DrugMetric: quantitative drug-likeness scoring based on chemical space distance.

The process of drug discovery is widely known to be lengthy and resource-intensive. Artificial Intelligence approaches bring hope for accelerating the identification of molecules with the necessary properties for drug development. Drug-likeness assessment is crucial for the virtual screening of candidate drugs. However, traditional methods like Quantitative Estimation of Drug-likeness (QED) struggle to distinguish between drug and non-drug molecules accurately. Additionally, some deep learning-based binary classification models heavily rely on selecting training negative sets. To address these challenges, we introduce a novel unsupervised learning framework called DrugMetric, an innovative framework for quantitatively assessing drug-likeness based on the chemical space distance. DrugMetric blends the powerful learning ability of variational autoencoders with the discriminative ability of the Gaussian Mixture Model. This synergy enables DrugMetric to identify significant differences in drug-likeness across different datasets effectively. Moreover, DrugMetric incorporates principles of ensemble learning to enhance its predictive capabilities. Upon testing over a variety of tasks and datasets, DrugMetric consistently showcases superior scoring and classification performance. It excels in quantifying drug-likeness and accurately distinguishing candidate drugs from non-drugs, surpassing traditional methods including QED. This work highlights DrugMetric as a practical tool for drug-likeness scoring, facilitating the acceleration of virtual drug screening, and has potential applications in other biochemical fields.

Amino acid metabolic reprogramming in the tumor microenvironment and its implication for cancer therapy.

Amino acids are essential building blocks for proteins, crucial energy sources for cell survival, and key signaling molecules supporting the resistant growth of tumor cells. In tumor cells, amino acid metabolic reprogramming is characterized by the enhanced uptake of amino acids as well as their aberrant synthesis, breakdown, and transport, leading to immune evasion and malignant progression of tumor cells. This article reviews the altered amino acid metabolism in tumor cells and its impact on tumor microenvironment, and also provides an overview of the current clinical applications of amino acid metabolism. Innovative drugs targeting amino acid metabolism hold great promise for precision and personalized cancer therapy.

Bioorthogonal Quinone Methide Decaging Enables Live-Cell Quantification of Tumor-Specific Immune Interactions.

Effective antitumor immunity hinges on the specific engagement between tumor and cytotoxic immune cells, especially cytotoxic T cells. Although investigating these intercellular interactions is crucial for characterizing immune responses and guiding immunotherapeutic applications, direct and quantitative detection of tumor-T cell interactions within a live-cell context remains challenging. We herein report a photocatalytic live-cell interaction labeling strategy (CAT-Cell) relying on the bioorthogonal decaging of quinone methide moieties for sensitive and selective investigation and quantification of tumor-T cell interactions. By developing quinone methide-derived probes optimized for capturing cell-cell interactions (CCIs), we demonstrated the capacity of CAT-Cell for detecting CCIs directed by various types of receptor-ligand pairs (e.g., CD40-CD40L, TCR-pMHC) and further quantified the strengths of tumor-T cell interactions that are crucial for evaluating the antitumor immune responses. We further applied CAT-Cell for ex vivo quantification of tumor-specific T cell interactions on splenocyte and solid tumor samples from mouse models. Finally, the broad compatibility and utility of CAT-Cell were demonstrated by integrating it with the antigen-specific targeting system as well as for tumor-natural killer cell interaction detection. By leveraging the bioorthogonal photocatalytic decaging chemistry on quinone methide, CAT-Cell provides a sensitive, tunable, universal, and noninvasive toolbox for unraveling and quantifying the crucial but delicate tumor-immune interactions under live-cell settings.

Exploration of the prognostic effect of costimulatory genes in bladder cancer.

