Background As common public facilities essential to daily life, hair and beauty salons frequently contain various airborne toxic and hazardous pollutants potentially leading to adverse health effects for salon practitioners. Objective To characterize the indoor air pollution profiles of common contaminants in hair and beauty salons in Shanghai and to evaluate the associated health risks for practitioners, in order to provide a scientific basis for strengthening the public health management in Shanghai and protecting the health of practitioners. Methods The air quality monitoring data of hair and beauty salons in Shanghai from 2016 to 2024 were obtained from the “Health Hazard Factors Monitoring Program for Public Places” of the National Institute of Environmental Health, Chinese Center for Disease Control and Prevention. Monitoring indicators included particulate matter ≤10 μm in aerodynamic diameter (PM₁₀), particulate matter ≤2.5 μm in aerodynamic diameter (PM_2.5), formaldehyde, ammonia, benzene, toluene, and xylene. Indicator compliance rates were calculated across various years in accordance with GB 9666-1996 Hygienic standard for barber shop and beauty shop and GB 37488-2019 Hygiene indicators and limit for public places; specifically, PM_2.5 was assessed against the limits stipulated in GB/T 18883-2022 Standards for indoor air quality. A questionnaire survey was conducted among salon practitioners to collect weekly working days and daily working hours. The non-carcinogenic risks associated with inhalation exposure to formaldehyde, ammonia, benzene, toluene, and xylene as well as the carcinogenic risks posed by formaldehyde and benzene were evaluated following WS/T 777-2021 Technical guide for environmental health risk assessment of chemical exposure and the U.S. Environmental Protection Agency inhalation risk model. Results The overall compliance rates of PM₁₀, formaldehyde, ammonia, benzene, and toluene in the air of hair and beauty salons in Shanghai from 2016 to 2024 were 92.13%, 96.59%, 96.15%, 94.93%, and 94.97%, respectively; the overall compliance rate of xylene was a little lower (85.92%), and the overall compliance rate of PM_2.5 was 57.18%. The P₅₀ concentrations of PM₁₀, PM_2.5, formaldehyde, ammonia, benzene, toluene, and xylene did not exceed the corresponding limits. The P₅₀ of non-carcinogenic risk indicator (hazard quotient, HQ) for formaldehyde, ammonia, benzene, toluene, and xylene were <1. The probabilities of non-carcinogenic risk HQ >1 for formaldehyde and xylene were 41.4% and 10.9%, respectively, which were higher than that of other pollutants. The P₅₀ of carcinogenic risk (CR) for formaldehyde and benzene were between 1.0×10⁻⁶ and 1.0×10⁻⁴, while the probabilities of CR >1.0×10⁻⁴ were 16.9% and 14.0%, respectively. Conclusion The overall compliance rate of common pollutant concentrations in the air of hair and beauty salons in Shanghai is high, and the hygienic condition meets the requirements of national standards. The non-carcinogenic health risks posed by formaldehyde and xylene to employees (with formaldehyde being more prominent), as well as the carcinogenic risks associated with formaldehyde and benzene, deserve heightened attention in future health supervision.

