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Background: The Centre for Disease Control and Prevention in Yangquan, China, has taken a series of preventive and control measures in response to the increasing trend of Kala-Azar. In response, we propose a new model to more scientifically evaluate the effectiveness of these interventions. Methods: We obtained the incidence data of Kala-Azar from 2017 to 2021 from the Centre for Disease Control and Prevention (CDC) in Yangquan. We constructed Poisson segmented regression model, harmonic Poisson segmental regression model, and improved harmonic Poisson segmented regression model, and used the three models to explain the intervention effect, respectively. Finally, we selected the optimal model by comparing the fitting effects of the three models. Results: The primary analysis showed an underlying upward trend of Kala-Azar before intervention [incidence rate ratio (IRR): 1.045, 95% confidence interval (CI): 1.027-1.063, p < 0.001]. In terms of long-term effects, the rise of Kala-Azar slowed down significantly after the intervention (IRR:0.960, 95%CI:0.927-0.995, p = 0.026), and the risk of Kala-Azar increased by 0.3% for each additional month after intervention (ß1 + ß3 = 0.003, IRR = 1.003). The results of the model fitting effect showed that the improved harmonic Poisson segmental regression model had the best fitting effect, and the values of MSE, MAE, and RMSE were the lowest, which were 0.017, 0.101, and 0.130, respectively. Conclusion: In the long term, the intervention measures taken by the Yangquan CDC can well curb the upward trend of Kala-Azar. The improved harmonic Poisson segmented regression model has higher fitting performance, which can provide a certain scientific reference for the evaluation of the intervention effect of seasonal infectious diseases.
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Leishmaniose Visceral , Humanos , China/epidemiologia , Leishmaniose Visceral/prevenção & controle , Leishmaniose Visceral/epidemiologia , Distribuição de Poisson , Incidência , Análise de Regressão , Masculino , Feminino , Modelos EstatísticosRESUMO
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.
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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.
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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.
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Fósforo , Áreas Alagadas , Sedimentos Geológicos/química , Águas Residuárias/química , CarbonoRESUMO
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.
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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 .
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Colágeno Tipo III , Preenchedores Dérmicos , Envelhecimento da Pele , Humanos , Envelhecimento da Pele/efeitos dos fármacos , Estudos Retrospectivos , Feminino , Pessoa de Meia-Idade , Adulto , Resultado do Tratamento , Masculino , Injeções Intradérmicas , Preenchedores Dérmicos/administração & dosagem , Preenchedores Dérmicos/efeitos adversos , Satisfação do Paciente/estatística & dados numéricos , Liofilização , Estética , Face , Técnicas Cosméticas , Estudos de CoortesRESUMO
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.
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Doença Pulmonar Obstrutiva Crônica , Fumantes , Humanos , Adolescente , Inteligência Artificial , Fumar Tabaco , FumarRESUMO
Background: Topical lidocaine microemulsion preparations with low toxicity, low irritation, strong transdermal capability and convenient administration are urgently needed. Methods: Box-Behnken design was performed for three preparation conditions of 5% lidocaine microemulsions: mass ratio of the mass ratio of surfactant/(oil phase + surfactant) (X1), the mass ratio of olive oil/(α-linolenic acid + linoleic acid) (X2) and the water content W% (X3). Then, five multi-objective genetic algorithms were used to optimize the three evaluation indices to optimize the effects of lidocaine microemulsion preparations. Finally, the ideal optimization scheme was experimentally verified. Results: Non-dominated Sorting Genetic Algorithm-II was used for 30 random searches. Among these, Scheme 2: X1 = 0.75, X2 = 0.35, X3 = 75%, which resulted in Y1 = 0.17 µg/(cm2·s) and Y2 = 0.74 mg/cm2; and the Scheme 19: X1 = 0.68, X2 = 1.42, X3 = 75% which resulted in Y1 = 0.14 µg/(cm2·s) and Y2 = 0.80 mg/cm2, provided the best matches for the objective function requirements. The maximum and average fitness of the method have reached stability after 3 generations of evolution. Experimental verification of the above two schemes showed that there were no statistically significant differences between the measured values of Y1 and Y2 and the predicted values obtained by optimization (p > 0.05) and are close to the target value. Conclusion: Two lidocaine microemulsion preparation protocols were proposed in this study. These preparations resulted in good transdermal performance or long anesthesia duration, respectively.
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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.
