Search Results (30,615)

Background/Objectives: Accurate evaluation of fallopian tube patency is an essential step in infertility assessment. This study investigated the diagnostic capability of saline infusion sonography combined with color Doppler flow (SIS-CF), using laparoscopic chromopertubation as the comparator method. Methods: A prospective diagnostic accuracy study was conducted between January and November 2025 at the Infertility Unit, Chiang Mai University Hospital, Thailand. Women requiring laparoscopic assessment for infertility evaluation or preconception investigation were consecutively enrolled. Each participant underwent SIS-CF immediately before laparoscopic chromopertubation within the same operative session. Primary outcomes included diagnostic indices for tubal patency. Secondary outcomes included procedural duration and perioperative safety. Results: Forty-four women (88 fallopian tubes) were included. SIS-CF demonstrated sensitivity of 89.4% (95% CI 78.1–95.9) and specificity of 95.5% (95% CI 77.2–99.9). Positive and negative predictive values were 98.3% and 75.0%, respectively. Likelihood ratios were 19.65 (positive) and 0.11 (negative), with overall accuracy of 90.9% (95% CI 82.9–95.8). Median procedure duration was 5.7 min for SIS-CF and 3.0 min for laparoscopic chromopertubation. No adverse events were recorded. Conclusions: SIS-CF demonstrated favorable diagnostic characteristics for evaluation of tubal patency. The technique provided functional information regarding tubal status while maintaining a minimally invasive profile. Further investigation is warranted to determine its role within routine infertility workups. Full article

Liquid biopsy has emerged as a transformative tool in oncology, enabling minimally invasive and dynamic characterization of tumor biology. In prostate cancer, marked by high heterogeneity and frequent bone metastases, tissue biopsy is often challenging, highlighting the clinical value of circulating biomarkers. Circulating tumor DNA (ctDNA) is the most clinically advanced analyte, supporting detection of actionable alterations such as BRCA1/2 and ATM mutations, guiding targeted therapies, and enabling real-time monitoring of treatment response and resistance. Circulating tumor cells (CTCs) and extracellular vesicles (EVs) provide complementary insights into tumor biology and disease progression. However, challenges remain, including limited sensitivity in low tumor burden and biological confounders such as clonal hematopoiesis (CH), which can lead to false-positive findings. Emerging approaches, including fragmentomics and methylation profiling, offer improved tumor specificity and may help overcome these limitations. Together, these advances support the integration of liquid biopsy into clinical practice for personalized management and longitudinal monitoring in prostate cancer. Full article

Water-level monitoring in rice paddies supports sustainable farming, responsible water management, and greenhouse gas emission mitigation. SAR-based remote sensing is an effective alternative for estimating water levels, especially in regions where optical observations are limited. This study evaluates ten ALOS-2/PALSAR-2 L-band SAR-derived polarimetric parameters for their contribution and effectiveness in water-level estimation across rice-growing phases using random forest regression in the Subang District, which is one of the largest rice-yield areas in West Java, Indonesia. Overall, L-band polarimetric information is clearly related to water-level dynamics throughout the rice-growing cycle, confirming its strong potential for quantitative water-level retrieval. The highest estimation accuracy was achieved by integrating all polarimetric parameter groups (MAE = 1.37 cm, RMSE = 1.79 cm, R² = 0.52, r = 0.73), indicating that no single group can adequately represent the complex scattering mechanisms governing water-level variability across an entire cropping season. Variable importance analysis shows a relatively uniform contribution (7.63–12.90%), suggesting synergies across parameters in water-level estimation. Phase-specific evaluation further reveals that Phase 2, corresponding to the vegetative-to-generative transition, is the optimal temporal window for L-band SAR-based water-level retrieval due to enhanced double-bounce scattering and reduced signal saturation. While Phase 2 data maximizes physical sensitivity and correlation, whole-phase modeling provides greater robustness and lower absolute errors, making it more suitable for L-band SAR-based operational water-level monitoring applications. Full article

