Mechanism of action of Orthosiphon stamineus against non-alcoholic fatty liver disease: Insights from systems pharmacology and molecular docking approaches

Non-alcoholic fatty liver disease (NAFLD) is one of the most common complications of a metabolic syndrome caused by excessive accumulation of fat in the liver. Orthosiphon stamineus also known as Orthosiphon aristatus is a medicinal plant with possible potential beneficial effects on various metabolic disorders. This study aims to investigate the in vitro inhibitory effects of O. stamineus on hepatic fat accumulation and to further use the computational systems pharmacology approach to identify the pharmacokinetic properties of the bioactive compounds of O. stamineus and to predict their molecular mechanisms against NAFLD. Methods: The effects of an ethanolic extract of O. stamineus leaves on cytotoxicity, fat accumulation and antioxidant activity were assessed using HepG2 cells. The bioactive compounds of O. stamineus were identified using LC/MS and two bioinformatics databases, namely the Traditional Chinese Medicine Integrated Database (TCMID) and the Bioinformatics Analysis Tool for the Molecular Mechanism of Traditional Chinese Medicine (BATMAN-TCM). Pathway enrichment analysis was performed on the predicted targets of the bioactive compounds to provide a systematic overview of the molecular mechanism of action, while molecular docking was used to validate the predicted targets. Results: A total of 27 bioactive compounds corresponding to 50 potential NAFLD-related targets were identified. O. stamineus exerts its anti-NAFLD effects by modulating a variety of cellular processes, including oxidative stress, mitochondrial β-oxidation, inflammatory signalling pathways, insulin signalling, and fatty acid homeostasis pathways. O. stamineus is significantly targeting many oxidative stress regulators, including JNK, mammalian target of rapamycin (mTOR), NFKB1, PPAR, and AKT1. Molecular docking analysis confirmed the expected high affinity for the potential targets, while the in vitro assay indicates the ability of O. stamineus to inhibit hepatic fat accumulation. Conclusion: Using the computational systems pharmacology approach, the potentially beneficial effect of O. stamineus in NAFLD was indicated through the combination of multiple compounds, multiple targets, and multicellular components.     

Phase 1/2 clinical trial of COVID-19 vaccine in Japanese participants: A report of interim findings

We initiated a randomized, placebo-controlled, phase 1/2 trial to evaluate the safety and immunogenicity of the S-268019-b recombinant protein vaccine, scheduled as 2 intramuscular injections given 21 days apart, in 60 randomized healthy Japanese adults. We evaluated 2 regimens of the S-910823 antigen (5 μg [n = 24] and 10 μg [n = 24]) with an oil-in-water emulsion formulation and compared against placebo (n = 12). Reactogenicity was mild in most participants. No serious adverse events were noted. For both regimens, vaccination resulted in robust IgG and neutralizing antibody production at days 36 and 50 and predominant T-helper 1-mediated immune reaction, as evident through antigen-specific polyfunctional CD4+ T-cell responses with IFN-γ, IL-2, and IL-4 production on spike protein peptides stimulation. Based on the interim analysis, the S-268019-b vaccine is safe, produces neutralizing antibodies titer comparable with that in convalescent serum from COVID-19-recovered patients. However, further evaluation of the vaccine in a large clinical trial is warranted.     

Artificial intelligence and clinical data suggest the T cell-mediated SARS-CoV-2 nonstructural protein intranasal vaccines for global COVID-19 immunity

Advanced computational methodologies suggested SARS-CoV-2, nonstructural proteins ORF1AB, ORF3a, as the source of immunodominant peptides for T cell presentation. T cell immunity is long-lasting and compatible with COVID-19 pathology. Based on the supporting clinical data, nonstructural SARS-CoV-2 protein vaccines could provide global immunity against COVID-19.     