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Tuberculose Pulmonar , Tuberculose , Humanos , Tuberculose/epidemiologia , Tuberculose Pulmonar/epidemiologia , Previsões , China/epidemiologia , Incidência , Modelos EstatísticosRESUMO
Diabetes mellitus (DM) has become the third chronic non-infectious disease affecting patients after tumor, cardiovascular and cerebrovascular diseases, becoming one of the major public health issues worldwide. Detection of early warning risk factors for DM is key to the prevention of DM, which has been the focus of some previous studies. Therefore, from the perspective of residents' self-management and prevention, this study constructed Bayesian networks (BNs) combining feature screening and multiple resampling techniques for DM monitoring data with a class imbalance in Shanxi Province, China, to detect risk factors in chronic disease monitoring programs and predict the risk of DM. First, univariate analysis and Boruta feature selection algorithm were employed to conduct the preliminary screening of all included risk factors. Then, three resampling techniques, SMOTE, Borderline-SMOTE (BL-SMOTE) and SMOTE-ENN, were adopted to deal with data imbalance. Finally, BNs developed by three algorithms (Tabu, Hill-climbing and MMHC) were constructed using the processed data to find the warning factors that strongly correlate with DM. The results showed that the accuracy of DM classification is significantly improved by the BNs constructed by processed data. In particular, the BNs combined with the SMOTE-ENN resampling improved the most, and the BNs constructed by the Tabu algorithm obtained the best classification performance compared with the hill-climbing and MMHC algorithms. The best-performing joint Boruta-SMOTE-ENN-Tabu model showed that the risk factors of DM included family history, age, central obesity, hyperlipidemia, salt reduction, occupation, heart rate, and BMI.
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Algoritmos , Diabetes Mellitus , Humanos , Teorema de Bayes , Fatores de Risco , Análise FatorialRESUMO
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.
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Saúde , Capital Social , Determinantes Sociais da Saúde , Humanos , Povo Asiático , China/epidemiologia , Escolaridade , Promoção da SaúdeRESUMO
Background and aim: The MBOAT7 rs641738 (C>T) variant has demonstrated an association with non-alcoholic fatty liver disease (NAFLD) in both adult and pediatric patients, while few studies have been conducted in elderly populations. Hence, a case-control study was undertaken to assess their correlation in elderly residents in a Beijing community. Materials and methods: A total of 1,287 participants were included. Medical history, abdominal ultrasound, and laboratory tests were recorded. Liver fat content and fibrosis stage were detected by Fibroscan. Genotyping of genomic DNA was performed using the 96.96 genotyping integrated fluidics circuit. Results: Of the recruited subjects, 638 subjects (56.60%) had NAFLD, and 398 subjects (35.28%) had atherosclerotic cardiovascular disease (ASCVD). T allele carriage was associated with higher ALT (p=0.005) and significant fibrosis in male NAFLD patients (p=0.005) compared to CC genotype. TT genotype was associated with reduced risk of metabolic syndrome (OR=0.589, 95%CI: 0.114-0.683, p=0.005) and type 2 diabetes (OR=0.804, 95%CI: 0.277-0.296, p=0.048) in NAFLD population when compared to the CC genotype. In addition, TT genotype was also associated with reduced risk of ASCVD (OR=0.570, 95%CI:0.340-0.953, p=0.032) and less obesity (OR=0.545, 95%CI: 0.346-0.856, p=0.008) in the whole population. Conclusion: MBOAT7 rs641738 (C>T) variant was associated with fibrosis in male NAFLD patients. The variant also reduced risk of metabolic traits and type 2 diabetes in NAFLD and ASCVD risk in Chinese elders.
