Impacts of different slash disposals on soil and water erosion of high-intensity management Eucalyptus grandis × urophylla plantation.

Background: The slash disposal-burning forest-in high-intensity management Eucalyptus grandis × urophylla plantation has accelerated soil degradation. Statement of the problem: Slash disposals is a contributing factor, but its specific role in the correlation between rainfall-runoff and soil erosion remains elusive. Objectives: his study investigated the characteristics of rainfall-runoff and soil erosion resistance in different methods of slash disposals in plantation. Methods: Three methods of slash disposal, namely burning forest (BF), moving away (MA), and spreading evenly (SE), were established. A field simulation experiment of rainfall was conducted, and path analysis was used. Results: The findings revealed that the water holding, infiltrating properties and the time the rainfall-runoff generated of SE were increased by approximately 10∼20 %, 100 %, and 80 %, respectively, compared with BF and MA. Water loss, soil loss and nutrient loss were significantly reduced by 62.23 % and 61.56 %, 69.06 % and 49.55 %, and 58.8 % and 65.42 % in SE and BF compared to MA. Path analysis suggested that different from BF and MA, the correlation between soil water properties and rainfall-runoff factors in SE was weakened, simultaneously considering the result that SE had the lower proportions of silt for sediment component (75.31 %), it stabilized the soil structure. Conclusions and prospect: Consequently, SE mitigated the erosion force by reducing rainfall-runoff and enhancing the anti-erosion of soil through improved water properties, making it a viable slash disposal. This work provides a detailed description of the soil erosion characteristics of plantation, including water, soil, and nutrient losses caused by rainfall-runoff, as well as the soil anti-erosion due to different slash disposals. These findings offer valuable insights for the management of high-intensity Eucalyptus grandis × urophylla plantations.

Radiomics and Deep Learning in Nasopharyngeal Carcinoma: A Review.

Nasopharyngeal carcinoma is a common head and neck malignancy with distinct clinical management compared to other types of cancer. Precision risk stratification and tailored therapeutic interventions are crucial to improving the survival outcomes. Artificial intelligence, including radiomics and deep learning, has exhibited considerable efficacy in various clinical tasks for nasopharyngeal carcinoma. These techniques leverage medical images and other clinical data to optimize clinical workflow and ultimately benefit patients. In this review, we provide an overview of the technical aspects and basic workflow of radiomics and deep learning in medical image analysis. We then conduct a detailed review of their applications to seven typical tasks in the clinical diagnosis and treatment of nasopharyngeal carcinoma, covering various aspects of image synthesis, lesion segmentation, diagnosis, and prognosis. The innovation and application effects of cutting-edge research are summarized. Recognizing the heterogeneity of the research field and the existing gap between research and clinical translation, potential avenues for improvement are discussed. We propose that these issues can be gradually addressed by establishing standardized large datasets, exploring the biological characteristics of features, and technological upgrades.

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

BACKGROUND: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet. PURPOSE: This multicenter study aimed to develop a deep learning-based radiomics model to preoperatively identify ESTM and evaluate its prognostic value. METHODS: A total of 959 GC patients were enrolled from two centers and split into a training cohort (N = 551) and a test cohort (N = 236) for ESTM evaluation. Additionally, an external survival cohort (N = 172) was included for prognostic analysis. Four models were established based on clinical characteristics and multiphase computed tomography (CT) images for preoperative identification of ESTM, including a deep learning model, a hand-crafted radiomic model, a clinical model, and a combined model. C-index, decision curve, and calibration curve were utilized to assess the model performances. Survival analysis was conducted to explore the ability of stratifying overall survival (OS). RESULTS: The combined model showed good discrimination of the ESTM [C-indices (95% confidence interval, CI): 0.770 (0.729-0.812) and 0.761 (0.718-0.805) in training and test cohorts respectively], which outperformed deep learning model, radiomics model, and clinical model. The stratified analysis showed this model was not affected by patient's tumor size, the presence of lymphovascular invasion, and Lauren classification (p < 0.05). Moreover, the model score showed strong consistency with the OS [C-indices (95%CI): 0.723 (0.658-0.789, p < 0.0001) in the internal survival cohort and 0.715 (0.650-0.779, p < 0.0001) in the external survival cohort]. More interestingly, univariate analysis showed the model score was significantly associated with occult distant metastasis (p < 0.05) that was missed by preoperative diagnosis. CONCLUSIONS: The model combining CT images and clinical characteristics had an impressive predictive ability of both ESTM and prognosis, which has the potential to serve as an effective complement to the preoperative TNM staging system.

