Picrasma quassioides leaves: Insights from chemical profiling and bioactivity comparison with stems.

BACKGROUND: In Chinese Pharmacopeia, Picrasma quassioides (PQ) stems and leaves are recorded as Kumu with antimicrobial, anti-cancer, anti-parasitic effects, etc. However, thick stems are predominantly utilized as medicine in many Asian countries, with leaves rarely used. By now, the phytochemistry and bioactivity of PQ leaves are not well investigated. METHODS: An Orbitrap Elite mass spectrometer was employed to comprehensively investigate PQ stems and leaves sourced from 7 different locations. Additionally, their bioactivities were evaluated against 5 fungi, 6 Gram-positive bacteria and 9 Gram-negative bacteria, a tumor cell line (A549), a non-tumor cell line (WI-26 VA4) and N2 wild-type Caenorhabditis elegans. RESULTS: Bioassay results demonstrated the efficacy of both leaves and stems against tumor cells, several bacteria and fungi, while only leaves exhibited anthelmintic activity against C. elegans. A total of 181 compounds were identified from PQ stems and leaves, including 43 ß-carbolines, 20 bis ß-carbolines, 8 canthinone alkaloids, 56 quassinoids, 12 triterpenoids, 13 terpenoid derivatives, 11 flavonoids, 7 coumarins, and 11 phenolic derivatives, from which 10 compounds were identified as indicator components for quality evaluation. Most alkaloids and triterpenoids were concentrated in PQ stems, while leaves exhibited higher levels of quassinoids and other carbohydrate (CHO) components. CONCLUSION: PQ leaves exhibit distinct chemical profiles and bioactivity with the stems, suggesting their suitability for medicinal purposes. So far, the antibacterial, antifungal, and anthelmintic activities of PQ leaves were first reported here, and considering PQ sustainability, the abundant leaves are recommended for increased utilization, particularly for their rich content of PQ quassinoids.

[Research progress of chemerin in polycystic ovary syndrome].

As a multifunctional adipokine, chemerin plays a crucial role in various pathophysiological processes through endocrine and paracrine manner. It can bind to three known receptors (ChemR23, GPR1 and CCRL2) and participate in energy metabolism, glucose and lipid metabolism, and inflammation, especially in metabolic diseases. Polycystic ovary syndrome (PCOS) is one of the most common endocrine diseases, which seriously affects the normal life of women of childbearing age. Patients with PCOS have significantly increased serum levels of chemerin and high expression of chemerin in their ovaries. More and more studies have shown that chemerin is involved in the occurrence and development of PCOS by affecting obesity, insulin resistance, hyperandrogenism, oxidative stress and inflammatory response. This article mainly reviews the production, subtypes, function and receptors of chemerin protein, summarizes and discusses the research status of chemerin protein in PCOS from the perspectives of metabolism, reproduction and inflammation, and provides theoretical basis and reference for the clinical diagnosis and treatment of PCOS.

SAGL: A self-attention-based graph learning framework for predicting survival of colorectal cancer patients.

BACKGROUND AND OBJECTIVE: Colorectal cancer (CRC) is one of the most commonly diagnosed cancers worldwide. The accurate survival prediction for CRC patients plays a significant role in the formulation of treatment strategies. Recently, machine learning and deep learning approaches have been increasingly applied in cancer survival prediction. However, most existing methods inadequately represent and leverage the dependencies among features and fail to sufficiently mine and utilize the comorbidity patterns of CRC. To address these issues, we propose a self-attention-based graph learning (SAGL) framework to improve the postoperative cancer-specific survival prediction for CRC patients. METHODS: We present a novel method for constructing dependency graph (DG) to reflect two types of dependencies including comorbidity-comorbidity dependencies and the dependencies between features related to patient characteristics and cancer treatments. This graph is subsequently refined by a disease comorbidity network, which offers a holistic view of comorbidity patterns of CRC. A DG-guided self-attention mechanism is proposed to unearth novel dependencies beyond what DG offers, thus augmenting CRC survival prediction. Finally, each patient will be represented, and these representations will be used for survival prediction. RESULTS: The experimental results show that SAGL outperforms state-of-the-art methods on a real-world dataset, with the receiver operating characteristic curve for 3- and 5-year survival prediction achieving 0.849±0.002 and 0.895±0.005, respectively. In addition, the comparison results with different graph neural network-based variants demonstrate the advantages of our DG-guided self-attention graph learning framework. CONCLUSIONS: Our study reveals that the potential of the DG-guided self-attention in optimizing feature graph learning which can improve the performance of CRC survival prediction.

