Metabolomics covers a wide range of applications in life sciences, biomedicine, and phytology. Data acquisition (to achieve high coverage and efficiency) and analysis (to pursue good classification) are two key segments involved in metabolomics workflows. Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups. However, insufficient feature extraction, inappropriate feature selection, overfitting, or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused. Using two ginseng varieties, namely Panax japonicus (PJ) and Panax japonicus var. major (PJvm), containing the similar ginsenosides, we integrated pseudo-targeted metabolomics and deep neural network (DNN) modeling to achieve accurate species differentiation. A pseudo-targeted metabolomics approach was optimized through data acquisition mode, ion pairs generation, comparison between multiple reaction monitoring (MRM) and scheduled MRM (sMRM), and chromatographic elution gradient. In total, 1980 ion pairs were monitored within 23 min, allowing for the most comprehensive ginseng metabolome analysis. The established DNN model demonstrated excellent classification performance (in terms of accuracy, precision, recall, F1 score, area under the curve, and receiver operating characteristic (ROC)) using the entire metabolome data and feature-selection dataset, exhibiting superior advantages over random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and multilayer perceptron (MLP). Moreover, DNNs were advantageous for automated feature learning, nonlinear modeling, adaptability, and generalization. This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples. This established approach holds promise for plant metabolomics and is not limited to ginseng.

Acute respiratory distress syndrome (ARDS) is a common respiratory emergency, but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures. Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS, but the application of hydrogen has flammable and explosive safety concerns. Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance, thus improving ARDS in patients and animal models. Coral calcium hydrogenation (CCH) is a new solid molecular hydrogen carrier prepared from coral calcium (CC). Whether and how CCH affects acute lung injury in ARDS remains unstudied. In this study, we observed the therapeutic effect of CCH on lipopolysaccharide (LPS) induced acute lung injury in ARDS mice. The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable, demonstrating a significant improvement compared to the untreated ARDS model group. CCH treatment significantly reduced pulmonary hemorrhage and edema, and improved pulmonary function and local microcirculation in ARDS mice. CCH promoted mitochondrial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2 (Trx2), improved lung mitochondrial dysfunction induced by LPS, and reduced oxidative stress damage. The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Acute respiratory distress syndrome(ARDS)is a common respiratory emergency,but current clinical treatment remains at the level of symptomatic support and there is a lack of effective targeted treatment measures.Our previous study confirmed that inhalation of hydrogen gas can reduce the acute lung injury of ARDS,but the application of hydrogen has flammable and explosive safety concerns.Drinking hydrogen-rich liquid or inhaling hydrogen gas has been shown to play an important role in scavenging reactive oxygen species and maintaining mitochondrial quality control balance,thus improving ARDS in patients and animal models.Coral calcium hydrogenation(CCH)is a new solid molecular hydrogen carrier prepared from coral calcium(CC).Whether and how CCH affects acute lung injury in ARDS re-mains unstudied.In this study,we observed the therapeutic effect of CCH on lipopolysaccharide(LPS)induced acute lung injury in ARDS mice.The survival rate of mice treated with CCH and hydrogen inhalation was found to be comparable,demonstrating a significant improvement compared to the untreated ARDS model group.CCH treatment significantly reduced pulmonary hemorrhage and edema,and improved pulmonary function and local microcirculation in ARDS mice.CCH promoted mitochon-drial peripheral division in the early course of ARDS by activating mitochondrial thioredoxin 2(Trx2),improved lung mitochondrial dysfunction induced by LPS,and reduced oxidative stress damage.The results indicate that CCH is a highly efficient hydrogen-rich agent that can attenuate acute lung injury of ARDS by improving the mitochondrial function through Trx2 activation.

Metabolomics covers a wide range of applications in life sciences,biomedicine,and phytology.Data acquisition(to achieve high coverage and efficiency)and analysis(to pursue good classification)are two key segments involved in metabolomics workflows.Various chemometric approaches utilizing either pattern recognition or machine learning have been employed to separate different groups.However,insufficient feature extraction,inappropriate feature selection,overfitting,or underfitting lead to an insufficient capacity to discriminate plants that are often easily confused.Using two ginseng varieties,namely Panax japonicus(PJ)and Panax japonicus var.major(PJvm),containing the similar ginsenosides,we integrated pseudo-targeted metabolomics and deep neural network(DNN)modeling to achieve accurate species differentiation.A pseudo-targeted metabolomics approach was optimized through data acquisition mode,ion pairs generation,comparison between multiple reaction monitoring(MRM)and scheduled MRM(sMRM),and chromatographic elution gradient.In total,1980 ion pairs were monitored within 23 min,allowing for the most comprehensive ginseng metabolome analysis.The established DNN model demonstrated excellent classification performance(in terms of accuracy,precision,recall,F1 score,area under the curve,and receiver operating characteristic(ROC))using the entire metabolome data and feature-selection dataset,exhibiting superior advantages over random forest(RF),support vector ma-chine(SVM),extreme gradient boosting(XGBoost),and multilayer perceptron(MLP).Moreover,DNNs were advantageous for automated feature learning,nonlinear modeling,adaptability,and generalization.This study confirmed practicality of the established strategy for efficient metabolomics data analysis and reliable classification performance even when using small-volume samples.This established approach holds promise for plant metabolomics and is not limited to ginseng.

