Hydroxychloroquine/chloroquine for the treatment of hospitalized patients with COVID-19: An individual participant data meta-analysis

Genetic, Epigenetic, and Hormonal Regulation of Stress Phenotypes in Major Depressive Disorder: From Maladaptation to Resilience

Major Depressive Disorder (MDD) is a complex psychiatric disorder with varied molecular mechanisms underlying its aetiology, diagnosis, and treatment. This review explores the crucial roles of stress, genetics, epigenetics, and hormones in shaping susceptibility and resilience to mood disorders. We discuss how acute stress can be beneficial, while prolonged stress disrupts brain function, leading to MDD. The review also highlights the significance of various animal models in understanding depression pathophysiology, including zebrafish, mice, and rats, which exhibit distinct sex differences in stress responses. Furthermore, we delve into the molecular bases of susceptible and resilient phenotypes, focusing on genetic aspects such as gene polymorphisms, mutations, and telomere length alterations. The review also examines epigenetic aspects including DNA methylation, histone acetylation and deacetylation, histone methylation and HMTs, and miRNA, which contribute to the development of MDD. Additionally, we explore the role of hormones such as estrogen, progesterone, and prolactin in modulating stress responses and influencing MDD susceptibility and resilience. Finally, we discuss the clinical implications of these findings, including recent clinical methods for determining MDD susceptibility and resiliency phenotypes. By consolidating the current knowledge and insights, this review aims to provide a comprehensive understanding of the molecular basis of susceptibility and resilience in mood disorders, contributing to the ongoing efforts in combating this debilitating disorder.

Potential clinical impact of predictive modeling of heterogeneous treatment effects: scoping review of the impact of the PATH Statement

Background

The PATH Statement (2020) proposed predictive modeling for examining heterogeneity in treatment effects (HTE) in randomized clinical trials (RCTs). It distinguished risk modeling, which develops a multivariable model predicting individual baseline risk of study outcomes and examines treatment effects across risk strata, from effect modeling, which directly estimates individual treatment effects from models that include treatment, multiple patient characteristics and interactions of treatment with selected characteristics.

Purpose

To identify, describe and evaluate findings from reports that cite the Statement and present predictive modeling of HTE in RCTs.

Data Extraction

We identified reports using PubMed, Google Scholar, Web of Science, SCOPUS through July 5, 2024. Using double review with adjudication, we assessed consistency with Statement recommendations, credibility of HTE findings (applying criteria adapted from the Instrument to assess Credibility of Effect Modification Analyses (ICEMAN)), and clinical importance of credible findings.

Results

We identified 65 reports (presenting 31 risk models, 41 effect models). Contrary to Statement recommendations, only 25 of 48 studies with positive overall findings included a risk model; most effect models included multiple predictors with little prior evidence for HTE. Claims of HTE were noted in 23 risk modeling and 31 effect modeling reports, but risk modeling met credibility criteria more frequently (87 vs 32 percent). For effect models, external validation of HTE findings was critical in establishing credibility. Credible HTE from either approach was usually judged clinically important (24 of 30). In 19 reports from trials suggesting overall treatment benefits, modeling identified subgroups of 5-67% of patients predicted to experience no benefit or net treatment harm. In five that found no overall benefit, subgroups of 25-60% of patients were nevertheless predicted to benefit.

Conclusions

Multivariable predictive modeling identified credible, clinically important HTE in one third of 65 reports. Risk modeling found credible HTE more frequently; effect modeling analyses were usually exploratory, but external validation served to increase credibility.

Viral sepsis: diagnosis, clinical features, pathogenesis, and clinical considerations

Sepsis, characterized as life-threatening organ dysfunction resulting from dysregulated host responses to infection, remains a significant challenge in clinical practice. Despite advancements in understanding host-bacterial interactions, molecular responses, and therapeutic approaches, the mortality rate associated with sepsis has consistently ranged between 10 and 16%. This elevated mortality highlights critical gaps in our comprehension of sepsis etiology. Traditionally linked to bacterial and fungal pathogens, recent outbreaks of acute viral infections, including Middle East respiratory syndrome coronavirus (MERS-CoV), influenza virus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), among other regional epidemics, have underscored the role of viral pathogenesis in sepsis, particularly when critically ill patients exhibit classic symptoms indicative of sepsis. However, many cases of viral-induced sepsis are frequently underdiagnosed because standard evaluations typically exclude viral panels. Moreover, these viruses not only activate conventional pattern recognition receptors (PRRs) and retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs) but also initiate primary antiviral pathways such as cyclic guanosine monophosphate adenosine monophosphate (GMP-AMP) synthase (cGAS)-stimulator of interferon genes (STING) signaling and interferon response mechanisms. Such activations lead to cellular stress, metabolic disturbances, and extensive cell damage that exacerbate tissue injury while leading to a spectrum of clinical manifestations. This complexity poses substantial challenges for the clinical management of affected cases. In this review, we elucidate the definition and diagnosis criteria for viral sepsis while synthesizing current knowledge regarding its etiology, epidemiology, and pathophysiology, molecular mechanisms involved therein as well as their impact on immune-mediated organ damage. Additionally, we discuss clinical considerations related to both existing therapies and advanced treatment interventions, aiming to enhance the comprehensive understanding surrounding viral sepsis.

