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.

Natural products are an important source for innovative drugs,but unclear molecular targets and mechanisms limit their further development and application.The authors proposed a new method for the target identification of natural products based on proteolysis-targeting chimera(PROTAC)technology and quantitative proteomics,and established the targeted degradomics(TGDO) technology for the identification of weak-affinity tar-gets.This article summarizes the standardized workflow and the application of TGDO for target identification of natural products.

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

This research investigated the contamination level,distribution of drug-resistant strains,and molecular epidemiologi-cal characteristics of Campylobacter,and further explored transmission pathways and prevention strategies.Cecum,chicken carcass,chicken product,and environmental samples,as well as swabs from workers'hands,were collected from a slaughterhouse in a large broiler group in the Jiaodong area between August 2023 and July 2024.Quantitative contamination assessment of Campylobacter in chicken carcasses and chicken products was performed.After microbial mass spectrometry identification,the representative strains of different links were selected for drug resistance testing and whole genome sequencing(WGS).On the basis of the sequencing results,the resistance genes,virulence genes,multilocus sequence typing(MLST),and phylogenetic characteristics of representative strains were analyzed.Homology comparisons were performed between isolates and strains from patients with diarrhea in the NCBI database.A total of 297 Campylobacter strains were isolated from 806 samples,and the overall detection rate was 36.85%.The detection rate of Campylobacter was highest in the evisceration process(47.33%),followed by the cutting process(35.64%).Overall,the Campylo-bacter detection rate first increased,then decreased,and subsequently increased.Drug sensitivity testing revealed that 90 isolates were resistant to nalidixic acid and ciprofloxacin,and 94.97%of isolates were resistant to tetracycline.WGS showed that both Campylo-bacter jejuni(C.jejuni)and Campylobacter coli(C.coli)carried many drug resistance and virulence genes.ST-14176 of C.jejuni was isolated for the first time herein.The predominant ST-8261 strain of C.jejuni and ST-860,ST-829,and ST-1586 strains of C.coli are known to cause human diarrhea.LOS expression genes associated with Guillain-Barré syndrome(GBS)were detected in both C.jejuni isolates from the slaughter chain and patients with GBS.Some strains exhibited close genetic relatedness to human-derived Campylo-bacter strains from the NCBI database.The detection rate of Campylobacter in the slaughterhouse first increased,then decreased,and subsequently increased,and the quantitative contamination level of each link was similar to the detection rate.Quantitative analysis of chicken carcasses/products revealed that the average bacterial load was highest in eviscerated carcasses(102.80 cfu/g),and the high-est amount of Campylobacter in chicken products reached 451.80 cfu/g.Abundant drug resistance genes and virulence genes were iden-tified,and the drug resistance genes were highly correlated with the drug resistance rate.Therefore,surveillance intensity and control measures for Campylobacter in slaughter processes should be strengthened.

Objective:To explore and compare the clinical application value of 8-channel head phased-array coil, an 8-channel temporomandibular joint (TMJ)-specific surface coil, and a single-channel surface coil in TMJ MRI examinations.Methods:A total of 600 temporomandibular disorders (TMD) patients (1 200 joints) who underwent TMJ MRI examination in the First People′s Hospital of Jinzhong from June 2020 to January 2025 were retrospectively screened. Based on inclusion/exclusion criteria, 120 TMD patients (240 joints) with closed-mouth oblique sagittal proton density weighted imaging (OSag PDWI), coronal T2 fat-suppression weighted imaging (OCor fs T2WI) and open-mouth oblique sagittal proton density weighted imaging (OSag PDWI) were included. Patients were divided into groups A, B, and C, with 40 cases in each group. Group A (31female, 9male, median age 24 years old), underwent 8-channel head phased-array coil imaging. Group B (29 female, 11male, median age 23.5 years old) underwent TMJ imaging with an 8-channel surface coil. Group C (29 female, 11male, median age 22.5 years old) underwent single-channel surface coil imaging. There were no significant differences in age, gender, type or disease types among groups ( P>0.05). Six healthy volunteers without TMD (4 female, 2 male, range 19 to 45 years old) underwent imaging with all three coils as self-control. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and image quality were compared for five regions of interest (ROI) in both patients and volunteers. Results:Under the same sequence and the same parameters, SNR and CNR in group B were higher than those in group A, and SNR and CNR in group C were also higher than those in group A, the differences were statistically significant ( P<0.05). However, there were significant differences in SNR and CNR between group B and group C in the closed and open positions of ROI1, the open positions of ROI3 and the open positions of ROI5 ( P<0.05), and there were no significant differences in other positions ( P>0.05). Group B had the best image quality, followed by group C and group A had the worst image quality. There were significant differences in the visualization of OSag PDWI in the closed mouth position, OCor T2WI in the coronal position, and OSag PDWI in the open mouth position, such as condyle, anterior attachment, joint disc, double lamina area, joint cavity and lateral pterygoid muscle ( P<0.05). There were significant differences between group B and group C in showing the joint cavity in the closed mouth position and showing the structure of the bilaminar area in the open mouth position ( P<0.05). There was no significant difference in other regions of interest ( P>0.05). The subjective scores of condyle, anterior attachment, articular disc, bilaminar area, articular cavity, lateral pterygos muscle and other structures were medium to high in group A, high in group B, and high or high in group C by two radiologists independently. In the five rois, the 8-channel TMJ surface coil showed more details, especially in the articular disc, condyle and lateral pterygoid muscle regions, and had more advantages in both volunteers and patients. Conclusions:The 8-channel TMJ-specific surface coil provides significantly clearer visualization of critical anatomical details within the ROIs, demonstrating the highest clinical application value and is recommended as the preferred choice.