SLC及其受体CCR7与Ⅰ期非小细胞肺癌淋巴结微转移的相关性

目的 探讨溶质载体蛋白（SLC）及其受体趋化因子受体7（CCR7）与I期非小细胞肺癌（NSCLC）淋巴结微转移的相关性。方法 选取2019年1月~2020年3月于我院就诊的I期NSCLC患者127例为研究对象，按照淋巴结微转移情况分为对照组92例和转移组35例，所有患者入院后均通过根治术切除病灶，通过免疫组化方式检测病灶中SLC7A11及CCR7含量，并收集患者临床资料、实验室检查资料及影像学检查资料。通过Logistic回归分析评价SLC7A11及CCR7与淋巴结微转移之间的关系。最后通过建立ROC曲线分析两者及其联合检测对NSCLC患者微淋巴结转移的预测价值。结果 两组患者SLC7A11及CCR7表达水平存在显著差异（P＜0.05）。转移组患者病灶直径、支气管受累及TLG显著高于对照组（P＜0.05）。病灶直径（OR=49.254,95%CI=11.062~507.604）是影响NSCLC淋巴结微转移的独立危险因素（P＜0.05）。SLC7A11（OR=8.622）及CCR7（OR=8.709）表达水平是影响NSCLC淋巴结微转移的独立因素（P＜0.05）。SLC7A11、CCR7及联合诊断对NSCLC淋巴结微转移具有较好的检测价值（均P＜0.05）。联合检测特异度显著高于 SLC7A11及CCR7单独检测（2=7.292，15.125；均P＜0.01）。结论 SLC家族的中SLC7A11及其受体CCR7与NSCLC患者微淋巴结转移显著相关。     

骨形态发生蛋白2及碱性成纤维生长因子2对大鼠骨髓间充质干细胞膜片增殖和成骨分化的影响

文题释义： 细胞膜片技术：是在体外接种培养高密度的细胞，使其相互融合生长至100%而形成的透明致密膜状物。该技术不需要胰酶消化即可收集细胞，因此保留了大量的胞外基质、细胞间连接以及细胞-基质连接等结构。目前细胞膜片技术已成为组织工程领域的研究热点，已被推广应用于牙周膜、角膜、心脏、软骨、食管等多种组织器官修复。 成骨细胞：主要由内外骨膜和间充质始祖细胞分化而来，在复杂的骨形成过程中发挥着主要的功能，承担着骨基质的合成、分泌和矿化。骨髓间充质干细胞具有多向分化潜能，能定向分化为成骨细胞，其成骨分化过程可受多种因素的影响，如细胞因子的调控、遗传因素和激素水平等。背景：现阶段骨形态发生蛋白2和碱性成纤维生长因子2对骨髓间充质干细胞膜片增殖、成骨分化的影响和作用机制还尚未可知，如何将生长因子与组织工程细胞膜片技术相整合，最终将其用于骨缺损修复具有重要意义。 目的：探讨单独及联合应用骨形态发生蛋白2和碱性成纤维生长因子2对骨髓间充质干细胞膜片增殖和成骨分化的影响。 方法：体外分离培养鉴定SD大鼠骨髓间充质干细胞并构建细胞膜片，选用不同质量浓度的骨形态发生蛋白2和碱性成纤维生长因子2单独及联合诱导骨髓间充质干细胞膜片，CCK-8法结合碱性磷酸酶活性检测确定2种因子促进膜片增殖和成骨分化的最佳有效质量浓度；然后对骨髓间充质干细胞膜片进行成骨诱导，通过大体及显微镜观察、Vonkossa染色、茜素红染色、RT-PCR检测相关成骨标志物来评估诱导效果。 结果与结论：单独应用骨形态发生蛋白2可增强骨髓间充质干细胞膜片的碱性磷酸酶活性，最佳质量浓度为100 μg/L(P < 0.001)，单独应用碱性成纤维生长因子2能加速骨髓间充质干细胞膜片的增殖，最佳质量浓度为20 μg/L(P < 0.001)，而联合应用既可以促进膜片增殖又能提高其碱性磷酸酶活性(P < 0.001)；经成骨诱导后，4组膜片在形态学上无明显差异，均能诱导骨髓间充质干细胞膜片的成骨分化，其中联合组钙结节最明显(P < 0.001)，可显著促进膜片晚期成骨分化并抑制其早期成骨分化，具有明显的协同促进作用(P < 0.001)。结果表明，骨形态发生蛋白2和碱性成纤维生长因子2联合应用时具有协同作用，既可以促进骨髓间充质干细胞膜片增殖，又能显著增强其成骨诱导能力。ORCID: 0000-0003-1918-579X(何惠宇) 中国组织工程研究杂志出版内容重点：干细胞；骨髓干细胞；造血干细胞；脂肪干细胞；肿瘤干细胞；胚胎干细胞；脐带脐血干细胞；干细胞诱导；干细胞分化；组织工程     

