ObjectiveTo observe the effect of functional pelvic floor muscle training on pelvic floor muscle function, incontinence symptoms and quality of life in female patients with stress urinary incontinence (SUI). MethodsFrom February, 2024 to July, 2025, 40 female patients with SUI were recruited from Beijing Bo'ai Hospital and advertisements. They were randomly divided into control group (n = 20) and experimental group (n = 20). The control group received conventional pelvic floor muscle contraction and relaxation training, and the experimental group received functional pelvic floor muscle contraction and relaxation training, for eight weeks. The data of pelvic floor muscle strength, pelvic floor muscle surface electromyography (sEMG), 1-hour urine pad test and Incontinence Quality of Life questionnaire (I-QOL) were collected before and after training. The compliance rate was counted after training. ResultsOne case dropped down in the control group and three cases dropped down in the experimental group. After treatment, the pelvic floor muscle strength increased in both groups (|Z| > 3.317, P < 0.01), the pre-resting average value, sustained contraction average value and durable contraction average value of sEMG improved in the control group (|t| > 2.731, P < 0.05), the mass of 1-hour urine pad significantly reduced (t > 9.215, P < 0.001) and the I-QOL score significantly increased (|t| > 13.229, P < 0.001) in both groups; compared with the control group, the pelvic floor muscle strength significantly increased (Z = -2.281, P = 0.023), the mass of 1-hour urine pad significantly reduced (t = 4.215, P < 0.001), the I-QOL score significantly increased (t = -4.501, P < 0.001), and the compliance rate significantly increased (Z = -2.798, P < 0.01) in the experimental group . ConclusionFunctional pelvic floor muscle training can significantly improve pelvic floor muscle strength, incontinence symptoms and quality of life in female patients with SUI.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Objective To establish an analytical strategy covering multi-dataset processing, recursive feature elimination (RFE) screening and multi-model evaluation based on multiple machine learning algorithms, so as to screen radiation-sensitive genes and verify the feasibility of the evaluation strategy. Methods Qualified radiation transcriptome datasets were retrieved from public gene expression databases. Following standardized data preprocessing and feature preselection, 13 machine learning algorithms were adopted to construct models. The performance of each model was compared and validated in independent datasets. Results A total of 38 eligible datasets were included. Sixteen differentially expressed genes unreported in existing literature were screened out, among which ugcrhl, pdcl3, mct4, h2-g2 and fam120aos were correlated with radiation phenotypes. Ensemble learning algorithms including random forest and gradient boosting exhibited the optimal comprehensive performance. Independent dataset verification confirmed that the screened genes overlapped with known radiation-sensitive genes, and the model performance was consistent with the findings. Conclusion The machine learning strategy constructed in this study can effectively explore potential radiation-sensitive genes, and provides methodological support for subsequent relevant studies.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Purpose: This study assessed the performance of the ultrasound-guided attenuation parameter (UGAP) in diagnosing and grading hepatic steatosis in patients with metabolic dysfunctionassociated steatotic liver disease (MASLD). Magnetic resonance imaging proton density fat fraction (MRI-PDFF) served as the reference standard. Methods: Patients with hepatic steatosis were enrolled in this prospective study and underwent UGAP measurements. MRI-PDFF values of ≥5%, ≥15%, and ≥25% were used as references for the diagnosis of steatosis grades ≥S1, ≥S2, and S3, respectively. Spearman correlation coefficients and area under the receiver operating characteristic curves (AUCs) were calculated. Results: Between July 2023 and June 2024, the study included 88 patients (median age, 40 years; interquartile range [IQR], 36 to 46 years), of whom 54.5% (48/88) were men and 45.5% (40/88) were women. Steatosis grades exhibited the following distribution: 22.7% (20/88) had S0, 50.0% (44/88) had S1, 21.6% (19/88) had S2, and 5.7% (5/88) had S3. The success rate for UGAP measurements was 100%. The median UGAP value was 0.74 dB/cm/MHz (IQR, 0.65 to 0.82 dB/ cm/MHz), and UGAP values were positively correlated with MRI-PDFF (r=0.77, P<0.001). The AUCs of UGAP for the diagnoses of ≥S1, ≥S2, and S3 steatosis were 0.91, 0.90, and 0.88, respectively. In the subgroup analysis, 98.4% (60/61) of patients had valid controlled attenuation parameter (CAP) values. UGAP measurements were positively correlated with CAP values (r=0.65, P<0.001). Conclusion Using MRI-PDFF as the reference standard, UGAP demonstrates good diagnostic performance in the detection and grading of hepatic steatosis in patients with MASLD.