BACKGROUND:Rheumatoid arthritis is widely prevalent worldwide,with its high incidence and universality that considerably affects patients' quality of life.Previous studies have focused on a few immune cells or cytokines,whereas this study comprehensively provides a more complete view of the immune mechanisms in rheumatoid arthritis.OBJECTIVE:To explore the causal relationship between 731 immune cell phenotypes and rheumatoid arthritis using the Mendelian randomization method,thereby providing evidence of causality.METHODS:The 731 immune cell phenotypes used in this study were sourced from the GWAScatalog database,jointly developed by the National Human Genome Research Institute(NHGRI)and the European Bioinformatics Institute(EBI).The rheumatoid arthritis data were from the Finngen database,developed by the Finnish Institute for Molecular Medicine(FIMM).The inverse variance weighting method was employed as the primary analytical approach.Additionally,multiple analytical methods,including MR-Egger,weighted mode,simple mode,and weighted median,were concurrently utilized to complement the final results.Sensitivity analyses(Cochran's Q test,MR-Egger regression,and MR-presso analysis)were also conducted to verify the stability and feasibility of the data.RESULTS AND CONCLUSION:(1)After excluding results through heterogeneity testing,the inverse variance weighting analysis indicated that 10 absolute cell counts,15 median fluorescence intensities of surface antigen levels,1 morphological characteristic,and 9 relative cell counts had a causal relationship with the occurrence of rheumatoid arthritis.(2)According to cell classification,this study found that seven types of B cells,seven types of classical dendritic cells,six types of mature T cells,four types of monocytes,three types of myeloid cells,three types of TBNK cells(lymphocyte subset T cells,B cells and natural killer cells),and five types of Tregs had a causal association with the occurrence of rheumatoid arthritis.(3)Through comprehensive bidirectional two-sample MR analysis,we demonstrated the complex causal relationships between multiple immune phenotypes and rheumatoid arthritis,highlighting the intricate interaction patterns between the immune system and rheumatoid arthritis.These results provide new biomarkers for the early screening and diagnosis of rheumatoid arthritis in China,and help to improve the diagnostic accuracy and sensitivity.

BACKGROUND:Rheumatoid arthritis is widely prevalent worldwide,with its high incidence and universality that considerably affects patients' quality of life.Previous studies have focused on a few immune cells or cytokines,whereas this study comprehensively provides a more complete view of the immune mechanisms in rheumatoid arthritis.OBJECTIVE:To explore the causal relationship between 731 immune cell phenotypes and rheumatoid arthritis using the Mendelian randomization method,thereby providing evidence of causality.METHODS:The 731 immune cell phenotypes used in this study were sourced from the GWAScatalog database,jointly developed by the National Human Genome Research Institute(NHGRI)and the European Bioinformatics Institute(EBI).The rheumatoid arthritis data were from the Finngen database,developed by the Finnish Institute for Molecular Medicine(FIMM).The inverse variance weighting method was employed as the primary analytical approach.Additionally,multiple analytical methods,including MR-Egger,weighted mode,simple mode,and weighted median,were concurrently utilized to complement the final results.Sensitivity analyses(Cochran's Q test,MR-Egger regression,and MR-presso analysis)were also conducted to verify the stability and feasibility of the data.RESULTS AND CONCLUSION:(1)After excluding results through heterogeneity testing,the inverse variance weighting analysis indicated that 10 absolute cell counts,15 median fluorescence intensities of surface antigen levels,1 morphological characteristic,and 9 relative cell counts had a causal relationship with the occurrence of rheumatoid arthritis.(2)According to cell classification,this study found that seven types of B cells,seven types of classical dendritic cells,six types of mature T cells,four types of monocytes,three types of myeloid cells,three types of TBNK cells(lymphocyte subset T cells,B cells and natural killer cells),and five types of Tregs had a causal association with the occurrence of rheumatoid arthritis.(3)Through comprehensive bidirectional two-sample MR analysis,we demonstrated the complex causal relationships between multiple immune phenotypes and rheumatoid arthritis,highlighting the intricate interaction patterns between the immune system and rheumatoid arthritis.These results provide new biomarkers for the early screening and diagnosis of rheumatoid arthritis in China,and help to improve the diagnostic accuracy and sensitivity.

