Beneficial effects of n-hexane bark extract of Onosma echioides L. on diabetic peripheral neuropathy

Onosma echioides Linn (Boraginaceae) is the most frequently used curative herb widely used for kidney obstruction, sciatic pain, and gout. The present study was designed to investigate the therapeutic effects of n-hexane bark extract of O. echioides (OE) L. root in vivo against Streptozotocin-induced diabetic neuropathy in SD rats. For in vivo activity, the experiment was categorized into five different groups (n = 5). Group-I was considered as nondiabetic/normal control (NC) treated with 0.5% carboxymethyl cellulose (CMC), Group II as diabetic control, Group-III, IV, and V served as diabetic treated with OE 50, OE 100, and pregabalin at a dose of 50, 100, and 10 mg/kg body weight, orally, respectively. Body weight, blood glucose, oral glucose tolerance test, behavioral studies (motor coordination test, thermal hyperalgesia, cold allodynia, locomotor activity, oxidative biomarkers (thio barbituric acid reactive substances [TBARS], superoxide dismutase [SOD], glutathione [GSH], and catalase), and histopathology of the sciatic nerve were performed. Treatment with OE showed a dose-dependent increase in neuroprotective activity by improving the myelination and decreasing the axonal swelling of nerve fibers. The verdicts of behavioral activities showed a remarkable effect on animals after the treatment of extract and standard drug pregabalin. In conclusion, our findings supported the traditional application of OE and explored its importance in the management of diabetic neuropathy. Additional clinical experiments may provide novel therapeutic drugs for diabetes and its complications.     

Assessment of Optimal Selected Prognostic Factors

The identification and assessment of prognostic factors is one of the major tasks in clinical research. The assessment of one single prognostic factor can be done by recently established methods for using optimal cutpoints. Here, we suggest a method to consider an optimal selected prognostic factor from a set of prognostic factors of interest. This can be viewed as a variable selection method and is the underlying decision problem at each node of various tree building algorithms. We propose to use maximally selected statistics where the selection is defined over the set of prognostic factors and over all cutpoints in each prognostic factor. We demonstrate that it is feasible to compute the approximate null distribution. We illustrate the new variable selection test with data of the German Breast Cancer Study Group and of a small study on patients with diffuse large B‐cell lymphoma. Using the null distribution for a p‐value adjusted regression trees algorithm, we adjust for the number of variables analysed at each node as well. (© 2004 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

环状RNA在肝细胞癌中的作用及机制

肝细胞癌（hepatocellular carcinoma,HCC）治疗困难、预后很差,是肿瘤相关死亡中的第4大癌症,严重危害人类生命健康,但其具体发病机制却仍未完全阐明。因此,探索能调控肝细胞癌发生发展,作为肝细胞癌的诊断标志物或能预测患者预后的关键分子仍十分必要。环状RNA是前体mRNA通过反向剪接产生的由3′, 5′ 磷酸二酯键首尾连接形成的共价闭合环状结构,主要有外显子circRNA（exonic circRNA,ecircRNA）、环状内含子RNA（circular intronic RNA,ciRNA）及外显子 内含子circRNA（exon-intron circRNA,EIciRNA）三大类。由于环状RNA具有普遍性、高度保守性和稳定性,其可以参与多种癌症的发生发展过程,并且可作为肿瘤的早期诊断标志物及预后因子,因此,这是一类新型且非常有潜力应用于临床诊治各阶段的分子。近年来,有大量关于环状RNA与肝细胞癌的研究。这些研究表明,环状RNA在肝细胞癌发生发展进程中发挥的作用十分重要,并且其机制多样。因此,本文主要关注环状RNA在肝细胞癌中的最新进展,总结不同环状RNA分子对于肝细胞癌细胞恶性表型、肿瘤干细胞及肿瘤微环境中免疫细胞的作用,以及其在肝细胞癌临床转移、分期、诊断、预后等各阶段中发挥的功能及其具体作用机制。此外,本文还提出了目前研究中存在的一些问题和不足,以期为未来的研究提供一些新的思路及策略。     

Oxidative processes as indicators of chemical stress in marine bivalves

Deleterious effects of environmental contaminants could be due to enhanced prooxidant forces overcoming antioxidant defences. Before practical biomarkers based on free radical biology will be generally accepted and validated in situ, additional research is required concerning normal physiological and environmental influences on the relevant systems. The aims of this study were to evaluate in situ the importance of oxyradical production in the presence and absence of pollutants and to characterize some antioxidant systems in Mytilus edulis L. Specimens of M. edulis L. were transplanted from a reference site (Franquelin) to Baie Comeau (Baie des Anglais), on the North shore of the St. Lawrence maritime estuary, where are found aluminium and pulp and paper plants. An oxidative stress was observed in mussels submitted to a chronic exposure in the polluted environment. Variations of pro-and anti-oxidant molecules involved in oxidative processes were related in part to seasonal and physico-chemical influences. Catalase activity, malondialdehyde and glutathione concentrations will be useful as biomarkers of stress in situ since they react to anthropogenic influence and to abiotic factors such as emersion period and temperature.     