Aging populations have intensified the demand for thermally comfortable and energy-efficient housing, particularly for elderly residents whose diminished thermoregulatory capacity renders them disproportionately vulnerable to indoor temperature fluctuations. Existing senior apartments in cold-climate regions frequently fail to meet age-specific thermal comfort standards, yet systematic retrofit optimization frameworks explicitly tailored to elderly occupants remain scarce. This study presents a data-driven multi-objective optimization framework for building envelope retrofitting, which is validated using on-site temperature measurements from a representative 1980s brick–concrete senior apartment building in Beijing. The framework integrates Latin Hypercube Sampling (LHS) for design space exploration, a Long Short-Term Memory (LSTM) surrogate model for simultaneous prediction of three performance objectives, and Non-dominated Sorting Genetic Algorithm II (NSGA-II) for Pareto-optimal solution generation, with final selection performed via a weighted Mahalanobis distance-based Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Optimization targets—annual energy consumption, indoor thermal discomfort hours, and retrofit cost—are parameterized using the age-sensitive comfort thresholds specified in GB 50340-2016. The LSTM surrogate achieved R² values of 0.91–0.93 across all objectives with training–testing differences below 0.02. The optimal retrofit package—Polyvinyl Chloride (PVC) Low Emissivity (Low-E) double-glazed windows (5 + 6A + 5), glass fiber roof insulation (65.25 mm), and Extruded Polystyrene (XPS) external wall insulation (65.39 mm)—reduces annual energy consumption by 47.1% (from 40,867 to 21,626 kWh) and annual thermal discomfort hours by 62.4% (from 2454 °C·h to 923 °C·h). SHapley Additive exPlanations (SHAP)-based sensitivity analysis further identifies wall U-value and roof thickness as the dominant performance drivers. A reproducible and computationally efficient pathway is provided by the proposed framework for evidence-based envelope retrofit decision-making in existing senior residential buildings. Full article

Background: Acute cellular rejection (ACR) after heart transplantation remains incompletely explained despite standardized immunosuppression. Environmental exposures may contribute to residual immune activation; however, prior studies have focused primarily on air pollution rather than residential land-use composition. Objectives: To determine whether buffer-specific residential environmental composition is associated with rejection risk and whether these associations are scale-dependent and domain-specific. Methods: In this retrospective single-center cohort study, 30 heart transplant recipients contributed 267 biopsy-linked observations. Residential land-use composition was quantified within 300 m, 500 m, 700 m, and 1000 m buffers and aggregated into five domains: trees, other green surroundings, roads, water, and industrial land. Associations with ACR were evaluated using clustered logistic regression models adjusted for time since transplantation. Results: The strongest and only statistically robust associations after FDR correction were observed within the 300 m buffer. Tree-dominant (OR 1.42, 95% CI 1.22–1.65, q = 0.010) and industrial land exposure (OR 1.50, 95% CI 1.28–1.76, q = 0.010) were independently associated with increased odds of ACR. At 500 m, the association with trees persisted nominally (OR 1.39, 95% CI 1.03–1.88, p = 0.034), but did not remain significant after FDR correction, whereas water exposure showed a non-significant trend (OR 1.28, p = 0.057), which did not reach statistical significance. No associations were observed beyond 700 m across all models. Conclusions: Residential environmental composition may be associated with acute cellular rejection after heart transplantation in a scale-dependent manner, with signals confined to the immediate residential environment. Tree-dominant exposure within 300 m showed an association in clustered models; however, this finding was attenuated in mixed-effects sensitivity analyses. These results should be considered exploratory and hypothesis-generating study. Full article

