Background: The rise in obesity has emphasised a focus on lifestyle and dietary habits. We aimed to address the debate between low-carbohydrate and low-fat diets and compare their effects on body weight, low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), total cholesterol, and triglycerides in an adult population. Method: Medline and Web of Science were searched for randomised controlled trials (RCTs) comparing low-fat and low-carbohydrate diets up to September 2019. Three independent reviewers extracted data. Risk of bias was assessed using the Cochrane tool. The meta-analysis was stratified by follow-up time using the random-effects models. Results: This meta-analysis of 38 studies assessed a total of 6499 adults. At 6–12 months, pooled analyses of mean differences of low-carbohydrate vs. low-fat diets favoured the low-carbohydrate diet for average weight change (mean difference −1.30 kg; 95% CI −2.02 to −0.57), HDL (0.05 mmol/L; 95% CI 0.03 to 0.08), and triglycerides (TG) (−0.10 mmol/L; −0.16 to −0.04), and favoured the low-fat diet for LDL (0.07 mmol/L; 95% CI 0.02 to 0.12) and total cholesterol (0.10 mmol/L; 95% CI 0.02 to 0.18). Conclusion and Relevance: This meta-analysis suggests that low-carbohydrate diets are effective at improving weight loss, HDL and TG lipid profiles. However, this must be balanced with potential consequences of raised LDL and total cholesterol in the long-term.

Current data suggest that infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing corona virus disease-19 (COVID-19) seems to follow a more severe clinical course in patients with cardiovascular disease (CVD), hypertension, and overweight/obesity. It appears that lipid-lowering pharmacological interventions, in particular statins, might reduce the risk of cardiovascular complications caused by COVID-19 and might potentially have an additional antiviral activity. It has been shown that high cholesterol levels are associated with more lipid rafts, subdomains of the plasma membrane that can harbour angiotensin-converting enzyme 2 (ACE2) receptors for the S-protein of SARS-CoV-2. Evidence of the importance of cholesterol for viral entry into host cells could suggest a role for cholesterol-lowering therapies in reducing viral infectivity. In addition to their lipid-lowering and plaque-stabilisation effects, statins possess pleiotropic effects including anti-inflammatory, immunomodulatory, and antithrombotic activities. Lower rates of mortality and intubation have been reported in studies investigating statin therapy in influenza infection, and statin therapy was shown to increase viral clearance from the blood during chronic hepatitis C infection. Statins may also serve as potential SARS-CoV-2 main protease inhibitors, thereby contributing to the control of viral infection. In this review, we elaborate on the role of cholesterol level in the process of the coronavirus infection and provide a critical appraisal on the potential of statins in reducing the severity, duration, and complications of COVID-19.

The role for steroids in acute spinal cord injury (ASCI) remains unclear; while some studies have demonstrated the risks of steroids outweigh the benefits,a meta-analyses conducted on heterogeneous patient populations have shown significant motor improvement at short-term but not at long-term follow-up. Given the heterogeneity of the patient population in previous meta-analyses and the publication of a recent trial not included in these meta-analyses, we sought to re-assess and update the safety and short-term and long-term efficacy of steroid treatment following ASCI in a more homogeneous patient population. A literature search was conducted on PubMed, EMBASE and Cochrane Library through June 2019 for studies evaluating the utility of steroids within the first 8 h following ASCI. Neurological and safety outcomes were extracted for patients treated and not treated with steroids. Pooled effect estimates were calculated using the random-effects model. Twelve studies, including five randomized controlled trials (RCTs) and seven observational studies (OBSs), were meta-analyzed. Overall, methylprednisolone was not associated with significant short-term or long-term improvements in motor or neurological scores based on RCTs or OBSs. An increased risk of hyperglycemia was shown in both RCTs (RR: 13.7; 95% CI: 1.93, 97.4; 1 study) and OBSs (RR: 2.9; 95% CI: 1.55, 5.41; 1 study). Risk for pneumonia was increased with steroids; while this increase was not statistically significant in the RCTs (pooled RR: 1.16; 95% C.I: 0.59, 2.29; 3 studies), it reached statistical significance in the OBSs (pooled RR: 2.00; 95% C.I: 1.32, 3.02; 6 studies). There was no statistically significant increased risk of gastrointestinal bleeding, decubitus ulcers, surgical site infections, sepsis, atelectasis, venous thromboembolism, urinary tract infections, or mortality among steroid-treated ASCI patients compared to untreated controls in either RCTs or OBSs. Methylprednisolone therapy within the first 8 h following ASCI failed to show a statistically significant short-term or long-term improvement in patients' overall motor or neurological scores compared to controls who were not administered steroids. For the same comparison, there was an increased risk of pneumonia and hyperglycemia compared to controls. Routine use of methylprednisone following ASCI should be carefully considered in the context of these results.

Time-Restricted Eating is an eating pattern based on the circadian rhythm which limits daily food intake (usually to ≤12 h/day), unique in that no overt restriction is imposed on the quality, nor quantity, of food intake. This paper aimed to examine the effects of two patterns of TRE, traditional TRE, and Ramadan fasting, on two markers of circadian rhythm, cortisol and melatonin. PubMed and Web of Science were searched up to December 2020 for studies examining the effects of time restricted eating on cortisol and melatonin. Fourteen studies met our inclusion criteria. All Ramadan papers found statistically significant decrease in melatonin (p < 0.05) during Ramadan. Two out of the three Ramadan papers noted an abolishing of the circadian rhythm of cortisol (p < 0.05). The non-Ramadan TRE papers did not examine melatonin, and cortisol changes were mixed. In studies comparing TRE to control diets, Stratton et al. found increased cortisol levels in the non-TRE fasting group (p = 0.0018) and McAllister et al. noted no difference. Dinner-skipping resulted in significantly reduced evening cortisol and non-significantly raised morning cortisol. Conversely, breakfast skipping resulted in significantly reduced morning cortisol. This blunting indicates a dysfunctional HPA axis, and may be associated with poor cardio-metabolic outcomes. There is a paucity of research examining the effects of TRE on cortisol and melatonin. The contrasting effect of dinner and breakfast-skipping should be further examined to ascertain whether timing the feeding window indeed has an impact on circadian rhythmicity.

Given the growing use of machine learning (ML) technologies in health care, regulatory bodies face unique challenges in governing their clinical use. Under the regulatory framework of the Food and Drug Administration, approved ML algorithms are practically locked, preventing their adaptation in the ever-changing clinical environment, defeating the unique adaptive trait of ML technology in learning from real-world feedback. At the same time, regulations must enforce a strict level of patient safety to mitigate risk at a systemic level. Given that ML algorithms often support, or at times replace, the role of medical professionals, we have proposed a novel regulatory pathway analogous to the regulation of medical professionals, encompassing the life cycle of an algorithm from inception, development to clinical implementation, and continual clinical adaptation. We then discuss in-depth technical and nontechnical challenges to its implementation and offer potential solutions to unleash the full potential of ML technology in health care while ensuring quality, equity, and safety. References for this article were identified through searches of PubMed with the search terms “Artificial intelligence,” “Machine learning,” and “regulation” from June 25, 2017, until June 25, 2022. Articles were also identified through searches of the reference list of the articles. Only papers published in English were reviewed. The final reference list was generated based on originality and relevance to the broad scope of this paper.