Combined analysis of metagenome and transcriptome revealed the adaptive mechanism of different golden Camellia species in karst regions.

Camellia sect. Chrysantha is an important rare and protected plant species. Some golden Camellia species grow in karst soil while others grow in acidic soil. In order to study the adaptation mechanism of golden Camellia to the karst environment, four species of golden Camellia growing in the karst soil (Camellia pubipetala, Camellia perpetua, Camellia grandis, and Camellia limonia) and four species growing in the acidic soil (Camellia nitidissima, Camellia euphlebia, Camellia tunghinensis, and Camellia parvipetala) were selected for this study. Combining the metagenome and transcriptome, the structure and function of the rhizosphere microbial communities and the gene expression in roots of golden Camellia were analyzed. The results showed that the rhizosphere microbial communities in different golden Camellia were significantly different in abundance of Acidobacteria, Actinobacteria, Candidatus_Rokubacteria, Nitrospirae, Planctomycetes, and Candidatus_Tectomicrobia. The proportion of Candidatus_Rokubacteria was significantly higher in the rhizosphere soil of four species of golden Camellia grown in karst areas, compared to C. nitidissima, C. euphlebia, and C. tunghinensis. The linear discriminant analysis Effect Size showed that C. parvipetala was similar to karst species in the enrichment of ABC transporters and quorum sensing. During the transcriptome analysis, numerous upregulated genes in four karst species, including CYP81E, CHS, F3H, C12RT1, NAS, and CAD, were found to be enriched in the secondary metabolite synthesis pathway in the KEGG library, when compared to C. tunghinensis. This study provides information for plant adaptation mechanisms on the rhizosphere soil microbial composition and gene expression in secondary metabolic pathways to karst habitats and its distribution in karst areas.

Relationship between the circulating N-terminal pro B-type natriuretic peptide and the risk of carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a CSCD-TCM plus study in China.

OBJECTIVE: Circulating N-terminal pro B-type natriuretic peptide (NT-proBNP) is a marker for heart failure in patients with coronary heart disease (CHD) and associated with glycemic abnormalities. Studies on the association and diagnostic value of NT-proBNP in carotid plaques (CAP) in patients with CHD are limited. METHODS: The relationships between NT-proBNP and the risk of CAP in different glucose metabolic states, sexes, and age categories were also examined using 5,093 patients diagnosed with CHD. The NT-proBNP tertiles were used to divide patients into three groups in which the NT-proBNP levels, blood glucose levels, the occurrence of CAP, and the number and nature of CAP were measured using normoglycemic (NG), prediabetes (Pre-DM), and diabetes mellitus (DM) glucose metabolic statuses. Logistic regression analyses were used to compare the relationship between NT-proBNP and the risk of CAP occurrence and the number and nature of CAP. The diagnostic value of NT-proBNP for CAP risk was measured using receiver operating characteristic (ROC) curves. RESULTS: We found a 37% relative increase in the correlation between changes in NT-proBNP per standard deviation (SD) and the incidence of CAP. After adjusting for potential confounders, NT-proBNP at the T3 level was found to be associated with an increased CAP odds ratio (OR) when T1 was used as the reference. This relationship was also present in males, patients aged > 60 years, or both pre-DM and DM states. NT-proBNP was more likely to present as hypoechoic plaques at T1 and as mixed plaques at T3. We also measured the diagnostic accuracy of CAP for NT-proBNP in patients with CHD, with an AUC value of 0.627(95% CI 0.592-0.631), sensitivity of 50.7%, and specificity of 68.0%. CONCLUSION: An increase in NT-proBNP was significantly associated with the risk of CAP in patients with CHD, especially in males and patients aged > 60 years, and exhibited specific characteristics under different glucose metabolism states. Trial registration The study was approved by the Ethics Committee of Tianjin University of Traditional Chinese Medicine (Approval number TJUTCM-EC20210007) and certified by the Chinese Clinical Trials Registry on April 4, 2022 (Registration number ChiCTR2200058296) and March 25, 2022 by ClinicalTrials.gov (registration number NCT05309343).

