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: 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: 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 understand the incidence risk and risk factors of tuberculosis (TB) among middle school students in Chongqing, so as to provide a basis for formulating TB prevention and control strategies. Methods: From September to December 2022, 32 181 middle school students were selected as the study cohort from 15 administrative districts in Chongqing by using the stratified cluster random sampling method. All cohort members were screened with the tuberculin skin test (TST), and relevant information was collected from January 1, 2023 to December 31, 2024. On the basis of active screening, the follow up data of the participants were compared with the National Tuberculosis Management Information System to obtain the incidence status of the study subjects. The Log rank test was used to compare the TB incidence rates among students with different characteristics, and a Cox proportional hazards model was established to analyze the incidence risk and risk factors of TB. Results: The TST screening rate of the cohort members was 93.0%. During the 2 year follow up period, a total of 36 TB cases occurred, with a cumulative incidence rate of 111.87/100 000 and an incidence density of 55.95/100 000. Among them, the cumulative incidence rate of students from public schools (170.44/ 100 000 ) was higher than that of students from private schools (41.16/100 000), the cumulative incidence rate of students in schools located in high epidemic areas (153.95/100 000) was higher than that in medium epidemic areas (69.00/100 000), and the difference was statistically significant ( χ 2=11.49, 4.73, both P <0.05). The Log-rank test for different TST results showed that the difference in TB comulative incidence rate between students with strongly positive TST results (216.55/ 100 000 ) and those with negative TST results (81.40/100 000) was statistically significant ( χ 2=5.85, P <0.05). Univariate analysis using the Cox proportional hazards model revealed that the risk of TB was lower in students from private schools ( HR=0.25, 95% CI = 0.10-0.59) and students in medium epidemic areas ( HR=0.46, 95%CI =0.23-0.94); whereas the risk of TB was increased in students with strongly positive TST results ( HR=1.39, 95%CI =1.05-1.84) (all P <0.05). Multivariate Cox regression analysis showed that the risk of TB in students from private schools was lower than that of students from public schools ( HR=0.23, 95%CI=0.08-0.62, P <0.05). Conclusions The annual average incidence rate of TB among middle school students in Chongqing is at a relatively high level. It is necessary to strengthen the management and intervention for student groups, including those in public schools, those in schools located in high epidemic areas, and those with strongly positive TST results, so as to reduce the incidence rate of TB.

Objective: To explore the independent and interactive effects of school green space exposure and outdoor activity duration on screening myopia among primary and secondary school students, so as to provide theoretical support for the prevention and control of screening myopia in children and adolescents. Methods: From September to November 2023, 117 487 primary and secondary school students from 497 schools were selected using a cluster random sampling method, covering 98 counties (cities, districts) in Jiangsu Province. Data on the students screening myopia status and associated health influencing factors were collected and analyzed. School green space exposure was quantified using the normalized difference vegetation index (NDVI), which was extracted with ArcGIS Pro software; meanwhile, information on students outdoor activity duration was gathered through self reported questionnaires. Multivariate Logistic regression was applied to assess the independent and interactive effects of green space exposure and outdoor activity duration on screening myopia among primary and secondary school students. Results: Univariate analysis showed that there were statistically significant differences in screening myopia detection rates among primary and secondary school students of different genders, NDVI groups, every outdoor activity duration, monitoring points, school stages, parents educational level, and whether they lived on campus or had parents with screening myopia ( χ 2=88.91-1 950.08, all P <0.05); as the school age and sedentary time increased, the detection rate of screening myopia in primary and secondary school students also increased ( χ 2 trend =8 410.15, 2 028.91, both P <0.05). Independent effects showed that compared to the low NDVI group, the medium and high NDVI groups had lower risks of screening myopia ( OR =0.93, 0.95, both P <0.05). Compared to those with outdoor activity duration<2 h/d, students with outdoor activity duration≥2 h/d had a lower risk of screening myopia ( OR =0.96, P <0.05). When stratified by school level, compared to the low NDVI group, the medium NDVI group had lower risks of screening myopia in primary and junior high schools (primary school: OR =0.91; junior high school: OR =0.88, both P <0.05). Compared to those with outdoor activity duration<2 h/d, junior high school students with outdoor activity duration≥2 h/d had a lower risk of screening myopia ( OR = 0.90, P <0.05). When stratified by monitoring site, urban primary and secondary school students in the medium and high NDVI groups and those with outdoor activity duration≥2 h/d had lower risks of screening myopia ( OR =0.92, 0.92, 0.93, all P <0.05). Interactive effects showed that when medium or high NDVI was combined with outdoor activity duration≥2 h/d, the risks of screening myopia among primary and secondary school students were lower (medium NDVI×≥2 h/d: OR =0.89; high NDVI×≥ 2 h/d : OR =0.89, both P <0.05), and the combined effect was superior to that of a single factor. Conclusion Green space exposure and outdoor activity duration have negative correlations with screening myopia among primary and secondary students, and the combined effect is better than that of a single factor.