Predicting Colorectal Cancer Survival Using Time-to-Event Machine Learning: Retrospective Cohort Study.

BACKGROUND: Machine learning (ML) methods have shown great potential in predicting colorectal cancer (CRC) survival. However, the ML models introduced thus far have mainly focused on binary outcomes and have not considered the time-to-event nature of this type of modeling. OBJECTIVE: This study aims to evaluate the performance of ML approaches for modeling time-to-event survival data and develop transparent models for predicting CRC-specific survival. METHODS: The data set used in this retrospective cohort study contains information on patients who were newly diagnosed with CRC between December 28, 2012, and December 27, 2019, at West China Hospital, Sichuan University. We assessed the performance of 6 representative ML models, including random survival forest (RSF), gradient boosting machine (GBM), DeepSurv, DeepHit, neural net-extended time-dependent Cox (or Cox-Time), and neural multitask logistic regression (N-MTLR) in predicting CRC-specific survival. Multiple imputation by chained equations method was applied to handle missing values in variables. Multivariable analysis and clinical experience were used to select significant features associated with CRC survival. Model performance was evaluated in stratified 5-fold cross-validation repeated 5 times by using the time-dependent concordance index, integrated Brier score, calibration curves, and decision curves. The SHapley Additive exPlanations method was applied to calculate feature importance. RESULTS: A total of 2157 patients with CRC were included in this study. Among the 6 time-to-event ML models, the DeepHit model exhibited the best discriminative ability (time-dependent concordance index 0.789, 95% CI 0.779-0.799) and the RSF model produced better-calibrated survival estimates (integrated Brier score 0.096, 95% CI 0.094-0.099), but these are not statistically significant. Additionally, the RSF, GBM, DeepSurv, Cox-Time, and N-MTLR models have comparable predictive accuracy to the Cox Proportional Hazards model in terms of discrimination and calibration. The calibration curves showed that all the ML models exhibited good 5-year survival calibration. The decision curves for CRC-specific survival at 5 years showed that all the ML models, especially RSF, had higher net benefits than default strategies of treating all or no patients at a range of clinically reasonable risk thresholds. The SHapley Additive exPlanations method revealed that R0 resection, tumor-node-metastasis staging, and the number of positive lymph nodes were important factors for 5-year CRC-specific survival. CONCLUSIONS: This study showed the potential of applying time-to-event ML predictive algorithms to help predict CRC-specific survival. The RSF, GBM, Cox-Time, and N-MTLR algorithms could provide nonparametric alternatives to the Cox Proportional Hazards model in estimating the survival probability of patients with CRC. The transparent time-to-event ML models help clinicians to more accurately predict the survival rate for these patients and improve patient outcomes by enabling personalized treatment plans that are informed by explainable ML models.

Prognostic Nutritional Index Combined with Triglyceride-Glucose Index to Contrast a Nomogram for Predicting Contrast-Induced Kidney Injury in Type 2 Diabetes Mellitus Patients with Acute Coronary Syndrome After Percutaneous Coronary Intervention.

