Urinary incontinence is a common complication following robot-assisted radical prostatectomy (RARP). Urethral length has been identified as a factor affecting postoperative continence recovery. In this meta-analysis, we examined the association between use of the maximal urethral length preservation (MULP) technique and postoperative urinary continence in patients undergoing RARP. We conducted a comprehensive search of PubMed, Web of Science, Embase, and the Cochrane Library up to December 31, 2023. The quality of the literature was assessed using the Newcastle-Ottawa Scale. A random-effects meta-analysis was performed to synthesize data and calculate the odds ratio (OR) from eligible studies on continence and MULP. Six studies involving 1869 patients met the eligibility criteria. MULP was positively associated with both early continence (1 month after RARP; Z = 3.62, P = 0.003, OR = 3.10, 95% confidence interval [CI]: 1.68-5.73) and late continence (12 months after RARP; Z = 2.34, P = 0.019, OR = 2.10, 95% CI: 1.13-3.90). Oncological outcomes indicated that MULP did not increase the overall positive surgical margin rate or the positive surgical margin status at the prostate apex (both P > 0.05). In conclusion, the use of the MULP technique in RARP significantly improved both early and late postoperative continence outcomes without compromising oncological outcomes.
Humans ; Prostatectomy/adverse effects* ; Robotic Surgical Procedures/methods* ; Male ; Urethra/surgery* ; Urinary Incontinence/prevention & control* ; Postoperative Complications/etiology* ; Prostatic Neoplasms/surgery* ; Organ Sparing Treatments/methods*

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.

Prostate-specific membrane antigen(PSMA)positron emission tomography(PET)is currently a precise diagnostic imaging technology for prostate cancer(PCa).Artificial intelligence(AI)technologies,particularly machine learning and deep learning algorithms,when combined with PSMA PET,showed extensive potential applications in various aspects of PCa management.These include the diagnosis and differential diagnosis of primary tumors,staging,recurrence detection,and treatment planning for PCa.At present,a few AI models have received clinical approval.This paper reviews the application progress of AI combined with PSMA PET in the diagnosis and treatment of PCa,explores the current limitations of AI technologies in clinical practice,and aim to provide references to future diagnosis and treatment studies for PCa.

Objective To screen Coxsackievirus A16(CA16)vaccine candidate strains and provide preliminary basis for the development of vaccine against hand,foot and mouth disease(HFMD).Methods Cell inoculation and continuous passage were performed on the 35 clinical specimens of patients with hand,foot,and mouth disease from different areas of Kunming.The harvested solution with cytopathic effects was identified using CA16 standard serum.Positive samples were screened for vaccine strains through the virus titer determination,plaque purification,VP1 sequence homology and evolutionary analysis.Results Among the 35 collected clinical oropharyngeal swab samples,23 showed cellular lesions,and 10 were identified as CA16 virus by serology,with a positive rate of 43.5%.After the adaptive cultivation,the virus titers of 10 strains gradually increased,but stabilized at 7.0 lgCCID50 from 48 hours onwards.The plaque of the strain showed that all strains can form plaques of 0.2～0.8 cm,showing the regular differences,and the size of KM/M08 plaques was relatively uniform.All strains were B1b type,with nucleotide homology ranging from 93.3～100%.Among them,KM/M08 had the highest homology with other strains,ranging from 95.2～100%.Conclusion Through the analysis of virus proliferation kinetics curve,gene homology,and evolution,KM/M08 meets the expected screening criteria in all aspects of evaluation and is proposed as a vaccine candidate strain for further researches.

Objective:To investigate the CD163 expression characteristics in intracranial aneurysm (IA) tissue and blood of patients with IA and its feasibility as an early clinical screening indicator for IA.Methods:A total of 28 patients with IA admitted to Department of Neurosurgery, First Affiliated Hospital of Xinjiang Medical University from January 2021 to November 2023 were selected as IA group, and 28 healthy subjects from Health Management Center, First Affiliated Hospital of Xinjiang Medical University at the same time period were selected as control group. Eight saccular IA tissues and 12 superficial temporal artery tissues were collected from patients from IA group accepted IA clipping, and real-time fluorescent quantitative PCR (RT-qPCR) was used to detect the CD163 mRNA expression in these tissues. RT-qPCR was also used to detect the CD163 mRNA expression in the blood of the 2 groups. Seven patients with IA and 7 control subjects from the above 2 groups were randomly selected, respectively; and plasma CD163 protein content was detected by enzyme-linked immunosorbent assay (ELISA). Multivariate Logistic regression was used to analyze the influencing factors for IA. Receiver operating characteristic (ROC) curve was used to analyze the diagnostic values of blood CD163 mRNA expression and plasma CD163 protein content in IA. Results:CD163 mRNA expression in IA tissues was significantly higher than that in superficial temporal artery tissues (41.870±20.355 vs. 6.080±5.444, P<0.05). CD163 mRNA expression in the blood of IA patients was significantly higher than that in the controls (1.969[1.124, 2.318] vs. 1.124[0.933, 1.379], P<0.05). CD163 mRNA expression in the blood of ruptured IA group, unruptured IA group, and control group was gradually decreased, with significant differences ( P<0.05). CD163 mRNA expression in the blood of female IA patients was not statistically different compared with that in male IA patients ( P>0.05). ELISA showed that the CD163 protein content in plasma of the IA group was significantly higher than that in the control group [10.537±1.879] ng/L vs. [8.598±0.885] ng/L, P<0.05). Multivariate Logistic regression analysis showed that age and CD163 mRNA expression in the blood were independent influencing factors for IA occurrence ( OR=0.844, 95% CI: 0.750-0.951, P=0.005; OR=0.111, 95% CI: 0.024-0.506, P=0.004). ROC curve showed that the area under the curve (AUC) of CD163 mRNA expression in blood in diagnosing IA was 0.759 (95% CI: 0.618-0.890, P=0.002), and that of CD163 protein content in plasma in diagnosing IA was 0.864 (95% CI: 0.610-1.000, P=0.035). Conclusion:CD163 mRNA expressions in blood and IA tissues and CD163 protein content in plasma are high in patients with IA; CD163 mRNA expression in blood is an independent risk factor for IA; CD163 protein in plasma can be used as a molecular marker for screening IA.