Capsaicin exerts anti-benign prostatic hyperplasia effects via inhibiting androgen receptor signaling pathway

Background:Benign prostatic hyperplasia(BPH)is a common condition in middle-aged and elderly men.Enlargement of the prostate causes lower urinary tract symptoms.Capsaicin is a phytochemical extracted from chili peppers and exerts many pharmacological actions,such as anti-tumor and anti-inflammatory effects.Methods:Our study investigated the effect of capsaicin in vitro and in a mouse model in vivo.A prostatic stromal myofibroblast cell line(WPMY-1)was co-incubated with testosterone(1µM)and different concentrations of capsaicin(10–100µM)for 24 and 48 h.Capsaicin(10–100µM)significantly inhibited testosterone-treated WPMY-1 cell growth at 48 h by MTT assay.The testosterone propionate(7.5 mg/kg)-induced BPH mouse model was used to examine the anti-proliferative effect of capsaicin.Treatment with capsaicin(10 mg/kg)for 14 days significantly attenuated prostatic hyperplasia.Finasteride was used as a positive control.Results:Capsaicin significantly decreased prostate weight and prostate index(prostate/body weight ratio)in BPH mice.The expression of 5α-reductase type II,androgen receptor(AR)and prostate specific antigen(PSA)protein expression and PSA serum were all significantly reduced in capsaicin-treated BPH mice.In addition,capsaicin also activated transient receptor potential vanilloid 1 mediated apoptosis and autophagy in BPH mice.Conclusion:These results demonstrate multiple positive effects of capsaicin in controlling prostate growth and suggest its therapeutic potential in the treatment of BPH.

An Interpretable Depression Prediction Model for the Elderly Based on ISSA Optimized LightGBM

Depression is one of the most severe mental health illnesses among senior citizens.Aiming at the low accuracy and poor interpretability of traditional prediction models,a novel interpretable depression predictive model for the elderly based on the improved sparrow search algorithm(ISSA)optimized light gradient boosting machine(LightGBM)and Shapley Additive exPlainations(SHAP)is proposed.First of all,to achieve better optimization ability and convergence speed,various strategies are used to improve SSA,including initialization population by Halton sequence,generating elite population by reverse learning and multi-sample learning strategy with linear control of step size.Then,the ISSA is applied to optimize the hyper-parameters of light gradient boosting machine(LightGBM)to improve the prediction accuracy when facing massive high-dimensional data.Finally,SHAP is used to provide global and local interpretation of the prediction model.The effectiveness of the proposed method is validated by a series of comparative experiments based on a real-world dataset.

非小细胞肺癌T7噬菌体展示文库的构建(英文)

Objective:Currently,only a limited numbers of tumor markers for non small lung cancer(NSCLC) diagnosis,new biomarker,such as serum autoantibodies may improve the early detection of lung cancer.Our objective is construction human lung squamous carcinoma and adenocarcinoma T7 phage display cDNA library from the tissues of NSCLC patients.Methods:mRNA was isolated from a pool of total RNA extract from NSCLC tissues obtained from 5 adenocarcinomas and 5 squamous carcinomas,and then mRNA was reverse transcribed into double stranded cDNA.After digestion,the cDNA was inserted into T7Select 10-3 vector.The phage display cDNA library was constructed by package reaction in vitro and plate proliferation.Plaque assay and PCR were used to evaluate the library.Results:Two T7 phage display cDNA library were established.Plaque assay show the titer of lung squamas carcinoma library was 1.8 × 106 pfu,and the adenocarcinoma library was 5 × 106 pfu.The phage titer of the amplified library were 3.2 × 1010 pfu/mL and 2.5 × 1010 pfu/mL.PCR amplifica-tion of random plaque show insert ratio were 100%(24/24) in adenocarcinoma library and 95.8% in human lung squamas carcinoma library(23/24).Insert range from 300 bp to 1 500 bp.Conclusion:Two phage display cDNA library from NSCLC were constructed.

