The general occurrence of “plastid initials” and their development into plastids in cortical cells of woody plants in winter

Summary Electron microscopic studies revealed that plastid initials, presumed precursors of plastids, occur in cortical cells of the following plants studied in February and March: Betula ermanii Cham.; Prunus sargentii Rhed.; Pyrus communis L.; Ribes sinanense F. Maekawa; Salix matsudana Koidz. forma tortuosa Rhed.; and Sambucus sieboldiana var. miquelii Hara. Since plastid initials were found previously in Malus pumila Mill., Morus bombycis Koidz. and Populus euramericana cv. gelrica (Sagisaka 1991), plastid initials have been found in all woody plants examined to date. In P. euramericana cv. gelrica, at later stages of the development of the initials in March, the conglomerates of plastid initials became heterogeneous in terms of size, extent of thylakoid formation and ability to form starch granules. The formation of prolamellar structures was frequently observed in cells of Magnolia kobus var. borealis Sarg., which was sampled on April 19. These observations suggest the course of events in the development of the plastid initial and the continuity of the life of amyloplasts over a year in the life of woody plants.     

PDGF-AA activates AKT and ERK signaling for testicular interstitial Leydig cell growth via primary cilia

Testes control the development of male reproductive system. The testicular interstitial Leydig cells (Leydig cells) synthesize testosterone for promoting spermatogenesis and secondary sexual characteristics. Type A platelet-derived growth factor (PDGF-AA) is one of the most important growth factors in regulating Leydig cell growth and function. Knockout of PDGF-AA or its congenital receptor PDGFR-α leads to poor testicular development caused by reducing Leydig cell numbers, supporting PDGF-AA/PDGFR-α signaling regulates Leydig cell development. Primary cilium is a cellular antenna that functions as an integrative platform to transduce extracellular signaling for proper development and differentiation. Several receptors including PDGFR-α are observed on primary cilia for initiating signaling cascades in distinct cell types. Here we showed that PDGF-AA/PDGFR-α signaling promoted Leydig cells growth, migration, and invasion via primary cilia. Upon PDGF-AA treatment, AKT and ERK signaling were activated to regulate these cellular events. Interestingly, active AKT and ERK were detected around the base of primary cilia. Depletion of ciliary genes (IFT88 and CEP164) alleviated PDGF-AA-activated AKT and ERK, thus reducing Leydig cell growth, migration, and invasion. Thus, our study not only reveals the function of PDGF-AA/PDGFR-α signaling in maintaining testicular physiology but also uncovers the role of primary cilium and downstream signaling in regulating Leydig cell development.     

汉族ABO血型的皮纹特征分析

作者对芜湖地区３８２例（男２２０人;女１６２人）汉族ＡＢＯ血型的皮纹特征进行了分析,其中Ｏ型１３０人,Ａ型１１３人,Ｂ型１０１人,ＡＢ型３８人。分析比较了指纹类型、指纹组合格局、指嵴纹计数、掌嵴纹计数、ａｔｄ角,掌部真实花纹,掌褶纹和拇趾球纹等项参数,结果表明,ＡＢＯ各血型的皮纹参数间有若干统计学差异。     

核纤层蛋白B1通过影响端粒酶活性调控肝癌细胞生长

核纤层蛋白B1(nuclear lamina protein B1,LMNB1)高表达于肝癌组织中,通过敲低LMNB1探讨其对肝癌细胞增殖的影响及其机制。利用siRNA在肝癌细胞中敲低LMNB1,Western blotting检测敲低效果,使用端粒重复序列扩增法(telomeric repeat amplification protocol assay,TRAP)检测其端粒酶活性变化。利用实时定量聚合酶链反应(quantitative real-time polymerase chain reaction,qPCR)检测其端粒长度变化。并通过CCK-8、克隆形成、Transwell、划痕实验检测其生长,侵袭和迁移能力变化。利用慢病毒系统构建稳定敲低LMNB1的HepG2细胞,检测其端粒长度及端粒酶活性变化,采用SA-β-gal衰老染色检测细胞衰老情况,通过裸鼠皮下成瘤实验及对肿瘤后续的组化染色,SA-β-gal衰老染色,端粒荧光原位杂交(fluorescence in situ hybridization,FISH)检测其对成瘤性的影响。最后利用生物信息分析的方法寻找LMNB1在临床肝癌组织中的表达情况,及其与临床分期、病人生存期的关系。HepG2和Hep3B中敲低LMNB1后端粒酶活性显著降低,细胞增殖、迁移和侵袭能力显著降低,细胞和裸鼠成瘤实验证明稳定敲低LMNB1后端粒酶活性降低的同时端粒长度缩短,细胞发生衰老,此外细胞成瘤性降低,Ki-67表达降低,生物信息分析结果显示,LMNB1高表达于肝癌组织,且与肿瘤分期和患者生存相关。LMNB1在肝癌细胞中过表达,其有望成为评估肝癌患者临床预后的指标和精准治疗的靶点。     

基于拉曼技术的单细胞生长检测方法

目的 单细胞生长检测可以更加科学地揭示微生物代谢变化的规律,为后期微生物工程应用提供指导。针对微生物生长应用于食品安全期和最佳食用期的精准检测问题,本文提出一种基于拉曼技术的单细胞生长检测方法。方法 首先,通过同步培养实验采集了枯草芽孢杆菌两个批次共900个单细胞拉曼光谱（SCRS）数据,其中600个用于训练和测试,另一批次300个用于模型验证。其次,基于主成分分析的特征关系矩阵,提出CP-SP特征评估方法以筛选SCRS特征用于模型检测。再基于XGBoost构建检测模型,并应用网格搜索和交叉验证对检测模型进行调优。最后,应用混淆矩阵、ROC曲线评估模型对细胞滞后期、对数期和稳定期的检测准确率、敏感性和特异性。结果 选用CP-SP筛选的第一、第二和第四主成分较特征贡献率前3个主成分的分类性能提高了3.1%,调优后的细胞生长检测模型测试准确率为96.0%,验证准确率为92.3%。结论 基于拉曼技术的单细胞生长检测方法能准确识别单细胞生长状态且具有较高的泛化能力,可为食品安全和保鲜制定精准调控机制提供科学指导。     

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,     

腺苷在低氧适应家兔脑血流调节中的作用

本研究观察了腺苷在低氧适应家兔脑血流(CBF)调节中的作用。结果表明:缺氧时适应组CBF改变不明显,对照组CBF明显增加;缺氧时适应组脑腺苷的含量明显低于对照组,而脑腺苷酸的含量明显高于对照组;适应组脑微血管对腺苷的反应与对照组相近。提示缺氧时低氧适应家兔CBF改变不明显,同低氧适应后脑腺苷含量较低、腺苷酸含量较高有关。     

Antisense EGFR sequence reverses the growth properties of human liver carcinoma cell line BEL-7404 in vitro

A recombinant plasmid containing a full length human epidermal growth factor receptor (EGFR) cDNA sequence in antisense orientation was transferred into cells of a human liver carcinoma cell line BEL-7404. Compared with the control cell clone JX-0 transferred with the vector plasmid and the parent BEL-7404 cells, the antisense EGFR transferred cell clone JX-1 showed a decreased EGFR gene expression and reduced significantly the growth potential either in anchorage-dependent or anchorage-independent growth. Furthermore, JX-1 cells appeared to be distinctly dependent on serum concentration for monolayer growth. The results suggested that antisense EGFR could partly block the EGFR gene expression and reverse the malignant growth properties of human liver carcinoma cells in vitro.     

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.