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Aciltransferases , Diabetes Mellitus Tipo 2 , Proteínas de Membrana , Hepatopatia Gordurosa não Alcoólica , Idoso , Humanos , Masculino , Aciltransferases/genética , Estudos de Casos e Controles , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/complicações , População do Leste Asiático , Fibrose , Predisposição Genética para Doença , Proteínas de Membrana/genética , Hepatopatia Gordurosa não Alcoólica/epidemiologia , Hepatopatia Gordurosa não Alcoólica/genética , Hepatopatia Gordurosa não Alcoólica/complicações , Polimorfismo de Nucleotídeo ÚnicoRESUMO
Background: Progression of disease within 24 months (POD24) is a risk factor for poor survival in follicular lymphoma (FL), and there is currently no optimal prognostic model to accurately predict patients with early disease progression. How to combine traditional prognostic models with new indicators to establish a new prediction system, to predict the early progression of FL patients more accurately is a future research direction. Methods: This study retrospectively analyzed patients with newly diagnosed FL patients in Shanxi Provincial Cancer Hospital from January 2015 to December 2020. Data from patients undergoing immunohistochemical detection (IHC) were analyzed using χ2 test and multivariate Logistic regression. Also, we built a nomogram model based on the results of LASSO regression analysis of POD24, which was validated in both the training set and validation set, and additional external validation was performed using a dataset (n = 74) from another center, Tianjin Cancer Hospital. Results: The multivariate Logistic regression results suggest that high-risk PRIMA-PI group, Ki-67 high expression represent risk factors for POD24 (P<0.05). Next, PRIMA-PI and Ki67 were combined to build a new model, namely, PRIMA-PIC to reclassify high and low-risk groups. The result showed that the new clinical prediction model constructed by PRIMA-PI with ki67 has a high sensitivity to the prediction of POD24. Compared to PRIMA-PI, PRIMA-PIC also has better discrimination in predicting patient's progression-free survival (PFS) and overall survival (OS). In addition, we built nomogram models based on the results of LASSO regression (histological grading, NK cell percentage, PRIMA-PIC risk group) in the training set, which were validated using internal validation set and external validation set, we found that C-index and calibration curve showed good performance. Conclusion: As such, the new predictive model-based nomogram established by PRIMA-PI and Ki67 could well predict the risk of POD24 in FL patients, which boasts clinical practical value.
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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.
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Doença Hepática Induzida por Substâncias e Drogas , Humanos , Estudos Retrospectivos , Doença Hepática Induzida por Substâncias e Drogas/diagnóstico , Doença Hepática Induzida por Substâncias e Drogas/etiologia , Alanina Transaminase , PrognósticoRESUMO
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.
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Carcinoma Hepatocelular , Hepatite C Crônica , Hepatite C , Neoplasias Hepáticas , Adulto , Humanos , Masculino , Feminino , Carcinoma Hepatocelular/etiologia , Neoplasias Hepáticas/epidemiologia , Neoplasias Hepáticas/etiologia , Neoplasias Hepáticas/diagnóstico , Fatores de Risco , Estudos Prospectivos , Estudos Retrospectivos , Hepatite C/complicações , Hepatite C/tratamento farmacológico , Hepatite C/diagnóstico , Cirrose Hepática/complicações , Cirrose Hepática/epidemiologia , Cirrose Hepática/diagnóstico , Resposta Viral Sustentada , Hepacivirus , Antivirais/uso terapêutico , Hepatite C Crônica/complicações , Hepatite C Crônica/tratamento farmacológicoRESUMO
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.
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Influenza Humana , Humanos , Influenza Humana/epidemiologia , Previsões , Incidência , Redes Neurais de Computação , China/epidemiologia , Modelos EstatísticosRESUMO
Background: Post-stroke depression (PSD) is most prevalent during the rehabilitative period following a stroke. Recent studies verified the effects of repetitive transcranial magnetic stimulation therapy (rTMS) and mindfulness-based stress reduction (MBSR) in patients with depression. However, the effectiveness and prospect of application in PSD patients remain unclear. This study sought to evaluate the effectiveness of a combined intervention based on rTMS and MBSR for the physical and mental state of PSD patients. Methods: A randomized, double-blind, sham-controlled study design was employed. Participants were recruited from the Rehabilitation Medicine Centre and randomly assigned to receive either MBSR combined with active or sham rTMS or sham rTMS combined with general psychological care. We used a 17-item Hamilton Depression Rating Scale (HAMD-17), a mini-mental state examination (MMSE), the Modified Barthel Index (MBI), and the Pittsburgh Sleep Quality Index (PSQI) to evaluate depressed symptoms, cognitive function, activities of daily living (ADL), and sleep quality at baseline, post-intervention, and the 8-week follow-up. A two-factor analysis of variance was used to compare differences between groups, and Pearson's linear correlation was used to analyze the possible relationship between variables and potential predictors of depression improvement. Results: Seventy-two participants were randomized to rTMS−MBSR (n = 24), sham rTMS−MBSR (n = 24), or sham rTMS−general psychological care (n = 24). A total of 71 patients completed the questionnaire, a 99% response rate. There were significant time and group interaction effects in HAMD-17, MMSE, MBI, and PSQI scores (p < 0.001). The repeated-measure ANOVA showed a significant improvement of all variables in rTMS−MBSR compared to sham rTMS−MBSR and sham rTMS combined with general psychological care (p < 0.05). Additional results demonstrated that cognitive function, sleep quality, and activities of daily living are associated with depressive symptoms, and cognitive function is a potential variable for improved depression. Conclusion: Depressive symptoms can be identified early by assessing cognitive function, and rTMS−MBSR might be considered a potentially helpful treatment for PSD.
