Search Results (570)

Background/Objectives: Post-cardiac arrest syndrome (PCAS) induces systemic ischemia–reperfusion injury accompanied by sepsis-like coagulopathy. This coagulopathy presents heterogeneously, yet distinct coagulation phenotypes and their impact on hypoxic–ischemic brain injury (HIBI) remain poorly defined. We aimed to identify coagulation phenotypes using latent class analysis (LCA) and assess their association with 6-month neurological outcomes. Methods: We retrospectively analyzed adult out-of-hospital cardiac arrest (OHCA) patients treated with targeted temperature management (TTM) between 2011 and 2019 from a prospective registry at a tertiary academic center. LCA was performed using coagulation biomarkers measured at admission and 24 h post-return of spontaneous circulation: D-dimer, fibrinogen, antithrombin III (ATIII), platelet count, and PT-INR. The primary outcome was poor neurological outcome (Cerebral Performance Category 3–5) at 6 months. Secondary outcomes included in-hospital mortality and cerebral edema severity assessed by gray-to-white matter ratio (GWR) on brain CT. Results: Among 325 patients, LCA identified three phenotypes: Class 1 (Preserved Coagulation, 36.9%), Class 2 (Hypercoagulable State, 41.5%) characterized by elevated D-dimer with preserved fibrinogen and ATIII, and Class 3 (Consumptive Coagulopathy, 21.5%) marked by profound D-dimer elevation with fibrinogen <150 mg/dL and ATIII <60%. Class 3 exhibited the lowest GWR and highest neuron-specific enolase levels. In multivariable analysis adjusting for age, low-flow time, initial rhythm, and lactate, Class 3 independently predicted poor neurological outcome (adjusted OR 4.52; 95% CI 2.15–9.48), whereas Class 2 did not. Conclusions: PCAS-related coagulopathy is heterogeneous. A consumptive coagulopathy phenotype identifies a high-risk subgroup associated with severe brain injury and poor long-term neurological outcomes. Early identification of this phenotype may enable targeted prognostication and guide future phenotype-specific interventional strategies.: Full article

This study developed an integrated vision–actuation system for precision weeding in indoor soil bin environments, with cabbage as a case example. The system integrates lightweight object detection, 3D co-ordinate mapping, path planning, and a three-axis synchronized conveyor-type actuator to enable precise weed identification and automated removal. By integrating ECA and CBAM attention mechanisms into YOLO11, we developed the YOLO11-WeedNet model. This integration significantly enhanced the detection performance for small-scale weeds under complex lighting and cluttered backgrounds. Based on the optimal model performance achieved during experimental evaluation, the model achieved 96.25% precision, 86.49% recall, 91.10% F1-score, and a mean Average Precision (mAP@0.5) of 91.50% calculated across two categories (crop and weed). An RGB-D fusion localization method combined with a protected-area constraint enabled accurate mapping of weed spatial positions. Furthermore, an enhanced Artificial Hummingbird Algorithm (AHA⁺) was proposed to optimize the execution path and reduce the operating trajectory while maintaining real-time performance. Indoor soil bin tests showed positioning errors of less than 8 mm on the X/Y axes, depth control within ±1 mm on the Z-axis, and an average weeding rate of 88.14%. The system achieved zero contact with cabbage seedlings, with a processing time of 6.88 s per weed. These results demonstrate the feasibility of the proposed system for precise and automated weeding at the cabbage seedling stage. Full article

