Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

OBJECTIVE: The analgesic effect of acupuncture has been widely accepted. Nevertheless, the mechanism behind its analgesic effect remains elusive, thus impeding the progress of research geared toward enhancing the analgesic effect of acupuncture. This paper investigated the role of acupuncture needle surface textures on acupuncture's analgesic effect by creating four experimental acupuncture needles with different patterns of surface augmentation. METHODS: Four types of acupuncture needles with different surface textures (the lined needle, circle needle, sandpaper needle, and threaded needle) were designed. Additionally, the force/torque measurement system used a robot arm and mechanical sensor to measure the force on the needle during insertion and manipulation. To perform acupuncture analgesia experiments, four experimental acupuncture needles and a normal needle were inserted into the Zusanli (ST36) acupoint of rats with inflammatory pain. By comparing the force and torque and the analgesic efficacy of the different acupuncture needles, these experiments tested the role of acupuncture needle body texture on acupuncture analgesia. RESULTS: The analgesic effects of different acupuncture needle body textures varied. Specifically, the force required to penetrate the skin with the lined needle was not greater than that for the normal needle; however, the needle with inscribed circles and the sandpaper-roughened needle both required greater force for insertion. Additionally, the torque of the lined needle reached 2 × 10^-4 N·m under twisting manipulation, which was four times greater the torque of a normal needle (5 × 10^-5 N·m). Furthermore, the lined needle improved pain threshold and mast cell degranulation rate compared to the normal needle. CONCLUSION Optimizing the texture of acupuncture needles can enhance acupuncture analgesia. The texture of our experimental acupuncture needles had a significant impact on the force needed to penetrate the skin and the torque needed to manipulate the needle; it was also linked to variable analgesic effects. This study provides a theoretical basis for enhancing the analgesic efficacy of acupuncture through the modification of needles and promoting the development of acupuncture therapy. Please cite this article as: Sun MZ, Wang X, Li YC, Liu YH, Yu Y, Ren LJ, Gu W, Yao W. Effects of acupuncture needle modification on acupuncture analgesia. J Integr Med. 2025; 23(1): 66-78.
Needles ; Acupuncture Analgesia/methods* ; Animals ; Rats ; Male ; Acupuncture Points ; Rats, Sprague-Dawley

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective To investigate the key factors influencing the implementation of long-acting injection-promoting policies and propose effective improving paths.Methods Qualitative interviews were carried out for stakeholders involved in the promotion of long-acting injections,based on the consolidated framework for implementation research.Additionally,countermeasures for identified barriers were proposed based on expert recommendations for implementation changes.Results A total of 46 health administrators,healthcare workers,and patients in Beijing were interviewed.The study identified several barriers in the strength and quality of evidence,adaptability,relative advantage,complexity and cost,patient needs and resources,external collaboration,external policies and incentives,organizational structural characteristics,and self-efficacy.Conclusions From the perspectives and experiences of stakeholders,the promotion of long-acting injections has shown initial success but still faces multiple obstacles.It is recommended that efforts should be made to coordinate and adapt policies,improve and incentivize relative organizations,and continuously strengthen the advocacy and education for individuals.
Humans ; Beijing ; Delayed-Action Preparations ; Health Personnel ; Health Policy ; Injections

Objective To gain insights into the care service preferences and willingness of patients with severe mental disorders in Beijing,analyze the main factors affecting their participation in care services,and provide data support and decision-making reference for the optimal design of care services for patients with severe mental disorders and the improvement of relevant policies.Methods In July 2022,a questionnaire survey was conducted for a part of Beijing community patients with severe mental disorders selected by multi-stage stratified sampling,including the basic personal information and the preferences of discrete choice experiment.A mixed Logit model was used to perform regression analysis on the care service preferences,and the trade off between general and monetary attributes was quantified by willingness to pay(WTP).Results A total of 242 questionnaires were distributed,and 181 valid questionnaires were collected,with a response rate of 74.79%.The regression coefficients for the four attributes-service type,service content,service frequency,and service cost-all showed statistical significance(all P<0.05).Patients' most preferred attribute level was a service frequency covering about 90% of the time per month/year( β=1.059),while the least preferred was full-time residential care( β=-1.025).Increasing the service frequency from 30% to 90% corresponded to a WTP of 492.5 yuan,while changing the service type from home-based care to full-time residential care resulted in a WTP of -476.6 yuan.Moreover,there were differences in care service preferences and WTP among patient groups with different characteristics(all P<0.05).Conclusions Service type,service content,service frequency,and service cost all significantly affect the care service preferences of patients with severe mental disorders.There is heterogeneity in care service preferences among patient groups with different characteristics.
Humans ; Mental Disorders/therapy* ; Patient Preference ; Beijing ; Surveys and Questionnaires ; Male ; Female ; Choice Behavior ; Adult ; Middle Aged

