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Purpose MRI-guided transurethral ultrasound ablation (TULSA) is a novel, minimally invasive therapy for prostate cancer designed to preserve urinary continence and erectile function. The original TULSA pivotal trial mandated preservation of 3 mm of apical tissue to protect the external sphincter. We evaluated oncologic and functional outcomes in patients with prostate cancer located at the extreme apex, including lesions abutting or involving the sphincter. Materials and methods We performed a retrospective analysis of a subgroup from a prospective TULSA cohort at a single center. Patients included had MRI-visible extreme apical lesions abutting or involving the external sphincter and ≥6 months of follow-up with prostate-specific antigen (PSA) or MRI. The extreme apex is considered to be the most distal, tapering portion of the gland adjacent to the prostatic urethral termination and membranous urethra, immediately proximal to the external urinary sphincter (rhabdosphincter).  Treatment planning incorporated intraoperative diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) maps, and T2-weighted imaging. For lesions near the sphincter, a reduced 5 mm safety margin was applied, treating ≤50% of sphincter length or arc. Follow-up included serial PSA testing every three months and MRI, International Prostate Symptom Score (IPSS), and International Index of Erectile Function (IIEF) at six to nine months. Local recurrence was assessed using Prostate Imaging for Recurrence Reporting (PI-RR) criteria. Results Sixty-eight patients (61 primary and 7 salvage) were treated. Median age was 63 years, with a median follow-up of 12 months. PSA declined from a median of 7 ng/mL to 0.5 ng/mL. Among 59 patients with follow-up MRI, 88% demonstrated no evidence of disease. Six of seven patients with suspicious imaging underwent repeat TULSA, with favorable early outcomes. Functional outcomes were excellent: all patients were pad-free by three months, 80% maintained erections sufficient for penetration, and IPSS scores remained stable. Adverse events were mild and self-limited; no rectal injuries occurred. Conclusions MRI-guided TULSA is a safe and effective treatment for prostate cancer involving the extreme apical region. Despite the technical challenges posed by tumors abutting or partially involving the external urinary sphincter, carefully planned and controlled ablation - limited to ≤50% of sphincter involvement - can achieve excellent oncologic outcomes, with no evidence of residual disease on follow-up imaging and no suggestion of positive margins in treated patients. Importantly, this approach preserves urinary continence, with all patients remaining pad-free, while maintaining favorable functional outcomes. These findings support the feasibility of TULSA for the treatment of even bulky apical tumors without compromising cancer control or quality of life.

Acute alcohol consumption may interfere with the dynamics between internal and external load during exercise, potentially attenuating cardiovascular responses. This study investigated the association between distance covered during a running test and mean heart rate, while examining the moderating role of the rating of perceived exertion (RPE) under conditions with and without acute alcohol ingestion. This crossover experimental study included 12 physically active male university students (23.7 ± 3.7 years). Participants completed two intermittent running sessions (control and alcohol conditions), separated by ≥48 h. In the alcohol condition, participants consumed 0.4 g of ethanol/kg of body mass. Heart rate was continuously monitored using a Polar RCX5 monitor, and total distance covered and RPE (Borg 6-20 scale) were assessed immediately after test completion. Analyses included paired comparisons, Pearson correlations, and linear regression models with interaction terms. No significant associations between variables were observed in the control condition. With alcohol consumption, distance covered was positively associated with mean heart rate, and RPE significantly moderated this relationship. Acute alcohol ingestion may modify the interaction between external load, perceived exertion, and cardiovascular response during running. These results highlight the importance of integrated monitoring of internal and external load, especially in contexts involving recent alcohol consumption.

Aromatic hydrocarbons such as benzene, toluene, and ethylbenzene are extensively used as solvents in coatings, resin, and artificial leather industries. Azeotropic mixtures involving these compounds are commonly encountered in chemical manufacturing, where accurate azeotropic temperature and composition are essential for designing and optimizing separation processes such as extractive and pressure-swing distillation. In this study, two quantitative structure-property relationship (QSPR) models were developed to predict the azeotropic temperature and composition of binary mixtures containing aromatic hydrocarbons using only molecular structural information. The models show excellent agreement with experimental data (R2 = 0.9454 and 0.9448, R adj 2 = 0.9400 and 0.9413). Internal validation via leave-one-out cross-validation yields R cv 2 = 0.9308 and 0.9364, while external validation using an independent test set yields Q ext 2 = 0.8939 and 0.9364, indicating strong robustness and superior predictive performance compared to previously reported models. Molecular geometries were optimized using HyperChem 8.0, employing MM + and PM3 methods. Molecular descriptors were calculated using the Online Chemical Modeling Environment (OCHEM). Binary mixture descriptors were derived from pure-component descriptors via Kay's mixing rule. The genetic function approximation (GFA) algorithm was used to select the most relevant descriptors, and predictive models were constructed using multiple linear regression (MLR). Model robustness and predictive capacity were evaluated using leave-one-out cross-validation and an external test set, with applicability domains assessed via Williams plots. All computational procedures and modeling analyses were performed using OCHEM, SPSS, and HyperChem 8.0.

