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Background: Artificial intelligence (AI)–enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. Methods: We trained a convolutional neural network (ECG-AI) to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit 3 Cox proportional hazards models, composed of ECG-AI 5-year AF probability, CHARGE-AF clinical risk score (Cohorts for Heart and Aging in Genomic Epidemiology–Atrial Fibrillation), and terms for both ECG-AI and CHARGE-AF (CH-AI), respectively. We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve) and calibration in an internal test set and 2 external test sets (Brigham and Women’s Hospital [BWH] and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. Results: The training set comprised 45 770 individuals (age 55±17 years, 53% women, 2171 AF events) and the test sets comprised 83 162 individuals (age 59±13 years, 56% women, 2424 AF events). Area under the receiver operating characteristic curve was comparable using CHARGE-AF (MGH, 0.802 [95% CI, 0.767–0.836]; BWH, 0.752 [95% CI, 0.741–0.763]; UK Biobank, 0.732 [95% CI, 0.704–0.759]) and ECG-AI (MGH, 0.823 [95% CI, 0.790–0.856]; BWH, 0.747 [95% CI, 0.736–0.759]; UK Biobank, 0.705 [95% CI, 0.673–0.737]). Area under the receiver operating characteristic curve was highest using CH-AI (MGH, 0.838 [95% CI, 0.807 to 0.869]; BWH, 0.777 [95% CI, 0.766 to 0.788]; UK Biobank, 0.746 [95% CI, 0.716 to 0.776]). Calibration error was low using ECG-AI (MGH, 0.0212; BWH, 0.0129; UK Biobank, 0.0035) and CH-AI (MGH, 0.012; BWH, 0.0108; UK Biobank, 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r : MGH, 0.61; BWH, 0.66; UK Biobank, 0.41). Conclusions: AI-based analysis of 12-lead ECGs has similar predictive usefulness to a clinical risk factor model for incident AF and the approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

OBJECTIVE5-aminolevulinic acid (5-ALA)–induced protoporphyrin IX (PpIX) fluorescence is an effective surgical adjunct for the intraoperative identification of tumor tissue during resection of high-grade gliomas. The use of 5-ALA-induced PpIX fluorescence in glioblastoma (GBM) has been shown to double the extent of gross-total resection and 6-month progression-free survival. The heterogeneity of 5-ALA-induced PpIX fluorescence observed during surgery presents a technical and diagnostic challenge when utilizing this tool intraoperatively. While some regions show bright fluorescence after 5-ALA administration, other regions do not, despite that both regions of the tumor may be histopathologically indistinguishable. The authors examined the biological basis of this heterogeneity using computational methods.METHODSThe authors collected both fluorescent and nonfluorescent GBM specimens from a total of 14 patients undergoing surgery and examined their gene expression profiles.RESULTSIn this study, the authors found that the gene expression patterns characterizing fluorescent and nonfluorescent GBM surgical specimens were profoundly different and were associated with distinct cellular functions and different biological pathways. Nonfluorescent tumor tissue tended to resemble the neural subtype of GBM; meanwhile, fluorescent tumor tissue did not exhibit a prominent pattern corresponding to known subtypes of GBM. Consistent with this observation, neural GBM samples from The Cancer Genome Atlas database exhibited a significantly lower fluorescence score than nonneural GBM samples as determined by a fluorescence gene signature developed by the authors.CONCLUSIONSThese results provide a greater understanding regarding the biological basis of differential fluorescence observed intraoperatively and can provide a basis to identify novel strategies to maximize the effectiveness of fluorescence agents.

