Serum untargeted metabolomics analysis of the preventive mechanism of TAETEA Prebiotea on non-alcoholic fatty liver in rats.

Pu-erh tea belongs to the six tea categories of black tea, according to the processing technology and quality characteristics, is divided into two types of raw tea and ripe tea. Raw tea is made from fresh leaves of tea as raw materials, through the process of greening, kneading, sun drying, steam molding and other processes made of tightly pressed tea. Ripe tea is made from Yunnan large-leafed sun green tea, using a specific process, post-fermentation (rapid post-fermentation or slow post-fermentation) processing of loose tea and tightly pressed tea. TAETEA Prebiotea is Puerh Ripe Tea, TAETEA Prebiotea has the effect of increasing insulin level and improving hyperglycemia in mice, and it also has the effect of regulating blood lipids, which can reduce the level of serum total cholesterol (TC) and triglycerides (TG), increase the level of high-density lipoprotein cholesterol (HDL-C), and improve the metabolism of lipids. Therefore, further experiments were conducted by us, and TAETEA Prebiotea was formulated into a suitable dose for the intervention of non alcoholic fatty liver disease (NAFLD) model rats, and at the end of the experiments, the samples of each group of experiments were analyzed and detected by the method of UHPLC-Q-Exactive LC-MS liquid-mass spectrometry methodology, and the relevant metabolites as well as metabolic pathways were analyzed by the method of Non targeted metabolomics analysis. As a result, 71 differential metabolites could be screened, of which 35 differential metabolites were up-regulated after intervention and 36 differential metabolites were down-regulated after intervention. Based on the KEGG pathway enrichment and Pathway Impact bubble diagram analysis, glycine, serine, threonine metabolism, arginine and proline metabolism, protein digestion and absorption, and central carbon metabolism in cancer may be the main metabolic pathways in which TAETEA Prebiotea exerted preventive effects on NAFLD rats, C00148 (Proline), C00300 (Creatine) and C00719 (Betaine) are the differential metabolites that play important regulatory roles.

Jiegeng decoction ameliorated acute pharyngitis through suppressing NF-κB and MAPK signaling pathways.

ETHNOPHARMACOLOGICAL RELEVANCE: Jiegeng decoction (JGD), consisting of Glycyrrhizae Radix et Rhizoma and Platycodonis Radix at the ratio of 2:1, is a classical Chinese medicine prescription firstly recorded in "Treatise on Febrile Diseases". JGD has been extensively utilized to treat sore throat and lung diseases for thousands of years in China. However, the pharmacological effect and mechanism of JGD on acute pharyngitis (AP) remain unclear. AIM OF THE STUDY: Our research aimed to reveal the pharmacological effect of JGD on AP and its potential mechanisms. MATERIALS AND METHODS: The chemical components of JGD were analyzed based on the UPLC-MS analysis. The anti-inflammatory effect of JGD was evaluated by NO production using the Griess assay in RAW 264.7 cells. The mRNA expression of iNOS, IL-1ß, IL-10, TNF-α, IL-6 and MCP-1 was determined by qRT-PCR in vitro. A 15% ammonia-induced AP model was established. The histopathology, the inflammatory cytokines IL-6 and MCP-1 in serum and the apoptosis-related genes caspease-8 and caspease-3 were determined by H&E staining, ELISA and qRT-PCR, respectively. The expression levels of p-p65, p65, p-JNK, JNK, p-p38, p38, p-ERK1/2, ERK1/2, and COX2 were measured through western blotting. RESULTS: Nine compounds, including liquiritin, liquiritin apiosde, liquiritigenin, platycodin D, platycoside A, licorice saponin J2, licorice saponin G2, glycyrrhizic acid, and licochalcone A, were identified. JGD significantly inhibited NO production and regulated the mRNA expression levels of cytokines in LPS-stimulated RAW 264.7 cells. The results of in vivo experiments confirmed that JGD ameliorated AP through improving the pathological state of pharyngeal tissue, decreasing the serum levels of IL-6 and MCP-1 and preventing the tissue mRNA expression of caspease-8 and caspease-3. Furthermore, JGD also inhibited the NF-κB and MAPK pathways in the AP model. CONCLUSIONS: This study suggested that JGD could alleviate AP through its anti-inflammation via NF-κB and MAPK pathways, which supported the traditional application of JGD for the treatment of throat diseases.

