Unveiling the Role of Artificial Intelligence (AI) in Polycystic Ovary Syndrome (PCOS) Diagnosis: A Comprehensive Review.

Polycystic Ovary Syndrome (PCOS) is one of the most widespread endocrine and metabolic disorders affecting women of reproductive age. Major symptoms include hyperandrogenism, polycystic ovary, irregular menstruation cycle, excessive hair growth, etc., which sometimes may lead to more severe complications like infertility, pregnancy complications and other co-morbidities such as diabetes, hypertension, sleep apnea, etc. Early detection and effective management of PCOS are essential to enhance patients' quality of life and reduce the chances of associated health complications. Artificial intelligence (AI) techniques have recently emerged as a popular methodology in the healthcare industry for diagnosing and managing complex diseases such as PCOS. AI utilizes machine learning algorithms to analyze ultrasound images and anthropometric and biochemical test result data to diagnose PCOS quickly and accurately. AI can assist in integrating different data sources, such as patient histories, lab findings, and medical records, to present a clear and complete picture of an individual's health. This information can help the physician make more informed and efficient diagnostic decisions. This review article provides a comprehensive analysis of the evolving role of AI in various aspects of the management of PCOS, with a major focus on AI-based diagnosis tools.

Utility of thiol/disulphide homeostasis as a biomarker for acute appendicitis: a systematic review and meta-analysis.

The aim of this study was to analyze the role of thiol/disulfide homeostasis (TDH) parameters as an indicator of oxidative stress in acute appendicitis (AA). PubMed, EMBASE, Web of Science, and Scopus databases were systematically searched. Studies reporting on TDH in AA (both complicated and uncomplicated cases) were included. The comparator group were healthy controls. The TDH domain was compared between the groups using anti-oxidant parameters, namely native thiol and total thiol levels, and native thiol/total thiol ratio; and oxidant parameters, namely disulfide level, disulfide/native thiol ratio, and disulfide/total thiol ratio. The statistical analysis was performed using a random-effects model. The methodological quality of the studies was assessed utilizing the Newcastle-Ottawa scale. Eleven studies with a total of 926 subjects, comprising 457 patients with uncomplicated appendicitis, 147 with complicated appendicitis, and 322 healthy controls were included. Our study demonstrated significantly increased oxidative stress in AA as compared to healthy controls in all TDH parameters and significantly lower total thiol levels in complicated AA as compared to uncomplicated AA. Due to a poor methodological quality in five out of eleven studies, future prospective studies with adequate power are essential to validate these observations and refine the diagnostic approaches to AA.

Impact of comorbidity on patients with COVID-19 in India: A nationwide analysis.

Background: The emergence of coronavirus disease (COVID-19) as a global pandemic has resulted in the loss of many lives and a significant decline in global economic losses. Thus, for a large country like India, there is a need to comprehend the dynamics of COVID-19 in a clustered way. Objective: To evaluate the clinical characteristics of patients with COVID-19 according to age, gender, and preexisting comorbidity. Patients with COVID-19 were categorized according to comorbidity, and the data over a 2-year period (1 January 2020 to 31 January 2022) were considered to analyze the impact of comorbidity on severe COVID-19 outcomes. Methods: For different age/gender groups, the distribution of COVID-19 positive, hospitalized, and mortality cases was estimated. The impact of comorbidity was assessed by computing incidence rate (IR), odds ratio (OR), and proportion analysis. Results: The results indicated that COVID-19 caused an exponential growth in mortality. In patients over the age of 50, the mortality rate was found to be very high, ~80%. Moreover, based on the estimation of OR, it can be inferred that age and various preexisting comorbidities were found to be predictors of severe COVID-19 outcomes. The strongest risk factors for COVID-19 mortality were preexisting comorbidities like diabetes (OR: 2.39; 95% confidence interval (CI): 2.31-2.47; p < 0.0001), hypertension (OR: 2.31; 95% CI: 2.23-2.39; p < 0.0001), and heart disease (OR: 2.19; 95% CI: 2.08-2.30; p < 0.0001). The proportion of fatal cases among patients positive for COVID-19 increased with the number of comorbidities. Conclusion: This study concluded that elderly patients with preexisting comorbidities were at an increased risk of COVID-19 mortality. Patients in the elderly age group with underlying medical conditions are recommended for preventive medical care or medical resources and vaccination against COVID-19.