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.

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.

Objective:To construct an artificial intelligence model for the diagnosis of facial vitiligo, so as to realize artificial intelligence-assisted diagnosis of facial vitiligo.Methods:Based on digital single-lens reflex (SLR) camera images of vitiligo skin lesions and YOLO (You Only Look Once) v3 algorithm, a skin lesion detection model Vit3 was established, and its performance was evaluated by comparing its detection results and labeling results of dermatologists. On the basis of the Vit3 model, both optical and ultraviolet images of vitiligo and non-vitiligo skin lesions were taken by using an artificial intelligence-based facial skin image collector, and the gray values of vitiligo and non-vitiligo skin lesion areas on the ultraviolet images were measured by using an image processing technique. According to the gray-value threshold between vitiligo and non-vitiligo skin lesions, a facial vitiligo diagnosis model Vit4 was established. Cochran′s Q test was used to compare the diagnostic results of the Vit4 model and dermatologists, and the diagnostic performance of the Vit4 model was evaluated. Results:For 100 SLR camera images of vitiligo skin lesions (167 lesional sites) and 100 SLR camera images of normal skin, the diagnostic sensitivity of the Vit3 model was 92.81% (155/167) . For 97 pairs of facial skin images (including 50 vitiligo lesions, 30 pityriasis alba lesions, 7 amelanotic nevus leisons, and 10 normal skin tissues) , the diagnostic accuracy rate, sensitivity and specificity of the Vit4 model were 88.66% (86/97) , 88.00% (44/50) and 89.36% (42/47) respectively, and there was no significant difference in the diagnostic accuracy rate between the Vit4 model and dermatologists (92.78%[90/97], χ2=2.323, P > 0.05) . Conclusion:The artificial intelligence model Vit4 was established for the diagnosis of facial vitiligo with favorable diagnostic performance, and could serve as an objective and convenient method for the auxiliary diagnosis of facial vitiligo.

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT)features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitalswere retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in thetraining cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in thevalidation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, ordeath. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. Anomogram was constructed based on the combination of clinical and CT features, and its prognostic performance wasexternally tested in the validation group. The predictive value of the combined model was compared with models built on theclinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohortexperienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67–6.71;p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04–0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03–4.48;p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76–0.88),and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82–0.96). The combinedmodel provided the best performance over the clinical or radiological model (p < 0.050). Conclusion Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverseoutcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predictingadverse outcomes of patients with COVID-19.

Objective To evaluate the efficacy and safety of a 755 nm picosecond Alexandrite la-ser with a diffractive lens array in the treatment of facial photoaged skin .Methods Twenty-six pa-tients with facial photoaging were recruited and received 3 treatments at 4-week intervals .Laser energy was applied over the entire face at a fixed spot size of 6 mm ,with a fluence of 0 .71 J/cm2 and 5Hz . Blinded clinical assessment was performed by 2 independent dermatologists on a 5-point global pho-toaging scale (GPS) .Patients were also questioned on the extent of improvement of rhytides ,skin tightening ,and complexion with a 4-point global aesthetic improvement scale (GAIS) and satisfaction . Adverse events were also evaluated .Results Twenty-six patients completed the treatment .Compared with the baseline ,there was a significant improvement in facial photoaged skin after 3 treatments ,and these positive outcomes were maintained up to the 3-month follow-up ,according to the GPS and GAIS scores .Moderate pain and transient erythema were observed as the two main discomforts associated with the treatment .Most patients were satisfied with the treatment .Conclusions This 755 nm pico-second Alexandrite laser with a diffractive lens array optic is effective in the treatment offacial pho -toaged skin ,and the therapy also seems safe and well tolerated .

Objective To investigate the heterogeneity of the left ventricular reference value in echocardiography mesurements by Meta analysis. Methods A literature retrieval in PubMed,Embase, Medline and other databases from January 2005 to September 2017 related to left ventricular normal reference value in healthy adults was performed. Cohrane′s Q test was used to test the heterogeneity,and I2 was used as a statistic for the description of heterogeneity. Subgroup analysis,sensitivity analysis and Meta regression were used to analyze the sources of heterogeneity. Results The Meta analysis enrolled 9 studies including 5 933 normal cases. The heterogeneity test showed that the left ventricular normal reference value of echocardiography was highly heterogeneous among the studies. In subgroup analysis, some measurements′ heterogeneity were significantly reduced. Meta regression analysis showed that the contribution of racial specificity to heterogeneity was relatively high in some measurements. Conclusions The heterogeneity of left ventricle is mainly related to racial specificity,but it cannot fully explain the heterogeneity between studies. Further studies are needed to demonstrate the involved factors of heterogeneity of left ventricular normal reference values.

Critical ultrasonography(CUS) is different from the traditional diagnostic ultrasound,the examiner and interpreter of the image are critical care medicine physicians.The core content of CUS is to evaluate the pathophysiological changes of organs and systems and etiology changes.With the idea of critical care medicine as the soul,it can integrate the above information and clinical information,bedside real-time diagnosis and titration treatment,and evaluate the therapeutic effect so as to improve the outcome.CUS is a traditional technique which is applied as a new application method.The consensus of experts on critical ultrasonography in China released in 2016 put forward consensus suggestions on the concept,implementation and application of CUS.It should be further emphasized that the accurate and objective assessment and implementation of CUS requires the standardization of ultrasound image acquisition and the need to establish a CUS procedure.At the same time,the standardized training for CUS accepted by critical care medicine physicians requires the application of technical specifications,and the establishment of technical specifications is the basis for the quality control and continuous improvement of CUS.Chinese Critical Ultrasound Study Group and Critical Hemodynamic Therapy Collabration Group,based on the rich experience of clinical practice in critical care and research,combined with the essence of CUS,to learn the traditional ultrasonic essence,established the clinical application technical specifications of CUS,including in five parts:basic view and relevant indicators to obtain in CUS;basic norms for viscera organ assessment and special assessment;standardized processes and systematic inspection programs;examples of CUS applications;CUS training and the application of qualification certification.The establishment of applied technology standard is helpful for standardized training and clinical correct implementation.It is helpful for clinical evaluation and correct guidance treatment,and is also helpful for quality control and continuous improvement of CUS application.

Objective To investigate the biological features of epidermal growth factor receptor inhibitor AG1478 on cervical carcinoma cell. Methods The proliferation of HeLa cells under AG1478 stimulation was determined by CCK8 assay. The expression of EGFR downstream signaling protein and apoptotic relative protein were examined by Western blot and transcription of apotosis?related genes were measured by RT?qPCR in AG 1478 treated HeLa cells. Nuclear transport of phosphorylated ERK were measured through ICC assay. TUNEL assay was used to determine early stage of apoptosis. Results CCK8 assay showed that AG1478 inhibit the proliferation of HeLa cells and also block phosphorylated level of EGFR ,ERK and AKT. Furthermore ,nuclear transport of phosphorylated ERK upon EGF stimulation were blocked and pro?apoptotic proteins were up?regluated with activat ed cleaved Caspases. Conclusion AG1478 inhibited the proliferation and induced apoptosis in HeLa cells and it could be a potential therapeutic drug for cervical carcinoma.