Objective: Our objective was to develop and validate a nomogram model aiming at predicting the risk of contrast-induced acute kidney injury (CI-AKI) following percutaneous coronary intervention (PCI) in patients suffering from type 2 diabetes mellitus (T2DM) and also diagnosed with acute coronary syndrome (ACS). Methods: The study gathered data from 722 T2DM patients with ACS who received PCI treatment at the Affiliated Hospital of Xuzhou Medical University between February 2019 and December 2022, serving as the training set. Considering the validation set, the study included 217 patients who received PCI at the East Affiliated Hospital of Xuzhou Medical University. The patients were classified into CI-AKI and non-CI-AKI groups. The study employed univariate and multivariate logistic analysis for identifying independent risk factors for CI-AKI, followed by developing a predictive nomogram model for CI-AKI risk using R software. The predictive performance and clinical utility of the nomogram were assessed through internal and external validation, utilizing the areas under the receiver operating characteristic curve (AUC-ROC), the Hosmer-Lemeshow test and calibration correction curve, and decision curve analysis (DCA). Results: The nomogram comprised four variables: age, estimated glomerular filtration rate (eGFR), triglyceride-glucose (TyG) index, and prognostic nutritional index (PNI). The AUC-ROC were 0.785 (95% confidence interval (CI) 0.729-0.841) and 0.802 (95% CI 0.699-0.905) for the training and validation cohorts, respectively, indicating a high discriminative ability of the nomogram. The calibration assessment and decision curve analysis have substantiated the strong concordance and clinical usefulness of the aforementioned. Conclusion: The nomogram exhibits favorable discrimination and accuracy, enabling it to visually and individually identify pre-procedure high-risk patients, and possesses a predictive capacity regarding CI-AKI incidence after PCI in patients diagnosed with both T2DM and ACS.

Comorbidity Patterns in Patients Newly Diagnosed With Colorectal Cancer: Network-Based Study.

BACKGROUND: Patients with colorectal cancer (CRC) often present with multiple comorbidities, and many of these can affect treatment and survival. However, previous comorbidity studies primarily focused on diseases in commonly used comorbidity indices. The comorbid status of CRC patients with respect to the entire spectrum of chronic diseases has not yet been investigated. OBJECTIVE: This study aimed to systematically analyze all chronic diagnoses and diseases co-occurring, using a network-based approach and large-scale administrative health data, and provide a complete picture of the comorbidity pattern in patients newly diagnosed with CRC from southwest China. METHODS: In this retrospective observational study, the hospital discharge records of 678 hospitals from 2015 to 2020 in Sichuan Province, China were used to identify new CRC cases in 2020 and their history of diseases. We examined all chronic diagnoses using ICD-10 (International Classification of Diseases, 10th Revision) codes at 3 digits and focused on chronic diseases with >1% prevalence in at least one subgroup (1-sided test, P<.025), which resulted in a total of 66 chronic diseases. Phenotypic comorbidity networks were constructed across all CRC patients and different subgroups by sex, age (18-59, 60-69, 70-79, and ≥80 years), area (urban and rural), and cancer site (colon and rectum), with comorbidity as a node and linkages representing significant correlations between multiple comorbidities. RESULTS: A total of 29,610 new CRC cases occurred in Sichuan, China in 2020. The mean patient age at diagnosis was 65.6 (SD 12.9) years, and 75.5% (22,369/29,610) had at least one comorbidity. The most prevalent comorbidities were hypertension (8581/29,610, 29.0%; 95% CI 28.5%-29.5%), hyperplasia of the prostate (3816/17,426, 21.9%; 95% CI 21.3%-22.5%), and chronic obstructive pulmonary disease (COPD; 4199/29,610, 14.2%; 95% CI 13.8%-14.6%). The prevalence of single comorbidities was different in each subgroup in most cases. Comorbidities were closely associated, with disorders of lipoprotein metabolism and hyperplasia of the prostate mediating correlations between other comorbidities. Males and females shared 58.3% (141/242) of disease pairs, whereas male-female disparities occurred primarily in diseases coexisting with COPD, cerebrovascular diseases, atherosclerosis, heart failure, or renal failure among males and with osteoporosis or gonarthrosis among females. Urban patients generally had more comorbidities with higher prevalence and more complex disease coexistence relationships, whereas rural patients were more likely to have co-existing severe diseases, such as heart failure comorbid with the sequelae of cerebrovascular disease or COPD. CONCLUSIONS: Male-female and urban-rural disparities in the prevalence of single comorbidities and their complex coexistence relationships in new CRC cases were not due to simple coincidence. The results reflect clinical practice in CRC patients and emphasize the importance of measuring comorbidity patterns in terms of individual and coexisting diseases in order to better understand comorbidity patterns.

Risk prediction of heart failure in patients with ischemic heart disease using network analytics and stacking ensemble learning.

