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

ObjectiveTo explore the syndrome elements and evolving patterns of patients with hepatitis B virus-related acute on chronic liver failure （HBV-ACLF） at different stages. MethodsClinical information of 1,058 hospitalized HBV-ACLF patients， including 618 in the early stage， 355 in the middle stage， and 85 in the late stage， were collected from 18 clinical centers across 12 regions nationwide from January 1， 2012 to February 28， 2015. The “Hepatitis B-related Chronic and Acute Liver Failure Chinese Medicine Clinical Questionnaire” were designed to investigate the basic information of the patients， like the four diagnostic information （including symptoms， tongue， pulse） of traditional Chinese medicine （TCM）， and to count the frequency of the appearance of the four diagnostic information. Factor analysis and cluster analysis were employed to determine and statistically analyze the syndrome elements and patterns of HBV-ACLF patients at different stages. ResultsThere were 76 four diagnostic information from 1058 HBV-ACLF patients， and 53 four diagnostic information with a frequency of occurrence ≥ 5% were used as factor analysis entries， including 36 symptom information， 12 tongue information， and 5 pulse information. Four types of TCM patterns were identified in HBV-ACLF， which were liver-gallbladder damp-heat pattern， qi deficiency and blood stasis pattern， liver-kidney yin deficiency pattern， and spleen-kidney yang-deficiency pattern. In the early stage， heat （39.4%， 359/912） and dampness （27.5%， 251/912） were most common， and the pattern of the disease was dominated by liver-gallbladder damp-heat pattern （74.6%， 461/618）； in the middle stage， dampness （30.2%， 187/619） and blood stasis （20.7%， 128/619） were most common， and the patterns of the disease were dominated by liver-gallbladder damp-heat pattern （53.2%， 189/355）， and qi deficiency and blood stasis pattern （27.6%， 98/355）； and in the late stage， the pattern of the disease was dominated by qi deficiency （26.3%， 40/152） and yin deficiency （20.4%， 31/152）， and the patterns were dominated by qi deficiency and blood stasis pattern （36.5%， 31/85）， and liver-gallbladder damp-heat pattern （25.9%， 22/85）. ConclusionThere are significant differences in the distribution of syndrome elements and patterns at different stages of HBV-ACLF， presenting an overall trend of evolving patterns as "from excess to deficiency， transforming from excess to deficiency"， which is damp-heat → blood stasis → qi-blood yin-yang deficiency.

ObjectiveTo construct a macro-evaluation tool for dampness syndrome （DS） animal model， which will provide a basis for experimental research on dampness syndrome in traditional Chinese medicine （TCM）. MethodsConceptual framework of this study was clarified through discussions within the core working group， and dimensions of the evaluation of the animal model of DS were identified according to TCM principles. We searched CNKI， Wanfang， VIP and SinoMed databases from the inception to June 30th， 2023， on experiments involving dampness syndrome animals to create a pool of items about DS animal models. The core items were selected and extracted for factor analysis and cluster analysis. An expert importance rating questionnaire was developed based on the results of the literature review， analyzing the distribution of item scores， importance averages， and coefficient of variation. Through a comprehensive analysis of literature， expert importance scoring， and specific expert opinions， items that did not meet anyone of the criteria of average importance rating ≥2.04， coefficient of variation ≤30%， or literature eva-luation frequency ≥2% were removed， thereafter， the macro-evaluation tool for DS animal model was preliminarily constructed. ResultsSpirit and body state， autonomic activity state， body surface characteristics， diet， urination and defecation， tongue manifestation， and motor behavior assessment were constructed as the seven dimensions in the evaluation of DS animal model. A total of 348 papers about animal experiments were included and analyzed， resulting in a saturated pool of 72 items， which was refined to 38 core items of DS animal models. Factor analysis obtained 16 common factors， which were further clustered into two categories， named dampness transforming from heat syndrome and dampness transforming from cold syndrome. The expert importance scoring showed that the Kendall harmony coefficient was 0.359 （P＜0.05） indicating a high level of agreement， coordination and reliability among the experts. Notably， 50% or more of the experts considered the items thick and greasy tongue coating， unclean perianal area， loose stools， lethargy， unformed stools， and listless expression as very important. The median scores for all items were 2.04（1.73， 2.37）， with a coefficient of variation ranging from 19.73% to 53.38%. After expert evaluation， the macro-evaluation tool for DS animal model in TCM with 33 items and corresponding criteria for assessing the formation of DS models was finally contructed. ConclusionThe Macro-evaluation tool for DS animal model in TCM is highly scientific， credible， and operable， and can be utilized in DS animal experiments after its characteristics are actually evaluated.

