BACKGROUND:Studies have found that probiotics have a certain preventive and therapeutic effect on peri-implantitis,and there are further explorations in the mechanism against peri-implantitis.OBJECTIVE:To review the mechanism and clinical application of probiotics in the treatment of peri-implantitis.METHODS:Relevant literature was searched on PubMed,Web of Science,CNKI,and WanFang Data,using the search terms of"probiotics,peri-implantitis,flora imbalance,immunoregulation,inflammatory reaction,mechanism of action"in Chinese and English.A total of 90 articles were finally included.RESULTS AND CONCLUSION:Probiotics have the following mechanisms.They can activate the anti-inflammatory mechanism by inhibiting the secretion of inflammatory factors and promoting the production of anti-inflammatory factors.They can destroy the cell wall of pathogenic bacteria by secreting microbial complexes and bacteriocins,reduce the pH value of biofilms,improve the composition of microorganisms in microecology,induce the change of bacterial community structure,and restore the balance of microbial population around implants.They have immunomodulatory effects and can enhance the resistance of the host oral mucosa to pathogenic bacteria in the surrounding area of the implant.In addition,probiotics can produce antibacterial compounds,offset the adhesion of pathogenic microorganisms,and regulate immune function.Through the above mechanisms,probiotics have certain potential in the adjuvant treatment of peri-implantitis,which can improve the clinical parameters of peri-implantitis and affect the microbiota.Probiotic therapy provides a new treatment option,but more long-term prospective studies are needed to further verify its effect.

OBJECTIVE:Disorders in iron metabolism increase the risk of type 2 diabetes mellitus.Hepcidin play an important role in maintaining iron homeostasis in the body,but its level increases with increased inflammation.Changes in hepcidin and iron homeostasis and the extent of their association with inflammation in people with and without type 2 diabetes mellitus are unknown.Meta-analysis was used to evaluate the effect of inflammation on serum hepcidin and iron metabolism related parameters in patients with type 2 diabetes mellitus.METHODS:CNKI,PubMed,Web of Science and EBSCOhost databases were searched by computer to collect observational studies related to inflammatory index and hepcidin in patients with type 2 diabetes mellitus.The search time was from September 1,2000 to September 30,2024.Three researchers independently screened the literature,extracted data and evaluated the quality of the included literature.Meta-analysis was performed by Review Manager 5.3,Stata 17.0 and GraphPad Prism 8.0.2 software.RESULTS:A total of 15 articles(17 studies)involving 3 159 participants,including 1 357 patients with type 2 diabetes mellitus,were included.Meta-analysis results showed that compared with the control group,patients with type 2 diabetes mellitus had higher levels of serum hepcidin[standardized mean difference(SMD)=0.35,95％confidence interval(CI)(0.05,0.65),P＜0.05],serum ferritin(SMD=0.49,95％CI(0.21,0.78),P＜0.01)and serum transferrin(SMD=0.19,95％CI(0.00,0.37),P＜0.05).Subgroup analysis results indicated that inflammation had a significant effect on serum hepcidin(SMD=0.76,95％CI(0.17,1.34),P＜0.05)and serum ferritin(SMD=0.77,95％CI(0.06,1.47),P＜0.05)in patients with type 2 diabetes mellitus.CONCLUSION:Hepcidin concentration is positively correlated with type 2 diabetes mellitus.Inflammation is one of the risk factors of type 2 diabetes mellitus.Early prevention of inflammation has certain significance in preventing iron metabolism disorder in patients with type 2 diabetes mellitus.

OBJECTIVE To provide references for the accurate identification and management of immune-related cutaneous adverse events （irCAEs） caused by durvalumab， and ensuring safe clinical drug use. METHODS Clinical pharmacists participated in the diagnosis and treatment process of a patient with gallbladder cancer who developed irCAEs caused by durvalumab. The clinical pharmacists systematically reviewed the patient’s past medical history and medication history， and assisted physicians in assessing the association between adverse drug reactions and administered drugs. Meanwhile， the clinical pharmacists conducted a graded assessment of the adverse reaction， proposed recommendations such as discontinuing durvalumab and adjusting the administration regimen of glucocorticoids， assisted physicians in restarting immunotherapy， and carried out medication education and other pharmaceutical care. RESULTS The occurrence of irCAEs in this patient was “highly likely” related to durvalumab and was classified as severe. The physicians adopted the clinical pharmacist’s opinion， and after symptomatic treatment， the patient’s skin symptoms improved， and discharged with medication. After the completion of glucocorticoid therapy for the patient， the physician restarted immunotherapy with tislelizumab， and no related adverse reactions occurred again in the patient. CONCLUSIONS Durvalumab can cause irCAEs such as severe skin maculopapular rash. In clinical practice， it is crucial to promptly identify and discontinue suspicious drugs， immediately implement effective symptomatic treatment measures， and actively resume immunotherapy to ensure the continuity and safety of the patient’s treatment.