Association of the systemic immuno-inflammation index, neutrophil-to-lymphocyte ratio, and platelet-to-lymphocyte ratio with diabetic microvascular complications.

Background: The systemic immuno-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) are widely used and have been shown to be predictive indicators of various diseases. Diabetic nephropathy (DN), retinopathy (DR), and peripheral neuropathy (DPN) are the most prominent and common microvascular complications, which have seriously negative impacts on patients, families, and society. Exploring the associations with these three indicators and diabetic microvascular complications are the main purpose. Methods: There were 1058 individuals with type 2 diabetes mellitus (T2DM) in this retrospective cross-sectional study. SII, NLR, and PLR were calculated. The diseases were diagnosed by endocrinologists. Logistic regression and subgroup analysis were applied to evaluate the association between SII, NLP, and PLR and diabetic microvascular complications. Results: SII, NLR, and PLR were significantly associated with the risk of DN [odds ratios (ORs): 1.52, 1.71, and 1.60, respectively] and DR [ORs: 1.57, 1.79, and 1.55, respectively] by multivariate logistic regression. When NLR ≥2.66, the OR was significantly higher for the risk of DPN (OR: 1.985, 95% confidence interval: 1.29-3.05). Subgroup analysis showed no significant positive associations across different demographics and comorbidities, including sex, age, hypertension, HbA1c (glycated hemoglobin), and dyslipidemia. Conclusion: This study found a positive relationship between NLR and DN, DR, and DPN. In contrast, SII and PLR were found to be only associated with DN and DR. Therefore, for the diagnosis of diabetic microvascular complications, SII, NLR and PLR are highly valuable.

Maternal RNA transcription in Dlk1-Dio3 domain is critical for proper development of the mouse placental vasculature.

The placenta is a unique organ for ensuring normal embryonic growth in the uterine. Here, we found that maternal RNA transcription in Dlk1-Dio3 imprinted domain is essential for placentation. PolyA signals were inserted into Gtl2 to establish a mouse model to prevent the expression of maternal RNAs in the domain. The maternal allele knock-in (MKI) and homozygous (HOMO) placentas showed an expanded junctional zone, reduced labyrinth and poor vasculature impacting both fetal and maternal blood spaces. The MKI and HOMO models displayed dysregulated gene expression in the Dlk1-Dio3 domain. In situ hybridization detected Dlk1, Gtl2, Rtl1, miR-127 and Rian dysregulated in the labyrinth vasculature. MKI and HOMO induced Dlk1 to lose imprinting, and DNA methylation changes of IG-DMR and Gtl2-DMR, leading to abnormal gene expression, while the above changes didn't occur in paternal allele knock-in placentas. These findings demonstrate that maternal RNAs in the Dlk1-Dio3 domain are involved in placental vasculature, regulating gene expression, imprinting status and DNA methylation.

Comparison of the effects of imputation methods for missing data in predictive modelling of cohort study datasets.

BACKGROUND: Missing data is frequently an inevitable issue in cohort studies and it can adversely affect the study's findings. We assess the effectiveness of eight frequently utilized statistical and machine learning (ML) imputation methods for dealing with missing data in predictive modelling of cohort study datasets. This evaluation is based on real data and predictive models for cardiovascular disease (CVD) risk. METHODS: The data is from a real-world cohort study in Xinjiang, China. It includes personal information, physical examination data, questionnaires, and laboratory biochemical results from 10,164 subjects with a total of 37 variables. Simple imputation (Simple), regression imputation (Regression), expectation-maximization(EM), multiple imputation (MICE) , K nearest neighbor classification (KNN), clustering imputation (Cluster), random forest (RF), and decision tree (Cart) were the chosen imputation methods. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are utilised to assess the performance of different methods for missing data imputation at a missing rate of 20%. The datasets processed with different missing data imputation methods were employed to construct a CVD risk prediction model utilizing the support vector machine (SVM). The predictive performance was then compared using the area under the curve (AUC). RESULTS: The most effective imputation results were attained by KNN (MAE: 0.2032, RMSE: 0.7438, AUC: 0.730, CI: 0.719-0.741) and RF (MAE: 0.3944, RMSE: 1.4866, AUC: 0.777, CI: 0.769-0.785). The subsequent best performances were achieved by EM, Cart, and MICE, while Simple, Regression, and Cluster attained the worst performances. The CVD risk prediction model was constructed using the complete data (AUC:0.804, CI:0.796-0.812) in comparison with all other models with p<0.05. CONCLUSION: KNN and RF exhibit superior performance and are more adept at imputing missing data in predictive modelling of cohort study datasets.

