Efficacy and Safety of Type III Collagen Lyophilized Fibers Using Mid-to-Deep Dermal Facial Injections for the Correction of Dynamic Facial Wrinkles.

BACKGROUND: This study aimed to evaluate the therapeutic efficacy and safety of injecting Type III collagen lyophilized fibers into the mid-to-deep layers of the facial dermis to ameliorate dynamic facial wrinkles. METHODS: In this retrospective analysis, clinical data were collected from patients exhibiting dynamic facial wrinkles (encompassing frown lines, forehead lines, and crow's feet) with a wrinkle severity rating scale (WSRS) score of 3 or higher. In the control group, 75 participants received collagen implant injections into the mid-to-deep facial dermal layers, whereas 76 participants in the experimental group received injections of Type III collagen lyophilized fibers in similar layers. The study analyzed and compared clinical efficacy, WSRS score alterations, patient satisfaction, and safety profiles between the groups over the 30-day and 90-day treatment periods. RESULTS: At the 30-day mark, the therapeutic efficacy was not significantly different between the two groups (P > 0.05). However, at 90 days, the treatment efficacy in the experimental group surpassed that in the control group, showing a statistically significant difference (P < 0.05). After 30 days of treatment, the WSRS score improvement in the experimental group was significantly superior to that in the control group (P < 0.05). Conversely, at the 90-day mark, the results revealed no significant variation in WSRS score improvements between the two groups (P > 0.05). Regarding treatment satisfaction among researchers and participants post-30 and 90-day treatment in both groups, no statistically significant differences were observed (P > 0.05). Similarly, the incidence of adverse reactions between the groups was not statistically significant (P > 0.05). CONCLUSIONS: Injections of lyophilized type III collagen fibers into the mid-to-deep layers of the facial dermis have a definitive therapeutic effect on dynamic facial wrinkles. This treatment not only substantially diminishes wrinkle severity but also has a commendable safety profile. LEVEL OF EVIDENCE I: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Comparison of acknowledged hepatocellular carcinoma risk scores in high-risk hepatitis C patients with sustained virological response.

Hepatitis C patients with advanced fibrosis or cirrhosis are at high risk of developing hepatocellular carcinoma (HCC) even after sustained virological response (SVR). Several HCC risk scores have been developed but which one is most suitable for this population is unclear. In this study, we compared the prediction ability of the aMAP model, THRI model, PAGE-B model and Models of HCV in a prospective hepatitis C cohort in order to propose better model(s) to clinical practice. Adult hepatitis C patients with baseline advanced fibrosis (141 cases), compensated cirrhosis (330 cases) and decompensated cirrhosis (80 cases) were included and followed up every 6 months for about 7 years or until HCC development. Demographic data, medical history and laboratory results were recorded. HCCs were diagnosed by radiography, AFP or liver histology. The median follow-up period was 69.93(60.99-74.93) months, during which 53 (9.62%) patients developed HCC. The areas under the receiver operating characteristic curve of aMAP, THRI, PAGE-B and Models of HCV scores were 0.74, 0.72, 0.70 and 0.63 respectively. The predictive power of the aMAP model score was comparable to that of THRI, PAGE-Band higher than that of Models of HCV (p < 0.05). Dividing patients into non-high-risk and high-risk groups, the cumulative incidence rates of HCC based on aMAP, THRI, PAGE-B and Models of HCV was 5.57% vs. 24.17%, 1.10% vs. 13.90%, 5.80% vs. 15.90% and 6.41% vs. 13.81% (all p < 0.05). The AUC of the four models were all below 0.7 in male while all were higher than 0.7 in female. The performance of all the models was not influenced by fibrosis stage. aMAP, THRI model and PAGE-B model were all performed well while THRI model and PAGE-B model were easier to calculate. There was no need to select score according to fibrosis stage but should be caution when explain the results in male patients.

Study on the prediction effect of a combined model of SARIMA and LSTM based on SSA for influenza in Shanxi Province, China.