BACKGROUND: A prognostic model of bladder cancer was constructed based on costimulatory molecules, and its stability and accuracy were verified in different datasets. METHOD: The expression profile of bladder cancer RNA and the corresponding clinical data in The Cancer Genome Atlas (TCGA) database were analyzed employing computational biology, and a prognostic model was constructed for costimulating molecule-related genes. The model was applied in GSE160693, GSE176307, Xiangya_Cohort, GSE13507, GSE19423, GSE31684, GSE32894, GSE48075, GSE69795 and GSE70691 in TCGA dataset and Gene Expression Omnibus database. The role of costimulating molecules in bladder cancer tumor subtypes was also explored. By consistent cluster analysis, bladder cancer in the TCGA dataset was categorized into two subtypes: C1 and C2. The C1 subtype exhibited a poor prognosis, high levels of immune cell infiltration and significant enrichment of natural killer cells, T cells and dendritic cells in the C1 subtype. In addition, the ImmuneScore calculated by the ESTIMATE algorithm differed greatly between the two subtypes, and the ImmuneScore of the C1 subtype was greater than the C2 subtype in a significant manner. RESULTS: This study also assessed the relationship between costimulating molecules and immunotherapy response. The high-risk group responded poorly to immunotherapy, with significant differences in the amount of most immune cells between the two groups. Further, three indices of the ESTIMATE algorithm and 22 immune cells of the CIBERSORT algorithm were significantly correlated with risk values. These findings suggest the potential value of costimulating molecules in predicting immunotherapy response. CONCLUSION: A costimulatory molecule-based prognostic model for bladder cancer was established and validated across multiple datasets. This model introduces a novel mode for tailoring treatments to each individual with bladder cancer, and offers valuable insights for informed clinical choices. Simultaneously, this research also delved into the significance of costimulating molecules within distinct bladder cancer subtypes, shedding novel insights into improving immunotherapy strategies for the treatment of bladder cancer.

Machine learning methods, databases and tools for drug combination prediction.

Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

The underestimated role of mitochondria in vitiligo: From oxidative stress to inflammation and cell death.

Vitiligo is an acquired depigmentary disorder characterized by the depletion of melanocytes in the skin. Mitochondria shoulder multiple functions in cells, such as production of ATP, maintenance of redox balance, initiation of inflammation and regulation of cell death. Increasing evidence has implicated the involvement of mitochondria in the pathogenesis of vitiligo. Mitochondria alteration will cause the abnormalities of mitochondria functions mentioned above, ultimately leading to melanocyte loss through various cell death modes. Nuclear factor erythroid 2-related factor 2 (Nrf2) plays a critical role in mitochondrial homeostasis, and the downregulation of Nrf2 in vitiligo may correlate with mitochondria damage, making both mitochondria and Nrf2 promising targets in treatment of vitiligo. In this review, we aim to discuss the alterations of mitochondria and its role in the pathogenesis of vitiligo.

Decaging-to-labeling: Development and investigation of quinone methide warhead for protein labeling.

Biomolecule labeling in living systems is crucial for understanding biological processes and discovering therapeutic targets. A variety of labeling warheads have been developed for multiple biological applications, including proteomics, bioimaging, sequencing, and drug development. Quinone methides (QMs), a class of highly reactive Michael receptors, have recently emerged as prominent warheads for on-demand biomolecule labeling. Their highly flexible functionality and tunability allow for diverse biological applications, but remain poorly explored at present. In this regard, we designed, synthesized, and evaluated a series of new QM probes with a trifluoromethyl group at the benzyl position and substituents on the aromatic ring to manipulate their chemical properties for biomolecule labeling. The engineered QM warhead efficiently labeled proteins both in vitro and under living cell conditions, with significantly enhanced activity compared to previous QM warheads. We further analyzed the labeling efficacy with the assistance of density functional theory (DFT) calculations, which revealed that the QM generation process, rather than the reactivity of QM, contributes more predominantly to the labeling efficacy. Noteworthy, twelve nucleophilic residues on the BSA were labeled by the probe, including Cys, Asp, Glu, His, Lys, Asn, Gln, Arg, Ser, Thr, Trp and Tyr. Given their high efficiency and tunability, these new QM warheads may hold great promise for a broad range of applications, especially spatiotemporal proteomic profiling for in-depth biological studies.

Community assembly and microbial interactions in an alkaline vanadium tailing pond.

