King Abdullah International Medical Research Center

There is a great need to categorize cancer types for early cancer detection and treatment. RNA-Seq data is essential for getting insight into the differentially expressed genes. Due to its high dimensionality and complexity, performing an analysis on RNA-Seq data is quite challenging. In the past, RNA-Seq data were analyzed for a single cancer type as a two-class problem (either positive or negative) and did not contain information from other classes of cancer types. To classify different cancer types and discover the most promising genes, RNA-Seq data for different types of cancer should be examined. Multiple repositories offer RNA-Seq-based cancer types data. The present study incorporates a dataset from the Mendeley repository for classification. RNA-Seq values are then converted to their respective 2D images using some transformations. The classification problem is handled by five Transfer Learning (TL) algorithms (VGG16, VGG19, Resnet50, Resnet101, and Resnet152). Four different splitting strategies are applied for each classifier presented in the results and discussion section. A comparative analysis is also carried out with and without data augmentation. Results show that classifiers perform best at a split of 70-30. VGG16 attained the best position on overall results by achieving an accuracy of 95%. Hence, VGG16 is the leading TL algorithm for classification among all the accessible models and is not difficult to execute and easy to comprehend.

Chronic diseases are the leading causes of death and disability, and their care consumes a large proportion of healthcare expenditures. Early detection and appropriate management of chronic diseases can significantly mitigate their health, societal, and economic consequences. Monitoring the national prevalence of chronic diseases and their risk factors is essential to inform health policy and guide public health interventions. The Saudi national Health INdicators survEy (SHINE) aimed to assess the prevalence of selected non-communicable diseases (NCDs) and their risk factors among adults in Saudi Arabia. The SHINE was a national telephone-based survey conducted in 2023 by the Saudi Public Health Authority. Participants aged 18 years or older were randomly selected from a national sampling frame of cell phone numbers, encompassing all administrative regions of Saudi Arabia. Data were collected on the prevalence of NCDs, including hypertension, diabetes, heart disease, renal disease, and asthma, as well as risk factors such as obesity and tobacco use. Multivariable regression analyses were conducted to assess associations between risk factors and NCDs. The total sample size was 2,650 participants. Hypertension and diabetes were the most commonly reported chronic conditions, with a national prevalence of 16.1% and 13.0%, respectively. Approximately 10% of the population reported having been diagnosed with two or more chronic conditions. Obesity was reported by 32.8% of the population, and tobacco use was reported by 24.9%. Body mass index was the strongest independent predictor of self-reported diagnosed diabetes, hypertension, asthma, and multimorbidity after adjustment for sociodemographic characteristics. NCDs impose a significant health burden on the Saudi population, particularly among aging Saudi nationals. Continuous monitoring of the prevalence of chronic diseases and their risk factors is vital to inform and optimize national prevention efforts.

[This corrects the article DOI: 10.3389/fmed.2026.1763735.].

Though invasive bacterial infections (IBIs) are rare, they can lead to significant morbidity or mortality in young infants. Internationally, the etiology of IBIs in young infants has been described with some studies showing a decreasing incidence of IBIs due to Group B streptococcus (GBS). The aim of this study was to describe the etiology of IBIs in a Saudi population, and to determine if there is antibiotic mismatch with commonly used empiric antibiotics. A retrospective cross-sectional study was done on all infants 90 days and under who presented to the emergency department at a tertiary care children’s hospital over a 7-year period and were diagnosed with an IBI (bacteremia and/or bacterial meningitis). A total of 46 young infants were diagnosed with community-acquired IBIs. Twenty-eight (60.9%) had bacteremia and 18 (39.1%) had bacterial meningitis. Overall, gram positive organisms were the causative pathogen in 30 (65.2%) infants; GBS was the most common cause accounting for 15 cases followed by Staphylococcus aureus (4). Amongst the 16 gram negative organisms, the most common isolated pathogens were Escherichia coli (5) and Klebsiella pneumoniae (4); 3 harbored extended-spectrum beta-lactamases (ESBLs). Nine patients (19.6%) had a long-term term morbidity from their infection, while 5 (10.9%) died. With common empiric antibiotic regimens, coverage would be provided for 75–80% of the pathogens. Community-acquired IBIs are in general rare in young infants, with GBS continuing to be the most common pathogenic organism in our population. Empiric antibiotics of choice in this age group may need to be adjusted especially if there is suspicion of a S. aureus infection or increasing isolates of ESBLs. Further multi-center and prospective studies are needed to assess if the etiology of IBIs is changing in our population and to guide empiric antibiotic coverage. Not applicable.

