胸腺肽α1联合阿帕替尼对原发性肝癌TACE术后患者免疫功能及疗效的影响

目的研究胸腺肽α1联合阿帕替尼对经导管动脉栓塞化疗(TACE)术后的原发性肝癌(PHC)患者免疫功能和疗效的影响。方法选取2016年1月-2018年4月于新乡医学院第一附属医院就诊的84例确诊为PHC并接受TACE治疗的患者,使用随机数字表法分为对照组及实验组。所有患者在TACE术后第1天开始口服阿帕替尼,而实验组同时给予胸腺肽α1治疗。比较两组的免疫功能、肝功能和不良反应。结果对照组随访1年后病死率较实验组高(P<0.05),两组治疗前和治疗后1、3及6个月的CD3^+、CD4^+及CD8^+水平比较,差异有统计学意义(P<0.05),随着治疗时间的延长,患者血清CD3^+、CD4^+水平均呈升高趋势,血清CD8^+水平呈下降趋势(P<0.05)。两组患者治疗前和治疗后1、3及6个月的ALT和AST比较,差异有统计学意义(P<0.05),随着治疗时间的延长,两组AST和TBIL呈降低趋势(P<0.05)。两组治疗后Child-pugh分级比较,差异有统计学意义(P<0.05)。实验组疾病控制率较对照组高(P<0.05)。两组患者不良反应比较,差异无统计学意义(P>0.05)。结论胸腺肽α1联合阿帕替尼可显著提高TACE后PHC患者的免疫力和疾病控制率,肝功能恢复效果佳,且无难以控制的不良反应。

Celastrol inhibits migration, proliferation and transforming growth factor-β2-induced epithelial-mesenchymal transition in lens epithelial cells

AIM: To investigate the mechanism of celastrol in inhibiting lens epithelial cells(LECs) fibrosis, which is the pathological basis of cataract.METHODS: Human LEC line SRA01/04 was treated with celastrol and transforming growth factor-β2(TGF-β2). Wound-healing assay, proliferation assay, flow cytometry, real-time polymerase chain reaction(PCR), Western blot and immunocytochemical staining were used to detect the pathological changes of celastrol on LECs. Then, we cultured Sprague-Dawley rat lens in medium as a semi-in vivo model to find the function of celastrol further.RESULTS: We found that celastrol inhibited the migration of LECs, as well as proliferation(P<0.05). In addition, it induced the G2/M phase arrest by cell cyclerelated proteins(P<0.01). Moreover, celastrol inhibited epithelial-mesenchymal transition(EMT) by the blockade of TGF-β/Smad and Jagged/Notch signaling pathways.CONCLUSION: Our study demonstrates that celastrol could inhibit TGF-β2-induced lens fibrosis and raises the possibility that celastrol could be a potential novel drug in prevention and treatment of fibrotic cataract.

A data-driven method for syndrome type identification and classification in traditional Chinese medicine

The efficacy of traditional Chinese medicine （TCM） treatments for Western medicine （WM） diseases relies heavily on the proper classification of patients into TCM syndrome types. The authors developed a data-driven method for solving the classification problem, where syndrome types were identified and quantified based on statistical patterns detected in unlabeled symptom survey data. The new method is a generalization of latent class analysis （LCA）, which has been widely applied in WM research to solve a similar problem, i.e., to identify subtypes of a patient population in the absence of a gold standard. A well-known weakness of LCA is that it makes an unrealistically strong independence assumption. The authors relaxed the assumption by first detecting symptom co-occurrence patterns from survey data and used those statistical patterns instead of the symptoms as features for LCA. This new method consists of six steps： data collection, symptom co-occurrence pattern discovery, statistical pattern interpretation, syndrome identification, syndrome type identification and syndrome type classification. A software package called Lantern has been developed to support the application of the method. The method was illustrated using a data set on vascular mild cognitive impairment.

Identification and classification of traditional Chinese medicine syndrome types among senior patients with vascular mild cognitive impairment using latent tree analysis

OBJECTIVE： To treat patients with vascular mild cognitive impairment （VMCI） using traditional Chinese medicine （TCM）, it is necessary to classify the patients into TCM syndrome types and to apply different treatments to different types. In this paper, we investigate how to properly carry out the classification for patients with VMCI aged 50 or above using a novel data-driven method known as latent tree analysis （LTA）. METHOD： A cross-sectional survey on VMCI was carried out in several regions in Northern China between February 2008 and February 2012 which resulted in a data set that involves 803 patients and 93 symptoms. LTA was performed on the data to reveal symptom co-occurrence patterns, and the patients were partitioned into clusters in multiple ways based on the patterns. The patient clusters were matched up with syndrome types, and population statistics of the clusters are used to quantify the syndrome types and to establish classification rules. RESULTS： Eight syndrome types are identified： Qi deficiency, Qi stagnation, Blood deficiency, Blood stasis, Phlegm-dampness, Fire-heat, Yang deficiency, and Yin deficiency. The prevalence and symptom occurrence characteristics of each syndrome type are determined. Quantitative classification rules are established for determining whether a patient belongs to each of the syndrome types. CONCLUSION： A solution for the TCM syndrome classification problem for patients with VMCI and aged 50 or above is established based on the LTA of unlabeled symptom survey data. The results can be used as a reference in clinic practice to improve the quality of syndrome differentiation and to reduce diagnosis variances across physicians. They can also be used for patient selection in research projects aimed at finding biomarkers for the syndrome types and in randomized control trials aimed at determining the efficacy of TCM treatments of VMCI.