Rational computational design and development of an immunogenic multiepitope vaccine incorporating transmembrane proteins of Staphylococcus lugdunensis.

Staphylococcus lugdunensis has emerged as a significant human pathogen, responsible for a range of infections from skin and soft tissue infections to endocarditis and bacteremia. Notably, abscess formation is a common manifestation, reflecting its potential shift from a benign skin commensal to a serious pathogen, akin to infective endocarditis. With the rising prevalence of antibiotic resistance, there is a pressing need for novel therapeutic strategies.

Imaging phenotypes predict overall survival in glioma more accurate than basic demographic and cell mutation profiles.

Background: Magnetic resonance imaging (MRI), digital pathology imaging (PATH), demographics, and IDH mutation status predict overall survival (OS) in glioma. Identifying and characterizing predictive features in the different modalities may improve OS prediction accuracy.

Purpose: To evaluate the OS prediction accuracy of combinations of prognostic markers in glioma patients.

Feature Transformation for Efficient Blood Glucose Prediction in Type 1 Diabetes Mellitus Patients.

Diabetes Mellitus, a metabolic disease, causes the body to lose control over blood glucose regulation. With recent advances in self-monitoring systems, a patient can access their personalized glycemic profile and may utilize it for efficient prediction of future blood glucose levels. An efficient diabetes management system demands the accurate estimation of blood glucose levels, which, apart from using an appropriate prediction algorithm, depends on discriminative data representation.

Combining MRI and Histologic Imaging Features for Predicting Overall Survival in Patients with Glioma.

Purpose To test the hypothesis that combined features from MR and digital histopathologic images more accurately predict overall survival (OS) in patients with glioma compared with MRI or histopathologic features alone. Materials and Methods Multiparametric MR and histopathologic images in patients with a diagnosis of glioma (high- or low-grade glioma [HGG or LGG]) were obtained from The Cancer Imaging Archive (original images acquired 1983-2008). An extensive set of engineered features such as intensity, histogram, and texture were extracted from delineated tumor regions in MR and histopathologic images.

Role of Hybrid Deep Neural Networks (HDNNs), Computed Tomography, and Chest X-rays for the Detection of COVID-19.

COVID-19 syndrome has extensively escalated worldwide with the induction of the year 2020 and has resulted in the illness of millions of people. COVID-19 patients bear an elevated risk once the symptoms deteriorate. Hence, early recognition of diseased patients can facilitate early intervention and avoid disease succession.

Draft Genome Sequences of Three Species Isolated from Biofouled Reverse-Osmosis Membranes.

Here, we present the draft genome sequences of three bacteria belonging to the genus which were isolated from biofouled reverse-osmosis (RO) membranes harvested from a full-scale desalination plant. The sizes of the assembled genomes for RO1, RO2, and RO3 were 4.22 Mb, 4.

Glioma Grading via Analysis of Digital Pathology Images Using Machine Learning.

Cancer pathology reflects disease progression (or regression) and associated molecular characteristics, and provides rich phenotypic information that is predictive of cancer grade and has potential implications in treatment planning and prognosis. According to the remarkable performance of computational approaches in the digital pathology domain, we hypothesized that machine learning can help to distinguish low-grade gliomas (LGG) from high-grade gliomas (HGG) by exploiting the rich phenotypic information that reflects the microvascular proliferation level, mitotic activity, presence of necrosis, and nuclear atypia present in digital pathology images. A set of 735 whole-slide digital pathology images of glioma patients (median age: 49.

Segmentation and Grade Prediction of Colon Cancer Digital Pathology Images Across Multiple Institutions.

Distinguishing benign from malignant disease is a primary challenge for colon histopathologists. Current clinical methods rely on qualitative visual analysis of features such as glandular architecture and size that exist on a continuum from benign to malignant. Consequently, discordance between histopathologists is common.

A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages.

Neuroimaging has made it possible to measure pathological brain changes associated with Alzheimer's disease (AD) in vivo. Over the past decade, these measures have been increasingly integrated into imaging signatures of AD by means of classification frameworks, offering promising tools for individualized diagnosis and prognosis. We reviewed neuroimaging-based studies for AD and mild cognitive impairment classification, selected after online database searches in Google Scholar and PubMed (January, 1985-June, 2016).

An Unusual Cause of Right Iliac Fossa Pain: Caecal Diverticulum Perforation.

Caecal diverticulum perforation is a rare condition. It mimics acute appendicitis and is seldom suspected pre-operatively. Commonly it is discovered during exploration.