The spreading of infectious diseases caused by the emergence of Multidrug-Resistant (MDR) pathogens is a global threat that has led to numerous deaths annually. In view of this, there is an overwhelming need to discover new bioactive compounds with effective antimicrobial properties. Concurrently, the genus Streptomyces has a growing reputation as a potential biological source of various antibiotics and other bioactive metabolites. Streptomyces sp. has been isolated from different sources, including terrestrial and marine habitats with a myriad of promising compounds that could be used to treat MDR pathogens. Therefore, this study presents a systematic review of the antibacterial activities of Streptomyces-derived secondary metabolites. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and checklist were employed in this studyto collect relevant articles from two research databases, namely PubMed and Science Direct. The selection process includes identification, screening, eligibility, and inclusion of articles. Several keywords and criteria were established for the screening and selection process. Based on the results, a total of 26 articles were selected from 70 potential articles. The articles were published between 2015 and 2020 with most studies being published in 2020, indicating an increased interest in Streptomyces and its derived compounds. Approximately 51 different PMMB 2022, 5, 1; a0000281 2 of 25 Streptomyces-derived compounds have been identified, ranging from crude extracts, pure compounds, and partially purified compounds. Various parameters were also used to assess their antibacterial activities, particularly the Minimum Inhibitory Concentration (MIC) (69%) and the zone of inhibition (11%). Moreover, the antibacterial activities of these compounds were effective on numerous gram-positive and gram-negative bacteria. Furthermore, 46% and 54% of the selected studies were focused on inhibiting MDR and nonMDR pathogens, respectively. In conclusion, both crude and purified compounds from Streptomyces sp. exhibited strong antibacterial effects. It is expected that extensive future research would develop a standard method to compare the antibacterial strength of eachextracted compound from Streptomyces sp. and determine the most effective bioactive compounds to treat diseases caused by MDR pathogens.

This study explores Artificial Intelligence (AI)’s transformative role in diabetes care and monitoring, focusing on innovations that optimize patient outcomes. AI, particularly machine learning and deep learning, significantly enhances early detection of complications like diabetic retinopathy and improves screening efficacy. The methodology employs a bibliometric analysis using Scopus, VOSviewer, and Publish or Perish, analyzing 235 articles from 2023-2025. Results indicate a strong interdisciplinary focus, with Computer Science and Medicine being dominant subject areas (36.9% and 12.9% respectively). Bibliographic coupling reveals robust international collaborations led by the U.S. (1558.52 link strength), UK, and China, with key influential documents by Zhu (2023c) and Annuzzi (2023). This research highlights AI’s impact on enhancing monitoring, personalized treatment, and proactive care, while acknowledging challenges in data privacy and ethical deployment. Future work should bridge technological advancements with real-world implementation to create equitable and efficient diabetes care systems.

This review explores the transformative role of artificial intelligence (AI) in the early detection and prognosis prediction of diabetic retinopathy (DR), a leading cause of vision loss in diabetic patients. AI, particularly deep learning and convolutional neural networks (CNNs), has demonstrated remarkable accuracy in analyzing retinal images, identifying early-stage DR with high sensitivity and specificity. These advancements address critical challenges such as intergrader variability in manual screening and the limited availability of specialists, especially in underserved regions. The integration of AI with telemedicine has further enhanced accessibility, enabling remote screening through portable devices and smartphone-based imaging. Economically, AI-based systems reduce healthcare costs by optimizing resource allocation and minimizing unnecessary referrals. Key findings highlight the dominance of Medicine (819 documents) and Computer Science (613 documents) in research output, reflecting the interdisciplinary nature of this field. Geographically, China, the United States, and India lead in contributions, underscoring global efforts to combat DR. Despite these successes, challenges such as algorithmic bias, data privacy, and the need for explainable AI (XAI) remain. Future research should focus on multi-center validation, diverse AI methodologies, and clinician-friendly tools to ensure equitable adoption. By addressing these gaps, AI can revolutionize DR management, reducing the global burden of diabetes-related blindness through early intervention and scalable solutions.