Coordination chemistry suggests that independently observed benefits of metformin and Zn2+ against COVID-19 are not independent

Independent trials indicate that either oral Zn2+ or metformin can separately improve COVID-19 outcomes by approximately 40%. Coordination chemistry predicts a mechanistic relationship and therapeutic synergy. Zn2+ deficit is a known risk factor for both COVID-19 and non-infectious inflammation. Most dietary Zn2+ is not absorbed. Metformin is a naked ligand that presumably increases intestinal Zn2+ bioavailability and active absorption by cation transporters known to transport metformin. Intracellular Zn2+ provides a natural buffer of many protease reactions; the variable "set point" is determined by Zn2+ regulation or availability. A Zn2+-interactive protease network is suggested here. The two viral cysteine proteases are therapeutic targets against COVID-19. Viral and many host proteases are submaximally inhibited by exchangeable cell Zn2+. Inhibition of cysteine proteases can improve COVID-19 outcomes and non-infectious inflammation. Metformin reportedly enhances the natural moderating effect of Zn2+ on bioassayed proteome degradation. Firstly, the dissociable metformin-Zn2+ complex could be actively transported by intestinal cation transporters; thereby creating artificial pathways of absorption and increased body Zn2+ content. Secondly, metformin Zn2+ coordination can create a non-natural protease inhibitor independent of cell Zn2+ content. Moderation of peptidolytic reactions by either or both mechanisms could slow (a) viral multiplication (b) viral invasion and (c) the pathogenic host inflammatory response. These combined actions could allow development of acquired immunity to clear the infection before life-threatening inflammation. Nirmatrelvir (Paxlovid®) opposes COVID-19 by selective inhibition the viral main protease by a Zn2+-independent mechanism. Pending safety evaluation, predictable synergistic benefits of metformin and Zn2+, and perhaps metformin/Zn2+/Paxlovid® co-administration should be investigated.

Losartan in hospitalized patients with COVID-19 in North America: An individual participant data meta-analysis

BACKGROUND

Angiotensin-converting enzyme inhibitors and angiotensin II receptor blockers (ARBs) have been hypothesized to benefit patients with COVID-19 via the inhibition of viral entry and other mechanisms. We conducted an individual participant data (IPD) meta-analysis assessing the effect of starting the ARB losartan in recently hospitalized COVID-19 patients.

METHODS

We searched ClinicalTrials.gov in January 2021 for U.S./Canada-based trials where an angiotensin-converting enzyme inhibitors/ARB was a treatment arm, targeted outcomes could be extrapolated, and data sharing was allowed. Our primary outcome was a 7-point COVID-19 ordinal score measured 13 to 16 days post-enrollment. We analyzed data by fitting multilevel Bayesian ordinal regression models and standardizing the resulting predictions.

RESULTS

325 participants (156 losartan vs 169 control) from 4 studies contributed IPD. Three were randomized trials; one used non-randomized concurrent and historical controls. Baseline covariates were reasonably balanced for the randomized trials. All studies evaluated losartan. We found equivocal evidence of a difference in ordinal scores 13-16 days post-enrollment (model-standardized odds ratio [OR] 1.10, 95% credible interval [CrI] 0.76-1.71; adjusted OR 1.15, 95% CrI 0.15-3.59) and no compelling evidence of treatment effect heterogeneity among prespecified subgroups. Losartan had worse effects for those taking corticosteroids at baseline after adjusting for covariates (ratio of adjusted ORs 0.29, 95% CrI 0.08-0.99). Hypotension serious adverse event rates were numerically higher with losartan.

CONCLUSIONS

In this IPD meta-analysis of hospitalized COVID-19 patients, we found no convincing evidence for the benefit of losartan versus control treatment, but a higher rate of hypotension adverse events with losartan.

Clinical Features Related to Severity and Mortality among COVID-19 Patients in a Pre-Vaccine Period in Luanda, Angola

Background: Infection due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is associated with clinical features of diverse severity. Few studies investigated the severity and mortality predictors of coronavirus disease 2019 (COVID-19) in Africa. Herein, we investigated the clinical features of severity and mortality among COVID-19 patients in Luanda, Angola. Methods: This multicenter cohort study involved 101 COVID-19 patients, between December 2020 and April 2021, with clinical and laboratory data collected. Analysis was done using independent-sample t-tests and Chi-square tests. The results were deemed significant when p < 0.05. Results: The mean age of patients was 51 years (ranging from 18 to 80 years) and 60.4% were male. Fever (46%), cough (47%), gastrointestinal symptoms (26.7%), and asthenia (26.7%), were the most common symptoms. About 64.4% of the patients presented coexistent disorders, including hypertension (42%), diabetes (17%), and chronic renal diseases (6%). About 23% were non-severe, 77% were severe, and 10% died during hospitalization. Variations in the concentration of neutrophil, urea, creatinine, c-reactive protein, sodium, creatine kinase, and chloride were independently associated with severity and/or mortality (p < 0.05). Conclusion: Several factors contributed to the severity and mortality among COVID-19 patients in Angola. Further studies related to clinical features should be carried out to help clinical decision-making and follow-up of COVID-19 patients in Angola.