Multiple sclerosis (MS) is a chronic neurological disorder commonly diagnosed through magnetic resonance imaging (MRI). Manual interpretation of MRI scans is time‐consuming and prone to observer variability. This study explores the use of deep learning (DL) models to automate MS lesion detection in MRI images. Four convolutional neural network (CNN) architectures—AlexNet, VGG16 pretrained on ImageNet, ResNet‐10, and DenseNet‐121—were utilized with slight modifications to optimize their performance for medical imaging tasks. The dataset, comprising 2831 FLAIR and T2 MRI slices from 60 patients, was extensively preprocessed, including normalization, resizing, and augmentation. The models were trained using the Adam optimizer and binary cross‐entropy loss function and evaluated on metrics such as accuracy, precision, recall, and F1‐score. VGG16 demonstrated the highest performance, with an area under the curve (AUC) of 0.94, followed by DenseNet‐121 with an AUC of 0.91, and both AlexNet‐C and ResNet‐10 with an AUC of 0.90. VGG16 also achieved the highest precision (0.81) and recall (0.93), resulting in the top F1‐score (0.86). ResNet‐10, however, showed the best balance between efficiency and performance, making it highly suitable for deployment in resource‐constrained environments, while DenseNet‐121 and AlexNet‐C were resource‐inefficient given their comparable performance to ResNet‐10. These findings underscore the potential of DL models to automate MS lesion detection, offering reliable, efficient, and scalable solutions for clinical diagnostics.

ObjectiveIntegrons facilitate the acquisition and expression of antibiotic resistance genes, with Classes 1 and 2 commonly found in clinical E. coli. The AcrAB–TolC efflux pump is a major RND system that expels diverse antimicrobial agents and contributes to resistance. This study is aimed at determining the prevalence of Class 1/2 integrons and the AcrAB–TolC efflux pump in UPEC isolates from UTI patients in Babol, Iran.MethodsThis cross‐sectional study analyzed 155 UPEC isolates collected from UTI patients in Babol, northern Iran (2021–2023). Isolates were identified using standard biochemical and genotypic methods. Antimicrobial susceptibility was assessed by disk diffusion. Polymerase chain reaction (PCR) was used to detect the presence of integron and efflux pump genes. Statistical analysis was performed using SPSS (Version 16), with significance defined at p value < 0.05.ResultsFindings revealed that 85.2% of the isolates carried Class 1 integrons, whereas 23.2% contained Class 2 integrons. Integron‐positive isolates presented an increased level of antibiotic resistance, particularly to trimethoprim–sulfamethoxazole (75% and 88.9%) and nalidixic acid (90.9% and 91.7%). Additionally, over 88% of the isolates had AcrAB–TolC efflux pump genes. MDR rates were 75% among intI1‐positive isolates and 83.3% among intI2‐positive isolates.ConclusionIn conclusion, integrons contribute to the spread of antibiotic resistance among UPEC isolates, whereas the AcrAB–TolC efflux pump contributes to the ability of bacteria to expel toxic substances. There is a need for effective infection management policies and proper application of antibiotics to limit the dissemination of resistance genes.

