Explore the Application Law of Disperse Stagnation and Purge Heat with Bitter-Acrid Medicinals in Treating Endogenous Cough

内伤咳嗽以肺失宣肃、肺气上逆为基本病机。“苦辛通降助肺法”即借助药物气味的升降作用以使肺气宣降复常的治法，用于治疗内伤咳嗽可通过苦辛配伍来助肺宣降、调节气机。张之文教授为全国温病学泰斗，提出以“苦辛通降助肺法”治疗内伤咳嗽，临床效果显著。本研究以张老医案为基础，通过数据挖掘来管窥“苦辛通降助肺法”治疗内伤咳嗽的应用规律，为内伤咳嗽的治疗提供新的思路和方法。     

Primary Conservative Therapy for Symptomatic Isolated Mesenteric Artery Dissection with Severely Compressed True Lumen or Large Dissecting Aneurysm

PurposeTo investigate the safety and effectiveness of primary conservative therapy for patients with symptomatic isolated mesenteric artery dissection (IMAD) with a severely compressed true lumen and/or a large dissecting aneurysm.Materials and MethodsA total of 35 consecutive patients (all men; median age, 53 y) with symptomatic IMAD with a severely compressed true lumen and/or a large dissecting aneurysm but without intestinal necrosis or arterial rupture who were treated with primary conservative therapy between November 2018 and February 2020 were assessed. A severely compressed true lumen was defined as luminal stenosis > 70%. A large dissecting aneurysm was defined as dissecting aneurysm diameter ≥ 1.5 times larger than the normal mesenteric artery diameter.ResultsThere was a strong positive relationship among abdominal pain, degree of luminal stenosis, and length of dissection (R = 0.811; P < .001). Conservative treatment was successful in all patients. Abdominal pain was eliminated within 4.7 d ± 4.8 (range, 2–31 d) in all patients, within 3.6 d ± 1.2 (range, 2–6) in the 31 patients with minor or moderate abdominal pain, and within 13.3 d ± 11.9 (range, 6–31 d) in the 4 patients with severe abdominal pain. Complete or partial remodeling of the mesenteric artery was achieved in 6 (17.1%) and 29 (82.9%) patients, respectively, during 8.6 mo ± 4.3 of follow-up.ConclusionsPrimary conservative therapy can be used safely and effectively in patients with symptomatic IMAD with a severely compressed true lumen and/or a large dissecting aneurysm but without intestinal necrosis or arterial rupture.     

幽门螺杆菌及胃蛋白酶原、胃泌素17水平在胃癌前病变诊断中的应用价值

目的 探讨幽门螺杆菌(H.pylori)与胃蛋白酶原(PG)、胃泌素17(G17)与胃癌前病变的相关性。方法 选取在我院行胃镜检查的患者856例,其中471例病理检出癌前病变的患者为病例组,385例未检出癌前病变的患者为对照组。病例组按病理诊断结果分为慢性萎缩性胃炎(CAG)组157例,肠化(IM)组153例,低级别上皮内瘤变(LGIN)组82例,高级别上皮内瘤变(HGIN)组79例。检测各组H.pylori感染分型及PG、G17水平。结果 CAG组Ⅰ型感染率高于对照组(P<0.05);CAG组PGⅠ 低于对照组(P<0.05);IM组、LGIN组、HGIN组PGⅡ高于对照组(P<0.05);CAG组、IM组、LGIN组、HGIN组PGⅠ/PGⅡ(PGR)均低于对照组(P<0.05);LGIN组、HGIN组G17均高于对照组(P<0.05);PGⅠ、PGⅡ、G17与H.pylori感染阳性存在正相关,PGR与H.pylori感染阳性存在负相关(P<0.05)。结论 H.pylori感染分型、PG、G17水平与胃癌前病变密切相关,检测H.pylori感染分型、PG、G17对于早期筛查胃癌前病变具有重要价值。     