Crianças com Transtorno do Espectro Autista (TEA) apresentam dificuldades na aprendizagem e no desenvolvimento cognitivo, e as tecnologias assistivas podem ser inseridas e utilizadas como facilitadoras desse desenvolvimento. Assim, este artigo realizou a análise de quatro aplicativos voltados para a alfabetização de crianças autistas, destacando "Lina Educa", "ABC Autismo", "Aprendendo com Biel e seus amigos" e "Livox", analisando suas funcionalidades e as possibilidades de uso, juntamente com um quadro comparativo evidenciando as características semelhantes e diferentes entre eles. Os dois principais métodos de ensino abordados foram TEACCH e ABA, sendo que estes têm se mostrado abrangentes e eficazes, segundo psicólogos e psicopedagogos, para o ensino-aprendizagem de crianças em processos de aprendizagem e domínio do código alfabético. Portanto, essas técnicas implementadas aos aplicativos validam a aplicabilidade para alfabetização de crianças com TEA.

Purpose Surgical excision is currently recommended for all occurrences of atypical ductal hyperplasia (ADH) found on core needle biopsies for malignancy diagnoses and treatment of lesions. The excision of all ADH lesions may lead to overtreatment, which results in invasive surgeries for benign lesions in many women. A machine learning method to predict ADH upgrade may help clinicians and patients decide whether combined active surveillance and hormonal therapy is a reasonable alternative to surgical excision. Methods The following six machine learning models were developed to predict ADH upgrade from core needle biopsy: gradient-boosting trees, random forest, radial support vector machine (SVM), weighted K-nearest neighbors (KNN), logistic elastic net, and logistic regression. The study cohort consisted of 128 lesions from 124 women at a tertiary academic care center in New Hampshire who had ADH on core needle biopsy and who underwent an associated surgical excision from 2011 to 2017. Results The best-performing models were gradient-boosting trees (area under the curve [AUC], 68%; accuracy, 78%) and random forest (AUC, 67%; accuracy, 77%). The top five most important features that determined ADH upgrade were age at biopsy, lesion size, number of biopsies, needle gauge, and personal and family history of breast cancer. Using the random forest model, 98% of all malignancies would have been diagnosed through surgical biopsies, whereas 16% of unnecessary surgeries on benign lesions could have been avoided (ie, 87% sensitivity at 45% specificity). Conclusion These results add to the growing body of support for machine learning models as useful aids for clinicians and patients in decisions about the clinical management of ADH.

BackgroundElectronic health records (EHRs) promise to enable broad-ranging discovery with power exceeding that of conventional research cohort studies. However, research using EHR datasets may be subject to selection bias, which can be compounded by missing data, limiting the generalizability of derived insights.MethodsMass General Brigham (MGB) is a large New England-based healthcare network comprising seven tertiary care and community hospitals with associated outpatient practices. Within an MGB-based EHR warehouse of >3.5 million individuals with at least one ambulatory care visit, we approximated a community-based cohort study by selectively sampling individuals longitudinally attending primary care practices between 2001-2018 (n=520,868), which we named the Community Care Cohort Project (C3PO). We also utilized pre-trained deep natural language processing (NLP) models to recover vital signs (i.e., height, weight, and blood pressure) from unstructured notes in the EHR. We assessed the validity of C3PO by deploying established risk models including the Pooled Cohort Equations (PCE) and the Cohorts for Aging and Genomic Epidemiology Atrial Fibrillation (CHARGE-AF) score, and compared model performance in C3PO to that observed within typical EHR Convenience Samples which included all individuals from the same parent EHR with sufficient data to calculate each score but without a requirement for longitudinal primary care. All analyses were facilitated by the JEDI Extractive Data Infrastructure pipeline which we designed to efficiently aggregate EHR data within a unified framework conducive to regular updates.ResultsC3PO includes 520,868 individuals (mean age 48 years, 61% women, median follow-up 7.2 years, median primary care visits per individual 13). Estimated using reports, C3PO contains over 2.9 million electrocardiograms, 450,000 echocardiograms, 12,000 cardiac magnetic resonance images, and 75 million narrative notes. Using tabular data alone, 286,009 individuals (54.9%) had all vital signs available at baseline, which increased to 358,411 (68.8%) after NLP recovery (31% reduction in missingness). Among individuals with both NLP and tabular data available, NLP-extracted and tabular vital signs obtained on the same day were highly correlated (e.g., Pearson r range 0.95-0.99, pConclusionsIntentional sampling of individuals receiving regular ambulatory care and use of NLP to recover missing data have the potential to reduce bias in EHR research and maximize generalizability of insights.

