ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

OBJECTIVE To observe and compare the synergistic rates of combined use of polymyxin B with 12 types of traditional antibiotics against carbapenem-resistant Klebsiella pneumoniae(CRKP)in vitro antimicrobial ac-tion.METHODS Totally 30 strains of CRKP were randomly drawn from 312 strains of CRKP that were isolated from the clinical departments of Shiyan People's Hospital between 2020 to 2023.The carbapenemases were detec-ted by Carba NP test.The six genotypes KPC,OXA-48,OXA-23,NDM,VIM and IMP were detected by means of fluorescent quantitative polymerase chain reaction(PCR),the drug resistance of the strains was detected by mi-cro broth dilution method,and the synergistic effect of polymyxin B combined with 12 types of traditional antibi-otics on in vitro antimicrobial action was detected by using micro chessboard dilution method.RESULTS All of the isolated CRKP strains produced carbapenemases,with the KPC genotype dominant.The drug resistance rates to cephalosporins,carbapenems and quinolones reached up to 100.00％,and no polymyxin B-resistant strains were detected.The synergistic rates of minocycline and rifampicin combined with polymyxin B to the in vitro antimicro-bial action were the highest,which were 73.33％and 50.00％,respectively;the synergistic rates of levofloxacin and ciprofloxacin combined with polymyxin B were the lowest,and both were 0.The synergistic rates of meropen-em and imipenem combined with polymyxin B to the in in vitro antimicrobial action were 36.67％and 30.00％,re-spectively.The synergistic rates of ceftazidime,cefepime,piperacillin-tazobactam and cefoperazone-sulbactam combined with polymyxin B to the in vitro antimicrobial action were less than 30.00％.CONCLUSIONS Polymyxin B combined with minocycline and rifampicin should be taken as the first choice for treatment of the CRKP-induced infection.The synergistic rate of the carbapenems combined with polymyxin B is higher than that of the cephalo-sporins combined with polymyxin B to the antimicrobial action.Levofloxacin and ciprofloxacin combined with pol-ymyxin B do not have synergistic effect on the in vitro antimicrobial action.

Gastric cancer with peritoneal metastasis (GCPM) is a common and lethal manifestation of advanced gastric cancer, with a median survival of only 5-11 months. This consensus was developed by 30 experts from Asia (China, Japan, Korea, and Singapore) using the Delphi method and the GRADE evidence grading system. A total of 29 statements were formulated, covering the diagnosis and assessment of GCPM, indications for laparoscopic exploration and NIPS (normothermic intraperitoneal and systemic treatment), treatment regimens, prevention and management of complications, criteria for conversion surgery, and postoperative intraperitoneal therapy. The consensus aims to standardize clinical practice and improve the prognosis of patients with GCPM.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

OBJECTIVE To observe and compare the synergistic rates of combined use of polymyxin B with 12 types of traditional antibiotics against carbapenem-resistant Klebsiella pneumoniae(CRKP)in vitro antimicrobial ac-tion.METHODS Totally 30 strains of CRKP were randomly drawn from 312 strains of CRKP that were isolated from the clinical departments of Shiyan People's Hospital between 2020 to 2023.The carbapenemases were detec-ted by Carba NP test.The six genotypes KPC,OXA-48,OXA-23,NDM,VIM and IMP were detected by means of fluorescent quantitative polymerase chain reaction(PCR),the drug resistance of the strains was detected by mi-cro broth dilution method,and the synergistic effect of polymyxin B combined with 12 types of traditional antibi-otics on in vitro antimicrobial action was detected by using micro chessboard dilution method.RESULTS All of the isolated CRKP strains produced carbapenemases,with the KPC genotype dominant.The drug resistance rates to cephalosporins,carbapenems and quinolones reached up to 100.00％,and no polymyxin B-resistant strains were detected.The synergistic rates of minocycline and rifampicin combined with polymyxin B to the in vitro antimicro-bial action were the highest,which were 73.33％and 50.00％,respectively;the synergistic rates of levofloxacin and ciprofloxacin combined with polymyxin B were the lowest,and both were 0.The synergistic rates of meropen-em and imipenem combined with polymyxin B to the in in vitro antimicrobial action were 36.67％and 30.00％,re-spectively.The synergistic rates of ceftazidime,cefepime,piperacillin-tazobactam and cefoperazone-sulbactam combined with polymyxin B to the in vitro antimicrobial action were less than 30.00％.CONCLUSIONS Polymyxin B combined with minocycline and rifampicin should be taken as the first choice for treatment of the CRKP-induced infection.The synergistic rate of the carbapenems combined with polymyxin B is higher than that of the cephalo-sporins combined with polymyxin B to the antimicrobial action.Levofloxacin and ciprofloxacin combined with pol-ymyxin B do not have synergistic effect on the in vitro antimicrobial action.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Background: Melasma is a common and chronic pigmentary disorder with complex pathogenesis, and the relationship between melasma and metabolic syndrome remains elusive. Thus, metabolomics might contribute to the early detection of potential metabolic abnormalities in individuals with melasma. Objective: The present study aims to analyze changes in plasma metabolites of female melasma patients and identify disease markers as well as explore potential therapeutic targets. Methods: Plasma samples from 20 female patients with melasma and 21 healthy female controls that were comparable in terms of age and body mass index were collected for untargeted metabolomics investigations. Ultra-high performance liquid chromatography-mass spectrometry was used to analyze metabolites in the plasma. Metabolic pathway analyses were employed to identify significantly differentially expressed metabolites in melasma patients. Receiver operating characteristic curves were constructed, and correlation analyses were performed using the modified Melasma Area and Severity Index and oxidative stress levels. Results: In contrast to healthy subjects, melasma patients showed significant alterations in 125 plasma metabolites, including amino acids, lipids, and carbohydrate-related metabolites.KEGG pathway analysis suggested that primary pathways associated with the development of melasma include tryptophan metabolism, as well as the biosynthesis of phenylalanine, tyrosine, and tryptophan. Importantly, based on receiver operating characteristic curves and correlation analyses, several metabolites were identified as robust biomarkers for melasma. Conclusion Collectively, this study identified significant changes in plasma metabolites in melasma patients, providing new insights into the pathogenesis of melasma and opening novel therapeutic avenues.