Association of PI3K/AKT/mTOR pathway autophagy-related gene polymorphisms with pulmonary tuberculosis susceptibility in a Chinese population.

BACKGROUND: Autophagy can inhibit the survival of intracellular microorganisms including Mycobacterium tuberculosis (Mtb), and the PI3K/AKT/mTOR pathway plays a crucial role. This study investigated the association between PI3K/AKT/mTOR pathway autophagy-related gene polymorphisms and pulmonary tuberculosis (PTB) susceptibility. METHODS: KEGG pathway and gene ontology (GO) databases were searched for genes belonging to the PI3K/AKT/mTOR and autophagy pathways. Thirty SNPs in nine genes were identified and tested for their associations with tuberculosis in 130 patients with PTB and 271 controls. We constructed genetic risk scores (GRSs) and divided the participants into 3 subgroups based on their GRSs:0-5, 6-10, and 11-16. RESULTS: This analysis revealed that the AKT1 (rs12432802), RPTOR (rs11654508, rs12602885, rs2090204, rs2589144, and rs2672897), and TSC2 (rs2074969) polymorphisms were significantly associated with PTB risk. A decreasing trend was observed (P trend 0.020), in which a lower GRS was associated with a higher risk of PTB ([6-10] vs. [0-5]: OR (95%CI) 0.590 (0.374-0.931); [11-16] vs. [0-5]: OR (95%CI) 0.381 (0.160-0.906)). CONCLUSIONS: Polymorphisms in AKT1, RPTOR, and TSC2 may influence susceptibility to PTB.

A multi-view co-training network for semi-supervised medical image-based prognostic prediction.

Prognostic prediction has long been a hotspot in disease analysis and management, and the development of image-based prognostic prediction models has significant clinical implications for current personalized treatment strategies. The main challenge in prognostic prediction is to model a regression problem based on censored observations, and semi-supervised learning has the potential to play an important role in improving the utilization efficiency of censored data. However, there are yet few effective semi-supervised paradigms to be applied. In this paper, we propose a semi-supervised co-training deep neural network incorporating a support vector regression layer for survival time estimation (Co-DeepSVS) that improves the efficiency in utilizing censored data for prognostic prediction. First, we introduce a support vector regression layer in deep neural networks to deal with censored data and directly predict survival time, and more importantly to calculate the labeling confidence of each case. Then, we apply a semi-supervised multi-view co-training framework to achieve accurate prognostic prediction, where labeling confidence estimation with prior knowledge of pseudo time is conducted for each view. Experimental results demonstrate that the proposed Co-DeepSVS has a promising prognostic ability and surpasses most widely used methods on a multi-phase CT dataset. Besides, the introduction of SVR layer makes the model more robust in the presence of follow-up bias.

The impacts of the national centralized drug procurement policy on the use of policy-related antibiotic agents: the case of Shaanxi province, China.

OBJECTIVES: The purpose of NCDP policy was to reduce the price of drugs. However, it is unclear that a reduction in the price of a single antibiotic will lead to an increase in other alternatives, which is crucial for antibiotic management. The aim of this study was to evaluate the impact of policy on the consumption of policy-related antibiotic. METHODS: Quasi-experimental interrupted time series methods were used to assess the effects of the policy. RESULTS: After the implementation of the policy, the consumption of the winning products increased rapidly, with a significant difference in growth (ß2 = 88.03). For nonwinning products, the level of purchase volume decreased (ß2=-22.83) in the intervention group, and after adding the comparison group, this number fell further in statistical significance (ß2=-114.53). Among all the nonwinning products, the level of purchase volume (ß2=-73.59) and expenditures (ß2=-346.71) of the generic drugs that passed the conformance evaluation decreased significantly after the policy in the difference model. The purchase volume of J01DC, J01DD and total antibiotics significantly increased in control group compared with the intervention. CONCLUSION: The implementation of the volume-based procurement policy promoted the use of winning products and decreased the usage of its alternative watch antibiotics.

