Biomarkers

Pica is a clinically significant and often overlooked feeding and eating disorder in which individuals have a persistent urge to eat or chew non-nutritious or non-food items and is linked to nutritional, hematological, behavioral, and environmental issues. Human studies have regularly found links between pica and iron deficiency and anemia, as well as between changes in hematological parameters, decreased ferritin, and decreased zinc concentrations; toxic-element exposure may also be a part of certain behaviors (like geophagia). The review critically summarises the potential use of multimodal artificial intelligence (AI) and machine learning (ML) to support predictive risk stratification, early detection, and clinical management of pica, highlighting the importance of combining etiological and pathophysiological biomarkers, clinical, behavioural, toxicological and electronic health-record data. Biomarker evidence also indicates human exposure to multiple biomarkers together may be a more complete risk profile than single biomarkers, since there are human data from which to draw conclusions. AI and natural language processing could be used to identify undetected pica-related behaviors and track changes over time to clinical and laboratory data. However, there are still limitations in the development and validation of pica-specific AI models, and existing evidence from related conditions mainly methodologically supports the use of AI. Further studies are needed on large, prospective, diverse human cohorts, standardized pica phenotyping, longitudinal assessment of biomarkers, explainable AI, external validation, and prospective clinical assessment. Finally, multimodal AI should be used as a clinically interpretable decision support system, not as an independent diagnostic system.

Lassa fever and septic fever are important infectious diseases which cause high morbidity, mortality and healthcare burden, especially in resource-limited areas. The clinical features of both diseases are so similar, and include fever, hypotension, respiratory failure, and multi-organ failure, that early diagnosis and treatment are challenging. The clinical features, epidemiological features, risk factors, early clinical predictors and biomarkers of disease severity and mortality are analyzed. Prognosis assessment and therapeutic monitoring is an important role of a number of biomarkers, such as lactate, procalcitonin, cystatin-C, and inflammatory cytokines. The review also notes that artificial intelligence, machine learning, 3D imaging technologies and epidemiological surveillance systems are increasingly playing a key role in the prediction, monitoring and clinical decision-making of diseases. The combination of biomarker-based diagnostics, AI-driven predictive modelling, and digital healthcare technologies could greatly enhance the management of infectious diseases and the preparedness of healthcare systems. The multidisciplinary research and international co-operation continue to be critical to decrease the global burden of severe infectious diseases.