Occult diabetic kidney disease (DKD) is a subtle yet high-risk microvascular complication of type 2 diabetes mellitus (T2DM). Early-stage DKD often goes undetected because traditional screening markers remain within the normal range. This study aimed to develop and validate an explainable machine learning (ML) model using routine clinical and laboratory data for the early detection of occult DKD. Its potential value for primary care screening was also evaluated. This multicenter retrospective study included 1,916 hospitalized patients with T2DM. The derivation cohort consisted of 1,066 patients from Wanbei Coal-Electricity Group General Hospital and was used to train the model. An independent cohort of 850 patients from the First Affiliated Hospital of Anhui Medical University served for external validation. Thirty-two routine clinical variables were initially considered. Eight ML algorithms were compared to identify the optimal model. SHapley Additive exPlanations (SHAP) was employed to rank feature importance, reduce variables, and interpret the model. Finally, a quartile-based risk stratification system and a web-based tool were developed. Among the eight algorithms, logistic regression (LR) showed the best performance. Using SHAP rankings, a simplified LR model was built with eight features: HGB, HbA1c, HTN, UA, sex, MicroVCs, CVD, and A/G. The model performed well in both the training cohort (AUC = 0.824) and the external validation cohort (AUC = 0.786). SHAP analysis identified HbA1c, uric acid (UA), and hemoglobin (HGB) as the top contributors. The risk stratification system demonstrated clear separation, with the incidence of occult DKD rising from 1.5% in the lowest-risk quartile (Q1) to 55.8% in the highest-risk quartile (Q4). Additionally, decision curve analysis demonstrated that the model provides substantial clinical net benefit, and the final model was implemented as an interactive web-based calculator for real-time risk assessment. An explainable ML model was successfully developed to accurately predict occult DKD using routine clinical data. The model combines good performance with clear interpretation. It may serve as a practical tool for large-scale screening and early intervention in primary care.

To evaluate and compare the presence and distribution of residual fibers from two different brands of adhesive applicators within the adhesive interface after active application of a universal adhesive. Eighteen sound human molars were prepared with standardized Class II cavities and randomly assigned to two groups (n = 9) according to the applicator used: group P (Proclinic SAU, Spain) or group K (Kerr, USA). A one-step self-etch universal adhesive (Scotchbond Universal Plus; Solventum, USA) was actively applied following the manufacturer's instructions. After polymerization, specimens were examined under ultraviolet (UV) light using an optical microscope. Residual fibers were identified, quantified, and categorized according to their location (external cavity surfaces, cavosurface margins, internal line angles, axial walls, and cavity floors). Data were analyzed using the Fisher-Freeman-Halton exact test (P 0.05). Residual fibers were detected in all specimens, predominantly on external cavity surfaces, cavosurface margins, and axial walls. Statistically significant differences were observed between the two applicator brands (P 0.05), with group P showing a higher number and greater length of fibers than group K. Both applicator brands released microscopic fibers that became incorporated into the adhesive interface, revealing an unrecognized source of contamination and leading to rejection of the null hypothesis. Fiber distribution was not homogeneous across cavity surfaces. The proposed methodology proved effective for detecting and localizing applicator-derived residues, highlighting an overlooked source of contamination that may influence adhesive performance and restoration durability.

Artificial ion-sensing systems rely on external power to sustain interfacial potentials, facing persistent stability challenges. During continuous operation, progressive energy depletion results in potential decay, manifesting as signal drift and eventual system failure. This problem stems from the absence of an efficient active regulation mechanism analogous to biological ion pumps, which harness ATP hydrolysis to actively transport ions against electrochemical gradients, dynamically compensating for potential dissipation. Inspired by this mechanism, we developed an oxygen-driven bioinspired ion pump that exploits oxygen-sensitive O─Zn bonds within NH4 +-intercalated V2O5 to achieve efficient Zn2+ extraction and reverse pumping in oxygen-rich environments, successfully emulating biological active transport. This design enables sustained electrode potential stability through dynamic ion pumping while significantly enhancing the ion-storage capacity of V2O5. Theoretical simulations elucidated the mechanism linking O─Zn bond dissociation to adsorption site energy states under oxygen enrichment, alongside the resulting Zn2+ pumping process. The constructed self-powered respiration sensor demonstrated stable operation for 480 h in ambient air without external power, exhibiting a minimal performance degradation rate of only 0.2% (compared to 13.9% in oxygen-free environments). This work proposes an oxygen-driven bioinspired ion-pumping strategy, offering a novel pathway to overcome persistent energy supply challenges in potentiometric sensors.