Objective To propose a brain electrical phase prediction method based on long short-term memory network(LSTM)to improve the accuracy and robustness of phase synchronization prediction in transcranial magnetic stimulation(TMS).Methods First,an LSTM consisting of an input layer,an LSTM layer,an ReLU activation layer,a fully connected layer and a regression layer was constructed to capture the EEG signal features through the synergistic action of input gates,forgetting gates and output gates.Second,eye-open resting-state EEG data from 30 healthy subjects were trained using the LSTM to obtain a predictive model for EEG signal and EEG phase prediction.Finally,the LSTM method and the traditional autoregressive(AR)method were compared in terms of the phase prediction errors at the overall and individual levels and the prediction performance for peaks and troughs.A regression model was used to explore the relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error with the LSTM method.Results The LSTM method achieved a total phase prediction error of 0.04°±5.69°,which was lower than that of the traditional AR method(-3.36°±51.13°).For each subject,the LSTM method demonstrated superior phase prediction accuracy compared to the traditional AR method(P＜0.001).The accuracy for predicting peaks(troughs)by the LSTM method(about 89％)was higher than that by the traditional AR method(about 10％).Unlike the traditional AR method,the LSTM method didnot result in linear relationships between instantaneous EEG amplitude,signal-to-noise ratio and phase prediction error,with Pvalues being 0.58 and 0.18,respectively.Conclusion The LSTM-based brain electrical phase prediction method shows high accuracy and robustness when used for EEG phase-synchronized TMS.[Chinese Medical Equipment Journal,2025,46(3):1-8]

This research investigated the contamination level,distribution of drug-resistant strains,and molecular epidemiologi-cal characteristics of Campylobacter,and further explored transmission pathways and prevention strategies.Cecum,chicken carcass,chicken product,and environmental samples,as well as swabs from workers'hands,were collected from a slaughterhouse in a large broiler group in the Jiaodong area between August 2023 and July 2024.Quantitative contamination assessment of Campylobacter in chicken carcasses and chicken products was performed.After microbial mass spectrometry identification,the representative strains of different links were selected for drug resistance testing and whole genome sequencing(WGS).On the basis of the sequencing results,the resistance genes,virulence genes,multilocus sequence typing(MLST),and phylogenetic characteristics of representative strains were analyzed.Homology comparisons were performed between isolates and strains from patients with diarrhea in the NCBI database.A total of 297 Campylobacter strains were isolated from 806 samples,and the overall detection rate was 36.85%.The detection rate of Campylobacter was highest in the evisceration process(47.33%),followed by the cutting process(35.64%).Overall,the Campylo-bacter detection rate first increased,then decreased,and subsequently increased.Drug sensitivity testing revealed that 90 isolates were resistant to nalidixic acid and ciprofloxacin,and 94.97%of isolates were resistant to tetracycline.WGS showed that both Campylo-bacter jejuni(C.jejuni)and Campylobacter coli(C.coli)carried many drug resistance and virulence genes.ST-14176 of C.jejuni was isolated for the first time herein.The predominant ST-8261 strain of C.jejuni and ST-860,ST-829,and ST-1586 strains of C.coli are known to cause human diarrhea.LOS expression genes associated with Guillain-Barré syndrome(GBS)were detected in both C.jejuni isolates from the slaughter chain and patients with GBS.Some strains exhibited close genetic relatedness to human-derived Campylo-bacter strains from the NCBI database.The detection rate of Campylobacter in the slaughterhouse first increased,then decreased,and subsequently increased,and the quantitative contamination level of each link was similar to the detection rate.Quantitative analysis of chicken carcasses/products revealed that the average bacterial load was highest in eviscerated carcasses(102.80 cfu/g),and the high-est amount of Campylobacter in chicken products reached 451.80 cfu/g.Abundant drug resistance genes and virulence genes were iden-tified,and the drug resistance genes were highly correlated with the drug resistance rate.Therefore,surveillance intensity and control measures for Campylobacter in slaughter processes should be strengthened.

