Infectious Disorders - Drug Targets - Volume 25, Issue 8, 2025

Mycobacterium fortuitum is a rapidly growing human pathogenic bacterium that has been linked to a number of clinical conditions. Its ability to quickly develop intricate biofilms makes its treatment challenging. Development of drug resistance has been reported in cases of M. fortuitum, further reducing treatment options available against the pathogen.

In order to identify the proteins involved in biofilm development, this work attempted to analyze the real-time proteome data of M. fortuitum using machine learning strategies. The aim of the study was to provide novel drug targets that may be used to treat patients more quickly and efficiently.

The proteomic data was analyzed using the Support Vector Machine (SVM), Artificial Neural Network (ANN), and k-nearest Neighbors (kNN) techniques. Proteins linked to biofilm formation, which were over-expressed and under-expressed, were used in the training set of the models. The trained models were then evaluated using abundant proteins found in M. fortuitum proteome analysis. The pre-processing and optimization were done to improve the performance of the models.

The kNN algorithm achieved the highest accuracy level of 82.98%, followed by SVM at 82.75% and ANN at 78%. Using other machine learning methods, including Random Forest, Naive Bayes, and Logistic Regression, the performance of these models was further verified. The outcomes demonstrated for the prediction of proteins, kNN consistently produced the best accuracy.

The study shows that machine learning techniques, in particular kNN, can be used for successful analyses of proteome data obtained from M. fortuitum in order to identify proteins associated with the formation of biofilms. This methodology may be used for the prediction of drug targets using a proteome database. Identification of drug targets can help in designing better treatment strategies against the pathogen.

This research aims to develop an advanced deep-learning framework for detecting respiratory diseases, including COVID-19, pneumonia, and tuberculosis (TB), using chest X-ray scans.

A Deep Neural Network (DNN)-based system was developed to analyze medical images and extract key features from chest X-rays. The system leverages various DNN learning algorithms to study X-ray scan color, curve, and edge-based features. The Adam optimizer is employed to minimize error rates and enhance model training.

A dataset of 1800 chest X-ray images, consisting of COVID-19, pneumonia, TB, and typical cases, was evaluated across multiple DNN models. The highest accuracy was achieved using the VGG19 model. The proposed system demonstrated an accuracy of 94.72%, with a sensitivity of 92.73%, a specificity of 96.68%, and an F1-score of 94.66%. The error rate was 5.28% when trained with 80% of the dataset and tested on 20%. The VGG19 model showed significant accuracy improvements of 32.69%, 36.65%, 42.16%, and 8.1% over AlexNet, GoogleNet, InceptionV3, and VGG16, respectively. The prediction time was also remarkably low, ranging between 3 and 5 seconds.

The proposed deep learning model efficiently detects respiratory diseases, including COVID-19, pneumonia, and TB, within seconds. The method ensures high reliability and efficiency by optimizing feature extraction and maintaining system complexity, making it a valuable tool for clinicians in rapid disease diagnosis.

Vulvar cancer, a rare malignancy of the female genital tract, accounts for approximately 4% of all gynaecological cancers. Among vulvar malignancies, Squamous Cell Carcinoma (SCC) constitutes about 90% of the cases, frequently arising from precursor lesions, such as Vulvar Intraepithelial Neoplasia (VIN). This case report describes an unusual presentation of both premalignant and malignant vulvar lesions in a postmenopausal, post-hysterectomized woman, highlighting diffuse p16 positivity on immunohistochemistry. This finding underscores the potential role of Human Papillomavirus (HPV) in the pathogenesis of vulvar SCC.

A 73-year-old multiparous, post-menopausal woman presented with a five-month history of vulvar growth accompanied by intense vulval itching and vaginal discharge. Initially referred by the dermatology department as a case of condyloma acuminatum for gynaecological evaluation, her local examination revealed three distinct lesions on the vulva: an exophytic, cauliflower-like warty lesion on the left labia majora; a blackish, pigmented maculopapular lesion on the right labia majora; and a friable, warty lesion over the clitoris extending beneath the clitoral hood. A wide local excision was performed, and histopathological examination of the left and right labial growths indicated VIN Grade 3. The biopsy from the clitoral lesion revealed features of SCC. Immunohistochemical analysis demonstrated diffuse p16 positivity in the tumor cells of the clitoral lesion, supporting an HPV-associated etiology. Subsequently, the patient underwent a modified radical vulvectomy with bilateral lymphadenectomy. Histopathological findings confirmed SCC of the vulva, staged as IB, with no lymph node involvement.