BACKGROUND: Influenza is an acute respiratory infectious disease that is highly infectious and seriously damages human health. Reasonable prediction is of great significance to control the epidemic of influenza. METHODS: Our Influenza data were extracted from Shanxi Provincial Center for Disease Control and Prevention. Seasonal-trend decomposition using Loess (STL) was adopted to analyze the season characteristics of the influenza in Shanxi Province, China, from the 1st week in 2010 to the 52nd week in 2019. To handle the insufficient prediction performance of the seasonal autoregressive integrated moving average (SARIMA) model in predicting the nonlinear parts and the poor accuracy of directly predicting the original sequence, this study established the SARIMA model, the combination model of SARIMA and Long-Short Term Memory neural network (SARIMA-LSTM) and the combination model of SARIMA-LSTM based on Singular spectrum analysis (SSA-SARIMA-LSTM) to make predictions and identify the best model. Additionally, the Mean Squared Error (MSE), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) were used to evaluate the performance of the models. RESULTS: The influenza time series in Shanxi Province from the 1st week in 2010 to the 52nd week in 2019 showed a year-by-year decrease with obvious seasonal characteristics. The peak period of the disease mainly concentrated from the end of the year to the beginning of the next year. The best fitting and prediction performance was the SSA-SARIMA-LSTM model. Compared with the SARIMA model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 38.12, 17.39 and 21.34%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 42.41, 18.69 and 24.11%, respectively, in prediction performances. Furthermore, compared with the SARIMA-LSTM model, the MSE, MAE and RMSE of the SSA-SARIMA-LSTM model decreased by 28.26, 14.61 and 15.30%, respectively, in fitting performance; the MSE, MAE and RMSE decreased by 36.99, 7.22 and 20.62%, respectively, in prediction performances. CONCLUSIONS: The fitting and prediction performances of the SSA-SARIMA-LSTM model were better than those of the SARIMA and the SARIMA-LSTM models. Generally speaking, we can apply the SSA-SARIMA-LSTM model to the prediction of influenza, and offer a leg-up for public policy.

A hybrid model for tuberculosis forecasting based on empirical mode decomposition in China.

BACKGROUND: Pulmonary Tuberculosis is a major public health problem endangering people's health, a scientifically accurate predictive model is of great practical significance for the prevention and treatment of pulmonary tuberculosis. METHODS: The reported incidence data of pulmonary tuberculosis were from the National Public Health Science Data Center ( https://www.phsciencedata.cn/ ). The ARIMA, LSTM, EMD-SARIMA, EMD-LSTM, EMD-ARMA-LSTM models were established using the reported monthly incidence of tuberculosis reported in China from January 2008 to December 2018. The MSE, MAE, RMSE and MAPE were used to evaluate the performance of the models to determine the best model. RESULTS: Comparing decomposition-based single model with undecomposed single model, it was found that: when predicting the incidence trend in the next year, compared with SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-SARIMA decreased by 39.3%, 19.0%, 22.1% and 19.8%, respectively. The MSE, MAE, RMSE and MAPE of EMD-LSTM were reduced by 40.5%, 12.8%, 22.9% and 12.7%, respectively, compared with the LSTM model; Comparing the decomposition-based hybrid model with the decomposition-based single model, it was found that: when predicting the incidence trend in the next year, compared with EMD-SARIMA model, the MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM model decreased by 21.7%, 10.6%, 11.5% and 11.2%, respectively. The MSE, MAE, RMSE and MAPE of EMD-ARMA-LSTM were reduced by 16.7%, 9.6%, 8.7% and 12.3%, respectively, compared with EMD-LSTM model. Furthermore, the performance of the model were consistent when predicting the incidence trend in the next 3 months, 6 months and 9 months. CONCLUSION: The prediction performance of the decomposition-based single model is better than that of the undecomposed single model, and the prediction performance of the combined model using the advantages of different models is better than that of the decomposition-based single model, so the EMD-ARMA-LSTM combination model can improve the prediction accuracy better than other models, which can provide a theoretical basis for predicting the epidemic trend of pulmonary tuberculosis and formulating prevention and control policies.

The relationship between social capital and health from a configuration perspective: an evidence from China.