The application of advanced imaging techniques, particularly computed tomography (CT), photogrammetric scanning, and three-dimensional reconstructions of body surfaces and skeletal remains, is becoming a crucial component of Forensic Anthropology. These tools enable a non-invasive and highly standardized analysis of both intact cadavers and human remains recovered from terrestrial or aquatic environments, providing reliable support in identification processes, traumatological reconstruction, and the assessment of taphonomic processes. In the context of estimating the Post-Mortem Interval (PMI) and the Post-Mortem Submersion Interval (PMSI), digital imaging allows for the objective and reproducible documentation of morphological changes associated with decomposition, saponification, skeletonization, and taphonomic patterns specific to the recovery environment. Specifically, CT enables the precise assessment of gas accumulation, transformations in residual soft tissues, and structural bone modifications, while photogrammetry and 3D reconstructions facilitate the longitudinal monitoring of transformative processes in both terrestrial and underwater contexts. These observations enhance the reliability of PMI/PMSI estimates through integrated models that combine morphometric, taphonomic, and environmental data. Beyond PMI/PMSI estimation, imaging techniques play a central role in anthropological bioprofiling, facilitating the estimation of age, sex, and stature, the analysis of dental characteristics, and the evaluation of antemortem or perimortem trauma, including damage caused by terrestrial or fauna. Three-dimensional documentation also provides a permanent, shareable archive suitable for comparative analyses, ensuring transparency and reproducibility in investigations. Although not a complete substitute for traditional autopsy or anthropological examination, imaging serves as an essential complement, particularly in cases where the integrity of remains must be preserved or where environmental conditions hinder the direct handling of osteological material. Future directions include the development of AI-based predictive models for PMI/PMSI estimation using automated analysis of post-mortem changes, greater standardization of imaging protocols for aquatic remains, and the use of digital sensors and multimodal techniques to characterize microstructural alterations not detectable by the naked eye. The integration of high-resolution imaging and advanced analytical algorithms promises to further enhance the reconstructive accuracy and interpretative capacity of Forensic Anthropology. Full article

Background/Objectives: Manual preparation of semisolid formulations (creams, ointments, gels) is prone to variability in mixing energy and time, which may compromise uniform API distribution. This study aimed to evaluate an Electronic Mortar and Pestle (EMP; Unguator™) as a standardized compounding tool, with objectives to: (i) validate stability-indicating UHPLC methods; (ii) assess content uniformity across jar strata; (iii) quantify the impact of mixing time and rotation speed via design of experiments (DOE); and (iv) verify cleaning effectiveness and cross-contamination risk. Methods: Five representative formulations were compounded: urea 40%, clobetasol 0.05%, diclofenac 2.5% in hyaluronic acid 3% gel, urea 10% + salicylic acid 1%, and hydroquinone 5%. UHPLC methods were validated per ICH Q2(R2) and stress-tested under acid, base, oxidative, thermal, and UV conditions. Homogeneity was assessed by stratified sampling (top/middle/bottom). A 3² factorial DOE (time: 2/6/10 min; speed: 600/1500/2400 rpm) modeled effects on % label claim and RSD. Cleaning validation employed hydroquinone as a tracer, with swab sampling pre-/post-use and post-sanitization analyzed by HPLC. Results: All UHPLC methods met specificity, linearity, precision, accuracy, and sensitivity criteria and were stability-indicating (Rs ≥ 1.5). Formulations achieved 90–110% label claim with strata CV ≤ 5%. DOE revealed speed as the dominant factor for clobetasol, urea, and diclofenac, while time was more influential for salicylic acid; gels exhibited curvature, indicating diminishing returns at high rpm. Model-predicted optima were implementable on the Unguator™ with minor rounding of rpm/time. Cleaning validation confirmed post-sanitization residues below LOQ and <10 ppm acceptance. Conclusions: The Unguator™ provides a practical, parameter-controlled route for compounding pharmacies to standardize semisolid preparations, achieving reproducible layer-to-layer content uniformity within predefined criteria under the evaluated conditions through programmable set-points and validated cycles. DOE-derived rpm–time relationships define an operational design space within the studied ranges and support selection of implementable device settings and set-points. Importantly, the DOE-derived “optima” in this study are optimized for assay-based content uniformity (mean % label claim and strata variability). Cleaning validation supports a closed, low-cross-contamination workflow, facilitating consistent routines for both routine and complex formulations. Overall, the work implements selected QbD elements (QTPP—Quality Target Product Profile; CQA—Critical Quality Attribute definition; CPP—Critical Process Parameter identification; operational design space; and a proposed control strategy) and should be viewed as a step toward broader lifecycle QbD implementation in compounding. Full article

