OBJECTIVE: Acupuncture therapies are known for their effectiveness in treating a variety of gastric diseases, although the mechanisms underlying these effects are not fully understood. This study tested the effectiveness of electroacupuncture (EA) at acupoints Zhongwan (RN12) and Weishu (BL21) for managing gastric motility disorder (GMD) and investigated the underlying mechanisms involved. METHODS: A GMD model was used to evaluate the impact of EA on various aspects of gastric function including the amplitude of gastric motility, electrogastrogram, food intake, and the rate of gastric emptying. Immunofluorescence techniques were used to explore the activation of spinal neurons by EA, specifically examining the presence of cholera toxin B subunit (CTB)-positive neurons and fibers emanating from acupoints RN12 and BL21. The stimulation of γ-aminobutyric acid (GABA)-ergic neurons in the spinal dorsal horn, the inhibition of sympathetic preganglionic neurons in the spinal lateral horn, and their collective effects on the activity of sympathetic nerves were examined. RESULTS: EA at RN12 and BL21 significantly improved gastric motility compromised by GMD. Notably, EA activated spinal neurons, with CTB-positive neurons and fibers from RN12 and BL21 being detectable in both the dorsal root ganglia and the spinal dorsal horn. Further analysis revealed that EA at these acupoints not only stimulated GABAergic neurons in the spinal dorsal horn but also suppressed sympathetic preganglionic neurons in the spinal lateral horn, effectively reducing excessive activity of sympathetic nerves triggered by GMD. CONCLUSION EA treatment at RN12 and BL21 effectively enhances gastric motility in a GMD model. The therapeutic efficacy of this approach is attributed to the activation of spinal neurons and the modulation of the spinal GABAergic-sympathetic pathway, providing a neurobiological foundation for the role of acupuncture in treating gastric disorders. Please cite this article as: Zhang MT, Liang YF, Dai Q, Gao HR, Wang H, Chen L, Huang S, Wang XY, Shen GM. A spinal neural circuit for electroacupuncture that regulates gastric functional disorders. J Integr Med. 2025; 23(1): 56-65.
Electroacupuncture ; Animals ; Male ; Acupuncture Points ; Stomach Diseases/physiopathology* ; Rats, Sprague-Dawley ; Gastrointestinal Motility ; Rats ; Gastric Emptying ; Neurons ; Spinal Cord ; Stomach/physiopathology*

Wastewater-based epidemiology has emerged as a transformative surveillance tool for estimating substance consumption and monitoring disease prevalence, particularly during the COVID-19 pandemic. It enables the population-level monitoring of illicit drug use, pathogen prevalence, and environmental pollutant exposure. In this perspective, we summarize the key challenges specific to the Chinese context: (1) Sampling inconsistencies, necessitating standardized 24-hour composite protocols with high-frequency autosamplers (≤ 15 min/event) to improve the representativeness of samples; (2) Biomarker validation, requiring rigorous assessment of excretion profiles and in-sewer stability; (3) Analytical method disparities, demanding inter-laboratory proficiency testing and the development of automated pretreatment instruments; (4) Catchment population dynamics, reducing estimation uncertainties through mobile phone data, flow-based models, or hydrochemical parameters; and (5) Ethical and data management concerns, including privacy risks for small communities, mitigated through data de-identification and tiered reporting platforms. To address these challenges, we propose an integrated framework that features adaptive sampling networks, multi-scale wastewater sample banks, biomarker databases with multidimensional metadata, and intelligent data dashboards. In summary, wastewater-based epidemiology offers unparalleled scalability for equitable health surveillance and can improve the health of the entire population by providing timely and objective information to guide the development of targeted policies.
China/epidemiology* ; Humans ; Wastewater/analysis* ; COVID-19/epidemiology* ; Public Health ; Wastewater-Based Epidemiological Monitoring ; SARS-CoV-2