Numerical simulation of sequential fracturing in horizontal wells for shale gas and oil extraction requires careful consideration of mechanical interactions between proppant and fracture surfaces—a challenge that remains largely unresolved. This study proposes a novel phase-field model featuring a strain-based formulation and a width-dependent proppant reaction force. Unlike previous studies, we integrate an empirical propped force solution, adapted from established work to account for rock properties and proppant support, to capture nonlinear fracture closure. Results show that reaction stress models significantly dictate propped geometry. The model’s fracture length, width, and closure predictions are validated against theoretical solutions. We conducted a sensitivity analysis to evaluate how fracture deflection angles and widths vary with dimensionless fracture spacing, in situ stress contrast, and proppant strength. Numerical results show that proppants induce pronounced morphological asymmetry and distinct geometric discrepancies. Specifically, the heterogeneous support provided by proppants and the resulting stress redistribution alter fracture propagation paths, leading to an 8% reduction in fracture length and a marked difference in fracture orientation of approximately 80° between supported and unsupported fractures, highlighting the important role of proppants in governing fracture geometry. Both dimensionless fracture spacing and in situ stress contrast strongly influence fracture deflection, with proppant strength also contributing. The propped-force formulation is further extended to nonplanar fractures, enabling application to sequential fracturing with multiple fractures. These results highlight fracture propagation mechanisms and demonstrate the robustness of the proposed phase-field model. Full article

Background: Elevated anticardiolipin IgG (aCL IgG) has been reported in end-stage renal disease (ESRD), but its association with specific etiologies of kidney failure remains unexplored. The unique pathophysiology of diabetic–hypertensive nephropathy may be associated with a microenvironment that could potentially contribute to antiphospholipid antibody production and thrombotic complications. This study aimed to investigate whether aCL IgG elevation in hemodialysis (HD) patients is associated with combined diabetes–hypertension (DM + HTN) etiology and thrombocytopenia, thereby identifying a clinically distinct potential high-risk subgroup. In this hypothesis-generating study, we focused on within-HD patient comparisons rather than healthy controls. Methods: We enrolled 242 participants: 150 healthy controls (included only to establish local reference ranges) and 92 patients with maintenance HD. The study was conducted from 01 September to 20 November 2025 in Madinah, Saudi Arabia. Serum aCL IgG was measured by chemiluminescence immunoassay (positive ≥ 12 GPL units). Comprehensive hematological and biochemical parameters were analyzed. Multivariable logistic regression identified predictors of aCL positivity. Results: In the HD cohort, 21% demonstrated aCL positivity; this represents a substantially higher rate than the 2% observed in local healthy controls (p < 0.001). This elevation was not uniform across etiologies. Strikingly, 94.7% (18/19) of aCL-positive HD patients had DM + HTN aetiology, compared with only 17.8% of aCL-negative patients (p < 0.001). Thrombocytopenia was significantly more severe in aCL-positive patients (median platelets: 100 vs. 191 × 10⁹/L, p < 0.001). In multivariable analysis, DM + HTN etiology (HTN-alone vs. DM + HTN odds ratio [OR]: 0.0013, 95% confidence interval [CI]: 0.00002–0.0999, p = 0.003; confirmed by Firth’s penalized logistic regression sensitivity analysis, and lower platelet count (OR: 0.92 per 1 × 10⁹/L increase, 95% CI: 0.87–0.98, p = 0.006) independently predicted aCL positivity. Conclusions: These hypothesis-generating findings suggest a potential association between metabolic–vascular disease and antiphospholipid immunity in ESRD. Causality cannot be inferred from this cross-sectional design. At present, routine aCL screening is not recommended outside of research protocols; prospective studies are needed to confirm these associations. Full article

The increasing availability of automated development workflows and data-driven methods raises the question of when approaches based on artificial intelligence (AI) provide potential benefits over established engineer-driven workflows in lightweight structural design. This paper presents a quantitative comparison between a conventional engineer-driven process and an AI-assisted, automated workflow for an injection-molded component with fixed installation space, identical boundary conditions, and manufacturing constraints. In the conventional process, topology optimization is followed by manual CAD reconstruction and iterative finite element analysis. In the AI-assisted process, an automated workflow generates many design variants that are simulated and used to train a regression-based surrogate model for rapid exploration of the design space. The conventional workflow yields a manufacturable structure with a high stiffness-to-mass ratio and controlled stresses, whereas the geometry selected from the surrogate model’s prediction shows reduced stiffness, higher stress peaks, and manufacturability issues. The analysis of the best-performing design identified ex post within the training data, rather than directly by the surrogate, illustrates the potential of the automated workflow but also highlights insufficient predictive accuracy for locally stress-sensitive quantities. On the process level, the AI-assisted workflow exhibits clear scaling advantages and a distinct break-even point in terms of development effort, suggesting that such methods are currently best suited as complementary tools for early-stage design space exploration. The quantitative effort values and the break-even point, however, are case-specific and should be interpreted as order-of-magnitude indicators rather than universally valid thresholds. Full article

