SIRT1 acts as a potential tumor suppressor in oral squamous cell carcinoma

Background

Oral squamous cell carcinoma (OSCC) is the most common malignant tumor in oral cancer, however, the mechanism underlying OSCC tumorigenesis is unknown. SIRT1, has been considered a prominent tumor-suppressing/promoting gene in various solid tumors, although the precise role of SIRT1 in OSCC progression remains unknown.

基于ResNet深度网络的人类蛋白质图谱图像分类方法研究

将基于深度学习的图像分类方法引入人类蛋白质图谱图像分类中,利用ResNet深度网络构建面向人类蛋白质图谱图像分类的深度卷积神经网络,通过混合模式的蛋白质显微镜图像进行验证。结果表明该方法比其他自动分类法具有更高的准确率和精度,大大节约人力和时间。     

基于集成神经网络的类风湿关节炎中医证候分类研究

目的 针对基于AI技术的类风湿关节炎（RA）中医证候多标签分类中存在标签关联性差、泛化性能低等问题,提出构建一种集成神经网络模型来实现RA中医证候分类,并探究其中的特征重要性和风险因素,为RA的诊断和治疗提供参考。方法 本文提出一种集成神经网络模型实现RA中医证候分类。该模型采用一种基于多层神经网络的基分类器提取临床RA多标签样本的深层特征,增强RA特征区分度,根据协方差理论衡量标签相关性,调节分类器链的输入空间,减少RA错误信息传播和冗余度,采用集成学习方法减小分类器链中不合理标签序列对RA特征分类的影响。结果 该模型在10折交叉验证性能参数中表现出优秀的性能,其中汉明损失、1-错误率、准确率和F1值分别为0.0036、0.0248、97.52%、99.18%。与其他常用多标签分类器相比,该模型的性能更为优秀具有更好的分类性能。此外,本文分析了RA中医证候特征重要性,并挖掘了潜在的风险因素。结论 基于集成神经网络模型的RA中医证候分类器具有较高的分类精度和效率,对于RA的临床诊断和治疗具有重要参考价值。     

老年性胆道疾病患者急诊外科手术治疗的临床观察

目的 探讨老年性胆道疾病患者急诊外科手术治疗的术式与疗效.方法 回顾性分析本院收治的56例老年性胆道疾病患者的病例资料,所有患者均为急诊手术治疗,并随机分为两组,治疗组使用改良胆总管空肠Roux-en-y吻合术进行治疗,对照组使用胆管十二指肠吻合术进行治疗,并对两组疗效进行对比.结果 治疗组各项指标均高于对照组,差异有统计学意义（P〈0.05）.结论 改良胆总管空肠Roux-en-y吻合术治疗老年胆道疾病患者的远期疗效要高于胆管十二指肠吻合术,其可作为急诊手术的首选手术术式.     

Artificial Neural Network Modelling of the Effect of Vanadium Addition on the Tensile Properties and Microstructure of High-Strength Tempcore Rebars

In high-strength rebar, the various microstructures obtained by the Tempcore process and the addition of V have a complex effect on the strength improvement of rebar. This study investigated the mechanism of strengthening of high-strength Tempcore rebars upon the addition of vanadium through artificial neural network (ANN) modelling. Various V contents (0.005, 0.072 and 0.14 wt.%) were investigated, and a large amount of bainite and V(C, N) were precipitated in the core of the Tempcore rebar in the high-V specimens. In addition, as the V content increased, the number of these fine precipitates (10–30 nm) increased. The precipitation strengthening proposed by the Ashby–Orowan model is a major contributing factor to the yield-strength increase (35 MPa) of the Tempcore rebar containing 0.140 wt.% V. The ANN model was developed to predict the yield and tensile strengths of Tempcore rebar after the addition of various amounts of V and self-tempering at various temperatures, and it showed high reproducibility compared to the experimental values (R-square was 93% and the average relative error was 2.6%). ANN modelling revealed that the yield strength of the Tempcore rebar increased more significantly with increasing V content (0.01–0.2 wt.%.) at relatively high self-tempering temperatures (≥530 °C). These results provide guidelines for selecting the optimal V content and process conditions for manufacturing high-strength Tempcore rebars.     