Intensive development of vanadium-titanium mines leads to an increasing discharge of vanadium (V) into the environment, imposing potential risks to both environmental system and public health. Microorganisms play a key role in the biogeochemical cycling of V, influencing its transformation and distribution. In addition, the characterization of microbial community patterns serves to assess potential threats imposed by elevated V exposure. However, the impact of V on microbial community remains largely unknown in alkaline V tailing areas with a substantial amounts of V accumulation and nutrient-poor conditions. This study aims to explore the characteristics of microbial community in a wet tailing pond nearby a large-scale V mine. The results reveal V contamination in both water (0.60 mg/L) and sediment tailings (340 mg/kg) in the tailing pond. Microbial community diversity shows distinctive pattern between environmental metrices. Genera with the functional potential of metal reduction\resistance, nitrogen metabolism, and carbon fixation have been identified. In this alkaline V tailing pond, V and pH are major drivers to induce community variation, particularly for functional bacteria. Stochastic processes primarily govern the assemblies of microbial community in the water samples, while deterministic process regulate the community assemblies of sediment tailings. Moreover, the co-occurrence network pattern unveils strong selective pattern for sediment tailings communities, where genera form a complex network structure exhibiting strong competition for limited resource. These findings reveal the patterns of microbial adaptions in wet vanadium tailing ponds, providing insightful guidelines to mitigate the negative impact of V tailing and develop sustainable management for mine-waste reservoir.

Single amino acid variation in MAB21L1 is dominantly associated with congenital eye defects.

BACKGROUND: Diagnosis of a genetic disease and determination of the causative molecular lesion rely on the availability of the disease-associated pedigrees. Microphthalmia is a congenital eye defect due to an insufficiently developed visual system; its prevalence is 1-3 in 10 000 live births. OBJECTIVE: We analysed a pedigree exhibiting autosomal dominant inheritance of microphthalmia to determine the genetic lesion; used AlphaFold2 to predict the changes in the protein's 3-Dimensional structure; and compared wild-type and variant proteins in cultured cells or Drosophila model was used to explore the cellular or developmental function of the encoded product. RESULTS: We identified a novel missense variation, F52L, in MAB21L1 that is absent in population databases and present exclusively in the individuals diagnosed with microphthalmia in this pedigree. Common structural changes were predicted for the disease-associated variants clustered at amino acids 49-52, and these variant products were also predominantly trapped in the cytoplasm of cultured human lens epithelia. To recapitulate its dominant effect in development, we expressed the Drosophila homologue corresponding to MAB21L1F52L and caused malformation of sensory organs. CONCLUSION: Mutations at the residues 49-52 of MAB21L1 compromise eye development. We recommend including MAB21L1 in the genetic testing panel for congenital eye disorders.

Multiple mediation of the association between childhood emotional abuse and adult obesity by anxiety and bulimia - a sample from bariatric surgery candidates and healthy controls.

Bulimia, which means a person has episodes of eating a very large amount of food (bingeing) during which the person feels a loss of control over their eating, is the most primitive reason for being overweight and obese. The extended literature has indicated that childhood emotional abuse has a close relationship with adverse mood states, bulimia, and obesity. To comprehensively understand the potential links among these factors, we evaluated a multiple mediation model in which anxiety/depression and bulimia were mediators between childhood emotional abuse and body mass index (BMI). A set of self-report questionnaires, including the Childhood Trauma Questionnaire (CTQ), Beck Anxiety Inventory, Beck Depression Inventory (BDI), and Eating Disorder Inventory (EDI), was sent out. Clinical data from 37 obese patients (age: 29.65 ± 5.35, body mass index (BMI): 37.59 ± 6.34) and 37 demographically well-matched healthy people with normal body weight (age: 31.35 ± 10.84, BMI: 22.16 ± 3.69) were included in the investigation. We first performed an independent t-test to compare all scales or subscale scores between the two groups. Then, we conducted Pearson correlation analysis to test every two variables' pairwise correlation. Finally, multiple mediation analysis was performed with BMI as the outcome variable, and childhood emotional abuse as the predictive variable. Pairs of anxiety, bulimia, and depression, bulimia were selected as the mediating variables in different multiple mediation models separately. The results show that the obese group reported higher childhood emotional abuse (t = 2.157, p = 0.034), worse mood state (anxiety: t = 5.466, p < 0.001; depression: t = 2.220, p = 0.030), and higher bulimia (t = 3.400, p = 0.001) than the healthy control group. Positive correlations were found in every pairwise combination of BMI, childhood emotional abuse, anxiety, and bulimia. Multiple mediation analyses indicate that childhood emotional abuse is positively linked to BMI (ß = 1.312, 95% CI = 0.482-2.141). The model using anxiety and bulimia as the multiple mediating variables is attested to play roles in the relationship between childhood emotional abuse and obesity (indirect effect = 0.739, 95% CI = 0.261-1.608, 56.33% of the total effect). These findings confirm that childhood emotional abuse contributes to adulthood obesity through the multiple mediating effects of anxiety and bulimia. The present study adds another potential model to facilitate our understanding of the eating psychopathology of obesity.