益心舒胶囊联合常规疗法治疗非瓣膜性心房颤动临床研究

目的：观察益心舒胶囊联合常规疗法治疗非瓣膜性心房颤动的疗效。方法：纳入96 例气阴亏虚兼瘀血内阻型非瓣膜性心房颤动患者，采用随机数字表法分为对照组和观察组各48 例。2 组患者均予常规降压、降糖、调脂以及调整心态、饮食等治疗措施，并服用华法林钠片治疗，观察组加予益心舒胶囊治疗。2 组疗程均为3 个月。观察2 组治疗前及治疗3 个月后凝血功能指标、心房颤动发作情况、血浆N 末端B 型利钠肽原（NT-proBNP） 以及血液流变学水平的变化。结果：治疗后，2 组凝血酶原时间（PT）、活化部分凝血活酶时间（APTT）、凝血酶时间（TT）、国际标准化比值（INR） 均较治疗前增加（P＜0.01），观察组PT、APTT、TT、INR 均长于或高于对照组（P＜0.01）。2 组心房颤动发作时间与发作频率均较治疗前缩短或降低（P＜0.01），观察组心房颤动发作时间与发作频率均短于或低于对照组（P＜0.01）。2 组血浆NT-proBNP 水平均较治疗前降低（P＜0.01），观察组血浆NT-proBNP 水平均低于对照组（P＜0.01）。2 组红细胞比容（HCT）、纤维蛋白原（Fb）、全血黏度、血浆比黏度均较治疗前降低（P＜0.01），观察组4 项指标值均低于对照组（P＜0.01）。结论：在常规治疗基础上加服益心舒胶囊治疗气阴亏虚兼瘀血内阻型非瓣膜性心房颤动，在改善患者的凝血功能、心功能以及缓解临床症状方面疗效明显，且可有效改善血液流变学水平。     

基于网络药理学和分子对接技术研究黄芪-赤芍治疗COPD的作用机制＊

目的 基于网络药理学和分子对接技术探究黄芪-赤芍配伍对治疗慢性阻塞性肺疾病（chronic obstructive pulmonary disease，COPD）的作用机制。方法 利用TCMSP，Pharmmaper数据库，筛选黄芪-赤芍治疗COPD的活性成分和潜在靶点；结合Genecards数据库挖掘的COPD相关靶点，对黄芪-赤芍药对与COPD靶点进行PPI网络构建，交互处理得到黄芪-赤芍药对治疗COPD的关键靶点，并进行GO分析和KEGG通路富集分析；并采用分子对接技术将主要活性成分与TNF-α（肿瘤坏死因子），IL-6（白细胞介素6）等进行分子对接；最后利用A549炎症细胞与人脐静脉内皮细胞缺氧损伤模型进行体外细胞实验对结果加以验证。结果 黄芪-赤芍药对中44个有效成分作用于COPD，核心成分为：槲皮素、山奈酚、丁子香萜、芍药苷、（2R，3R）-4-methoxyl-distylin、二氢异黄酮；黄芪-赤芍药对通过IL6、PTGS2、TNF等113个靶蛋白，调控Ras、PI3KAkt、IL-17等多条信号通路治疗COPD，且分子对接结果显示槲皮素、山奈酚、丁子香萜、芍药苷与IL-6、PTGS2、TNF大分子蛋白有良好的结合性，体外细胞试验证实，槲皮素与山奈酚均能减少IL-8，MMP-9炎症因子的分泌，具有不同程度的抗炎效果；芍药苷有明显的扩血管、抗血栓之效。结论 黄芪-赤芍药对治疗COPD具有多成分、多靶点、多通路、整体调节的作用特点。初步揭示了黄芪-赤芍药对通过抑制炎症反应、调节上皮细胞生长增强保护屏障等预测出黄芪-赤芍药对治疗COPD的潜在作用机制，以期为其活性成分的药效物质基础提供理论研究和思路。     

AI-assisted superresolution cosmological simulations

Cosmological simulations of galaxy formation are limited by finite computational resources. We draw from the ongoing rapid advances in artificial intelligence (AI; specifically deep learning) to address this problem. Neural networks have been developed to learn from high-resolution (HR) image data and then make accurate superresolution (SR) versions of different low-resolution (LR) images. We apply such techniques to LR cosmological N-body simulations, generating SR versions. Specifically, we are able to enhance the simulation resolution by generating 512 times more particles and predicting their displacements from the initial positions. Therefore, our results can be viewed as simulation realizations themselves, rather than projections, e.g., to their density fields. Furthermore, the generation process is stochastic, enabling us to sample the small-scale modes conditioning on the large-scale environment. Our model learns from only 16 pairs of small-volume LR-HR simulations and is then able to generate SR simulations that successfully reproduce the HR matter power spectrum to percent level up to 16 h−1Mpc and the HR halo mass function to within 10% down to 1011 M⊙. We successfully deploy the model in a box 1,000 times larger than the training simulation box, showing that high-resolution mock surveys can be generated rapidly. We conclude that AI assistance has the potential to revolutionize modeling of small-scale galaxy-formation physics in large cosmological volumes.

