Domain generalization for enhanced predictions of hospital readmission on unseen domains among patients with diabetes.

A prediction model to assess the risk of hospital readmission can be valuable to identify patients who may benefit from extra care. Developing hospital-specific readmission risk prediction models using local data is not feasible for many institutions. Models developed on data from one hospital may not generalize well to another hospital.

Hematological Parameters of Patients Positive for Coronavirus With and Without Comorbidities.

Background: The causative agent of the coronavirus disease (COVID-19) is a virus from the SARS-CoV-2 group of viruses that cause severe acute respiratory syndrome. The aim of the study was to examine the differences in hematological analyses of patients suffering from COVID-19 with and without comorbidities, to determine the degree of the clinical picture based on the MEWS scale and to examine the persistence of inflammatory parameters with the severity of the clinical picture.

Methods: The research is a cross-sectional retrospective study, conducted in the laboratory diagnostics service of Tesanj General Hospital.

Chemical Composition and Protective Possibilities of Juglans Nigra Leaves and Green Husks Extracts: DNA Binding and Micronucleus Assay in Human Lymphocytes.

To better understand the mechanism of action of the compounds in the ethanolic extracts of leaves and green husks, their binding to CT-DNA was investigated. This study was conducted to elucidate the in vitro protective effect of extracts against chromosomal damage in mitogen-induced human lymphocytes and investigate the possible application of selec+ted extracts as a natural source of polyphenolic compounds. Using HPLC-MS analysis, 103 different compounds were identified as having a higher number of active species, which is consistent with their activity.

Reactivity of anti-SARS-CoV-2 antibodies in Serbian voluntary blood donors.

Background: The COVID-19 pandemic has major implications on the entire blood supply system worldwide. Seroepidemiological studies are certainly necessary for better understanding the global burden that the COVID-19 pandemic represents.

Objectives: In this study, we analysed the association between demographic factors, COVID-19 severity, vaccination status and the reactivity of anti-SARS-CoV-2 IgG antibodies in Serbian blood donors.

A Social Network Analysis of Hemodialysis Clinics: Attitudes Toward Living Donor Kidney Transplant among Influential Patients.

Key Points: Hemodialysis clinic social networks spread attitudes and behaviors toward kidney transplants. Identifying and characterizing influential patients is a first step in future hemodialysis clinic social network interventions to promote kidney transplantation.

Background: Hemodialysis clinics help develop patient social networks that may spread kidney transplant (KT) attitudes and behaviors.

Optimization of radioprotective dose of Juglans nigra leaf extract using response surface methodology.

J. nigra leaf contains mixture of various pharmacologically active compounds and it is assumed that they may have the potential radioprotective effect. The purpose of this work was to predict radioprotective doses by correlating changes in organ distribution of radiopharmaceuticals with extract dose levels and rat body weight using response surface methodology (RSM) based on a second-order polynomial equation.

A transformer model for learning spatiotemporal contextual representation in fMRI data.

Representation learning is a core component in data-driven modeling of various complex phenomena. Learning a contextually informative representation can especially benefit the analysis of fMRI data because of the complexities and dynamic dependencies present in such datasets. In this work, we propose a framework based on transformer models to learn an embedding of the fMRI data by taking the spatiotemporal contextual information in the data into account.

Dialysis Patients' Social Networks and Living Donation Offers.

Rationale & Objective: Most living kidney donors are members of a hemodialysis patient's social network. Network members are divided into core members, those strongly connected to the patient and other members; and peripheral members, those weakly connected to the patient and other members. We identify how many hemodialysis patients' network members offered to become kidney donors, whether these offers were from core or peripheral network members, and whose offers the patients accepted.

Deep Learning vs Traditional Models for Predicting Hospital Readmission among Patients with Diabetes.

A hospital readmission risk prediction tool for patients with diabetes based on electronic health record (EHR) data is needed. The optimal modeling approach, however, is unclear. In 2,836,569 encounters of 36,641 diabetes patients, deep learning (DL) long short-term memory (LSTM) models predicting unplanned, all-cause, 30-day readmission were developed and compared to several traditional models.

Discovering research articles containing evolutionary timetrees by machine learning.

Motivation: Timetrees depict evolutionary relationships between species and the geological times of their divergence. Hundreds of research articles containing timetrees are published in scientific journals every year. The TimeTree (TT) project has been manually locating, curating and synthesizing timetrees from these articles for almost two decades into a TimeTree of Life, delivered through a unique, user-friendly web interface (timetree.

The Association of Glucose Control with Circulating Levels of Red Blood Cell-Derived Vesicles in Type 2 Diabetes Mellitus Patients with Atrial Fibrillation.

Hyperglycemia is a trigger for structural alteration of red blood cells (RBCs) and their ability to release extracellular vesicles (EVs). The aim of the study was to elucidate whether glucose control in T2DM patients with concomitant HF and AF affects a circulating number of RBC-derived EVs. We prospectively included 417 T2DM patients with HF, 51 of them had atrial fibrillation and 25 healthy volunteers and 30 T2DM non-HF individuals.