Integrative analysis of the bladder cancer from a cell cycle NCAM1 perspective at both single cell and bulk resolution.

INTRODUCTION: Bladder cancer (BLCA) is a prevalent and deadly form of urinary cancer, and there is a need for effective therapies, particularly for muscle-invasive bladder cancer (MIBC). Cell cycle inhibitors show promise in restoring control of the cell cycle in BLCA cells, but their clinical prognosis evaluation is limited. METHODS: Transcriptome and scRNA-seq data were collected from the Cancer Genome Atlas Program (TCGA)-BLCA and GSE190888 cohort, respectively. R software and the Seurat package were used for data analysis, including cell quality control, dimensionality reduction, and identification of differentially expressed genes. Genes related to the cell cycle were obtained from the genecards website, and a protein-protein interaction network analysis was performed using cytoscape software. Functional enrichment analysis, immune infiltration analysis, drug sensitivity analysis, and molecular docking were conducted using various tools and packages. BLCA cell lines were cultured and transfected for in vitro experimental assays, including RT-qPCR analysis, and CCK-8 cell viability assays. RESULTS: We identified 32 genes as independent risk or protective factors for BLCA prediction. Functional enrichment analysis revealed their involvement in cell cycle regulation, apoptosis, and various signaling pathways. Using these genes, we developed a nomogram for predicting BLCA survival, which displayed high prognosis stratification efficacy in BLCA patients. Four cell cycle associated key genes identified, including NCAM1, HBB, CKD6, and CTLA4. We also identified the main cell types in BLCA patients and investigated the functional differences between epithelial cells based on their expression levels of key genes. Furthermore, we observed a high positive correlative relationship between the infiltration of cancer-associated fibroblasts and the risk score value. Finally, we conducted in vitro experiments to demonstrate the suppressive role of NCAM1 in BLCA cell proliferation. CONCLUSION: These findings suggest that cell cycle associated genes could serve as potential biomarkers for predicting BLCA prognosis and may represent therapeutic targets for the development of more effective therapies. Hopefully, these findings provide valuable insights into the molecular mechanisms and potential therapeutic targets in BLCA from the perspective of cell cycle. Moreover, NCAM1 was a novel cell proliferation suppressor in the BLCA carcinogenesis.

A partially flipped physiology classroom improves the deep learning approach of medical students.

This study aimed to compare the impact of the partially flipped physiology classroom (PFC) and the traditional lecture-based classroom (TLC) on students' learning approaches. The study was conducted over 5 mo at Xiangya School of Medicine from February to July 2022 and comprised 71 students majoring in clinical medicine. The experimental group (n = 32) received PFC teaching, whereas the control group (n = 39) received TLC. The Revised Two-Factor Study Process Questionnaire (R-SPQ-2F) was used to assess the impact of different teaching methods on students' learning approaches. After the PFC, students got significantly higher scores on deep learning approach (Z = -3.133, P < 0.05). Conversely, after the TLC students showed significantly higher scores on surface learning approach (Z = -2.259, P < 0.05). After the course, students in the PFC group scored significantly higher in deep learning strategy than those in the TLC group (Z = -2.196, P < 0.05). The PFC model had a positive impact on deep learning motive and strategy, leading to an improvement in the deep approach, which is beneficial for the long-term development of students. In contrast, the TLC model only improved the surface learning approach. The study implies that educators should consider implementing PFC to enhance students' learning approaches.NEW & NOTEWORTHY In this article, we compare the impact of the partially flipped classroom (PFC) and the traditional lecture classroom (TLC) in a physiology course on medical students' learning approaches. We found that the PFC benefited students by significantly enhancing their deep learning motive, strategy, and approach, which was good for them. However, the TLC model only improved the surface learning motive and approach.