As telescopes and satellites become more powerful, observational data on galaxies, quasars, and the matter in intergalactic space becomes more detailed and covers a greater range of epochs and environments in the Universe. Our cosmological simulations (see, e.g., ref. 1) must also become more detailed and more wide-ranging in order to make predictions and test the effects of different physical processes and different dark-matter candidates. Even with supercomputers, we are forced to decide whether to maximize either resolution or volume, or else compromise on both. These limitations can be overcome through the development of methods that leverage techniques from the artificial intelligence (AI) revolution (see, e.g., ref. 2) and make superresolution (SR) simulations possible. In the present work, we begin to explore this possibility, combining knowledge and existing superscalable codes for petascale-plus cosmological simulations (3) with machine learning (ML) techniques to effectively create representative volumes of the Universe that incorporate information from higher-resolution models of galaxy formation. Our first attempts, presented here, involve simulations with dark matter and gravity only, and extensions to full hydrodynamics will follow. This hybrid approach, which will imply offloading simulations to neural networks (NNs) and other ML algorithms, has the promise to enable the prediction of quasar, supermassive black hole, and galaxy properties in a way that is statistically identical to full hydrodynamic models, but with a significant speed-up.Adding details to images below the resolution scale (SR image enhancement) has become possible with the latest advances in deep learning (DL; ML with NN; ref. 4), including generative adversarial networks (GANs; ref. 5). The technique has applications in many fields, from microscopy to law enforcement (6). It has been used for observational astronomical images by (7), to recover galaxy features from below the resolution scale in degraded Hubble Space Telescope images. Besides SR image enhancement, DL has started to find applications in cosmological simulations. For example, refs. 8 and 9 showed how NNs can predict the nonlinear formation of structures given simple linear theory predictions. NN models have also been trained to predict galaxies (10, 11) and 21-cm emission from neutral hydrogen (12) from simulations that only contain dark matter. GANs have been used in ref. 13 to generate image slices of cosmological models and to generate dark-matter halos from density fields (14). ML techniques other than DL find many applications, too. For example, Kamdar et al. (15) have applied extremely randomized trees to dark-matter simulations to predict hydrodynamic galaxy properties.Generating mocks for future sky surveys requires large volumes and high accuracy, a task that quickly becomes computationally prohibitive. To alleviate the cost, recently, Dai and Seljak (16) developed a Lagrangian-based parametric ML model to predict various hydrodynamical outputs from the dark-matter density field. In other work, Dai et al. (17, 18) sharpened the particle distribution using a potential gradient descent method starting from low-resolution (LR) simulations. Note, however, that these approaches did not aim to enhance the spatial or mass resolution of a simulation.On the DL side, recently, Ramanah et al. (19) explored using the SR technique to map density fields of LR cosmological simulations to that of the high-resolution (HR) ones. While the goal is similar, our work has the following three key differences. First, instead of focusing on the dark-matter density field, we aim to enhance the number of particles and predict their displacements, from which the density fields can be inferred. This approach allows us to preserve the particle nature of the N-body simulations and therefore to interpret the SR outputs as simulations themselves. Second, we test our technique at a higher SR ratio. Compared to ref. 19, which increased the number of Eulerian voxels by 8 times, we increase the number of particles and thus the mass resolution by a factor of 512. Finally, to facilitate future applications of SR on hydrodynamic simulations in representative volumes, we test our method at much smaller scales and in large simulations whose volume is much bigger than that of the training data.     

Oesophageal carcinoma with solitary patellar metastasis: a rare case report

The incidence of tumours found in the patella, including primary and metastatic tumours, is low. Solitary metastasis of oesophageal carcinoma (OC) in the patella is even rarer. A 50-year-old man presented to our clinic because of pain and limited range of motion in the right knee for 4 hours and after a fall. On the basis of the patient’s medical history, he was diagnosed with OC 2 months previously and underwent two cycles of paclitaxel liposome combined with tiggio chemotherapy (oral tiggio, 40 mg, two times/day, with a treatment cycle of 3 weeks). A 99mTc-methylene diphosphonate bone scintigraphy scan showed increased radioactivity in the right patella. A right knee biopsy showed the presence of patellar metastasis from OC. Unfortunately, the patient denied additional treatment and was discharged for personal reasons. At the 1-month follow-up, which was conducted by a telephone survey, we learned that the patient had died of acute pulmonary embolism. X-rays and computed tomography are useful for diagnosing patellar metastases, but 99mTc-methylene diphosphonate bone scintigraphy can help physicians diagnose patellar metastasis of OC more rapidly. Biopsy with pathology is the gold standard for diagnosing patellar metastases. Additionally, timely surgical treatment prolongs the survival time of these patients.     

