Despite breakthroughs in immunotherapy for solid tumors, significant variations in treatment efficacy persist. Up to 80% of cancer patients suffer from malnutrition, which leads to: lymphoid atrophy and reduced T-cell reserves; deficiency of substrates required for T-cell activation and expansion; concurrent inflammation hindering T-cell infiltration into tumors; and cachexia accelerating PD-1 antibody clearance. Clinical studies confirm that severe malnutrition significantly impairs immune responses and increases the risk of treatment toxicity. Therefore, implementing standardized nutritional therapy is crucial for optimizing the reserve, activation, expansion, and infiltration capacity of immune cells, thereby providing a sound immune system foundation for immunotherapy. Immunonutrition therapy, by enhancing immunonutrients such as arginine, omega-3 polyunsaturated fatty acids, and nucleotides, reduces the secretion of pro-inflammatory mediators and promotes T-cell activation and proliferation. This enhances anti-tumor immune responses, prolongs survival, and advances cancer treatment towards multimodal combination and precision approaches.

Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

A portion of patients undergoing implant restoration require bone augmentation procedures to ensure that there is sufficient bone volume around the implant. For the patients with horizontal bone ridge defects at edentulous sites, with or without mild to moderate vertical bone defects, the shell technique serves as a reliable and minimally invasive bone augmentation method with effective space maintenance. The shell technique involves fixating 1 mm cortical bone blocks to the recipient site, using retention screws and filling the gap between the bone block and recipient bed with particulate bone substitute materials, and covering the barrier membrane to achieve bone augmentation. The overlying tension-free soft tissue closure seals the surgical site while local peripheral blood releases osteoclasts and cytokines that gradually degrade the bone block. The rigid fixation of the bone block ensures a stable internal environment for osteogenesis and a new bone regeneration cycle. Although this technique demonstrates favorable bone augmentation outcomes, it is highly technique-sensitive. There are certain differences in the application scenarios and osteogenic processes for autologous and allogeneic bone shells. The selection of bone blocks and particulate bone substitute materials significantly influences the osteogenic biological process and the predictability of bone augmentation results. Complications associated with the shell technique possess distinct characteristics, such as the immunogenicity of allogeneic bone fragments, soft tissue cracking, and bone fragment loosening. Their prevention and subsequent management substantially impact the success rate of osteogenesis. This article delves into the biological mechanisms of osteogenesis in the bone block technique, summarizing the indications, clinical outcomes, classification of bone blocks, and surgical workflow management, as well as complication prevention and management, aiming to provide a reference for the future application and development of the bone shell technique.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

ObjectiveTo analyze the research hotspots and development trends of brain-computer interface (BCI) in the treatment for spinal cord injury (SCI). MethodsRelevant literatures on BCI applied in SCI treatment, published from the inception of the Web of Science Core Collection to July, 2025, were retrieved. Visualization analysis was performed using CiteSpace, VOSviewer and Tableau Desktop. ResultsA total of 437 literatures were included, and the annual number of publications showed an overall increasing trend. The United States ranked first in the number of publications; Graz University of Technology was the institution with the highest number of publication; Gernot R Mueller-Putz was the most productive author, while Jonathan R Wolpaw was the most cited author. Brain-computer interface and artificial intelligence were identified as the high-frequency and bursting keywords in this field. The researches were characterized by the cross-integration of five core disciplines: neuroscience and rehabilitation medicine, biomedical engineering, computer science and artificial intelligence, neurophysiology, and materials science. ConclusionResearches on BCI in SCI treatment are accelerating continuously, and technological integration is becoming the core trend.

OBJECTIVE To study the improvement effect and mechanism of Dendrobium officinale on skin damage in mice with xeroderma. METHODS The mice were randomly divided into control group， model group， and D. officinale group， with 5 mice in each group. Except for the control group （which only underwent shaving treatment）， the mice in all other groups were induced to develop a xeroderma model using an acetone-ether mixture for five consecutive days. The mice in D. officinale group were treated with 200 μL of D. officinale suspension （0.2 mg/mL） two hours after the first modeling each day. Mice in the control group and the model group were applied with an equal volume of pure water； once a day， until the end of the modeling process. After last medication， skin lesions and pathological morphology of the mice were observed. Immunofluorescence was used to detect the expressions of Filaggrin， Loricrin and Ki67 proteins in skin tissue of the mice. The core pathways through which D. officinale improves skin damage in xeroderma were screened using 16S rRNA sequencing combined with gene ontology and Kyoto Encyclopedia of Genes and Genomes （KEGG） enrichment analyses， and subsequent validation was conducted. RESULTS Compared with the control group， the mice in the model group exhibited obvious scratching behavior， with a large amount of scale on the skin， excessive epidermal keratinization， and thickened stratum spinosum. The skin scale score， epidermal thickness， and the expression levels of Ki67， Filaggrin， and Loricrin proteins in the skin tissue were significantly increased/elevated （ P ＜0.05）. Compared with model group， the mice in the D. officinale group exhibited reduced scratching behavior and scaling， along with a mitigated degree of skin keratinization. The aforementioned quantitative indicators were significantly decreased/reduced （ P ＜0.05）. The results of core pathway screening revealed that the KEGG pathways involving differentially expressed genes included signaling pathways such as interleukin-17 （IL-17） and tumor necrosis factor （TNF）. Further validation experim ents found that after intervention with D. officinale ， mRNA expression of downstream effector molecules CCN1， Hbegf， Tnfrsf12a， and Thbs1 genes in skin tissues were all significantly reduced （ P ＜0.05）. CONCLUSIONS D. officinale can repair skin damage in mice with xeroderma， and its mechanism of action is related to restoring the balance of proliferation and differentiation in keratinocytes and down-regulating the mRNA expressions of CCN1， Hbegf， Tnfrsf12a， and Thbs1.

