The coronavirus disease 2019 (COVID-19) pandemic had an unprecedented global impact,resulting in both positive and negative consequences. The virus not only affected millions oflives worldwide but also caused long-term harm to multiple organ systems in many survivors,thereby substantially impairing quality of life. This persistent condition is now referred to as long COVID (LC). The aim of this study is to raise awareness of LC-related organ system impacts and to highlight the key role of artificial intelligence (AI) in mitigating these effects. The present research conducts a narrative review focusing on LC-related impacts. In this context, unstructured searches were conducted to identify a total of 69 relevant studies indexed in Embase, PubMed, Web of Science, or Scopus, each of which was reviewed by at least 2 experts with sufficient domain knowledge in health sciences. Based on the authors’ perspectives and insights, the review narratively examines damage to human organ systems attributable to LC and explores the role of AI in addressing LC-related challenges. Significant ethical, practical, and societal concerns arising from the extensive use of AI, particularly major issues such asdata privacy and algorithmic bias, are also discussed. LC has caused lasting impacts on humanorgan systems, while AI is offering substantial potential for LC-related care.

BACKGROUND: Diabetic ketoacidosis (DKA) is characterized by a biochemical triad of hyperglycemia, acidosis, and ketonemia. This condition is life-threatening despite improvements in diabetic care. The purpose of this study was to evaluate the clinical and biochemical prognostic markers of DKA. We assessed correlations in prognostic markers with DKA-associated morbidity and mortality. METHODS: Two hundred and seventy patients that were hospitalized with DKA over a period of 2 years were evaluated clinically and by laboratory tests. Serial assays of serum electrolytes, glucose, and blood pH were performed, and clinical outcome was noted as either discharged to home or death. RESULTS: The analysis indicated that significant predictors included sex, history of type 1 diabetes mellitus or type 2 diabetes mellitus, systolic blood pressure, diastolic blood pressure, total leukocyte count, Acute Physiology and Chronic Health Evaluation II (APACHE II) score, blood urea nitrogen, serum creatinine, serum magnesium, serum phosphate, serum osmolality, serum glutamic oxaloacetic transaminases, serum glutamic pyruvic transaminases, serum albumin, which were further regressed and subjected to multivariate logistic regression (MLR) analysis. The MLR analysis indicated that males were 7.93 times more likely to have favorable outcome compared with female patients (odds ratio, 7.93; 95% confidence interval, 3.99 to 13.51), while decreases in mean APACHE II score (14.83) and serum phosphate (4.38) at presentation may lead to 2.86- and 2.71-fold better outcomes, respectively, compared with higher levels (APACHE II score, 25.00; serum phosphate, 6.04). CONCLUSION: Sex, baseline biochemical parameters such as APACHE II score, and phosphate level were important predictors of the DKA-associated mortality.
Acidosis ; APACHE ; Blood Pressure ; Blood Urea Nitrogen ; Creatinine ; Diabetes Mellitus, Type 1 ; Diabetes Mellitus, Type 2 ; Diabetic Ketoacidosis* ; Electrolytes ; Female ; Glucose ; Humans ; Hydrogen-Ion Concentration ; Hyperglycemia ; Hyperglycemic Hyperosmolar Nonketotic Coma ; Ketosis ; Leukocyte Count ; Logistic Models ; Magnesium ; Male ; Mortality ; Osmolar Concentration ; Serum Albumin ; Transaminases