With the increasing deployment of grid-forming static var generators (GFM-SVG) in modern power systems, the reliability of the valve hall that houses the core power modules has become a critical concern. To overcome the limitations of conventional wired monitoring systems—complex cabling, poor scalability, and incomplete state perception—this paper proposes and implements a multi-source fusion wireless data acquisition system specifically designed for GFM-SVG valve halls. The system integrates acoustic, visual, and infrared sensing nodes into a wireless sensor network (WSN) to cooperatively capture thermoacoustic visual multi-physics information of key components. A dual-mode communication scheme, using Wireless Fidelity (Wi-Fi) as the primary link and Fourth-Generation Mobile Communication Network (4G) as a backup channel, is adopted together with data encryption, automatic reconnection, and retransmission-checking mechanisms to ensure reliable operation in strong electromagnetic interference environments. The main innovation lies in a multi-source information fusion algorithm based on an improved Dempster–Shafer (D–S) evidence theory, which is combined with the object detection capability of the You Only Look Once, Version 8 (YOLOv8) model to effectively handle the uncertainty and conflict of heterogeneous data sources. This enables accurate identification and early warning of multiple types of faults, including local overheating, abnormal acoustic signatures, and coolant leakage. Experimental results demonstrate that the proposed system achieves a fault-diagnosis accuracy of 98.5%, significantly outperforming single-sensor approaches, and thus provides an efficient and intelligent operation-and-maintenance solution for ensuring the safe and stable operation of GFM-SVG equipment. Full article

Background and Objectives: Early identification of cardiac dysfunction in multi-system inflammatory syndrome in children (MIS-C) is crucial for effective management. Our primary objective was to predict left ventricular systolic dysfunction (LVSD) through a multicenter collaborative assessing admission laboratory data and echocardiogram findings. Methods: Laboratory and clinical data were collected by retrospective chart review from a cohort of pediatric patients admitted and treated for MIS-C in our institutions. Laboratory data including absolute lymphocyte count, albumin, sedimentation rate, C-reactive protein, procalcitonin, d-dimer, fibrinogen, ferritin, interleukin-6 level, and lymphocyte subsets (T, B and NK quantitation, TBNK) were collected. We built a LASSO logistic regression model to predict which MIS-C patients would have left ventricular systolic dysfunction LVSD using only laboratory data obtained within the first 24 h of admission. Results: Of the 1474 MIS-C patients evaluated, 297 had LVSD. The linear kinetic analysis found differences in albumin, lymphocyte count, C-reactive proteins and fibrinogen for systolic dysfunction patients, and of these C-reactive proteins, fibrinogen and procalcitonin were more predictive earlier. The best model for coronary artery abnormalities (CAAs) performed poorly, with a mean cross-validated AUC of 0.57. The model performed well with a cross-validated AUC of 0.845. Conclusions: This model identified widely available biomarkers to successfully predict systolic dysfunction in MIS-C patients. Those at high risk of systolic dysfunction had higher peak laboratory values for C-reactive protein, fibrinogen, and procalcitonin early on. A regularized logistic regression model was validated to provide excellent discrimination for LVSD. Full article

Background/Objectives: Early identification of vasculitic acute kidney injury (AKI) is crucial for timely immunosuppression and improved renal outcomes; however, noninvasive adjunctive diagnostic tools remain limited. Renal elastography, a noninvasive technique that quantifies renal cortical stiffness, has been primarily investigated in chronic kidney disease, whereas evidence in acute kidney injury is scarce. This study aimed to evaluate the diagnostic utility of renal shear wave elastography for differentiating vasculitic from non-vasculitic AKI and to explore the association between baseline renal cortical stiffness and vasculitic renal outcomes. Materials and Methods: This prospective observational study included three groups: vasculitic AKI, non-vasculitic AKI, and healthy controls. Renal cortical stiffness was measured at admission using two-dimensional shear-wave elastography (2D-SWE) by radiologists blinded to clinical information. After clinicopathological confirmation of definitive diagnoses, between-group comparisons were performed and the diagnostic performance of elastography was evaluated. Additionally, in a biopsy-confirmed immunoglobulin A vasculitis nephritis (IgAVN) cohort (n = 12), baseline elastography measurements were examined in relation to one-year renal outcomes to explore potential prognostic associations. Results: The vasculitic AKI group exhibited significantly higher mean renal cortical stiffness values (9.5 ± 1.9 kPa) compared with both healthy controls (5.53 ± 0.92 kPa) and the non-vasculitic AKI group (6.61 ± 1.89 kPa) (both p < 0.01). Mean renal cortical stiffness demonstrated good diagnostic performance for distinguishing vasculitic from non-vasculitic AKI (AUC 0.86, 95% CI 0.73–0.97), with an optimal threshold of 6.79 kPa yielding 91% sensitivity and 72% specificity. In the prospective one-year follow-up of the IgAVN subcohort (n = 12), patients with unfavorable renal outcomes tended to have higher baseline renal cortical stiffness compared with those with favorable outcomes [median (min–max), 11.2 (10.8–13.3) vs. 9.1 (5.6–11.2), p = 0.046]. Conclusions: These findings suggest that renal elastography may aid in distinguishing vasculitic from non-vasculitic acute kidney injury and may provide exploratory information on the relationship between baseline cortical stiffness and renal outcomes in IgAVN. Full article