Anti-inflammatory labdane diterpenoids from the aerial parts of Leonurus sibiricus.

Eleven undescribed labdane diterpenoids, sibiricusins K-U, and seven known analogues were obtained from the MeOH extract of the aerial parts of Leonurus sibiricus. The structures of the compounds were established by detailed spectroscopic data analysis, single-crystal X-ray diffraction analysis and ECD calculations. Among them, sibiricusins L-N featured a rare α, ß-unsaturated-γ-lactam moiety. Fourteen of the isolates were evaluated for their anti-inflammatory effect on the production of NO in LPS-induced RAW264.7 cells through Griess assay. Sibiricusin O displayed the strongest activity with an IC50 value of 9.0 ± 1.7 µM.

Superlow Power Consumption Memristor Based on Borphyrin-Deoxyribonucleic Acid Composite Films as Artificial Synapse for Neuromorphic Computing.

Memristor synapses based on green and pollution-free organic materials are expected to facilitate biorealistic neuromorphic computing and to be an important step toward the next generation of green electronics. Metalloporphyrin is an organic compound that widely exists in nature with good biocompatibility and stable chemical properties, and has already been used to fabricate memristors. However, the application of metalloporphyrin-based memristors as synaptic devices still faces challenges, such as realizing a high switching ratio, low power consumption, and bidirectional conductance modulation. We developed a memristor that improves the resistive switching (RS) characteristics of Zn(II)meso-tetra(4-carboxyphenyl) porphine (ZnTCPP) by combining it with deoxyribonucleic acid (DNA) in a composite film. The as-fabricated ZnTCPP-DNA-based device showed excellent RS memory characteristics with a sufficiently high switching ratio of up to ∼104, super low power consumption of ∼39.56 nW, good cycling stability, and data retention capability. Moreover, bidirectional conductance modulation of the ZnTCPP-DNA-based device can be controlled by modulating the amplitudes, durations, and intervals of positive and negative pulses. The ZnTCPP-DNA-based device was used to successfully simulate a series of synaptic functions including long-term potentiation, long-term depression, spike time-dependent plasticity, paired-pulse facilitation, excitatory postsynaptic current, and human learning behavior, which demonstrates its potential applicability to neuromorphic devices. A two-layer artificial neural network was used to demonstrate the digit recognition ability of the ZnTCPP-DNA-based device, which reached 97.22% after 100 training iterations. These results create a new avenue for the research and development of green electronics and have major implications for green low-power neuromorphic computing in the future.

Global prevalence of myopic macular degeneration in general population and patients with high myopia: A systematic review and meta-analysis.

The prevalence of myopic macular degeneration (MMD) in the general population and patients with high myopia worldwide has not been fully investigated. Therefore, we screened all population-based studies that reported the prevalence of MMD, and pooled prevalence of MMD using a random-effect model. Subgroup analyses were performed to explore the differences in MMD prevalence in the general population and patients with high myopia according to ethnicity, region of residence (urban/rural), and grading system. Finally, 16 studies were included in this meta-analysis. Results obtained from 2,963 patients from seven countries on four continents indicated that the pooled prevalence of MMD in patients with high myopia was 49.0% (95% CI: 31.5%-66.7%). Results obtained from 71,052 participants from 10 countries on four continents suggested that the pooled prevalence of MMD in the general population was 1.7% (95% CI: 1.1%-2.6%). In the general population, living in urban areas and East Asians were associated with a high prevalence of MMD. Among patients with high myopia, only East Asians were at a higher risk of developing MMD. In conclusion, MMD was particularly prevalent in patients with high myopia. Compared with Europeans, East Asians (Chinese and Japanese) have a higher propensity of developing MMD, both in the general population and in patients with high myopia. It remains unclear whether the higher prevalence of MMD in patients with high myopia in East Asia is caused by differences in given age or given degree of myopia.Systematic review registration number: 202270014 (INPLASY.COM).

