion. Mild symptom severity was marked by upregulation of classical antiviral pathways, including those regulating IRF1 and IRF7, whereas in moderate disease, these classical antiviral signals diminished, suggesting dysregulated and less effective responses. Together, these observations offer novel insight into the epigenome of early mild SARS-CoV-2 infection and suggest that detection of chromatin remodeling in early disease may offer promise for a new class of diagnostic tools for COVID-19.

Summary: Assays detecting blood transcriptome changes are studied for infectious disease diagnosis. Blood-based RNA alternative splicing (AS) events, which have not been well characterized in pathogen infection, have potential normalization and assay platform stability advantages over gene expression for diagnosis. Here, we present a computational framework for developing AS diagnostic biomarkers. Leveraging a large prospective cohort of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and whole-blood RNA sequencing (RNA-seq) data, we identify a major functional AS program switch upon viral infection. Using an independent cohort, we demonstrate the improved accuracy of AS biomarkers for SARS-CoV-2 diagnosis compared with six reported transcriptome signatures. We then optimize a subset of AS-based biomarkers to develop microfluidic PCR diagnostic assays. This assay achieves nearly perfect test accuracy (61/62 = 98.4%) using a naive principal component classifier, significantly more accurate than a gene expression PCR assay in the same cohort. Therefore, our RNA splicing computational framework enables a promising avenue for host-response diagnosis of infection. Motivation: Host-based response assays (HRAs) can often diagnose infectious disease earlier and more precisely than pathogen-based tests. However, the role of RNA alternative splicing (AS) in HRAs remains unexplored, as existing HRAs are restricted to gene expression signatures. We report a computational framework for the identification, optimization, and evaluation of blood AS-based diagnostic assay development for infectious disease. Using SARS-CoV-2 infection as a case study, we demonstrate the improved accuracy of AS biomarkers for COVID-19 diagnosis when compared against six reported transcriptome signatures and when implemented as a microfluidic PCR diagnostic assay.

Abstract Background In the early stages of the COVID-19 pandemic our institution was interested in forecasting how long surgical patients receiving elective procedures would spend in the hospital. Initial examination of our models indicated that, due to the skewed nature of the length of stay, accurate prediction was challenging and we instead opted for a simpler classification model. In this work we perform a deeper examination of predicting in-hospital length of stay. Methods We used electronic health record data on length of stay from 42,209 elective surgeries. We compare different loss-functions (mean squared error, mean absolute error, mean relative error), algorithms (LASSO, Random Forests, multilayer perceptron) and data transformations (log and truncation). We also assess the performance of two stage hybrid classification-regression approach. Results Our results show that while it is possible to accurately predict short length of stays, predicting longer length of stay is extremely challenging. As such, we opt for a two-stage model that first classifies patients into long versus short length of stays and then a second stage that fits a regresssor among those predicted to have a short length of stay. Discussion The results indicate both the challenges and considerations necessary to applying machine-learning methods to skewed outcomes. Conclusions Two-stage models allow those developing clinical decision support tools to explicitly acknowledge where they can and cannot make accurate predictions.

Abstract Background Measuring host gene expression is a promising diagnostic strategy to discriminate bacterial and viral infections. Multiple signatures of varying size, complexity, and target populations have been described. However, there is little information to indicate how the performance of various published signatures compare to one another. Methods This systematic comparison of host gene expression signatures evaluated the performance of 28 signatures, validating them in 4589 subjects from 51 publicly available datasets. Thirteen COVID-specific datasets with 1416 subjects were included in a separate analysis. Individual signature performance was evaluated using the area under the receiving operating characteristic curve (AUC) value. Overall signature performance was evaluated using median AUCs and accuracies. Results Signature performance varied widely, with median AUCs ranging from 0.55 to 0.96 for bacterial classification and 0.69–0.97 for viral classification. Signature size varied (1–398 genes), with smaller signatures generally performing more poorly (P < 0.04). Viral infection was easier to diagnose than bacterial infection (84% vs. 79% overall accuracy, respectively; P < .001). Host gene expression classifiers performed more poorly in some pediatric populations (3 months–1 year and 2–11 years) compared to the adult population for both bacterial infection (73% and 70% vs. 82%, respectively; P < .001) and viral infection (80% and 79% vs. 88%, respectively; P < .001). We did not observe classification differences based on illness severity as defined by ICU admission for bacterial or viral infections. The median AUC across all signatures for COVID-19 classification was 0.80 compared to 0.83 for viral classification in the same datasets. Conclusions In this systematic comparison of 28 host gene expression signatures, we observed differences based on a signature’s size and characteristics of the validation population, including age and infection type. However, populations used for signature discovery did not impact performance, underscoring the redundancy among many of these signatures. Furthermore, differential performance in specific populations may only be observable through this type of large-scale validation.