BACKGROUND: Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts. METHODS: We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models. RESULTS: The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749). CONCLUSIONS: The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population. TRIAL REGISTRATION Chictr.org, ChiCTR 1800018895.
Humans ; Aged ; Sarcopenia/diagnosis* ; Deep Learning ; Aging ; Algorithms ; Biomarkers

Objective:To investigate the effect of miRNA (miR) -193b-5p on melanogenesis and its possible mechanisms.Methods:Human primary melanocytes were isolated from discarded normal foreskin tissues of healthy males after circumcision, and cultured in vitro. miR-NC mimics (miR-NC mimic group) and miR-193b-5p mimics (miR-193b-5p mimic group) were transfected into human primary melanocytes and human MNT1 melanoma cells, separately. After transfection, real-time quantitative PCR (RT-qPCR) was performed to determine the overexpression efficiency of miR-193b-5p at 48 hours, Western blot analysis to determine the expression of melanogenesis-related proteins tyrosinase (TYR) and microphthalmia-associated transcription factor (MITF) in human primary melanocytes and human MNT1 melanoma cells at 72 hours, and the melanin content in the above cells was determined by a sodium hydroxide solubilization method at 1 week. The target gene of miR-193b-5p was predicted by using Targetscan algorithms and verified by dual-luciferase reporter assay, and RT-qPCR and Western blot analysis were performed to analyze changes in mRNA and protein expression of the target gene respectively after the overexpression of miR-193b-5p. Two-independent-samples t test was used for comparisons between two groups. Results:In human primary melanocytes and human MNT1 melanoma cells, the miR-193b-5p expression levels were significantly higher in the miR-193b-5p mimic groups than in the miR-NC mimic groups ( t = 65.57, 22.49, respectively, both P < 0.001) , and the melanin content was significantly lower in the miR-193b-5p mimic groups (0.091 ± 0.007, 0.130 ± 0.004, respectively) than in the miR-NC mimic groups (0.117 ± 0.002, 0.188 ± 0.032, t = 5.98, 3.24, P < 0.01, < 0.05, respectively) . Western blot analysis showed that the expression of melanogenesis-related proteins TYR and MITF in both human primary melanocytes and human MNT1 melanoma cells was significantly lower in the miR-193b-5p mimic groups than in the miR-NC mimic groups (all P < 0.01) . TargetScan analysis and dual-luciferase reporter assay revealed a binding site for miR-193b-5p in the 3′ untranslated region of the transcriptional regulator CITED2. After up-regulation of miR-193b-5p expression in human primary melanocytes and human MNT1 melanoma cells, the CITED2 mRNA and protein expression levels significantly decreased compared with the miR-NC mimic groups (all P < 0.05) . Conclusion:miR-193b-5p overexpression can down-regulate the expression of melanogenesis-related proteins TYR and MITF, and then inhibit melanogenesis, which may be related to the targeted inhibition of CITED2 expression.

Tumor-targeted immunotherapy is a remarkable breakthrough, offering the inimitable advantage of specific tumoricidal effects with reduced immune-associated cytotoxicity. However, existing platforms suffer from low efficacy, inability to induce strong immunogenic cell death (ICD), and restrained capacity of transforming immune-deserted tumors into immune-cultivated ones. Here, an innovative platform, perfluorooctyl bromide (PFOB) nanoemulsions holding MnO₂ nanoparticles (MBP), was developed to orchestrate cancer immunotherapy, serving as a theranostic nanoagent for MRI/CT dual-modality imaging and advanced ICD. By simultaneously depleting the GSH and eliciting the ICD effect via high-intensity focused ultrasound (HIFU) therapy, the MBP nanomedicine can regulate the tumor immune microenvironment by inducing maturation of dendritic cells (DCs) and facilitating the activation of CD8⁺ and CD4⁺ T cells. The synergistic GSH depletion and HIFU ablation also amplify the inhibition of tumor growth and lung metastasis. Together, these findings inaugurate a new strategy of tumor-targeted immunotherapy, realizing a novel therapeutics paradigm with great clinical significance.