OBJECTIVE To systematically review the current application of discrete choice experiment （DCE） in the value assessment and preference measurement of orphan medicinal product （OMP）， and to provide a reference for the standardized use of this methodology in China. METHODS The systematic search was conducted across Chinese and English databases including CNKI， Wanfang Data， VIP， CBM， PubMed， Web of Science， Medline， and Embase. Original studies that employed DCE to evaluate the value or preferences related to OMP were included. The methodological quality and reporting completeness of the included studies were assessed using the ISPOR Conjoint Analysis Checklist and the DIRECT Checklist， respectively. Respondent populations， attribute setting， and the relative importance of attributes were summarized and analyzed. RESULTS Eight eligible studies were included； all studies demonstrated high-quality reporting and methodological rigor. Respondents comprised the general public， patients/caregivers， policymakers， and other stakeholders. The number of DCE attributes ranged from 4 to 13 （median＝7.5）. Through thematic synthesis， these attributes were categorized into three dimensions， namely “disease-related” “treatment-related” and “economic/financial-related” along with 14 secondary criteria. The most frequently included secondary criteria were treatment efficacy （13 occurrences）， disease severity （9 occurrences）， safety （7 occurrences）， unmet medical need （6 occurrences）， and treatment cost （5 occurrences）. Rankings of relative importance identified treatment efficacy as the most valued criterion across most studies， followed by health insurance financing. CONCLUSIONS DCE applications in the value assessment of OMP have begun to converge on a relatively consistent core attribute framework and selection preference. Future research should further promote the use of DCE to inform attribute and criterion selection in multi-criteria decision analysis frameworks for OMP.

OBJECTIVE: To investigate the effects of Bushen Huoxue Granule on the ubiquitin-proteasome system (UPS) in an in vitro model of Parkinson's disease. METHODS: After treated with 1-methyl-4-phenylpyridinium (MPP⁺, 1 mmol/L) for 24 h, the cells were incubated with drug-free serum, Madopar-containing serum or Bushen Huoxue Granule-containing serum (BCS, 5%, 10%, and 20%) for another 24 h. The levels of α-synuclein (α-syn), tyrosine hydroxylase (TH) and UPS-related proteins were detected by Western blot. The expression levels of α-syn in PC12 cells were also analyzed by Western blot after treated with proteasome inhibitor MG132 and WT-α-syn plasmid transfection, respectively, as well as the alterations induced by subsequent BCS intervention. Immunocytochemistry was performed to determine the changes in α-syn phosphorylation at serine 129 (pSer129-α-syn) expression. The 20S proteasome levels were measured by enzyme-linked immunosorbnent assay. RESULTS: BCS (volume fraction ⩽20%) intervention could alleviate the MMP⁺-induced cell viability decrease (P<0.05). In the MPP⁺ treated cells, α-syn was up-regulated, while TH and proteins of UPS such as ubiquitin (Ub), Ub binding with Ub-activating enzyme (UBE1), Parkin and Ub C-terminal hydrolase-1 (UCHL-1) were down-regulated (P<0.05). BCS intervention could attenuate the above changes (P<0.05). The activity of BCS on blocking α-syn accumulation was weakened by MG132 (P<0.05). While α-syn level was significantly increased in cells transfected with plasmid, and reduced by BCS intervention (P<0.05). pSer129-α-syn was increased in MPP⁺-induced PC12 cells, whereas decreased by later BCS intervention (P<0.05). The 20S proteasome activity of MPP⁺-induced PC12 cells was decreased, but increased after BCS intervention (P<0.05). CONCLUSION BCS intervention protected UPS function, increased 20S proteasome activity, promoted pathological α-syn clearance, restored cell viability, and reversed the damage caused by MPP⁺ in the in vitro model of Parkinson's disease.
PC12 Cells ; alpha-Synuclein/metabolism* ; Rats ; Animals ; 1-Methyl-4-phenylpyridinium/toxicity* ; Proteasome Endopeptidase Complex/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Ubiquitin/metabolism* ; Cell Survival/drug effects* ; Phosphorylation/drug effects* ; Tyrosine 3-Monooxygenase/metabolism*

