Machine learning-based remission prediction in rheumatoid arthritis patients treated with biologic disease-modifying anti-rheumatic drugs: findings from the Kuwait rheumatic disease registry.

Background: Rheumatoid arthritis (RA) is a common condition treated with biological disease-modifying anti-rheumatic medicines (bDMARDs). However, many patients exhibit resistance, necessitating the use of machine learning models to predict remissions in patients treated with bDMARDs, thereby reducing healthcare costs and minimizing negative effects.

Objective: The study aims to develop machine learning models using data from the Kuwait Registry for Rheumatic Diseases (KRRD) to identify clinical characteristics predictive of remission in RA patients treated with biologics.

Hybrid healthcare unit recommendation system using computational techniques with lung cancer segmentation.

Introduction: Our research addresses the critical need for accurate segmentation in medical healthcare applications, particularly in lung nodule detection using Computed Tomography (CT). Our investigation focuses on determining the particle composition of lung nodules, a vital aspect of diagnosis and treatment planning.

Methods: Our model was trained and evaluated using several deep learning classifiers on the LUNA-16 dataset, achieving superior performance in terms of the Probabilistic Rand Index (PRI), Variation of Information (VOI), Region of Interest (ROI), Dice Coecient, and Global Consistency Error (GCE).

The Delphi method to analyze the expert views on possible futures of the smart city adoption and development in developing countries: the case of Jordan.

Smart cities are characterized by the integration of various technologies and the use of data to achieve several objectives. These objectives include the creation of efficiencies, boosting economic development, expanding sustainability, and improving the overall quality of life for individuals residing and working within the urban environment. The aim of this study is to analyze the future of smart cities with respect to developing countries, specifically Jordan as the case.

Impact of MLL::AF9 Gene Rearrangement on Survival of Acute Myeloid Leukaemia Patients: An Insight into Pakistani Population.

Objective: To ascertain the frequency of the MLL::AF9 gene rearrangement and its association with survival in Pakistani patients suffering from acute myeloid leukaemia (AML).

Study Design: Analytical study. Place and Duration of the Study: Department of Haematology, National Institute of Blood Diseases and Bone Marrow Transplantation, Karachi, Pakistan, from 2015 to 2020.

Exploring the Interactions between Obesity and Diabetes: Implications for Understanding Metabolic Dysregulation in a Saudi Arabian Adult Population.

The rising prevalence of obesity in Saudi Arabia is a major contributor to the nation's high levels of cardiometabolic diseases such as type 2 diabetes. To assess the impact of obesity on the diabetic metabolic phenotype presented in young Saudi Arabian adults, participants ( = 289, aged 18-40 years) were recruited and stratified into four groups: healthy weight (BMI 18.5-24.

Feasibility and outcomes of living-donor liver transplantation utilizing the right hemi-liver graft with portal vein anatomical variations.

Purpose: Portal vein (PV) reconstruction is a key factor for successful living-donor liver transplantation (LDLT). Anatomical variations of right PV (RPV) are encountered among potential donors.

Methods: To evaluate a single center experience of reconstruction techniques for the right hemi-liver grafts with PV variations during the period between May 2004 and 2022.

Antimicrobial resistance and machine learning: past, present, and future.

Machine learning has become ubiquitous across all industries, including the relatively new application of predicting antimicrobial resistance. As the first bibliometric review in this field, we expect it to inspire further research in this area. The review employs standard bibliometric indicators such as article count, citation count, and the Hirsch index (H-index) to evaluate the relevance and impact of the leading countries, organizations, journals, and authors in this field.

Federated Fusion of Magnified Histopathological Images for Breast Tumor Classification in the Internet of Medical Things.

Breast tumor detection and classification on the Internet of Medical Things (IoMT) can be automated with the potential of Artificial Intelligence (AI). Deep learning models rely on large datasets, however, challenges arise when dealing with sensitive medical data. Restrictions on sharing these medical data result in limited publicly available datasets thereby impacting the performance of the deep learning models.

DEBCM: Deep Learning-Based Enhanced Breast Invasive Ductal Carcinoma Classification Model in IoMT Healthcare Systems.

Accurate breast cancer (BC) diagnosis is a difficult task that is critical for the proper treatment of BC in IoMT (Internet of Medical Things) healthcare systems. This paper proposes a convolutional neural network (CNN)-based diagnosis method for detecting early-stage breast cancer. In developing the proposed method, we incorporated the CNN model for the invasive ductal carcinoma (IDC) classification using breast histology image data.

Predictive factors of postendoscopic retrograde cholangiopancreatography pancreatitis for biliary complications in living-donor liver transplantation recipients.

Objectives: Endoscopic retrograde cholangiopancreatography (ERCP) has shown great safety and efficacy in the management of post-living-donor liver transplantation (LDLT) biliary complications. Pancreatitis is the most commonest and the most feared complication after ERCP.

Methods: We reviewed the data of liver transplant recipients who underwent ERCP for biliary complications after LDLT between 2011 and 2022.

Ensemble Learning-Based Hybrid Segmentation of Mammographic Images for Breast Cancer Risk Prediction Using Fuzzy C-Means and CNN Model.

The research interest in this field is that females are not aware of their health conditions until they develop tumour, especially when breast cancer is concerned. The breast cancer risk factors include genetics, heredity, and sedentary lifestyle. The prime concern for the mortality rate among females is breast cancer, and breast cancer is on the rise, both in rural and urban India.

The Effect of Topical Agents on the Tensile Strength of Absorbable and Non-absorbable Suture Materials-An in Vitro Study.