Objective: To investigate the association between outdoor light at night (LAN) exposure and sleep quality among primary and secondary school students, and to examine the pathways of negative affects including depressive, stress and anxiety symptoms, so as to provide a theoretical basis for optimizing the school environment and enhancing the physical and mental well being of students. Methods: In December 2024, a total of 36 885 students from 154 primary and secondary schools in Suzhou, Nantong, and Changzhou were included via a stratified cluster sampling method. Sleep quality and negative affect were assessed by using the Pittsburgh Sleep Quality Index (PSQI), Center for Epidemiologic Studies Depression Scale (CES-D), Generalized Anxiety Disorder-7 (GAD-7), and Depression, Anxiety and Stress Scale-21 (DASS-21), respectively. Outdoor LAN exposure data were obtained from the national polar orbiting partnership visible infrared imaging radiometer suite nighttime light(NPP-VIIRS NTL) dataset provided by the National Earth System Science Data Center. Multivariate Logistic regression model was employed to analyze the association between LAN exposure and sleep quality across different regions, with stratification by monitoring site. Spearman correlation analysis was used to examine the relationships between LAN, negative affect, and sleep quality. The mediating role of negative affect was tested by using Model 4 of the PROCESS macro. Results: The detection rates among students were 13.95%( n =5 147) for depressive symptom, 16.72%( n =6 166) for stress symptom, and 17.49%( n =6 451) for anxiety symptom. The median outdoor LAN exposure was 28.85(19.10, 41.44)nW/(cm 2 · ( sr). After adjusting for confounders, multivariate Logistic regression analysis showed that high LAN exposure ( Q 4) was positively associated with sleep problems (urban areas: OR =1.28, 95% CI = 1.17- 1.41; rural areas: OR =1.21, 95% CI =1.07-1.36; both P <0.05). Spearman correlation analysis revealed significant positive correlations between LAN exposure, depressive symptoms, stress symptoms, anxiety symptoms, and sleep quality ( r =0.03-0.75, all P < 0.01). The mediation analysis indicated that all dimensions of negative affect significantly mediated the relationship between high LAN exposure and poor sleep quality (all P <0.01). Specifically, the indirect effects were 0.03 (95% CI =0.02-0.05) for depressive symptom, 0.05(95% CI =0.03-0.08) for stress symptom, and 0.07(95% CI =0.05-0.09) for anxiety symptom. Overall, 31.9% of the total effect was mediated by negative affect, with anxiety (14.89%) being the strongest mediator, followed by stress (10.64%) and depression(6.38%). Conclusion Reducing high levels of outdoor LAN exposure and implementing interventions targeting negative affect may contribute to improved sleep quality among primary and secondary school students.

OBJECTIVE: To screen the optimal regimen of acupuncture and moxibustion for obstructive sleep apnea hypopnea syndrome (OSAHS), so as to provide the evidences for clinical decision-making. METHODS: From 7 databases in Chinese and English i.e. the Full-Text Database of China Journal Network (CNKI), Wanfang Data Knowledge Service Platform (Wanfang), VIP Information Chinese Journal Service Platform (VIP), Chinese Biomedical Literature Database (SinoMed), PubMed, Web of Science (WOS) and Cochrane Library, randomized controlled trial (RCT) articals of OSAHS treated with acupuncture and moxibustion were searched. The quality of evidence was evaluated with the modified Jadad scale, the evaluation index was established and the optimal regimen of acupuncture and moxibustion for OSAHS was screened by multi-index decision analysis. RESULTS: A total of 10 RCTs were included, and the filiform needling therapy was optimal in treatment of OSAHS. The acupoints included Lianquan (CV23), Danzhong (CV17), Zhongwan (CV12), and bilateral Kongzui (LU6), Pishu (BL20), Fenglong (ST40), Zusanli (ST36), Yinlingquan (SP9) and Zhaohai (KI6). Zusanli (ST36) received the reinforcing method, Pishu (BL20) and Fenglong (ST40) were stimulated with the reducing technique, and the rest acupoints with the uniform reinforcing-reducing. Each acupoint was manually manipulated once every 10 min during the needle retention for 30 min. Acupuncture was delivered once a day, 5 times a week and for consecutive 4 weeks. Among the included literature, the severity of disease was not reported in detail, the filiform needling was the dominant intervention, the local acupoints such as Lianquan (CV23) and Panglianquan (Extra) were mainly selected. The apnea-hypopnea index and the minimum oxygen saturation were taken as the evaluation indexes, and the effect was evaluated in reference to the generally accepted standards. The attention to safety evaluation was insufficient, the report on methodology was not adequate and the quality was low. CONCLUSION Filiform needling is the dominant therapy of acupuncture and moxibustion for OSAHS, and the local acupoints are considered specially. But the quality of clinical research should be improved.
Humans ; Moxibustion ; Acupuncture Therapy ; Sleep Apnea, Obstructive/therapy* ; Acupuncture Points ; Randomized Controlled Trials as Topic

Recent advances in large models demonstrate significant prospects for transforming the field of medical imaging. These models, including large language models, large visual models, and multimodal large models, offer unprecedented capabilities in processing and interpreting complex medical data across various imaging modalities. By leveraging self-supervised pretraining on vast unlabeled datasets, cross-modal representation learning, and domain-specific medical knowledge adaptation through fine-tuning, large models can achieve higher diagnostic accuracy and more efficient workflows for key clinical tasks. This review summarizes the concepts, methods, and progress of large models in medical imaging, highlighting their potential in precision medicine. The article first outlines the integration of multimodal data under large model technologies, approaches for training large models with medical datasets, and the need for robust evaluation metrics. It then explores how large models can revolutionize applications in critical tasks such as image segmentation, disease diagnosis, personalized treatment strategies, and real-time interactive systems, thus pushing the boundaries of traditional imaging analysis. Despite their potential, the practical implementation of large models in medical imaging faces notable challenges, including the scarcity of high-quality medical data, the need for optimized perception of imaging phenotypes, safety considerations, and seamless integration with existing clinical workflows and equipment. As research progresses, the development of more efficient, interpretable, and generalizable models will be critical to ensuring their reliable deployment across diverse clinical environments. This review aims to provide insights into the current state of the field and provide directions for future research to facilitate the broader adoption of large models in clinical practice.
Humans ; Diagnostic Imaging/methods* ; Precision Medicine/methods* ; Image Processing, Computer-Assisted/methods*