E learning

INTRODUCTION: The identification of a new adverse event (AE) caused by a drug product is one of the key activities in the pharmaceutical industry to ensure the safety profile of a drug product. Machine learning (ML) has the potential to assist with signal detection and supplement traditional pharmacovigilance (PV) surveillance methods. This pilot ML modeling study was designed to detect potential safety signals for two AbbVie products and test the model's capability of detecting safety signals earlier than humans...

OBJECTIVE: In dementia research, the Driving Scenes test from the Neuropsychological Assessment Battery has been shown to relate to memory, dementia diagnosis, and functional impairment. The aim of the current study was to examine Driving Scenes and its component scores, and their relationships with cognition and daily functioning, in a mixed dementia clinic sample. METHOD: One hundred U.S. military veterans between the ages of 55 and 88 were administered a full neuropsychological protocol that included Driving Scenes...

OBJECTIVES: Upper tract urothelial carcinoma is a rare, aggressive lesion, with early detection a key to its management. This study aimed to utilise computed tomographic urogram data to develop machine learning models for predicting tumour grading and staging in upper urothelial tract carcinoma patients and to compare these predictions with histopathological diagnosis used as reference standards. METHODS: Protocol-based computed tomographic urogram data from 106 patients were obtained and visualised in 3D...

BACKGROUND: Studies on haemosporidian diversity, including origin of human malaria parasites, malaria's zoonotic dynamic, and regional biodiversity patterns, have used target gene approaches. However, current methods have a trade-off between scalability and data quality. Here, a long-read Next-Generation Sequencing protocol using PacBio HiFi is presented. The data processing is supported by a pipeline that uses machine-learning for analysing the reads. METHODS: A set of primers was designed to target approximately 6 kb, almost the entire length of the haemosporidian mitochondrial genome...

The main issue with clinical infections is multidrug resistance to traditional antibiotics. As they are essential to innate immunity, shielding hosts from pathogenic microbes, traditional herbal remedies are an excellent supplier of antimicrobial peptides (AMPs), vital parts of defensive systems. Nevertheless, little is known about the bioactive peptide components of most ethnobotanical species. Our goal in this study was to find new, likely AMPs from Portulaca oleracea (P. oleracea) using in silico studies...

Optical coherence tomography (OCT) is a medical imaging method that generates micron-resolution 3D volumetric images of tissues in-vivo. Photothermal (PT)-OCT is a functional extension of OCT with the potential to provide depth-resolved molecular information complementary to the OCT structural images. PT-OCT typically requires long acquisition times to measure small fluctuations in the OCT phase signal. Here, we use machine learning with a neural network to infer the amplitude of the photothermal phase modulation from a short signal trace, trained in a supervised fashion with the ground truth signal obtained by conventional reconstruction of the PT-OCT signal from a longer acquisition trace...

In this article, the impact of radiofrequency electromagnetic field (RF-EMF) exposure from a simulated base station for the 5G New Radio (5G NR) telecommunication on rats was studied. The base station affects all age groups of the population, thus, for the first time, the experiment was conducted on male Wistar rats of three different ages (juvenile, adult, and presenile). The base station exposure parameters were chosen according to ICNIRP recommendations for limiting the exposure to radiofrequency electromagnetic field: frequency 2...

Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise...

PURPOSE: Chronic neck pain (CNP) is prevalent and challenging to treat. Despite evidence of massage's effectiveness for CNP, multiple accessibility barriers exist. The Trial Outcomes for Massage: Care Ally-Assisted vs. Therapist Treated (TOMCATT) study examined a care ally-assisted massage (CA-M) approach compared to a waitlist control prior to a study design modification (WL-C0 ). METHODS: CA-M consisted of in-person training for veteran/care-ally dyads to learn a standardized 30-minue massage routine, instructional DVD, and printed treatment manual...

The proviral integration for the Moloney murine leukemia virus (PIM) kinases, belonging to serine/threonine kinase family, have been found to be overexpressed in various types of cancers, such as prostate, breast, colon, endometrial, gastric, and pancreatic cancer. The three isoforms PIM kinases i.e., PIM1, PIM2, and PIM3 share a high degree of sequence and structural similarity and phosphorylate substrates controlling tumorigenic phenotypes like proliferation and cell survival. Targeting short-lived PIM kinases presents an intriguing strategy as in vivo knock-down studies result in non-lethal phenotypes, indicating that clinical inhibition of PIM might have fewer adverse effects...