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.

Objectives:To evaluate the predictive value of Tabular Prior-data Fitted Network(TabPFN) for short-term outcome in patients with spontaneous intracerebral hemorrhage (sICH), and compared with the Extreme Gradient Boosting (XGboost) model and traditional logistic regression (LR) model. Methods:Patients with sICH admitted to the Department of Neurology, Hefei Second People's Hospital from January 2018 to March 2024 were included retrospectively. The demographic and baseline data were collected. At 3 months after onset, the modified Rankin Scale score was used to determine the outcome, 0-2 was defined as good outcome and >2 was defined as poor outcome. All enrolled patients were randomly divided into a training set and a testing set at a ratio of 7:3. Feature selection was performed using recursive feature elimination (RFE) method, and then the selected feature variables were included into TabPFN, XGboost, and LR models for training and testing. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the models. Shapley additive explanations (SHAP) method was used for model interpretation.Results:A total of 547 patients with sICH were enrolled, including 367 males (67.1%), with a median age of 65 (interquartile range, 54-76) years. Two hundred twenty-six patients (41.3%) had poor outcome. Age, baseline blood pressure (systolic blood pressure, diastolic blood pressure), baseline laboratory tests (white blood cell count, red blood cell count, platelet count, neutrophil count, hemoglobin, fasting blood glucose, creatinine, uric acid, urea nitrogen, alanine aminotransferase, aspartate aminotransferase), hematoma rupture into the ventricle, island sign, baseline hematoma volume, and baseline National Institutes of Health Stroke Scale (NIHSS) score were selected as characteristic variables using RFE method. ROC curve analysis showed that the ROC AUC for TabPFN, Xgboost, and LR models predicting poor short-term outcome in the testing set were 0.918 (95% confidence interval [ CI] 0.870-0.966], 0.883 (95% CI 0.826-0.940), and 0.905 (95% CI 0.854-0.957), respectively. SHAP analysis showed that the top four important variables in the TabPFN model were baseline NIHSS score, baseline hematoma volume, baseline aspartate aminotransferase, and age. Conclusions:The TabPFN model is superior to the LR model and the XGBoost model in predicting poor outcome in patients with sICH. In the TabPFN model, baseline NIHSS score, baseline hematoma volume, aspartate aminotransferase, and age are the most important predictors of poor outcome in patients with sICH.Objectives To evaluate the predictive value of Tabular Prior-data Fitted Network(TabPFN) for short-term outcome in patients with spontaneous intracerebral hemorrhage (sICH), and compared with the Extreme Gradient Boosting (XGboost) model and traditional logistic regression (LR) model. Methods Patients with sICH admitted to the Department of Neurology, Hefei Second People's Hospital from January 2018 to March 2024 were included retrospectively. The demographic and baseline data were collected. At 3 months after onset, the modified Rankin Scale score was used to determine the outcome, 0-2 was defined as good outcome and >2 was defined as poor outcome. All enrolled patients were randomly divided into a training set and a testing set at a ratio of 7:3. Feature selection was performed using recursive feature elimination (RFE) method, and then the selected feature variables were included into TabPFN, XGboost, and LR models for training and testing. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was used to evaluate the predictive ability of the models. Shapley additive explanations (SHAP) method was used for model interpretation. Results A total of 547 patients with sICH were enrolled, including 367 males (67.1%), with a median age of 65 (interquartile range, 54-76) years. Two hundred twenty-six patients (41.3%) had poor outcome. Age, baseline blood pressure (systolic blood pressure, diastolic blood pressure), baseline laboratory tests (white blood cell count, red blood cell count, platelet count, neutrophil count, hemoglobin, fasting blood glucose, creatinine, uric acid, urea nitrogen, alanine aminotransferase, aspartate aminotransferase), hematoma rupture into the ventricle, island sign, baseline hematoma volume, and baseline National Institutes of Health Stroke Scale (NIHSS) score were selected as characteristic variables using RFE method. ROC curve analysis showed that the ROC AUC for TabPFN, Xgboost, and LR models predicting poor short-term outcome in the testing set were 0.918 (95% confidence interval [ CI] 0.870-0.966], 0.883 (95% CI 0.826-0.940), and 0.905 (95% CI 0.854-0.957), respectively. SHAP analysis showed that the top four important variables in the TabPFN model were baseline NIHSS score, baseline hematoma volume, baseline aspartate aminotransferase, and age. Conclusions The TabPFN model is superior to the LR model and the XGBoost model in predicting poor outcome in patients with sICH. In the TabPFN model, baseline NIHSS score, baseline hematoma volume, aspartate aminotransferase, and age are the most important predictors of poor outcome in patients with sICH.