ObjectiveTo investigate the application value of a multimodal model integrating radiomics features， deep learning features， and clinical structured data in predicting severe acute pancreatitis （SAP）， and to provide more accurate tools for the early identification of SAP in clinical practice. MethodsThe patients with acute pancreatitis （AP） who attended The First Affiliated Hospital of Soochow University， Jintan Hospital Affiliated to Jiangsu University， and Suzhou Yongding Hospital from January 1， 2017 to December 31， 2023 were included. Related data were collected， including demographic information， previous medical history， etiology， laboratory test data， and systemic inflammatory response syndrome （SIRS） within 24 hours after admission， as well as imaging data within 72 hours after admission， while related scores were calculated， including Ranson score， modified CT severity index （MCTSI）， bedside index for severity in acute pancreatitis （BISAP）， and systemic inflammatory response syndrome， albumin， blood urea nitrogen and pleural effusion （SABP） score. The model was constructed in the following process： （1） three-dimensional CT images were used to extract and identify radiomics features， and a radiomics classification model was established based on the extreme gradient Boost （XGBoost） algorithm； （2） U-Net is used to perform semantic segmentation of three-dimensional CT images， and then the results of segmentation were imported into 3D ResNet50 to construct a deep learning classification model； （3） the predicted values of the above two models were integrated with clinical structured data to establish a multimodal model based on the XGBoost algorithm. The variable importance plot and local interpretability plot were used to perform visual interpretation of the model. The independent samples t-test was used for comparison of normally distributed continuous data between groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between groups； the chi-square test or Fisher’s exact test was used for comparison of categorical data between groups. The receiver operating characteristic （ROC） curve was plotted for each model and existing scoring systems， and the area under the ROC curve （AUC） was calculated to assess their performance； the Delong test was used for comparison of AUC. ResultsA total of 609 patients who met the criteria were included， among whom 114 （18.7%） developed SAP. In this study， the data of 426 patients from The First Affiliated Hospital of Soochow University was used as the training set， and the data of 183 patients from Jintan Hospital Affiliated to Jiangsu University and Suzhou Yongding Hospital were used as the independent test set. The multimodal model had an AUC of 0.914 in the test set， which was significantly higher than the AUC of traditional scoring systems such as MCTSI （AUC=0.827）， Ranson score （AUC=0.675）， BISAP （AUC=0.791）， and SABP score （AUC=0.648）； in addition， the multimodal model showed a significant improvement in performance compared with the radiomics classification model （AUC=0.739） and the deep learning classification model （AUC=0.685） （the Delong test： Z=-3.23， -4.83， -3.48， -4.92， -4.31， and -4.59， all P <0.01）. The top 10 variables in terms of importance in the multimodal model were pleural effusion， predicted value of the deep learning model， predicted value of the radiomics model， triglycerides， calcium ions， SIRS， white blood cell count， age， platelets， and C-reactive protein， suggesting that the above variables had significant contributions to the performance of the model in predicting SAP. ConclusionBased on structured data， radiomic features， and deep learning features， this study constructs a multicenter prediction model for SAP based on the XGBoost algorithm， which has a better predictive performance than existing traditional scoring systems and unimodal models.

Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91％of the total clinical isolates and 4.07％of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89％were isolated from children and 33.11％were isolated from adults.More than 90％of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79％in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06％were isolated from children and 19.94％isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0％.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.

Objective:To examine the effect of antidepressant treatment on the longitudinal depressive burden in patients with bipolar depression.Methods:Subjects were recruited from a national multicenter, naturalistic observational project: Comprehensive Assessment and Follow-up Descriptive Study on Bipolar Disorder study (CAFE-BD). A total of 110 patients with bipolar depression (51 males, 59 females; aged 18-64 years, mean age 34.4±11.1 years) were consecutively enrolled between January 2012 and December 2013 from outpatients and inpatients of nine medical institutions, including six psychiatric hospitals and three general hospitals. Based on the use of antidepressants as defined in this study, patients were classified into a medicated group (Ads, n=74) and a non-medicated group (nAds, n=36). Diagnosis of bipolar depression was confirmed using the MINI (Chinese version), and baseline and follow-up assessments were conducted using the Assessment of Mood Disorders Evaluation (ADE) and the Clinical Monitoring Form (CMF). Depression burden indicators, including aggregate depression scores (SUM-D), number of depressive symptoms (NUM-D), and total depression burden, were compared between the Ads group and nAds group at mid-term (the 6 th month) and endpoint (the 12 nd month). Longitudinal changes in these indicators were also analyzed. Results:The proportion of bipolar depressive patients on antidepressants was 67% (74/110). Among them, 85% (63/74) were taking antidepressants at baseline; this dropped to 76% (56/74) at mid-term, and 64% (47/74) at the endpoint. SUM-D were higher in the Ads group than in the nAds group at baseline (9 (6.5, 11) vs 7.38 (5.5, 9.0); W=1 712.00, P=0.015), and there was no statistically significant difference in NUM-D and total depressive burden between two groups at any time points ( P>0.05). Compared to baseline, the Ads group had significantly lower SUM-D (0.5 (0, 1), 1.33 (0.5, 2.5) vs. 9 (6.5, 11); W=2 770.00, 2 743.00), NUM-D (0 (0, 0), 0 (0, 1) vs. 7 (5, 8); W=2 621.00, 2 601.50) and total depressive burden (c 2=64.36, 59.00) at both mid-term and endpoint (all P<0.001); While SUM-D (0.59 (0.4, 0.7), 1 (0.8, 2.5) vs. 7.38 (5.5, 9.0); W=664.50, W=666.00), NUM-D (0 (0, 0), 0 (0, 1) vs. 6 (4, 7); W=527.00, 528.00) and total depression burden ( χ 2=31.00, 31.00) in the nAds group were also significantly decreased at both mid-term and endpoint (all P<0.001). There were no statistically significant differences in the changes in depression burden indicators between the two groups from baseline to mid-follow-up or endpoint, nor from mid-follow-up to endpoint ( P>0.05). Conclusion:In a 12-month real-world naturalistic follow-up study, both medicated and non-medicated bipolar depression groups experienced significant and similar reductions in depression burden.