BACKGROUND: Heart failure (HF) is a major complication following ischemic heart disease (IHD) and it adversely affects the outcome. Early prediction of HF risk in patients with IHD is beneficial for timely intervention and for reducing disease burden. METHODS: Two cohorts, cases for patients first diagnosed with IHD and then with HF (N = 11,862) and control IHD patients without HF (N = 25,652), were established from the hospital discharge records in Sichuan, China during 2015-2019. Directed personal disease network (PDN) was constructed for each patient, and then these PDNs were merged to generate the baseline disease network (BDN) for the two cohorts, respectively, which identifies the health trajectories of patients and the complex progression patterns. The differences between the BDNs of the two cohort was represented as disease-specific network (DSN). Three novel network features were exacted from PDN and DSN to represent the similarity of disease patterns and specificity trends from IHD to HF. A stacking-based ensemble model DXLR was proposed to predict HF risk in IHD patients using the novel network features and basic demographic features (i.e., age and sex). The Shapley Addictive exPlanations method was applied to analyze the feature importance of the DXLR model. RESULTS: Compared with the six traditional machine learning models, our DXLR model exhibited the highest AUC (0.934 ± 0.004), accuracy (0.857 ± 0.007), precision (0.723 ± 0.014), recall (0.892 ± 0.012) and F1 score (0.798 ± 0.010). The feature importance showed that the novel network features ranked as the top three features, playing a notable role in predicting HF risk of IHD patient. The feature comparison experiment also indicated that our novel network features were superior to those proposed by the state-of-the-art study in improving the performance of the prediction model, with an increase in AUC by 19.9%, in accuracy by 18.7%, in precision by 30.7%, in recall by 37.4%, and in F1 score by 33.7%. CONCLUSIONS: Our proposed approach that combines network analytics and ensemble learning effectively predicts HF risk in patients with IHD. This highlights the potential value of network-based machine learning in disease risk prediction field using administrative data.

Explainable prediction of daily hospitalizations for cerebrovascular disease using stacked ensemble learning.

BACKGROUND: With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources. METHODS: In this study, a stacking ensemble model comprised of four base learners (ridge regression, random forest, gradient boosting decision tree, and artificial neural network) and a meta learner (elastic net) was proposed for predicting the daily number of hospital admissions (HAs) for CD using the historical HAs data, air quality data, and meteorological data in Chengdu, China from 2015 to 2018. To solve the label imbalance problem, a re-weighting method based on label distribution smoothing was integrated into the meta learner. We trained the model using the data from 2015 to 2017 and evaluated its predictive ability using the data in 2018 based on four metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). In addition, the SHapley Additive exPlanations (SHAP) framework was applied to provide explanation for the prediction of our stacking model. RESULTS: Our proposed model outperformed all the base learners and long short-term memory (LSTM) on two datasets. Particularly, compared with the optimal results obtained by individual models, the MAE, RMSE, and MAPE of the stacking model decreased by 13.9%, 12.7%, and 5.8%, respectively, and the R2 improved by 6.8% on CD dataset. The model explanation demonstrated that environmental features played a role in further improving the model performance and identified that high temperature and high concentrations of gaseous air pollutants might strongly associate with an increased risk of CD. CONCLUSIONS: Our stacking model considering environmental exposure is efficient in predicting daily HAs for CD and has practical value in early warning and healthcare resource allocation.

A Predictive Model for Contrast-Induced Acute Kidney Injury After Percutaneous Coronary Intervention in Elderly Patients with ST-Segment Elevation Myocardial Infarction.

Purpose: Development and validation of a nomogram model to predict the risk of Contrast-Induced Acute Kidney Injury (CI-AKI) after emergency percutaneous coronary intervention (PCI) in elderly patients with acute ST-segment elevation myocardial infarction (STEMI). Patients and Methods: Retrospective analysis of 542 elderly (≥65 years) STEMI patients undergoing emergency PCI in our hospital from January 2019 to June 2022, with all patients randomized to the training cohort (70%; n=380) and the validation cohort (30%; n=162). Univariate analysis, LASSO regression, and multivariate logistic regression analysis were used to determine independent risk factors for developing CI-AKI in elderly STEMI patients. R software is used to generate a nomogram model. The predictive power of the nomogram model was compared with the Mehran score 2. The area under the ROC curve (AUC), calibration curves, and decision curve analysis (DCA) was used to evaluate the prediction model's discrimination, calibration, and clinical validity, respectively. Results: The nomogram model consisted of five variables: diabetes mellitus (DM), left ventricular ejection fraction (LVEF), Systemic immune-inflammatory index (SII), N-terminal pro-brain natriuretic peptide (NT-proBNP), and highly sensitive C-reactive protein(hsCRP). In the training cohort, the AUC is 0.84 (95% CI: 0.790-0.890), and in the validation cohort, it is 0.844 (95% CI: 0.762-0.926). The nomogram model has better predictive ability than Mehran score 2. Based on the calibration curves, the predicted and observed values of the nomogram model were in good agreement between the training and validation cohort. Decision curve analysis (DCA) and clinical impact curve showed that the nomogram prediction model has good clinical utility. Conclusion: The established nomogram model can intuitively and specifically screen high-risk groups with a high degree of discrimination and accuracy and has a specific predictive value for CI-AKI occurrence in elderly STEMI patients after PCI.

