Background: The clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technology has recently been discovered for gene editing and cancer therapy and its applications are expanding. This review and meta-analysis aim to assess the present and future of CRISPR/Cas9 based gene editing in cancer treatment and the way forward. Methods: The search was conducted in PubMed from 2015 to 2025 and 89 relevant studies were identified. The study design, CRISPR/Cas9 targets, delivery methods, therapeutic efficacy and limitations were extracted from the studies. Results: We reviewed the efficacy, challenges, and potential for translation of CRISPR/Cas9 in oncogene and tumor suppressor gene targeting and immune modulation. Several preclinical researches showed that CRISPR/Cas9 mediated disruption of oncogenes or restoration of tumor suppressor genes led to significant tumor regression. The evaluation was also extended to off target effects and integration with immunotherapy. Conclusion: From the findings of this work, it can be concluded that CRISPR/Cas9 is a promising tool, but there are several limitations including off target effects, delivery systems and ethical issues that need to be solved in order to improve the clinical significance.

Background: South Asia comprises genetically diverse populations because its populations have experienced multiple migrations combined with endogamy and isolation throughout history. Research on large-scale genetic variation patterns in this region remains scarce. This study aims to perform both a systematic review and meta-analysis of research about genetic diversity in South Asian populations. Methods: The PubMed database yielded 3798 studies within the time frame from 2010 to 2025. The analysis included 57 studies that fulfilled the required criteria after initial screening. The random-effects model processed pooled F_ST values together with heterozygosity estimates and allele frequency variation across ethnic subgroups. Results: The genetic differentiation (F_ST) measurements between significant South Asian groups extended from 0.02 to 0.15. The homozygosity levels were significantly higher in tribal populations (mean runs of homozygosity = 0.38) than in caste groups. The combined F_ST value reached 0.072 with a 95% confidence interval ranging from 0.061 to 0.084. The highest heterozygosity values existed among North Indian speakers of the Indo-European language, while isolated tribal populations showed the lowest heterozygosity levels. Conclusion: The genetic structure of South Asia extends deep into its population because of geographical barriers as well as linguistic and social organization systems. The diverse genetic makeup of populations affects both disease risk profiles and precision medical approaches for individual groups.

OBJECTIVES: This systematic review evaluates artificial intelligence (AI)-based predictive models developed for early sepsis detection in adult hospitalized patients. It explores model types, input features, validation strategies, performance metrics, clinical integration, and implementation challenges. DATA SOURCES: A systematic search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and CENTRAL for studies published between January 2015 and March 2025. STUDY SELECTION: Eligible studies included those developing or validating AI models for adult inpatient sepsis prediction using electronic health record data and reporting at least one performance metric (area under the curve [AUC], sensitivity, specificity, or F1 score). Studies focusing on pediatric populations, lacking quantitative evaluation, or unpublished in peer-reviewed journals were excluded. DATA EXTRACTION: Data extraction followed preferred reporting items for systematic reviews and meta-analyses guidelines. Extracted variables included study design, patient population, model type, input features, validation approach, and performance outcomes. DATA SYNTHESIS: A total of 52 studies met the inclusion criteria. Most used retrospective designs, with limited prospective or real-time clinical validation. Commonly used algorithms included random forests, neural networks, support vector machines, and deep learning architectures (long short-term memory, convolutional neural network). Input data varied from structured sources (vital signs, laboratory values, demographics) to unstructured clinical notes processed via natural language processing. Reported AUC values ranged from 0.79 to 0.96, indicating strong predictive performance across models. CONCLUSIONS: AI models demonstrate significant promise for early sepsis detection, outperforming conventional scoring systems in many cases. However, generalizability, interpretability, and clinical implementation remain major challenges. Future research should emphasize externally validated, explainable, and scalable AI solutions integrated into real-time clinical workflows.

Predictive analytics have emerged as a powerful tool in cardiology, revolutionizing how patient care is delivered by leveraging artificial intelligence (AI) and machine learning (ML) algorithms. Healthcare professionals can now forecast the occurrence and progression of cardiovascular diseases with unprecedented accuracy. This breakthrough technology possesses the ability to fundamentally transform the field of cardiology, facilitating early detection, personalized treatment methodologies, and improving patient outcomes. The utilization of predictive analytics based on AI and ML represents a significant advancement that can optimize resource allocation, enhance clinical decision-making, and most importantly, benefit those affected by heart conditions. As the technology continues to grow more sophisticated through ongoing research and development, its full potential to revolutionize cardiovascular medicine and benefit patients worldwide has yet to be fully realized.

Asthma is a widespread chronic respiratory disease that requires effective management to reduce exacerbations and improve patient outcomes. Albuterol (salbutamol), a short-acting beta-2-agonist (SABA), remains a mainstay treatment for acute symptom relief due to its rapid bronchodilatory effect. However, accumulating evidence over the past decade has raised concerns about its cardiovascular safety, particularly in vulnerable populations such as children, elderly individuals, and those with underlying cardiac conditions. This review synthesizes clinical findings from the last ten years (2015–2025) evaluating the cardiovascular effects of albuterol, including tachycardia, arrhythmias, QTc prolongation, and hypotension. Literature across PubMed, Cochrane, and Google Scholar was analyzed to assess frequency, severity, and risk factors associated with these events. Notably, intravenous administration was associated with markedly higher rates of adverse effects, while inhaled formulations remained safer with moderate risk. Pediatric patients on continuous therapy showed increased susceptibility to electrolyte imbalances and hypotension. Although alternatives like levalbuterol demonstrated a reduced cardiovascular risk profile, they were linked with increased healthcare costs and longer hospital stays. The review highlights the importance of risk stratification, personalized dosing, and enhanced monitoring, particularly in high-risk groups, to maximize the therapeutic benefits of SABAs while minimizing cardiovascular harm. Overall, the findings underscore the need for ongoing pharmacovigilance and tailored clinical decision-making when prescribing albuterol in asthma care.