Chronic pain remains a critical clinical issue worldwide, with adverse effects on the quality of life of oncology patients. Meanwhile, the overuse of opioids to treat or alleviate chronic cancer pain has contributed to a global opioid crisis. The increasing accessibility of high-quality clinical datasets and computational frameworks has promoted the use of machine learning (ML) techniques in clinical practice to manage opioid consumption. This review investigates the current bibliography referring to the role of applied ML techniques in opioid administration in patients with chronic cancer pain. The objective of the current scoping review, according to population, intervention, comparison, and outcome (PICO) standards, was to evaluate the effectiveness of ML techniques in monitoring opioid consumption in patients with chronic cancer pain. This review includes scientific journal papers published from 2010 to 2024 that use healthcare data from patients with chronic cancer pain, apply machine learning techniques, and may address the potential consequences of the misuse of opioids. A systematic literature search, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, was performed in PubMed and Google Scholar databases. Data extracted include the study's goal, dataset used, cohort selected, types of ML models created, model evaluation metrics, and the details of the ML tools and techniques used to create the models. After conducting the screening process, 50 articles were identified, but only four focused specifically on or included data of patients with chronic cancer pain where ML techniques were applied. The four included studies showed high performance (area under the curve {AUC}: >0.8) in predicting opioid adherence, misuse, and long-term use. Although generalizability remains limited due to small sample sizes and a lack of external validation, it sets distinct limits in applying these methods in clinical use. After a thorough review of recent literature, ML models demonstrated promising accuracy in predicting opioid adherence, misuse, and long-term use among patients with chronic cancer pain. However, these findings are based on studies with limited sample sizes and a lack of external validation, which restricts their generalizability. Future research should focus specifically on populations with chronic cancer pain and expand predictive models to incorporate a combination of clinical, psychosocial, biometric, and genomic data. This approach may enable more accurate, personalized, and safer opioid management in oncology care.

Major depressive disorder (MDD) affects approximately 1 in 6 adults during their lifetime, yet antidepressant selection relies predominantly on trial-and-error, with response rates of only 42% to 53%. While machine learning (ML) models have shown promise in predicting treatment outcomes, most focus on single treatments rather than comparative selection across therapeutic alternatives, limiting their clinical utility for the medication choice decisions that clinicians face in practice. This systematic review evaluates ML approaches that examine 2 or more pharmacological interventions for predicting treatment outcomes in MDD, with a focus on their capacity to facilitate comparative treatment selection between medications or medication classes for individual patients. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we searched PubMed, Scopus, and Web of Science for studies published from 2015 to 2025. We included studies involving adults with MDD that used ML models to predict treatment outcomes across 2 or more pharmacological treatments and reported medication-specific prediction outcomes. Risk of bias was assessed using PROBAST-AI (Prediction Model Risk of Bias Assessment Tool for Artificial Intelligence). We conducted a narrative synthesis organized by modeling strategies, data integration approaches, validation methodologies, and performance patterns. From 5370 initial records, 19 studies met the inclusion criteria, with dataset sample sizes ranging from 49 to 77,226 participants. Studies employed 3 distinct modeling strategies: drug-specific supervised models trained independently for each medication, subtype- or trajectory-based approaches using clustering methods to identify differential response patterns, and a unified differential prediction framework generating calibrated cross-treatment predictions. Performance varied substantially, with area under the curve values ranging from 0.59 to 0.95 and classification accuracies between 62% and 95.4%, though high performance was concentrated in studies with small samples, high-dimensional neurobiological features, and internal-only validation. Only 7 studies conducted external validation, which generally yielded more conservative performance estimates. Feature informativeness was more consistently associated with performance variation than algorithm complexity. Most studies did not formally distinguish between prognostic features predicting general outcomes and predictive features identifying differential medication responses, and none applied formal explainability techniques. ML for comparative antidepressant selection remains in an early stage of development. Only 1 study implemented a unified framework directly supporting patient-level treatment ranking. Key barriers to clinical translation include insufficient distinction between prognostic and predictive markers, limited cross-trial validation, near-absent calibration reporting, and absent explainability. Future research should prioritize unified comparative frameworks with calibrated predictions, rigorous external validation on diverse cohorts, explicit modeling of heterogeneous treatment effects, and integration of explainability into model development.