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

An efficient analytical method was developed for simultaneous detection of alkylamines and alkylamides in food packaging plastics using liquid chromatography-high resolution mass spectrometry(LC-HRMS).Based on the physicochemical properties of alkylamines and alkylamides,as well as the complexity of plastic samples,sample pretreatment and chromatographic-mass spectrometric parameters were optimized.The samples were extracted by vortex-ultrasonic extraction with a methanol-acetonitrile mixture for 15 min,followed by nitrogen evaporation to concentrate the extract,reconstitution,and analysis.The chromatographic mobile phase consisted of 0.1%formic acid aqueous solution and acetonitrile,and a gradient elution was used.The electrospray ionization(ESI)source was operated in positive ion mode,and mass spectrometry data were collected in full scan and data-dependent acquisition modes.Quantification was performed using an isotope-labeled internal standard method.The results showed that within the quantification range of 1-1000 ng/mL,the calibration curves exhibited good linearity(R2>0.99).Some compounds interfered with the validation experiments at higher concentrations,so only 10 kinds of target analytes were validated.Using a mixed food packaging plastic matrix,the recoveries at spiking levels of 40,400,and 4000 ng/g were mostly between 66.0%and 117.1%,with relative standard deviations ranging from 0.6%to 10.6%.The method was applied to detect 14 food packaging plastic samples,and the results showed that the concentrations of alkylamines and alkylamides ranged from not detected to 8924 ng/g.This method offered high sensitivity and accuracy,and was suitable for the screening and quantitative determination of alkylamines and alkylamides in plastics.

Hypothermia is a clinical syndrome characterized by core body temperature<35℃,caused by significant heat loss from body surface in cold environment.As a systemic cold injury,it can be lethal if treatment is delayed.Emergent diagnosis and treatment of hypothermia are expected to improve the prognosis of patients.In 2005,the U.S.Army Research Institute of Environmental Medicine(USARIEM)issued guidelines for the prevention and management of cold injuries,but there has been no corresponding standard in China.Therefore,Emergency Branch of Chinese Medical Rescue Association,Emergency Medical Equipment Society of China Association of Medical Equipment,Integrated Rehabilitation Medical Branch of Chinese Medical Rescue Association,and Pre-Hospital Emergency Care Working Committee of Chinese Aging Well Association jointly developed the Chinese Expert Consensus on Emergent Treatment of Hypothermia(2025 edition).The consensus covers the pathophysiology,etiology and epidemiology,diagnosis and severity grading,prehospital treatment,and in-hospital treatment of hypothermia,including 15 recommendations in total,aiming to provide guidance for the relevant clinical rescue work.

