Background and Aims: Antibiotic resistance (ABR) is a global public health emergency which has seen an uptick in low‐ to middle‐income countries in recent times due to a plethora of aggravating factors and has led to a whole host of setting‐specific pathogens registering high rates of resistance, causing outbreaks with graver mortality and morbidity. This review analyzes available literature to determine the causes and effects of ABR and recommend solutions to the problem in a Pakistani setting.Methods: Sources for this narrative review were identified via electronic databases using keyword search methods. The information was retrieved using databases such as PubMed and Science Direct. Additionally, websites such as CDC and World Health Organization were used to attain pertinent information. All the sources were selected as per their relevance and appropriateness toward the purpose of this review.Results: This review details the causes by dividing them into three primary strata, namely (1) under‐regulation, (2) over‐prescription and self‐medication, and (3) lack of medical stewardship. This is made much graver when the COVID‐19 pandemic and the subsequent erratic treatment response is considered, with the pandemic augmenting already high levels of consumption. These factors have led a cascade of effects including, but not limited to, a considerable increase in ABR in pathogens to first‐line drugsConclusion: ABR is a serious and growing issue which will result in undesirable personal, local, and national consequences if unchecked. Mitigation and reversal of this trend is necessary by developing existing programs and investing in novel therapies and pharmaceutical research and strengthening regulatory policies and mechanisms.

The deadly earthquake in southeast Afghanistan on June 22, 2022 was a tragedy amidst the country's humanitarian crisis. It cost more than a thousand people's lives, caused three times more injuries, and destroyed many houses, mainly in Spera, Giyan, and Barmal districts. WHO and other NGOs responded to incidence, focusing on physical injuries, food shortage, and shelter, while mental health was not adequately emphasized. Almost half of the Afghanistan population suffers from mental disorders due to decades of civil war, economic instability, and natural disasters. The recent earthquake further exacerbates the mental conditions among earthquake victims and their families, making them vulnerable to severe mental disorders. The absence of local mental facilities and proper roads delayed the early response and made the follow-up difficult leading to serious mental issues and costly management. Although WHO and HealthNet TPO sent their mental health professionals to consult the earthquake victims and train health care workers, the sociocultural beliefs made the approach difficult and its result suspicious. Moreover, the stigma around mental health and the lack of female HCWs stop people from seeking mental healthcare.

Artificial intelligence (AI) is transforming biomarker discovery in neurology by overcoming key limitations of conventional approaches that are often slow, reductionist, and unable to integrate complex multimodal data. In this narrative review, we searched the data bases; PubMed, Scopus, IEEE Xplore, CINAHL, Embase and the Cochrane Library from inception to 2025 to evaluate how AI supports biomarker identification, diagnosis, prognostication, and treatment stratification across neurovascular, neurodegenerative, neuro-oncological and seizure disorders. Evidence demonstrates that AI-driven imaging and multi-omics biomarkers can detect disease earlier, improve prediction accuracy, and support personalised care. For example, AI models improve stroke outcome prediction beyond conventional scores, identify intracranial aneurysms with sensitivities exceeding 90%, predict conversion from mild cognitive impairment to Alzheimer’s disease with accuracies approaching 85–90%, and extract radiogenomic biomarkers in gliomas that outperform traditional diagnostic strategies. However, real-world translation remains constrained by dataset bias, limited external validation, interpretability challenges, and gaps in generalisability, particularly in underrepresented populations. Overall, AI-driven biomarker discovery offers a powerful pathway toward precision neurology, with the greatest impact expected when technical innovation is paired with robust clinical validation, regulatory integration, and equitable data representation.