Metabolic defects shared by Alzheimer's disease and diabetes: A focus on mitochondria

Type 2 diabetes (T2D) and Alzheimer's disease (AD) are two global epidemics that share several metabolic defects, such as insulin resistance, impaired glucose metabolism, and mitochondrial defects. Importantly, strong evidence demonstrates that T2D significantly increases the risk of cognitive decline and dementia, particularly AD. Here, we provide an overview of the metabolic defects that characterize and link both pathologies putting the focus on mitochondria. The biomarker potential of mitochondrial components and the therapeutic potential of some drugs that target and modulate mitochondria are also briefly discussed.     

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.     

Quantitative Detection of 15 Serum Bile Acid Metabolic Products by LC/MS/MS in the Diagnosis of Primary Biliary Cholangitis

To determine 15 bile acid metabolic products in human serum by liquid chromatography-tandem mass spectrometry (LC/MS/MS) and value their diagnostic outcome in primary biliary cholangitis (PBC). Serum from 20 healthy controls and 26 patients with PBC were collected and went LC/MS/MS analysis of 15 bile acid metabolic products. The test results were analyzed by bile acid metabolomics, and the potential biomarkers were screened and their diagnostic performance was judged by statistical methods such as principal component and partial least squares discriminant analysis and area under curve (AUC). 8 differential metabolites can be screened out: Deoxycholic acid (DCA), Glycine deoxycholic acid (GDCA), Lithocholic acid (LCA), Glycine ursodeoxycholic acid (GUDCA), Taurolithocholic acid (TLCA), Tauroursodeoxycholic acid (TUDCA), Taurodeoxycholic acid (TDCA), Glycine chenodeoxycholic acid (GCDCA). The performance of the biomarkers was evaluated by the AUC, specificity and sensitivity. In conclusion, DCA, GDCA, LCA, GUDCA, TLCA, TUDCA, TDCA and GCDCA were identified as eight potential biomarkers to distinguish between healthy people and PBC patients by multivariate statistical analysis, which provided reliable experimental basis for clinical practice.     

Proteomics evaluation of five economical commercial abundant protein depletion kits for enrichment of diseases-specific biomarkers from blood serum

Blood serum is arguably the most analyzed biofluid for disease prediction and diagnosis. Herein, we benchmarked five different serum abundant protein depletion (SAPD) kits with regard to the identification of disease-specific biomarkers in human serum using bottom-up proteomics. As expected, the IgG removal efficiency among the SAPD kits is highly variable, ranging from 70% to 93%. A pairwise comparison of database search results showed a 10%–19% variation in protein identification among the kits. Immunocapturing-based SAPD kits against IgG and albumin outperformed the others in the removal of these two abundant proteins. Conversely, non-antibody-based methods (i.e., kits using ion exchange resins) and kits leveraging a multi-antibody approach were proven to be less efficient in depleting IgG/albumin from samples but led to the highest number of identified peptides. Notably, our results indicate that different cancer biomarkers could be enriched up to 10% depending on the utilized SAPD kit compared with the undepleted sample. Additionally, functional analysis of the bottom-up proteomic results revealed that different SAPD kits enrich distinct disease- and pathway-specific protein sets. Overall, our study emphasizes that a careful selection of the appropriate commercial SAPD kit is crucial for the analysis of disease biomarkers in serum by shotgun proteomics.     

Milk metabolites can characterise individual differences in animal resilience to a nutritional challenge in lactating dairy goats

The aim of this study is built in two phases: to quantify the ability of novel milk metabolites to measure between-animal variability in response and recovery profiles to a short-term nutritional challenge, then to derive a resilience index from the relationship between these individual variations. At two different stages of lactation, sixteen lactating dairy goats were exposed to a 2-d underfeeding challenge. The first challenge was in late lactation, and the second was carried out on the same goats early in the following lactation. During the entire experiment period, samples were taken at each milking for milk metabolite measures. For each metabolite, the response profile of each goat was characterised using a piecewise model for describing the dynamic pattern of response and recovery profiles after the challenge relative to the start of the nutritional challenge. Cluster Analysis identified three types of response/recovery profiles per metabolite. Using cluster membership, multiple correspondence analyses (MCAs) were performed to further characterise response profile types across animals and metabolites. This MCA analysis identified three groups of animals. Further, discriminant path analysis was able to separate these groups of multivariate response/recovery profile type based on threshold levels of three milk metabolites: β-hydroxybutyrate, free glucose and uric acid. Further analyses were done to explore the possibility of developing an index of resilience from milk metabolite measures. Different types of performance response to short-term nutritional challenge can be distinguished using multivariate analyses of a panel of milk metabolites.