Developing and evaluating a gamified self-management application for inflammatory bowel disease using the ADDIE model and Sukr framework.

Background: The prevalence and chronic nature of Inflammatory Bowel Diseases (IBD) is a significant global concern. As the essential part of treatments approach, patient adherence to treatment protocols and self-management practices are crucial to = IBD management. Healthcare initiatives focused on chronic conditions are strongly needed to consider various aspects of gamification and how it can positively affect self-management.

AI predictive models and advancements in microdissection testicular sperm extraction for non-obstructive azoospermia: a systematic scoping review.

Study Question: How accurately can artificial intelligence (AI) models predict sperm retrieval in non-obstructive azoospermia (NOA) patients undergoing micro-testicular sperm extraction (m-TESE) surgery?

Summary Answer: AI predictive models hold significant promise in predicting successful sperm retrieval in NOA patients undergoing m-TESE, although limitations regarding variability of study designs, small sample sizes, and a lack of validation studies restrict the overall generalizability of studies in this area.

What Is Known Already: Previous studies have explored various predictors of successful sperm retrieval in m-TESE, including clinical and hormonal factors. However, no consistent predictive model has yet been established.

The relationship of macular vessel density and foveal avascular zone with pulse wave velocity and central blood pressure in healthy adults: Persian cohort study.

Background: To evaluate the correlation of macular vessel density (VD) and foveal avascular zone (FAZ) parameters measured on optical coherence tomography angiography (OCTA) with systemic arterial stiffness using pulse wave velocity (PWV), pulse wave analysis, arterial age, and central blood pressure (CBP) measurements in healthy subjects.

Methods: In a comparative, cross-sectional, observational study, healthy adults who participated in the PERSIAN Cohort study at Mashhad University of Medical Sciences were included in this study. The study involved using a spectral domain OCTA device to obtain 3 × 3 and 6 × 6 mm scans of the macular superficial capillary plexus (SCP) VD, deep capillary plexus (DCP) VD, and FAZ vascular analysis.

Ensemble machine learning models for sperm quality evaluation concerning success rate of clinical pregnancy in assisted reproductive techniques.

This study aimed to investigate the influence of various sperm quality characteristics, including morphology, motility, and count, on the success rates of clinical pregnancy achieved through assisted reproductive technologies (ART) such as in-vitro fertilization (IVF), intracytoplasmic sperm injection (ICSI), and intrauterine insemination (IUI). The secondary objective was to assess the impact of these sperm parameters on the clinical pregnancy rate that resulted in the detection of a fetal heartbeat during the 11th week of gestation, a crucial milestone in successful ART-derived pregnancies. The researchers employed a retrospective analysis, evaluating data from 734 couples undergoing IVF/ICSI and 1197 couples undergoing IUI across two infertility centers.

Remote monitoring system to support positive airway pressure therapy in patients with obstructive sleep apnea: a multi-center randomized controlled trial.

Purpose: To develop and evaluate an intervention tailored to patients' needs to increase the rate of positive airway pressure (PAP) adherence in patients afflicted with obstructive sleep apnea (OSA), who undergo PAP therapy.

Methods: A multi-center, 3 parallel-arm, randomized, controlled trial was conducted. Participants with OSA who undergo a PAP therapy were randomized to one of three groups: control arm (usual care), educational booklet arm, and mobile-based application arm.

Comparison of Regression Methods to Predict the First Spike Latency in Response to an External Stimulus in Intracellular Recordings for Cerebellar Cells.

The significance of intracellular recording in neurophysiology is emphasized in this article, with considering the functions of neurons, particularly the role of first spike latency in response to external stimuli. The study employs advanced machine learning techniques to predict first spike latency from whole cell patch recording data. Experiments were conducted on Control (Salin) and Experiment (Harmaline) groups, generating a dataset for developing predictive models.

Effect of probiotics as an adjunctive therapy with Ritalin among ADHD children and adolescents: a triple-blind randomized controlled trial.

Objective: Attention Deficit Hyperactivity Disorder (ADHD) is one of the most common mental disorders in children. Evidence regarding the impact of probiotics supplementation in ADHD children is limited and controversial. Thus, this study aimed to assess the effect of probiotics as an adjunctive therapy with Ritalin among ADHD children and adolescents.

Effect of dairy products fortified with vitamin d on restless legs syndrome in women with premenstrual syndrome, abdominal obesity and vitamin d deficiency: a pilot study.

Backgrounds: Restless legs syndrome (RLS) is an unpleasant condition that affects the quality of life of patients. Its prevalence in increased in women with premenstrual syndrome (PMS). Vitamin D plays a key role in female reproduction through its impact on calcium homeostasis and neurotransmitters.

Evaluation of no-show rate in outpatient clinics with open access scheduling system: A systematic review.

Background: Patients' missed appointments can cause interference in the functions of the clinics and the visit of other patients. One of the most effective strategies to solve the problem of no-show rate is the use of an open access scheduling system (OA). This systematic review was conducted with the aim of investigating the impact of OA on the rate of no-show of patients in outpatient clinics.

Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol.

Background: For women in the first trimester, amniocentesis or chorionic villus sampling is recommended for screening. Machine learning has shown increased accuracy over time and finds numerous applications in enhancing decision-making, patient care, and service quality in nursing and midwifery. This study aims to develop an optimal learning model utilizing machine learning techniques, particularly neural networks, to predict chromosomal abnormalities and evaluate their predictive efficacy.

Effect of a gamified mobile-based self-management application on disease activity index, quality of life, and mental health in adults with inflammatory bowel disease: A protocol of a randomized controlled trial study.

