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We explored the mechanisms and molecular targets of Ejiao Siwu Decoction (EJSW) for treating primary immune thrombocytopenia (ITP) using network pharmacology and molecular docking. Active compounds of EJSW were identified by high-performance liquid chromatography-diode array detector (HPLC-DAD) and high-performance liquid chromatography-mass spectrometry (HPLC-MS) and their targets were obtained from HERB and SwissTargetPrediction, and ITP targets were obtained from Comparative Toxicogenomics Database (CTD) and GeneCards. STRING and Cytoscape were used for protein-protein interaction (PPI) network analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses by WebGestalt yielded a gene-pathway network, Autodock molecular docking was applied to screen targets and active compounds, and cytokines were detected using a cytometric bead array (CBA) human inflammation kit. We identified 14 compounds and 129 targets, and 1,726 ITP targets. RAC-alpha serine/threonine-protein kinase (AKT1), tumour necrosis factor (TNF), interleukin-6 (IL6), caspase-3 (CASP3) and tumour suppressor protein (TP53) were core targets (nodes and edges). Functional annotation identified cofactor binding and coenzyme binding, and 20 significantly enriched pathways. Active compounds of EJSW were successfully docked with ITP targets. Tumour necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1β) were upregulated in ITP patients, vascular endothelial growth factor A (VEGF-A) and vascular endothelial growth factor D (VEGF-D) were downregulated, and EJSW treatment reversed these trends. EJSW may regulate key ITP targets based on the in silico analyses, and protect vascular integrity through AGE-RAGE signalling, complement and coagulation cascades, and VEGF signalling by downregulating TNF-α, IL-1β and other inflammatory factors.

Abstract Background This study aimed to evaluate the effect of sacubitril valsartan (SV) on heart failure (HF) hospitalization and cardiovascular mortality in patients on hemodialysis with HF with preserved ejection fraction (EF; HFpEF). Methods This single-center, prospective study enrolled 155 stable hemodialysis patients with EF > 40% who were followed up for 12 months. Fifty-nine patients were treated with SV; the others were matched for EF (57.89 ± 9.35 vs. 58.00 ± 11.82, P = 0.9) at a ratio of 1:1 and included as controls. The target dosage of SV was 200 mg/day. Results Twenty-three (23/155; 14.84%) had HF with mid-range EF (HFmrEF), while 132 (85.16%) had HFpEF. After SV treatment, the peak early diastolic transmitral flow velocity/peak early diastolic mitral annular tissue velocity(E/e’) improved from 17.19 ± 8.74 to 12.80 ± 5.52 (P = 0.006), the left ventricular (LV) end-diastolic diameter decreased from 53.14 ± 7.67 mm to 51.56 ± 7.44 mm (P = 0.03), and the LV mass index decreased from 165.7 ± 44.6 g/m2 to 154.8 ± 24.0 g/m2 (P = 0.02). LVEF (P = 0.08) and LV global longitudinal strain (P = 0.7) did not change significantly. The composite outcome of first and recurrent HF hospitalization or cardiovascular death showed no difference between group. However, the Acute Dialysis Quality Initiative Workgroup (ADQI) HF class improved in 39 and 15 patients and worsened in 1 and 11 patients in the SV and control groups, respectively (P 40%.

Abstract Background Diabetic retinopathy (DR) has become a leading cause of global blindness as a microvascular complication of diabetes. Regular screening of diabetic retinopathy is strongly recommended for people with diabetes so that timely treatment can be provided to reduce the incidence of visual impairment. However, DR screening is not well carried out due to lack of eye care facilities, especially in the rural areas of China. Artificial intelligence (AI) based DR screening has emerged as a novel strategy and show promising diagnostic performance in sensitivity and specificity, relieving the pressure of the shortage of facilities and ophthalmologists because of its quick and accurate diagnosis. In this study, we estimated the cost-effectiveness of AI screening for DR in rural China based on Markov model, providing evidence for extending use of AI screening for DR. Methods We estimated the cost-effectiveness of AI screening and compared it with ophthalmologist screening in which fundus images are evaluated by ophthalmologists. We developed a Markov model-based hybrid decision tree to analyze the costs, effectiveness and incremental cost-effectiveness ratio (ICER) of AI screening strategies relative to no screening strategies and ophthalmologist screening strategies (dominated) over 35 years (mean life expectancy of diabetes patients in rural China). The analysis was conducted from the health system perspective (included direct medical costs) and societal perspective (included medical and nonmedical costs). Effectiveness was analyzed with quality-adjusted life years (QALYs). The robustness of results was estimated by performing one-way sensitivity analysis and probabilistic analysis. Results From the health system perspective, AI screening and ophthalmologist screening had incremental costs of $180.19 and $215.05 but more quality-adjusted life years (QALYs) compared with no screening. AI screening had an ICER of $1,107.63. From the societal perspective which considers all direct and indirect costs, AI screening had an ICER of $10,347.12 compared with no screening, below the cost-effective threshold (1–3 times per capita GDP of Chinese in 2019). Conclusions Our analysis demonstrates that AI-based screening is more cost-effective compared with conventional ophthalmologist screening and holds great promise to be an alternative approach for DR screening in the rural area of China.

The transcription suppressor factor FBI-1 (the factor that binds to inducer of short transcripts-1) is an important regulator of hepatocellular carcinoma (HCC). In this work, the results showed that FBI-1 promoted the Warburg effect and enhances the resistance of hepatocellular carcinoma cells to molecular targeted agents. Knockdown of FBI-1 via its small-interfering RNA (siRNA) inhibited the ATP level, lactate productions, glucose uptake or lactate dehydrogenase (LDH) activation of HCC cells. Transfection of siFBI-1 also decreased the expression of the Warburg-effect-related factors: hypoxia-inducible factor-1 alpha (HIF-1α), lactate dehydrogenase A (LDHA), or GLUT1, and the epithelial–mesenchymal transition-related factors, Vimentin or N-cadherin. The positive correlation between the expression of FBI-1 with HIF-1α, LDHA, or GLUT1 was confirmed in HCC tissues. Mechanistically, the miR-30c repressed the expression of HIF-1α by binding to the 3′-untranslated region (3′-UTR) of HIF-1α in a sequence-specific manner, and FBI-1 enhanced the expression of HIF-1α and HIF-1α pathway’s activation by repressing the expression of miR. By modulating the miR-30c/HIF-1α, FBI-1 promoted the Warburg effect or the epithelial–mesenchymal transition of HCC cells and promoted the resistance of HCC cells to molecular targeted agents.