BACKGROUND: The debate on the relationship between social capital and health is still ongoing. To enhance previous research, this study uses data drawn from China to analyse the situations in which social capital is related to good health and the various configurations that result in good health outcomes. METHODS: Using the data of China Family Panel Studies, the conditions of age, gender, marriage, education, income, structural social capital and cognitive social capital were included to analyse the sufficient and necessary conditions for achieving good general health and their different configurations using the fsQCA method. RESULTS: None of the listed conditions were prerequisites for excellent general health in terms of either their presence or their absence. The sufficiency analysis found 11 configurations with an average of 3-4 conditions per configuration; in no configuration was the condition of social capital present alone. Structured social capital and cognitive social capital exhibited negative states in configurations 1 and 2, respectively. The most prevalent factor in all configurations was the condition of age. CONCLUSIONS: The relationship between social capital and health is both positive and negative, with cognitive social capital playing a larger role in the positive relationship than structural social capital. Social capital is neither a necessary nor a sufficient condition for health, and it must be combined with a variety of other factors to promote health. A variety of methods can be used to promote an individual's health, as different populations require different approaches to good general health, and no single pathway applies to all populations. In the Chinese population, an individual's age is a significant determinant of their health status.

An explainable artificial intelligence framework for risk prediction of COPD in smokers.

BACKGROUND: Since the inconspicuous nature of early signs associated with Chronic Obstructive Pulmonary Disease (COPD), individuals often remain unidentified, leading to suboptimal opportunities for timely prevention and treatment. The purpose of this study was to create an explainable artificial intelligence framework combining data preprocessing methods, machine learning methods, and model interpretability methods to identify people at high risk of COPD in the smoking population and to provide a reasonable interpretation of model predictions. METHODS: The data comprised questionnaire information, physical examination data and results of pulmonary function tests before and after bronchodilatation. First, the factorial analysis for mixed data (FAMD), Boruta and NRSBoundary-SMOTE resampling methods were used to solve the missing data, high dimensionality and category imbalance problems. Then, seven classification models (CatBoost, NGBoost, XGBoost, LightGBM, random forest, SVM and logistic regression) were applied to model the risk level, and the best machine learning (ML) model's decisions were explained using the Shapley additive explanations (SHAP) method and partial dependence plot (PDP). RESULTS: In the smoking population, age and 14 other variables were significant factors for predicting COPD. The CatBoost, random forest, and logistic regression models performed reasonably well in unbalanced datasets. CatBoost with NRSBoundary-SMOTE had the best classification performance in balanced datasets when composite indicators (the AUC, F1-score, and G-mean) were used as model comparison criteria. Age, COPD Assessment Test (CAT) score, gross annual income, body mass index (BMI), systolic blood pressure (SBP), diastolic blood pressure (DBP), anhelation, respiratory disease, central obesity, use of polluting fuel for household heating, region, use of polluting fuel for household cooking, and wheezing were important factors for predicting COPD in the smoking population. CONCLUSION: This study combined feature screening methods, unbalanced data processing methods, and advanced machine learning methods to enable early identification of COPD risk groups in the smoking population. COPD risk factors in the smoking population were identified using SHAP and PDP, with the goal of providing theoretical support for targeted screening strategies and smoking population self-management strategies.

Development and validation of a novel model to predict liver-related mortality in patients with idiosyncratic drug-induced liver injury.

BACKGROUND: Early identification of patients with high mortality risk is critical for optimizing the clinical management of drug-induced liver injury (DILI). We aimed to develop and validate a new prognostic model to predict death within 6 months in DILI patients. METHODS: This multicenter study retrospectively reviewed the medical records of DILI patients admitted to three hospitals. A DILI mortality predictive score was developed using multivariate logistic regression and was validated with area under the receiver operating characteristic curve (AUC). A high-mortality-risk subgroup was identified according to the score. RESULTS: Three independent DILI cohorts, including one derivation cohort (n = 741) and two validation cohorts (n = 650, n = 617) were recruited. The DILI mortality predictive (DMP) score was calculated using parameters at disease onset as follows: 1.913 × international normalized ratio + 0.060 × total bilirubin (mg/dL) + 0.439 × aspartate aminotransferase/alanine aminotransferase - 1.579 × albumin (g/dL) - 0.006 × platelet count (109/L) + 9.662. The predictive performance for 6-month mortality of DMP score was desirable, with an AUC of 0.941 (95% CI: 0.922-0.957), 0.931 (0.908-0.949) and 0.960 (0.942-0.974) in the derivation, validation cohorts 1 and 2, respectively. DILI patients with a DMP score ≥ 8.5 were stratified into high-risk group, whose mortality rates were 23-, 36-, and 45-fold higher than those of other patients in the three cohorts. CONCLUSIONS: The novel model based on common laboratory findings can accurately predict mortality within 6 months in DILI patients, which should serve as an effective guidance for management of DILI in clinical practice.