Background And Aims: Inflammatory bowel disease (IBD) is a chronic inflammatory gastrointestinal tract disease subdivided into Crohn's disease (CD) and ulcerative colitis (UC). There is currently no cure for IBD, and individuals with IBD frequently experience a lower health-related quality of life (HRQOL) than the general population. Gamification has become an increasingly popular topic in recent years.

Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches.

Introduction: Open globe injuries (OGI) represent a main preventable reason for blindness and visual impairment, particularly in developing countries. The goal of this study is evaluating key variables affecting the prognosis of open globe injuries and validating internally and comparing different machine learning models to estimate final visual acuity.

Materials And Methods: We reviewed three hundred patients with open globe injuries receiving treatment at Khatam-Al-Anbia Hospital in Iran from 2020 to 2022.

Inflammatory bowel disease patients' perspectives of non-medical needs.

Background: Inflammatory bowel disease (IBD) imposes a huge burden on the healthcare systems and greatly declines the patient's quality of life. However, there is a paucity of detailed data regarding information and supportive needs as well as sources and methods of obtaining information to control different aspects of the disease from the perspectives of the patients themselves. This study aimed to establish the IBD patients' preferences of informational and supportive needs through Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA).

Models to predict length of stay in the emergency department: a systematic literature review and appraisal.

Introduction: Prolonged Length of Stay (LOS) in ED (Emergency Department) has been associated with poor clinical outcomes. Prediction of ED LOS may help optimize resource utilization, clinical management, and benchmarking. This study aims to systematically review models for predicting ED LOS and to assess the reporting and methodological quality about these models.

A systematic review of machine learning-based tumor-infiltrating lymphocytes analysis in colorectal cancer: Overview of techniques, performance metrics, and clinical outcomes.

The incidence of colorectal cancer (CRC), one of the deadliest cancers around the world, is increasing. Tissue microenvironment (TME) features such as tumor-infiltrating lymphocytes (TILs) can have a crucial impact on diagnosis or decision-making for treating patients with CRC. While clinical studies showed that TILs improve the host immune response, leading to a better prognosis, inter-observer agreement for quantifying TILs is not perfect.

A hybrid feature selection algorithm to determine effective factors in predictive model of success rate for in vitro fertilization/intracytoplasmic sperm injection treatment: A cross-sectional study.

Background: Previous research has identified key factors affecting in vitro fertilization or intracytoplasmic sperm injection success, yet the lack of a standardized approach for various treatments remains a challenge.

Objective: The objective of this study is to utilize a machine learning approach to identify the principal predictors of success in in vitro fertilization and intracytoplasmic sperm injection treatments.

Materials And Methods: We collected data from 734 individuals at 2 infertility centers in Mashhad, Iran between November 2016 and March 2017.

A comparative study of explainable ensemble learning and logistic regression for predicting in-hospital mortality in the emergency department.

This study addresses the challenges associated with emergency department (ED) overcrowding and emphasizes the need for efficient risk stratification tools to identify high-risk patients for early intervention. While several scoring systems, often based on logistic regression (LR) models, have been proposed to indicate patient illness severity, this study aims to compare the predictive performance of ensemble learning (EL) models with LR for in-hospital mortality in the ED. A cross-sectional single-center study was conducted at the ED of Imam Reza Hospital in northeast Iran from March 2016 to March 2017.

Author Response.

Rahmatinejad Z, Hoseini B, Pourmand A, Reihani H, Rahmatinejad F, Eslami S, Author Response. Indian J Crit Care Med 2024;28(2):183-184.

The effect of computerised decision support alerts tailored to intensive care on the administration of high-risk drug combinations, and their monitoring: a cluster randomised stepped-wedge trial.

Background: Drug-drug interactions (DDIs) can harm patients admitted to the intensive care unit (ICU). Yet, clinical decision support systems (CDSSs) aimed at helping physicians prevent DDIs are plagued by low-yield alerts, causing alert fatigue and compromising patient safety. The aim of this multicentre study was to evaluate the effect of tailoring potential DDI alerts to the ICU setting on the frequency of administered high-risk drug combinations.

Development and Validation of an Educational Book on Self-Management in Inflammatory Bowel Disease Based on Patient Preferences and Expert Opinions: A Methodological Study.

Background: Self-management education resources for inflammatory bowel disease (IBD) using concepts remain infrequent. We aim to describe the development and evaluation process of educational material for self-management in IBD based on patient preferences and expert opinions.

Research Design And Methods: The method of this study includes two main phases of development and validation in five steps in the following order: (1) identification of information needs for patients with IBD; (2) content development with a comprehensive literature review and scientific texts related to IBD; (3) measuring the face validity of the content based on the expert opinions in the field of IBD; (4) validation of the content with the experts in the field of IBD; and (5) validation by target audiences.

Computer-aided diagnosis of keratoconus through VAE-augmented images using deep learning.

Detecting clinical keratoconus (KCN) poses a challenging and time-consuming task. During the diagnostic process, ophthalmologists are required to review demographic and clinical ophthalmic examinations in order to make an accurate diagnosis. This study aims to develop and evaluate the accuracy of deep convolutional neural network (CNN) models for the detection of keratoconus (KCN) using corneal topographic maps.

Application of ensemble machine learning approach to assess the factors affecting size and polydispersity index of liposomal nanoparticles.

Liposome nanoparticles have emerged as promising drug delivery systems due to their unique properties. Assessing particle size and polydispersity index (PDI) is critical for evaluating the quality of these liposomal nanoparticles. However, optimizing these parameters in a laboratory setting is both costly and time-consuming.