As a global public health concern, parasitic diseases have been a great threat to human health for a long period of time. Following decades of integrated control efforts, the incidence of soil-transmitted and food-borne parasitic diseases has been remarkable reduced in most endemic foci of China, and is now low across the country. However, there are still multiple factors affecting the transmission of soil-transmitted and food-borne parasitic diseases, which pose multiple challenges to parasitic diseases surveillance in the new era. This article analyzed the current status and challenges of soil-transmitted and food-borne parasitic diseases surveillance in Jiangsu Province, a low-endemicity area, and proposed countermeasures to tackle the challenges, so as to provide insights into key parasitic diseases surveillance in the new era.

The intensive care unit (ICU) is a highly equipment-intensive area with a wide variety of medical devices, and the accuracy and timeliness of medical equipment data collection are highly demanded. The integration of the Internet of Things (IoT) into ICU medical devices is of great significance for enhancing the quality of medical care and nursing, as well as for the advancement of digital and intelligent ICUs. This study focuses on the construction of the IOT for ICU medical devices and proposes innovative solutions, including the overall architecture design, devices connection, data collection, data standardization, platform construction and application implementation. The overall architecture was designed according to the perception layer, network layer, platform layer and application layer; three modes of device connection and data acquisition were proposed; data standardization based on Integrating the Healthcare Enterprise-Patient Care Device (IHE-PCD) was proposed. This study was practically verified in the Chinese People's Liberation Army General Hospital, a total of 122 devices in four ICU wards were connected to the IoT, storing 21.76 billion data items, with a data volume of 12.5 TB, which solved the problem of difficult systematic medical equipment data collection and data integration in ICUs. The remarkable results achieved proved the feasibility and reliability of this study. The research results of this paper provide a solution reference for the construction of hospital ICU IoT, offer more abundant data for medical big data analysis research, which can support the improvement of ICU medical services and promote the development of ICU to digitalization and intelligence.
Intensive Care Units ; Internet of Things ; Humans ; Internet ; Data Collection

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
Humans ; Male ; Female ; Adolescent ; Case-Control Studies ; Depression/diagnosis* ; Early Diagnosis ; Rest ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Models, Psychological ; Reproducibility of Results ; Affect/physiology* ; Photic Stimulation/methods* ; Video Recording ; Brain/physiopathology*

Protrusive facial deformities, characterized by the forward displacement of the teeth and/or jaws beyond the normal range, affect a considerable portion of the population. The manifestations and morphological mechanisms of protrusive facial deformities are complex and diverse, requiring orthodontists to possess a high level of theoretical knowledge and practical experience in the relevant orthodontic field. To further optimize the correction of protrusive facial deformities, this consensus proposes that the morphological mechanisms and diagnosis of protrusive facial deformities should be analyzed and judged from multiple dimensions and factors to accurately formulate treatment plans. It emphasizes the use of orthodontic strategies, including jaw growth modification, tooth extraction or non-extraction for anterior teeth retraction, and maxillofacial vertical control. These strategies aim to reduce anterior teeth and lip protrusion, increase chin prominence, harmonize nasolabial and chin-lip relationships, and improve the facial profile of patients with protrusive facial deformities. For severe skeletal protrusive facial deformities, orthodontic-orthognathic combined treatment may be suggested. This consensus summarizes the theoretical knowledge and clinical experience of numerous renowned oral experts nationwide, offering reference strategies for the correction of protrusive facial deformities.
Humans ; Orthodontics, Corrective/methods* ; Consensus ; Malocclusion/therapy* ; Patient Care Planning ; Cephalometry

Antibody (Ab) humanization is critical to reduce immunogenicity and enhance efficacy in the preclinical phase of the development of therapeutic Abs originated from animal models. Computational suggestions have long been desired, but available tools focused on immunogenicity calculation of whole Ab sequences and sequence segments, missing the individual residue sites. This study introduces Site-specific Immunogenicity for Therapeutic Antibody (SITA), a novel computational framework that predicts B-cell immunogenicity score for not only the overall antibody, but also individual residues, based on a comprehensive set of amino acid descriptors characterizing physicochemical and spatial features for antibody structures. A transfer-learning-inspired framework was purposely adopted to overcome the scarcity of Ab-Ab structural complexes. On an independent testing dataset derived from 13 Ab-Ab structural complexes, SITA successfully predicted the epitope sites for Ab-Ab structures with a receiver operating characteristic (ROC)-area unver the ROC curve (AUC) of 0.85 and a precision-recall (PR)-AUC of 0.305 at the residue level. Furthermore, the SITA score can significantly distinguish immunogenicity levels of whole human Abs, therapeutic Abs and non-human-derived Abs. More importantly, analysis of an additional 25 therapeutic Abs revealed that over 70% of them were detected with decreased immunogenicity after modification compared to their parent variants. Among these, nearly 66% Abs successfully identified actual modification sites from the top five sites with the highest SITA scores, suggesting the ability of SITA scores for guide the humanization of antibody. Overall, these findings highlight the potential of SITA in optimizing immunogenicity assessments during the process of therapeutic antibody design.

