Interactive web-based identification and visualization of transcript shared sequences.

We have developed TraC (Transcript Consensus), a web-based tool for detecting and visualizing shared sequences among two or more mRNA transcripts such as splice variants. Results including exon-exon boundaries are returned in a highly intuitive, data-rich, interactive plot that permits users to explore the similarities and differences of multiple transcript sequences. The online tool (http://labs.pathology.jhu.edu/nauen/trac/) is free to use. The source code is freely available for download (https://github.com/nauenlab/TraC).

Stress cardiomyopathy: Provoked chaotic T-wave lability.

Potentially lethal arrhythmia, occurring at presentation or during hospitalization, are a part of the clinical spectrum of stress cardiomyopathy. There is significant association between the catecholamine provoked nonalternating beat to beat fluctuations in T-wave morphology, termed T-wave lability (TWL), and the clinical risk of arrhythmia. We report four patients with diagnosis of stress cardiomyopathy, in whom serial electrocardiography showed macrovoltage nonalternating fluctuations in T-wave morphology.

Acute stent thrombosis: Should preventative measures start in the emergency department?

Stent thrombosis is a potentially life threatening condition caused by several factors or a combination factors, such as resistance to platelet agents and type of anticoagulation used as well as stent types. We report a case of acute thrombosis and discuss potential areas of intervention with literature review.

Underestimated and unreported prolonged QTc by automated ECG analysis in patients on methadone: can we rely on computer reading?

BACKGROUND: Recognition of prolonged corrected QT (QTc) interval is of particular importance, especially when using medications known to prolong QTc interval. Methadone can prolong the QTc interval and has the potential to induce torsades de pointes. OBJECTIVE: The objective of this study is to investigate the accuracy of computerized ECG analysis in correctly identifying and reporting QTc interval in patients on methadone. METHODS: We conducted a retrospective review of ECGs in the Muse electronic database of patients on methadone who are above 18 years old between January 2012 and December 2013 at an urban community hospital. ECGs were analyzed by the Marquette 12SL ECG Analysis Program (GE'Healthcare) reviewed by a cardiologist. RESULTS: A total of 826 ECGs of patients on methadone were examined manually for the QTc interval, of which 625 (75.7%) had QTc less than 470 ms, 149 (18%) had QTc between 470-499 ms and 52 (6.3%) had QTc more than 499 ms. QTc between 470-499 ms was underestimated by machine in 19 (12.8%) ECGs and QTc more than 499 ms was underestimated in 10 (19.6%) when compared to manually calculated QTc. QTc prolongation was underreported in 63 ECGs (48.5%) of those whose QTc between 470-499 ms and in 1 ECG (2.4%) of those whose QTc was more than 499 ms. CONCLUSIONS: QTc can be underestimated or unreported by the computer analysis. Physicians not only should calculate QTc manually but also examine the actual QTc value displayed on the report before concluding that this parameter is normal, especially in patients who are at risk of QTc prolongation.

Antibiotics for methicillin-resistant Staphylococcus aureus skin and soft tissue infections: the challenge of outpatient therapy.

PURPOSE: Methicillin-resistant Staphylococcus aureus (MRSA) infections are becoming increasingly prevalent in both community and hospital settings. Certain strains are notorious for causing skin and soft tissue infections in patients with no established risk factors. In this article, we report our findings on the dynamic antibiotic resistance pattern of MRSA and outpatient prescription trend for skin and soft tissue infections within our community. METHODS: We conducted a retrospective medical record review of 1876 patients evaluated in the emergency department of an urban community hospital from 2003 to 2012. Data regarding culture isolates and associated antimicrobial resistance, antibiotic treatment, site of specimen collection, age, race, and sex were collected and analyzed. RESULTS: Analysis of 1879 culture specimens yielded 2193 isolates. In some cases, a single specimen yielded polymicrobial growth. Staphylococcus aureus represented 996 isolates (45.4%); 463 were methicillin-susceptible (21.1%) and 533 (24.3%) were methicillin-resistant. Most patients were prescribed a single- or poly-drug regimen of trimethoprim/sulfamethoxazole, cephalexin, and clindamycin. Antimicrobial resistance analysis indicated that MRSA became increasingly resistant to the aforementioned antibiotics over time: 10% and 6% in 2012 vs 3.5% and 3.4% in 2007 for clindamycin and trimethoprim/sulfamethoxazole, respectively. CONCLUSION: Methicillin-resistant Staphylococcus aureus is a particularly virulent, rapidly adaptive pathogen that is becoming increasingly difficult to combat with existing antibiotics. Care must be taken to ensure appropriate treatment and follow-up of patients with known MRSA infections.