Comparative transcriptome analysis of longissimus dorsi tissues with different intramuscular fat contents from Guangling donkeys.

BACKGROUND: Donkey meat has low fat and high protein contents and is rich in various unsaturated fatty acids and trace elements that are beneficial to human digestion and absorption. IMF (intramuscular fat), also known as marbling, is an important indicator of the lean meat to fat ratio, which directly affects the tenderness and juiciness of the meat. At present, the underlying molecular variations affecting IMF content among donkey breeds are unclear. The Guangling donkey is an indigenous species in China. This study explored candidate regulatory genes that affect IMF content in Guangling donkeys. The IMF content of the longissimus dorsi muscle in 30 Guangling donkeys was measured. Six donkeys of similar age were selected according to age factors and divided into two groups, the high (H) and low (L) fat groups, according to their IMF content. RESULTS: RNA-seq technology was used to compare the muscle transcriptome between the two groups. More than 75.0% of alternative splicing (AS) events were of the skipped exon (SE) type. A total of 887 novel genes were identified; only 386 novel genes were aligned to the annotation information of various databases. Transcriptomics analysis revealed 167 differentially expressed genes (DEGs), of which 64 were upregulated and 103 were downregulated between the H and L groups. Gene ontology analysis showed that the DEGs were enriched in multiple biological processes and pathways that are related to adipocyte differentiation, lipid synthesis, and neutral lipid metabolism. KEGG pathway analysis suggested that arachidonic acid metabolism, the HIF-1 signalling pathway, fructose and mannose metabolism, glycerophospholipid metabolism, and the AMPK signalling pathway were involved in lipid deposition. In addition, a gene-gene interaction network was constructed that revealed that the DEGs, including SCD, LEPR, CIDEA, DLK1, DGAT2, ITGAL, HMOX1, WNT10B, and DGKA, had significant roles in adipocyte differentiation and adipogenesis. The selected DEGs were further validated by qRT-PCR. CONCLUSION: This study improves the in-depth understanding of gene regulation and protein expression regarding IMF deposition and lays a basis for subsequent molecular breeding studies in Guangling donkeys.

Research on the predictive effect of a combined model of ARIMA and neural networks on human brucellosis in Shanxi Province, China: a time series predictive analysis.

BACKGROUND: Brucellosis is a major public health problem that seriously affects developing countries and could cause significant economic losses to the livestock industry and great harm to human health. Reasonable prediction of the incidence is of great significance in controlling brucellosis and taking preventive measures. METHODS: Our human brucellosis incidence data were extracted from Shanxi Provincial Center for Disease Control and Prevention. We used seasonal-trend decomposition using Loess (STL) and monthplot to analyse the seasonal characteristics of human brucellosis in Shanxi Province from 2007 to 2017. The autoregressive integrated moving average (ARIMA) model, a combined model of ARIMA and the back propagation neural network (ARIMA-BPNN), and a combined model of ARIMA and the Elman recurrent neural network (ARIMA-ERNN) were established separately to make predictions and identify the best model. Additionally, the mean squared error (MAE), mean absolute error (MSE) and mean absolute percentage error (MAPE) were used to evaluate the performance of the model. RESULTS: We observed that the time series of human brucellosis in Shanxi Province increased from 2007 to 2014 but decreased from 2015 to 2017. It had obvious seasonal characteristics, with the peak lasting from March to July every year. The best fitting and prediction effect was the ARIMA-ERNN model. Compared with those of the ARIMA model, the MAE, MSE and MAPE of the ARIMA-ERNN model decreased by 18.65, 31.48 and 64.35%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 60.19, 75.30 and 64.35%, respectively. Second, compared with those of ARIMA-BPNN, the MAE, MSE and MAPE of ARIMA-ERNN decreased by 9.60, 15.73 and 11.58%, respectively, in fitting performance; in terms of prediction performance, the MAE, MSE and MAPE decreased by 31.63, 45.79 and 29.59%, respectively. CONCLUSIONS: The time series of human brucellosis in Shanxi Province from 2007 to 2017 showed obvious seasonal characteristics. The fitting and prediction performances of the ARIMA-ERNN model were better than those of the ARIMA-BPNN and ARIMA models. This will provide some theoretical support for the prediction of infectious diseases and will be beneficial to public health decision making.