Recent advances in the application of Mendelian randomization to chronic kidney disease.

OBJECTIVE: Chronic kidney disease (CKD) is a condition influenced by both genetic and environmental factors and has been a focus of extensive research. Utilizing Mendelian randomization, researchers have begun to untangle the complex causal relationships underlying CKD. This review delves into the advances and challenges in the application of MR in the field of nephrology, shifting from a mere summary of its principles and limitations to a more nuanced exploration of its contributions to our understanding of CKD. METHODS: Key findings from recent studies have been pivotal in reshaping our comprehension of CKD. Notably, evidence indicates that elevated testosterone levels may impair renal function, while higher sex hormone-binding globulin (SHBG) levels appear to be protective, predominantly in men. Surprisingly, variations in plasma glucose and glycated hemoglobin levels seem unaffected by genetically induced changes in the estimated glomerular filtration rate (eGFR), suggesting an independent pathway for renal function impairment. RESULTS: Furthermore, lifestyle factors such as physical activity and socioeconomic status emerge as significant influencers of CKD risk and kidney health. The relationship between sleep duration and CKD is nuanced; short sleep duration is linked to increased risk, while long sleep duration does not exhibit a clear causal effect. Additionally, lifestyle factors, including diet, exercise, and mental wellness activities, play a crucial role in kidney health. New insights also reveal a substantial causal connection between both central and general obesity and CKD onset, while no significant links were found between genetically modified LDL cholesterol or triglyceride levels and kidney function. CONCLUSION: This review not only presents the recent achievements of MR in CKD research but also illuminates the path forwards, underscoring critical unanswered questions and proposing future research directions in this dynamic field.

Artificial Channels Based on Bottlebrush Polymers: Enhanced Ion Transport Through Polymer Topology Control.

Synthetic structures mimicking the transport function of natural ion channel proteins have a wide range of applications, including therapeutic treatments, separation membranes, sensing, and biotechnologies. However, the development of polymer-based artificial channels has been hampered due to the limitation on available models. In this study, we demonstrate the great potential of bottlebrush polymers as accessible and versatile molecular scaffolds for developing efficient artificial ion channels. Adopting the bottlebrush configuration enhanced ion transport activity of the channels compared to their linear analogs. Matching the structure of lipid bilayers, the bottlebrush channel with a hydrophilic-hydrophobic-hydrophilic triblock architecture exhibited the highest activity among the series. Functionalized with urea groups, these channels displayed high anion selectivity. Additionally, we illustrated that the transport properties could be fine-tuned by modifying the chemistry of ion binding sites. This work not only highlights the importance of polymer topology control in channel design, but also reveals the great potential for further developing bottlebrush channels with customized features and diverse functionalities.

Upregulated Cytoskeletal Proteins Promote Pathological Angiogenesis in Moyamoya Disease.