Halal pharmaceuticals refer to drugs that are formulated with acceptable ingredients consistent with Islamic principles and conditions. In the pharmaceutical industry, the standard of halal pharmaceuticals is the crucial document that should be followed to standardize the quality and the safety of the products. Melicope ptelefolia (M. ptelefolia), known as tenggek burung was claimed to have many health benefits, including antioxidant, 150 anti-inflammatory and antimicrobial properties. This study aims to formulate halal cream containing M. ptelefolia extract. All the ingredients used were evaluated for their halal and safety status based on four supporting documents: Halal Certificate, Certificate of Analysis (CoA), International Nomenclature of Cosmetic Ingredients (INCI) and Material Safety Data Sheet (MSDS). The leaves of M. ptelefolia was extracted using methanol solvent and diluted into four different concentrations, 25% w/v, 50% w/v, 75% w/v, and 100% w/v. The extractwas then tested for S. aureus and P. aeruginosa antimicrobial screening using disk diffusion method. Based on the antimicrobial screening, four types of creams were formulated namely MP0, MP2, MP4 and MP6. It was then evaluated by its color, homogeneity, appearance, phase separation, effect on pH and temperature. The cream formulation was then evaluated for its antimicrobial activities against S. aureus. All the M. ptelefolia extract concentration demonstrates antimicrobial properties against S. Aureus but not P. aeruginosa.M. ptelefolia extract was then incorporated into the cream formulation with different concentration. Based on the evaluation, all the cream formulations are stable at various temperatures. The results showed that MP6 and storage temperature at 25°C has the highest inhibition zone. In conclusion, stable halal formulation of M. ptelefolia as antimicrobial cream was successfully formulated for treatment against S. aureus.

Neptunia oleracea also known as water mimosa is widely consumed by people worldwide including Malaysia. This plant was claimed able to treat several diseases such as epileptic convulsion, syphilis, jaundice, and bloody diarrhea. Moreover, it has been reported to possess antimicrobial, antioxidant, and anti-ulcer properties that benefit human health. Hence, this study investigates the content of macronutrients, micronutrients, phytochemical constituents, total phenolic content (TPC), total flavonoid content (TFC), and antioxidant properties in the aqueous leaf extract of this plant. The study found that N. oleracea leaves were possessed with high fiber (52%), moisture (23.33%), fat (5.33%), protein (5.25%), carbohydrate (6.42%), ash (7.67%), and mineral content with a sequence of Potassium > Calcium > Sodium > Phosphorus. A positive result of tannins, steroids, phenols, and flavonoids were detected. High values of TPC and TFC which were 136.19 ± 0.62 mg GAE/g and 829.17 ± 19.09 mg GAE/g of dry weight respectively were obtained. Meanwhile, the hydrogen peroxide scavenging activity of the plant extract produced an IC50 of 70.09 µg/mL. The presence of a high content of polyphenolic compounds may reflect the antioxidant properties of this plant. In conclusion, the aqueous extract of N. oleracea leaves showed the presence of various phytochemical constituents and has a high content of TPC and TFC which may be derived from its nutritional content. Hence, consumption of N. oleracea leaves is useful for human health and disease prevention.

The integration of Artificial Intelligence (AI) in the early detection and prognosis of Chronic Kidney Disease (CKD) is revolutionizing nephrology by providing enhanced diagnostic accuracy and improved patient outcomes. This research explores the transformative role of AI, particularly machine learning (ML) and deep learning (DL), in predicting CKD progression and facilitating timely interventions. AI-driven models analyze diverse patient data, including imaging, laboratory results, and genetic information, to identify subtle patterns often overlooked by traditional methods. These technologies allow for earlier identification of kidney dysfunction, potentially slowing disease progression and increasing life expectancy. The study highlights the importance of using ensemble learning techniques and feature selection methods to refine AI models, improving their predictive capabilities. Furthermore, the research emphasizes the potential of AI to support clinical decision-making by offering objective, data-driven risk assessments, which are crucial in the personalized management of CKD. The use of convolutional neural networks (CNNs) in renal imaging has shown promise in detecting early-stage kidney damage, while support vector machines (SVM) and artificial neural networks (ANN) have demonstrated high accuracy in diagnosing CKD. The growing integration of AI into healthcare workflows is expected to reduce diagnostic delays, enhance prognostic evaluations, and optimize treatment strategies. This study also discusses the interdisciplinary collaboration between medicine, computer science, and engineering, which is essential for advancing AI applications in CKD. With the increasing availability of high-quality data and computational tools, AI is poised to play a central role in transforming CKD management, offering a proactive approach to care that can lead to better patient outcomes and healthcare resource efficiency.