Chronic pain has emerged as a prevalent medical challenge in contemporary society.Patients suffering from chronic pain frequently develop comorbid psychological disorders,including anxiety,depression,post-traumatic stress disorder,and various psychiatric syndromes.These psychological complications not only affect patients' pain perception and responses,but may also constitute critical obstacles during pain management interventions.Acupuncture is a long-established clinical practice that has demonstrated remarkable efficacy in alleviating diverse pain types and has shown favorable therapeutic outcomes in ameliorating emotional disturbances such as anxiety and depression.The precise mechanisms underlying acupuncture-induced analgesia and anxiolytic effects,however,remain to be fully elucidated.In this context,it is essential to establish suitable and stable animal models to allow in-depth investigations into the pathogenesis of pain-related emotional disorders and the mechanistic foundations of acupuncture.This article presents a comprehensive review of recent literature regarding the selection of experimental animals,model-establishment method ologies,and behavioral-assessment paradigms pertaining to animal model platforms of chronic pain with comorbid anxiety.We also provide an in-depth discussion of research advancements regarding acupuncture intervention parameters,including needling techniques,acupoint selection,treatment duration,and efficacy evaluation within these animal models.This review proposes comprehensive and reference strategies for constructing preclinical animal models to investigate the mechanisms of acupuncture in managing chronic pain with comorbid anxiety,thus supporting scientific advancements in related research fields.

AIM:This study investigated the potential mechanism of acupuncture regulating adenosine A1 re-ceptor(ADORA1)in the mouse caudate putamen(CPu)to improve neuroplasticity and alleviate inflammatory pain.METHODS:Male C57BL/6 mice were randomly divided into five groups,namely,saline,complete Freund's adjuvant(CFA),acupuncture(MA),acupuncture+ADORA1 shRNA(MA+shRNA),and acupuncture+negative shRNA(MA+NCshRNA)groups.Twenty-one days before modeling,the mice in the MA+shRNA and MA+NCshRNA groups were in-jected with ADORA1 shRNA and control virus into the CPu.Modeling was performed 21 d later by injection of CFA into the right plantar skin of mice in the model and acupuncture groups to induce adjuvant-mediated arthritis.On day 2 after modeling,mice in the acupuncture groups received acupuncture at bilateral"Zusanli"points,30 min per session,once a day,for a total of 7 days.The paw thermal radiation pain threshold was used as an indicator of the effects on pain in the mice.Changes in the protein levels of ADORA1,synaptophysin(SYP),synapsin(SYN)I,SYN II,and brain-derived neurotrophic factor(BDNF)in the CPu were assessed by Western blot,and transmission electron microscopy was used to examine the dendritic structure and synaptic ultrastructure of neurons within the CPu.RESULTS:(1)Compared with the saline group,CFA modeling significantly reduced the thermal radiation pain threshold in mice(P<0.01),as well as the protein levels of ADORA1(P<0.01).Compared with the CFA group,the thermal pain threshold in the MA group in-creased between days 1 and 7 of acupuncture(P<0.05 or P<0.01),and ADORA1 protein levels increased(P<0.01).(2)Compared with the saline group,SYN I,SYN II,and BDNF protein levels were increased in the CFA group(P<0.05 or P<0.01),while relative to the CFA group,the levels of SYN I,SYN II,and BDNF were reduced in the acupuncture group(P<0.05 or P<0.01).No significant changes in SYP protein levels were observed in any of the groups.(3)Golgi staining and Sholl analysis showed that compared with the saline group,there were reductions in the total dendritic length and number of intersection points in the CFA group,while the dendritic spine density increased(P<0.05).Relative to the CFA group,the total dendritic length and the number of intersection points were increased in the MA group,while the dendritic spine density decreased(P<0.05).(4)Transmission electron microscopy revealed that compared with the Sa-line group,the synaptic clefts were narrower in the CFA group,while the density of synaptic vesicles in the presynaptic membrane was increased.Compared with the CFA group,synaptic clefts in the MA group were wider,and synaptic vesi-cle densities in the presynaptic membrane were reduced.(5)Twenty-one days after viral transfection,the thermal pain threshold in the MA and MA+NCshRNA groups increased from day 1 to day 7 of acupuncture relative to the CFA and MA+shRNA groups(P<0.05 or P<0.01),while the expression of ADORA1 was increased in both the MA and MA+NCshRNA groups(P<0.05 or P<0.01).(6)Compared with the CFA and MA+shRNA groups,the protein expression of SYN II and BDNF in the MA and MA+NCshRNA groups was reduced(P<0.05 or P<0.01).No significant changes in SYN I protein were observed in any of the groups.CONCLUSION:Acupuncture on Zusanli point can alleviate chronic pain in CFA-treated mice,potentially mediated by up-regulation of ADORA1 expression in the CPu brain region,thereby improving neuroplasticity.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