BACKGROUND: Moyamoya disease (MMD) is a rare progressive vascular disease that leads to intracranial internal carotid artery stenosis and eventual occlusion. However, its pathogenesis remains unclear. The purpose of this study is to explore the role of abnormally expressed proteins in the pathogenesis of MMD. METHODS: Data-independent acquisition mass spectrometry identifies the differentially expressed proteins in MMD serum by detecting the serum from 60 patients with MMD and 20 health controls. The differentially expressed proteins were validated using enzyme linked immunosorbent assays. Immunofluorescence for superficial temporal artery and middle cerebral artery specimens was used to explore the morphological changes of vascular wall in MMD. In vitro experiments were used to explore the changes and mechanisms of differentially expressed proteins on endothelial cells. RESULTS: Proteomic analysis showed that a total of 14 726 peptides and 1555 proteins were quantified by mass spectrometry data. FLNA (filamin A) and ZYX (zyxin) proteins were significantly higher in MMD serum compared with those in health controls (Log2FC >2.9 and >2.8, respectively). Immunofluorescence revealed an intimal hyperplasia in superficial temporal artery and middle cerebral artery specimens of MMD. FLNA and ZYX proteins increased the proportion of endothelial cells in S phase and promoted their proliferation, angiogenesis, and cytoskeleton enlargement. Mechanistic studies revealed that AKT (serine/threonine kinase)/GSK-3ß (glycogen synthase kinase 3ß)/ß-catenin signaling pathway plays a major role in these FLNA- and ZYX-induced changes in endothelial cells. CONCLUSIONS: This study provides proteomic data on a large sample size of MMD. The differential expression of FLNA and ZYX in patient with MMD and following in vitro experiments suggest that these upregulated proteins are related to the pathology of cerebrovascular intimal hyperplasia in MMD and are involved in MMD pathogenesis, with diagnostic and therapeutic ramifications.

A radiomics model fusing clinical features to predict microsatellite status preoperatively in colorectal cancer liver metastasis.

PURPOSE: To study the combined model of radiomic features and clinical features based on enhanced CT images for noninvasive evaluation of microsatellite instability (MSI) status in colorectal liver metastasis (CRLM) before surgery. METHODS: The study included 104 patients retrospectively and collected CT images of patients. We adjusted the region of interest to increase the number of MSI-H images. Radiomic features were extracted from these CT images. The logistic models of simple clinical features, simple radiomic features, and radiomic features with clinical features were constructed from the original image data and the expanded data, respectively. The six models were evaluated in the validation set. A nomogram was made to conveniently show the probability of the patient having a high MSI (MSI-H). RESULTS: The model including radiomic features and clinical features in the expanded data worked best in the validation group. CONCLUSION: A logistic regression prediction model based on enhanced CT images combining clinical features and radiomic features after increasing the number of MSI-H images can effectively identify patients with CRLM with MSI-H and low-frequency microsatellite instability (MSI-L), and provide effective guidance for clinical immunotherapy of CRLM patients with unknown MSI status.

Exploring the impact of online and offline teaching methods on the cognitive abilities of medical students: a comparative study.

BACKGROUND: Online education has become increasingly popular, but research on the effectiveness of different teaching models in developing cognitive skills is limited. This study investigated the relationship between different teaching models (online and offline) and the development of cognitive skills in clinical medicine students. METHODS: Survey data were collected from 2018 entry students who participated in online teaching and 2019 entry students in offline teaching at Xiangya School of Medicine, Central South University. National Quality Open Courses (NQROC) were provided to both groups of students. The study examined the total score of physiology final exam, score of each type of question, and NQROC learning engagement in different score segments under the two teaching models. Non-parametric statistical methods were utilized to analyze the total score of physiology final exam, score of each type of question, and the NQROC learning engagement. Spearman's rank correlation was utilized to analyze the relationship between the score of physiology final exam and the students' NQROC learning engagement. RESULTS: The study found no statistically significant difference in the total score, short-answer questions (SAQs) score, and case study questions (CSQs) score between online and offline teaching models. However, the multiple-choice questions (MCQs) score was higher in the online teaching model (Z=-4.249, P < 0.001), suggesting that online teaching may be an effective way to improve lower-order cognitive skills among students. In contrast, low-achieving students had higher total scores (Z=-3.223, P = 0.001) and scores in both MCQs (Z=-6.263, P < 0.001) and CSQs (Z=-6.877, P < 0.001) in the online teaching model. High-achieving students in the online teaching model had higher total scores (Z=-3.001, P = 0.003) and MCQs scores (Z=-5.706, P < 0.001) but lower scores in CSQs (Z=-2.775, P = 0.006). Furthermore, students' NQROC learning engagement was greater in the online teaching model. CONCLUSIONS: The results of this study suggested that online teaching was not statistically significantly different from offline in cognitive domains and was more desirable than offline in strengthening lower-order cognitive skills. However, it was important to note that offline teaching may be more effective in reinforcing higher-order cognitive skills among high-achieving students. In conclusion, this study provided important insights into the effectiveness of different teaching models in developing cognitive skills among medical students and highlighted the potential benefits of online teaching in enhancing students' lower-order cognitive skills.

