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 evaluate the value of miniprobe endoscopic ultrasound(EUS)in guiding endoscopic treatment of small-diameter(maximum diameter less than 1 cm)and low-grade(G1 grade)rectum neuroendocrine neoplasm(R-NEN),and to provide evidence and clues for its clinical application and further research.Methods The clinical data of 85 cases of low-grade(G1 grade)R-NEN with a maximum diameter of less than 1 cm who underwent endoscopic treatment in our center from January 2014 to December 2020 were retrospectively analyzed.The patients were divided into the EUS group(37 cases)and control group(48 cases)according to whether EUS was performed before endoscopic treatment.The positive rate of incision margin,the incidence of complications,the recurrence rate,the hospital stay,the cost of hospitalization and endoscopic therapy were compared between the two groups.Results The positive rate of incision margin in the EUS group was significantly lower than that in control group(P<0.05).There was no significant difference in the incidence of complications,tumor recurrence rate,hospital stay or hospital costs between the two groups(P>0.05).There was statistically significant difference in the endoscopic therapy between the two groups(P<0.05).Conclusion Evaluating the lesion depth of small-diameter and low-grade(G1 grade)R-NEN before surgery by miniprobe EUS and selecting endoscopic surgery according to its results of can significantly reduce the residual risk of resection margin tumors.

The gut microbiota plays a pivotal role in the immunomodulatory and protumorigenic microenviron-ment of colorectal cancer(CRC).However,the effect of ginsenoside Rk3(Rk3)on CRC and gut microbiota remains unclear.Therefore,the purpose of this study is to explore the potential effect of Rk3 on CRC from the perspective of gut microbiota and immune regulation.Our results reveal that treatment with Rk3 significantly suppresses the formation of colon tumors,repairs intestinal barrier damage,and regulates the gut microbiota imbalance caused by CRC,including enrichment of probiotics such as Akkermansia muciniphila and Barnesiella intestinihominis,and clearance of pathogenic Desulfovibrio.Subsequent metabolomics data demonstrate that Rk3 can modulate the metabolism of amino acids and bile acids,particularly by upregulating glutamine,which has the potential to regulate the immune response.Furthermore,we elucidate the regulatory effects of Rk3 on chemokines and inflammatory factors associated with group 3 innate lymphoid cells(ILC3s)and T helper 17(Th17)signaling pathways,which inhibits the hyperactivation of the Janus kinase-signal transducer and activator of transcription 3(JAK-STAT3)signaling pathway.These results indicate that Rk3 modulates gut microbiota,regulates ILC3s immune response,and inhibits the JAK-STAT3 signaling pathway to suppress the development of colon tumors.More importantly,the results of fecal microbiota transplantation suggest that the inhibitory effect of Rk3 on colon tumors and its regulation of ILC3 immune responses are mediated by the gut microbiota.In summary,these findings emphasize that Rk3 can be utilized as a regulator of the gut microbiota for the prevention and treatment of CRC.

Objective To construct an artificial intelligence assisted diagnosis model based on deep learning for dynamically recognizing gastric lesions and their locations under gastroscopy in real time,and to evaluate its ability to detect and recognize gastric lesions and their locations.Methods The gastroscopy videos of 104 patients in our hospital was retrospectively analyzed,and the video frames were manually annotated.The annotated picture frames of lesion category were divided into the training set and the validation set according to the ratio of 8∶2,and the annotated picture frames of location category were divided into the training set and the validation set according to the patient sources at the ratio of 8∶2.These sets were utilized for training and validating the respective models.YoloV4 model was used for the training of lesion recognition,and ResNet152 model was used for the training of location recognition.The accuracy,sensitivity,specificity,positive predictive value,negative predictive value and location recognition accuracy of the auxiliary diagnostic model were evaluated.Results A total of 68 351 image frames were annotated,with 54 872 frames used as the training set,including 41 692 frames for lesion categories and 13 180 frames for location categories.The validation set consisted of 13 479 frames,comprising 10 422 frames for lesion categories and 3 057 frames for location categories.The lesion recognition model achieved an overall accuracy of 98.8%,with a sensitivity of 96.6%,specificity of 99.3%,positive predictive value of 96.3%,and negative predictive value of 99.3% in validation set.Meanwhile,the location recognition model demonstrated an top-5 accuracy of 87.1% .Conclusion The artificial intelligence assisted diagnosis model based on deep learning for real-time dynamic recognition of gastric lesions and their locations under gastroscopy has good ability in lesion detection and location recognition,and has great clinical application prospects.