Hypothermia is a clinical syndrome characterized by core body temperature<35℃,caused by significant heat loss from body surface in cold environment.As a systemic cold injury,it can be lethal if treatment is delayed.Emergent diagnosis and treatment of hypothermia are expected to improve the prognosis of patients.In 2005,the U.S.Army Research Institute of Environmental Medicine(USARIEM)issued guidelines for the prevention and management of cold injuries,but there has been no corresponding standard in China.Therefore,Emergency Branch of Chinese Medical Rescue Association,Emergency Medical Equipment Society of China Association of Medical Equipment,Integrated Rehabilitation Medical Branch of Chinese Medical Rescue Association,and Pre-Hospital Emergency Care Working Committee of Chinese Aging Well Association jointly developed the Chinese Expert Consensus on Emergent Treatment of Hypothermia(2025 edition).The consensus covers the pathophysiology,etiology and epidemiology,diagnosis and severity grading,prehospital treatment,and in-hospital treatment of hypothermia,including 15 recommendations in total,aiming to provide guidance for the relevant clinical rescue work.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To construct a predictive model for mortality in patients with multiple trauma combined with thoracic injuries and evaluate its predictive value.Methods:A retrospective cohort study was conducted to analyze the clinical data of 184 patients with multiple trauma combined with thoracic injuries admitted to the International Zhuang Medicine Hospital Affiliated to Guangxi University of Chinese Medicine from April 2019 to December 2023, including 129 males and 55 females, aged 19-85 years [(46.1±13.7)years]. According to the prognostic outcomes at 3-month follow-up after discharge, the patients were divided into survival group ( n=145) and death group ( n=39). Data were recorded in both groups at admission, including gender, age, and cause of injury, laboratory tests such as systolic blood pressure, oxygen saturation (SaO 2), hemoglobin (Hb), neutrophil-to-lymphocyte ratio (NLR), and lactate, combined injuries such as the number of combined injuries, number of rib fracture, bilateral rib fracture, first-rib fracture, sternum fracture, thoracic vertebral fracture, bilateral pulmonary contusion, bilateral pneumothorax, subarachnoid hemorrhage, subdural hematoma, epidural hematoma, skull fracture, skull base fracture, cervical vertebral fracture, brain herniation, cerebral contusion, lumbar vertebral fracture, pelvic and abdominal cavity hematoma, liver injury, kidney injury, spleen injury, clavicle fracture, scapular fracture, femoral fracture, and pelvic fracture, and injury scores such as shock index (SI), modified shock index (MSI), injury severity score (ISS), revised trauma score (RTS), Glasgow coma score (GCS), and thoracic trauma severity (TTS) score. Univariate binary logistic regression analysis was used to screen for risk factors of death in patients with multiple trauma combined with thoracic injuries. LASSO regression and multivariate logistic regression analysis were employed to identify predictive variables and independent risk factors for mortality in those patients and to construct a regression equation. A nomogram prediction model based on the regression equation was developed using R language. Receiver operating characteristic (ROC) curves were plotted to evaluate the discrimination of the model. The ROC curves were internally validated using the Bootstrap method with 1 000 resamples. The calibration of the model was assessed using the Hosmer-Lemeshow (H-L) goodness-of-fit test. The clinical application value of the model was evaluated using decision curve analysis (DCA) and clinical impact curve (CIC) analysis. Results:There were statistically significant differences between the survival group and the death group in systolic blood pressure, SaO 2, NLR, lactate, number of combined injuries, subarachnoid hemorrhage, subdural hematoma, skull fracture, skull base fracture, brain herniation, liver injury, SI, MSI, ISS, RTS, GCS, and TTS ( P<0.05 or 0.01). The results of the univariate binary logistic regression analysis showed that the above-mentioned related variables except for systolic blood pressure were all significantly associated with death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Five predictive variables, TTS, GCS, brain herniation, ISS, and lactate were obtained in LASSO regression analysis. The results of the multivariate logistic regression analysis showed that GCS ( OR=0.70, 95% CI 0.58, 0.83), brain herniation ( OR=46.18, 95% CI 4.27, 499.26), TTS ( OR=1.71, 95% CI 1.30, 2.24), and lactate ( OR=1.35, 95% CI 1.01, 1.80) were independent risk factors for death in patients with multiple trauma combined with thoracic injuries ( P<0.05 or 0.01). Based on the aforementioned independent risk factors, a regression formula was constructed as follows: P=e x/(1+e x), with the x=-0.36×"GCS"+3.83×"brain herniation"+0.53×"TTS"+0.30×"lactate levels"-11.03. The area under the ROC curve (AUC) of the predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on the equation was 0.97 (95% CI 0.93, 1.00). The AUC was internally validated using the Bootstrap method with 1 000 samples, resulting in an AUC of 0.97 (95% CI 0.91, 1.00). The results of the H-L goodness-of-fit test showed that the bias-corrected calibration curve of the model was in good consistence with the actual curve and both of them were close to the ideal curve. In the evaluation of the clinical application value of the predictive model, the DCA results showed that the predictive model could achieve good clinical net benefit. The CIC results showed that when the threshold probability was greater than 0.7, the model-identified high-risk patients for death highly matched the patients who actually died. Conclusion:The predictive model for mortality in patients with multiple trauma combined with thoracic injuries based on GCS, brain herniation, TTS, and lactate has good predictive performance and clinical application value.

Objective To evaluate the efficacy and safety of brexpiprazole in treating acute schizophrenia.Methods Patients with schizophrenia were randomly divided into treatment group and control group.The treatment group was given brexpiprozole 2-4 mg·d-1 orally and the control group was given aripiprazole 10-20 mg·d-1orally,both were treated for 6 weeks.Clinical efficacy of the two groups,the response rate at endpoint,the changes from baseline to endpoint of Positive and Negative Syndrome Scale(PANSS),Clinical Global Impression-Improvement(CGI-S),Personal and Social Performance scale(PSP),PANSS Positive syndrome subscale,PANSS negative syndrome subscale were compared.The incidence of treatment-related adverse events in two groups were compared.Results There were 184 patients in treatment group and 186 patients in control group.After treatment,the response rates of treatment group and control group were 79.50％(140 cases/184 cases)and 82.40％(150 cases/186 cases),the scores of CGI-I of treatment group and control group were(2.00±1.20)and(1.90±1.01),with no significant difference(all P＞0.05).From baseline to Week 6,the mean change of PANSS total score wese(-30.70±16.96)points in treatment group and(-32.20±17.00)points in control group,with no significant difference(P＞0.05).The changes of CGI-S scores in treatment group and control group were(-2.00±1.27)and(-1.90±1.22)points,PSP scores were(18.80±14.77)and(19.20±14.55)points,PANSS positive syndrome scores were(-10.30±5.93)and(-10.80±5.81)points,PANSS negative syndrome scores were(-6.80±5.98)and(-7.30±5.15)points,with no significant difference(P＞0.05).There was no significant difference in the incidence of treatment-related adverse events between the two group(69.00％vs.64.50％,P＞0.05).Conclusion The non-inferiority of Brexpiprazole to aripiprazole was established,with comparable efficacy and acceptability.