OBJECTIVE: To investigate the mechanism of human embryonic stem cell-derived mesenchymal stem cells (hESC-MSC) in alleviating brain injury after resuscitation in swine with cardiac arrest (CA). METHODS: Twenty-nine healthy male large white swine were randomly divided into Sham group (n = 9), cardiopulmonary resuscitation (CPR) group (n = 10) and hESC-MSC group (n = 10). The Sham group only completed animal preparation. In CPR group and hESC-MSC group, the swine model of CA-CPR was established by inducing ventricular fibrillation for 10 minutes with electrical stimulation and CPR for 6 minutes. At 5 minutes after successful resuscitation, hESC-MSC 2.5×10⁶/kg was injected via intravenous micropump within 1 hour in hESC-MSC group. Venous blood samples were collected before resuscitation and at 4, 8, 24, 48 and 72 hours of resuscitation. The levels of neuron specific enolase (NSE) and S100B protein (S100B) were detected by enzyme linked immunosorbent assay (ELISA). At 24, 48 and 72 hours of resuscitation, neurological deficit score (NDS) and cerebral performance category (CPC) were used to evaluate the neurological function of the animals. Three animals from each group were randomly selected and euthanized at 24, 48, and 72 hours of resuscitation, and the hippocampus tissues were quickly obtained. Immunofluorescence staining was used to detect the distribution of hESC-MSC in hippocampus. Immunohistochemical staining was used to detect the activation of astrocytes and microglia and the survival of neurons in the hippocampus. The degree of apoptosis was detected by TdT-mediated dUTP nick end labeling (TUNEL). RESULTS: The serum NSE and S100B levels of brain injury markers in CPR group and hESC-MSC group were significantly higher than those in Sham group at 24 hours of resuscitation, and then gradually increased. The levels of NSE and S100B in serum at each time of resuscitation in hESC-MSC group were significantly lower than those in CPR group [NSE (μg/L): 20.69±3.62 vs. 28.95±3.48 at 4 hours, 27.04±5.56 vs. 48.59±9.22 at 72 hours; S100B (μg/L): 2.29±0.39 vs. 3.60±0.73 at 4 hours, 2.38±0.15 vs. 3.92±0.50 at 72 hours, all P < 0.05]. In terms of neurological function, compared with the Sham group, the NDS score and CPC score in the CPR group and hESC-MSC group increased significantly at 24 hours of resuscitation, and then gradually decreased. The NDS and CPC scores of hESC-MSC group were significantly lower than those of CPR group at 24 hours of resuscitation (NDS: 111.67±20.21 vs. 170.00±21.79, CPC: 2.33±0.29 vs. 3.00±0.00, both P < 0.05). The expression of hESC-MSC positive markers CD73, CD90 and CD105 in the hippocampus of hESC-MSC group at 24, 48 and 72 hours of resuscitation was observed under fluorescence microscope, indicating that hESC-MSC could homing to the damaged hippocampus. In addition, compared with Sham group, the proportion of astrocytes, microglia and apoptotic index in hippocampus of CPR group were significantly increased, and the proportion of neurons was significantly decreased at 24, 48 and 72 hours of resuscitation. Compared with CPR group, the proportion of astrocytes, microglia and apoptotic index in hippocampus of hESC-MSC group decreased and the proportion of neurons increased significantly at 24 hours of resuscitation [proportion of astrocytes: (14.33±1.00)% vs. (30.78±2.69)%, proportion of microglia: (12.00±0.88)% vs. (27.89±5.68)%, apoptotic index: (12.89±3.86)% vs. (52.33±7.77)%, proportion of neurons: (39.44±3.72)% vs. (28.33±1.53)%, all P < 0.05]. CONCLUSIONS Application of hESC-MSC at the early stage of resuscitation can reduce the brain injury and neurological dysfunction after resuscitation in swine with CA. The mechanism may be related to the inhibition of immune cell activation, reduction of cell apoptosis and promotion of neuronal survival.
Animals ; Heart Arrest/therapy* ; Cardiopulmonary Resuscitation ; Swine ; Humans ; Male ; Human Embryonic Stem Cells/cytology* ; Mesenchymal Stem Cell Transplantation ; Mesenchymal Stem Cells/cytology* ; Phosphopyruvate Hydratase/blood* ; Brain Injuries/therapy* ; S100 Calcium Binding Protein beta Subunit ; Apoptosis ; Disease Models, Animal

