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 accuracy and safety of pedicle screw placement using 3D-printed auxiliary guides in scoliosis correction surgery for adolescents. METHODS: A retrospective analysis was conducted on the clinical data of 51 patients who underwent posterior scoliosis correction surgery from January 2020 to March 2023. Among them, there were 35 cases of adolescent idiopathic scoliosis and 16 cases of congenital scoliosis. The patients were divided into two groups based on the auxiliary tool used:the 3D-printed auxiliary guide screw placement group (3D printing group) and the free-hand screw placement group (free-hand group, without auxiliary tools). The 3D printing group included 32 patients (12 males and 20 females) with an average age of (12.59±2.60) years;the free-hand group included 19 patients (7 males and 12 females) with an average age of (14.58±3.53) years. The two groups were compared in terms of screw placement accuracy and safety, spinal correction rate, intraoperative blood loss, number of intraoperative fluoroscopies, operation time, hospital stay, and preoperative and last follow-up scores of the Scoliosis Research Society-22 (SRS-22) questionnaire. RESULTS: A total of 707 pedicle screws were placed in the two groups, with 441 screws in the 3D printing group and 266 screws in the free-hand group. All patients in both groups successfully completed the surgery. There was a statistically significant difference in operation time between the two groups (P<0.05). The screw placement accuracy rate of the 3D printing group was 95.46% (421/441), among which the Grade A placement rate was 89.34% (394/441);the screw placement accuracy rate of the free-hand group was 86.47% (230/266), with a Grade A placement rate of 73.31% (195/266). There were statistically significant differences in the accuracy of Grade A, B, and C screw placements between the two groups (P<0.05), while no statistically significant differences were observed in intraoperative blood loss, number of fluoroscopies, correction rate, or hospital stay (P>0.05). In the SRS-22 questionnaire scores, the scores of functional status and activity ability, self-image, mental status, and pain of patients in each group at the last follow-up were significantly improved compared with those before surgery (P<0.05), but there were no statistically significant differences in all scores between the two groups (P>0.05). CONCLUSION In scoliosis correction surgery, compared with traditional free-hand screw placement, the use of 3D-printed auxiliary guides for screw placement significantly improves the accuracy and safety of screw placement and shortens the operation time.
Humans ; Male ; Scoliosis/surgery* ; Female ; Adolescent ; Printing, Three-Dimensional ; Retrospective Studies ; Pedicle Screws ; Child

Compounds isolated from Epimedium include the total flavonoids of Epimedium , icariin, and its metabolites (icaritin, icariside I, and icariside II), which have similar molecular structures. Modern pharmacological research and clinical practice have proved that Epimedium and its active components have a wide range of pharmacological effects, especially in improving sexual function, hormone regulation, anti-osteoporosis, immune function regulation, anti-oxidation, and anti-tumor activity. To date, we still need a comprehensive source of knowledge about the pharmacological effects of Epimedium and its bioactive compounds on the male reproductive system. However, their actions in other tissues have been reviewed in recent years. This review critically focuses on the Epimedium , its bioactive compounds, and the biochemical and molecular mechanisms that modulate vital pathways associated with the male reproductive system. Such intrinsic knowledge will significantly further studies on the Epimedium and its bioactive compounds that protect the male reproductive system and provide some guidances for clinical treatment of related male reproductive disorders.
Male ; Epimedium/chemistry* ; Humans ; Genitalia, Male/drug effects* ; Flavonoids/therapeutic use* ; Animals