Assuntos
Atenção Plena , Acidente Vascular Cerebral , Humanos , Estimulação Magnética Transcraniana/métodos , Depressão/etiologia , Depressão/terapia , Atividades Cotidianas , Acidente Vascular Cerebral/complicações , Resultado do Tratamento , Método Duplo-CegoRESUMO
BACKGROUND AND OBJECTIVE: Since the early symptoms of chronic obstructive pulmonary disease (COPD) are not obvious, patients are not easily identified, causing improper time for prevention and treatment. In present study, machine learning (ML) methods were employed to construct a risk prediction model for COPD to improve its prediction efficiency. METHODS: We collected data from a sample of 5807 cases with a complete COPD diagnosis from the 2019 COPD Surveillance Program in Shanxi Province and extracted 34 potentially relevant variables from the dataset. Firstly, we used feature selection methods (i.e., Generalized elastic net, Lasso and Adaptive lasso) to select ten variables. Afterwards, we employed supervised classifiers for class imbalanced data by combining the cost-sensitive learning and SMOTE resampling methods with the ML methods (Logistic Regression, SVM, Random Forest, XGBoost, LightGBM, NGBoost and Stacking), respectively. Last, we assessed their performance. RESULTS: The cough frequently at age 14 and before and other 9 variables are significant parameters for COPD. The Stacking heterogeneous ensemble model showed relatively good performance in the unbalanced datasets. The Logistic Regression with class weighting enjoyed the best classification performance in the balancing data when these composite indicators (AUC, F1-Score and G-mean) were used as criteria for model comparison. The values of F1-Score and G-mean for the top three ML models were 0.290/0.660 for Logistic Regression with class weighting, 0.288/0.649 for Stacking with synthetic minority oversampling technique (SMOTE), and 0.285/0.648 for LightGBM with SMOTE. CONCLUSIONS: This paper combining feature selection methods, unbalanced data processing methods and machine learning methods with data from disease surveillance questionnaires and physical measurements to identify people at risk of COPD, concluded that machine learning models based on survey questionnaires could provide an automated identification for patients at risk of COPD, and provide a simple and scientific aid for early identification of COPD.
Assuntos
Doença Pulmonar Obstrutiva Crônica , Humanos , Adolescente , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Doença Pulmonar Obstrutiva Crônica/epidemiologia , Aprendizado de Máquina , Modelos Logísticos , Máquina de Vetores de SuporteRESUMO
Background and Aims: Decompensated cirrhotic patients with hepatitis C (HCV) are often under-represented in clinical trials. We aimed to evaluate pooled data on the efficacy and safety of sofosbuvir (SOF)-based regimens in these patients. Methods: We conducted a systemic review and meta-analysis by searching multiple databases for studies published from October 2010 to October 2020. Outcomes of interest were sustained virologic response (SVR) and safety of SOF-based regimens in decompensated HCV patients. Two reviewers independently performed the study selection and data extraction. Results: We included 33 studies that enrolled 5,302 HCV patients. The pooled SVR rate in decompensated patients with SOF-based regimens was 85.1% (95% CI: 82.8-87.3). Patients on SOF/velpatasvir±ribavirin achieved a significantly higher SVR (91.0%, 95% CI: 87.7-93.9) than that of SOF/ledipasvir±ribavirin [(86.3%, 95% CI: 84.6-87.8); p=0.004)], or on SOF/daclatasvir±ribavirin (82.4%, 95% CI: 78.2-86.2%; p<0.001). Adding ribavirin to SOF-based regimens (pooled SVR 84.9%, 95% CI: 81.7-87.9) did not significantly increase the SVR [(83.8% (95% CI: 76.8-89.8%; p=0.76)] in decompensated patients, which was also true in subgroup analyses for each regimen within the same treatment duration. However, adding ribavirin significantly increased the frequency of adverse events from 52.9% (95% CI: 28.0-77.1) to 89.2% (95% CI: 68.1-99.9) and frequency of severe events. The pooled incidence of hepatocellular carcinoma and case-fatality of decompensated patients were 3.1% (95% CI: 1.5-5.0) and 4.6% (95% CI: 3.1-6.3), respectively. The overall heterogeneity was high. There was no publication bias. Conclusions: The analysis found that 12 weeks of SOF/velpatasvir without ribavirin is the preferred therapy, with a significantly higher SVR compared with other SOF-based regimens in decompensated HCV patients.