Characterizing and Predicting Post-Acute Sequelae of SARS CoV-2 infection (PASC) in a Large Academic Medical Center in the US

The incidence and predisposing factors for irritable bowel syndrome following COVID-19: a systematic review and meta-analysis

BACKGROUND

Irritable bowel syndrome (IBS) is a common functional gastrointestinal (GI) disorder. Several studies have analyzed the long-term GI symptoms and IBS following coronavirus disease 2019 (COVID-19). The purpose of this study is to evaluate the incidence and predisposing factors for IBS following COVID-19 by a systematic review and meta-analysis.

METHODS

Electronic databases were searched to identify relevant studies. Primary outcomes were the pooled incidence rate of IBS following COVID-19 and the pooled relative risk (RR) for IBS in the COVID-19 group compared to the non-COVID-19 group. Secondary outcomes were the pooled RR and the standardized mean difference (SMD) for predisposing factors in the IBS group compared to the non-IBS group. Heterogeneity was evaluated using Cochran's Q test and I2 statistics.

RESULTS

Ten studies were included in this study. The pooled incidence rate of IBS in COVID-19 patients was 12%. The pooled incidence rate of IBS-D, IBS-C and IBS-M was 5%, 2% and 1%. The pooled incidence rate of IBS in 6 and 12 months was 10% and 3%. The pooled RR for IBS in COVID-19 patients was 1.23 [95% confidence interval (CI) = 0.50-3.01] compared to non-COVID-19 patients. The pooled RR or SMD for mild, moderate, and severe disease activity, procalcitonin (PCT), depression or anxiety in IBS patients following COVID-19 was 0.94 (95% CI = 0.74-1.21), 1.19 (95% CI = 0.65-2.21), 1.30 (95% CI = 0.63-2.66), 6.73 (95% CI = 6.08-7.38) and 3.21 (95% CI = 1.79-5.75).

CONCLUSION

The incidence of IBS following COVID-19 was 12%. But it was not higher than the general population. We also found some predisposing factors for IBS including depression or anxiety, PCT.

Cardiovascular and Neurological Complications of COVID-19: A Narrative Review

A novel coronavirus emerged in China in late 2019 as a disease named coronavirus disease 2019. This pathogen was initially identified as causing a respiratory syndrome, but later, it was found that COVID-19 could also affect other body systems, such as the neurological and cardiovascular systems. For didactic purposes, cardiovascular and neurological manifestations of SARS-CoV-2 have been classified in three different groups: acute complications, late complications, and post-vaccine complications. Therefore, the following study has the goal to summarize and disseminate the present knowledge about the cardiovascular and neurological manifestations of COVID-19 based on the latest and most up-to-date data available and, thus, promote more prepared medical care for these conditions as the medical team is updated. Based on what is brought on this revision and its understanding, the medical service becomes more aware of the causal relationship between some conditions and COVID-19 and can better prepare for the most prevalent conditions to associate and, consequently, to treat patients earlier. Therefore, there is a chance of better prognoses in this context and the need to increase the number of studies about complications related to SARS-CoV-2 infection for a better understanding of other associated conditions.

The role of machine learning in health policies during the COVID-19 pandemic and in long COVID management

The ongoing COVID-19 pandemic is arguably one of the most challenging health crises in modern times. The development of effective strategies to control the spread of SARS-CoV-2 were major goals for governments and policy makers. Mathematical modeling and machine learning emerged as potent tools to guide and optimize the different control measures. This review briefly summarizes the SARS-CoV-2 pandemic evolution during the first 3 years. It details the main public health challenges focusing on the contribution of mathematical modeling to design and guide government action plans and spread mitigation interventions of SARS-CoV-2. Next describes the application of machine learning methods in a series of study cases, including COVID-19 clinical diagnosis, the analysis of epidemiological variables, and drug discovery by protein engineering techniques. Lastly, it explores the use of machine learning tools for investigating long COVID, by identifying patterns and relationships of symptoms, predicting risk indicators, and enabling early evaluation of COVID-19 sequelae.