细胞膜脂质流动性的不均一性与细胞分裂动力学的关系

细胞腊脂流动性在不同类属细胞存在着差异,其差异的程度取决于细胞的生理和生化特性。正常淋巴细胞、肺腺癌、肺鳞癌细胞以及结核细胞膜脂流动发生 分别为２．５１７±０．２６７、２２．５５７±３．７７１,３２．８７５±９．７０９和４．０２６±０．７２２。细胞分裂动力学与膜脂质流动性有关系。细胞分裂及其程度是膜脂质流动性差异的物质基础,同时并受细胞膜脂质的组成,脂蛋白生化以及外界因素的ｐＨ、Ｃａ＾２＋等影响,     

蝮蛇抗栓酶治疗小儿脑炎致急性偏瘫血中6—K—PGF1α,TXB2的变化

应用蝮蛇抗栓酶治疗小儿脑炎致急性偏瘫共25例。沿疗前后检测血中的6-K-PGF_1x及 TXB_2.结果表明:治疗后血中 6-酮-前列腺素 F_1x 明显增高,血栓素 B_2明显降低。推测蝮蛇抗栓酶之所以具有去纤、溶栓、扩血管等作用,可能与血中前列腺素、血栓素 B_2水平变化有关。     

转基因玉米HGK60在不同遗传背景下抗虫性鉴定及农艺性状分析

为研究转基因玉米HGK60在不同遗传背景下遗传稳定性和抗虫效果,利用转Bt cry1Ah基因的转基因玉米HGK60为供体,通过回交转育的方式将cry1Ah基因分别导入玉米自交系郑58、昌7-2、lx05-4、lx03-2,获得转基因玉米自交系HGK60-郑58、HGK60-昌7-2、HGK60-lx03-2、HGK60-lx05-4,并杂交获得HGK60-郑单958（HGK60-郑58 × HGK60-昌7-2）和HGK60-鲁单9066（HGK60-lx05-4 × HGK60-lx03-2）,转化体特异性PCR证明cry1Ah基因已转入不同遗传背景玉米中,ELISA检测不同遗传背景转基因玉米叶片中Cry1Ah蛋白表达情况,结果表明在不同遗传背景玉米自交系和杂交种中Cry1Ah蛋白表达没有显著差异;田间人工接虫和室内玉米螟抗虫性鉴定结果表明,不同遗传背景的转基因玉米高抗玉米螟,室内接虫后4 d幼虫死亡率达到100%;对不同遗传背景转基因玉米HGK60进行农艺性状分析,结果显示与受体对照玉米相比,两者之间农艺性状没有显著差异,转基因玉米HGK60可用于抗虫玉米品种的选育。     

白花蛇舌草的化学成分研究

为研究白花蛇舌草(Hedyotis diffusa)的化学成分,采用硅胶柱色谱、制备HPLC色谱等方法,从白花蛇舌草全草的70%乙醇提取物中分离得到9个化合物。根据理化性质及其波谱或光谱数据,结构分别鉴定为：豆甾醇(1)、β-谷甾醇(2)、rubiadin(3)、2,6-二羟基-1-甲氧基蒽醌(4)、β-谷甾醇-3-O-β-D-葡萄糖苷(5)、对香豆酸(6)、山奈酚(7)、槲皮素(8)和2-羟基-3-甲氧基-7-甲基蒽醌(9)。化合物4为新化合物;氯仿提取部位的Fr.4、Fr.4-2及Fr.4-4流分具有较强体外抗人子宫内膜癌Ishikawa细胞活性,其IC₅₀值分别为52.8、78.1和27.5μg/mL。     

越南槐根的抗HIV活性成分研究

为研究越南槐(Sophora tonkinensis)根的抗HIV蛋白酶活性成分及其分子对接机制,采用硅胶、MCI、Sephadex LH-20等多种色谱分离方法,对越南槐根的化学成分进行分离,采用HIV蛋白酶对化合物进行体外抗HIV活性筛选,运用分子对接手段初步探究活性化合物与HIV-1蛋白酶的结合机制。结果表明,从越南槐中共分离得到8个化合物,根据波谱数据分别鉴定为三叶豆紫檀苷(1)、苦参碱(2)、N-acetylnicotinamide (3)、2′-O-甲基腺苷(4)、毛蕊异黄酮苷(5)、玫瑰花苷(6)、环广豆根素(7)、芒柄花苷(8),此外还分离得到塑化剂衍生物邻苯二甲酸二(2-乙基)己酯(9)和邻苯二甲酸二异丁酯(10)。抗HIV蛋白酶活性测试显示化合物1和2的IC50分别为13.2和38.6μg/m L,分子对接表明其与HIV蛋白酶有一定的结合作用。化合物3～5为首次从该植物中分离得到,化合物1和2显示中等的抗HIV蛋白酶活性。     