To develop and evaluate a combined model integrating musculoskeletal ultrasound (MSK US) with a machine learning (ML) algorithm for assessing disease activity in rheumatoid arthritis (RA). A total of 203 patients with clinically confirmed RA were prospectively enrolled from December 2023 to September 2025. A cohort of 142 patients from the First Affiliated Hospital, College of Clinical Medicine of Henan University of Science and Technology served as the training cohort, while 61 patients from Affiliated Hospital of Traditional Chinese Medicine, Xinjiang Medical University (Fourth Clinical Medical College, Xinjiang Medical University) constituted the independent external test cohort. Three predictive models were developed: (1) an MSK US model incorporating two-dimensional grayscale ultrasound, power Doppler ultrasound (PDUS), and superb micro-vascular imaging (SMI); (2) a radiomics model based on two-dimensional grayscale images using the extremely randomized trees (ExtraTrees) algorithm; and (3) a combined model integrating the first two. Model performance in assessing RA disease activity was evaluated and compared using receiver operating characteristic (ROC) curve analysis. Calibration curves and decision curve analysis (DCA) were subsequently used to validate the overall performance of the optimal model. Multivariate logistic regression analysis identified erythrocyte sedimentation rate (ESR)>54 mm/h, C-reactive protein (CRP)>32.83 mg/L, and SMI synovial blood flow grade III as independent predictors of clinically active RA. The area under the ROC curve (AUC) values for the MSK US model, radiomics model, and combined model were 0.935 (95% confidence interval [CI]: 0.893-0.978), 0.976 (95% CI: 0.955-0.997), and 0.998 (95% CI: 0.998-1.000), respectively, in the training cohort; in the independent external test cohort, the AUC values for the three models were 0.904 (95% CI: 0.825-0.983), 0.823 (95%CI:0.714-0.933), and 0.929 (95%CI:0.866-0.992),respectively. The discriminative performance of the combined model was significantly superior to that of either the MSK US model or the radiomics model alone. Calibration curves demonstrated good agreement between the observed risk levels and the predicted risk probabilities. Decision curve analysis indicated that the model provided significant net benefit across threshold probability ranges of 0.02-0.80 in the training cohort and 0.12-0.78 in the test cohort. The combined model developed based on MSK US and radiomics demonstrated satisfactory performance for assessing disease activity in RA, enabling clinicians to dynamically monitor RA disease activity and evaluate treatment response, thereby providing a reliable imaging basis for the selection of routine medical treatment strategies.

Perioperative factors can influence planned discharge and survival prediction for lung transplant patients. In this study, we retrospectively analyzed perioperative clinical data of lung transplant recipients and assessed the predictive effects of relevant indexes on timely discharge of patients within 90 days post -transplant. We conducted a retrospective study on 81 lung transplant patients seen from March 1, 2017, to March 1, 2024, using data from the hospital information system. We used univariate and multi-variate logistic regression, ROC curve analysis, and Kaplan -Meier survival analysis to analyze perioperative indicators. To strengthen the robustness of our findings, we performed external validation. Univariate logistic analysis showed that preoperative hemoglobin, albumin, blood loss, lactic acid, and postoperative hemoglobin were prognostic factors, whereas multivariate logistic analysis showed that postoperative lactic acid was an independent risk factor (P < .05 ). A negative correlation was shown between postoperative hemoglobin and lactate (r = -0.433, P < .001 ). External validation results provided additional confirmation of our study's findings. Further analysis and exploration with larger cohort studies are needed to enhance the generalizability and reliability of the findings that showed preoperative hemoglobin, albumin levels, blood loss, lactic acid, and postoperative hemoglobin were factors affecting prognosis and that postoperative lactic acid level was an independent risk factor discharge status.

Dislocation remains a potential complication after total hip arthroplasty (THA), even with the anterior approach, which is generally associated with lower instability rates. Obturator dislocation, although well-documented in native hips following high-energy trauma, is extremely rare in the context of THA and has not been previously described in patients with preoperative spinopelvic assessment. We report two cases of obturator dislocation following direct anterior approach THA in young female patients without medical comorbidities. Both dislocations occurred during extreme hip flexion combined with abduction and external rotation. In one case, closed reduction attempts initially failed, requiring reduction on a traction table. Radiographic and 3D analyses revealed shared features, including significant medialization of the acetabular component (10&#x2009;mm), low combined anteversion (16&#xb0; and 24&#xb0;), and lumbopelvic mobility patterns dominated by hip flexion. Preoperative functional imaging showed no signs of spinopelvic imbalance or stiffness. Both patients exhibited a "hip user" profile with low pelvic incidence and distal lumbar lordosis apex. These cases highlight multiple converging factors contributing to instability: loss of offset due to cup medialization, stem retroversion resulting in low combined anteversion, and postoperative increase in compensatory hip flexion. Notably, no classic spinopelvic risk factors were present. These findings underscore the need for precise implant positioning, especially for acetabular positioning and femoral version.