With the rapid advancement and iterative development of new artificial intelligence technologies, there remains a regulatory vacuum in corresponding governance measures among governments worldwide. Simultaneously, a technological and governance gap exists between developing countries and developed economies. In response, the World Health Organization (WHO) has released "Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models" to assist governments in strengthening governance capabilities in this field. This paper provides an in-depth analysis of the Guidance, aiming to identify challenges and risks associated with the application of multimodal large models in healthcare. Guided by ethical principles for advancing health through artificial intelligence, the paper examines the three-tier governance framework and recommendations outlined in the Guidance. Additionally, it evaluates the current state of AI governance in China, offering insights and reference points for improving AI governance in China′s healthcare sector.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

With the rapid advancement and iterative development of new artificial intelligence technologies, there remains a regulatory vacuum in corresponding governance measures among governments worldwide. Simultaneously, a technological and governance gap exists between developing countries and developed economies. In response, the World Health Organization (WHO) has released "Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models" to assist governments in strengthening governance capabilities in this field. This paper provides an in-depth analysis of the Guidance, aiming to identify challenges and risks associated with the application of multimodal large models in healthcare. Guided by ethical principles for advancing health through artificial intelligence, the paper examines the three-tier governance framework and recommendations outlined in the Guidance. Additionally, it evaluates the current state of AI governance in China, offering insights and reference points for improving AI governance in China′s healthcare sector.

Chronic pain has emerged as a prevalent medical challenge in contemporary society.Patients suffering from chronic pain frequently develop comorbid psychological disorders,including anxiety,depression,post-traumatic stress disorder,and various psychiatric syndromes.These psychological complications not only affect patients' pain perception and responses,but may also constitute critical obstacles during pain management interventions.Acupuncture is a long-established clinical practice that has demonstrated remarkable efficacy in alleviating diverse pain types and has shown favorable therapeutic outcomes in ameliorating emotional disturbances such as anxiety and depression.The precise mechanisms underlying acupuncture-induced analgesia and anxiolytic effects,however,remain to be fully elucidated.In this context,it is essential to establish suitable and stable animal models to allow in-depth investigations into the pathogenesis of pain-related emotional disorders and the mechanistic foundations of acupuncture.This article presents a comprehensive review of recent literature regarding the selection of experimental animals,model-establishment method ologies,and behavioral-assessment paradigms pertaining to animal model platforms of chronic pain with comorbid anxiety.We also provide an in-depth discussion of research advancements regarding acupuncture intervention parameters,including needling techniques,acupoint selection,treatment duration,and efficacy evaluation within these animal models.This review proposes comprehensive and reference strategies for constructing preclinical animal models to investigate the mechanisms of acupuncture in managing chronic pain with comorbid anxiety,thus supporting scientific advancements in related research fields.