Application of a novel hybrid algorithm of Bayesian network in the study of hyperlipidemia related factors: a cross-sectional study.

BACKGROUND: This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs). METHODS: Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model. RESULTS: The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia. CONCLUSIONS: The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.

Exploratory study on classification of diabetes mellitus through a combined Random Forest Classifier.

BACKGROUND: Diabetes Mellitus (DM) has become the third chronic non-communicable disease that hits patients after tumors, cardiovascular and cerebrovascular diseases, and has become one of the major public health problems in the world. Therefore, it is of great importance to identify individuals at high risk for DM in order to establish prevention strategies for DM. METHODS: Aiming at the problem of high-dimensional feature space and high feature redundancy of medical data, as well as the problem of data imbalance often faced. This study explored different supervised classifiers, combined with SVM-SMOTE and two feature dimensionality reduction methods (Logistic stepwise regression and LAASO) to classify the diabetes survey sample data with unbalanced categories and complex related factors. Analysis and discussion of the classification results of 4 supervised classifiers based on 4 data processing methods. Five indicators including Accuracy, Precision, Recall, F1-Score and AUC are selected as the key indicators to evaluate the performance of the classification model. RESULTS: According to the result, Random Forest Classifier combining SVM-SMOTE resampling technology and LASSO feature screening method (Accuracy = 0.890, Precision = 0.869, Recall = 0.919, F1-Score = 0.893, AUC = 0.948) proved the best way to tell those at high risk of DM. Besides, the combined algorithm helps enhance the classification performance for prediction of high-risk people of DM. Also, age, region, heart rate, hypertension, hyperlipidemia and BMI are the top six most critical characteristic variables affecting diabetes. CONCLUSIONS: The Random Forest Classifier combining with SVM-SMOTE and LASSO feature reduction method perform best in identifying high-risk people of DM from individuals. And the combined method proposed in the study would be a good tool for early screening of DM.

Association of TM6SF2 rs58542926 T/C gene polymorphism with hepatocellular carcinoma: a meta-analysis.

BACKGROUND: Hepatocellular carcinoma (HCC) is the sixth-most common malignancy worldwide. Multiple previous studies have assessed the relationship between TM6SF2 gene polymorphism and the risk of developing HCC, with discrepant conclusions reached. To assess the association of TM6SF2 rs58542926 T/C gene polymorphism with liver cancer, we performed the current meta-analysis. METHODS: This study queried the MEDLINE, PubMed, EMBASE, and CENTRAL databases from inception to April 2019. Case-control studies assessing the relationship between TM6SF2 rs5854292 locus polymorphism and liver cancer were selected according to inclusion and exclusion criteria. The Stata 12.0 software was employed for data analysis. RESULTS: A total of 5 articles, encompassing 6873 patients, met inclusion criteria and were included in the meta-analysis. Statistical analysis showed that the TM6SF2 gene polymorphism was significantly associated with liver cancer in the allele contrast, dominant, recessive and over dominant models (T vs C, OR = 1.621, 95%CI 1.379-1.905; CT + TT vs CC. OR = 1.541, 95%CI 1.351-1.758; TT vs CT + CC, OR = 2.897, 95%CI 1.690-4.966; CC + TT vs TC, OR = 0.693, 95%CI 0.576-0.834). The Egger's test revealed no significant publication bias. CONCLUSION: The present findings suggest a significant association of TM6SF2 gene polymorphism with HCC risk in the entire population studied.

Safety evaluation of chemically modified beta-lactoglobulin administered intravaginally.

Currently, there is no specific antiviral therapy for treatment of HPV infection. Jiang and colleagues previously reported that anhydride-modified proteins have inhibitory activities against multiple viruses including HPV. Here, we evaluated the safety of 3-hydroxyphthalic anhydride-modified bovine beta-lactoglobulin, designated JB01, vaginally applied in women infected by high-risk HPV. After the vaginal application of JB01 in 38 women for 3 months, no serious adverse events were reported, and normalization of the vaginal micro-environment has been observed. It can be concluded that JB01-BD is safe for vaginal use in HPV-infected women, suggesting its potential application for the treatment of HPV infection.