Cuproptosis, a copper-dependent regulated cell death pathway formally characterized in 2022, has rapidly attracted scientific interest in cancer metabolism and mitochondrial biology. The accelerated growth of related publications warrants systematic bibliometric assessment to clarify research focus, thematic evolution, and early translational relevance within this emerging field. This study mapped the global landscape of cuproptosis research using bibliometric methods, with emphasis on publication trends, thematic structure, collaboration patterns, and citation networks. A total of 1469 Scopus-indexed original research articles published between 2022 and 2025 were analyzed using Bibliometrix for performance indicators, Bradford's and Lotka's laws, and thematic mapping; VOSviewer for keyword co-occurrence and collaboration networks; and CiteSpace for reference co-citation analysis. Results demonstrated rapid publication growth characteristic of an early-stage scientific field, with 33.4% of articles concentrated in 13 core journals. Research output was highly geographically concentrated, with Chinese institutions contributing 89.8% of publications, while international collaboration remained limited (14.7%). Keyword and co-citation analyses revealed a thematic shift from foundational mechanistic studies toward prognostic modeling, copper ionophore exploration (e.g., elesclomol and disulfiram), immune-related analyses, and bioinformatics-driven patient stratification. Ferredoxin 1 (FDX1) emerged as a central regulatory node in co-citation networks, reflecting its role in defining the molecular framework of cuproptosis rather than validated drug development. Overall, cuproptosis research remains largely mechanistic and preclinical, with only early indications of translational relevance. The strong geographic concentration of publications and limited international collaboration highlight structural imbalances in knowledge production and the need for broader global validation. This bibliometric mapping clarifies the field's early developmental stage and identifies priorities for future research, including cross-regional collaboration, experimental standardization, and cautious advancement toward clinical application.

Background: Diabetics with coronavirus disease 2019 (COVID-19) manifest more adverse clinical outcomes with elevated rates of death. It has been suggested that the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pathway of entrance into the host cell might be assisted by dipeptidyl peptidase-4 (DPP4), leading to inflammation and cytokine storm, with replication into the airways and unfavorable effects in the lungs. Consequently, the goal of this systematic review is to investigate the most recent data on the effect of DPP-4i (dipeptidyl peptidase-4 inhibitor) medications on clinical outcomes, mainly mortality among COVID-19 patients. Methods: By conducting a systematic search using PubMed and the Cochrane library, observational studies were identified to examine the association between DPP-4i medications and clinical outcomes including mortality, intensive care unit and hospital admissions. The methodologies of included studies were assessed utilizing the Newcastle-Ottawa Scale (NOS). Results: A total of nineteen studies were included with sample sizes varying from over 100 patients to 2.8 million and variant follow-up durations from 30 days up to discharge or death. Most of the population across the studies had COVID-19 for the first time, and the majority were hospitalized. Similarly, mortality definition varied among studies with different time points consisting of 30-day mortality, in-hospital mortality, or all-cause mortality. The majority of the studies identified no effect on mortality by DPP-4i, while a considerable proportion revealed beneficial effects; only four studies showed increased mortality. Conclusions: Real-world data from this review suggested a safe use of DPP-4i among COVID-19 patients; however, randomized clinical trials are required to confirm the beneficial outcomes and safe use.

Cancer continues to be a leading cause of morbidity and mortality globally, which necessitates the development of more effective and less toxic therapies. Quinoxaline derivatives, a class of heterocyclic compounds with versatile chemical structures, have emerged as promising candidates for anticancer drug development. These derivatives exhibit a range of biological activities and have shown significant efficacy against various tumors. However, the therapeutic potential of quinoxaline derivatives, particularly in clinical applications, remains underexplored, highlighting the need for further research to optimize their use as anticancer agents. This systematic review aims to provide an updated analysis of quinoxaline derivatives' anticancer potential, focusing on their mechanisms of action and therapeutic applications in oncology. Following PRISMA guidelines, a thorough search was conducted in MEDLINE/PubMed and Science Direct for studies from 2020 to 2024. Relevant keywords were used to identify studies on quinoxaline derivatives and their anticancer properties, resulting in the selection of 77 studies based on pharmacological activity and mechanisms. The review included 77 studies exploring various anticancer activities of quinoxaline derivatives. Key findings indicate that these compounds effectively inhibit cancer cell growth through multiple mechanisms, including the inhibition of topoisomerases, kinases, and other critical proteins involved in cancer progression. Additionally, they demonstrate strong antiproliferative and pro-apoptotic effects across several cancer cell lines, highlighting their broad therapeutic potential. Quinoxaline derivatives represent a promising class of compounds in anticancer therapy due to their diverse biological activities and multiple molecular targets. The development of these derivatives as targeted therapies holds the potential for more effective cancer treatments.