Contraceptive content shared on social media: an analysis of Twitter.

BACKGROUND: Information on social media may affect peoples' contraceptive decision making. We performed an exploratory analysis of contraceptive content on Twitter (recently renamed X), a popular social media platform. METHODS: We selected a random subset of 1% of publicly available, English-language tweets related to reversible, prescription contraceptive methods posted between January 2014 and December 2019. We oversampled tweets for the contraceptive patch to ensure at least 200 tweets per method. To create the codebook, we identified common themes specific to tweet content topics, tweet sources, and tweets soliciting information or providing advice. All posts were coded by two team members, and differences were adjudicated by a third reviewer. Descriptive analyses were reported with accompanying qualitative findings. RESULTS: During the study period, 457,369 tweets about reversible contraceptive methods were published, with a random sample of 4,434 tweets used for final analysis. Tweets most frequently discussed contraceptive method decision-making (26.7%) and side effects (20.5%), particularly for long-acting reversible contraceptive methods and the depot medroxyprogesterone acetate shot. Tweets about logistics of use or adherence were common for short-acting reversible contraceptives. Tweets were frequently posted by contraceptive consumers (50.6%). A small proportion of tweets explicitly requested information (6.2%) or provided advice (4.2%). CONCLUSIONS: Clinicians should be aware that individuals are exposed to information through Twitter that may affect contraceptive perceptions and decision making, particularly regarding long-acting reversible contraceptives. Social media is a valuable source for studying contraceptive beliefs missing in traditional health research and may be used by professionals to disseminate accurate contraceptive information.

Precision Phenotyping for Curating Research Cohorts of Patients with Post-Acute Sequelae of COVID-19 (PASC) as a Diagnosis of Exclusion.

Scalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.

Temporal characterization of Alzheimer's Disease with sequences of clinical records.

BACKGROUND: Alzheimer's Disease (AD) is a complex clinical phenotype with unprecedented social and economic tolls on an ageing global population. Real-world data (RWD) from electronic health records (EHRs) offer opportunities to accelerate precision drug development and scale epidemiological research on AD. A precise characterization of AD cohorts is needed to address the noise abundant in RWD. METHODS: We conducted a retrospective cohort study to develop and test computational models for AD cohort identification using clinical data from 8 Massachusetts healthcare systems. We mined temporal representations from EHR data using the transitive sequential pattern mining algorithm (tSPM) to train and validate our models. We then tested our models against a held-out test set from a review of medical records to adjudicate the presence of AD. We trained two classes of Machine Learning models, using Gradient Boosting Machine (GBM), to compare the utility of AD diagnosis records versus the tSPM temporal representations (comprising sequences of diagnosis and medication observations) from electronic medical records for characterizing AD cohorts. FINDINGS: In a group of 4985 patients, we identified 219 tSPM temporal representations (i.e., transitive sequences) of medical records for constructing the best classification models. The models with sequential features improved AD classification by a magnitude of 3-16 percent over the use of AD diagnosis codes alone. The computed cohort included 663 patients, 35 of whom had no record of AD. Six groups of tSPM sequences were identified for characterizing the AD cohorts. INTERPRETATION: We present sequential patterns of diagnosis and medication codes from electronic medical records, as digital markers of Alzheimer's Disease. Classification algorithms developed on sequential patterns can replace standard features from EHRs to enrich phenotype modelling. FUNDING: National Institutes of Health: the National Institute on Aging (RF1AG074372) and the National Institute of Allergy and Infectious Diseases (R01AI165535).

Using Shapes of COVID-19 Positive Patient-Specific Trajectories for Mortality Prediction.

Machine learning can be used to identify relevant trajectory shape features for improved predictive risk modeling, which can help inform decisions for individualized patient management in intensive care during COVID-19 outbreaks. We present explainable random forests to dynamically predict next day mortality risk in COVID -19 positive and negative patients admitted to the Mount Sinai Health System between March 1st and June 8th, 2020 using patient time-series data of vitals, blood and other laboratory measurements from the previous 7 days. Three different models were assessed by using time series with: 1) most recent patient measurements, 2) summary statistics of trajectories (min/max/median/first/last/count), and 3) coefficients of fitted cubic splines to trajectories. AUROC and AUPRC with cross-validation were used to compare models. We found that the second and third models performed statistically significantly better than the first model. Model interpretations are provided at patient-specific level to inform resource allocation and patient care.

