OBJECTIVE To systematically evaluate the relationship of semaglutide with malignant neoplasms in type 2 diabetes mellitus （T2DM） patients. METHODS Retrieved from the Cochrane Library， PubMed， Embase， ClinicalTrials.gov， CNKI， Wanfang data and CBM， randomized controlled trials （RCTs） about semaglutide in the treatment of T2DM patients with outcome measures including malignant tumor events were collected from the establishment of the database to June 2024. Meta- analysis was performed by using RevMan 5.3 software to assess the risk of malignant neoplasms. RESULTS A total of 24 RCTs （26 trials） involving 24 145 patients were included. Results of meta-analysis showed that compared to placebo， there was no statistical significance in the risk of semaglutide in pancreatic cancer [RR＝0.39， 95%CI（0.10， 1.50）， P＝0.17]， thyroid cancer [RR＝1.29， 95%CI（0.38， 4.36）， P＝0.68]， prostate cancer [RR＝1.05， 95%CI（0.36， 3.12）， P＝0.92]， skin cancer [RR＝1.27， 95%CI（0.80， 2.02）， P＝0.31]， gastrointestinal cancer [RR＝1.00， 95%CI（0.47， 2.14）， P＝1.00]， colorectal cancer [RR＝0.96， 95%CI（0.40， 2.26）， P＝0.92]， lung cancer [RR＝1.62， 95%CI（0.74， 3.55）， P＝0.23]， breast cancer [RR＝1.25， 95%CI（0.45， 3.51）， P＝0.67] or all malignant neoplasms [RR＝0.96， 95%CI（0.76， 1.21）， P＝0.73]. Compared to other antidiabetic drugs， there was no statistical significance in the risk of semaglutide in pancreatic cancer [RR＝0.62， 95%CI（0.18， 2.09）， P＝0.44]， thyroid cancer [RR＝1.09， 95%CI（0.25， 4.78）， P＝0.90]， prostate cancer [RR＝2.09， 95%CI（0.46， 9.47）， P＝0.34]， skin cancer [RR＝1.76， 95%CI（0.65， 4.72）， P＝0.26]， gastrointestinal cancer [RR＝0.68， 95%CI（0.19， 2.35）， P＝0.54]， colorectal cancer [RR＝0.60， 95%CI（0.20， 1.78）， P＝0.36]， lung cancer [RR＝1.00， 95%CI（0.24， 4.11）， P＝1.00]， breast cancer [RR＝0.82， 95%CI（0.25， 2.66）， P＝0.74] or all malignant neoplasms [RR＝1.36， 95%CI（0.96， 1.94）， P＝0.09]. CONCLUSIONS Semaglutide does not increase the risk of any type of malignant neoplasms in T2DM patients.

OBJECTIVE To summarize the current situation of pharmaceutical clinic service in our hospital over the past five years， and explore sustainable development strategies for service models of pharmaceutical clinics. METHODS A retrospective analysis was conducted on the consultation records of patients who registered and established files at the pharmaceutical clinic in our hospital from January 2019 to December 2023. Statistical analysis was performed on patients’ general information， medication- related problems， and types of pharmaceutical services provided by pharmacists. RESULTS A total of 963 consultation records were included， among which females aged 20-39 years accounted for the highest proportion （66.04%）； obstetrics and gynecology- related consultations accounted for the largest number of cases. Additionally， 80 patients attended follow-up visits at our hospital’s pharmaceutical clinic. A total of 1 029 medication-related issues were resolved， including 538 cases of drug consultations （52.28%）， 453 medication recommendations （44.02%）， 22 medication restructuring（2.14%）， and 16 medication education （1.55%）； the most common types of medication-related problems identified were adverse drug events（70.07%）. CONCLUSIONS Although the pharmaceutical clinic has achieved recognition from clinicians and patients， challenges such as low awareness among healthcare providers and the public persist. Future efforts should focus on strengthening information technology construction， enhancing pharmacist training， and establishing various forms of outpatient pharmaceutical service models.

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 investigate the safety and feasibility of transurethral blue laser vaporization of the prostate (BVP) under intravenous anesthesia without intubation. Methods: Clinical data of 30 benign prostatic hyperplasia (BPH) (prostate volume <40 mL) patients undergoing BVP under intravenous anesthesia without intubation in our hospital during Jul.and Nov.2024 were retrospectively analyzed.Preoperative and 1-month postoperative international prostate symptom score (IPSS), quality of life score (QoL), maximum urinary flow rate (Qmax), and postvoid residual volume (PVR) were compared.The operation time, cumulative blue laser activation time, recovery time, postoperative bladder irrigation time, postoperative catheter indwelling time, postoperative 2-hour visual analog scale (VAS) score and incidence of surgical and anesthetic complications were recorded. Results: All 30 patients successfully completed BVP under intravenous anesthesia without intubation.The operation time was (12.5±5.0) min, cumulative laser activation time (9.8±4.1) min, recovery time (6.8±1.2) min, postoperative bladder irrigation time (11.0±4.6) h, postoperative catheter indwelling time (2.7±1.1) days and postoperative 2-hour VAS score was (3.0±1.3).No cases required conversion to intubated general anesthesia, and no severe perioperative surgical or anesthetic complications occurred.Significant improvements in IPSS, QoL, Qmax, and PVR were observed 1 month postoperatively (P＜0.001). Conclusion: BVP under intravenous anesthesia without intubation in the treatment of prostate volume <40 mL BPH is clinically feasible, significantly improving lower urinary tract symptoms without significant surgical or anesthetic complications.

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis