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.

Traditional Chinese medicine(TCM) decoction pieces are a core carrier for the inheritance and innovation of TCM, and their quality and safety are critical to public health and the sustainable development of the industry. Conventional quality control models, while having established a well-developed system through long-term practice, still face challenges such as relatively long inspection cycles, insufficient objectivity in characterizing complex traits, and urgent needs for improving the efficiency of integrating multidimensional quality information when confronted with the dual demands of large-scale production and precision quality control. With the rapid development of artificial intelligence, machine learning can deeply analyze multidimensional data of the morphology, spectroscopy, and chemical fingerprints of decoction pieces by constructing high-dimensional feature space analysis models, significantly improving the standardization level and decision-making efficiency of quality evaluation. This article reviews the research progress in the application of machine learning in the processing, production, and rapid quality evaluation of TCM decoction pieces. It further analyzes current challenges in technological implementation and proposes potential solutions, offering theoretical and technical references to advance the digital and intelligent transformation of the industry.
Machine Learning ; Drugs, Chinese Herbal/standards* ; Quality Control ; Medicine, Chinese Traditional/standards* ; Humans

To investigate the impact of preoperative serum follicle-stimulating hormone (FSH) levels on the probability of testicular sperm retrieval, we conducted a study of nonobstructive azoospermic (NOA) men with different testicular volumes (TVs) who underwent microdissection testicular sperm extraction (micro-TESE). A total of 177 NOA patients undergoing micro-TESE for the first time from April 2019 to November 2022 in Shenzhen Zhongshan Obstetrics and Gynecology Hospital (formerly Shenzhen Zhongshan Urology Hospital, Shenzhen, China) were retrospectively reviewed. The subjects were divided into four groups based on average TV quartiles. Serum hormone levels in each TV group were compared between positive and negative sperm retrieval subgroups. Overall sperm retrieval rate was 57.6%. FSH levels (median [interquartile range]) were higher in the positive sperm retrieval subgroup compared with the negative outcome subgroup when average TV was <5 ml (first quartile [Q1: TV <3 ml]: 43.32 [17.92] IU l -1 vs 32.95 [18.56] IU l -1 , P = 0.048; second quartile [Q2: 3 ml ≤ TV <5 ml]: 31.31 [15.37] IU l -1 vs 25.59 [18.40] IU l -1 , P = 0.042). Elevated serum FSH levels were associated with successful micro-TESE sperm retrieval in NOA men whose average TVs were <5 ml (adjusted odds ratio [OR]: 1.06 per unit increase; 95% confidence interval [CI]: 1.01-1.11; P = 0.011). In men with TVs ≥5 ml, larger TVs were associated with lower odds of sperm retrieval (adjusted OR: 0.84 per 1 ml increase; 95% CI: 0.71-0.98; P = 0.029). In conclusion, elevated serum FSH levels were associated with positive sperm retrieval in micro-TESE in NOA men with TVs <5 ml. In men with TV ≥5 ml, increases in average TVs were associated with lower odds of sperm retrieval.
Humans ; Male ; Azoospermia/surgery* ; Sperm Retrieval/statistics & numerical data* ; Adult ; Follicle Stimulating Hormone/blood* ; Retrospective Studies ; Testis/pathology* ; Microdissection ; Organ Size

Methods: Data from students at Chengdu University of Traditional Chinese Medicine were collected and organized according to the 24 solar terms from January 21, 2020, to April 6, 2022. The data were used to identify nine TCM constitutions, including balanced constitution, Qi deficiency constitution, Yang deficiency constitution, Yin deficiency constitution, phlegm dampness constitution, damp heat constitution, stagnant blood constitution, Qi stagnation constitution, and specific-inherited predisposition constitution. Deep learning algorithms were employed to construct multi-layer perceptron (MLP), long short-term memory (LSTM), and deep belief network (DBN) models for the prediction of TCM constitutions based on the nine constitution types. To optimize these TCM constitution prediction models, this study introduced the attention mechanism (AM), grey wolf optimizer (GWO), and particle swarm optimization (PSO). The models’ performance was evaluated before and after optimization using the F1-score, accuracy, precision, and recall. Results: The research analyzed a total of 31 655 pieces of data. (i) Before optimization, the MLP model achieved more than 90% prediction accuracy for all constitution types except the balanced and Qi deficiency constitutions. The LSTM model's prediction accuracies exceeded 60%, indicating that their potential in TCM constitutional prediction may not have been fully realized due to the absence of pronounced temporal features in the data. Regarding the DBN model, the binary classification analysis showed that, apart from slightly underperforming in predicting the Qi deficiency constitution and damp heat constitution, with accuracies of 65% and 60%, respectively. The DBN model demonstrated considerable discriminative power for other constitution types, achieving prediction accuracy rates and area under the receiver operating characteristic (ROC) curve (AUC) values exceeding 70% and 0.78, respectively. This indicates that while the model possesses a certain level of constitutional differentiation ability, it encounters limitations in processing specific constitutional features, leaving room for further improvement in its performance. For multi-class classification problem, the DBN model’s prediction accuracy rate fell short of 50%. (ii) After optimization, the LSTM model, enhanced with the AM, typically achieved a prediction accuracy rate above 75%, with lower performance for the Qi deficiency constitution, stagnant blood constitution, and Qi stagnation constitution. The GWO-optimized DBN model for multi-class classification showed an increased prediction accuracy rate of 56%, while the PSO-optimized model had a decreased accuracy rate to 37%. The GWO-PSO-DBN model, optimized with both algorithms, demonstrated an improved prediction accuracy rate of 54%. Conclusion This study constructed MLP, LSTM, and DBN models for predicting TCM constitution and improved them based on different optimisation algorithms. The results showed that the MLP model performs well, the LSTM and DBN models were effective in prediction but with certain limitations. This study also provided a new technology reference for the establishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.