High triglyceride levels increase the risk of diabetic microvascular complications: a cross-sectional study.

BACKGROUND: The prevalence of microvascular complications in type 2 diabetes mellitus (T2DM) is increasing. The effect of lipid profiles on diabetic microvascular complications remains debated. This research aimed to study the correlation between lipid profiles and microvascular complications. METHODS: This retrospective cross-sectional study included 1096 T2DM patients. The patients were divided into the control, diabetic retinopathy (DR), nephropathy (DKD), and peripheral neuropathy (DPN) groups based on the existence of corresponding complications. The lipid profiles were analyzed, and the effect on complications was assessed by logistic regression. RESULTS: Compared with the control group, the diabetic microvascular complications group had a higher dyslipidemia rate. The rate of high TGs increased significantly with an increasing number of complications. High TG levels contributed to the risk of DKD, DR, and DPN [odds ratios (ORs): 2.447, 2.267, 2.252; 95% confidence interval: 1.648-3.633, 1.406-3.655, 1.472-3.445]. In the age (years) > 55, T2DM duration (years) > 10, and HbA1c (%) ≥ 7 groups, the risk of high TGs was higher for DKD (ORs: 2.193, 2.419, 2.082), DR (ORs: 2.069, 2.317, 1.993), and DPN (ORs: 1.811, 1.405, 1.427). CONCLUSION: High TG levels increase the risk of diabetic microvascular complications, and patients with older age, longer T2DM duration, and higher HbA1c levels are recommended to keep lipid levels more strictly.

Labdane and ent-halimane diterpenoids with STAT3-inhibitory activity from Leonurus sibiricus.

Nine undescribed labdane diterpenoids (1-9) and one undescribed ent-halimane diterpenoid (10) were isolated from the aerial parts of Leonurus sibiricus, together with four known analogues (11-14) during our searching for naturally occurring antitumor agents. Their structures were established by detailed spectroscopic analyses and electronic circular dichroism analysis. Compound 4 possessed a rare 10-epi labdane scaffold. All compounds except 5 were evaluated for their inhibitory activities against interleukin (IL)-6-stimulated signal transducer and activator of transcription (STAT3) expression using a luciferase reporter assay. Compound 1 showed the most inhibitory effect with the IC50 value 20.31 µM. Compound 1 inhibited the activation of JAK2/STAT3 signal pathway through binding to Gln326 of STAT3 in CNE cells. The antiproliferative evaluation of compound 1 against CNE, CAL-27, A549 and PANC-1 cells demonstrated that CNE cells were the most sensitive to 1. Furthermore, compound 1 showed moderate efficacy in inhibiting cell migration, invasion, and epithelial-mesenchymal transition in CNE cells. In addition, compound 1 also promoted ferroptosis in CNE cells in a dose-dependent manner. These results suggest that compound 1 might be a potential candidate lead for treating nasopharyngeal carcinoma.

Anti-inflammatory polyoxygenated cyclohexene derivatives from Uvaria macclurei.

Two undescribed polyoxygenated seco-cyclohexene derivatives named macclureins A and B, and three undescribed polyoxygenated cyclohexene derivatives macclureins C-E, together with 15 known analogues were isolated from the twigs and leaves of Uvaria macclurei. Their structures were established by extensive spectroscopic and circular dichroism analyses. Macclurein C is a chlorinated polyoxygenated cyclohexene. All isolates were evaluated for their anti-inflammatory activities on NO generation in the LPS-stimulated RAW 264.7 cells. (-)-Zeylenone showed the most potent effect against NO production with the IC50 value of 20.18 µM. Meanwhile, (-)-zeylenone also decreased the mRNA expression of pro-inflammatory factors IFN-γ, iNOS, IL-6 and TNF-α via downregulating NF-κB signaling pathway. Further in vivo experiments using a mouse model of sepsis showed that (-)-zeylenone significantly alleviated sepsis severity by measuring weight, murine sepsis score, survival rate and the serum levels of pro-inflammatory factors TNF-α and IL-6.