Objective: To fabricate a hydrogel loaded with inositol hexaphosphate-zinc and preliminarily evaluate its performance in self-mineralization and osteoinduction, thereby providing a theoretical basis for the development of bone regeneration materials. Methods: The hydrogel framework (designated DF0) was formed by copolymerizing methacryloyloxyethyltrimethylammonium chloride and four-armed poly(ethylene glycol) acrylate, followed by sequentially loading inositol hexaphosphate anions via electrostatic interaction and zinc ions via chelation. The hydrogel loaded only with inositol hexaphosphate anions was named DF1, while the co-loaded hydrogel was named DF2. The self-mineralization efficacy of the DF0 , DF1 and DF2 hydrogels was characterized using scanning electron microscopy, transmission electron microscopy (TEM), energy dispersive spectroscopy (EDS), and selected area electron diffraction (SAED). The biocompatibility was assessed via live/dead cell staining and a CCK-8 assay. The osteoinductive capacity of the DF0 , DF1 and DF2 hydrogels on MC3T3-E1 cells was assessed via alkaline phosphatase (ALP) and Alizarin Red S (ARS) staining. In the aforementioned cell experiments, cells cultured in standard medium served as the control group Results: The DF0, DF1, and DF2 hydrogels were successfully synthesized. Notably, DF1 and DF2 exhibited distinct self-mineralization within 6 days. Results from TEM, EDS, and SAED confirmed that the mineralization products were amorphous calcium phosphate in group DF1, and amorphous calciumzinc phosphate in group DF2. Biocompatibility tests revealed that none of the hydrogels (DF0, DF1, and DF2) adversely affected cell viability or proliferation. In osteogenic induction experiments, both ALP and ARS staining were intensified in the DF1 and DF2 groups, with the most profound staining observed in the DF2 group. Conclusion The developed inositol hexaphosphate-zinc hydrogel (DF2) demonstrates the dual capacity to generate calcium-phosphate compounds through self-mineralization while exhibiting excellent osteoinductive properties. This biocompatible, dual-promoting osteogenic hydrogel presents a novel strategy for bone regeneration.

Objective: To evaluate the effect of community comprehensive management model intervention among patients with dyslipidemia, so as to provide the reference for optimizing community management strategies and improving the target achievement rate for blood lipids among this population. Methods: From May to June 2023, a multi-stage stratified random sampling method was employed to select patients with dyslipidemia from primary healthcare institutions in Jiaxing City, Zhejiang Province. Eligible participants were randomly assigned to either a control group or an intervention group. The control group received routine management, while the intervention group was subjected to a community comprehensive management model in addition to the routine care. Both groups were followed up for 24 months. Data on demographic characteristics, lifestyle behaviors, physical examination indices, and blood biochemical indicators were collected at baseline and after the intervention through questionnaires, physical examinations, and laboratory tests. Changes in obesity rate, central obesity rate, target achievement rates for blood lipids, blood pressure, and blood glucose, as well as lifestyle modifications, were analyzed. Differences between the two groups before and after the intervention were assessed using generalized estimating equations (GEE). Results: The control group consisted of 560 patients, including 303 females (54.11%) and 430 individuals aged ≥65 years (76.79%). The intervention group also included 560 patients, with 300 females (53.57%) and 431 individuals aged ≥65 years (76.96%). Before the intervention, no statistically significant differences were observed between the two groups in terms of gender, age, educational level, history of chronic diseases, and atherosclerotic cardiovascular disease risk stratification (all P>0.05). After 24 months of intervention, interaction effects between group and time were observed for obesity rate, central obesity rate, target achievement rate for blood lipids, target achievement rate for blood glucose, composite target achievement rate, physical activity rate, and medication adherence (all P<0.05). Specifically, the intervention group demonstrated lower rates of obesity and central obesity, and higher target achievement rate of blood lipids, target achievement rate of blood glucose, composite target achievement rate, physical activity rate, and medication adherence compared to the control group. Conclusion The community comprehensive management model contributed to improvements in multiple metabolic parameters (including body weight, waist circumference, blood lipids, and blood glucose) among patients with dyslipidemia, and was associated with increased physical activity rate and medication adherence.