This case emphasizes the diverse presentation of vulvar lesions and the critical role of HPV in vulvar carcinogenesis, particularly in postmenopausal women.

Persistent bacteremia, despite the susceptibility of the causative organism to appropriate antimicrobial therapy, presents a major clinical challenge. In such cases, early identification and control of the infectious source are essential to prevent complications and reduce mortality.

We report the case of a 59-year-old woman with persistent Methicillin-resistant Staphylococcus Aureus (MRSA) bacteremia following spinal surgery. Despite multiple days of intravenous antibiotic therapy, her blood cultures remained positive for MRSA. A tagged white blood cell (Technetium-99) scan revealed an abscess in the right sacroiliac joint. Surgical drainage of the abscess led to clinical improvement and resolution of bacteremia. Interestingly, cultures of the abscess fluid grew Enterococcus faecalis rather than MRSA.

This case underscores the importance of early source control in the management of persistent bacteremia. Even when the pathogen isolated from the presumed source differs from that in the bloodstream, drainage can play a critical role in resolving systemic infection.

Early source control should be pursued in persistent bacteremia, regardless of initial culture results. Imaging studies may assist in locating occult sources, and successful drainage may contribute to clinical improvement even when the primary bloodstream pathogen is not isolated from the source.

There are several diagnostic techniques for detecting Helicobacter pylori, the most common of which are upper GI endoscopic biopsies and stool specimens as optimal samples. The goal of this study was to detect and compare H. pylori infection using the following techniques: rapid urease test (RUT), polymerase chain reaction (PCR), culture, histopathology, and stool antigen test (SAT), as well as to assess their validity in detecting H. pylori infection.

Patients with dyspepsia who presented to the Department of Gastroenterology's Outpatient Department and In-Patient Department between September 2021 and December 2022 were screened (Rome IV criteria). Endoscopy was used to diagnose and recruit patients with Functional dyspepsia (FD) and Peptic ulcer disease (PUD). Each biopsy sample was subjected to a battery of microbiological testing. Patients were considered infected with H. pylori if any three of five tests were found to be positive. The outcomes of all diagnostic modalities were documented and analysed.

A total of 171 patients were enrolled; the majority of them were male (62.60%), with a median age of 43 years. In 120 cases (70.18%), H. pylori was identified. The RUT showed the following results: sensitivity, specificity, positive predictive value, negative predictive value, and accuracy: 91.67%, 74.51%, 89.43%, 79.17%, and 86.55%; PCR (ureC gene): 91.67%, 100%, 100%, 83.61%, and 94.15%; Histopathology: 61.67%, 100%, 100%, 52.58%, and 73.10%; and SAT: 87.50%, 94.12%, 97.22%, 76.19%, and 89.47%, respectively.

The present study sheds light on the various diagnostic modalities and their efficacy in detecting H. pylori infection. Since several diagnostics are available for detecting H. pylori infection, the question of which method to use arises. Thus, the sensitivity, specificity, availability, rapidity in obtaining results, and availability of the test, with added value such as detection of pathogenic qualities, must all be considered.

Effective disease control and prevention are central to global public health, especially amid increasing chronic diseases, re-emerging infectious threats, and socioeconomic disparities. This systematic review aims to identify and synthesize key strategies that contribute to improved disease management outcomes worldwide.

This systematic review was conducted using the keywords “disease management,” “disease prevention,” “public health strategies,” “surveillance,” “vaccination,” “vector control,” and “social determinants of health (SDOH)” in electronic databases including PubMed, Scopus, Web of Science, and Google Scholar from 2001 to 2024. The search strategy was based on the PRISMA statement, and the research question was designed and formulated using the PICO framework. Twenty-two articles were selected for inclusion in the study.

Enhanced surveillance systems enable timely outbreak detection and inform public health responses. Vaccination strategies, including mobile units and public awareness campaigns, significantly improve coverage, especially in underserved areas. Environmental sanitation through WASH programs reduces disease transmission. Vector control using Integrated Vector Management has proven effective in controlling malaria and dengue. Addressing social determinants of health (SDOH) through targeted policies reduces health inequities. The One Health approach promotes cross-sector collaboration for controlling zoonotic diseases.

Findings highlight the importance of combining epidemiological tools, community engagement, policy reform, and intersectoral collaboration. Socioeconomic and environmental contexts significantly influence health outcomes and the success of interventions.

A multifaceted, integrated strategy is crucial for effective disease prevention and control. Public health systems must prioritize surveillance, vaccination, sanitation, equity-oriented policies, and cross-sectoral collaboration to improve global health resilience and reduce disparities.