COVRECON: automated integration of genome- and metabolome-scale network reconstruction and data-driven inverse modeling of metabolic interaction networks.

MOTIVATION: One central goal of systems biology is to infer biochemical regulations from large-scale OMICS data. Many aspects of cellular physiology and organismal phenotypes can be understood as results of metabolic interaction network dynamics. Previously, we have proposed a convenient mathematical method, which addresses this problem using metabolomics data for the inverse calculation of biochemical Jacobian matrices revealing regulatory checkpoints of biochemical regulations. The proposed algorithms for this inference are limited by two issues: they rely on structural network information that needs to be assembled manually, and they are numerically unstable due to ill-conditioned regression problems for large-scale metabolic networks. RESULTS: To address these problems, we developed a novel regression loss-based inverse Jacobian algorithm, combining metabolomics COVariance and genome-scale metabolic RECONstruction, which allows for a fully automated, algorithmic implementation of the COVRECON workflow. It consists of two parts: (i) Sim-Network and (ii) inverse differential Jacobian evaluation. Sim-Network automatically generates an organism-specific enzyme and reaction dataset from Bigg and KEGG databases, which is then used to reconstruct the Jacobian's structure for a specific metabolomics dataset. Instead of directly solving a regression problem as in the previous workflow, the new inverse differential Jacobian is based on a substantially more robust approach and rates the biochemical interactions according to their relevance from large-scale metabolomics data. The approach is illustrated by in silico stochastic analysis with differently sized metabolic networks from the BioModels database and applied to a real-world example. The characteristics of the COVRECON implementation are that (i) it automatically reconstructs a data-driven superpathway model; (ii) more general network structures can be investigated, and (iii) the new inverse algorithm improves stability, decreases computation time, and extends to large-scale models. AVAILABILITY AND IMPLEMENTATION: The code is available in the website https://bitbucket.org/mosys-univie/covrecon.

Ultrasensitive Electrochemiluminescence Biosensor Based on 2D Co3O4 Nanosheets as a Coreaction Accelerator and Highly Ordered Rolling DNA Nanomachine as a Signal Amplifier for the Detection of MicroRNA.

A novel ultrasensitive electrochemiluminescence (ECL) biosensor was constructed using two-dimensional (2D) Co3O4 nanosheets as a novel coreaction accelerator of the luminol/H2O2 ECL system for the detection of microRNA-21 (miRNA-21). Impressively, coreaction accelerator 2D Co3O4 nanosheets with effective mutual conversion of the Co2+/Co3+ redox pair and abundant active sites could promote the decomposition of coreactant H2O2 to generate more superoxide anion radicals (O2•-), which reacted with luminol for significantly enhancing ECL signals. Furthermore, the trace target miRNA-21 was transformed into a large number of G-wires through the strand displacement amplification (SDA) process to self-assemble the highly ordered rolling DNA nanomachine (HORDNM), which could tremendously improve the detection sensitivity of biosensors. Hence, on the basis of the novel luminol/H2O2/2D Co3O4 nanosheet ternary ECL system, the biosensor implemented ultrasensitive detection of miRNA-21 with a detection limit as low as 4.1 aM, which provided a novel strategy to design an effective ECL emitter for ultrasensitive detection of biomarkers for early disease diagnosis.

Developing a male-specific age predictive model based on Y-CpGs for forensic analysis.