Kinetic response of wild and mutant core codon 70 strains of HCV genotype 1b to pegylated interferon-α and ribavirin therapy.

BACKGROUND: Amino acid (aa) 70 substitution (R70Q/H) in the core protein of hepatitis C virus (HCV) genotype 1b has been shown to be one of the key factors in determining resistance for pegylated interferon-α plus ribavirin combination therapy (PEG-IFNα/RBV). But the exact mechanisms remain unclear. The aim of this study was to investigate the dynamic response of wild and mutant core codon 70 strains to PEG-IFNα/RBV treatment. METHODS: One hundred twelve Chinese patients with chronic HCV 1b infection were enrolled and received a standard protocol of 48 weeks of PEG-IFNα/RBV therapy and 24 consecutive weeks of follow-up. Serial blood samples were obtained at pretreatment baseline, and again at weeks 2, 4, 8, 12, and 24 during therapy for the quantification of 70R and 70Q/H strains. Dynamic characteristics and association with early virological response (EVR), sustained virological response (SVR) and IL28B genotypes were analyzed. RESULTS: Of the 112 patients enrolled in this study, 93.8% (105/112) were infected with mixture of 70R and 70Q/H strains before treatment. The 70Q/H strain was dominant in 20.5% of patients. 42.9% of patients with dominant 70Q/H exhibited EVR versus 88.6% of patients with dominant 70R (P < 0.001). Furthermore, 35.0% of patients with dominant 70Q/H exhibited SVR versus 77.4% with dominant 70R (P < 0.001). However, regardless of the dominant strain, virological response types or the IL28B SNP genotypes, 70Q/H strains always exhibited the same response to treatment as the 70R strains and the percentage of HCV harboring the 70Q/H substitution did not change significantly during treatment. CONCLUSIONS: Although the ratio of 70Q/H to 70R is related to the virological response, 70Q/H strains always exhibited the same response as the 70R strains during PEG-IFNα/RBV treatment. Substitution of R70Q/H alone is not enough to lead to resistance to therapy. Positive selection for 70Q/H induced by IFNα was not observed.

Synthesis, Structure and Spectroscopy Study of a 1D Copper Coordination Polymer Based on a Carboxybenzyl Viologen Ligand and SCN-Anion.

A zwitterionic viologen derivative ligand, 1,1'-bis(4-carboxybenzyl)-4 4'-bipyridinium dichloride (H2BpybcCl2) as a multifunctional ligand, has been synthesized incorporating a 4,4'-bipyridine core with two carboxylate groups as a. building block, specifically designed for the rational construction of metal-organic frameworks. H2BpybcCl2 ligand is a multifunctional ligand that contains viologen's specific functions and carboxylate coordination groups. The coordination polymers of viologen carboxylate with copper thiocyanate are not reported to date. A novel copper coordination polymer, [Cu(SCN)2 (Bpybc)] (I) was by solution diffusion method and characterized by single-crystal X-ray diffraction, XRD, elemental analyses, IR spectroscopy, UV-Vis DRS, TG analysis and liquid-state luminescent properties. Compound I crystallized in the monoclinic system with C2/c space group. Crystal data for complex I is as follow: a=19. 508(4) A, b=9. 474(2) Å, c =16. 963(3) Å, α=90°, ß=124. 92(3)°, γ=90°. Two SCN-anions were coordinated to the Cu2+ cation forming a [Cu(SCN)2] unit. Complex I was built up by [Cu(SCN)2] units bridged sequentially by ladder-shaped Bpybc ligands to form one-dimensional zigzag chains running along the [203] direction. The chains were held together by π-π interaction between the pyridine rings and phenyl rings, thus yielding a 3-D extended supramolecular network. The UV-Visible absorption spectra show the absorption bands of π-π* transitions of Bpybc ligands and d-->d transition of Cu2+. The liquid-state luminescent property of compound I was investigated at room temperature. Attractively, the complex exhibits strong blue emission peak at 533 nm (λEx=360 nn) that can be assigned to intraligand transition of Bpybc ligand when it was excited at 360 nm.