Prognostic Value of Systemic Immune-Inflammation Index and NT-proBNP in Patients with Acute ST-Elevation Myocardial Infarction.

Objective: Our aim was to assess systemic immune-inflammation index (SII) and NT-proBNP value either in singly or in combination to predict acute ST-elevation myocardial infarction (STEMI) patient prognosis. Methods: Analyzed retrospectively the clinical features and laboratory data of STEMI confirmed patients in our hospital from January to December 2020. The levels of SII and NT-proBNP were detected. The Kaplan-Meier approach and Spearman's rank correlation coefficient were used to construct the overall major adverse cardiac event (MACE) curve. Multivariate Cox regression analysis was applied to detect MACE predictors. In addition, the Delong test and receiver operating characteristic (ROC) curve analyzed each factor performance on its own and composite multivariate index to predict MACEs. Results: The MACE group showed statistically significant differences in SII, NT- proBNP in comparison to the non-MACE group (P=0.003, P <0.001). Based on Kaplan-Meier analysis, SII and NT-proBNP showed positive correlation with MACE (log-rank P < 0.001). SII and NT-proBNP were independent predicting factors for long-term MACEs in multivariate Cox regression analysis (P <0.001, HR: 2.952, 95% CI 1.565-5.566; P <0.001, HR: 2.112, 95% CI 1.662-2.683). SII and NT-proBNP exhibited a positive correlation (R = 0.187, P < 0.001) in correlation analysis. According to the ROC statistical analysis, the combination exhibited 78.0% sensitivity and 88.0% specificity in the prediction of MACE. According to the results of the AUC and Delong test, the combined SII and NT-proBNP performed better as a prognostic index than each of the individual factor indexes separately (Z = 2.622, P = 0.009; Z = 3.173, P < 0.001). Conclusion: SII and NT-proBNP were independent indicators of clinical prognosis in acute STEMI patients, and they correlated positively. These factors could be combined to improve clinical prognosis.

Predictive value of systemic immune-inflammatory index combined with CHA2DS2-VASC score for contrast-induced acute kidney injury in patients with acute coronary syndrome undergoing percutaneous coronary intervention.

OBJECTIVE: To investigate the value of systemic immune-inflammation index (SII) combined with CHA2DS2-VASC score in predicting the risk of contrast-induced acute kidney injury (CI-AKI) in patients with acute coronary syndrome (ACS) after percutaneous coronary intervention (PCI) treatment. METHODS: 1531 consecutive patients with ACS and undergoing PCI were recruited from January 2019 to December 2021. All patients were divided into CI-AKI and non-CI-AKI groups according to the pre-procedure and post-procedure creatinine changes, and the baseline data were compared between the two groups. Binary logistic regression analysis was used to investigate the factors influencing CI-AKI in ACS patients after PCI. Receiver operating characteristic (ROC) curves were plotted to evaluate the predictive value of SII, CHA2DS2-VASC, and their combined levels on CI-AKI after PCI. RESULTS: Patients with high SII and high CHA2DS2-VASC score had a higher incidence of CI-AKI. For SII, the area under the ROC curve (AUC) for predicting CI-AKI was 0.686. The optimal cut-off value was 736.08 with a sensitivity of 66.8% and a specificity of 66.3% [95% confidence interval (CI) 0.662-0.709; P < 0.001]. For CHA2DS2-VASC score, the AUC was 0.795, the optimal cut-off value was 2.50 with a sensitivity of 80.3% and a specificity of 62.7% (95% CI 0.774-0.815; P < 0.001). When combining SII and CHA2DS2-VASC score, the AUC was 0.830, the optimal cut-off value was 0.148 with a diagnostic sensitivity of 76.1% and a specificity of 75.2% (95% CI 0.810-0.849; P < 0.001). The results showed that SII combined with CHA2DS2-VASC score resulted in improved predictive accuracy of CI-AKI. Multifactorial logistic regression analysis showed that albumin level (OR = 0.967, 95% CI 0.936-1.000; P = 0.047), lnSII level (OR = 1.596, 95% CI 1.010-1.905; P < 0.001), and CHA2DS2-VASC score level (OR = 1.425, 95% CI 1.318-1.541; P < 0.001) were independent risk factors for CI-AKI in patients with ACS treated with PCI. CONCLUSION: High SII and high CHA2DS2-VASC score are risk factors for the development of CI-AKI, and the combination of the two improves the accuracy of predicting the occurrence of CI-AKI in patients with ACS undergoing PCI.