Bietti crystalline dystrophy (BCD) is a hereditary retinal disease caused by loss-of-function mutations in the CYP4V2 gene. Gene replacement therapy using rAAV-hCYP4V2 represents a promising therapeutic strategy, requiring robust bioassays for product quality control. This study developed and validated a sensitive LC-MS/MS method for quantifying CYP4V2 enzyme activity. Lysates from HeLa-AAVR cells transduced with rAAV-hCYP4V2 (MOI = 3 × 10⁵) were used, with lauric acid as substrate supplemented with cytochrome P450 reductase, cytochrome b5, and NADPH. The ω-hydroxylated product (12-hydroxy lauric acid) was quantified using tolbutamide as an internal standard. Method validation followed ICH guidelines. Results demonstrated excellent specificity with negligible background in negative controls. Linearity was achieved over 0.5–100 ng/mL (R² > 0.99), with an average recovery of 100.6%. Intra-batch and inter-batch precision RSDs were <47.8% and <28.4%, respectively. Product stability was maintained for ≥4 weeks at −80°C. The method was successfully applied to three AAV serotypes (AAV2, AAV8, and AAV2/8), with all RSDs < 23.9%. This validated LC-MS/MS bioassay provides a crucial quality control tool for potency assessment, process development, batch release, and stability studies of rAAV-hCYP4V2 gene therapy products. Full article

Camera rotation estimation is a key component in video stabilization and motion analysis. In many practical scenarios, inertial measurements are unavailable or temporally unreliable, while classical geometric pipelines degrade under blur, low texture, and low illumination. This paper investigates whether substantially downsampled optical flow can retain sufficient structure for accurate frame-to-frame rotation regression. We present VisualRNet, a lightweight rotation-specific visual regression framework trained with cross-modal IMU supervision. Our design uses coordinate-aware feature encoding, depthwise separable convolutions, lightweight attention, and a compact 6D rotation head to model the spatial structure of rotational flow fields. On Deep-FVS, VisualRNet achieves a mean rotation error of 0.3151${}^{\circ }$ on the test set. The VisualRNet regression head contains 7.7 K parameters, 0.002 GFLOPs, and runs at 729 FPS, while the full pipeline with the FastFlowNetv2 frontend contains 1.374 M parameters, 7.194 GFLOPs, and runs at approximately 113 FPS. A cross-camera adaptation experiment on TUM VI further indicates that the learned motion representation can be aligned to a new camera system with limited calibration data. These results support low-resolution optical flow as a practical input for visual rotation estimation and suggest particular value in stabilization-oriented and cost-sensitive applications where approximate rotational trend matters more than full scene geometry. Full article