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95–0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012–0.030 in C3PO vs. 0.028–0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.

Identifying patient characteristics that influence the rate of colorectal polyp recurrence can provide important insights into which patients are at higher risk for recurrence. We used natural language processing to extract polyp morphological characteristics from 953 polyp-presenting patients’ electronic medical records. We used subsequent colonoscopy reports to examine how the time to polyp recurrence (731 patients experienced recurrence) is influenced by these characteristics as well as anthropometric features using Kaplan-Meier curves, Cox proportional hazards modeling, and random survival forest models. We found that the rate of recurrence differed significantly by polyp size, number, and location and patient smoking status. Additionally, right-sided colon polyps increased recurrence risk by 30% compared to left-sided polyps. History of tobacco use increased polyp recurrence risk by 20% compared to never-users. A random survival forest model showed an AUC of 0.65 and identified several other predictive variables, which can inform development of personalized polyp surveillance plans.

The intersection of medicine and machine learning (ML) has the potential to transform healthcare. We describe how physiology, a foundational discipline of medical training and practice with a rich quantitative history, could serve as a starting point for the development of a common language between clinicians and ML experts, thereby accelerating real-world impact.

Colorectal cancer is the second leading cause of cancer-related deaths in the United States. Colorectal cancer risk can be effectively managed through early detection and removal of precancerous lesions, known as colorectal polyps, with routine colonoscopy screening. The current guidelines for colonoscopy screening and surveillance do not consider detailed clinical information and polyp characteristics from prior colonoscopies. Developing a colonoscopy surveillance plan based upon a patient’s personalized polyp recurrence risk is important for preventing the progression of colorectal cancer. To address this clinical need, in this paper, we proposed and developed a natural language processing and machine learning model to predict colorectal polyp recurrence risk using features derived from patient colonoscopy and pathology reports in electronic medical record systems. Colonoscopy records and the associated pathology reports from 952 patients in a tertiary academic care center in New Hampshire were obtained from 2011 to 2017. Polyp characteristics were extracted from these records using a natural language processing pipeline. The extracted features from these records along with other demographic and anthropometric information were used to develop and compare six machine learning models for their ability to predict polyp recurrence. Our evaluation of these models revealed a range of performance advantages, such as an area under the curve as high as 65%, and it further highlighted important features in predicting polyp recurrence from demographic and medical health record sources. Our predictive analysis highlights the potential of personalized risk modeling for colorectal cancer screening, which can reduce unnecessary screenings, healthcare costs, and psychological stress, while improving patient health outcomes.

Objective: The emergence of Carbapenem-resistant E. cloacae complex has been a serious challenge to manage in the clinic because of multi-drug resistance. Tigecycline is regarded as one of the last-resort for Carbapenem-resistant E. cloacae complex infections. Here, we report the draft genome sequence of a tigecycline resistant NDM-1-producing E. hormaechei strain ETR1 that was isolated from a male in China.Methods: Whole-genomic DNA of E. hormaechei strain ETR1 was extracted and was sequenced using an Illumina-HiseqTM X Ten platform. The generated sequence reads were assembled using CLC Genomics Workbench. The draft genome was annotated using Rapid Annotation using Subsystem Technology. Bioinformatics analysis was further performed.Results: The 5,141,975 bp genome contains various antimicrobial resistance genes conferring resistance to beta-lactam, fosfomycin, fluoroquinolone and tetracycline. Notably, the strain was also resistant to carbapenem and tigecycline. In addition, virulence factor encoding genes were also identified.Conclusion: The genome sequence will provide valuable information to understand antibiotic resistance mechanisms and pathogenic mechanisms in this strain. Close surveillance is urgently needed to monitor the spread of tigecycline resistant NDM-1-producing isolates.