Functional microorganisms in Baijiu Daqu: Research progress and fortification strategy for application.

Daqu is a saccharifying and fermenting starter in the production of Chinese Baijiu; its quality directly affects the quality of Baijiu. The production of Daqu is highly environment-dependent, and after long-term natural domestication, it is rich in a wide variety of microorganisms with a stable composition, which provide complex and diverse enzymes and flavor (precursor) substances and microbiota for Jiupei (Fermented grains) fermentation. However, inoculation with a relatively stable microbial community can lead to a certain upper limit or deficiencies of the physicochemical properties (e.g., saccharification capacity, esterification capacity) of the Daqu and affect the functional expression and aroma formation of the Daqu. Targeted improvement of this problem can be proposed by selecting functional microorganisms to fortify the production of Daqu. This review introduced the isolation, screening, identification and functional characteristics of culture-dependent functional microorganisms in Baijiu-brewing, the core functional microbiota community of Daqu, and the related research progress of functional microorganisms fortified Daqu, and summarized the fortifying strategies of functional microorganisms, aiming to further deepen the application of functional microorganisms fortification in Daqu fermentation and provide ideas for the flavor regulation and quality control of Baijiu.

Association of PI3K/AKT/mTOR pathway autophagy-related gene polymorphisms with pulmonary tuberculosis susceptibility in a Chinese population

ABSTRACT Background: Autophagy can inhibit the survival of intracellular microorganisms including Mycobacterium tuberculosis (Mtb), and the PI3K/AKT/mTOR pathway plays a crucial role. This study investigated the association between PI3K/AKT/mTOR pathway autophagy-related gene polymorphisms and pulmonary tuberculosis (PTB) susceptibility. Methods: KEGG pathway and gene ontology (GO) databases were searched for genes belonging to the PI3K/AKT/mTOR and autophagy pathways. Thirty SNPs in nine genes were identified and tested for their associations with tuberculosis in 130 patients with PTB and 271 controls. We constructed genetic risk scores (GRSs) and divided the participants into 3 subgroups based on their GRSs:0-5, 6-10, and 11-16. Results: This analysis revealed that the AKT1 (rs12432802), RPTOR (rs11654508, rs12602885, rs2090204, rs2589144, and rs2672897), and TSC2 (rs2074969) polymorphisms were significantly associated with PTB risk. A decreasing trend was observed (P trend 0.020), in which a lower GRS was associated with a higher risk of PTB ([6-10] vs. [0-5]: OR (95%CI) 0.590 (0.374-0.931); [11-16] vs. [0-5]: OR (95%CI) 0.381 (0.160-0.906)). Conclusions: Polymorphisms in AKT1, RPTOR, and TSC2 may influence susceptibility to PTB.

A novel risk model based on white blood cell-related biomarkers for acute kidney injury prediction in patients with ischemic stroke admitted to the intensive care unit.

Background: Conventional systemic inflammatory biomarkers could predict prognosis in patients with ischemic stroke (IS) admitted to the intensive care unit (ICU). Acute kidney injury (AKI) is common in patients with IS admitted to ICU, but few studies have used systemic inflammatory biomarkers to predict AKI in critically ill patients with IS. This study aimed to establish a risk model based on white blood cell (WBC)-related biomarkers to predict AKI in critically ill patients with IS. Methods: Data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort, and data were extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) for a validation cohort. Logistic regression analysis was used to determine the significant predictors of WBC-related biomarkers on AKI prediction, and a risk model was established based on those significant indicators in multivariate logistic regression. The receiver operating characteristics (ROC) curve was utilized to obtain the best cut-off value of the risk model. The Kaplan-Meier curve was used to evaluate the prognosis-predictive ability of the risk model. Results: The overall incidence of AKI was 28.4% in the training cohort and 33.2% in the validation cohort. WBC to lymphocyte ratio (WLR), WBC to basophils ratio (WBR), WBC to hemoglobin ratio (WHR), and neutrophil to lymphocyte ratio (NLR) could independently predict AKI, and a novel risk model was established based on WLR, WBR, WHR, and NLR. This risk model depicted good prediction performance both in AKI and other clinical outcomes including hemorrhage, persistent AKI, AKI progression, ICU mortality, and in-hospital mortality both in the training set and in the validation set. Conclusion: A risk model based on WBC-related indicators exhibited good AKI prediction performance in critically ill patients with IS which could provide a risk stratification tool for clinicians in the ICU.