Quantitation of substitutions at amino acid 70 in hepatitis C virus genotype 1b.

BACKGROUND: Substitutions of amino acid (aa) 70 in the core region of hepatitis C virus genotype 1b (HCV 1b) are a predictor of the non-virological response to pegylated interferon plus ribavirin (PEG-IFN/RBV) therapy. The aim of our study was to develop quantitative real-time reverse transcription polymerase chain reaction (qPCR) assays to quantify wild-type (70 W) and mutant (70 M) strains of HCV 1b. METHODS: We used the TaqMan system to quantify strains 70 W and 70 M. Codon 70 in the HCV 1b core region can be either CGN or CAN, therefore degenerate TaqMan minor groove binder (MGB) probes with inosine were used. We determined detection limits, sensitivity and specificity of the methods developed. Direct sequencing and cloning of the HCV core region were used to confirm the reliability of our new system. Serum samples from 138 Chinese patients infected with HCV 1b were examined with the system we developed and compared with results obtained from the Roche TaqMan RT-PCR HCV RNA quantitation system. RESULTS: Degenerate MGB probes were able to clearly distinguish 70 W from 70 M. The detection limit was 10³ copies/mL. Cross-reactivity tests confirmed the specificity of our method. Our system can effectively quantify 70 W and 70 M for 99.6% of patients with HCV 1b. Further tests involving cloning and sequencing confirmed the reliability of our system. CONCLUSIONS: We developed an assay system using degenerate TaqMan MGB probes with inosine to quantify wild-type and mutant viral RNAs of the HCV 1b core region at aa 70. Our developed assay system had high levels of sensitivity and accuracy, and could prove useful in investigating dynamic changes during PEG-IFN/RBV therapy to assess virological responses.

Development and validation of a nomogram for assessing hepatocellular carcinoma risk after SVR in hepatitis C patients with advanced fibrosis and cirrhosis.

BACKGROUND: Hepatitis C patients with advanced fibrosis or cirrhosis are at high risk of developing hepatocellular carcinoma (HCC), even after sustained virological response (SVR). Clinical recommendations impose a significant burden on patients by recommending lifelong screening for HCC every six months. The goals of this study were to develop a nomogram that accurately stratifies risk of HCC and improve the screening approach that is currently in use. METHOD: Risk factors for HCC were identified using univariate and multivariate analyses in this prospective study. We developed and validated a nomogram for assessing hepatocellular carcinoma risk after SVR in patients with advanced fibrosis and cirrhosis. RESULTS: During the median follow-up period of 61.00 (57.00-66.00) months in the derivation cohort, 37 patients (9.61%) developed HCC. Older age (HR = 1.08, 95% CI 1.02-1.14, p = 0.009), male gender (HR = 2.38, 95% CI 1.10-5.13, p = 0.027), low serum albumin levels (HR = 0.92, 95% CI 0.86-1.00, p = 0.037), and high liver stiffness measurement (LSM) (HR = 1.03, 95% CI 1.01-1.06, p = 0.001) were found to be independent predictors of HCC development. Harrell's C-index for the derivation cohort was 0.81. The nomogram's 3-, 5- and 7-years time-dependent AUROCSs were 0.84 (95% CI 0.80-0.88), 0.83 (95% CI 0.79-0.87), and 0.81 (95% CI 0.77-0.85), respectively (all p > 0.05). According to the nomogram, patients are categorized as having low, intermediate, or high risk. The annual incidence rates of HCC in the three groups were 0.18%, 1.29%, and 4.45%, respectively (all p < 0.05). CONCLUSIONS: Older age, male gender, low serum albumin levels, and high LSM were risk factors for HCC after SVR in hepatitis C patients with advanced fibrosis and cirrhosis. We used these risk factors to establish a nomogram. The nomogram can identify a suitable screening plan by classifying hepatitis C patients according to their risk of HCC.

The urgent need to reduce phosphorus discharges for sustainable mangrove wetland management.