Objective:To rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

Objective To evaluate the clinical efficacy of hip-knee-ankle active and passive exercise therapy in patients with early-to mid-stage knee osteoarthritis(KOA).Methods A total of 180 patients with early to mid-stage knee osteoarthritis(KOA)were recruited from the First Affiliated Hospital of Hebei University of Tradi-tional Chinese Medicine between March 2023 and March 2024.Patients were randomly assigned to one of four groups:active movement group,passive movement group,combined movement group,and control group,with 45 patients in each group.The active movement group received hip-knee-ankle active movement therapy daily until the end of follow-up.The passive movement group underwent hip-knee-ankle passive movement therapy three times per week for two weeks.The combined movement group received both active and passive therapies.The control group was administered oral celecoxib capsules(200 mg once daily for two weeks).Joint function was assessed in all four groups before treatment,at two weeks post-treatment,and at 14 weeks post-treatment.The primary outcome measure was the WOMAC joint function score,while secondary outcomes included the WOMAC pain score,stiffness score,and quality of life score(SF-12).Results A total of 160 patients completed the trial,with 39 in the active group,42 in the passive group,40 in the combined group,and 39 in the control group.There were no significant differences in baseline characteristics among the groups(P>0.05).Compared to baseline,the WOMAC scores for function,pain,and stiffness in the passive,combined,and control groups decreased significantly at both 2 and 14 weeks post-treatment(P<0.05),while the SF-12 scores increased significantly(P<0.05).Between 2 and 14 weeks post-treat-ment,the active and combined groups showed further significant decreases in WOMAC function,pain,and stiffness scores(P<0.05)and increases in SF-12 scores(P<0.05).At 2 weeks post-treatment,compared to the control group,the passive and combined groups exhibited significantly lower WOMAC function scores(P<0.05),with no significant difference between the passive and combined groups(P>0.05).By 14 weeks post-treatment,the active and combined groups demonstrated significantly lower WOMAC function scores(P<0.05),with the combined group showing a significantly lower score than the active group(P<0.05).Conclusion The four therapeutic approaches demonstrate a certain degree of efficacy in improving joint function for patients with early and mid-stage KOA.The passive therapy group exhibits superior short-term outcomes,while the active therapy group shows better long-term benefits.The combined therapy group presents notable advantages in both short-term and long-term effi-cacy,although its short-term effectiveness does not surpass that of the passive therapy group.It is recommended for patients with early and mid-stage KOA who have underlying gastrointestinal and cardiovascular conditions.

Objective To evaluate the clinical efficacy of hip-knee-ankle active and passive exercise therapy in patients with early-to mid-stage knee osteoarthritis(KOA).Methods A total of 180 patients with early to mid-stage knee osteoarthritis(KOA)were recruited from the First Affiliated Hospital of Hebei University of Tradi-tional Chinese Medicine between March 2023 and March 2024.Patients were randomly assigned to one of four groups:active movement group,passive movement group,combined movement group,and control group,with 45 patients in each group.The active movement group received hip-knee-ankle active movement therapy daily until the end of follow-up.The passive movement group underwent hip-knee-ankle passive movement therapy three times per week for two weeks.The combined movement group received both active and passive therapies.The control group was administered oral celecoxib capsules(200 mg once daily for two weeks).Joint function was assessed in all four groups before treatment,at two weeks post-treatment,and at 14 weeks post-treatment.The primary outcome measure was the WOMAC joint function score,while secondary outcomes included the WOMAC pain score,stiffness score,and quality of life score(SF-12).Results A total of 160 patients completed the trial,with 39 in the active group,42 in the passive group,40 in the combined group,and 39 in the control group.There were no significant differences in baseline characteristics among the groups(P>0.05).Compared to baseline,the WOMAC scores for function,pain,and stiffness in the passive,combined,and control groups decreased significantly at both 2 and 14 weeks post-treatment(P<0.05),while the SF-12 scores increased significantly(P<0.05).Between 2 and 14 weeks post-treat-ment,the active and combined groups showed further significant decreases in WOMAC function,pain,and stiffness scores(P<0.05)and increases in SF-12 scores(P<0.05).At 2 weeks post-treatment,compared to the control group,the passive and combined groups exhibited significantly lower WOMAC function scores(P<0.05),with no significant difference between the passive and combined groups(P>0.05).By 14 weeks post-treatment,the active and combined groups demonstrated significantly lower WOMAC function scores(P<0.05),with the combined group showing a significantly lower score than the active group(P<0.05).Conclusion The four therapeutic approaches demonstrate a certain degree of efficacy in improving joint function for patients with early and mid-stage KOA.The passive therapy group exhibits superior short-term outcomes,while the active therapy group shows better long-term benefits.The combined therapy group presents notable advantages in both short-term and long-term effi-cacy,although its short-term effectiveness does not surpass that of the passive therapy group.It is recommended for patients with early and mid-stage KOA who have underlying gastrointestinal and cardiovascular conditions.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Objective:To rapidly and accurately detect volumetric modulated arc therapy (VMAT) plans with potentially inaccurate radiation doses.Methods:The measurement-based dosimetric verification result of 196 VMAT plans obtained using ArcCHECK phantoms were retrospectively collected. Independent dosimetric calculation and verification were conducted for these plans using the ArcherQA system based on a fast Monte Carlo algorithm. The gamma passing rates of dosimetric verification using ArcCHECK phantom and the ArcherQA system were compared, followed by their correlation analysis and linear regression fitting. The ArcherQA system′s gamma passing rate threshold used to detect positive dosimetric verification result obtained using ArcCHECK phantoms, as well as the specificity of the detection, were calculated. Based on this gamma passing rate threshold, another 50 VMAT plans were selected as a test set to assess the ArcherQA system′s ability to detect positive measurement-based dosimetric verification result.Results:The average gamma passing rates for the dosimetric verification of the VMAT plans using the ArcherQA system and ArcCHECK phantoms were 97.28% and 96.57% (3%/3 mm, TH=10%), respectively. Both rates had a correlation coefficient of 0.71 ( P < 0.01) and a linear fitting coefficient of 0.54 ( R2=0.51). When the gamma passing rate for dosimetric verification using ArcCHECK phantoms was set at 90% (3%/2 mm, TH=10%), the gamma passing rate threshold for dosimetric verification using the ArcherQA system should be adjusted to 94.8% to detect all VMAT plans with positive dosimetric verification result obtained using ArcCHECK phantoms, with a specificity of 67.8%. Using this threshold, the ArcherQA system detected all VMAT plans in the test set for which ArcCHECK phantom-based measurement yielded positive dosimetric verification result. Conclusions:By determining an appropriate gamma passing rate threshold, the ArcherQA system can rapidly and accurately detect VMAT plans with potentially inaccurate doses, thus ensuring treatment accuracy and improving work efficiency.