Ultrasound-Microwave Combined Extraction of Novel Polysaccharide Fractions from Lycium barbarum Leaves and Their In Vitro Hypoglycemic and Antioxidant Activities.

Ultrasound-microwave combined extraction (UMCE), gradient ethanol precipitation, chemical characterization, and antioxidant and hypoglycemic activities of Lycium barbarum leaf polysaccharides (LLP) were systematically studied. The optimal conditions for UMCE of LLP achieved by response surface method (RSM) were as follows: microwave time of 16 min, ultrasonic time of 20 min, particle size of 100 mesh, and ratio of liquid to solid of 55:1. Three novel polysaccharide fractions (LLP30, LLP50, LLP70) with different molecular weights were obtained by gradient ethanol precipitation. Polysaccharide samples exhibited scavenging capacities against ABTS and DPPH radicals and inhibitory activities against α-glucosidase and α-amylase. Among the three fractions, LLP30 possessed relatively high antioxidant and hypoglycemic activities in vitro, which showed a potential for becoming a nutraceutical or a phytopharmaceutical for prevention and treatment of hyperglycemia or diabetes.

[Specific DNA barcodes, germplasm resources, and genetic diversity of Eleutherococcus senticosus].

Eleutherococcus senticosus is one of the Dao-di herbs in northeast China. In this study, the chloroplast genomes of three E. senticosus samples from different genuine producing areas were sequenced and then used for the screening of specific DNA barcodes. The germplasm resources and genetic diversity of E. senticosus were analyzed basing on the specific DNA barcodes. The chloroplast genomes of E. senticosus from different genuine producing areas showed the total length of 156 779-156 781 bp and a typical tetrad structure. Each of the chloroplast genomes carried 132 genes, including 87 protein-coding genes, 37 tRNAs, and 8 rRNAs. The chloroplast genomes were relatively conserved. Sequence analysis of the three chloroplast genomes indicated that atpI, ndhA, ycf1, atpB-rbcL, ndhF-rpl32, petA-psbJ, psbM-psbD, and rps16-psbK can be used as specific DNA barcodes of E. senticosus. In this study, we selected atpI and atpB-rbcL which were 700-800 bp and easy to be amplified for the identification of 184 E. senticosus samples from 13 genuine producing areas. The results demonstrated that 9 and 10 genotypes were identified based on atpI and atpB-rbcL sequences, respectively. Furthermore, the two barcodes identified 23 genotypes which were named H1-H23. The haplotype with the highest proportion and widest distribution was H10, followed by H2. The haplotype diversity and nucleotide diversity were 0.94 and 1.82×10~(-3), respectively, suggesting the high genetic diversity of E. senticosus. The results of the median-joining network analysis showed that the 23 genotypes could be classified into 4 categories. H2 was the oldest haplotype, and it served as the center of the network characterized by starlike radiation, which suggested that population expansion of E. senticosus occurred in the genuine producing areas. This study lays a foundation for the research on the genetic quality and chloroplast genetic engineering of E. senticosus and further research on the genetic mechanism of its population, providing new ideas for studying the genetic evolution of E. senticosus.

[Study on Analytical Method of Leachable Substances of Allogeneic Products].

Products made from allogeneic tissue are largely used in clinical treatment due to its wide source compared with autologous tissue, causing less secondary trauma of patients and the good biocompatibility. Various organic solvents and other substances introduced in the production process of allogeneic products will leach down into the human through clinical treatment, thus bringing varying degrees of harm to patients. Therefore, it is very necessary to detect and control the leachables in such products. Based on the classification and summary of leachable substances existing in the allogeneic products, the preparation of extract and the establishment of the detection techniques for known and unknown leachable are briefly introduced in this study, in order to provide research method for the study of leachable substances of allogeneic products.