Tonsillectomy is one of the most commonly performed surgical procedures on children. Indications for tonsillectomy include recurrent tonsillitis, obstructive sleep apnea, and peritonsillar abscess, among others. Bipolar diathermy and coblation surgeries are the most common used surgeries. Bipolar Diathermy efficiently manages postoperative hemorrhage but often results in increased postoperative pain due to higher thermal tissue destruction. In contrast, coblation is a procedure that offers a lower output of heat and results in lesser pain and shorter healing times. The present study compares the above two procedures to evaluate the postoperative outcomes regarding pain, healing, and safety of pediatric tonsillectomy patients. This was a prospective, randomized, experimental study on 120 pediatric patients ranging from 5 to 14 years who experienced recurrent or chronic tonsillitis or obstructive symptoms, amongst them sleep apnea. Both groups were operated on with extracapsular tonsillectomy under general anaesthesia, with standardized preoperative management and uniform anaesthetic protocols. The aforementioned parameters of pain, hemorrhage, and healing process were similarly followed up for postoperative evaluations at one, two, three, and 4 weeks. Mean scores of pain were very highly significantly lower in patients of Group A at visits than in patients of Group B. Patients of Group A received scores for visit for the first week at 9.45 ± 0.58 versus 9.82 ± 0.22 (p = 0.001) and scores for visit in the fourth week at 0.35 ± 0.40 versus 1.92 ± 1.78 (p = 0.001). Group A compared with group B had early healing time and fewer cases of bleeding complications. Time to pain resolution was shorter in Group A with a mean of 15.5 ± 2.0 days compared with a mean of 24.8 ± 3.4 days in Group B, (p = 0.001). The study results show that in pediatric patients, coblation-assisted tonsillectomy proved to have advantageous benefits over bipolar diathermy tonsillectomy. Coblation provided less postoperative pain and quick healing, with an overall shorter pain duration and fewer bleeding complications.

BackgroundDiagnosing oral lesions from benign conditions to oral cancer remains challenging due to overlapping visual features and reliance on histopathology. Large language models (LLMs) can integrate textual and visual cues, but their diagnostic accuracy and clinical utility in real decision-making contexts remain uncertain. To systematically evaluate the diagnostic performance, clinical usefulness, and limitations of LLMs in identifying oral lesions.MethodsPubMed, CINAHL, Embase, Web of Science, and Google Scholar were searched to 20 July 2025. Eligible studies applied LLMs (e.g., ChatGPT, Gemini, DeepSeek, Copilot, Claude) for diagnosis or differential diagnosis of oral lesions using text, images, or multimodal inputs. Outcomes included diagnostic accuracy, agreement metrics, and qualitative assessments of explanation quality and clinical applicability. Risk of bias was assessed using an adapted QUADAS-2. Narrative synthesis was performed due to heterogeneity.ResultsSeventeen studies (>1,200 cases) were included. Diagnostic accuracy ranged from 25%–96%, varying by model version, input modality, and lesion complexity. Multimodal inputs consistently improved performance, with Cohen's κ up to 0.85–0.90. Advanced models (GPT-4o, DeepSeek-R1, o1-preview) outperformed earlier versions and approached expert performance in some tasks, although specialists generally retained superior Top-1 accuracy. Clinical utility was highest when LLMs were used to structure differential reasoning, highlight red-flag features, and support communication, but limited in tasks requiring fine morphological interpretation or severity grading. Overall risk of bias was low to moderate.ConclusionsLLMs demonstrate variable diagnostic performance and context-dependent supportive utility as adjunctive tools in oral lesion assessment, particularly in multimodal settings. They should complement, rather than replace, expert clinical judgment. Future research should prioritize real-world workflow evaluation, standardized prompting strategies, and prospective clinical validation.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/view/CRD420251090315, identifier CRD420251090315.