Hygroscopicity research has long been a key focus and hot topic in Chinese materia medica（CMM）. Elucidating hygroscopic mechanisms plays a vital role in formulation design， process optimization， and storage condition selection. Hygroscopic models serve as essential tools for characterizing CMM hygroscopic mechanisms， with various types available. The double exponential model is a kinetic mathematical model constructed based on the law of conservation of energy and Fick's first law of diffusion， tailored to the physical properties of CMM extracts. In recent years， this model has been extensively applied to simulate the dynamic moisture absorption behavior of CMM extracts and solid dosage forms under varying humidity conditions. It has revealed the correlation between moisture absorption kinetic parameters and material properties， offering a new perspective for characterizing the moisture uptake behavior of CMM. This paper systematically reviews the application progress of this model in the field of CMM， analyzes its advantages， disadvantages， and challenges in this domain， and explores its potential application trends in other fields. It aims to provide references for elucidating the moisture absorption mechanisms of CMM and researching moisture-proofing technologies， while also offering insights for its broader application in food and polymer materials.

To systematically analyzed the reporting status of core elements in publicly available clinical practice guideline(hereafter referred to as "guideline") protocols published domestically and internationally over the past decade, identified existing problems, and provided evidence to inform the standardized writing and publication of future guideline protocols.

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection. Septic shock is the primary cause of mortality in sepsis, with its core pathophysiological mechanism being severe ischemia and hypoxia in critical units—composed of microcirculation and the mitochondria of functional cells—resulting from disruptions in blood flow and oxygen flow following a dysregulated host response. Due to the systemically convergent yet clinically heterogeneous nature of the host response, current understanding and management strategies for hemodynamics remain inconsistent, often leading to inadequate resuscitation or overtreatment. To improve the quality of care, based on a systematic review of the "blood flow-oxygen flow" theory, an expert panel emphasizes reevaluating septic shock from an integrated perspective of blood flow and oxygen flow, and has formulated the Expert Consensus on Blood Flow and Oxygen Delivery Phenotyping and Clinical Management of Septic Shock (2025). The consensus proposes that clinical typing of blood flow-oxygen flow should comprehensively consider cardiac function, vascular tone, oxygen flow utilization status in critical units, and disease trajectory, while optimizing subtype identification by integrating host response phenotypes and artificial intelligence technologies. It advocates establishing a continuous assessment system through multi-site oxygen flow monitoring for organ perfusion, peripheral perfusion monitoring, and critical care ultrasound. Under the framework of the "critical care triangle", the consensus promotes the implementation of individualized, bundled management strategies, providing guidance for multiple management points to restore blood flow-oxygen flow matching, reduce the risk of organ failure, and decrease patient mortality.

ObjectiveTo investigate somatization symptoms in adolescents during frequent earthquakes in Hefei， and to explore their correlation with earthquake experiences. MethodsA cross-sectional survey was used to select 324 adolescents in Hefei as the survey objects. The self-rating scale of somatization symptoms （SSS） and the fatigue intensity scale （FIS） were used to evaluate the somatization symptoms and fatigue degree of middle school students， and multivariate Logistic regression analysis was used to explore the related factors of somatization symptoms and fatigue among middle school students. ResultsA total of 324 adolescents were included， and the overall detection rate of somatization symptoms was 6.5%， and the detection rate of moderate or above fatigue was 20.1%. The results of regression analysis showed that adolescents who were concerned about the earthquake for a longer time （≥1 h） had a higher risk of somatization symptoms （OR=5.430， 95%CI： 1.547-19.058）， and adolescents who received pre-earthquake training had a lower degree of fatigue （OR=0.535， 95%CI： 0.292-0.981） （P<0.05）. ConclusionDuring the frequent earthquakes， adolescents have more somatization symptoms and fatigue. Therefore， it is crucial to enhance health education， reduce the emphasis on event-related reports， and implement earthquake prevention and disaster reduction training to improve the physical and mental health of adolescents.

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.