An innovative on-site real-time avian influenza virus(AIV)detection method was estab-lished by integratingrecombinase-aided amplification(RAA)with the clustered regularly inter-spaced short palindromic repeats(CRISPR)/CRISPR-associated protein(Cas)system.After analy-zing 120 sequences of the M gene of avian influenza viruses of different subtypes publicly available on NCBI,the RAA primers and crRNA were designed based on the identified highly conserved segment and used for RAA nucleic acid amplification.After the amplified products were transferred to a CRISPR/Cas13a detection system,the fluorescence values were monitored throughout the re-action process to indicate the results.The sensitivity and specificity of the RAA-CRISPR/Cas13a method were validated using gradient dilutions(106-100 copies/μL)of positive plasmids and sev-en other avian viruses.Fifty clinical samples were tested using this method and compared with the national standard fluorescence RT-PCR method.The results indicated that the detection limit for RAA-CRISPR/Cas13a method was 102 copies/μL,a two-fold improvement over the standard RAA.Specificity assay showed the established method only detected AIV with no cross-reactivity with other seven avian viruses.Compared to the national standard fluorescence RT-PCR method,this method exhibited 100％specificity,95.24％accuracy,and 98.00％consistency in detection of clinical samples.In conclusion,a universal and rapid RAA-CRISPR/Cas13a for detection of AIV was established with the capacity of achieving detection within 60 minutes at 37 ℃,which provides a rapid,sensitive,and specific on-site detection method for AIV.

Objective: To study the incidence of bloodstream infections, pathogen distribution, and antibiotic resistance profile in patients with hematological malignancies. Methods: From January 2018 to December 2021, we retrospectively analyzed the clinical characteristics, pathogen distribution, and antibiotic resistance profiles of patients with malignant hematological diseases and bloodstream infections in the Department of Hematology, Nanfang Hospital, Southern Medical University. Results: A total of 582 incidences of bloodstream infections occurred in 22,717 inpatients. From 2018 to 2021, the incidence rates of bloodstream infections were 2.79%, 2.99%, 2.79%, and 2.02%, respectively. Five hundred ninety-nine types of bacteria were recovered from blood cultures, with 487 (81.3%) gram-negative bacteria, such as Klebsiella pneumonia, Escherichia coli, and Pseudomonas aeruginosa. Eighty-one (13.5%) were gram-positive bacteria, primarily Staphylococcus aureus, Staphylococcus epidermidis, and Enterococcus faecium, whereas the remaining 31 (5.2%) were fungi. Enterobacteriaceae resistance to carbapenems, piperacillin/tazobactam, cefoperazone sodium/sulbactam, and tigecycline were 11.0%, 15.3%, 15.4%, and 3.3%, with a descending trend year on year. Non-fermenters tolerated piperacillin/tazobactam, cefoperazone sodium/sulbactam, and quinolones at 29.6%, 13.3%, and 21.7%, respectively. However, only two gram-positive bacteria isolates were shown to be resistant to glycopeptide antibiotics. Conclusions: Bloodstream pathogens in hematological malignancies were broadly dispersed, most of which were gram-negative bacteria. Antibiotic resistance rates vary greatly between species. Our research serves as a valuable resource for the selection of empirical antibiotics.
Humans ; Bacteremia/epidemiology* ; Cefoperazone ; Sulbactam ; Retrospective Studies ; Drug Resistance, Bacterial ; Microbial Sensitivity Tests ; Hematologic Neoplasms ; Sepsis ; Anti-Bacterial Agents/pharmacology* ; Gram-Negative Bacteria ; Gram-Positive Bacteria ; Piperacillin, Tazobactam Drug Combination ; Escherichia coli