Traditional Chinese medicine (TCM) is a well-accepted therapy for atopic dermatitis (AD). However, there are currently no evidence-based guidelines integrating TCM and Western medicine for the treatment of AD, limiting the clinical application of such combined approaches. Therefore, the China Association of Chinese Medicine initiated the development of the current guideline, focusing on key issues related to the use of TCM in the treatment of AD. This guideline was developed in accordance with the principles of the guideline formulation manual published by the World Health Organization. A comprehensive review of the literature on the combined use of TCM and Western medicine to treat AD was conducted. The findings were extensively discussed by experts in dermatology and pharmacy with expertise in both TCM and Western medicine. This guideline comprises 23 recommendations across seven major areas, including TCM syndrome differentiation and classification of AD, principles and application scenarios of TCM combined with Western medicine for treating AD, outcome indicators for evaluating clinical efficacy of AD treatment, integration of TCM pattern classification and Western medicine across disease stages, daily management of AD, the use of internal TCM therapies and proprietary Chinese medicines, and TCM external treatments. Please cite this article as: Du XR, Wu MY, Tao MC, Lin Y, Gu CY, Wu MF, Cao Y, Chen DC, Li W, Wang HW, Wang Y, Wang Y, Lu HZ, Liu X, Su XF, Li FL. Clinical practice guidelines for the diagnosis and treatment of atopic dermatitis with integrative traditional Chinese and Western medicine. J Integr Med. 2025; 23(6):641-653.
Dermatitis, Atopic/drug therapy* ; Humans ; Medicine, Chinese Traditional/methods* ; Integrative Medicine ; Drugs, Chinese Herbal/therapeutic use* ; Practice Guidelines as Topic

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective:To explore the effectiveness of a hysteroscopic endometrial lesion diagnosis model de-veloped based on deep learning(DL)algorithm combined with gradient-weighted class activation mapping(Grad-CAM)visualization technology.Methods:303 hysteroscopy videos(4781 images)of 291 patients who un-derwent hysteroscopy examination in the Department of Gynecology,Renmin Hospital of Wuhan University from June 1,2021 to December 31,2022 were selected.The dataset was divided into a training set(3703 images)and a test set(1078 images)by weight sampling method.After the training set was used for model learning and train-ing,two model architectures,residual neural network(ResNet18)and efficient neural network(EfficientNet-B0),were selected to verify the model in the test set by five-class and two-class classification tasks,respectively.Tak-ing histopathology as the gold standard,the diagnostic efficacy was evaluated to select the optimal model,and the Grad-CAM layer was embedded in the optimal model to output hysteroscopy images of Grad-CAM.Results:①In the five-class classification tasks,the accuracy of EfficientNet-B0 model(93.23％)was higher than that of Res-Net18 model(84.23％);the area under the curve(AUC)of EfficientNet-B0 model in the diagnosis of five disea-ses,including atypical endometrial hyperplasia,endometrial polyps,endometrial cancer,endometrial atypical hy-perplasia,and submucous myoma,was slightly higher than that of ResNet18 model,and the AUC of both models was almost above 0.980.②In the binary classification task of accuracy and the evaluation of specificity,the two models were similar,both above 93.00％,and the sensitivity of EfficientNet-B0 model(91.14％)was significantly better than that of ResNet18 model(77.22％).③EfficientNet-B0 model combined with Grad-CAM algorithm could identify the abnormal areas in the image.After biopsy and pathological examination,it was confirmed that about 95％of the marked areas in the model's output heatmap were lesion areas.Conclusions:The hysteroscopy di-agnostic model developed by EfficientNet-B0 model combined with Grad-CAM has high diagnostic accuracy,sen-sitivity,and specificity,and has application value in the diagnosis of endometrial lesions.

Objective To explore the protective mechanism of baicalin regulating NOD like receptor thermal protein domain associated protein 3(NLRP3)inflammasomes against acne.Methods Compound acne models were prepared by intradermal injection of Propionibacterium acnes into the auricle.Rats were randomly divided into control group(normal rats were given physiological saline by gavage),model group(acne model rats were given physiological saline by gavage),experimental-L,-M,-H groups(acne model rats were given 25,50,and 100 mg·kg-1 of baicalin by gavage),and positive control group(acne model rats were given 3.125 mg·kg-1 of isotretinoin by gavage),with 10 rats in each group.Observe the morphology of rat auricles;enzyme linked immunosorbent assay(ELISA)was used to detect the level of inflammation in serum;hematoxylin-eosin staining was used to detect pathological changes in rat auricle tissue;Western blotting was used to detect the protein expression level in the auricle tissue.Results After drug treatment,the auricular thickness of rats in the control,model,experimental-H and positive control groups were(0.42±0.05),(0.75±0.10),(0.49±0.05)and(0.50±0.05)mm;the serum levels of tumor necrosis factor-α were(20.46±2.13),(62.32±5.47),(23.27±2.26)and(25.41±2.28)pg·mL-1;interleukin-1 β levels were(11.38±1.26),(31.62±2.58),(15.61±1.35)and(16.72±1.38)pg·mL-1;interleukin-6 levels were(10.62±1.02),(25.43±2.51),(13.27±1.15)and(14.01±1.17)pg·mL-1;NLRP3 protein expression levels in auricular tissues were 0.23±0.03,0.81±0.08,0.30±0.04 and 0.32±0.04;and Caspase-1 protein expression levels were 0.31±0.04,0.76±0.08,0.39±0.04 and 0.41±0.04;matrix metalloproteinase-2 protein expression levels were 0.35±0.04,0.86±0.10,0.40±0.05 and 0.42±0.05.Compared with the model group,the above indexes in the experimental-H group were statistically significant(all P＜0.05).Conclusion Baicalin can inhibit the inflammatory response in acne rats,and its mechanism of action may be related to the inhibition of the NLRP3 inflammasome signaling pathway.