In forensic work, predicting the age of the criminal suspect or victim could provide beneficial clues for investigation. Epigenetic age estimation based on age-correlated DNA methylation has been one of the most widely studied methods of age estimation. However, almost all available epigenetic age prediction models are based on autosomal CpGs, which are only applicable to single-source DNA samples. In this study, we screened the available methylation data sets to identify loci with potential to meet the objectives of this study and then established a male-specific age prediction model based on 2 SNaPshot systems that contain 13 Y-CpGs and the mean absolute deviation (MAD) values were 4-6 years. The multiplex methylation SNaPshot systems and age-predictive model have been validated for sensitivity (the DNA input could be as low as 0.5 ng) and male specificity. They are supposed to have feasibility in forensic practice. In addition, it demonstrated that the method was also applicable to bloodstains, which were commonly found at crime scenes. The results showed good performance (the training set: R2 = 0.9341, MAD = 4.65 years; the test set: R2 = 0.8952, MAD = 5.73 years) in case investigation for predicting male age. For mixtures, when the male to female DNA ratio is 1:1, 1:10, the deviation between the actual age and the predicted age obtained by the model was less than 8 years, which offers great hope for future prediction of the age of males in mixtures and will be a powerful tool for special cases, such as sexual assault. Furthermore, the work provides a basis for the application of Y-CpGs in forensic science.

Aluminum(III)-Based Organic Nanofibrous Gels as an Aggregation-Induced Electrochemiluminescence Emitter Combined with a Rigid Triplex DNA Walker as a Signal Magnifier for Ultrasensitive DNA Assay.

Due to effective tackling of the problems of aggregation-caused quenching of traditional ECL emitters, aggregation-induced electrochemiluminescence (AIECL) has emerged as a research hotspot in aqueous detection and sensing. However, the existing AIECL emitters still encounter the bottlenecks of low ECL efficiency, poor biocompatibility, and high cost. Herein, aluminum(III)-based organic nanofibrous gels (AOGs) are used as a novel AIECL emitter to construct a rapid and ultrasensitive sensing platform for the detection of Flu A virus biomarker DNA (fDNA) with the assistance of a high-speed and hyper-efficient signal magnifier, a rigid triplex DNA walker (T-DNA walker). The proposed AOGs with three-dimensional (3D) nanofiber morphology are assembled in one step within about 15 s by the ligand 2,2':6',2″-terpyridine-4'-carboxylic acid (TPY-COOH) and cheap metal ion Al3+, which demonstrates an efficient ECL response and outstanding biocompatibility. Impressively, on the basis of loop-mediated isothermal amplification-generated hydrogen ions (LAMP-H+), the target-induced pH-responsive rigid T-DNA walker overcomes the limitations of conventional single or duplex DNA walkers in walking trajectory and efficiency due to the entanglement and lodging of leg DNA, exhibiting high stability, controllability, and walking efficiency. Therefore, AOGs with excellent AIECL performance were combined with a CG-C+ T-DNA nanomachine with high walking efficiency and stability, and the proposed "on-off" ECL biosensor displayed a low detection limit down to 23 ag·µL-1 for target fDNA. Also, the strategy provided a useful platform for rapid and sensitive monitoring of biomolecules, considerably broadening its potential applications in luminescent molecular devices, clinical diagnosis, and sensing analysis.

Study on Dynamic Probability and Quantitative Risk Calculation Method of Domino Accident in Pool Fire in Chemical Storage Tank Area.

The domino event caused by fire is one of the common accidents in hydrocarbon storage tank farms, which further expands the severity and scope of the accident. Due to the different failure sequence of the storage tanks in a domino accident, the radiant heat generated by the failed storage tank to the target tank is different. Based on the influence of this synergistic effect, this study combined the Monte Carlo algorithm and FSEM, and proposed a fast real-time probability calculation method for a fire domino accident in a storage tank area, for the first time. This method uses the Monte Carlo algorithm to simulate all accident scenarios, and obtains the evolution of multiple escalation fire domino accidents under the synergistic effect according to FSEM, and then calculates the real-time failure probability and risk. Based on a comprehensive analysis of the accident propagation path, this method avoids the problem of a large amount of calculation, and is conducive to the rapid and effective analysis of the fire risk in a storage tank area and the formulation of corresponding risk reduction measures. The effectiveness and superiority of the proposed method were proved by a case study.

Superlow Power Consumption Artificial Synapses Based on WSe2 Quantum Dots Memristor for Neuromorphic Computing.

