This study aims to investigate the factors affecting the stone-free rate (SFR) of flexible ureteroscopy and laser lithotripsy (fURSL) for renal stones and establish predictive models by identifying their prognostic factors. We retrospectively examined 252 patients with renal stones who were treated with fURSL between July 2020 and April 2022. We analyzed the relationship between the patient’s clinical data (sex, age, and body mass index), stone status (side, size, location, stone/transverse process pixel ratio [STPR], and the CT value of stone [SCTV]), and SFR to determine the relevant factors and analyze their influence. Additionally, a nomogram was constructed based on these prediction results. A total of 252 patients were enrolled based on the inclusion and exclusion criteria. They were reviewed 7, 30, and 90 days postoperatively, with 46, 23, and 10 patients failing to discharge stones, respectively. Univariate Cox proportional hazard regression results revealed that the SFR was correlated with stone location, diameter (D1, diameter of stone measured by computed tomography [CT]; D2, diameter of stone measured by kidney-ureter-bladder abdominal radiography), SCTV, STPR, and operation time. Multivariate Cox proportional hazard regression was used to develop 2 predictive models for the SFR. The influencing factors of model 1 included D1, location, and SCTV, whereas those of model 2 were D2, location, and STPR. The results are shown in the nomogram. Receiver operating characteristic curves showed no significant difference between models 1 and 2 (P = .498), indicating that the nomogram was highly predictive. After 1000 resamples and internal self-validation, the C-indices of models 1 and 2 were 0.924 and 0.895, respectively, showing that the stone clearance predicted by the nomogram matched the actual situation. Stone location, size, and density (SCTV and STPR) were significant predictors of SFR after fURSL. The scoring system based on these factors may be used to guide optimal treatment strategy selection.

This study aimed to establish a clinical model to predict the risk of hemorrhoid recurrence after procedure for prolapse and hemorrhoids. The clinical data of patients who underwent stapler hemorrhoidal mucosal circumcision in Shanxi Bethune Hospital from April 2014 to June 2017 were collected retrospectively and followed up regularly after the operation. Finally, 415 patients were included, which were divided into training group (n = 290) and verification group (n = 125). Logistic regression method was used to select meaningful predictors. The prediction model was constructed with nomographs, and the model was evaluated with correction curve, receiver operating characteristic curve and C index. The decision analysis curve was used to determine the clinical utility of the nomogram. Birth history, muscle attachment, postoperative anal urgency, anal resting pressure, postoperative nutritional index, body mass index, Wexner score, and hemorrhoid grading were included in the nomogram. The area under the curve of the prediction model was 0.813 and 0.679, respectively, in the training and verification groups, and 0.839 and 0.746, respectively, for the 5-year recurrence rate. The C index (0.737) and clinical decision curve showed that the model had high clinical practical value. The prediction model of hemorrhoid recurrence risk after hemorrhoidectomy based on multiple clinical indicators can be used for individualized prediction of hemorrhoid recurrence in patients after hemorrhoidectomy, and early intervention measures can be given to individuals with a high recurrence risk to reduce the risk of recurrence.

Primary aldosteronism (PA), a leading cause of secondary hypertension, remains vastly underdiagnosed due to unreliable screening tools. We developed an integrated platform combining a high-gradient magnetic separation three-dimensional (HGMS-3D) chip and locked nucleic acid (LNA)–enhanced droplet digital polymerase chain reaction (ddPCR) for noninvasive detection of potassium inwardly rectifying channel subfamily J member 5 ( KCNJ5 ) mutations in plasma small extracellular vesicles (sEVs). The HGMS-3D chip uses a nickel mesh–based stereoscopic immunoaffinity capture system, achieving a vesicle isolation efficiency 4.4-fold higher than ultracentrifugation. Coupled with LNA-ddPCR, the platform detects KCNJ5 hotspot mutations [p.Gly151Arg, (G151R); p.Leu168Arg (L168R)] at minor allele frequencies of ≤0.05% ( R ² ≥ 0.99), overcoming plasma-derived noise. Clinical validation in 106 patients with PA demonstrated 64.58% sensitivity and 96.88% specificity for sEV-based mutation profiling. The assay identified one aldosterone-producing adenoma (APA) case missed by tissue genotyping, achieving area under the receiver operating characteristic curve (AUC) values of 0.767 ~ 0.852 across mutations. This noninvasive approach could enable curative treatment for millions with undiagnosed PA, advancing precision management of endocrine hypertension through sEV-based liquid biopsy.

Due to the rarity of primary cervical lymphoma (PCL), the long-term survival of patients with cervical lymphoma and factors influencing survival are unknown. This study aimed to compare the survivals of patients with PCL and those with other cervical tumors and construct a clinical prediction model to assess the prognosis of patients with PCL. Patients with PCL from the Surveillance, Epidemiology, and End Results database were allocated randomly in a 7:3 ratio to the training and validation sets. Cox proportional hazard and Fine-Gray models were used to verify independent factors influencing overall survival (OS) and disease-specific survival (DSS), and nomograms were constructed. Receiver operating characteristic curve analysis and decision curve analysis (DCA) were used to test the performance and clinical utility of the models, respectively. We included 206 patients with PCL. The areas under the curves (AUCs) and DCA showed that all models had clinical benefits; The models constructed in this study had a predictive performance for patients with PCL. It can guide clinicians to rationalize the treatment plan for patients.

Objectives: Non-contrast computed tomography (NCCT) is used before retrograde intrarenal surgery (RIRS) to describe stone size, location, and complexity, but the best way to summarize stone burden remains unsettled. Therefore, we compared multidimensional NCCT-derived burden metrics with cumulative stone diameter (CSD) with respect to their ability to predict stone-free rate (SFR) and operative time after RIRS. Methods: We reviewed 352 RIRSs performed by one surgical team from April 2024 to May 2026. The primary SFR endpoint was defined as no residual fragments or a maximum residual fragment size of ≤4 mm, with 1-month computed tomography (CT) used as the reference imaging modality. Strict 0 mm CT stone-free status was analyzed as a sensitivity endpoint. The candidate predictors included one-dimensional, two-dimensional, estimated three-dimensional, spatial-distribution, attenuation, and distribution-complexity metrics extracted from preoperative NCCT. The cohort was split into training (n = 247) and validation (n = 105) sets. Results: The main model retained area-equivalent diameter, staghorn stone, and stone volume distribution entropy, with a validation area under the receiver operating characteristic curve (AUC) of 0.779. A simpler CSD model combining CSD and staghorn stone achieved a validation AUC of 0.761, without a significant AUC difference (p = 0.201). In repeated patient-level full-pipeline validation, the median validation AUC was 0.761. Under the strict 0 mm CT endpoint, the validation AUC was 0.670. Entropy showed lower selection stability in sensitivity analyses. Reclassification favored the main model (validation continuous net reclassification improvement [NRI] 0.460, p = 0.007; integrated discrimination improvement [IDI] 0.038, p = 0.003). Regarding operative time, area-, volume-, and surface-area-based metrics performed better than CSD in regard to both the whole cohort and stone-free episodes. Conclusions: NCCT-derived burden metrics can refine preoperative assessment before RIRS, but their incremental value over CSD for binary SFR prediction was modest. CSD remains a practical approximation for SFR prediction, whereas multidimensional metrics better reflect operative workload.