Background/Objectives: Acute mesenteric ischemia (AMI) is a time-dependent condition associated with exceptionally high in-hospital mortality, particularly among elderly and comorbid patients. Early identification of patients at high risk of death remains challenging and has important implications for clinical decision-making. The objective of this study was to derive and internally validate a prognostic score for in-hospital mortality of patients with AMI. Materials and Methods: We conducted a multicenter, observational, retrospective cohort study including patients with AMI from 10 participating hospitals. A descriptive and analytical approach was performed. A Classification and Regression Tree (CART) model was used to determine cut-off points for continuous variables and assess their association with mortality. Based on these thresholds, a univariate analysis was performed, and variables with statistical significance (p < 0.05) were incorporated into a multivariate logistic regression model. A score—the RADIAL score—was then derived from the beta coefficients. The discriminative ability of the score was evaluated using the receiver operating characteristic (ROC) curve. Results: A total of 693 patients were studied. Thee mean age was 81 years (IQR 73–86) and 54.2% were women. A history of cardiovascular disease was present in 75.3% of participants. Overall mortality was 62.4%. Most patients (74%) were managed conservatively. Significant variables in the bivariate analysis included hypotension, age > 65 years, pH < 7.3, creatinine > 1.7 mg/dL, and absence of rectal bleeding. These variables were incorporated into the multivariate model. The resulting score showed an area under the ROC curve of 0.78 (95% CI: 0.74–0.82). Conclusions: The RADIAL score demonstrated robust predictive performance and allowed the identification of three mortality-risk groups: 30–40% (low), 50–60% (intermediate), and 80% (high). This tool may support clinical decision-making in the management of patients with AMI. Full article

Background/Objectives: Although 90% of prostate cancer (PCa) metastasis occurs in the bone, there are limited studies and rarely available genome-wide profiles at individual sample level for genomic copy number changes in the literature. Methods: We performed Affymetrix SNP 6.0 high-density microarray analysis to generate the genome-wide copy number change profiles for six cases of PCa bone metastases. A common genomic loss was confirmed by fluorescence in situ hybridization (FISH) in paraffin-embedded PCa bone metastasis samples together with primary PCa and benign prostate hyperplasia samples. We overexpressed the candidate miRNA in PCa cell lines and knocked down its target genes by siRNA transfection and investigated the effect on protein expression and cell viability, migration, and invasion abilities, respectively. Protein expression in PCa tissues was analyzed by immunohistochemical staining. Results: We provided high-resolution PCa bone metastasis profiles of six cases and identified potential bone metastasis-specific common genomic alterations, including a 1.6 mb region on 17q11.2, as well as those shared by non-bone metastatic PCa. The common 17q11.2 loss was confirmed by FISH in further 14/21 PCa bone metastasis samples but was only found in 9/151 primary PCa samples. The well-established tumor-suppressing miRNA located within this small genomic region, miR-193a-3p, was downregulated in both bone metastasis and primary PCa cases, leading to overexpression of cyclin D1 and uPA to promote cancer cell migration and invasion. Cyclin D1 was highly expressed in both localized PCa and bone metastasis samples, and the expression was significantly higher in the latter group (p = 0.013). Conclusions: We generated high-resolution copy number change profiles for bone metastasis samples. This led to the identification of a common, small genomic loss and downregulation of miR-193a-3p, which suppresses PCa bone metastasis through inhibition of its target proteins, providing new insight into bone metastasis development. Full article