ObjectiveTo investigate the role and mechanism of Lck/Yes-related novel protein tyrosine kinase （Lyn） on lipopolysaccharide （LPS）-induced M1-type polarization of macrophage. MethodsThe LentiCRISPR-V2 plasmid was digested with the restriction endonuclease BSMBI-V2， and the digested DNA fragments were recovered. The digested plasmid was ligated with Lyn-sgRNA using T4 ligase to generate the Lenti-Lyn-gRNA lentivirus. THP-1 cells were infected with the Lenti-Lyn-gRNA lentivirus to obtain a stable cell line with Lyn knockout， and a monoclonal THP-1 cell line with complete Lyn knockout （Lyn^⁻/⁻） was established subsequently. Wild-type Lyn （Lyn^WT） and Lyn^⁻/⁻ THP-1 cells were induced with 100 ng/mL phorbol myristate acetate （PMA） for 48 h to differentiate into M0 macrophages， which were further polarized into M1 macrophages by stimulation with 100 ng/mL LPS for 24 h. Quantitative real-time polymerase chain reaction （qPCR） was performed to detect the expression of M0 macrophage markers， including integrin αM （CD11b）， macrophage antigen （CD68）， and monocyte differentiation antigen （CD14）. The expression of Lyn in M1 macrophages differentiated from wild-type THP-1 cells （Lyn^WT-M1） was measured by qPCR， and the ratio of phosphorylated Lyn to total Lyn （P-Lyn/Lyn） in Lyn^WT-M1 cells was determined by Western blot. In M1 macrophages differentiated from Lyn-knockout THP-1 cells （Lyn^⁻/⁻-M1）， qPCR was used to detect the mRNA expression of inducible nitric oxide synthase （iNOS）， interleukin-6 （IL-6）， and chemokine （C-X-C motif） ligand 10 （CXCL-10）. Western blot was conducted to assess the protein expression of iNOS， as well as the protein levels of molecules related to the Janus kinase 1 （JAK1）-signal transducer and activator of transcription 1 （STAT1）signaling pathway， including JAK1， phosphorylated JAK1 （P-JAK1）， STAT1， and phosphorylated STAT1 （P-STAT1）. Additionally， the expression of the M1 macrophage marker cluster of differentiation 80 （CD80） was analyzed by flow cytometry. ResultsThe Lyn^-/- monoclonal cell line was successfully constructed. The expression of CD11b was significantly elevated in Lyn^-/- M0 macrophages， and the differentiation of M1 macrophages was successful. Knockdown of Lyn inhibited mRNA expression of iNOS， IL⁃6， CXCL⁃10， protein expression of iNOS and CD80 expression in M1 macrophages （P<0.05）. Western blot assay showed that Lyn knockdown inhibited protein expression of JAK1 and P-STAT1 （P<0.01）. ConclusionAfter CRISPR/Cas9-mediated Lyn knockout， the expression levels of JAK1 and P-STAT1， the key molecules in the JAK/STAT signaling pathway of M1 macrophages， are significantly downregulated； concomitantly， the expression of M1 macrophage-specific secretory factors （iNOS， IL⁃6， CXCL⁃10 mRNA） and CD80 is also downregulated， which may be achieved via targeted regulation of the JAK1/P-STAT1-mediated JAK/STAT signaling pathway.

ObjectiveTo provide evidence for strengthening tuberculosis control in Inner Mongolia by analyzing changes and influencing factors in public awareness of tuberculosis prevention and control information among residents between the early stage and 17 years after the implementation of the directly observed treatment， short-course （DOTS） strategy. MethodsBased on the "National Public Knowledge， Belief and Behavior Questionnaire on Tuberculosis Prevention and Control" designed by the Chinese Center for Disease Control and Prevention， a questionnaire survey was conducted among residents in Inner Mongolia using a multi-stage stratified random sampling method. Chi-square test was used for univariate analysis， and binary Logistic regression was employed to explore the influencing factors of public awareness of tuberculosis prevention and control information. ResultsThe overall awareness rate of core information on tuberculosis prevention and control among the public was 67.7% in 2006， and it decreased to 49.2% in 2023 （P<0.05）. Multivariate Logistic regression analysis showed， compared with the groups aged 15-29， illiterate and semi-illiterate， those with public medical care， and those with three or fewer family members， the awareness rate of the groups aged 60 and above （OR=0.689）， those with primary and junior high school education （OR=0.856）， and those with self-paid medical care （OR=0.468） was significantly lower in 2006. The awareness rate was relatively high in groups with more than 3 family members （OR=1.236） （P<0.05）. Compared with the groups aged 15-29， illiterate and semi-illiterate， married， Han ethnicity， employed， and those receiving public medical care， the awareness rate was higher among the groups aged 30-59 （OR=1.976）， 60 and above （OR=2.224）， those with high school education and above （OR=2.801）， and single （OR=2.244） in 2023. However， the awareness rates of ethnic minorities （OR=0.737）， the unemployed （OR=0.557）， and self-funded medical care （OR=0.497） groups were significantly lower （P<0.05）. ConclusionPublic awareness of TB prevention knowledge in Inner Mongolia remains suboptimal. Strengthening diversified health education campaigns， addressing social stigma， and improving healthcare access are critical to enhance regional TB control outcomes.