Polymorphisms of the BCL2 gene associated with susceptibility to tuberculosis.

Although tuberculosis (TB) is a serious public health concern, we still don't understand why only 10% of people infected will develop the disease. Apoptosis plays a role in the interaction of Mycobacterium tuberculosis (Mtb) with the human host and it may be modified by subtle alterations in the B-cell lymphoma 2 (BCL2) gene, an anti-apoptotic regulatory element. Therefore, we investigated whether there is an association between BCL2 polymorphisms and susceptibility to TB by analyzing 130 TB cases, 108 subjects with latent TB infection (LTBI), and 163 healthy controls (HC). Logistic regression was used to calculate odds ratios (ORs) and 95% confidential intervals (95% CIs) for possible associations between single nucleotide polymorphisms (SNPs) in BCL2 and the risk of tuberculosis. We found that the G allele of rs80030866 (OR=0.62, 95%CI:0.42-0.91, P=0.015), and also the G allele of rs9955190 (OR=0.58, 95%CI:0.38-0.88, P=0.011) were less frequent in the TB group compared with the LTBI group. In addition, individuals with rs2551402 CC genotype were more likely to have LTBI than those with AA genotype (OR=2.166, 95%CI:1.046-4.484, P=0.037). Our study suggests that BCL2 gene polymorphisms may be correlated with susceptibility to both TB and LTBI.

A Hydrazine-Hydrazone Adamantine Compound Shows Antimycobacterial Activity and Is a Probable Inhibitor of MmpL3.

Tuberculosis remains an important cause of morbidity and mortality throughout the world. Notably, an important number of multi drug resistant cases is an increasing concern. This problem points to an urgent need for novel compounds with antimycobacterial properties and to improve existing therapies. Whole-cell-based screening for compounds with activity against Mycobacterium tuberculosis complex strains in the presence of linezolid was performed in this study. A set of 15 bioactive compounds with antimycobacterial activity in vitro were identified with a minimal inhibitory concentration of less than 2 µg/mL. Among them, compound 1 is a small molecule with a chemical structure consisting of an adamantane moiety and a hydrazide-hydrazone moiety. Whole genome sequencing of spontaneous mutants resistant to the compounds suggested compound 1 to be a new inhibitor of MmpL3. This compound binds to the same pocket as other already published MmpL3 inhibitors, without disturbing the proton motive force of M. bovis BCG and M. smegmatis. Compound 1 showed a strong activity against a panel ofclinical strains of M. tuberculosis in vitro. This compound showed no toxicity against mammalian cells and protected Galleria mellonella larvae against M. bovis BCG infection. These results suggest that compound 1 is a promising anti-TB agent with the potential to improve TB treatment in combination with standard TB therapies.

Modelling and discussion on emission reduction transformation path of China's electric power industry under "double carbon" goal.

China has promised to peak carbon emission before 2030 and to achieve carbon neutrality before 2060 (i.e., "double carbon" goal). Under this background, the emission reduction transformation path of China's electric power industry is studied in this paper. First, several boundary conditions (i.e., assumptions) of electric power structure transformation (i.e., the costs of power generations, the costs of energy storage systems, the developments of carbon sinks, the emission factors, and the quotas of carbon sinks) are given considering the whole society electricity consumption in the future. Second, a transformation path optimization model is established aim to minimize the total cost in the electric power industry. Then, according to the optimization results, the transformation predictions for the power industry under the "30·60 scenario" (i.e., the scenario that can achieve carbon peak and carbon neutrality before 2030 and 2060) are analyzed in detail, and are compared with the ones of "2 °C scenario" and "1.5 °C scenario" defined by IPCC. Furthermore, the influence of different carbon prices on the transformation path is also analyzed. It can be concluded from the simulation results that the "30·60 scenario" is a scenario between "2 °C scenario" and "1.5 °C scenario", and carbon emission can be reduced rapidly under the guidance of high carbon prices.

3D-bioprinted peptide coupling patches for wound healing.