Japanese cancer screening programs during the COVID-19 pandemic: Changes in participation between 2017-2020

BackgroundThe impact of the coronavirus disease 2019 (COVID-19) pandemic on cancer screening participation is a global concern. A national database of screening performance is available in Japan for population-based cancer screening, estimated to cover approximately half of all cancer screenings.MethodsUtilizing the fiscal year (FY) 2017–2020 national database, the number of participants in screenings for gastric cancer (upper gastrointestinal [UGI] series or endoscopy), colorectal cancer (fecal occult blood test), lung cancer (chest X-ray), breast cancer (mammography), and cervical cancer (Pap smear) were identified. The percent change in the number of participants was calculated.ResultsCompared with the pre-pandemic period (FY 2017–2019), in percentage terms FY 2020 recorded the largest decline in gastric cancer UGI series (2.82 million to 1.91 million, percent change was −32.2 %), followed by screening for breast cancer (3.10 million to 2.57 million, percent change was −17.2 %), lung cancer (7.92 million to 6.59 million, percent change was −16.7 %), colorectal cancer (8.42 million to 7.30 million, percent change was −13.4 %), cervical cancer (4.26 million to 3.77 million, percent change was −11.6 %), and gastric cancer via endoscopy (1.02 million to 0.93 million, percent change was −9.0 %).ConclusionThe number of participants in population-based screenings in Japan decreased by approximately 10–30 % during the pandemic. The impact of these declines on cancer detection or mortality should be carefully monitored.     

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

ObjectivesWhen the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.Material and methodsThe sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.ResultsIn this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.ConclusionThe findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.     

Piper longum Constituents Induce PANC-1 Human Pancreatic Cancer Cell Death under Nutrition Starvation

Pancreatic cancer is a highly aggressive form of cancer with a poor prognosis, partly due to ‘austerity’, a phenomenon of tolerance to nutrient deprivation and survival in its hypovascular tumor microenvironment. Anti-austerity agents which preferentially diminish the survival of cancer cells under nutrition starvation is regarded as new generation anti-cancer agents. This study investigated the potential of Piper longum constituents as anti-austerity agents. The ethanolic extract of Piper longum was found to have preferential cytotoxicity towards PANC-1 human pancreatic cancer cells in a nutrient-deprived medium (NDM). Further investigation led to the identification of pipernonaline ( 3 ) as the lead compound with the strongest anti-austerity activity, inducing cell death and inhibiting migration in a normal nutrient medium, as well as strongly inhibiting the Akt/mTOR/autophagy pathway. Therefore, pipernonaline ( 3 ) holds promise as a novel antiausterity agent for the treatment of pancreatic cancer.     

Engeletin alleviates cerebral ischemia reperfusion-induced neuroinflammation via the HMGB1/TLR4/NF-κB network

High-mobility group box1 (HMGB1) induces inflammatory injury, and emerging reports suggest that it is critical for brain ischemia reperfusion. Engeletin, a natural Smilax glabra rhizomilax derivative, is reported to possess anti-inflammatory activity. Herein, we examined the mechanism of engeletin-mediated neuroprotection in rats having transient middle cerebral artery occlusion (tMCAO) against cerebral ischemia reperfusion injury. Male SD rats were induced using a 1.5 h tMCAO, following by reperfusion for 22.5 h. Engeletin (15, 30 or 60 mg/kg) was intravenously administered immediately following 0.5 h of ischemia. Based on our results, engeletin, in a dose-dependent fashion, reduced neurological deficits, infarct size, histopathological alterations, brain edema and inflammatory factors, namely, circulating IL-1β, TNF-α, IL-6 and IFN-γ. Furthermore, engeletin treatment markedly reduced neuronal apoptosis, which, in turn, elevated Bcl-2 protein levels, while suppressing Bax and Cleaved Caspase-3 protein levels. Meanwhile, engeletin significantly reduces overall expressions of HMGB1, TLR4, and NF-κB and attenuated nuclear transfer of nuclear factor kappa B (NF-κB) p65 in ischemic cortical tissue. In conclusion, engeletin strongly prevents focal cerebral ischemia via suppression of the HMGB1/TLR4/NF-κB inflammatory network.