Arterial thoracic outlet syndrome (ATOS) is characterized by upper extremity ischaemia or aneurysm-like disease caused by external compression of the subclavian or axillary arteries at the thoracic outlet. Arterial thoracic outlet syndrome is the least common type of thoracic outlet syndrome (TOS), accounting for 1%-2% of all TOS cases. Acute limb ischaemia (ALI) is a life-threatening condition requiring urgent assessment and management. Although ALI most commonly affects the lower limb, 20% of cases involve the upper limb. The first-line treatment for ALI is surgical thrombectomy; however, some reports have found endovascular treatment to be effective. A 45-year-old man complained of rest pain and paraesthesia in the left arm for the past week. Physical examination revealed coldness and pallor of the left upper limb and absence of the brachial pulse. Computed tomography revealed pseudarthrosis due to left cervical ribs, aneurysmal change of the left subclavian artery with thrombus, and distal occlusion beyond the brachial artery. Electrocardiography showed a normal sinus rhythm, and echocardiography showed no thrombus in the left ventricle. We diagnosed acute upper limb ischaemia due to subclavian artery aneurysms with TOS. He underwent emergent surgical thrombectomy via the left brachial artery. Surgical resection of the cervical and first rib was performed 1 month later, and endovascular treatment with a stent graft was performed for the aneurysmal change 3 months later. We report a rare case of TOS with aneurysmal change and thrombosis. Arterial thoracic outlet syndrome should be considered in acute upper limb ischaemia when the embolic cause is unknown.

To understand circadian rhythms and sleep in an understudied population, which is particularly prone to suffer chronodisruption (CD), eighteen blind volunteers of 51.5 &#xb1; 3.6 years (Mean &#xb1; SEM) and 26 volunteers (51.8 &#xb1; 1.2) with no visual impairments wore the ambulatory circadian monitoring (ACM) device Kronowise&#xae; for seven consecutive days in real-life conditions. Nine of the blind participants declared to have some sort of light perception while the other nine declared to lack conscious light perception. ACM combines measurements of distal skin temperature; motor activity, light exposure and feeding schedules, providing information about lifestyle and the bidirectional crosstalk between internal time and external synchronisers, which is paramount to determine a subject's CD degree. We found a extraordinarily diverse population in terms of blindness aetiology and thus, in the degree of affectation of the participants visual and circadian systems. Our results pointed to poorer circadian health and sleep in the blind participants, which could be directly related to the impact of disease over circadian photoreception but also to disruption of daily habits. Compared to controls, blind participants showed significantly lower light exposure and physical activity values during the day and higher time of movement during the night. Besides, we analysed feeding schedules in the blind participants for the first time and found that their last meal of the day happened later than in controls, thus blind participants' night fasting was shorter. Altogether, our results indicated substantial behavioural circadian alterations associated with the disease.

Understanding chemotaxis at the molecular level is challenging, as individual enzyme molecules cannot sense chemical gradients across their nanometer-sized bodies. Typical theoretical models encompass chemotaxis under constant, externally imposed gradients; however, this overlooks a critical feedback loop, where the active enzymes themselves reshape the imposed gradients through catalysis. In this work, we investigate the principles of active molecular chemotaxis using a Fokker-Planck model for an ATP-driven kinase-phosphatase system. Using experimentally relevant enzyme concentration ranges (&#x223c;nM), we demonstrate that the chemotactic velocity of enzymes does not simply respond linearly to chemical gradients, as commonly observed in microscale systems driven by diffusiophoresis. Instead, it emerges from a nonlinear coupling between the enzyme's spatial distribution, its conformational state (free/bound-state ratio), and chemical gradients modulated by catalytic reactions. As a result, the spatial profile of chemotactic velocity transitions between monotonic and nonmonotonic regimes, depending on substrate availability. Furthermore, we find that high catalyst concentrations can amplify the effective interaction between enzymes, forming a cascade that is critical for collective assemblies such as metabolon formation. To understand these complex interactions, we construct chemotactic velocity maps as a function of enzyme concentration, energy, and substrate availability, offering a set of design principles. This work clarifies the distinct roles of energy, gradients, and enzyme free/bound states in molecular motion, highlighting a fundamental difference between nano and microscale systems, and provides a theoretical framework for designing advanced autonomous active molecular systems.