Chronic wounds caused by severe trauma remain a serious challenge for clinical treatment. In this study, we developed a novel angiogenic 3D-bioprinted peptide patch to improve skin wound healing. The 3D-bioprinted technology can fabricate individual patches according to the shape characteristics of the damaged tissue. Gelatin methacryloyl (GelMA) and hyaluronic acid methacryloyl (HAMA) have excellent biocompatibility and biodegradability, and were used as a biomaterial to produce bioprinted patches. The pro-angiogenic QHREDGS peptide was covalently conjugated to the 3D-bioprinted GelMA/HAMA patches, extending the release of QHREDGS and improving the angiogenic properties of the patch. Our results demonstrated that these 3D-bioprinted peptide patches showed excellent biocompatibility, angiogenesis, and tissue repair both in vivo and in vitro. These findings indicated that 3D-bioprinted peptide patches improved skin wound healing and could be used in other tissue engineering applications.

Polymorphisms of the BCL2 gene associated with susceptibility to tuberculosis

ABSTRACT Although tuberculosis (TB) is a serious public health concern, we still don't understand why only 10% of people infected will develop the disease. Apoptosis plays a role in the interaction of Mycobacterium tuberculosis (Mtb) with the human host and it may be modified by subtle alterations in the B-cell lymphoma 2 (BCL2) gene, an anti-apoptotic regulatory element. Therefore, we investigated whether there is an association between BCL2 polymorphisms and susceptibility to TB by analyzing 130 TB cases, 108 subjects with latent TB infection (LTBI), and 163 healthy controls (HC). Logistic regression was used to calculate odds ratios (ORs) and 95% confidential intervals (95% CIs) for possible associations between single nucleotide polymorphisms (SNPs) in BCL2 and the risk of tuberculosis. We found that the G allele of rs80030866 (OR=0.62, 95%CI:0.42-0.91, P=0.015), and also the G allele of rs9955190 (OR=0.58, 95%CI:0.38-0.88, P=0.011) were less frequent in the TB group compared with the LTBI group. In addition, individuals with rs2551402 CC genotype were more likely to have LTBI than those with AA genotype (OR=2.166, 95%CI:1.046-4.484, P=0.037). Our study suggests that BCL2 gene polymorphisms may be correlated with susceptibility to both TB and LTBI.

An Investigation of the Risk Factors Associated With Anti-Tuberculosis Drug-Induced Liver Injury or Abnormal Liver Functioning in 757 Patients With Pulmonary Tuberculosis.

Objectives: To identify the risk factors associated with anti-tuberculosis drug-induced liver injury (AT-DILI) or abnormal living functioning from 757 patients with pulmonary tuberculosis (TB) registered at Nanshan Center for Chronic Disease Control (Nanshan CCDC), Shenzhen, Guangdong Province, China. Design and methods: We identified 757 TB patients who met our inclusion criteria by screening the Hospital Information System (HIS) at Nanshan CCDC. Next, we identified positive cases of AT-DILI or abnormal liver functioning based on results of the first-time liver function tests (LFTs) after taking anti-TB drugs. The χ2 test was used to relate the positive rate with a variety of factors. A logistic regression model was also used to identify statistically significant risk factors. Results: Of the 757 patients, the positive rate of AT-DILI or abnormal liver functioning was 37.9% (287/757). Univariate analysis revealed that the positive rate was 42.91% (212/494) for males and 28.52% (75/263) for females. The positive rate was significantly higher in males (p <0.001). Patients with an annual income of 9,231-13,845 USD had a significantly higher positive rate (67.35%; 33/49) than those with an income of 1,540-4616 USD (37.97%; 30/79) (p = 0.022). The most frequent prescription regime among positive cases was a 2 months supply of fixed dose combination Ethambutol Hydrochloride, Pyrazinamide, Rifampicin and Isoniazid Tablets (Ⅱ) 450 mg) followed by a 4 months supply of fixed dose combination Rifampin and Isoniazid Capsules (2FDC-HRZE half/4FDC-HR) at 56.03% (144/257). The least frequent prescription regime was a 2 months supply of fixed dose combination Rifampin, Isoniazid and Pyrazinamide Capsules with Ethambutol independently followed by a 4 months supply of fixed dose combination Rifampin and Isoniazid Capsules (2FDC-HRZ + EMB/4FDC-HR) at 24.27% (25/103). The difference between these two different regimes was significant (p = 0.022). With an increase in the duration of medication, patients under various prescription regimes all showed a gradual increase in the positive rate of AT-DILI or abnormal liver functioning. Conclusion: We identified several risk factors for the occurrence of AT-DILI or abnormal liver functioning, including gender, annual income, prescription regime, dosage, and treatment time.