Objective To analyze the risk factors and survival status of Epstein-Barr virus(EBV)infection in pa-tients with aplastic anemia(AA)after haploid allogeneic hematopoietic stem cell transplantation(Haplo-HSCT).Methods Clinical data of 78 AA patients who underwent Haplo-HSCT in the hematology department of a hospital from January 1,2019 to October 31,2022 were analyzed retrospectively.The occurrence and onset time of EBV viremia,EBV-related diseases(EBV diseases),and post-transplant lymphoproliferative disorders(PTLD)were ob-served,risk factors and survival status were analyzed.Results Among the 78 patients,38 were males and 40 were females,with a median age of 33(9-56)years old;53 patients experienced EBV reactivation,with a total inci-dence of 67.9％,and the median time for EBV reactivation was 33(13,416)days after transplantation.Among pa-tients with EBV reactivation,49 cases(62.8％)were simple EBV viremia,2 cases(2.6％)were possible EBV di-seases,and 2 cases(2.6％)were already confirmed EBV diseases(PTLD).Univariate analysis showed that age 1＜40 years old at the time of transplantation,umbilical cord blood infusion,occurrence of acute graft-versus-host disease(aGVHD)after transplantation,and concurrent cytomegalovirus(CMV)infection were independent risk fac-tors for EBV reactivation in AA patients after Haplo-HSCT.Multivariate analysis showed that concurrent CMV in-fection was an independent risk factor for EBV reactivation in A A patients after Haplo-HSCT(P=0.048).Ritu-ximab intervention before stem cell reinfusion was a factor affecting the duration of EBV reactivation(P＜0.05).The mortality of EBV viremia,EBV diseases,and PTLD alone were 8.2％,50.0％,and 100％,respectively.The 2-year overall survival rate of patients with and without EBV reactivation were 85.3％,and 90.7％,respectively,difference was not statistically significant(P=0.897).However,patients treated with rituximab had 2-year lower survival rate than those who did not use it,with a statistically significant difference(P=0.046).Conclusion EBV reactivation is one of the serious complications in AA patients after Haplo-HSCT,which affects the prognosis and survival of patients.

Huocao(a traditional Chinese herbal medicine) moxibustion is a characteristic technology in Yi medicine suitable for cold-dampness diseases. Huocao, as the moxibustion material, is confusedly used in clinical practice and little is known about its quality control. In this study, UPLC method was used to establish the chemical fingerprint of non-volatile components in Huocao, and the contents of eight phenolic acids such as chlorogenic acid were determined. Multivariate statistical analysis was performed to obtain the indicator components of Huocao for quality evaluation, and thus a comprehensive evaluation system for the quality of Huocao was built. The UPLC fingerprints of 49 batches of Huocao were established, and there were 20 common peaks, of which eight phenolic acids including neochlorogenic acid and chlorogenic acid were identified. Except for three batches of Huocao, the similarity of the other 46 batches was higher than 0.89, suggesting that the established fingerprint method could be used for quality control of the medicinal herb. The correlation coefficient between entropy weight score of the eight phenolic acids and comprehensive fingerprint score in Huocao was 0.875(P<0.01), which indicated that the eight phenolic acids could be used as indicator components for the quality evaluation of Huocao. Furthermore, in multivariate statistical analysis on the common peaks of fingerprint and the contents of the eight phenolic acids, chlorogenic acid, isochlorogenic acid A and isochlorogenic acid C were screened to be the indicator components. The results revealed that the proposed method achieved a simple and accurate quality control of Huocao based on UPLC fingerprint and multi-component content determination, which provided useful data for establishing the quality standard of Huocao.
Chlorogenic Acid ; Entropy ; Hydroxybenzoates ; Quality Control