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: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

Objective: To analyze the distribution characteristics of fluoride and trichloromethane in drinking water in rural schools in Guizhou Province and assess their health risks, so as to provide a scientific basis for ensuring the safety of drinking water in rural schools. Methods: During the dry season (March to May) and wet season (July to September) of 2020 to 2022, 788 rural primary and secondary schools in agricultural counties (districts) in Guizhou Province were selected for investigation by using a direct sampling method. A total of 1 566 drinking water samples were collected from these schools, and the mass concentrations of fluoride and trichloromethane in the water samples were detected. The Mann-Whitney U test was used for intergroup comparison, and a health risk assessment model was employed to evaluate the health risks of students oral intake of fluoride and trichloromethane. Results: From 2020 to 2022, the mass concentrations of fluoride and trichloromethane in the drinking water of rural schools in Guizhou Province all met the standards, and the ranges were no detection to 0.99 mg/L and (no detection to 0.06)×10 -3 mg/L, respectively. The mass concentrations of fluoride in dry and wet seasons were 0.05(0.05,0.10), 0.05(0.05,0.10) mg/L, the mass concentrations of trichloromethane were [0.02(0.02,1.00)]×10 -3 , [0.02(0.02,1.00)]×10 -3 mg/L, the mass concentrations of fluoride in factory water and terminal water were 0.05(0.05,0.05), 0.05(0.05,0.10) mg/L, and the differences were not statistically significant ( Z=-0.04, -0.88, - 0.98 , P >0.05). There was a statistically significant difference in the mass concentration of trichloromethane between factory water and peripheral water [0.02(0.02,0.02)×10 -3 , 0.02(0.02,1.05)×10 -3 mg/L]( Z=-2.16, P < 0.05 ). The non-carcinogenic risk assessment values for students oral exposure to fluoride and trichloromethane were in the range of 0.01(0.01,0.03)-0.03(0.03,0.06) and [0.26( 0.26 ,14.54)]×10 -4 -[0.52(0.52,48.62)]×10 -4 , respectively, all of which were at acceptable levels; the carcinogenic risk assessment values for oral exposure to trichloromethane were in the range of [0.08(0.08, 4.51 )]×10 -7 -[0.16(0.16,15.07)]×10 -7 , indicating a low risk. Conclusions The health risks of students expore to fluoride and trichloromethane in drinking water in rural schools of Guizhou Province are low. It is necessary to strengthen the standardized management of disinfection in some rural drinking water projects and the monitoring of fluoride in water sources to reduce the exposure risk to children.

In order to further standardize the vaccination of kidney transplant candidates and recipients in China, the Branch of Organ Transplantation of Chinese Medical Association has organized experts in kidney transplantation and infectious diseases. Based on the "Vaccination of Solid Organ Transplant Candidates and Recipients: Guidelines from the American Society of Transplantation Infectious Diseases Community of Practice", and in combination with the clinical reality of infectious diseases and vaccination after organ transplantation in China, as well as referring to relevant recommendations from home and abroad in recent years, these guidelines are formulated from aspects such as epidemiology, types of vaccines, vaccination principles, target population, and specific vaccine administration. The "Guidelines for Vaccination of Kidney Transplant Candidates and Recipients in China" aims to provide theoretical reference for medical workers in the field of kidney transplantation in China, regarding the vaccination of kidney transplant candidates and recipients. It is expected to better guide the vaccination of kidney transplant candidates and recipients, reduce the risk of postoperative infection, and improve survival outcomes.