Objective To systematically evaluate the risk prediction models for anastomotic leakage (AL) in patients with esophageal cancer after surgery. Methods A computer-based search of PubMed, EMbase, Web of Science, Cochrane Library, Chinese Medical Journal Full-text Database, VIP, Wanfang, SinoMed and CNKI was conducted to collect studies on postoperative AL risk prediction model for esophageal cancer from their inception to October 1st, 2023. PROBAST tool was employed to evaluate the bias risk and applicability of the model, and Stata 15 software was utilized for meta-analysis. Results A total of 19 literatures were included covering 25 AL risk prediction models and 7373 patients. The area under the receiver operating characteristic curve (AUC) was 0.670-0.960. Among them, 23 prediction models had a good prediction performance (AUC>0.7); 13 models were tested for calibration of the model; 1 model was externally validated, and 10 models were internally validated. Meta-analysis showed that hypoproteinemia (OR=9.362), postoperative pulmonary complications (OR=7.427), poor incision healing (OR=5.330), anastomosis type (OR=2.965), preoperative history of thoracoabdominal surgery (OR=3.181), preoperative diabetes mellitus (OR=2.445), preoperative cardiovascular disease (OR=3.260), preoperative neoadjuvant therapy (OR=2.977), preoperative respiratory disease (OR=4.744), surgery method (OR=4.312), American Society of Anesthesiologists score (OR=2.424) were predictors for AL after esophageal cancer surgery. Conclusion At present, the prediction model of AL risk in patients with esophageal cancer after surgery is in the development stage, and the overall research quality needs to be improved.

Objective:To analyze the clinicopathological characteristics and prognostic factors of patients with Castleman disease (CD).Methods:A retrospective case-series study was conducted. A total of 85 patients newly diagnosed with CD in the First Affiliated Hospital of Air Force Medical University between July 2007 and August 2024 were collected. Their clinical characteristics and prognostic factors were analyzed.Results:Among the 85 patients, 45 had unicentric Castleman disease (UCD) and 40 had multicentric Castleman disease (MCD). In the UCD group, females were more commonly affected (64.4%, 29/45), with a median age of onset of 39 years. The primary lesions were mainly located in the retroperitoneum, neck, abdomen, and axilla; and the hyaline vascular subtype was the predominant pathological type (69.4%, 25/36). In the MCD group, males were more frequently affected (62.5%, 25/40), with a median age of onset of 50 years; and the plasmacytic subtype was the main pathological type (68.2%, 15/22). Compared with UCD patients, MCD patients presented more systemic symptoms and signs [85.0% (34/40) vs. 13.3% (6/45), χ2 = 43.66, P < 0.001], splenomegaly [42.5% (17/40) vs. 2.2% (1/45), χ2 = 20.58, P < 0.001], hepatomegaly [25.0% (10/40) vs. 0 (0/45), χ2 = 10.46, P = 0.001], edema or effusion in serous cavity [67.5% (27/40) vs. 8.9%(4/45), χ2 = 31.40, P < 0.001], hematological system involvement [32.5% (13/40) vs. 0 (0/44), χ2 = 16.92, P < 0.001], and renal involvement [22.5% (9/40) vs. 2.3%(1/44), χ2 = 6.36, P = 0.012]. Laboratory findings showed that the levels of hemoglobin and albumin in MCD patients were lower than those in UCD patients, while white blood cell count in MCD patients was higher than that in UCD patients. Additionally, MCD patients exhibited elevated levels of C-reactive protein, interleukin-6, vascular endothelial growth factor, erythrocyte sedimentation rate, and ferritin compared to UCD patients (all P < 0.05). Among UCD patients, 40 cases underwent simple surgical resection, with no deaths during follow-up and the 5-year overall survival (OS) rate of 100.0%; among MCD patients, 34 cases received chemotherapy, 4 received siltuximab, 3 died during follow-up with a 5-year OS rate of 87.5%; and there was no statistically significant difference in OS between the MCD and UCD groups ( χ2 = 3.67, P = 0.055). Among MCD patients, the OS of those with renal involvement (9 cases) was worse than that of those without renal involvement (31 cases) ( χ2 = 8.39, P = 0.004). Conclusions:CD is a highly heterogeneous disorder. Surgical resection is the primary treatment for UCD, with a favorable prognosis. Chemotherapy is the main treatment for MCD, with a relatively poor prognosis.

Fe/Mn/Gd polymetallic nanooxide(FMGN)were prepared by one-step solvent thermal reaction by using Fe(acac)3,Mn(acac)2 and Gd(acac)3 as reaction precursors.Next,hyaluronic acid(HA)was used to modify FMGN to fabricate tumor-targeting T 1-T 2 dual-mode magnetic resonance imaging(MRI)contrast agent(HA-FMGN)for accurate diagnosis of prostate cancer.The structure and morphology of FMGN were observed by transmission electron microscope(TEM).It was found that FMGN exhibited a uniform nanocluster spherical structure when the feeding ratio of iron acetylacetonate,manganese acetylacetonate,and gadolinium acetylacetonate was 3:2:1.X-ray diffraction(XRD)analysis showed that FMGN had a typical inverse spinel structure of Mn doped Fe 3O 4,with Gd existing in the form of amorphous gadolinium oxide.The longitudinal relaxivity(r 1)and transverse relaxivity(r 2)of FMGN were 13.395 and 428.535 L/(mmol·s),respectively,measured by 0.5 T MRI analyzer,which proved that FMGN had excellent T 1-T 2 dual-mode MRI contrast capability.The cytotoxicity and hemolysis test found that HA-FMGN didn't damage red cells and induce toxicity for normal cells,indicating that HA-FMGN had excellent cell biocompatibility.The internalization efficacy of HA-FMGN was observed by CLSM,and the results showed that HA-FMGN possessed excellent prostate tumor-targeting ability.In vivo MRI experiment showed that HA-FMGN significantly enhanced T 1 and T 2 weighted MRI signal to noise ratio(SNR)of prostate tumor,which promoted the accurate diagnosis of orthotopic prostate cancer.