Differential expression experiments or other analyses often end in a list of genes. Pathway enrichment analysis is one method to discern important biological signals and patterns from noisy expression data. However, pathway enrichment analysis may perform suboptimally in situations where there are multiple implicated pathways – such as in the case of genes that define subtypes of complex diseases. Our simulation study shows that in this setting, standard overrepresentation analysis identifies many false positive pathways along with the true positives. These false positives hamper investigators’ attempts to glean biological insights from enrichment analysis. We develop and evaluate an approach that combines community detection over functional networks with pathway enrichment to reduce false positives. Our simulation study demonstrates that a large reduction in false positives can be obtained with a small decrease in power. Though we hypothesized that multiple communities might underlie previously described subtypes of high-grade serous ovarian cancer and applied this approach, our results do not support this hypothesis. In summary, applying community detection before enrichment analysis may ease interpretation for complex gene sets that represent multiple distinct pathways.

High-grade gliomas (HGGs) include the most common and the most aggressive primary brain tumor of adults and children. Despite multimodality treatment, most high-grade gliomas eventually recur and are ultimately incurable. Several studies suggest that the initiation, progression, and recurrence of gliomas are driven, at least partly, by cancer stem-like cells. A defining characteristic of these cancer stem-like cells is their capacity to self-renew. We have identified a hypoxia-induced pathway that utilizes the Hypoxia Inducible Factor 1α (HIF-1α) transcription factor and the JAK1/2-STAT3 (Janus Kinase 1/2 - Signal Transducer and Activator of Transcription 3) axis to enhance the self-renewal of glioma stem-like cells. Hypoxia is a commonly found pathologic feature of HGGs. Under hypoxic conditions, HIF-1α levels are greatly increased in glioma stem-like cells. Increased HIF-1α activates the JAK1/2-STAT3 axis and enhances tumor stem-like cell self-renewal. Our data further demonstrate the importance of Vascular Endothelial Growth Factor (VEGF) secretion for this pathway of hypoxia-mediated self-renewal. Brefeldin A and EHT-1864, agents that significantly inhibit VEGF secretion, decreased stem cell self-renewal, inhibited tumor growth, and increased the survival of mice allografted with S100β-v-erbB/p53−/− glioma stem-like cells. These agents also inhibit the expression of a hypoxia gene expression signature that is associated with decreased survival of HGG patients. These findings suggest that targeting the secretion of extracellular, autocrine/paracrine mediators of glioma stem-like cell self-renewal could potentially contribute to the treatment of HGGs.

The responses of rats to intracranial injections of cholinergic drugs implicate acetylcholine in the control of male mating behavior and suggest specific brain areas as mediators of these effects. In particular, past work has linked the medial preoptic area (MPOA) to the control of intromission frequency but implicated areas near the lateral ventricles in effects on the initiation and spacing of intromissions. Studies of responses to systemic cholinergic treatments suggest that acetylcholine is even more important for the control of mating behavior in male hamsters but provide no information on the relevant brain areas. To fill this gap, we observed the effects of central injections of the cholinergic agonist oxotremorine that approached the MPOA along contrasting paths. Both studies suggest that increased cholinergic activity in or near the MPOA can facilitate behavior by reducing the postejaculatory interval and possibly affecting other parts of the mechanisms controlling the initiation of copulation and the efficiency of performance early in an encounter. In addition, oxotremorine caused other changes in behavior that could not be tied to the MPOA and may reflect actions at more dorsal sites, possibly including the bed nucleus of the stria terminalis and medial septum. These effects were notably heterogeneous, including facilitatory and disruptive effects on male behavior along with a facilitation of lordosis responses to manual stimulation. These results emphasize the number and diversity of elements of sexual behavior in hamsters that are under the partial control of forebrain cholinergic mechanisms.

[This corrects the article DOI: 10.1016/j.heliyon.2023.e18251.]., (© 2024 The Author(s).)