Introduction: Topical agents are sometimes applied to surgical wounds after closure; these may include antiseptics or antibiotics. Minimal research has been undertaken to investigate the effect of topical regimens on the tensile strength of suture materials.

Aim: To investigate the effect of four commonly used wound care regimens on the tensile strength of suture materials.

Evaluation of the Synergistic Potential of Simultaneous Pan- or Isoform-Specific BET and SYK Inhibition in B-Cell Lymphoma: An In Vitro Approach.

Both bromodomain and extra-terminal domain (BET) proteins and spleen tyrosine kinase (SYK) represent promising targets in diffuse large B-cell (DLBCL) and Burkitt's lymphoma (BL). We evaluated the anti-lymphoma activity of the isoform-specific bivalent BET inhibitor AZD5153 (AZD) and the pan-BET inhibitor I-BET151 (I-BET) as single agents and in combination with SYK inhibitor Entospletinib (Ento) in vitro. The effect of the single agents on cell proliferation and metabolic activity was evaluated in two DLBCL and two BL cell lines.

Correction to: Exploring the impact of air pollution on COVID-19 admitted cases: Evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait.

[This corrects the article DOI: 10.1007/s42081-022-00165-z.].

Internet of Medical Things (IoMT)-Based Smart Healthcare System: Trends and Progress.

Internet of Medical Thing (IoMT) is the most emerging era of the Internet of Thing (IoT), which is exponentially gaining researchers' attention with every passing day because of its wide applicability in Smart Healthcare systems (SHS). Because of the current pandemic situation, it is highly risky for an individual to visit the doctor for every small problem. Hence, using IoMT devices, we can easily monitor our day-to-day health records, and thereby initial precautions can be taken on our own.

Exploring the impact of air pollution on COVID-19 admitted cases: Evidence from vector error correction model (VECM) approach in explaining the relationship between air pollutants towards COVID-19 cases in Kuwait.

In urban areas, air pollution is one of the most serious global environmental issues. Using time-series approaches, this study looked into the validity of the relationship between air pollution and COVID-19 hospitalization. This time series research was carried out in the state of Kuwait; stationarity test, cointegration test, Granger causality and stability test, and test on multivariate time-series using the Vector Error Correction Model (VECM) technique.

Early and intermediate term outcomes after laparoscopic one-anastomosis gastric bypass for morbidly obese patients: a single center experience.

Objectives: To evaluate our outcomes of laparoscopic one-anastomosis gastric bypass (LOAGB) as a primary weight loss procedure. We evaluated the impact of biliary reflux by combination of upper endoscopy (UGIE), ambulatory pH metry, and ambulatory biliary reflux monitoring.

Material And Methods: We reviewed the data of patients who underwent LOAGB during the period between July 2015 till August 2018.

AI-based production and application of English multimode online reading using multi-criteria decision support system.

Reading and writing English have greater significance in learning oral English and comprehensive skills. Artificial Intelligence (AI) is important in many aspects of our lives, including education, healthcare, business, and so on. AI has allowed for significant advancements in the educational system.

The Relation Between Host TLR9 -1486T/C, rs187084 Gene Polymorphisms and , , , and Virulence Genes among Gastric Cancer Patients.

To identify the associations between different genotypes of TLR9 -1486T/C (rs187084) with gastric cancer patients and reveal their relation to virulence genes (, , and ). Patients with gastric cancer were recruited to our study, diagnosed both endoscopically and histopathologically. were isolated from gastric samples by culture and PCR amplification of the gene.

IoT-Based Hybrid Ensemble Machine Learning Model for Efficient Diabetes Mellitus Prediction.

Nowadays, there is a growing need for Internet of Things (IoT)-based mobile healthcare applications that help to predict diseases. In recent years, several people have been diagnosed with diabetes, and according to World Health Organization (WHO), diabetes affects 346 million individuals worldwide. Therefore, we propose a noninvasive self-care system based on the IoT and machine learning (ML) that analyses blood sugar and other key indicators to predict diabetes early.

Detecting and Extracting Brain Hemorrhages from CT Images Using Generative Convolutional Imaging Scheme.

Purpose: The need for computerized medical assistance for accurate detection of brain hemorrhage from Computer Tomography (CT) images is more mandatory than conventional clinical tests. Recent technologies and advanced computerized algorithms follow Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to improve medical diagnosis platforms. This technology is making the diagnosis practice of brain issues easier for medical practitioners to analyze and identify diseases with an assured degree of precision and performance.

Integrated IoT-Based Secure and Efficient Key Management Framework Using Hashgraphs for Autonomous Vehicles to Ensure Road Safety.

Autonomous vehicles offer various advantages to both vehicle owners and automobile companies. However, despite the advantages, there are various risks associated with these vehicles. These vehicles interact with each other by forming a vehicular network, also known as VANET, in a centralized manner.

A Novel of New 7D Hyperchaotic System with Self-Excited Attractors and Its Hybrid Synchronization.

In this study, a novel 7D hyperchaotic model is constructed from the 6D Lorenz model via the nonlinear feedback control technique. The proposed model has an only unstable origin point. Thus, it is categorized as a model with self-excited attractors.

Diagnostic Approach for Accurate Diagnosis of COVID-19 Employing Deep Learning and Transfer Learning Techniques through Chest X-ray Images Clinical Data in E-Healthcare.

COVID-19 is a transferable disease that is also a leading cause of death for a large number of people worldwide. This disease, caused by SARS-CoV-2, spreads very rapidly and quickly affects the respiratory system of the human being. Therefore, it is necessary to diagnosis this disease at the early stage for proper treatment, recovery, and controlling the spread.