Adaptive immunity is a critical component of the human immune system, playing an essential role in identifying antigens and orchestrating a tailored immune response. This review delves into the significant strides made in the development of epitope prediction tools, their integration into vaccine design, and their pivotal role in enhancing immunotherapy strategies. The review emphasizes the transformative potential of these tools in refining our understanding and application of immune responses. Adaptive immunity distinguishes itself from innate immunity by its ability to recognize specific antigens and remember past infections, leading to quicker and more effective responses upon subsequent exposures. This facet of immunity involves complex interactions between various cell types, primarily B cells and T cells, which recognize distinct epitopes presented by antigens. Epitopes are small sequences or configurations on antigens that are recognized by the immune receptors on B cells and T cells, acting as the focal points of immune recognition and response. Epitopes can be broadly classified into two types: linear (or sequential) epitopes and conformational (or discontinuous) epitopes. Linear epitopes consist of a sequence of amino acids in a protein that are recognized by B cells and T cells in their primary structure form. Conformational epitopes, on the other hand, are formed by spatially distinct amino acids that come together in the tertiary structure of the protein, often recognized by the immune system only when the protein folds into its native conformation. The role of epitopes in the immune response is critical as they are the primary triggers for the activation of B cells and T cells. When an epitope is recognized, it can stimulate B cells to produce antibodies, mobilize helper T cells to secrete cytokines, or prompt cytotoxic T cells to kill infected cells. These actions form the basis of the adaptive immune response, tailored to eliminate specific pathogens or infected cells effectively. The prediction of B cell and T cell epitopes has evolved with advances in computational biology, leading to the development of several sophisticated tools that utilize a variety of algorithms to predict the likelihood of epitope regions on antigens. Tools employing machine learning methods, such as support vector machines (SVMs), XGBoost, random forest, analyze large datasets of known epitopes to classify new sequences as potential epitopes based on their similarity to known data. Moreover, deep learning has emerged as a powerful method in epitope prediction, leveraging neural networks capable of learning high-dimensional data from vast amounts of immunological inputs to identify patterns that may not be evident to other predictive models. Deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and ESM protein language model have demonstrated superior accuracy in mapping the nonlinear relationships inherent in protein structures and epitope interactions. The application of epitope prediction tools in vaccine design is transformative, enabling the development of epitope-based vaccines that can elicit targeted immune responses against specific parts of the pathogen. These vaccines, by focusing the immune response on highly specific regions of the pathogen, can offer high efficacy and reduced side effects. Similarly, in cancer immunotherapy, epitope prediction tools help identify tumor-specific antigens that can be targeted to develop personalized immunotherapeutic strategies, thereby enhancing the precision of cancer treatments. The future of epitope prediction technology appears promising, with ongoing advancements anticipated to enhance the precision and efficiency of these tools further. The integration of broader immunological data, such as patient-specific immune profiles and pathogen variability, along with advances in AI and machine learning, will likely drive the development of more adaptive, robust, and clinically relevant prediction models. This will not only improve the effectiveness of vaccines and immunotherapies but also contribute to our broader understanding of immune mechanisms, potentially leading to breakthroughs in the treatment and prevention of multiple diseases. In conclusion, the development and refinement of epitope prediction tools stand as a cornerstone in the advancement of immunological research and therapeutic design, highlighting a path toward more precise and personalized medicine. The ongoing integration of computational models with experimental immunology holds the promise of revolutionizing our approach to combating infectious diseases and cancer.