Heavy metals (HMs) are among the most dangerous environmental variables for a variety of life forms, including crops. Accumulation of HMs in consumables and their subsequent transmission to the food web are serious concerns for scientific communities and policy makers. The function of essential plant cellular macromolecules is substantially hampered by HMs, which eventually have a detrimental effect on agricultural yield. Among these HMs, three were considered, i.e., arsenic, cadmium, and chromium, in this review, from agro-ecosystem perspective...

Image based deep learning models are used to extract new information from standard H&E pathology slides, however, biological interpretation of the features detected by artificial intelligence (AI) remains a challenge. High-grade serous ovarian carcinoma (HGSC) is characterized by aggressive behavior and chemotherapy resistance, but also by striking variability in outcome. Our understanding of this disease is limited, partly due to considerable tumor heterogeneity. We previously trained an AI model to identify HGSC tumor regions that are highly associated with outcome status but are indistinguishable by conventional morphologic methods...

This study identified tumorigenic processes most dependent on murine HSP72 in the MMTV-PyMT mammary tumor model, which give rise to spontaneous mammary tumors that exhibit HSP72-dependent metastasis to the lung. RNA-seq expression profiling of Hspa1a/Hspa1b (Hsp72) WT and Hsp72-/- primary mammary tumors discovered significantly lower expression of genes encoding components of the extracellular matrix (ECM) in Hsp72 knockout mammary tumors compared to WT controls. In vitro studies found that genetic or chemical inhibition of HSP72 activity in cultured collagen-expressing human or murine cells also reduces mRNA and protein levels of COL1A1 and several other ECM-encoding genes...

BACKGROUND: The serotonergic system modulates brain processes via functionally distinct subpopulations of neurons with heterogeneous properties, including their electrophysiological activity. In extracellular recordings, serotonergic neurons to be investigated for their functional properties are commonly identified on the basis of "typical" features of their activity, i.e. slow regular firing and relatively long duration of action potentials. Thus, due to the lack of equally robust criteria for discriminating serotonergic neurons with "atypical" features from non-serotonergic cells, the physiological relevance of the diversity of serotonergic neuron activities results largely understudied...

The pancreas is a vital organ in digestive system which has significant health implications. It is imperative to evaluate and identify malignant pancreatic lesions promptly in light of the high mortality rate linked to such malignancies. Endoscopic Ultrasound (EUS) is a non-invasive precise technique to detect pancreas disorders, but it is highly operator dependent. Artificial intelligence (AI), including traditional machine learning (ML) and deep learning (DL) techniques can play a pivotal role to enhancing the performance of EUS regardless of operator...

BACKGROUND: Cartilage T2 can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee cartilage T2 mapping because of its acquisition time of under 5 minutes. Understanding the reproducibility errors associated with qDESS T2 is essential to profiling the technical performance of this biomarker. PURPOSE: To examine the combined acquisition and segmentation reproducibility of knee cartilage qDESS T2 using two different regional analysis schemes: 1) manual segmentation of subregions loaded during common activities and 2) automatic subregional segmentation...

INTRODUCTION: Attention and working memory are key cognitive functions that allow us to select and maintain information in our mind for a short time, being essential for our daily life and, in particular, for learning and academic performance. It has been shown that musical training can improve working memory performance, but it is still unclear if and how the neural mechanisms of working memory and particularly attention are implicated in this process. In this work, we aimed to identify the oscillatory signature of bimodal attention and working memory that contributes to improved working memory in musically trained children...

BACKGROUND: Irregular time series (ITS) are common in healthcare as patient data is recorded in an electronic health record (EHR) system as per clinical guidelines/requirements but not for research and depends on a patient's health status. Due to irregularity, it is challenging to develop machine learning techniques to uncover vast intelligence hidden in EHR big data, without losing performance on downstream patient outcome prediction tasks. METHODS: In this paper, we propose Perceiver, a cross-attention-based transformer variant that is computationally efficient and can handle long sequences of time series in healthcare...

BACKGROUND: The disproportionate effect of COVID-19 on long term care facility (LTCF) residents has highlighted the need for clear, consistent guidance on the management of pandemics in such settings. As research exploring the experiences of LTCFs during the pandemic and the implications of mass hospital discharge, restricting staff movement, and limiting visitation from relatives are emerging, an in-depth review of policies, guidance and recommendations issued during this time could facilitate wider understanding in this area...

PURPOSE: Early onset scoliosis (EOS) patient diversity makes outcome prediction challenging. Machine learning offers an innovative approach to analyze patient data and predict results, including LOS in pediatric spinal deformity surgery. METHODS: Children under 10 with EOS were chosen from the American College of Surgeon's NSQIP database. Extended LOS, defined as over 5 days, was predicted using feature selection and machine learning in Python. The best model, determined by the area under the curve (AUC), was optimized and used to create a risk calculator for prolonged LOS...