死亡结构域相关蛋白及其在宫颈病变中的研究进展

宫颈癌对妇女健康构成严重威胁,人乳头瘤病毒感染与宫颈病变及宫颈癌的发生密切相关。关于宫颈癌发生发展的机制仍在研究中。近年研究发现一种多功能核蛋白,即死亡结构域相关蛋白(death domain associated protein,Daxx),其与细胞内蛋白或病毒蛋白相互作用,参与调节细胞凋亡、转录调控、抗病毒等细胞活动,在不同途径中发挥不同的生理或病理作用。通过对Daxx功能及其作用机制的研究有助于进一步阐明宫颈癌发生发展的机制,有助于发现新的预防和治疗方法。综述Daxx的一般特性和研究现况及其在宫颈病变的研究进展。     

Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features

PurposeThe purpose of this study was to determine whether computed tomography (CT)-based machine learning of radiomics features could help distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsEighty-nine patients with AIP (65 men, 24 women; mean age, 59.7 ± 13.9 [SD] years; range: 21–83 years) and 93 patients with PDAC (68 men, 25 women; mean age, 60.1 ± 12.3 [SD] years; range: 36–86 years) were retrospectively included. All patients had dedicated dual-phase pancreatic protocol CT between 2004 and 2018. Thin-slice images (0.75/0.5 mm thickness/increment) were compared with thick-slices images (3 or 5 mm thickness/increment). Pancreatic regions involved by PDAC or AIP (areas of enlargement, altered enhancement, effacement of pancreatic duct) as well as uninvolved parenchyma were segmented as three-dimensional volumes. Four hundred and thirty-one radiomics features were extracted and a random forest was used to distinguish AIP from PDAC. CT data of 60 AIP and 60 PDAC patients were used for training and those of 29 AIP and 33 PDAC independent patients were used for testing.ResultsThe pancreas was diffusely involved in 37 (37/89; 41.6%) patients with AIP and not diffusely in 52 (52/89; 58.4%) patients. Using machine learning, 95.2% (59/62; 95% confidence interval [CI]: 89.8–100%), 83.9% (52:67; 95% CI: 74.7–93.0%) and 77.4% (48/62; 95% CI: 67.0–87.8%) of the 62 test patients were correctly classified as either having PDAC or AIP with thin-slice venous phase, thin-slice arterial phase, and thick-slice venous phase CT, respectively. Three of the 29 patients with AIP (3/29; 10.3%) were incorrectly classified as having PDAC but all 33 patients with PDAC (33/33; 100%) were correctly classified with thin-slice venous phase with 89.7% sensitivity (26/29; 95% CI: 78.6–100%) and 100% specificity (33/33; 95% CI: 93–100%) for the diagnosis of AIP, 95.2% accuracy (59/62; 95% CI: 89.8–100%) and area under the curve of 0.975 (95% CI: 0.936–1.0).ConclusionsRadiomic features help differentiate AIP from PDAC with an overall accuracy of 95.2%.