Transparency and reporting characteristics of randomized controlled trials with Chinese herbal medicine formulas interventions

To enhance the clarity and transparency of reporting in trials involving Chineseherbal medicine(CHM)formulas,the CONSORT extension specifically tailored for CHM formulas(CONSORT-CHM Formulas 2017)was introduced in 2017[1].This statement,along with its corresponding checklist,outlines key elements essential for reporting in randomized controlled trials(RCTs)related tothe rationale,design,analysis,and interpretation of interventions using CHM formulas.The CONSORT-CHM Formulas 2017 elaborated on 7 of 25 CONSORT checklist items,namely title,abstract and keywords,backgrounds and objectives,participants,interventions,outcomes,generalizability,and interpretation.

CShaperApp:Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

Caenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant development.In this study,we developed a desktop software CShaperApp to segment fluorescence-labeled images of cell membranes and analyze cellular morphologies interactively during C.elegans embryogenesis.Based on the previously proposed framework CShaper,CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine-tuned one adapted to their in-house dataset.Experimental results show that it takes about 30 min to process a three-dimensional time-lapse(4D)dataset,which consists of 150 image stacks at a~1.5-min interval and covers C.elegans embryogenesis from the 4-cell to 350-cell stages.The robustness of CShaperApp is also validated with the datasets from different laboratories.Furthermore,modularized implementation increases the flexibility in multi-task applications and promotes its flexibility for future enhancements.As cell morphology over development has emerged as a focus of interest in developmental biology,CShaperApp is anticipated to pave the way for those studies by accelerating the high-throughput generation of systems-level quantitative data collection.The software can be freely downloaded from the website of Github(cao13jf/CShaperApp)and is executable on Windows,macOS,and Linux operating systems.