Objective:To reveal the coupling mechanism of beam temporal profile and tissue oxygen content on radical kinetics, further explain the potential biological basis of the FLASH effect, and provide a reference for beam optimization and treatment planning design of FLASH radiotherapy (FLASH-RT).Methods:TOPAS-nBio v3.0 was used to simulate the physical and chemical processes of electron beams in water, and a full-scale kinetic model was established covering the generation, diffusion, reaction, and quenching of free radicals such as hydroxyl radical (·OH) and hydrated electrons (e aq-). Under different beam temporal profiles (single pulse, multi-pulses, continuous wave irradiation) and different oxygen concentration conditions, the evolution dynamics of free radicals were systematically simulated. At the same time, the data on e aq- content were obtained by experimental measurement of laser absorption spectroscopy to verify the accuracy of the model prediction. Results:The changing trend of e aq- concentration measured in the experiment was highly consistent with the simulation result, verifying the reliability of the constructed model. The beam time structure had a significant impact on the peak value and duration of free radical concentration. The single-pulse structure can cause the free radicals to rapidly increase and then quickly quench in a short time, while the continuous or long-pulse structure can cause the radical concentration to remain at a high level for a long time. The evolution of ·OH was not sensitive to the oxygen environment, while e aq- are greatly affected by the oxygen environment. The scavenging efficiency of free radicals in a hypoxic environment was significantly decreased, leading to an enhanced accumulation of oxidative damage to biological macromolecules. The lifespan of e aq- in an oxygen-rich environment decreased rapidly. Conclusions:Radical kinetics are regulated by both the beam temporal profile and oxygen content. FLASH-RT can utilize single-pulse or multi-pulses intervals to form periodic windows, reducing normal tissue damage by efficiently scavenging free radicals through antioxidants, while free radicals in tumor tissues continuously accumulate and amplify damage, thus generating a selective protective effect.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

OBJECTIVE T o explore the prevention and control measures for the hospital-acquired pulmonary mu-cormycosis caused by Cunninghamella bertholletiae.METHODS One case of patient with pulmonary mucormyco-sis caused by Cunninghamella bertholletiae who was treated in pediatric intensive care unit(PICU)of a three-A general hospital was enrolled in the study,the process of clinical diagnosis and treatment was summarized.A ret-rospective survey regarding four aspects including people,machine,materials and environment was conducted.The related factors leading to the hospital-acquired infections in the patient were analyzed.RESULTS The child was diagnosed with severe aplastic anemia and underwent hematopoietic stem cell transplantation,the child was treated with various invasive procedures during the treatment period and was infected with pulmona-ry mucormycosis caused by the rare Cunninghamella bertholletiae.The occurrence of the pulmonary mucormyco-sis was associated with the poor management of medical textile,insufficient environmental cleaning and disinfec-tion and nonstandard invasive procedures.CONCLUSIONS The Cunninghamella bertholletiae infection is less com-mon,but the risk of death is high.It is necessary for the medical institutions to complete the prevention and con-trol measures and intensify the health care workers'capabilities in identification of the pathogenic fungus so as to reduce the incidence of pulmonary mucormycosis caused by the pathogen.

Objective:To investigate the long-term outcomes of using 3D-printed preformed titanium meshes in repair and reconstruction of orbital region.Methods:A retrospective analysis was conducted on patients with tumors invading the naso-orbito-maxillary region who underwent surgical resection and repair/reconstruction with 3D-printed preformed titanium meshes. The patients were collected at three medical centers (the Third Affiliated Hospital of Sun Yat-sen University, Xijing Hospital of Air Force Military Medical University, and Shenzhen Longgang District Ear, Nose and Throat Hospital) from 2016 to 2023. Tumor extent was evaluated radiologically, and the surgical approaches, reconstruction outcomes, surgical complications, and long-term follow-up results were analyzed.Results:A total of 46 patients from the three centers were included in this study, comprising 27 males and 19 females, with an average age of 51 years (range: from 13 to 86 years). Among them, 4 patients had benign tumors, while the remaining 42 had malignant tumors. The median follow-up duration was 60.7 months (range: from 19.0 to 75.0 months). Postoperatively, symmetrical globe position was achieved in 38 cases without significant diplopia; 4 cases exhibited enophthalmos without diplopia, and 4 cases had enophthalmos with diplopia. Twelve patients received preoperative radiotherapy, and 30 patients received postoperative radiotherapy. Six patients developed enophthalmos, and 6 experienced titanium mesh exposure after radiotherapy. Following treatment completion, 3 patients underwent repair using frontal flaps, 1 using a superficial temporal artery island flap, and 2 using free flaps. All remaining patients showed no postoperative infections, and their wounds healed normally.Conclusion:The application of 3D-printed preformed titanium mesh enables precise repair of postoperative defects in patients with naso-orbital tumors, facilitating reliable reconstruction of the orbital and facial contours with straightforward operation and dependable outcomes.