BackgroundGoiter is the most prevalent endocrine illness, and it is considered endemic when > 10% of a community has it. The patient may not notice the nodules, which typically form early in endemic goiter and later in sporadic goiter.ObjectivesThe objective of this study is to determine the incidence, histopathological subtypes, and risk of thyroid malignancy in patients with goiter, and to evaluate the diagnostic performance of ACR TI‐RADS‐guided fine‐needle aspiration cytology (US‐FNAC) using the Bethesda System for Reporting Thyroid Cytopathology 2023.Patients and MethodsThis prospective, cross‐sectional descriptive study recruited patients with goiter in the Rizgary Teaching Hospital, Erbil, Iraq, from January 2020 to December 2024. Clinical and sonographic examinations were performed to confirm the diagnosis. To reduce the possibility of malignancy in goiter, a preoperative UG‐FNAB was performed. Patients underwent surgery; biopsies were analyzed, and histopathological analysis of surgically removed thyroid tissue was performed to determine the goiter type, Bethesda category, and malignancy status.ResultsAmong 128 diagnosed patients with goiter, 76 (59.4%) were diagnosed with BFN, in which most of them were females (n = 63, 82.9%), with a median age of 36 years. UG‐FNAB indicated colloid goiter in 28 (36.74%) cases, follicular neoplasm/suggestive malignancy in three (3.94%) cases, malignant in seven (9.2%) cases, and inconclusive in 35 (46.1%) cases. Based on the Bethesda Classification (2023), FNAC results indicated that Bethesda Category I (nondiagnostic or unsatisfactory) was more commonly detected (n = 35, 46.1%), followed by Category II (benign) (n = 28, 36.8%), whereas Category III (atypia of undetermined significance) was not detected (n = 0, 0%). Categories IV (follicular neoplasm or suspicious follicular neoplasm) and V (suspicious for malignancy) were found in three cases each (3.94% each), and Category VI (malignant) in seven cases (9.2%). The histopathological analysis found 15 (19.7%) individuals had a TC, including papillary carcinomas (n = 8, 53.3%), follicular carcinomas (n = 4, 26.6%), Hurthle cell carcinomas (n = 2, 13.3%), and anaplastic carcinoma (n = 1, 6.6%). Additionally, patients with BFN were 3.35 times more likely to develop thyroid cancer than those with a solitary thyroid nodule.ConclusionsThe incidence of goiter is slightly high among patients in this locality, especially median‐aged females. However, the incidence of TC is low when successfully treated with total thyroidectomy.

Hypertension, widely recognized as the “silent killer,” remains a leading cause of cardiovascular, renal, and neurological complications worldwide. This study proposes a dual‐scenario hybrid machine learning framework for hypertension risk prediction using noninvasive body composition features, aimed at enhancing both interpretability and predictive reliability. In Scenario 1, an unsupervised clustering analysis inspired by self‐labeling principles was performed exclusively on hypertensive individuals, where five physiological subgroups were identified via K‐Means clustering and validated using Silhouette (0.3371), Davies–Bouldin (1.0094), and Calinski–Harabasz (720.10) indices. Significant intercluster variability (p < 0.001) was observed across key indicators such as FATP, RLFATP, LLFATP, FATM, and age. Among the tested models, the support vector machine (SVM) with random oversampling achieved the best performance (accuracy = 99.08%, F1 = 98.04%, AUC = 99.98%), confirming effective subgroup discrimination. In Scenario 2, a comprehensive binary classification between healthy and hypertensive subjects was conducted using five models—ExtraTrees, KNN, SVM, Gaussian Naive Bayes, and Decision Tree—across multiple configurations. The cluster‐augmented dataset yielded the best results, with the ExtraTrees classifier achieving superior performance (accuracy = 98.23%, recall = 98.30%, precision = 98.17%, F1 = 98.23%, AUC = 99.87%). Clustering and feature selection both improved generalization, particularly for ensemble‐based learners. Overall, Scenario 2 demonstrated the highest predictive accuracy and stability, whereas Scenario 1 provided valuable interpretability through subgroup discovery. These findings highlight that integrating unsupervised clustering with supervised classification offers a robust and explainable framework for personalized hypertension risk prediction, contributing to early detection and precision healthcare.

ObjectiveTo compare the surgical feasibility and safety of three approaches (anterior, transgluteal, posterior) for constructing a porcine model of irreparable acetabular labral tear.MethodsAfter a cadaveric anatomical exploration in two pigs to define feasible corridors, 36 male Chinese miniature pigs were randomized (1:1:1) to anterior, transgluteal, or posterior approaches for model construction and labral reconstruction. Primary intraoperative outcomes were blood loss, operative time, and a 0–10 surgical exposure score; postoperative complications and survival were assessed over 3 months.ResultsThe anterior approach showed greater blood loss and longer operative time than posterior and transgluteal (both p < 0.001), while exposure was worst with anterior (p < 0.001) and only trended better with transgluteal versus posterior (p = 0.056). Postoperative events included sciatic nerve‐related claudication (4/12 posterior), acetabular chondral lesions (3/12 anterior), and poor wound healing (4/12 anterior; 2/12 posterior). One‐month survival was lower in anterior versus transgluteal (p = 0.025), with most deaths within 2 weeks; 2‐ and 3‐month survival were also lower for anterior versus transgluteal (p = 0.026; p = 0.011). At 2 months, survival was lower in anterior versus posterior (p = 0.011).ConclusionThe transgluteal approach appears to be a relatively safe and effective option for constructing porcine hip models; nevertheless, conclusions should be interpreted with caution given the undetermined mortality etiology in the anterior group.

