The efficacy and safety of Zuranolone for treatment of depression: A systematic review and meta-analysis.

RATIONALE: Zuranolone, a newly FDA-approved synthetic neurosteroid, shows promise in treating depression. OBJECTIVES: Our aim is to evaluate Zuranolone's efficacy and safety in treating depression. METHODS: Five databases were searched until September 2023 for relevant randomized clinical trials evaluating the efficacy and safety of zuranolone. The potential risk of bias in the included trials was evaluated by the Cochrane Risk of Bias II guideline Data were extracted and pooled using Review Manager Software (RevMan 5.3). RESULTS: An analysis of eight studies highlights Zuranolone's efficacy in treating depression compared to placebo across most of the outcomes. Notably, the 30mg and 50mg doses demonstrated significant improvements in reducing HAM-D scores by over 50% within a 15-day follow-up (RR) of 1.46 (95% CI [1.27, 1.68], p < 0.0001) and 1.14 (95% CI [1.01, 1.3], p = 0.04). Additionally, the HAM-D ≤ 7% score analysis revealed significant enhancements with the 30mg dose over both 15-day (RR = 1.82, 95% CI [1.44, 2.31], p < 0.0001) and 45-day (RR = 1.43, 95% CI [1.16, 1.77], p = 0.0008) durations. Adverse Events Drug Discontinuation demonstrated no overall significant difference (OR = 1.33, 95% CI: [0.79, 2.23], p = 0.282). Further, specific adverse events, such as headache, showed no significant overall difference between Zuranolone and placebo (OR = 1.11, 95% CI: [0.84, 1.47], p = 0.47), with dose-dependent analysis revealing less headache in the 30 mg group. CONCLUSION: Zuranolone demonstrates favorable tolerability and safety, particularly at 30mg and 50mg doses after 15 days, suggesting its potential and effective treatment for depression.

Does High-Dose Thromboprophylaxis Improve Outcomes in COVID-19 Patients? A Meta-analysis of Comparative Studies.

Background Thromboembolism remains a detrimental complication of novel coronavirus disease (COVID-19) despite the use of prophylactic doses of anticoagulation Objectives This study aimed to compare different thromboprophylaxis strategies in COVID-19 patients Methods We conducted a systematic database search until June 30, 2022. Eligible studies were randomized (RCTs) and nonrandomized studies that compared prophylactic to intermediate or therapeutic doses of anticoagulation in adult patients with COVID-19, admitted to general wards or intensive care unit (ICU). Primary outcomes were mortality, thromboembolism, and bleeding events. Data are analyzed separately in RCTs and non-RCTs and in ICU and non-ICU patients. Results. We identified 682 studies and included 53 eligible studies. Therapeutic anticoagulation showed no mortality benefit over prophylactic anticoagulation in four RCTs (odds ratio [OR] = 0.67, 95% confidence interval [CI], 0.18-2.54). Therapeutic anticoagulation didn't improve mortality in ICU or non-ICU patients. Risk of thromboembolism was significantly lower among non-ICU patients who received enhanced (therapeutic/intermediate) anticoagulation (OR = 0.21, 95% CI, 0.06-0.74). Two additional RCTs (Multiplatform Trial and HEP-COVID), not included in quantitative meta-analysis, analyzed non-ICU patients, and reported a similar benefit with therapeutic-dose anticoagulation. Therapeutic anticoagulation was associated with a significantly higher risk of bleeding events among non-randomized studies (OR = 3.45, 95% CI, 2.32-5.13). Among RCTs, although patients who received therapeutic-dose anticoagulation had higher numbers of bleeding events, these differences were not statistically significant. Studies comparing prophylactic and intermediate-dose anticoagulation showed no differences in primary outcomes. Conclusion There is a lack of mortality benefit with therapeutic-dose over prophylactic-dose anticoagulation in ICU and non-ICU COVID-19 patients. Therapeutic anticoagulation significantly decreased risk of thromboembolism risk in some of the available RCTs, especially among non-ICU patients. This potential benefit, however, may be counter balanced by higher risk of bleeding. Individualized assessment of patient's bleeding risk will ultimately impact the true clinical benefit of anticoagulation in each patient. Finally, we found no mortality or morbidity benefit with intermediate-dose anticoagulation.

NuCLS: A scalable crowdsourcing approach and dataset for nucleus classification and segmentation in breast cancer.

BACKGROUND: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. RESULTS: This article describes a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce an integrated tissue region- and cell-level annotation dataset for training that is the largest such resource for multi-scale analysis of breast cancer histology. This article presents data and analysis results for single and multi-rater annotations from both non-experts and pathologists. We present a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. We also present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes. CONCLUSIONS: This study is the most extensive systematic exploration of the large-scale use of wisdom-of-the-crowd approaches to generate data for computational pathology applications.

Structured crowdsourcing enables convolutional segmentation of histology images.

MOTIVATION: While deep-learning algorithms have demonstrated outstanding performance in semantic image segmentation tasks, large annotation datasets are needed to create accurate models. Annotation of histology images is challenging due to the effort and experience required to carefully delineate tissue structures, and difficulties related to sharing and markup of whole-slide images. RESULTS: We recruited 25 participants, ranging in experience from senior pathologists to medical students, to delineate tissue regions in 151 breast cancer slides using the Digital Slide Archive. Inter-participant discordance was systematically evaluated, revealing low discordance for tumor and stroma, and higher discordance for more subjectively defined or rare tissue classes. Feedback provided by senior participants enabled the generation and curation of 20 000+ annotated tissue regions. Fully convolutional networks trained using these annotations were highly accurate (mean AUC=0.945), and the scale of annotation data provided notable improvements in image classification accuracy. AVAILABILITY AND IMPLEMENTATION: Dataset is freely available at: https://goo.gl/cNM4EL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.