Phosphorus affects microbial metabolic activity, nitrogen and carbon cycling in mangrove sediment, but its influence on carbon stability and greenhouse gases emission remains unclear. This study compared greenhouse gases (CO2, N2O, and CH4) emissions from mangrove sediment receiving wastewater containing various phosphorus concentrations, and evaluated its long term effect on sediment carbon flux when phosphorus pollution is eliminated. Significant increases in greenhouse gases flux and decrease of total organic carbon and readily oxidizable organic carbon in the sediment were observed after phosphorus discharge. Specifically, the N2O flux was reduced significantly at high phosphorus levels while the CO2 flux and the microbial biomass organic carbon was increased. The copy numbers of ammonia oxidation (AOA-amoA, AOB-amoA) gene, denitrification (narG, nirK) gene and methanogenesis (mcrA) gene increased with the increasing phosphorus concentration. During the wastewater discharge period for 70 days, the global warming potential of sediment flux at high phosphorus discharge condition was more than 4 times that of the control group, and the loss of total organic carbon and readily oxidizable organic carbon was 4.66 % and 7.1 %, respectively. During the remediation period (71-101 days), the greenhouse gases flux decreased rapidly, ends up with a similar level of the control group. Our results indicate that using mangrove wetland for pollution minimization in the coastal aquaculture industry could increase greenhouse gases emisison significantly, it is therefore essential to reduce phosphorus discharges from various anthropogenic activities, and local authorities must set up more stringent discharge standards in the future.

Genome-wide comparative analysis reveals selection signatures for reproduction traits in prolific Suffolk sheep.

The identification of genome-wide selection signatures can reveal the potential genetic mechanisms involved in the generation of new breeds through natural or artificial selection. In this study, we screened the genome-wide selection signatures of prolific Suffolk sheep, a new strain of multiparous mutton sheep, to identify candidate genes for reproduction traits and unravel the germplasm characteristics and population genetic evolution of this new strain of Suffolk sheep. Whole-genome resequencing was performed at an effective sequencing depth of 20× for genomic diversity and population structure analysis. Additionally, selection signatures were investigated in prolific Suffolk sheep, Suffolk sheep, and Hu sheep using fixation index (F ST) and heterozygosity H) analysis. A total of 5,236.338 Gb of high-quality genomic data and 28,767,952 SNPs were obtained for prolific Suffolk sheep. Moreover, 99 selection signals spanning candidate genes were identified. Twenty-three genes were significantly associated with KEGG pathway and Gene Ontology terms related to reproduction, growth, immunity, and metabolism. Through selective signal analysis, genes such as ARHGEF4, CATIP, and CCDC115 were found to be significantly correlated with reproductive traits in prolific Suffolk sheep and were highly associated with the mTOR signaling pathway, the melanogenic pathway, and the Hippo signaling pathways, among others. These results contribute to the understanding of the evolution of artificial selection in prolific Suffolk sheep and provide candidate reproduction-related genes that may be beneficial for the establishment of new sheep breeds.

Inflammation activity affects liver stiffness measurement by magnetic resonance elastography in MASLD.

BACKGROUND: Magnetic resonance elastography (MRE) is recognized as the most precise imaging technology for assessing liver fibrosis in individuals with metabolic dysfunction-associated steatotic liver disease (MASLD). We aimed to investigate the clinical factors and pathological characteristics that may impact LSM in MASLD patients. METHODS: This cross-sectional study recruited 124 patients who concurrently performed MRE, MRI-PDFF, and biopsy-proven MASLD. Linear regression models, Spearman's correlation, and subgroup analysis were employed to identify the variables affecting LSM. RESULTS: The AUROC (95 % CI) of MRE for diagnosing fibrosis stage ≥ 1, 2, 3, and 4 was 0.80 (0.70-0.90), 0.76 (0.66-0.85), 0.92 (0.86-0.99), and 0.99 (0.99-1.00), with corresponding cutoffs of 2.56, 2.88, 3.35, and 4.76 kPa, respectively. Multivariate analyses revealed that AST was the only independent clinical variable significantly correlated with LSM. Furthermore, LSM exhibited a notable association with the grade of lobular inflammation and hepatocellular ballooning. Subgroup analysis showed that when AST ≥ 2 ULN or inflammation grade ≥ 2, LSM of patients with early fibrosis stages showed a slight but significant increase. CONCLUSION: MRE demonstrates significant diagnostic accuracy in predicting liver fibrosis stages for MASLD patients, especially for advanced liver fibrosis and cirrhosis. However, elevated AST and the severity of liver inflammation may impact its accuracy in staging early liver fibrosis.