The present work addresses the critical challenge of designing a lightweight antenna suitable for remote sensing applications specifically aimed at the identification of buried objects from Unmanned Aerial Vehicles (UAVs). The stability of the phase center and the radiation pattern are critical factors for enabling synthetic aperture radar (SAR) processing on moving platforms. The presented antenna structure is characterized by a simple, lightweight geometry, and allows for achieving a fractional bandwidth of nearly 100% with an excellent stability of the radiation pattern, that exhibits minimal variation within the operating band of the antenna. Specifically, the gain is in the range 4.4–6.3 dBi and the group delay spread is about 200 ps in the frequency range 1–2 GHz. We illustrate numerical simulations and measurements of an antenna prototype that validate the proposed approach, demonstrating the suitability of the design for the intended operational scenario. Full article

Fused deposition modeling (FDM) suffers from unpredictable mechanical properties in nominally identical prints. Current quality assurance relies on destructive testing or expensive post-process inspection, while existing machine learning approaches focus primarily on printing parameters rather than real-time thermal environments. The objective of this proof-of-concept study is to develop a low-cost, non-destructive framework for predicting tensile strength during FDM printing by directly measuring convective thermal gradients surrounding the print. To accomplish this, we introduce thermal fingerprinting: a novel non-destructive technique that combines Background-Oriented Schlieren (BOS) imaging with machine learning to predict tensile strength during printing. We captured thermal gradient fields surrounding PLA specimens (n = 30) under six controlled cooling conditions using consumer-grade equipment (Nikon D750 camera, household hairdryers) to demonstrate low-cost implementation feasibility. BOS imaging was performed at nine critical layers during printing, generating thermal gradient data that was processed into features for analysis. Our initial dual-model ensemble system successfully classified cooling conditions (100%) and showed promising correlations with tensile strength (initial 80/20 train–test validation: R² = 0.808, MAE = 0.279 MPa). However, more rigorous cross-validation revealed the need for larger datasets to achieve robust generalization (five-fold cross-validation R² = 0.301, MAE = 0.509 MPa), highlighting typical challenges in small-sample machine learning applications. This work represents the first successful application of Schlieren imaging to polymer additive manufacturing and establishes a methodological framework for real-time quality prediction. The demonstrated framework is directly applicable to real-time, non-contact quality assurance in FDM systems, enabling on-the-fly identification of mechanically unreliable prints in laboratory, industrial, and distributed manufacturing environments without interrupting production. Full article

Background: Health care work contributes significantly to greenhouse gas emissions. Primary health care is community-based and focused on wellness and disease prevention. Within primary health care, pharmacists are most frequently the stewards of medicines, supplies, and other tangible products that contribute to carbon footprints. Pharmacists are in a unique position to help adapt to and mitigate climate change-related issues. Objective: To examine pharmacists’ perspectives on climate adaptation and mitigation strategies in primary health care delivery in interprofessional settings. Methods: Semi-structured qualitative interviews with primary care pharmacists were undertaken. Constant-comparative data analysis was used to code and categorize findings. The COREQ system was applied to ensure rigor and quality of research. Results: A total of 21 primary care pharmacists participated in this research. Several core themes emerged as follows: (a) universal agreement that climate change is real and primary health care needs to evolve rapidly to address it; (b) recognition that primary health care is time-pressured and resource constrained so successful solutions need to be pragmatic and work within realities of practice; (c) identification of actionable priorities with high potential for mitigation impact; and (d) mobilization of a coalition to develop system-wide initiatives that could be implemented in primary health care. Conclusions: Collaborative approaches and those that focus on the implementation of regulatory requirements were identified as being most productive in this setting. Full article

