Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this study was to evaluate the predictive values of dynamic clinical indices by developing machine-learning (ML) models for early and accurate clinical assessment of the disease prognosis of ARDS. We conducted a retrospective observational study by applying dynamic clinical data collected in the ARDSNet FACTT Trial (n = 1000) to ML-based algorithms for predicting mortality.

Resistin predicts disease severity and survival in patients with pulmonary arterial hypertension.

Background: Abnormal remodeling of distal pulmonary arteries in patients with pulmonary arterial hypertension (PAH) leads to progressively increased pulmonary vascular resistance, followed by right ventricular hypertrophy and failure. Despite considerable advancements in PAH treatment prognosis remains poor. We aim to evaluate the potential for using the cytokine resistin as a genetic and biological marker for disease severity and survival in a large cohort of patients with PAH.

Artificial intelligence and body composition.

Aims: Although obesity is associated with chronic disease, a large section of the population with high BMI does not have an increased risk of metabolic disease. Increased visceral adiposity and sarcopenia are also risk factors for metabolic disease in people with normal BMI. Artificial Intelligence (AI) techniques can help assess and analyze body composition parameters for predicting cardiometabolic health.

A machine learning based approach towards high-dimensional mediation analysis.

Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics.

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine.

Radiogenomics, a combination of "Radiomics" and "Genomics," using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes.

Multi-animal pose estimation, identification and tracking with DeepLabCut.

Estimating the pose of multiple animals is a challenging computer vision problem: frequent interactions cause occlusions and complicate the association of detected keypoints to the correct individuals, as well as having highly similar looking animals that interact more closely than in typical multi-human scenarios. To take up this challenge, we build on DeepLabCut, an open-source pose estimation toolbox, and provide high-performance animal assembly and tracking-features required for multi-animal scenarios. Furthermore, we integrate the ability to predict an animal's identity to assist tracking (in case of occlusions).

Relationship Between TSH Levels and Cognition in the Young Adult: An Analysis of the Human Connectome Project Data.

Context: The nature of the relationship between serum thyrotropin (TSH) levels and higher cognitive abilities is unclear, especially within the normal reference range and in the younger population.

Objective: To assess the relationship between serum TSH levels and mental health and sleep quality parameters (fluid intelligence [Gf], MMSE (Mini-Mental State Examination), depression scores, and, finally, Pittsburgh Sleep Quality Index (PSQI) scores (working memory, processing speed, and executive function) in young adults.

Methods: This was a retrospective analysis of the data from the Human Connectome Project (HCP).

Machine learning approach to predicting albuminuria in persons with type 2 diabetes: An analysis of the LOOK AHEAD Cohort.

Albuminuria and estimated glomerular filtration rate (e-GFR) are early markers of renal disease and cardiovascular outcomes in persons with diabetes. Although body composition has been shown to predict systolic blood pressure, its application in predicting albuminuria is unknown. In this study, we have used machine learning methods to assess the risk of albuminuria in persons with diabetes using body composition and other determinants of metabolic health.

Real-time quantitation of thyroidal radioiodine uptake in thyroid disease with monitoring by a collar detection device.

Radioactive iodine (RAI) is safe and effective in most patients with hyperthyroidism but not all individuals are cured by the first dose, and most develop post-RAI hypothyroidism. Postoperative RAI therapy for remnant ablation is successful in 80-90% of thyroid cancer patients and sometimes induces remission of nonresectable cervical and/or distant metastatic disease but the effective tumor dose is usually not precisely known and must be moderated to avoid short- and long-term adverse effects on other tissues. The Collar Therapy Indicator (COTI) is a radiation detection device embedded in a cloth collar secured around the patient's neck and connected to a recording and data transmission box.

Body fat predicts exercise capacity in persons with Type 2 Diabetes Mellitus: A machine learning approach.

Diabetes mellitus is associated with increased cardiovascular disease (CVD) related morbidity, mortality and death. Exercise capacity in persons with type 2 diabetes has been shown to be predictive of cardiovascular events. In this study, we used the data from the prospective randomized LOOK AHEAD study and used machine learning algorithms to help predict exercise capacity (measured in Mets) from the baseline data that included cardiovascular history, medications, blood pressure, demographic information, anthropometric and Dual-energy X-Ray Absorptiometry (DXA) measured body composition metrics.

Artificial intelligence may offer insight into factors determining individual TSH level.

The factors that determine Serum Thyrotropin (TSH) levels have been examined through different methods, using different covariates. However, the use of machine learning methods has so far not been studied in population databases like NHANES (National Health and Nutritional Examination Survey) to predict TSH. In this study, we performed a comparative analysis of different machine learning methods like Linear regression, Random forest, Support vector machine, multilayer perceptron and stacking regression to predict TSH and classify individuals with normal, low and high TSH levels.

Using DeepLabCut for 3D markerless pose estimation across species and behaviors.

Noninvasive behavioral tracking of animals during experiments is critical to many scientific pursuits. Extracting the poses of animals without using markers is often essential to measuring behavioral effects in biomechanics, genetics, ethology, and neuroscience. However, extracting detailed poses without markers in dynamically changing backgrounds has been challenging.

A simple computer vision pipeline reveals the effects of isolation on social interaction dynamics in Drosophila.

Isolation profoundly influences social behavior in all animals. In humans, isolation has serious effects on health. Drosophila melanogaster is a powerful model to study small-scale, temporally-transient social behavior.

Network-specific sex differentiation of intrinsic brain function in males with autism.

Background: The male predominance in the prevalence of autism spectrum disorder (ASD) has motivated research on sex differentiation in ASD. Multiple sources of evidence have suggested a neurophenotypic convergence of ASD-related characteristics and typical sex differences. Two existing, albeit competing, models provide predictions on such neurophenotypic convergence.

Association of White Matter Structure With Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder.

Importance: Clinical overlap between autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) is increasingly appreciated, but the underlying brain mechanisms remain unknown to date.

Objective: To examine associations between white matter organization and 2 commonly co-occurring neurodevelopmental conditions, ASD and ADHD, through both categorical and dimensional approaches.

Design, Setting, And Participants: This investigation was a cross-sectional diffusion tensor imaging (DTI) study at an outpatient academic clinical and research center, the Department of Child and Adolescent Psychiatry at New York University Langone Medical Center.

Brains of verbal memory specialists show anatomical differences in language, memory and visual systems.

We studied a group of verbal memory specialists to determine whether intensive oral text memory is associated with structural features of hippocampal and lateral-temporal regions implicated in language processing. Professional Vedic Sanskrit Pandits in India train from childhood for around 10years in an ancient, formalized tradition of oral Sanskrit text memorization and recitation, mastering the exact pronunciation and invariant content of multiple 40,000-100,000 word oral texts. We conducted structural analysis of gray matter density, cortical thickness, local gyrification, and white matter structure, relative to matched controls.