Objective To develop a deep learning-based denoising model for accelerating Monte Carlo(MC)simulation of electronic portal imaging device(EPID)transit dose images.Methods A total of 500 EPID fields were collected from 100 lung cancer patients undergoing 5-field intensity-modulated radiotherapy,with 400 fields randomly selected as training set,50 fields as validation set,and 50 fields as test set.EPID transit dose image datasets with low particle counts(1×107)and high particle counts(1×109)were simulated using the GPU-accelerated MC dose calculation engine ARCHER.A denoising network model named SUNet was constructed based on Swin Transformer and U-Net,and trained using low-particle-count images as input and high-particle-count images as output.Following training,SUNet model was used to denoise low-particle-count EPID images in the test set.Denoising performance was evaluated using structural similarity index(SSIM),peak signal-to-noise ratio(PSNR),and Gamma passing rates(3%/2 mm),and the computational efficiency of MC simulation combined with SUNet model was analyzed.Results Compared with the original low-particle-count images,the SUNet-denoised images showed significantly improved quality,reduced noise points,and smoother dose distribution.When benchmarked against high-particle-count images,the SUNet-denoised images achieved an average SSIM greater than 0.9,an average PSNR higher than 32 dB,and an average gamma passing rate exceeding 90%.The MC simulation combined with SUNet model required only 1.88 s to simulate a single EPID transit dose image,representing an approximate 40-fold improvement in computational efficiency as compared with high-particle-count MC simulation.Conclusion The deep learning-based denoising model substantially accelerates MC simulation of EPID transit dose images while preserving both image quality and dose accuracy,which provides possibilities for EPID-basedin vivodose verification.

Objective To explore the impact of the three-level assistance model based on the narrative nursing theory on the mental health status of medical staff.Methods 140 medical staff working in a third class hospital in Xiangyang City were selected as the research object.The three-level assistance model based on narrative nursing theory was used to intervene them from September 2021 to July 2022.The symptom self-assessment scales before and after the intervention were compared.Results Before the intervention,the total score of SCL-90(156.37±32.56)points and the scores of various symptom factors of medical staff were higher;After the intervention,the total score of SCL-90(133.35±43.48)points and the scores of various symptom factors were lower than those before the intervention and the difference was statistically significant(P<0.05).Conclusion The three-level assistance model based on narrative nursing theory can reduce the total score of SCL-90 and the scores of various symptom factors,improve the mental health status and mental health level.

Immmunosuppressants are mainly used to reduce rejection after solid organ transplantation,so as to improve the success rate of organ transplantation.However,long-term use of immunosuppressants can also serious-ly impair the immune function of patients,thereby increasing the risk of viral infection and postoperative complica-tions,leading to transplant failure.Therefore,patients need to use both immunosuppressants and antiviral agents.If some immunosuppressants with antiviral effects are found,the patient's burden of taking medicines will be greatly reduced.Currently,the immunosuppressants with antiviral effect have been focused by researchers.The gradual re-vealing of the antiviral mechanism of these immunosuppressants will help to optimize the treatment plan of postope-rative rehabilitation of organ transplant recipients.Based on the mechanism of rejection of transplanted organ,this paper systematically describes the types of viruses which closely related to infection of organ transplant patients and the molecular mechanism of some immunosuppressants in antiviral aspects,which further provides a new idea for clinical prevention and treatment of viral infection due to organ transplantation.