AIM:This study investigated the potential mechanism of acupuncture regulating adenosine A1 re-ceptor(ADORA1)in the mouse caudate putamen(CPu)to improve neuroplasticity and alleviate inflammatory pain.METHODS:Male C57BL/6 mice were randomly divided into five groups,namely,saline,complete Freund's adjuvant(CFA),acupuncture(MA),acupuncture+ADORA1 shRNA(MA+shRNA),and acupuncture+negative shRNA(MA+NCshRNA)groups.Twenty-one days before modeling,the mice in the MA+shRNA and MA+NCshRNA groups were in-jected with ADORA1 shRNA and control virus into the CPu.Modeling was performed 21 d later by injection of CFA into the right plantar skin of mice in the model and acupuncture groups to induce adjuvant-mediated arthritis.On day 2 after modeling,mice in the acupuncture groups received acupuncture at bilateral"Zusanli"points,30 min per session,once a day,for a total of 7 days.The paw thermal radiation pain threshold was used as an indicator of the effects on pain in the mice.Changes in the protein levels of ADORA1,synaptophysin(SYP),synapsin(SYN)I,SYN II,and brain-derived neurotrophic factor(BDNF)in the CPu were assessed by Western blot,and transmission electron microscopy was used to examine the dendritic structure and synaptic ultrastructure of neurons within the CPu.RESULTS:(1)Compared with the saline group,CFA modeling significantly reduced the thermal radiation pain threshold in mice(P<0.01),as well as the protein levels of ADORA1(P<0.01).Compared with the CFA group,the thermal pain threshold in the MA group in-creased between days 1 and 7 of acupuncture(P<0.05 or P<0.01),and ADORA1 protein levels increased(P<0.01).(2)Compared with the saline group,SYN I,SYN II,and BDNF protein levels were increased in the CFA group(P<0.05 or P<0.01),while relative to the CFA group,the levels of SYN I,SYN II,and BDNF were reduced in the acupuncture group(P<0.05 or P<0.01).No significant changes in SYP protein levels were observed in any of the groups.(3)Golgi staining and Sholl analysis showed that compared with the saline group,there were reductions in the total dendritic length and number of intersection points in the CFA group,while the dendritic spine density increased(P<0.05).Relative to the CFA group,the total dendritic length and the number of intersection points were increased in the MA group,while the dendritic spine density decreased(P<0.05).(4)Transmission electron microscopy revealed that compared with the Sa-line group,the synaptic clefts were narrower in the CFA group,while the density of synaptic vesicles in the presynaptic membrane was increased.Compared with the CFA group,synaptic clefts in the MA group were wider,and synaptic vesi-cle densities in the presynaptic membrane were reduced.(5)Twenty-one days after viral transfection,the thermal pain threshold in the MA and MA+NCshRNA groups increased from day 1 to day 7 of acupuncture relative to the CFA and MA+shRNA groups(P<0.05 or P<0.01),while the expression of ADORA1 was increased in both the MA and MA+NCshRNA groups(P<0.05 or P<0.01).(6)Compared with the CFA and MA+shRNA groups,the protein expression of SYN II and BDNF in the MA and MA+NCshRNA groups was reduced(P<0.05 or P<0.01).No significant changes in SYN I protein were observed in any of the groups.CONCLUSION:Acupuncture on Zusanli point can alleviate chronic pain in CFA-treated mice,potentially mediated by up-regulation of ADORA1 expression in the CPu brain region,thereby improving neuroplasticity.

The rapid development of artificial intelligence in the field of healthcare has greatly improved diagnosis accuracy, disease prediction, personalized treatment and healthcare resource management. However, with the widespread application of medical artificial intelligence, challenges has emerged in the aspects of medical data, model development and evaluation, and societal considerations. Therefore, this study aims to explore challenges in the application of artificial intelligence in healthcare and suggest a series of feasible solutions to improve medical professional and researchers' understanding of medical artificial intelligence and enhance the quality of healthcare in clinical practice

Objective:To investigate the risk factors associated with one-year mortality following surgery for intertrochanteric fractures in elderly patients and develop a survival prediction model.Methods:A retrospective analysis was conducted on clinical data from 532 elderly patients with intertrochanteric fractures admitted to the People's Hospital of Xinjiang Uygur Autonomous Region and the People's Hospital of Xinyang between January 2020 and September 2022.Patient demographics, laboratory indicators, and surgical variables were documented.The primary outcome assessed was the one-year mortality rate.Risk factors were identified through univariate and multivariate Cox regression analyses, leading to the development of a prognostic model.The model's predictive performance was evaluated using the Concordance Index(C-Index), time-dependent receiver operating characteristic(ROC)curve, calibration curve, and decision curve analysis(DCA).Results:Multivariate Cox regression analysis identified several key factors associated with one-year mortality after intertrochanteric fractures in elderly patients.These factors included the modified five-item frailty index( OR=1.338, 95% CI: 1.147-1.561, P<0.001), ICU admission( OR=1.694, 95% CI: 1.230-2.333, P=0.001), preoperative hemoglobin levels( OR=1.281, 95% CI: 1.016-1.616, P=0.036), surgical waiting time( OR=1.570, 95% CI: 1.063-2.319, P=0.023), and age( OR=2.196, 95% CI: 1.712-2.816, P<0.001).The prediction model showed good consistency with a C-Index of 0.769(95% CI: 0.723-0.818)in the modeling group and 0.715(95% CI: 0.612-0.750)in the validation group.Time-dependent ROC areas under the curve were 0.802(95% CI: 0.722-0.850)and 0.718(95% CI: 0.640-0.808)for the modeling and validation groups, respectively.Calibration curves for both groups indicated a good model fit, and decision curve analysis demonstrated a positive net benefit, highlighting the clinical applicability of the model. Conclusions:The modified five-item frailty index, ICU admission, preoperative hemoglobin, surgical waiting time, and age independently predict one-year mortality after surgery for intertrochanteric fractures in elderly patients.This prognostic model, utilizing these factors, shows high predictive accuracy, assisting clinicians in quick personalized assessments and setting informed expectations in clinical practice.