Effect and mechanism of simultaneous cadmium-tetracycline removal by a self-assembled microbial-photocatalytic coupling system.

Electrochemical bacteria Shewanella oneidensis MR-4 (MR-4) was used to biologically generate cadmium sulfide (bio-CdS) nanocrystals and construct a self-assembled intimately coupled photocatalysis-biodegradation system (SA-ICPB) to remove cadmium (Cd) and tetracycline hydrochloride (TCH) from wastewater. The characterization using EDS, TEM, XRD, XPS, and UV-vis confirmed the successful CdS bio-synthesis and its visible-light response capacity (520 nm). 98.4% of Cd2+ (2 mM) was removed during bio-CdS generation within 30 min. The electrochemical analysis confirmed the photoelectric response capability of the bio-CdS as well as its photocatalytic efficiency. Under visible light, SA-ICPB entirely eliminated TCH (30 mg/L). In 2 h, 87.2% and 43.0% of TCH were removed separately with and without oxygen. 55.7% more chemical oxygen demand (COD) was removed with oxygen participation, indicating the degradation intermediates elimination by SA-ICPB required oxygen participation. Biodegradation dominated the process under aerobic circumstances. Electron paramagnetic resonance analysis indicated that h+ and ·O2- played a decisive role in photocatalytic degradation. Mass spectrometry analysis proved that TCH was dehydrated, dealkylated, and ring-opened before mineralizing. In conclusion, MR-4 can spontaneously generate SA-ICPB and rapidly-deeply eliminate antibiotics by coupling photocatalytic and microbial degradation. Such an approach was efficient for the deep degradation of persistent organic pollutants with antimicrobial properties.

Effect of bioaugmentation on tetracyclines influenced chicken manure composting and antibiotics resistance.

Antibiotic residue in husbandry waste has become a serious concern. In this study, contaminated chicken manure composting was conducted to reveal the bioaugmentation effect on tetracyclines residue and antibiotics resistance genes (ARGs). The bioaugmented composting removed most of the antibiotics in 7 days. Under bioaugmentation, 96.88 % of tetracycline and 92.31 % of oxytetracycline were removed, 6.32 % and 20.93 % higher than the control (P < 0.05). The high-temperature period was the most effective phase for eliminating antibiotics. The treatment showed a long high-temperature period (7 days), while no high-temperature period was in control. After composting, the treatment showed 13.87 % higher TN (26.51 g/kg) and 13.42 % higher NO3--N (2.45 g/kg) than control (23.28 and 2.16 g/kg, respectively) but 12.72 % lower C/N, indicating fast decomposition and less nutrient loss. Exogenous microorganisms from bioaugmentation significantly reshaped the microbial community structure and facilitated the enrichment of genera such as Truepera and Fermentimonas, whose abundance increased by 71.10 % and 75.37 % than the control, respectively. Remarkably, ARGs, including tetC, tetG, and tetW, were enhanced by 198.77 %, 846.77 %, and 62.63 % compared with the control, while the integron gene (intl1) was elevated by 700.26 %, indicating horizontal gene transfer of ARGs. Eventually, bioaugmentation was efficient in regulating microbial metabolism, relieving antibiotic stress, and eliminating antibiotics in composting. However, the ability to remove ARGs should be further investigated. Such an approach should be further considered for treating pollutants-influenced organic waste to eliminate environmental concerns.