As the emerging member of zero-dimension transition metal dichalcogenide, WSe2 quantum dots (QDs) have been applied to memristors and exhibited better resistance switching characteristics and miniaturization size. However, low power consumption and high reliability are still challenges for WSe2 QDs-based memristors as synaptic devices. Here, we demonstrate a high-performance, superlow power consumption memristor device with the structure of Ag/WSe2 QDs/La0.3Sr0.7MnO3/SrTiO3. The device displays excellent resistive switching memory behavior with a R OFF/R ON ratio of ~5 × 103, power consumption per switching as low as 0.16 nW, very low set, and reset voltage of ~0.52 V and~ -0.19 V with excellent cycling stability, good reproducibility, and decent data retention capability. The superlow power consumption characteristic of the device is further proved by the method of density functional theory calculation. In addition, the influence of pulse amplitude, duration, and interval was studied to gradually modulating the conductance of the device. The memristor has also been demonstrated to simulate different functions of artificial synapses, such as excitatory postsynaptic current, spike timing-dependent plasticity, long-term potentiation, long-term depression, and paired-pulse facilitation. Importantly, digit recognition ability based on the WSe2 QDs device is evaluated through a three-layer artificial neural network, and the digit recognition accuracy after 40 times of training can reach up to 94.05%. This study paves a new way for the development of memristor devices with advanced significance for future low power neuromorphic computing.

Prevalence of dyslipidemia, treatment rate and its control among patients with type 2 diabetes mellitus in Northwest China: a cross-sectional study.

BACKGROUND: The prevalence of cardiovascular disease (CVD) is high in China, especially in Northwest China, and dyslipidemia in diabetes is a major factor at risk for CVD. The dyslipidemia prevalence, treatment and control among type 2 diabetes mellitus (T2DM) patients in Northwest China were investigated. METHODS: In the cross-sectional retrospective research, 1386 medical records of T2DM patients were collected from the Endocrine Department of Tangdu Hospital. And patients' age, sex, diabetes duration, glycated hemoglobin (HbA1c), complications, lipid levels, and drug use were recorded. The patient characteristics, lipid level and lipid-lowering therapy were analyzed. RESULTS: In this study, the dyslipidemia prevalence among T2DM patients was 87.7%, the treatment rate was 68.0%. The overall control rate of low-density lipoprotein cholesterol (LDL-C) was 43.1%, and control rates reached 52.7% for high-risk subjects and 36.1% for very high-risk subjects. The overall control rate of non-high-density lipoprotein cholesterol (non-HDL-C) was 19.8%. HbA1c (%) ≥ 7 was indicated as a major factor predicting failure of LDL-C and non-HDL-C control [odds ratio (OR) 1.521; 2.206, 95% confidence interval (CI) 1.154-2.005; 1.583-3.076)]. CONCLUSION: Among patients with T2DM, it is high prevalence of dyslipidemia and low rate of treatment and control, and higher HbA1c level is the main factor for poor lipid control. It calls for more efforts to promote early screening, prevention and treatment of dyslipidemia for patients, thereby reducing the risk of CVD.

Persistence of Salmonella Typhimurium in apple-pear (Pyrus bretschneideri Rehd.) orchard soils influenced by bacterial communities and soil properties.

2In this study, we investigated the persistence of Salmonella Typhimurium in 26 soil samples from apple-pear orchards in Yanji, Longjing and Helong in northeastern China. The time to reach detection limit (ttds) of Salmonella Typhimurium in soils varied from 20 to 120 days. Redundancy analysis and variation partition analysis elucidated that bacterial communities, clay content, pH, electrical conductivity (EC) salinity, and NO3--N could explain more than 85% of overall variation of the persistence behaviors. Results of structural equation models and Mantel tests revealed that clay content and EC displayed both direct and indirect effect on ttds, while NO3--N and pH exhibited direct and indirect effect on the survival patterns, respectively. Furthermore, Actinobacteria, Acidobacteria and Deltaproteobacteria at class level showed highly close correlations with ttds. Our results revealed that certain biotic and abiotic factors could greatly contribute to the overall persistence of Salmonella in apple-pear orchard soils.

Prediction of COVID-19 Severity Using Chest Computed Tomography and Laboratory Measurements: Evaluation Using a Machine Learning Approach.

BACKGROUND: Most of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. OBJECTIVE: This study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. METHODS: A total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients' CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. RESULTS: We present a prediction model combining patients' radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients' laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. CONCLUSIONS: To our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.