Scientific Reports最新文献

Industrial defect detection replaces manual visual inspection with an efficient, accurate, and reproducible automated intelligent system, directly enhancing quality management in intelligent manufacturing and providing clear engineering value for production safety and corporate competitiveness; however, their production processes often generate various defects. Detecting these defects is challenging due to issues such as high inter-class similarity among defect types, weak semantic information in complex small objects, and the presence of multi-scale defective targets, all of which compromise product quality and safety. To overcome these challenges, we introduce MFDH-Net, an innovative industrial defect detection network. The framework begins with a Dual-domain Feature Extraction Network (DFE-Net), which employs a full-domain local sensing module to capture both global and local defect information bidirectionally. Next, a Multilevel Feature Aggregation Network (MFA-Net) is designed, incorporating a full-level fusion mechanism for multiscale features and an adaptive feature weighting strategy along bidirectional propagation paths, enabling deep feature interaction both within and across layers. Additionally, a Spatial Semantic Fusion Module (SSFM) is introduced to achieve spatial and pixel-level alignment, effectively mitigating semantic gaps in the feature pyramid that degrade detection performance. Finally, a Cross-aware Decoupling Head (CDH) extracts fine-grained defect representations, while a Cross-aware Attention Module (CAM) enhances sparse, defect-related features through horizontal and vertical dual attention weighting in spatial dimensions, thereby improving recognition of tiny defects in complex industrial backgrounds. The experimental results show that 94.3%, 96.6%, 71.7 and 98.9% of mAP@.5 are obtained on steel, PCB, GC10-Net and Automobile Surface Defect datase, respectively, and they reach 52FPS, obtaining SOTA performance, which provides reliable technical guarantee for intelligent manufacturing quality control.

Incontinence-associated dermatitis (IAD) is an inflammatory skin condition caused by prolonged exposure of the skin to urine and feces. It is common among older adults in long-term care and contributes to pain, discomfort, and increased risk for secondary infection. This study aimed to identify risk factors for IAD development and to construct a prognostic model for incontinent nursing home residents aged ≥ 65 years. A secondary analysis was conducted using data from a cluster-randomized controlled clinical trial performed in Berlin, Germany (April 2019 to June 2021). A total of 149 incontinent residents were included. At week 12, 20 residents (13.4%) newly developed IAD. The final cluster-adjusted multivariable model identified lower Barthel Index scores, severe mobility impairment, and the absence of xerosis on the legs as significant predictors. An interaction between double incontinence and Barthel Index scores indicated that the protective effect of higher functional independence was reduced in residents with double incontinence. The model demonstrated good discrimination (area under the curve 0.82), sensitivity of 78.6%, specificity of 74.2%, and satisfactory calibration (Hosmer-Lemeshow test p = 0.67). These findings support early risk identification and targeted prevention; validation in a prospective cohort is warranted to confirm the model's robustness and clinical utility.

This prospective study aimed to compare computed tomography (CT) and magnetic resonance imaging (MRI) in the preoperative assessment of acute midfacial trauma. Twenty patients received posttraumatic CT and MRI scans using a 3T scanner with a dedicated 15-channel dentomaxillofacial coil. Five MRI protocols were evaluated: UTE, DESS, Dark Bone, StarVIBE, and STIR. Three observers qualitatively assessed fracture detection, image quality, fracture line visibility, cortical delineation, and bone-to-soft-tissue contrast using a five-point scale. Descriptive statistics and inter-observer reliability (Krippendorff's α) were calculated. Forty-two fractures were analyzed. CT achieved excellent fracture detection (98% of fractures detected; α = 1.0) with the fastest evaluation times (30-82 s vs. 42-145 s). Among MRI protocols, UTE and StarVIBE performed best, detecting 88-89% of fractures, with excellent image quality and high inter-observer reliability (α = 0.80-0.91). Darkbone, DESS, and STIR consistently showed lower detection rates (up to 43%). UTE and StarVIBE were particularly effective for orbital, zygomaticomaxillary, and nasal bone fractures. Optimized gradient-echo-based MRI protocols provide radiation-free, CT-like imaging of midfacial fractures with superior soft-tissue contrast. While CT remains essential for emergency situations, a patient-, protocol-, and pathology-specific MR-based diagnostic approach offers a clinically feasible preoperative alternative in trauma management.Trial registration number: Swiss National Clinical Trials Portal: SNCTP000006343, ClinicalTrials.gov ID: NCT07012850 (trial registration date: May 9, 2025).