BEHRTDAY: Dynamic Mortality Risk Prediction using Time-Variant COVID-19 Patient Specific Trajectories.

Incorporating repeated measurements of vitals and laboratory measurements can improve mortality risk-prediction and identify key risk factors in individualized treatment of COVID-19 hospitalized patients. In this observational study, demographic and laboratory data of all admitted patients to 5 hospitals of Mount Sinai Health System, New York, with COVID-19 positive tests between March 1st and June 8th, 2020, were extracted from electronic medical records and compared between survivors and non-survivors. Next day mortality risk of patients was assessed using a transformer-based model BEHRTDAY fitted to patient time series data of vital signs, blood and other laboratory measurements given the entire patients' hospital stay. The study population includes 3699 COVID-19 positive (57% male, median age: 67) patients. This model had a very high average precision score (0.96) and area under receiver operator curve (0.92) for next-day mortality prediction given entire patients' trajectories, and through masking, it learnt each variable's context.

Severity of COVID-19-Related Illness in Massachusetts, July 2021 to December 2022.

This cohort study uses hospitalization and 30-day mortality risks to create a temporal profile of the severity of COVID-19 in Massachusetts from July 2021 to December 2022.

HiGlass: web-based visual exploration and analysis of genome interaction maps.

We present HiGlass, an open source visualization tool built on web technologies that provides a rich interface for rapid, multiplex, and multiscale navigation of 2D genomic maps alongside 1D genomic tracks, allowing users to combine various data types, synchronize multiple visualization modalities, and share fully customizable views with others. We demonstrate its utility in exploring different experimental conditions, comparing the results of analyses, and creating interactive snapshots to share with collaborators and the broader public. HiGlass is accessible online at http://higlass.io and is also available as a containerized application that can be run on any platform.

Space and Time Resolved Detection of Platelet Activation and von Willebrand Factor Conformational Changes in Deep Suspensions.

Tracking cells and proteins' phenotypic changes in deep suspensions is critical for the direct imaging of blood-related phenomena in in vitro replica of cardiovascular systems and blood-handling devices. This paper introduces fluorescence imaging techniques for space and time resolved detection of platelet activation, von Willebrand factor (VWF) conformational changes, and VWF-platelet interaction in deep suspensions. Labeled VWF, platelets, and VWF-platelet strands are suspended in deep cuvettes, illuminated, and imaged with a high-sensitivity EM-CCD camera, allowing detection using an exposure time of 1 ms. In-house postprocessing algorithms identify and track the moving signals. Recombinant VWF-eGFP (rVWF-eGFP) and VWF labeled with an FITC-conjugated polyclonal antibody are employed. Anti-P-Selectin FITC-conjugated antibodies and the calcium-sensitive probe Indo-1 are used to detect activated platelets. A positive correlation between the mean number of platelets detected per image and the percentage of activated platelets determined through flow cytometry is obtained, validating the technique. An increase in the number of rVWF-eGFP signals upon exposure to shear stress demonstrates the technique's ability to detect breakup of self-aggregates. VWF globular and unfolded conformations and self-aggregation are also observed. The ability to track the size and shape of VWF-platelet strands in space and time provides means to detect pro- and antithrombotic processes.

An interactive web-based application for Comprehensive Analysis of RNAi-screen Data.

RNAi screens are widely used in functional genomics. Although the screen data can be susceptible to a number of experimental biases, many of these can be corrected by computational analysis. For this purpose, here we have developed a web-based platform for integrated analysis and visualization of RNAi screen data named CARD (for Comprehensive Analysis of RNAi Data; available at https://card.niaid.nih.gov). CARD allows the user to seamlessly carry out sequential steps in a rigorous data analysis workflow, including normalization, off-target analysis, integration of gene expression data, optimal thresholds for hit selection and network/pathway analysis. To evaluate the utility of CARD, we describe analysis of three genome-scale siRNA screens and demonstrate: (i) a significant increase both in selection of subsequently validated hits and in rejection of false positives, (ii) an increased overlap of hits from independent screens of the same biology and (iii) insight to microRNA (miRNA) activity based on siRNA seed enrichment.