The risk for Candida infections in patients with diabetes mellitus (DM) is three times higher than in the general population. Despite a DM prevalence of 15.8% reported in Libya (2024), the laboratory identification and susceptibility testing of fungal infections in patients with DM are not routinely performed. This cross‐sectional study investigated the prevalence and antifungal drug resistance patterns of Candida species in the oral mucosa of Libyan patients with Type 2 DM. Oral samples were collected with a sterile cotton swab, and 182 Candida isolates were phenotypically identified using the API ID 32 C and VITEK 2 Compact systems. Isolates were screened for their susceptibility to fluconazole and five other antifungals using disk diffusion and VITEK AST‐YS07 cards. Statistically significant associations were found between Candida carriage and clinical presentation (p = 0.032), denture wearing (p = 0.025) and sex (p = 0.012). Although Candida albicans was the predominant species isolated (37.4%), the majority of isolates comprised non‐albicans Candida (NAC). Candida humicola and Candida dubliniensis coexisted with other Candida species. Most Candida species showed susceptibility or dose‐dependent susceptibility (DDS) to fluconazole with low resistance to the other antifungal drugs. Candida glabrata, Candida guilliermondii and Candida krusei were resistant to fluconazole, and multidrug resistance was observed in some C. albicans, C. dubliniensis and C. krusei isolates. Candida membranifaciens and Candida parapsilosis showed either DDS or resistance to fluconazole. The emerging resistance to second‐line antifungals requires the establishment of routine Candida identification and antifungal susceptibility testing to guide species‐specific treatment.

BackgroundComorbidity in patients with hypertension presents a significant challenge to effective disease management, leading to worsened health outcomes due to increased complications, higher healthcare costs, and elevated mortality rates. In resource‐limited settings like Ethiopia, the prevalence of comorbid disorders among patients with hypertension remains poorly understood. This lack of understanding is particularly alarming as noncommunicable diseases are on the rise, and the health systems in these areas are often inadequate. While different studies have assessed the prevalence and risk factors of hypertension, there is limited knowledge regarding the prevalence of comorbidity and related factors among patients with hypertension, particularly in Southern Ethiopia. To effectively develop integrated methods for the prevention, early diagnosis, and management of hypertension, a thorough understanding of the associated comorbidities and related factors is essential. Therefore, this study is aimed at determining the prevalence of comorbidity and its associated factors among patients with hypertension in healthcare facilities of Southern Ethiopia.MethodsAn institution‐based cross‐sectional study was conducted among patients with hypertension aged ≥ 18 years attending follow‐up at Dilla University Teaching Hospital from November 30, 2022, to January 30, 2023. A systematic random sampling technique was used to recruit 190 hypertensive patients from a hypertension registration logbook, collected using a pretested, structured‐administered questionnaire and supplemented by a medical record review. The data collection tool was translated into the Amharic language and back‐translated to ensure consistency. The data was analyzed using STATA Version 14. To characterize the data, descriptive statistics were applied. To determine the factors that are associated with comorbidity among patients with hypertension, a logistic regression model was fitted. Model fitness was checked using the Hosmer–Lemeshow goodness‐of‐fit test (p‐value = 0.633), indicating an adequate model fit. In the end, an adjusted odds ratio (AOR) with a 95% confidence interval (CI) was estimated and interpreted, and a p‐value of less than 0.05 was used to declare statistical significance.ResultThe prevalence of comorbidity among patients with hypertension was 56.8% (95% CI: 49.7%–63.7%). Several factors were found to be significantly associated with comorbidity. Patients aged 55 years and above were over four times more likely to have comorbid conditions (AOR = 4.42, 95% CI: 2.65–7.68), while those residing in urban areas had nearly seven times higher odds of comorbidity (AOR = 6.67, 95% CI: 2.73–8.72). Likewise, alcohol consumption (AOR = 3.50, 95% CI: 2.94–11.31), cigarette smoking (AOR = 4.87, 95% CI: 1.23–6.53), and having a body mass index (BMI) ≥ 25 kg/m2 (AOR = 4.55, 95% CI: 2.78–10.91) were independently associated with a higher likelihood of comorbidity. Conversely, being physically active was inversely associated with comorbidity, reducing the odds by nearly half (AOR = 0.52, 95% CI: 0.24–0.89).ConclusionMore than half of the patients with hypertension had at least one comorbid condition. Sociodemographic, behavioral, and clinical factors were significantly associated with these comorbidities. Based on our findings, we recommend routine screening for comorbid conditions, the implementation of educational programs to increase patient awareness about the risks of alcohol consumption and smoking, the promotion of healthy lifestyle practices, and improved access to healthcare services to ensure early detection and comprehensive management of hypertension and its related comorbidities.