Objective:To predict mechanical complications (MC) following spinal deformity surgery for adult spine deformity (ASD) using machine learning models, identify key risk factors, and develop a visualizable tool for individualized risk assessment.Methods:Clinical and radiological data from 525 patients with ASD who underwent surgery in our hospital between January 2017 and December 2021 were collected. Patients were randomly assigned to a training set (70%) and a test set (30%) for model development. The cohort included 88 males and 437 females, with a mean age of 42.2±18.1 years. Variables included demographic data, comorbidities, local and systemic radiological parameters, paraspinal muscle fat infiltration (FI), and vertebral bone quality (VBQ) scores. Multiple machine learning algorithms: Random Forest (RF), Gaussian Naive Bayes (GNB), Light GBM, Support Vector Machine (SVM), XGBoost (XGB), and Logistic Regression (LR) were trained and evaluated. Model performance was compared using the receiver operating characteristic curve (ROC) and precision-recall curve (PRC). SHAP (Shapley Additive Explanations) was used to rank risk factors, while LIME (Local Interpretable Model-Agnostic Explanations) was applied to visualize MC risk in individual cases.Results:Of the 525 patients, 135 (25.7%) developed postoperative MC. Among these, 80 (59.3%) experienced proximal junction kyphosis or failure (PJK/PJF), 7 (5.2%) had distal junction kyphosis or failure (DJK/DJF), 28 (20.7%) sustained rod fractures, and 29 (21.5%) showed significant loss of correction. In the validation cohort, the RF model achieved the highest area under the curve (AUC=0.80), followed by GNB (0.77), XGB (0.76), LR (0.74), LightGBM (0.73), and SVM (0.66). The RF model also demonstrated the best PRC value (0.58), highest sensitivity (0.65), and lowest Brier score (0.20). GNB, Light GBM, and LR models achieved the highest accuracy (0.78 each), while LightGBM exhibited the highest specificity (0.93). SHAP analysis identified higher preoperative VBQ scores, larger T 1 pelvic angle (TPA), and higher paraspinal muscle FI as the main risk factors for MC. Based on the RF model, a LIME-based tool was successfully constructed for individualized MC risk estimation. Conclusion:The RF model demonstrated the best overall predictive performance for MC. A machine learning-based prediction model has the potential to provide valuable guidance for surgical decision-making in ASD patients.

Objective:To investigate the feasibility and safety of implementing a domestic vehicle-mounted remote mobile robotic surgical system in thyroid surgery applications, integrated with 5G communication technology.Methods:Using the main system located on the vehicle-mounted mobile robot operating platform of the 960th Hospital of PLA Joint Logistics Support Force and the slave system of Weifang Traditional Chinese Hospital, the remote radical thyroidectomy 5G communication technology, and analyze the clinical and information transmission data of two female patients who underwent remote mobile robot thyroid cancer surgery on October 21, 2024 at Weifang Traditional Chinese Medicine Hospital.Results:The remote radical thyroidectomy was conducted by the robosurgeons utilizing a vehicle-mounted mobile robotic surgical system, and the procedure was successfully completed without necessitating intermediate open surgery. The operation durations for patient 1 and patient 2 were 135 minutes and 108 minutes, respectively, with 7 and 13 lymph nodes dissected, respectively. The average delay in surgical data transmission was recorded at 61.9 milliseconds, with no instances of signal interruption or frame loss. The procedure proceeded smoothly, without any jamming, and the audio and video transmissions were consistently clear. Follow up for 21 days after surgery showed no complications such as hoarseness, skin damage, or lymphatic fistula.Conclusion:The implementation of a vehicle-mounted remote mobile robotic surgery system for thyroid surgery has demonstrated safety and feasibility. Furthermore, the utilization of the 5G network offers rapid data transmission and minimal latency, closely approximating the therapeutic efficacy of traditional robotic thyroidectomy.