Analysis of the efficacy of endoscopic transnasal surgery for sinonasal and skull base adenoid cystic carcinoma / 中华耳鼻咽喉头颈外科杂志

Objective: To evaluate the efficacy of endoscopic transnasal surgery for sinonasal and skull base adenoid cystic carcinoma (ACC), and to analyze the prognostic factors. Methods: Data of 82 patients (43 females and 39 males, at a median age of 49 years old) with sinonasal and skull base ACC who were admitted to XuanWu Hospital, Capital Medical University between June 2007 and June 2021 were analyzed retrospectively. The patients were staged according to American Joint Committee on Cancer (AJCC) 8th edition. The disease overall survival(OS) and disease-free survival(DFS) rates were calculated by Kaplan-Meier analysis. Cox regression model was used for multivariate prognostic analysis. Results: There were 4 patients with stage Ⅱ, 14 patients with stage Ⅲ, and 64 patients with stage Ⅳ. The treatment strategies included purely endoscopic surgery (n=42), endoscopic surgery plus radiotherapy (n=32) and endoscopic surgery plus radiochemotherapy (n=8). Followed up for 8 to 177 months, the 5-year OS and DFS rates was 63.0% and 51.6%, respectively. The 10-year OS and DFS rates was 51.2% and 31.8%, respectively. The multivariate Cox regression analysis showed that late T stage and internal carotid artery (ICA) involvement were the independent prognostic factors for survival in sinonasal and skull base ACC (all P<0.05). The OS of patients who received surgery or surgery plus radiotherapy was significantly higher than that of patients who received surgery plus radiochemotherapy (all P<0.05). Conclusions: Endoscopic transonasal surgery or combing with radiotherapy is an effective procedure for the treatment of sinonasal and skull base ACC. Late T stage and ICA involvement indicate poor prognosis.

Transcutaneous auricular vagus nerve stimulation in poststroke cognitive impairment: protocol for a randomised controlled trial.

BACKGROUND: As one of the most common stroke sequelae, poststroke cognitive impairment significantly impacts 17.6%-83% of survivors, affecting their rehabilitation, daily living and quality of life. Improving cognitive abilities among patients in stroke recovery is therefore critical and urgent. Transcutaneous auricular vagus nerve stimulation (TAVNS) is a non-invasive, safe, cost-effective treatment with great potential for improving the cognitive function of poststroke patients. This clinical research will evaluate the effectiveness, and help elucidate the possible underlying mechanisms, of TAVNS for improving poststroke cognitive function. METHODS AND ANALYSIS: A single-centre, parallel-group, allocation concealment, assessor-blinded randomised controlled clinical trial. We will allocate 88 recruited participants to the TAVNS or sham group for an intervention that will run for 8 weeks, 5 days per week with twice daily sessions lasting 30 min each. Blood tests will be performed and questionnaires issued at baseline and 8-week and 12 week follow-ups. Primary outcomes will be changes in cognitive function scores. Secondary outcomes will be changes in activities of daily living, quality of life and serum oxidative stress indicators. ETHICS AND DISSEMINATION: The Ethics Committee of the First Affiliated Hospital of Hunan University of Chinese Medicine has approved the protocol (No. HN-LL-YJSLW-2022200). Findings will be published in peer-reviewed academic journals and presented at scientific conferences. TRIAL REGISTRATION NUMBER: ChiCTR2200057808.

Preprocedural SII Combined with High-Sensitivity C-Reactive Protein Predicts the Risk of Contrast-Induced Acute Kidney Injury in STEMI Patients Undergoing Percutaneous Coronary Intervention.