(DarkAI)Mapping the large-scale density field of dark matter using artificial intelligence

Herein,we present a deep-learning technique for reconstructing the dark-matter density field from the redshift-space distribution of dark-matter halos.We built a UNet-architecture neural network and trained it using the COmoving Lagrangian Acceleration fast simulation,which is an approximation of the N-body simulation with 5123 particles in a box size of 500 h^(-1)Mpc.Further,we tested the resulting UNet model not only with training-like test samples but also with standard N-body simulations,such as the Jiutian simulation with 61443particles in a box size of 1000 h^(-1)Mpc and the ELUCID simulation,which has a different cosmology.The real-space dark-matter density fields in the three simulations can be reconstructed reliably with only a small reduction of the cross-correlation power spectrum at 1%and 10%levels at k=0.1 and 0.3 h Mpc-1,respectively.The reconstruction clearly helps to correct for redshift-space distortions and is unaffected by the different cosmologies between the training(Planck2018)and test samples(WMAP5).Furthermore,we tested the application of the UNet-reconstructed density field to obtain the velocity&tidal field and found that this approach provides better results compared with the traditional approach based on the linear bias model,showing a 12.2%improvement in the correlation slope and a 21.1%reduction in the scatter between the predicted and true velocities.Thus,our method is highly efficient and has excellent extrapolation reliability beyond the training set.This provides an ideal solution for determining the three-dimensional underlying density field from the plentiful galaxy survey data.

Repurposed benzydamine targeting CDK2 suppresses the growth of esophageal squamous cell carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the leading causes of cancer death worldwide. It is urgent to develop new drugs to improve the prognosis of ESCC patients. Here, we found benzydamine, a locally acting non-steroidal anti-inflammatory drug, had potent cytotoxic effect on ESCC cells. Benzydamine could suppress ESCC proliferation in vivo and in vitro. In terms of mechanism, CDK2 was identified as a target of benzydamine by molecular docking, pull-down assay and in vitro kinase assay. Specifically, benzydamine inhibited the growth of ESCC cells by inhibiting CDK2 activity and affecting downstream phosphorylation of MCM2, c-Myc and Rb, resulting in cell cycle arrest. Our study illustrates that benzydamine inhibits the growth of ESCC cells by downregulating the CDK2 pathway.

赣江鱼类物种更新名录

赣江是长江的主要支流之一,淡水鱼类多样性极其丰富,但是目前对于该河流的鱼类多样性还未有充分的了解。本研究于2016年9月至2017年8月对赣江鱼类进行了实地调查,结合历史文献资料并参考最新的分类学成果,更新了赣江淡水鱼类名录。结果表明,赣江共有淡水鱼类180种,隶属于12目31科93属。其中,土著鱼类174种,外来鱼类6种。赣江鱼类以鲤形目为主;鲤科种类最多,其次是鲿科。更新的物种名录中,包含23个新记录土著种,其中有5个未被描述的新种;有36个历史资料记载的种类被剔除出物种名录;25个物种发生了分类地位的变更。此外,本次调查有28种鱼类未采集到,说明这些鱼类的种群数量正在急剧下降,而这些鱼类分别属于洄游性、流水性和产漂流性卵等独特生态类型,说明赣江鱼类多样性受到了严重的人为干扰。本研究所给出的赣江鱼类的更新物种名录,可为今后的赣江鱼类多样性保护提供理论基础。

机器人手术系统辅助下的结直肠癌经自然腔道取标本手术与常规辅助切口取标本手术的近期疗效对比研究

目的比较机器人无辅助切口NOSES组(R-NOSES)和机器人常规辅助切口组(R-LA)在结直肠癌治疗中的近期疗效差异。方法纳入203例于哈尔滨医科大学附属第二医院肿瘤中心行机器人辅助下结直肠癌根治术的患者的病例资料,包括患者的术前一般资料、手术相关资料、病理资料以及术后相关资料。对比分析R-NOSES和R-LA的近期疗效差异。结果本研究共纳入R-NOSES患者36例,R-LA患者167例。通过倾向性评分后,R-NOSES患者36例,R-LA患者25例,R-NOSES组的平均手术时间长于R-LA组(267.64±57.23 min vs. 239.80±33.68 min,t=-2.181,P=0.033)。R-NOSES组的平均术后住院时间(t=2.860,P=0.006)、平均术后进食流质食物时间(t=2.929,P=0.005)和平均术后首次排气时间(t=3.046,P=0.003)均短于R-LA组,R-NOSES组并发症比例较R-LA组少(χ^(2)=4.164,P=0.041),术后第一日疼痛评分低于R-LA组(t=2.994,P=0.005)。R-NOSES组术后躯体功能(t=-7.530,P<0.001)、角色功能(t=-6.359,P<0.001)、情绪功能(t=-4.812,P<0.001)恢复更好,整体健康情况优于R-LA组(t=-4.288,P<0.001)。结论与R-LA组相比,R-NOSES组虽然平均手术时间略有延长,但术后首次排气时间、术后首次进流食时间、术后住院时间均更短,术后并发症少,功能恢复更快,术后疼痛更少。