Conventional fully covered self-expandable metallic stents (FCSEMS) for hilar biliary obstruction (HBO) remains limited due to the potential for side branch obstruction. The multi-hole covered self-expandable metallic stent (MHSEMS) was designed to overcome the limitations of FCSEMS by incorporating side holes into the fully covered membrane. This preclinical study evaluated the feasibility and safety of MHSEMS compared to conventional FCSEMS in a porcine HBO model. HBO was induced via intraductal radiofrequency ablation in eight minipigs. After four weeks, animals were randomized to receive either an MHSEMS or a conventional FCSEMS and were monitored for 12 weeks. Outcomes included technical success, adverse events, stent removability, and histological analysis. Excluding two procedure-related mortalities (one in each group), technical success and 12-week stent removability were achieved in all animals (n = 6). Compared to the FCSEMS group, the MHSEMS group demonstrated trends toward lower total bilirubin levels and reduced histological inflammation score in contralateral ducts (median [range]: 7.5 [7.0-9.0] vs. 9.0 [8.0-10.0], Cliff's delta = 0.67). In conclusion, the MHSEMS was safely and completely removable within a clinically relevant timeframe. This preclinical pilot study suggests a possible signal toward lower histologic injury with the MHSEMS compared with the FCSEMS, supporting the need for larger, adequately powered clinical studies.

Medical application is an effective extension of traditional medicine, which can provide users with convenient medical health and management services. However, how to improve the continuous acceptance and use of medical applications and relieve medical pressure has become an urgent issue to be explored. This study was intended to reveal the factors influencing the continuance intention of medical applications among the elderly. This study integrated the Value-Based Adoption Model, technology acceptance model, expectation confirmation theory, and trust model to construct an acceptance model for the continuous use of medical applications in the elderly. Quantitative empirical research was carried out by questionnaire survey and structural equation model. The results showed that confirmation significantly affected perceived ease of use and perceived usefulness. The perceived value, trust, satisfaction, perceived ease of use and perceived usefulness had a direct and significant positive effect on the continuance intention of medical applications in the elderly. In addition, perceived usefulness and perceived ease of use had a positive effect on satisfaction and trust. Meanwhile, perceived usefulness, perceived trust and value play a mediating role to some extent. However, the impact of privacy concern on perceived value and continuance intention was not significant. This study has expanded the technology acceptance model and its application, which can provide relevant improvement and optimization measures for medical application developers, providers, managers and researchers. It is expected to improve the user's continuance intention of medical applications, promoting the healthy and sustainable development of mHealth.

Post-traumatic osteoarthritis (ptOA) following joint injury accounts for at least 12% of cases of symptomatic OA. Surgical restoration of joint biomechanics does not prevent ptOA development, suggesting other factors influence individual OA risk. One suggested factor is the immuno-inflammatory response in the joint, with both adaptive and innate immune responses implicated. Whether different joint injuries incite different acute inflammatory responses, potentially explaining variable ptOA risk, has not been explored. Male 10-12-week-old C57BL/6 mice were randomly assigned to one of 6 interventions: naïve non-operated-controls (NOC), sham surgery (SHAM), anterior cruciate ligament (ACL) sprain (ACLS - subcritical loading injury without ACL damage or ptOA development), ACLR (non-surgical ACL rupture), ACLT (surgical ACL transection), and surgical destabilization of the medial meniscus (DMM). Tissues were harvested at 8 time points spanning acute injury (day-1, -2, -7), and early (day-14), progressive (day-28, -42), and established/advanced (day-56, -70) ptOA development. Immune cells from the synovium and primary and secondary lymphatic organs were analysed using dedicated flow cytometry panels, and joints were evaluated using an established histopathological scoring system. Joint injuries that induced ptOA had a distinct spatiotemporal Th1 and Th 17 response in synovial tissue and draining lymph node with little systemic change. The Th1 response subsided once the cartilage damage reached its maximum, whereas Th17 cells continuously accumulated in the synovial tissue. Importantly, model-specific temporal differences in both Th1/Th2 and Th17/Treg cells were observed. Joint injury also led to a distinct pattern of monocyte cell numbers across tissues. In particular, a short-term (up to day-14) depletion of classically-activated Ly6Chigh monocytes in bone marrow and spleen was concordant with a synovial tissue influx indicating recruitment of systemic cells in ptOA-inducing injuries. This data indicates a predominant loco-regional and limited systemic response to joint injury. The magnitude and duration of the initial transient increase in activated-monocyte and M1 macrophages, and both acute and long-term Th1 and Th17 T-cell response, differentiated pt-OA-inducing from non-ptOA-inducing injury. Importantly, what you injure (ACL vs. meniscus) rather than how you injure it (surgery vs. non-surgical) determines the temporal pattern and systemic involvement of both the innate and adaptive immune response and the structural ptOA progression and severity.

Drowsiness poses a significant risk in safety-critical operations such as operating unmanned aerial vehicles (UAV). While behavioral indicators like eye closure and head pose are effective for detection, the interpretability of complex models remains a challenge. This work employs a Random Forest model not merely as a classifier, but as a diagnostic tool to analyze dataset biases and feature correlations in drowsiness detection. Using established benchmarks, we demonstrate how this interpretable framework provides actionable insight into feature importance and model decision boundaries. The analysis offers a method to audit training data and informs the more reliable application of high-performance black-box systems. Our approach underscores the value of model transparency for developing robust, trustworthy drowsiness detection in operational environments.