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Background The network pharmacology method was used to predict the active components of Banxia Xiexin decoction, its targets and the key signalling pathways that are activated in the treatment of depression and ulcerative colitis to explore the common mechanism. Methods The active components and targets of Banxia Xiexin decoction were obtained by searching the ETCM,TCMSP and TCMIP database. The disease targets of depression and ulcerative colitis were obtained by combining the following the DisGeNET, OMIM,Drugbank,CTD and PharmGKB disease databases. The drug and disease target genes were obtained from the intersection of the herbal medicine targets and the disease targets and were imported into the STRING platform for the analysis of PPI network. The network modules were constructed using Cytoscape software. An analysis of the functional annotations of GO terms and KEGG signalling pathways was performed for each network module. Then, the tissue distribution, sub-cellular distribution and protein attributes of the key targets in the pathway were analysed by the BioGPS, Genecards and DisGeNET databases. Results The mechanism of Banxia Xiexin Decoction in the treatment of depression and ulcerative colitis is related to drug reaction, steroid metabolism, lipid metabolism, inflammatory response, oxidative stress response, cell response to lipopolysaccharide, insulin secretion regulation, estradiol response and other biological functions, mainly through the regulation of 5-hydroxytryptamine synaptic, arachidonic acid metabolism, HIF-1 signaling pathway and NF-kappa B signaling pathway can achieve the effect of same treatment for different diseases. Conclusions The mechanism of Banxia Xiexin Decoction in treating different diseases involves direct or indirect correlation of multiple signal pathways, mainly involved in drug metabolism and lipid metabolism, but also through comprehensive intervention of the body’s nervous system, immune system, digestive system and other systems. The effective components of Banxia Xiexin Decoction are mainly act on eight key target proteins (such as ALB, IL6, VEGFA, TNF, PTGS2, MAPK1, STAT3, EGFR) to carry out multi-target effect mechanism, biological mechanism of treating different diseases with the same treatment, and related mechanism of overall treatment, which provide theoretical reference for further research on the material basis and mechanism of Banxiaxiexin decoction on antidepressant and prevention and treatment of ulcerative colitis.

Background. Dysphagia is a common sequelae after stroke. Noninvasive brain stimulation (NIBS) is a tool that has been used in the rehabilitation process to modify cortical excitability and improve dysphagia. Objective. To systematically evaluate the effect of NIBS on dysphagia after stroke and compare the effects of two different NIBS. Methods. Randomized controlled trials about the effect of NIBS on dysphagia after stroke were retrieved from databases of PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang Data, VIP, and CBM, from inception to June 2021. The quality of the trials was assessed, and the data were extracted according to the Cochrane Handbook for Systematic Reviews of Interventions. A statistical analysis was carried out using RevMan 5.3 and ADDIS 1.16.8. The effect size was evaluated by using the standardized mean difference (SMD) and a 95% confidence interval (CI). Results. Ultimately, 18 studies involving 738 patients were included. Meta-analysis showed that NIBS could improve the dysphagia outcome and severity scale (DOSS) score (standard mean difference SMD = 1.44 , 95% CI 0.80 to 2.08, P < 0.05 ) and the water swallow test score ( SMD = 6.23 , 95% CI 5.44 to 7.03, P < 0.05 ). NIBS could reduce the standardized swallowing assessment (SSA) score ( SMD = − 1.04 , 95% CI -1.50 to -0.58, P < 0.05 ), the penetration-aspiration scale (PAS) score ( SMD = − 0.85 , 95% CI -1.33 to -0.36, P < 0.05 ), and the functional dysphagia scale score ( SMD = − 1.05 , 95% CI -1.48 to -0.62, P < 0.05 ). Network meta-analysis showed that the best probabilistic ranking of the effects of two different NIBS on the DOSS score is rTMS   P = 0.52 > tDCS   P = 0.48 , the best probabilistic ranking of the SSA score is rTMS   P = 0.72 > tDCS   P = 0.28 , and the best probabilistic ranking of the PAS score is rTMS   P = 0.68 > tDCS   P = 0.32 . Conclusion. Existing evidence showed that NIBS could improve swallowing dysfunction and reduce the occurrence of aspiration after stroke, and that rTMS is better than tDCS. Limited by the number of included studies, more large-sample, multicenter, double-blind, high-quality clinical randomized controlled trials are still needed in the future to further confirm the results of this research.

Regulatory agencies are increasingly considering real-world evidence (RWE) to support label expansions of approved medicines. We conducted a comparative effectiveness study to emulate a proposed randomized trial of romiplostim vs. standard-of-care (SOC) therapy among patients with recently diagnosed (≤12 months) immune thrombocytopenia (ITP), that could support expansion of the romiplostim label. We discuss challenges that we encountered and solutions that were developed to address those challenges. Study size was a primary concern, particularly for romiplostim initiators, given the rarity of ITP and the stringent trial eligibility criteria. For this reason, we leveraged multiple data sources (Nordic Country Patient Registry for Romiplostim; chart review study of romiplostim initiators in Europe; Flatiron Health EMR linked with MarketScan claims). Additionally, unlike the strictly controlled clinical trial setting, platelet counts were not measured at regular intervals in the observational data sources, and therefore the end point of durable platelet response often used in trials could not be reliably measured. Instead, the median platelet count was chosen as the primary end point. Ultimately, while we observed a slightly higher median platelet count in the romiplostim group vs. SOC, precision was limited because of small study size (median difference was 11 × 10 9 /L (95% CI: −59, 81)). We underscore the importance of conducting comprehensive feasibility assessments to identify fit-for-purpose data sources with sufficient sample size, data elements, and follow-up. Beyond technical challenges, we also discuss approaches to increase the credibility of RWE, including systematic incorporation of clinical expertise into study design decisions, and separation between decision makers and the data.

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