Objective: To investigate the relationship between the incidence of contrast-induced acute kidney injury (CI-AKI) and the levels of the systemic immune-inflammatory index (SII, platelet × neutrophil/lymphocyte ratio) and high-sensitivity C-reactive protein (hsCRP) in patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI), to analyze further the predictive value of the combination of SII and hsCRP for CI-AKI. Methods: Retrospectively analyze the clinical data of STEMI patients who underwent PCI in our cardiology department from November 2019 to March 2021. Restricted cubic splines were used to determine the correlation between SII and hsCRP and the risk of CI-AKI. Patients were divided into the CI-AKI group (n=71) and the non-CI-AKI group (n=344) according to postoperative creatinine changes. Logistic regression was used to analyze the factors influencing CI-AKI. ROC curves were used to evaluate the predictive value of SII, hsCRP, and their combined levels on CI-AKI. Results: Restricted cubic spline analysis showed that when SII>653.73×109/L and hsCRP>5.52mg/dl, there was a positive correlation with the incidence of CI-AKI. And the incidence of CI-AKI rose with the inflammation status. The receiver operating characteristic curve of SII combined with hsCRP was 0.831, which was higher than SII or hsCRP alone. The logistic regression analysis showed that high-risk factors of CI-AKI were diabetes mellitus, platelet count, and highly elevated SII and hsCRP. Conclusion: Within a certain range, elevated inflammatory biomarkers SII and hsCRP were risk factors for CI-AKI after PCI in patients with STEMI. This study suggests that the combination of SII and hsCRP predicts the risk of CI-AKI more accurately than either biomarker alone.

Correction: Age- and Sex-Specific Differences in Multimorbidity Patterns and Temporal Trends on Assessing Hospital Discharge Records in Southwest China: Network-Based Study.

[This corrects the article DOI: 10.2196/27146.].

Bellidifolin Inhibits SRY-Related High Mobility Group-Box Gene 9 to Block TGF-ß Signalling Activation to Ameliorate Myocardial Fibrosis.

Myocardial fibrosis is the main morphological change of ventricular remodelling caused by cardiovascular diseases, mainly manifested due to the excessive production of collagen proteins. SRY-related high mobility group-box gene 9 (SOX9) is a new target regulating myocardial fibrosis. Bellidifolin (BEL), the active component of G. acuta, can prevent heart damage. However, it is unclear whether BEL can regulate SOX9 to alleviate myocardial fibrosis. The mice were subjected to isoproterenol (ISO) to establish myocardial fibrosis, and human myocardial fibroblasts (HCFs) were activated by TGF-ß1 in the present study. The pathological changes of cardiac tissue were observed by HE staining. Masson staining was applied to reveal the collagen deposition in the heart. The measurement for expression of fibrosis-related proteins, SOX9, and TGF-ß1 signalling molecules adopted Western blot and immunohistochemistry. The effects of BEL on HCFs, activity were detected by CCK-8. The result showed that BEL did not affect cell viability. And, the data indicated that BEL inhibited the elevations in α-SMA, Collagen I, and Collagen III by decreasing SOX9 expression. Additionally, SOX9 suppression by siRNA downregulated the TGF-ß1 expression and prevented Smad3 phosphorylation, as supported by reducing the expression of α-SMA, Collagen I, and Collagen III. In vivo study verified that BEL ameliorated myocardial fibrosis by inhibiting SOX9. Therefore, BEL inhibited SOX9 to block TGF-ß1 signalling activation to ameliorate myocardial fibrosis.

Network analytics and machine learning for predicting length of stay in elderly patients with chronic diseases at point of admission.

BACKGROUND: An aging population with a burden of chronic diseases puts increasing pressure on health care systems. Early prediction of the hospital length of stay (LOS) can be useful in optimizing the allocation of medical resources, and improving healthcare quality. However, the data available at the point of admission (PoA) are limited, making it difficult to forecast the LOS accurately. METHODS: In this study, we proposed a novel approach combining network analytics and machine learning to predict the LOS in elderly patients with chronic diseases at the PoA. Two networks, including multimorbidity network (MN) and patient similarity network (PSN), were constructed and novel network features were created. Five machine learning models (eXtreme Gradient Boosting, Gradient Boosting Decision Tree, Random Forest, Linear Support Vector Machine, and Deep Neural Network) with different input feature sets were developed to compare their performance. RESULTS: The experimental results indicated that the network features can bring significant improvements to the performances of the prediction models, suggesting that the MN and PSN are useful for LOS predictions. CONCLUSION: Our predictive framework which integrates network science with data mining can forecast the LOS effectively at the PoA and provide decision support for hospital managers, which highlights the potential value of network-based machine learning in healthcare field.

Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients' survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.