Identification of key genes and pathways in discoid lupus skin via bioinformatics analysis

Discoid lupus erythematosus (DLE) is the most common skin manifestation of lupus; however, the molecular mechanisms underlying DLE remain unknown. Therefore, we aimed to identify key differentially expressed genes (DEGs) in discoid lupus skin and investigate their potential pathways.To identify candidate genes involved in the occurrence and development of the disease, we downloaded the microarray datasets {"type":"entrez-geo","attrs":{"text":"GSE52471","term_id":"52471"}}GSE52471 and {"type":"entrez-geo","attrs":{"text":"GSE72535","term_id":"72535"}}GSE72535 from the Gene Expression Database (GEO). DEGs between discoid lupus skin and normal controls were selected using the GEO2R tool and Venn diagram software (http://bioinformatics.psb.ugent.be/webtools/Venn/). The Database for Annotation, Visualization, and Integrated Discovery (DAVID), Enrichr, and Cytoscape ClueGo were used to analyze the Kyoto Encyclopedia of Gene and Genome pathways and gene ontology. Protein-protein interactions (PPIs) of these DEGs were further assessed using the Search Tool for the Retrieval Interacting Genes version 10.0.Seventy three DEGs were co-expressed in both datasets. DEGs were predominantly upregulated in receptor signaling pathways of the immune response. In the PPI network, 69 upregulated genes were selected. Furthermore, 4 genes (CXCL10, ISG15, IFIH1, and IRF7) were found to be significantly upregulated in the RIG-I-like receptor signaling pathway, from analysis of Enrichr and Cytoscape ClueGo.The results of this study may provide new insights into the potential molecular mechanisms of DLE. However, further experimentation is required to confirm these findings.     

Electroacupuncture improves learning and memory functions in a rat cerebral ischemia/reperfusion injury model through PI3K/Akt signaling pathway activation

Electroacupuncture has been widely used to treat cognitive impairment after cerebral ischemia, but the underlying mechanism has not yet been fully elucidated. Studies have shown that autophagy plays an important role in the formation and development of cognitive impairment, and the phosphoinositide 3-kinase(PI3 K)/Akt signaling pathway plays an important role in autophagy regulation. To investigate the role played by the PI3 K/Akt signaling pathway in the electroacupuncture treatment of cerebral ischemia/reperfusion rat models, we first established a rat model of cerebral ischemia/reperfusion through the occlusion of the middle cerebral artery using the suture method. Starting at 2 hours after modeling, electroacupuncture was delivered at the Shenting(GV24) and Baihui(GV20) acupoints, with a dilatational wave(1–20 Hz frequency, 2 mA intensity, 6 V peak voltage), for 30 minutes/day over 8 consecutive days. Our results showed that electroacupuncture reduced the infarct volume in a rat model of cerebral ischemia/reperfusion injury, increased the mRNA expression levels of the PI3 K/Akt signaling pathwayrelated factors Beclin-1, mammalian target of rapamycin(mTOR), and PI3 K, increased the protein expression levels of phosphorylated Akt, Beclin-1, PI3 K, and mTOR in the ischemic cerebral cortex, and simultaneously reduced p53 mRNA and protein expression levels. In the Morris water maze test, the latency to find the hidden platform was significantly shortened among rats subjected to electroacupuncture stimulation compared with rats without electroacupuncture stimulation. In the spatial probe test, the number of times that a rat crossed the target quadrant was increased in rats subjected to electroacupuncture stimulation compared with rats without electroacupuncture stimulation. Electroacupuncture stimulation applied to the Shenting(GV24) and Baihui(GV20) acupoints activated the PI3 K/Akt signaling pathway and improved rat learning and memory impairment. This study was approved by the Animal Ethics Committee of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine, China(approval No. 8150150901) on March 10, 2016.     

新疆血脂异常患者ABCB1 C3435T与ABCB1 T2677G基因多态性分析

目的 分析ATP结合盒转运蛋白B1(ABCB1)C3435T与T2677G位点基因型和等位基因在新疆维吾尔族、汉族及回族血脂异常患者中的分布特征.方法 采用荧光染色原位杂交方法检测311例维吾尔族、409例汉族及53例回族血脂异常患者的ABCB1 C3435T与ABCB1 T2677G基因多态性,分析不同民族间基因型频率和等位基因频率是否存在差异性.结果 维吾尔族与汉族血脂异常患者ABCB1C3435T基因型频率及等位基因频率差异有统计学意义(P＜0.05),维吾尔族与回族血脂异常患者ABCB1 C3435T等位基因频率差异有统计学意义(P＜0.05);维吾尔族与汉族血脂异常患者ABCB1 T2677G基因型频差异有统计学意义(P＜0.05).结论 新疆不同民族血脂异常患者ABCB1基因多态性分布具有差异性.在研究ABCB1与他汀类药物疗效相关的机制时,民族差异是不容忽视的重要因素.