Background & objective: Nerve Growth Factor (NGF) plays a key role in the pathophysiology of bronchial asthma through contributing to inflammation of airway, remodeling of airway and neural regulation. This systematic review aimed for the evaluation of mechanisms that contributed to asthma and its relevance to ICU settings. Methodology: The PRISMA 2020 guidelines were followed in this review. Studies that explored NGF expression, regulation and therapeutic targeting in asthma were sought using a comprehensive search of PubMed, Scopus and Google Scholar. Study design, population, NGF measure methods, key findings, and ICU relevance were focused on data extraction. The assessment of risk of any bias was done using Evident Project Risk of Bias Tool. GRADE assessment was used for certainty of evidence. Results: Of the 10 studies included, 6 were based on animal models, 3 were based on human observational studies and the remaining one was a systematic review. Asthma patients exhibited markedly higher NGF levels than control subjects (mean: 3.45 ± 0.76 pg/mL). Eosinophilic counts (r = 0.74, P < 0.01) and inflammatory cytokines correlated in positive with NGF. Airway inflammation decreased by 45.2% (P = 0.002), and airway remodeling by 32% (P = 0.004) via anti-NGF therapy. Similar results were observed by vitamin D supplementation that caused NGF downregulation by 41% (P = 0.003) via the Nrf2/HO-1 pathway. Risk of bias assessment showed that four studies were classified as low risk, five as moderate, and one as high. Conclusion: NGF was significantly stimulated in asthma and contributed to inflammation, reformation and neuroimmune interactions. The correlation of NGF with severe exacerbations and ICU outcomes suggested its potential as a biomarker for therapeutic target. However large scale and long-term evaluation of NGF against asthma was required. Future studies should validate the role of NGF in asthma management particularly in ICU settings. Abbreviations: NGF: Nerve Growth Factor, ICU: Intensive Care Unit, BALF: Bronchoalveolar Lavage Fluid, AHR: Airway Hyperresponsiveness, IL-6: Interleukin-6, IL-1β: Interleukin-1 Beta, CRP: C-Reactive Protein, TRPV1: Transient Receptor Potential Vanilloid 1, Nrf2: Nuclear factor erythroid 2-related factor 2, HO-1: Heme Oxygenase-1, ELISA: Enzyme-Linked Immunosorbent Assay, RT-qPCR: Reverse Transcription Quantitative Polymerase Chain Reaction, GRADE: Grading of Recommendations, Assessment, Development, and Evaluations, PRISMA Preferred Reporting Items for Systematic Reviews and Meta-Analyses, RSV: Respiratory Syncytial Virus, OVA: Ovalbumin, RhoA: Ras homolog family member A, PM: Particulate Matter Keywords: Nerve Growth Factor; Asthma; Airway Remodeling; Inflammation; Neuroimmunomodulation; Intensive Care Units; Critical Care; Biomarkers Citation: Bukhari AAS, Shaikh ARK, Salman W, Bhatti FA, Malik W, Minhas M, Muddasser A, Khaliq H. Pathophysiological role of nerve growth factor (NGF) in asthma: insights into airway inflammation, remodeling, and neural regulation in intensive care settings. Anaesth. pain intensive care 2025;29(3):681-689. DOI: 10.35975/apic.v29i3.2778 Received: February 03, 2025; Revised: February 28, 2025; Accepted: March 05, 2025

Background: The Internet of Things (IoT) has emerged as a transformative force in healthcare, offering significant potential to enhance patient care through connected devices. Understanding the research trends and contributions in this field is crucial for guiding future innovations. Objective: This bibliometric review investigates the evolution of IoT applications in healthcare, focusing on their impact on patient management, treatment outcomes, and healthcare delivery.

This paper aims to discuss the evolution of machine learning based approaches for identifying higher risk individuals. It is relevant preventive measures with the reference to the individual characteristics of patients. It is big data that include genetic, environmental, and lifestyle data, these models improve the accuracy of risk prediction and identify protective strategies on the individual level. A paradigm shift in the cancer prevention strategies has occurred owing to the incorporation of Artificial Intelligence and machine learning in the modern world. The role of machine learning in early recognition of high-risk personalities of contracting cancer is examined to support malt measures. It overviews the available literature regarding the ethical dilemma, direction and obstacle on the ordeal of integrating AI guided targeted treatment in clinical setting. The results set the stage for exploring the possibilities of big data and AI for cancer prevention, which could eventually result in equitable benefits to the quality of individual patients’ care and expenses for the entire healthcare system. Cancer is among the most prevalent diseases resulting in morbidity and mortality of patients globally. The recent advancements in diagnostics, evaluation of the cancer prognosis and primary treatment of the patients, data-personalized therapy has not been systematically addressed. Artificial Intelligence deployed to forecast and orchestrate many cancers, has being seen as a talented instrument for enhancing healthcare predicates and patients. The AI applications in the field of Oncology are risk evaluation, diagnosis, prognosis and decision making in treatments with precise and accurate knowledge. Artificial intelligence, a broad category of which a segment involves machine learning which involves using past data to develop a solution to solve a problem has been able to predict most forms of cancer such as breast, brain, lung, liver, and prostate among others. It is observing that both AI and ML exhibit a more favorable performance compared to clinicians in terms of cancer prediction. These technologies also have the competency to enhance the diagnostic and prognostic possibilities for numerous diseases including but not exclusive to cancer, as well as enhance the quality of life of the affected patient. Thus, further enhancement of the existing Artificial Intelligence and Machine learning schemes is essential. In conclusion, it is evident that Artificial Intelligence and Machine learning strategies are interwoven, the overall health of the society. The incidence of the burden of cancer can be significantly enhanced with the provision of an experts’ proactive approach.

Innovations in Intensive Care Units (ICU) have increased survival rates, but several patients experience Post-Intensive Care Syndrome (PICS), which encompasses persistent physical, cognitive, and psychological disability. The research emphasizes the need to identify and treat PICS, as 25-60% of ICU survivors experience it. Lack of standardized diagnostic criteria or a follow-up system has resulted in continued disability and diminished quality of life in patients. The objective of this study was to analyze existing literature on PICS, define its prevalence, identify associated risk factors, and outline strategies for providing post-ICU care. Findings indicated high levels of muscle weakness, cognitive dysfunction, depression, and post-traumatic stress disorder among survivors. The research concludes that post-discharge care gaps can be addressed through early rehabilitation, remote monitoring, mental health support, and patient-centered recovery plans, thereby improving long-term outcomes. Keywords: Critical Illness; Intensive Care Units; Stress Disorders; Post-Traumatic; Cognitive Dysfunction; Rehabilitation Citation: Khaliq H, Minhas M, Ahmed MAA. Post-intensive care syndrome (PICS): A call for continuity beyond discharge. Anaesth. pain intensive care 2025;29(6):441-43. DOI: 10.35975/apic.v29i6.2885 Received: May 14, 2025; Revised: July 11, 2025; Accepted: July 11, 2025

Sepsis remains one of the most challenging issues related to the intensive care unit, where the morbidity and mortality remain high, despite progress in diagnostic and treatment. Early identification, early administration of antibiotics, vigorous fluid therapy, source control, and following up with evidence-based bundles are the best approaches to use in mortality reduction. Sepsis is a potentially fatal syndrome of dysregulated host response to infection, which is one of the major causes of ICU hospitalizations all over the world.1 The quest to develop biomarkers has provided hope in the context of precision medicine, the daily routine of sepsis care regularly fails in its simplest processes, which raises a major divide between aspiration and practice.2