OBJECTIVE: To explore the diagnostic value of shear wave elastography (SWE) for clinically significant prostate cancer (csPCa). METHODS: We retrospectively analyzed the clinical data of 359 cases with suspected prostate cancer (PCa) in Baoji Central Hospital from June 2017 to July 2023. All the patients underwent the following examinations in the order of serum prostate-specific antigen (PSA) testing, transrectal ultrasonography (TRUS), measurement of the stiffness of the entire prostate gland by SWE, and TRUS-guided prostate puncture biopsy. The stiffness of the entire prostate gland was defined as the average of Young's modulus at both sides of the base, middle, and apex of the prostate, including the maximum Young's modulus (Emax), mean Young's modulus (Emean), and minimum Young's modulus (Emin). We analyzed the correlation of the parameters of the stiffness of the entire prostate gland with the pathological results, focusing on their diagnostic performance for csPCa. RESULTS: Of the 359 cases, 189 were diagnosed by pathological puncture biopsy as BPH, 26 as non-csPCa, and 144 as csPCa. The PSA level, Emax, Emean and Emin were significantly higher in the csPCa than those in the BPH and non-csPCa groups (all P < 0.01), but showed no statistically significant difference between the BPH and non-csPCa groups (all P > 0.05). The area under the receiver operating characteristic curve (AUC), optimal cut-off value, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of Emax in the diagnosis of csPCa were 0.852, 143.92 kPa, 72.22%, 84.65%, 75.91%, 81.98% and 79.67%; those of Emean were 0.868, 82.42 kPa, 67.36%, 91.16%, 83.62%, 80.66% and 81.62%; and those of Emin were 0.682, 32.73 kPa, 47.22%, 89.30%, 73.91%, 71.54% and 72.14%, respectively. In the non-csPCa group, Emax, Emean and Emin were found below the optimal cut-off value in 73.08% (19/26), 92.31% (24/26) and 88.46% (23/26), respectively. CONCLUSION The stiffness of the entire prostate gland measured by SWE contributes to the diagnosis of csPCa, reduces unnecessary detection of non-csPCa, and provides some reference for its active surveillance.
Humans ; Male ; Prostatic Neoplasms/diagnosis* ; Elasticity Imaging Techniques ; Retrospective Studies ; Prostate/pathology* ; Prostate-Specific Antigen/blood* ; Aged ; Middle Aged

BACKGROUND: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. METHODS: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. RESULTS: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. CONCLUSION The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.

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.

In this study, the reductive amination method was used to label IR783 on Astragalus polysaccharides(APS) for the first time, which was verified by ultraviolet-visible spectroscopy and infrared spectroscopy. Quantitative analysis methods of APS-IR783 in plasma and various tissue were established using a multifunctional microplate reader. The pharmacokinetics and tissue distribution of APS-IR783 in mice were investigated after a single intravenous injection of 30 mg·kg~(-1) APS-IR783, and pharmacokinetic parameters were calculated using DAS 2.0 software. The results showed that the APS used had a mass fraction of 93.69%, a relative molecular weight of 1.55×10~5, and a polydispersity index(PDI, M_w/M_n) of 1.73, close to a homogeneous polysaccharide. The IR783 labeling yield reached 86.50%, and the content of IR783 in APS-IR783 was 0.72%. After a single intravenous injection of 30 mg·kg~(-1), the pharmacokinetic parameters of APS in mouse plasma were as follows: T_(max) was(0.67±0.26) h; C_(max) was(1 599.29±159.30) mg·L~(-1); T_(1/2α) and T_(1/2β) were(2.29±3.06) h and(0.44±0.05) h, respectively; AUC_(0-t) was(23 398.91±2 907.03) mg·h·L~(-1); AUC_(0-∞) was(27 710.55±3 506.55) mg·h·L~(-1); MRT_(0-∞) was(34.38±12.59) h; CL was 0.001 L·h~(-1)·kg~(-1); V_z was(0.042±0.017) L·kg~(-1). The in vivo biodistribution study demonstrated that the in vivo exposure ratios of APS in different tissue were in the following order: spleen > liver > kidney > lung > heart > small intestine > muscle > large intestine > brain > stomach, where the top five tissue accounted for 87.54% of the total area under the curve(AUC). This study successfully labeled APS with a water-soluble near-infrared fluorescent probe of IR783 for the first time and revealed the pharmacokinetics and tissue distribution of APS in mice. The paper provides detailed in vivo behavior of APS after intravenous injection, which lays the foundation for the development and utilization of APS and related natural medicines.
Animals ; Mice ; Polysaccharides/chemistry* ; Tissue Distribution ; Astragalus Plant/chemistry* ; Male ; Drugs, Chinese Herbal/chemistry* ; Fluorescent Dyes/pharmacokinetics* ; Female