Objective:To understand nurses' experiences providing discharge preparation services for elderly patients with multiple chronic diseases and offer references for developing intervention strategies for discharge preparation services for these patients.Methods:A purposive sampling method was used to select 24 nurses from the cardiovascular departments, geriatrics wards, and other internal medicine departments of three Grade A tertiary hospitals, four Grade B hospitals, and one community health service center in Guilin City between March and April 2024. An interview outline was developed based on the social-ecological system theory, and semi-structured interviews were conducted to collect data. A directed content analysis method was used to analyze the data.Results:A total of three themes were identified: micro-level (nurses have a positive attitude but face shortcomings in age-appropriate discharge preparation services), meso-level (team collaboration mechanisms are weak), and macro-level (there is a desire to link social resources) .Conclusions:Nurses' discharge preparation services for elderly patients with multiple chronic diseases are inadequate. It is recommended to enhance nurses' discharge preparation skills through core competency-based training, improve the development of innovative healthcare platforms, and promote establishing of a multi-dimensional social care support system to improve the quality of discharge preparation services for elderly patients with multiple chronic diseases.

Objective:To understand nurses' experiences providing discharge preparation services for elderly patients with multiple chronic diseases and offer references for developing intervention strategies for discharge preparation services for these patients.Methods:A purposive sampling method was used to select 24 nurses from the cardiovascular departments, geriatrics wards, and other internal medicine departments of three Grade A tertiary hospitals, four Grade B hospitals, and one community health service center in Guilin City between March and April 2024. An interview outline was developed based on the social-ecological system theory, and semi-structured interviews were conducted to collect data. A directed content analysis method was used to analyze the data.Results:A total of three themes were identified: micro-level (nurses have a positive attitude but face shortcomings in age-appropriate discharge preparation services), meso-level (team collaboration mechanisms are weak), and macro-level (there is a desire to link social resources) .Conclusions:Nurses' discharge preparation services for elderly patients with multiple chronic diseases are inadequate. It is recommended to enhance nurses' discharge preparation skills through core competency-based training, improve the development of innovative healthcare platforms, and promote establishing of a multi-dimensional social care support system to improve the quality of discharge preparation services for elderly patients with multiple chronic diseases.

Objective:To evaluate the diagnostic performance of radiomics model based on contrast-enhanced ultrasound(CEUS) in predicting pathological complete response(pCR) after neoadjuvant chemoradiotherapy(nCRT) in patients with locally advanced rectal cancer(LARC).Methods:One hundred and six patients with LARC who underwent total mesorectal excision after nCRT between April 2018 and April 2023 in the First Affiliated Hospital of Guangxi Medical University were retrospectively included, the patients were randomly divided into a training set of 63(14 pCR patients) and a validation set of 43(12 pCR patients) in a 6∶4 ratios. Radiomics features were extracted from the tumors′ region of interest of CEUS images based on PyRadiomics. Intra-class correlation coefficient(ICC), Mann-Whitney U test, and least absolute shrinkage and selection operator(LASSO) algorithms were used to reduce features dimension. Finally, 7 radiomics features relevanted to pCR were selected to construct an ultrasomics model using elastic network regression, based on the R language. A combined model was constructed by jointing clinical feature. The performance of the models was assessed with the area under the ROC curve(AUC). Results:The AUC of the ultrasomics model and the combined model was 0.695(95% CI=0.532-0.859) and 0.726(95% CI=0.584-0.868) respectively in the training set. The AUC of the ultrasomics model and the combined model was 0.763(95% CI=0.625-0.902) and 0.790(95% CI=0.653-0.928) respectively in the validation set. Both univariate and multivariate Logistic regression analyses showed that CA199( P<0.05) and ultrasomics score( P<0.001) could be an independent predictor of pCR after nCRT in patients with LARC. Conclusions:The CEUS-based radiomics scores has certain predictive value for whether LARC patients achieve pCR after nCRT, and may provide a non-invasive imaging biomarker for predicting LARC patients achieve pCR after nCRT.

Objective To observe the correlations of chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with blood eosinophil(EOS)level.Methods Chest CT data of 162 AECOPD patients with elevated eosinophils were retrospectively analyzed.The patients were divided into low EOS group(n=105)and high EOS group(n=57)according to the absolute counting of blood EOS.The quantitative CT parameters,including the number of whole lung bronchi and the volume of blood vessels,low-attenuation area percentage(LAA％)of whole lung,of left/right lung and each lobe of lung,as well as the luminal diameter(LD),wall thickness(WT),wall area(WA)and WA percentage of total bronchial cross-section(WA％)of grade 3 to 8 bronchi were compared between groups.Spearman correlations were performed to analyze the correlations of quantitative CT parameters with blood EOS level.Results LAA％of the whole lung,of the left/right lung and each lobe of lung,as well as of the upper lobe of right lung LDgrade4,middle lobe of right lung WTgrade5,upper lobe of right lung WAgrade4,middle lobe of right lung WAgrade5 and lower lobe of left lung WAgrade3 in low EOS group were all higher than those in high EOS group(all P＜0.05).Except for the upper lobe of right lung LDgrade4,the above quantitative CT indexes being significant different between groups were all weakly and negatively correlated with blood EOS level(r=-0.335 to-0.164,all P＜0.05).Conclusion Chest CT quantitative parameters of AECOPD patients were correlated with blood EOS level,among which LAA％,a part of WT and WA were all weakly negatively correlated with blood EOS level.

Objective:To establish a nomogram prediction model for recurrence within 2 years after radical resection of hepatocellular carcinoma (HCC) based on clinical and ultrasonographic characteristics.Methods:Clinical data from 405 HCC patients (including 327 males and 78 females), aged 60 (53, 66) years old, who underwent radical hepatectomy in the Zhongshan Hospital, Fudan University, from January to December 2021, were retrospectively collected. The patients were divided into two groups: the training group ( n=283) and the validation group ( n=122). Based on recurrence within 2 years after surgery, the 283 patients in the training group were further categorized into the recurrence group ( n=73) and the non-recurrence group ( n=210). Among the 122 patients in the validation group, 33 had recurrence within 2 years, while 89 did not. Data on age, microvascular invasion, alpha-fetoprotein (AFP), AFP lentil lectin-reactive fraction (AFP-L3), protein induced by vitamin K absence or antagonist-II (PIVKA-II), tumor number, and enhancement homogeneity were collected. Logistic regression analysis was performed on the training group to identify risk factors associated with postoperative recurrence, and a nomogram model for predicting HCC recurrence was constructed based on these factors. Calibration curves were used to compare the consistency between predicted and actual outcomes in both the training and validation groups. Results:Multivariate logistic regression analysis revealed that younger age ( OR=0.976, 95% CI: 0.953-1.000, P=0.004), higher AFP-L3 ( OR=1.066, 95% CI: 1.014-1.120, P=0.012), higher PIVKA-II ( OR=1.000, 95% CI: 1.000-1.001, P=0.042), multiple tumors ( OR=0.399, 95% CI: 0.225-0.706, P=0.038), and heterogeneous enhancement ( OR=0.472, 95% CI: 0.243-0.916, P=0.045) were significant risk factors for recurrence after partial hepatectomy in HCC patients. The nomogram constructed based on these variables had a C-index of 0.87 (95% CI: 0.81-0.93) in the training group and 0.83 (95% CI: 0.77-0.89) in the validation group. The calibration curves for predicting recurrence within 2 years after partial hepatectomy in HCC patients showed a high degree of fit in both the training and validation groups, indicating a good agreement between predicted and actual outcomes. Conclusion:The nomogram model constructed based on preoperative clinical and ultrasonographic characteristics can effectively predict the risk of recurrence within 2 years after radical resection of HCC.

Objective To explore the efficacy of the geriatric interdisciplinary team(GIT)service for elderly patients with multimorbidity and malnutrition.Methods We selected 109 elderly patients with diabetes mellitus(DM),chronic obstructive pulmonary disease(COPD)or chronic heart failure(CHF)and multi morbidity to the Department of Geriatrics of The 2nd People's Hospital of Kunming City from January 2022 to January 2023.The patients were randomly divided into the control group of 53 cases and the observation group of 56 cases.The control group received routine management of multimorbidity and malnutrition,while the observation group used the GIT service model to manage multimorbidity and malnutrition.The disease,nutritional status,and quality of life outcomes of the two groups of elderly patients were evaluated before and 3 months after the intervention.(1)HbA1c was detected in patients with DM in both groups,and the aggravation of patients with COPD or CHF was observed and recorded.(2)Nutrition-related indicators(Tp,ALB and Hb)and Barthel index were evaluated in both groups.Results(1)The HbA1c of patients with DM in the two groups decreased after intervention(P＜0.001),and the decrease in the observation group was more significant than that in the control group(P＜0.001).(2)The number of AECOPEs in patients with COPD in the two groups decreased,and the observation group was less than the control group(P＜0.05).(3)The number of CHF acute exacerbations in patients with CHF in the two groups decreased and the observation group was fewer than the control group(P＜0.05).(4)The TP,ALB,Hb and Barthel index scores of the two groups increased compared with those before intervention,and the observation group increased more significantly than the control group(P＜0.05).Conclusion The geriatric interdisciplinary team service for the elderly has a good effect in the intervention of elderly patients with multimorbidity and malnutrition.