The emergence of multidrug-resistant (MDR) in various microorganisms due to prolonged antibiotic treatment poses a growing worldwide health issue. It is essential to identify alternative, effective methods to address MDR bacterial diseases, as treating infections caused by these pathogens can be challenging. Bacteriophages, also known as phages, are viruses that target and destroy bacteria. They are being investigated and utilized as alternatives to existing therapies because of their effectiveness and specificity. Phages are often considered safe substitutes for antibiotics because they naturally occur in the environment. Phage replacement therapy involves complex biological processes driven by interactions between phages and bacteria. An integrative approach is essential for a comprehensive analysis of these processes and for understanding the relationships between different biomolecules and their functions. This includes combining data from various omics, such as transcriptomics, proteomics, and metabolomics. High-throughput technology has revolutionised research, enabling the development of numerous tools and methods for integrating and interpreting multiomics data from diverse samples. This paper presents an overview of omics technologies and highlights strategies for integrating them across layers. By comparing data from multiomics and single-omics studies, a deeper understanding of the information flow underlying phage therapy will be gained.

Malaria remains a major public health concern in Cameroon. Although Plasmodium falciparum is the predominant parasite, the prevalence of other Plasmodium species remains poorly documented due to the absence of a formal surveillance system. This study is aimed at assessing the prevalence of Plasmodium species through routine malaria surveillance using P.f/Pan rapid diagnostic tests (RDTs). A nationwide malaria surveillance program was conducted over 1 year (January-December 2024), covering health facilities across all regions and levels of care. Malaria cases were diagnosed using P.f/Pan RDTs, which detect P. falciparum and non-falciparum infections but cannot reliably differentiate individual non-falciparum species. Data were systematically compiled through the National Health Information System and analysed to determine the prevalence and distribution of Plasmodium species across regions. Of the 3,843,507 individuals tested, 43% were malaria-positive. P. falciparum monoinfection accounted for 82% of cases, mixed infections (P. falciparum + Pan) for 15%, and Pan infection for 3%. Regional analysis showed the highest prevalence of P. falciparum monoinfection in the Far North region (92.1%), followed by the North (85.7%), Central (82.5%), West (81.7%), and East (80.6%) regions. Seasonal variations revealed a peak in positive cases between June and July, whereas mixed and Pan infections remained consistently low. Findings confirm the presence of non-falciparum and mixed infections beyond P. falciparum. However, P.f/Pan RDTs have inherent limitations in species differentiation, underscoring the need for molecular techniques to improve species-specific identification. Strengthening malaria genomic surveillance, including monitoring of Plasmodium species diversity and HRP2/3 gene deletions that affect RDT performance, will be critical to enhance routine surveillance and guide effective malaria control strategies in Cameroon.