Disease-specific alpha-synuclein (αsyn) strains have been linked to different synucleinopathies. Current αsyn biomarkers are limited to binary detection of pathogenic αsyn in peripheral tissue biopsies or fluids, limiting differential diagnosis. Hence, there is an urgent need for methods that allow strain-specific detection and characterization of αsyn strain architecture. Notably, luminescent conjugated oligothiophenes (LCOs) have been successfully used to detect distinct protein strain conformers in prion diseases and Alzheimer’s disease, highlighting their utility in differentiating disease-specific amyloid structures. Species-dependent differences in αsyn structure are increasingly recognized as one of the critical aspects that shape how fibrils form, propagate and interact with molecular LCO probes. Here, we evaluate the potential of the LCO h-FTAA to differentiate species-specific αsyn strains and conduct a translational investigation using peripheral cardiac tissue of a gut-first synucleinopathy rodent model. Our in vitro data demonstrate strain-specific probe–fibril interactions, reflecting a differential strain architecture and cellular micro-environment. While h-FTAA binds with comparable efficiency to mouse (mo-) and human (hu-) pre-formed fibrils (PFFs), h-FTAA exhibits markedly lower quantum yield when bound to moPFFs versus huPFFs. Spectral imaging revealed h-FTAA-moPFF binding produces blue-shifted maxima (505–550 nm), contrasting with the red-shifted maxima (545–580 nm) of huPFFs. Fluorescence lifetime imaging microscopy confirmed h-FTAA’s intrinsic sensitivity to species-dependent variations through distinct temporal fluorescence signatures (moPFFs: ~0.60–1.5 ns vs. huPFFs: ~0.65–1.0 ns). Our translational investigation showed h-FTAA binding to peripheral cardiac pathology exhibits comparable red-shifted emission, but distinct fluorescence lifetimes of h-FTAA-bound aggregates in moPFF-injected (~1.0–1.4 ns) versus huPFF-injected (~0.69–0.8 ns) rats. Interestingly, we observed distinct blue-shifted emission profiles in a few selected regions of the heart of moPFF-injected rodents, further characterized by extra-long fluorescence decay shifts (~1.5–1.9 ns), reflecting differences in both aggregate conformation and maturity in moPFF-induced compared with huPFF-induced rats. Taken together, our findings underscore the potential of LCO ligands, like h-FTAA, to enable more precise disease staging and diagnosis through peripheral biopsies, complementing existing αsyn biomarker methods. Full article

To address the environmental sensitivity and low bioavailability of riboflavin, this study constructed a soybean protein isolate fibril (SPF)/κ-carrageenan (κC) composite gel delivery system. This study systematically investigated the effects of two independent variables (protein type: SPI/SPF; κC concentration: 2, 4, 6, 8 mg/mL) on the gel structural stability, riboflavin encapsulation performance, and in vitro digestive delivery characteristics of the system. Thioflavin T (ThT) fluorescence and ultraviolet (UV) absorption spectroscopy confirmed the successful preparation of SPF and verified specific intermolecular interactions between SPF and κC. Intermolecular forces, protein leaching rates, and differential scanning calorimetry (DSC) results indicated that compared with SPI-κC composite gels, κC regulates SPF molecular conformation via hydrogen bonding and hydrophobic interactions to exert a synergistic effect. This conformational regulation significantly reduced the protein leaching rates in SPF-κC composite gels, elevated the thermal denaturation temperatures (up to 79.82 °C), and enhanced the gel structural stability. As the κC concentration increased, the environmental stability of SPF-κC riboflavin-loaded composite gels were markedly enhanced, which effectively delayed the gel degradation during simulated gastrointestinal digestion. This was manifested as a reduced protein loss rate (reduced to 22.23%). At a κC concentration of 8 mg/mL, the in vitro release mechanism of riboflavin shifted from Fickian to non-Fickian diffusion. Full article

Chimeric antigen receptor (CAR) T-cell therapy is an effective treatment for hematologic malignancies. However, it is limited by high costs, risk of severe toxicities such as cytokine release syndrome and neurotoxicity, and heterogeneous patient responses. The current therapy monitoring depends largely on subjective symptom assessment, routine laboratory tests, and basic vital signs, without real-time, quantitative evaluation of CAR T-cell expansion or activation in clinical practice. This lack of timely immune monitoring hampers individualized care and contributes to increased treatment costs. To address this need, we present a proof-of-concept, label-free rapid optical imaging (ROI) biosensor with automated machine learning analysis for direct quantification of CAR T-cells from whole blood. This microfluidic platform integrates red blood cell (RBC) removal, CAR T-cell capture, and imaging-based quantification on a single chip, eliminating the need for centrifugation, staining, and operator-dependent interpretation. For validation, 50 μL whole blood samples spiked with Jurkat cells expressing CD19 CARs underwent RBC depletion by agglutination and microfiltration. The remaining blood components were then incubated on a sensor chip functionalized with recombinant CD19 protein. Captured CAR T-cells were imaged by brightfield microscopy and automatically enumerated using a machine learning algorithm trained on fluorescence-validated cells. The CD-19 cells’ capture performance was validated by flow cytometry and fluorescence imaging. The trained machine learning model validated at 88% sensitivity and 96% specificity. Buffer and whole blood calibration curves were established across clinically relevant concentrations (1–1000 cells/µL) with triple replicates. The results showed high correlation (0.975 and 0.990 R²) between the spiked concentration and the detected CAR T-cells, with a 95% certainty limit of detection (LOD) and quantification (LOQ) of 0.6 and 1.1 cells/µL for spiked buffer, and 14 and 67 cells/µL for spiked whole-blood, respectively. Full article