Objective To investigate the global burden of visceral leishmaniasis (VL) from 1990 to 2021 and predict the trends in the burden of VL from 2022 to 2035, so as to provide insights into global VL prevention and control. Methods The global age-standardized incidence, prevalence, mortality and disability-adjusted life years (DALYs) rates of VL and their 95% uncertainty intervals (UI) were captured from the Global Burden of Disease Study 2021 (GBD 2021) data resources. The trends in the global burden of VL were evaluated with average annual percent change (AAPC) and 95% confidence interval (CI) from 1990 to 2021, and gender-, age-, country-, geographical area- and socio-demographic index (SDI)-stratified burdens of VL were analyzed. The trends in the global burden of VL were projected with a Bayesian age-period-cohort (BAPC) model from 2022 to 2035, and the associations of age-standardized incidence, prevalence, mortality, and DALYs rates of VL with SDI levels were examined with a smoothing spline model. Results The global age-standardized incidence [AAPC = -0.25%, 95% CI: (-0.25%, -0.24%)], prevalence [AAPC = -0.06%, 95% CI: (-0.06%, -0.06%)], mortality [AAPC = -0.25%, 95% CI: (-0.25%, -0.24%)] and DALYs rates of VL [AAPC = -2.38%, 95% CI: (-2.44%, -2.33%)] all appeared a tendency towards a decline from 1990 to 2021, and the highest age-standardized incidence [2.55/10⁵, 95% UI: (1.49/10⁵, 4.07/10⁵)], prevalence [0.64/10⁵, 95% UI: (0.37/10⁵, 1.02/10⁵)], mortality [0.51/10⁵, 95% UI: (0, 1.80/10⁵)] and DALYs rates of VL [33.81/10⁵, 95% UI: (0.06/10⁵, 124.09/10⁵)] were seen in tropical Latin America in 2021. The global age-standardized incidence and prevalence of VL were both higher among men [0.57/10⁵, 95% UI: (0.45/10⁵, 0.72/10⁵); 0.14/10⁵, 95% UI: (0.11/10⁵, 0.18/10⁵)] than among women [0.27/10⁵, 95% UI: (0.21/10⁵, 0.33/10⁵); 0.06/10⁵, 95% UI: (0.05/10⁵, 0.08/10⁵)], and the highest mortality of VL was found among children under 5 years of age [0.24/10⁵, 95% UI: (0.08/10⁵, 0.66/10⁵)]. The age-standardized incidence (r = -0.483, P < 0.001), prevalence (r = -0.483, P < 0.001), mortality (r = -0.511, P < 0.001) and DALYs rates of VL (r = -0.514, P < 0.001) correlated negatively with SDI levels from 1990 to 2021. In addition, the global burden of VL was projected with the BAPC model to appear a tendency towards a decline from 2022 to 2035, and the age-standardized incidence, prevalence, mortality and DALYs rates were projected to be reduced to 0.11/10⁵, 0.03/10⁵, 0.02/10⁵ and 1.44/10⁵ in 2035, respectively. Conclusions Although the global burden of VL appeared an overall tendency towards a decline from 1990 to 2021, the burden of VL showed a tendency towards a rise in Central Asia and western sub-Saharan African areas. The age-standardized incidence and prevalence rates of VL were relatively higher among men, and the age-standardized mortality of VL was relatively higher among children under 5 years of age. The global burden of VL was projected to continue to decline from 2022 to 2035.

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.

Purpose: The aim of this study was to investigate the feasibility of an intelligent handheld ultrasound (US) device for assisting non-expert general practitioners (GPs) in detecting carotid plaques (CPs) in community populations. Methods: This prospective parallel controlled trial recruited 111 consecutive community residents. All of them underwent examinations by non-expert GPs and specialist doctors using handheld US devices (setting A, setting B, and setting C). The results of setting C with specialist doctors were considered the gold standard. Carotid intima-media thickness (CIMT) and the features of CPs were measured and recorded. The diagnostic performance of GPs in distinguishing CPs was evaluated using a receiver operating characteristic curve. Inter-observer agreement was compared using the intragroup correlation coefficient (ICC). Questionnaires were completed to evaluate clinical benefits. Results: Among the 111 community residents, 80, 96, and 112 CPs were detected in settings A, B, and C, respectively. Setting B exhibited better diagnostic performance than setting A for detecting CPs (area under the curve, 0.856 vs. 0.749; P<0.01). Setting B had better consistency with setting C than setting A in CIMT measurement and the assessment of CPs (ICC, 0.731 to 0.923). Moreover, measurements in setting B required less time than the other two settings (44.59 seconds vs. 108.87 seconds vs. 126.13 seconds, both P<0.01). Conclusion Using an intelligent handheld US device, GPs can perform CP screening and achieve a diagnostic capability comparable to that of specialist doctors.

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: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.