The company we keep. Using hemodialysis social network data to classify patients' kidney transplant attitudes with machine learning algorithms.

Background: Hemodialysis clinic patient social networks may reinforce positive and negative attitudes towards kidney transplantation. We examined whether a patient's position within the hemodialysis clinic social network could improve machine learning classification of the patient's positive or negative attitude towards kidney transplantation when compared to sociodemographic and clinical variables.

Methods: We conducted a cross-sectional social network survey of hemodialysis patients in two geographically and demographically different hemodialysis clinics.

Interplay between Myokine Profile and Glycemic Control in Type 2 Diabetes Mellitus Patients with Heart Failure.

Type 2 diabetes mellitus (T2DM) remains a powerful predictor of progressive heart failure (HF), but it is not clear whether altered glycemic control interferes with HF progression via an impaired profile of circulating myokines. The aim was to investigate plausible effects of glucose control on a myokine signature in T2DM patients affected by chronic HF. We selected 372 T2DM patients from the local database and finally included 314 individuals suffering from chronic HF and subdivided them into two groups according to glycosylated hemoglobin (HbA1c) levels (<6.

The backbone network of dynamic functional connectivity.

Temporal networks have become increasingly pervasive in many real-world applications, including the functional connectivity analysis of spatially separated regions of the brain. A major challenge in analysis of such networks is the identification of noise confounds, which introduce temporal ties that are nonessential, or links that are formed by chance due to local properties of the nodes. Several approaches have been suggested in the past for static networks or temporal networks with binary weights for extracting significant ties whose likelihood cannot be reduced to the local properties of the nodes.

Vector-borne and zoonotic infections and their relationships with regional and socioeconomic statuses: An ID-IRI survey in 24 countries of Europe, Africa and Asia.

Background: In this cross-sectional, international study, we aimed to analyze vector-borne and zoonotic infections (VBZI), which are significant global threats.

Method: VBZIs' data between May 20-28, 2018 was collected. The 24 Participatingcountries were classified as lower-middle, upper-middle, and high-income.

Does Whom Patients Sit Next to during Hemodialysis Affect Whether They Request a Living Donation?

Background: The seating arrangement of in-center hemodialysis is conducive to patients forming a relationship and a social network. We examined how seating in the in-center hemodialysis clinic affected patients forming relationships, whether patients formed relationships with others who have similar transplant behaviors (homophily), and whether these relationships influenced patients (social contagion) to request a living donation from family and friends outside of the clinic.

Methods: In this 30-month, prospective cohort study, we observed the relationships of 46 patients on hemodialysis in a hemodialysis clinic.

Does Sex, Race, and the Size of a Kidney Transplant Candidate's Social Network Affect the Number of Living Donor Requests? A Multicenter Social Network Analysis of Patients on the Kidney Transplant Waitlist.

Background: A kidney transplant candidate's social network serves as a pool of potential living donors. Sex and racial differences in network size, network strength, and living donor requests may contribute to disparities in living donor kidney transplantation.

Methods: In this multicenter cross-sectional study, we performed an egocentric network analysis via a telephone survey of 132 waitlisted candidates (53% female and 69% Black) to identify demographic and network factors associated with requesting living kidney donations.

DescribePROT: database of amino acid-level protein structure and function predictions.

We present DescribePROT, the database of predicted amino acid-level descriptors of structure and function of proteins. DescribePROT delivers a comprehensive collection of 13 complementary descriptors predicted using 10 popular and accurate algorithms for 83 complete proteomes that cover key model organisms. The current version includes 7.

Influence of medical domain knowledge on deep learning for Alzheimer's disease prediction.

Background And Objective: Alzheimer's disease (AD) is the most common type of dementia that can seriously affect a person's ability to perform daily activities. Estimates indicate that AD may rank third as a cause of death for older people, after heart disease and cancer. Identification of individuals at risk for developing AD is imperative for testing therapeutic interventions.

Predicting complications of diabetes mellitus using advanced machine learning algorithms.

Objective: We sought to predict if patients with type 2 diabetes mellitus (DM2) would develop 10 selected complications. Accurate prediction of complications could help with more targeted measures that would prevent or slow down their development.

Materials And Methods: Experiments were conducted on the Healthcare Cost and Utilization Project State Inpatient Databases of California for the period of 2003 to 2011.

Use of disease embedding technique to predict the risk of progression to end-stage renal disease.

The accurate prediction of progression of Chronic Kidney Disease (CKD) to End Stage Renal Disease (ESRD) is of great importance to clinicians and a challenge to researchers as there are many causes and even more comorbidities that are ignored by the traditional prediction models. We examine whether utilizing a novel low-dimensional embedding model disease2disease (D2D) learned from a large-scale electronic health records (EHRs) could well clusters the causes of kidney diseases and comorbidities and further improve prediction of progression of CKD to ESRD compared to traditional risk factors. The study cohort consists of 2,507 hospitalized Stage 3 CKD patients of which 1,375 (54.