Ensuring data security and privacy has emerged as a serious concern in the realm of blood supply chain. This is mainly because of sensitivity of donor information, the involvement of multiple stakeholders, and the need for transparent traceability. This paper proposes a novel privacy-preserving, permissioned blockchain framework for blood supply chain management that integrates Hyperledger Fabric, the InterPlanetary File System (IPFS), and a Zero-Knowledge Proof (ZKP)-based authentication protocol. The framework introduces a Pseudonymous Role-Bound Zero-Knowledge Authentication (PRZKA) mechanism that enables donors to authenticate and authorize access to their medical data without revealing their real identities. Context-specific pseudonyms derived through cryptographic hash-to-curve operations ensure unlinkability across different healthcare interactions, while Schnorr-style challenge–response proofs prevent replay attacks and credential misuse. Sensitive donor information is protected using Fabric Private Data Collections, whereas encrypted medical records are stored off-chain in IPFS, with only secure content identifiers recorded on the blockchain. Smart contracts enforce fine-grained, consent-aware access control policies and maintain immutable audit logs of all access events. The proposed system architecture combines an off-chain ZKP gateway with on-chain authorization logic to minimize blockchain overhead while preserving strong security guarantees. Furthermore, a performance evaluation framework is defined, including metrics, workload scenarios, and system configurations, to support future empirical validation. Security analysis indicates that the proposed framework enhances privacy, prevents identity linkage, and enables auditable, consent-driven data sharing compared with existing blockchain-based healthcare solutions. Full article

The present study provides a high-frequency empirical assessment of the financial performance, volatility, and market integration of thematic sustainability-oriented equity funds, focusing on clean energy and environmental innovation indices. Specifically, the study compares the financial performance of representative thematic green equity funds, such as ICLN and QCLN, and an emerging-market benchmark (ECON) with conventional developed-market indices (SPY, QQQ, GSPC, and XLE) using daily stock prices from 2010 to 2025. The analysis employs a transparent and replicable framework based on daily logarithmic and cumulative returns and incorporates the compound annual growth rate (CAGR), Sharpe and Sortino ratios, beta estimation, correlation analysis, and maximum drawdown. The research frequency is appropriate for a thorough analysis of short-term market structures and performance. The results indicate that sustainability-oriented equity indices exhibit higher volatility, deeper drawdowns, and greater sensitivity to broad market movements than conventional benchmarks. Sustainability-focused equity indices that emphasize clean energy exhibit higher market sensitivity (betas above 1) and strong correlations with traditional equity indices. Correlation and beta estimates suggest a high degree of integration with traditional equity markets, implying limited diversification benefits within an equity-only framework. Periods of relative outperformance appear to be associated with favorable policy conditions and energy market dynamics, but are not consistently sustained over the sample period. In addition, the overall results suggest that sustainability investments generate substantial environmental and social externalities. Risk-adjusted performance measures suggest weaker historical performance over the sample period relative to conventional benchmarks. These findings should be interpreted as a comparative historical assessment rather than a structural risk model. From a policy perspective, the findings suggest that stable and credible regulatory frameworks, including long-term climate policy support and investment-enabling institutions, may be important for improving the financial resilience and long-term viability of green equity instruments. From a sustainability transition perspective, the observed volatility and market dependence of sustainability-oriented equity indices may constrain their effectiveness as standalone market-based financing mechanisms without complementary institutional and policy support. Full article