An Artificial Intelligence System for Staging the Spheno-Occipital Synchondrosis.

Objective: The aim of this study was to develop, test and validate automated interpretable deep learning algorithms for the assessment and classification of the spheno-occipital synchondrosis (SOS) fusion stages from a cone beam computed tomography (CBCT).

Study Design: The sample consisted of 723 CBCT scans of orthodontic patients from private practices in the midwestern United States. The SOS fusion stages were classified by two orthodontists and an oral and maxillofacial radiologist.

Recent Advances in Molecular Docking Techniques: Transforming Perspectives in Distinct Drug Targeting and Drug Discovery Approaches.

Introduction: Drug targeting and drug discovery methodologies are advancing rapidly due to recent developments in molecular docking techniques. Molecular docking forecasts the interactions between a small molecule, such as a potential medicine, and a target protein or receptor.

Objectives: This comprehensive review focuses on significant advances in molecular docking algorithms such as Vina, Glide, and AutoDock, including their enhanced accuracy and efficiency in predicting drug-target interactions.

Automated classification of midpalatal suture maturation stages from CBCTs using an end-to-end deep learning framework.

Accurate classification of midpalatal suture maturation stages is critical for orthodontic diagnosis, treatment planning, and the assessment of maxillary growth. Cone Beam Computed Tomography (CBCT) imaging offers detailed insights into this craniofacial structure but poses unique challenges for deep learning image recognition model design due to its high dimensionality, noise artifacts, and variability in image quality. To address these challenges, we propose a novel technique that highlights key image features through a simple filtering process to improve image clarity prior to analysis, thereby enhancing the learning process and better aligning with the distribution of the input data domain.

A fully automated classification of third molar development stages using deep learning.

Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators.

Lycopene alleviates BCG-induced depressive phenotypes in mice by disrupting 5-HT3 receptor - IDO1 interplay in the brain.

The 5-HT3 receptor and indoleamine 2,3-dioxygenase 1 (IDO1) enzyme play a crucial role in the pathogenesis of depression as their activation reduces serotonin contents in the brain. Since molecular docking analysis revealed lycopene as a potent 5-HT3 receptor antagonist and IDO1 inhibitor, we hypothesized that lycopene might disrupt the interplay between the 5-HT3 receptor and IDO1 to mitigate depression. In mice, the depression-like phenotypes were induced by inoculating Bacillus Calmette-Guerin (BCG).

Ruddlesden-Popper 2D Perovskite-MoS Hybrid Heterojunction Photocathodes for Efficient and Scalable Photo-Rechargeable Li-Ion Batteries.

Photo-rechargeable batteries (PRBs) can provide a compact solution to power autonomous smart devices located at remote sites that cannot be connected with the grid. The study reports the Ruddlesden-Popper (RP) metal halide perovskite (MHP) and molybdenum disulfide (MoS) hybrid heterojunction-based photocathodes for Li-ion photo-rechargeable battery (Li-PRB) applications. Hybrid Lithium-ion batteries (LIBs) have demonstrated an average discharge specific capacity of 144.

Application of Artificial Intelligence in Assessing the Self-Management Practices of Patients with Type 2 Diabetes.

The use of Artificial intelligence in healthcare has evolved substantially in recent years. In medical diagnosis, Artificial intelligence algorithms are used to forecast or diagnose a variety of life-threatening illnesses, including breast cancer, diabetes, heart disease, etc. The main objective of this study is to assess self-management practices among patients with type 2 diabetes in rural areas of Pakistan using Artificial intelligence and machine learning algorithms.

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages.

Objective: A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed.

Methods: A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages.

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters.

Introduction: We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images.

Methods: A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages.

Learning Pain from Action Unit Combinations: A Weakly Supervised Approach via Multiple Instance Learning.

Patient pain can be detected highly reliably from facial expressions using a set of facial muscle-based action units (AUs) defined by the Facial Action Coding System (FACS). A key characteristic of facial expression of pain is the simultaneous occurrence of pain-related AU combinations, whose automated detection would be highly beneficial for efficient and practical pain monitoring. Existing general Automated Facial Expression Recognition (AFER) systems prove inadequate when applied specifically for detecting pain as they either focus on detecting individual pain-related AUs but not on combinations or they seek to bypass AU detection by training a binary pain classifier directly on pain intensity data but are limited by lack of enough labeled data for satisfactory training.

Ruddlesden-Popper 2D perovskites of type (CHCHNH)(CHNH)PbI (n = 1-4) for optoelectronic applications.

Ruddlesden-Popper (RP) phase metal halide organo perovskites are being extensively studied due to their quasi-two dimensional (2D) nature which makes them an excellent material for several optoelectronic device applications such as solar cells, photo-detectors, light emitting diodes (LEDs), lasers etc. While most of reports show use of linear carbon chain based organic moiety, such as n-Butylamine, as organic spacer in RP perovskite crystal structure, here we report a new series of quasi 2D perovskites with a ring type cyclic carbon group as organic spacer forming RP perovskite of type (CH)(MA)PbI; CH = 2-(1-Cyclohexenyl)ethylamine; MA = Methylamine). This work highlights the synthesis, structural, thermal, optical and optoelectronic characterizations for the new RP perovskite series n = 1-4.