Background/Objectives: Swimming requires precise motor control, sustained attention, and optimal cognitive–motor integration, making it an ideal model for investigating neural efficiency—the phenomenon whereby expert performers achieve optimal outcomes with reduced neural resource expenditure, operationalized as lower activation, sparser connectivity, and enhanced functional integration. This systematic review examined cognitive performance and neural adaptations in swimming athletes, investigating neuroimaging and behavioral outcomes distinguishing swimmers from non-athletes across performance levels. Methods: Following PRISMA 2020 guidelines, seven databases were searched (1999–2024) for studies examining cognitive/neural outcomes in swimmers using neuroimaging or validated assessments. A total of 24 studies (neuroimaging: n = 9; behavioral: n = 15) met the inclusion criteria. Risk of bias assessment used adapted Cochrane RoB2 and Newcastle–Ottawa Scale criteria. Results: Neuroimaging modalities included EEG (n = 4), fMRI (n = 2), TMS (n = 1), and ERP (n = 2). Key associations identified included the following: (1) Neural Efficiency: elite swimmers showed sparser upper beta connectivity (35% fewer connections, d = 0.76, p = 0.040) and enhanced alpha rhythm intensity (p ≤ 0.01); (2) Cognitive Performance: superior attention, working memory, and executive control correlated with expertise (d = 0.69–1.31), with thalamo-sensorimotor functional connectivity explaining 41% of world ranking variance (r² = 0.41, p < 0.001); (3) Attention: external focus strategies improved performance in intermediate swimmers but showed inconsistent effects in experts; (4) Mental Fatigue: impaired performance in young adult swimmers (1.2% decrement, d = 0.13) but not master swimmers (p = 0.49); (5) Genetics: COMT Val158Met polymorphism associated with performance differences (p = 0.026). Effect sizes ranged from small to large, with Cohen’s d = 0.13–1.31. Conclusions: Swimming expertise is associated with specific neural and cognitive characteristics, including efficient brain connectivity and enhanced cognitive control. However, cross-sectional designs (88% of studies) and small samples (median n = 36; all studies underpowered) preclude causal inference. The lack of spatially quantitative synthesis and visualization of neuroimaging findings represents a methodological limitation of this review and the field. The findings suggest potential applications for talent identification, training optimization, and mental health promotion through swimming but require longitudinal validation and development of standardized swimmer brain atlases before definitive recommendations. Full article

Background/Objectives: Gunshot wounds in living patients present significant challenges from both a clinical and a forensic perspective. Understanding the exact trajectory of a bullet is crucial not only for guiding treatment but also for providing reliable documentation in legal settings. This work introduces a practical diagnostic workflow that combines OsiriX (V. 14.1.1), a DICOM viewer with advanced 3D tools, with Autodesk Maya, a modeling platform used to recreate the external shooting scene. Methods: CT scans obtained with multidetector systems were analyzed in OsiriX using a structured, seven-step process that included multiplanar reconstructions, 3D renderings, and region-of-interest tracking. The reconstructed trajectories were then exported to Maya, where they were integrated into a virtual model of the shooting scene to correlate internal findings with the incident’s external dynamics. Results: The workflow allowed precise identification of entry and exit points, reliable reconstruction of bullet paths, and effective 3D visualization. While OsiriX provided detailed information for clinical and radiological purposes, the use of Maya enabled simulation of the external scene, improving forensic interpretation and courtroom presentation. The procedure proved reproducible across cases and compatible with emergency timelines. Conclusions: The combined use of OsiriX and Maya offers a reproducible and informative method for analyzing gunshot wounds in living patients. This approach not only supports surgical and diagnostic decisions but also enhances the forensic value of radiological data by linking internal trajectories to external shooting dynamics. Its integration into trauma imaging protocols and forensic workflows could represent a significant step toward standardized ballistic documentation. Full article

With the advancement of Light Detection and Ranging (LiDAR) technology and computer science, LiDAR–Inertial Simultaneous Localization and Mapping (SLAM) has become essential in autonomous driving, robotic navigation, and 3D reconstruction. However, dynamic objects such as pedestrians and vehicles, with complex terrain conditions, pose serious challenges to existing SLAM systems. These factors introduce artifacts into the acquired point clouds and result in significant vertical drift in SLAM trajectories. To address these challenges, this study focuses on controlling vertical drift errors in LiDAR–Inertial SLAM systems operating in dynamic environments. The research focuses on three key aspects: ground point segmentation, dynamic artifact removal, and vertical drift optimization. In order to improve the robustness of ground point segmentation operations, this study proposes a method based on a concentric sector model. This method divides point clouds into concentric regions and fits flat surfaces within each region to accurately extract ground points. To mitigate the impact of dynamic objects on map quality, this study proposes a removal algorithm that combines multi-frame residual analysis with curvature-based filtering. Specifically, the algorithm tracks residual changes in non-ground points across consecutive frames to detect inconsistencies caused by motion, while curvature features are used to further distinguish moving objects from static structures. This combined approach enables effective identification and removal of dynamic artifacts, resulting in a reduction in vertical drift. Full article