Algorithm for Determining Time Series of Phase Transformations in the Solid State Using Long-Short-Term Memory Neural Network

In the numerical analysis of manufacturing processes of metal parts, many material properties depending on, for example, the temperature or stress state, must be taken into account. Often these data are dependent on the temperature changes over time. Strongly non-linear material property relationships are usually represented using diagrams. In numerical calculations, these diagrams are analyzed in order to take into account the coupling between the properties. An example of these types of material properties is the dependence of the kinetics of phase transformations in the solid state on the rate and history of temperature change. In literature, these data are visualized Continuous Heating Transformation (CHT) and Continuous Cooling Transformation (CCT) diagrams. Therefore, it can be concluded that time series analysis is important in numerical modeling. This analysis can also be performed using neural networks. This work presents a new approach to storing and analyzing the data contained in the discussed CCT diagrams. The application of Long-Short-Term Memory (LSTM) neural networks and their architecture to determine the correct values of phase fractions depending on the history of temperature change was analyzed. Moreover, an area of research was elements that determine what type of information should be stored by LSTM network coefficients, e.g., whether the network should store information about changes of single phase transformations, or whether it would be better to extract data from differences between several networks with similar architecture. The purpose of the studied network is strongly different from typical applications of artificial neural networks. The main goal of the network was to store information (even by overfitting the network) rather than some form of generalization that allows computation for unknown cases. Therefore, the authors primarily investigated in the ability of the layer-based LSTM network to store nonlinear time series data. The analyses presented in this paper are an extension of the issues presented in the paper entitled “Model of the Austenite Decomposition during Cooling of the Medium Carbon Steel Using LSTM Recurrent Neural Network”.     

珠江三角洲新生儿转运网络10年工作及效果报告

目的 报告和评价珠江三角洲新生儿转运网络１０年工作及其效果。方法 介绍新生儿转运网络方法和分析有关效果的资料。结果 转运网络现有８７家医院;１０年共转运急,危重症新生儿８１２４例,转运途中病死率为０．４３％;第三级ＮＩＣＵ收容８０８９例,病死率为２．０７％。结论该ＮＴＮ所实行的运转机制合理适用,促进了网络单位包括三级ＮＩＣＵ的进步提高,是降低新生儿死亡率的重要措施。     

Study on Toughening and Temperature Sensitivity of Polyurethane Cement (PUC)

Polyurethane cement (PUC) is now commonly used in the reinforcement of old bridges, which exhibit various issues such as poor toughness, temperature-sensitive mechanical properties, and brittle failure. These problems can lead to the failure of the reinforcement effect of the PUC on old bridges in certain operating environments, leading to the collapse of such reinforced bridges. In order to alleviate these shortcomings, in this study, the toughness of PUC is improved by adding polyvinyl alcohol (PVA) fiber, carbon fiber, and steel fiber. In addition, we study the change law of the flexural strength of PUC between −40 °C and +40 °C. The control parameters evaluated are fiber type, fiber volume ratio, and temperature. A series of flexural tests and scanning electron microscope (SEM) test results show that the flexural strength first increases and then decreases with the increase in the volume-doping ratio of the three fibers. The optimum volume-mixing ratios of polyvinyl alcohol (PVA) fiber, carbon fiber, and steel fiber are 0.3%, 0.04% and 1%, respectively. Excessive addition of fiber will affect the operability and will adversely affect the mechanical properties. The flexural strength of both fiber-reinforced and control samples decreases with increasing temperature. Using the flexural test results, a two-factor (fiber content, temperature) BP neural network flexural strength prediction model is established. It is verified that the model is effective and accurate, and the experimental value and the predicted value are in good agreement.     

Oral Administration of East Asian Herbal Medicine for Inflammatory Skin Lesions in Plaque Psoriasis: A Systematic Review,Meta-Analysis,and Exploration of Core Herbal Materials

Psoriasis is an inflammatory autoimmune skin disease with various clinical manifestations. The aim of this review was to systematically evaluate the efficacy and safety of oral administration of East Asian herbal medicine (EAHM) for inflammatory skin lesions in psoriasis and to explore core herbal materials for drug discovery. A comprehensive search was conducted in 10 electronic databases for randomized controlled trials from their inception until 29 July 2021. Statistical analysis was performed in R version 4.1.2 and R studio. When heterogeneity in studies was detected, the cause was identified through sensitivity analysis, meta-regression, and subgroup analysis. Methodological quality was independently assessed using the revised tool for risk of bias in randomized trials. A total of 56 trials with 4966 psoriasis patients met the selection criteria. Meta-analysis favored EAHM monotherapy on Psoriasis Area Severity Index (PASI) 70 (RR: 1.2845; 95% CI: 1.906 to 1.3858, p < 0.0001), PASI 60 (RR: 1.1923; 95% CI: 1.1134 to 1.2769, p < 0.0001), continuous PASI score (MD: −2.3386, 95% CI: −3.3068 to −1.3704, p < 0.0001), IL-17, IL-23, TNF-α, and Dermatology Life Quality Index. Patients treated with EAHM monotherapy had significantly reduced adverse events incidence rate. In addition, based on additional examination of the herb data included in this meta-analysis, 16 core materials were identified. They are utilized in close proximity to one another, and all have anti-inflammatory properties. The findings in this study support that oral EAHM monotherapy may be beneficial for inflammatory skin lesions in psoriasis. Meanwhile, the identified core materials are expected to be utilized as useful drug candidate hypotheses through follow-up studies on individual pharmacological activities and synergistic effects.