Applications of a Chronic Care Model for Self-Management of Type 2 Diabetes: A Qualitative Analysis.

The main aim of this study was to explore the suitability, practicality, and acceptability of the self-management support and delivery system design components of the Chronic Care Model (CCM) in type 2 diabetes self-management in primary care settings in rural Pakistan. Thirty patients living with type 2 diabetes and 20 healthcare professionals (10 general practitioners and 10 nurses) were recruited from Al-Rehman Hospital at Abbottabad, Pakistan. The study data were collected using semi-structured interviews and analyzed using thematic analysis.

Healthcare Professionals' Perspectives of Patients' Experiences of the Self-Management of Type 2 Diabetes in the Rural Areas of Pakistan: A Qualitative Analysis.

The main objective of this research work was to explore the healthcare professionals' perspectives of type 2 diabetes patients' experiences of self-management of diabetes in the rural area of Pakistan. In this study, we have carried out a methodological approach to use a self-management framework to direct the interview guide for healthcare professionals to examine their perceptions and expectations of their diabetes patients' adherence to the medications prescribed. Twenty healthcare professionals were recruited in this study consisting of ten general practitioners and ten nurses from various clinics (medical centres) of Al-Rehman Hospital at Abbottabad, Pakistan.

Identifying the predictors of Covid-19 infection outcomes and development of prediction models.

Background: The infection of Corona Virus Disease (Covid-19) is challenging health problems worldwide. COVID-19 pandemic is spreading all over the world with the number of infected cases increased to 54.4 million with 1.

Application of structural equation modeling in assessing the self-management activities of patients with type 2 diabetes.

Objective: This study aimed at assessing the self-management activities of type 2 diabetes patients using Structural Equation Modeling (SEM) which measures and analyzes the correlations between observed and latent variables. This statistical modeling technique explored the linear causal relationships among the variables and accounted for the measurement errors.

Methods: A sample of 200 patients was recruited from the middle-aged population of rural areas of Pakistan to explore the self-management activities of type 2 diabetes patients using the validated version of the Urdu Summary of Diabetes Self-care Activities (U-SDSCA) instrument.

The Summary of an Urdu Version of Diabetes Self-Care Activities Measure: Psychometric Evaluation and Validation.

The English version of the Summary of Diabetes Self-Care Activities (SDSCA) measure is the most frequently used self-reporting instrument assessing diabetes self-management. This study is aimed at translating English SDSCA into the Urdu version and validating and evaluating its psychometric properties. The Urdu version of SDSCA was developed based on the guidelines provided by the World Health Organization for translation and adaptation of instruments.

Predicting Hospital Re-admissions from Nursing Care Data of Hospitalized Patients.

Readmission rates in the hospitals are increasingly being used as a benchmark to determine the quality of healthcare delivery to hospitalized patients. Around three-fourths of all hospital re-admissions can be avoided, saving billions of dollars. Many hospitals have now deployed electronic health record (EHR) systems that can be used to study issues that trigger readmission.

Framework for Mining and Analysis of Standardized Nursing Care Plan Data.

Despite an unprecedented amount of health-related data being amassed from various technological innovations, our ability to process this data and extract hidden knowledge has yet to catch up with this explosive growth. Although nursing care plans can be an effective tool to support the achievement of desired patient outcomes, their online collection, storage, and processing is lagging far behind. As a result, the impact of nursing care is not well understood from qualitative as well as quantitative perspectives.

Predictive Modeling for End-of-Life Pain Outcome using Electronic Health Records.

Electronic health record (EHR) systems are being widely used in the healthcare industry nowadays, mostly for monitoring the progress of the patients. EHR data analysis has become a big data problem as data is growing rapidly. Using a nursing EHR system, we built predictive models for determining what factors influence pain in end-of-life (EOL) patients.

Automated Real-Time Conjunctival Microvasculature Image Stabilization.

The bulbar conjunctiva is a thin, vascularized membrane covering the sclera of the eye. Non-invasive imaging techniques have been utilized to assess the conjunctival vasculature as a means of studying microcirculatory hemodynamics. However, eye motion often confounds quantification of these hemodynamic properties.

Feasibility of level-set analysis of enface OCT retinal images in diabetic retinopathy.

Pathology segmentation in retinal images of patients with diabetic retinopathy is important to help better understand disease processes. We propose an automated level-set method with Fourier descriptor-based shape priors. A cost function measures the difference between the current and expected output.

Predictive Modeling for Comfortable Death Outcome Using Electronic Health Records.

Electronic health record (EHR) systems are used in healthcare industry to observe the progress of patients. With fast growth of the data, EHR data analysis has become a big data problem. Most EHRs are sparse and multi-dimensional datasets and mining them is a challenging task due to a number of reasons.