Correction: Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study.

[This corrects the article DOI: 10.2196/29226.].

Low Dose of Emodin Inhibits Hypercholesterolemia in a Rat Model of High Cholesterol.

BACKGROUND Emodin has been widely used in traditional Chinese medicine, but few studies have tried to understand the mechanism of its anti-hypercholesterolemic effect. MATERIAL AND METHODS To delineate the underlying pathways, high-cholesterol diet (HCD)-fed Sprague-Dawley rats were orally administrated emodin or the lipid-lowering medicine simvastatin. Emodin was administered at 10, 30, or 100 mg/kg, while simvastatin was administered at 10 mg/kg. Parameters measured included lipid profiles (serum total cholesterol, triglycerides, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol, aorta endothelium-dependent vasorelaxation in response to acetylcholine, and nitric oxide (NO) production. RT-qPCR and western blotting were performed to evaluate aortic endothelial nitric oxide synthase (eNOS), phosphorylated eNOS (p-eNOS), and hepatic LDL receptor (LDLR). Indices of liver and serum oxidation were also measured. RESULTS The atherogenic index was increased by the HCD but significantly reduced in all treatment groups. The HCD-fed experimental group treated with emodin at 10 mg/kg had significantly lower serum total-C and LDL-C and improved aorta vasorelaxation and enhanced NO production. Also, emodin significantly attenuated the lipid profiles and restored endothelial function, as reflected by upregulated expression of hepatic LDLR and p-eNOS, respectively. Furthermore, emodin at 10 mg/kg significantly enhanced superoxide dismutase activity, lowered the malondialdehyde level in both liver and serum, and enhanced catalase activity in serum. CONCLUSIONS The ability of emodin to inhibit hypercholesterolemia in HCD-fed rats was associated with lower serum total-C and LDL-C, restoration of aortic endothelial function, and improved antioxidant capacity. Low-dose emodin showed better protection of aortic endothelium and better antioxidant activity than did higher doses.

Predicting Antituberculosis Drug-Induced Liver Injury Using an Interpretable Machine Learning Method: Model Development and Validation Study.

BACKGROUND: Tuberculosis (TB) is a pandemic, being one of the top 10 causes of death and the main cause of death from a single source of infection. Drug-induced liver injury (DILI) is the most common and serious side effect during the treatment of TB. OBJECTIVE: We aim to predict the status of liver injury in patients with TB at the clinical treatment stage. METHODS: We designed an interpretable prediction model based on the XGBoost algorithm and identified the most robust and meaningful predictors of the risk of TB-DILI on the basis of clinical data extracted from the Hospital Information System of Shenzhen Nanshan Center for Chronic Disease Control from 2014 to 2019. RESULTS: In total, 757 patients were included, and 287 (38%) had developed TB-DILI. Based on values of relative importance and area under the receiver operating characteristic curve, machine learning tools selected patients' most recent alanine transaminase levels, average rate of change of patients' last 2 measures of alanine transaminase levels, cumulative dose of pyrazinamide, and cumulative dose of ethambutol as the best predictors for assessing the risk of TB-DILI. In the validation data set, the model had a precision of 90%, recall of 74%, classification accuracy of 76%, and balanced error rate of 77% in predicting cases of TB-DILI. The area under the receiver operating characteristic curve score upon 10-fold cross-validation was 0.912 (95% CI 0.890-0.935). In addition, the model provided warnings of high risk for patients in advance of DILI onset for a median of 15 (IQR 7.3-27.5) days. CONCLUSIONS: Our model shows high accuracy and interpretability in predicting cases of TB-DILI, which can provide useful information to clinicians to adjust the medication regimen and avoid more serious liver injury in patients.