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Deep learning approach to super-resolution correction of brain MRI motion artifacts for accurate hippocampal volumetry
The effect of religious belief and altruism on organ donation attitude in Turkey
Decoupling in a joint communication and sensing system with metasurface
Abstract The increasing demand for integrated communication and sensing has led to the development of Joint Communication and Sensing (JCAS) systems. However, strong self-interference (SI) between transmitting (TX) and receiving (RX) antennas remains a major obstacle, significantly degrading system performance in compact MIMO arrays. Traditional signal-processing-based cancellation methods face limitations in wideband scenarios due to high complexity and potential signal distortion. In this work, a novel metasurface-assisted decoupling structure is presented. The metasurface based on modified split-ring resonators (MSRRs) can suppress surface currents and reduce the coupling between TX and RX arrays. To further enhance isolation and reduce front-end self-interference in sensing-centric full-duplex JCAS, a multi-frequency null-space projection (NSP) beamforming algorithm is integrated with the antenna array design, forming a hardware–algorithm co-optimization framework. As proof of concept, a 2 $$\times$$ 2 patch antenna array incorporating the proposed metasurface operating in the 9–10 GHz band has been designed, fabricated, and characterized. The measurement results validate effectiveness. The findings suggest that the proposed decoupling approach offers a promising solution for enhancing electromagnetic isolation and overall system performance in next-generation JCAS applications such as intelligent transportation and indoor wireless sensing.
Elucidation of the process of delayed colonic perforation after endoscopic thermal injury in a rat model
Abstract Delayed perforation is a rare but serious complication following endoscopic resection of colonic lesions; however, its pathogenesis remains unclear. We hypothesized that its mechanism mirrors Jackson’s burn model, where a zone of stasis progresses to a zone of coagulation, ultimately leading to perforation. In this study, thermal injury was endoscopically induced in rat colons, and histopathological changes were analyzed at 12-hour intervals. To assess the role of bacterial infection, pseudo-germ-free rats were established by administering a course of antibiotics. The incidence of delayed colonic perforation was compared between the control and pseudo-germ-free rats. Degeneration and thinning of the muscularis propria persisted for 36 hours post-injury, with destruction of this layer observed at 48 hours. Dilated vessels in the mucosa and submucosa peaked at 24 hours, forming a zone of stasis. By 36 hours, mucosal necrosis had intensified, myeloperoxidase-positive cells had increased in the submucosa, and Escherichia coli had translocated into deeper layers, coinciding with expansion of the zone of coagulation. Moreover, pseudo-germ-free rats exhibited significantly fewer perforations. These findings suggest that delayed colonic perforation follows a pathological process similar to thermal burns and that bacterial infection accelerates this progression. Therefore, local infection control may be essential for preventing this serious complication.
Prevalence and predictors of hypotension on hospital arrival in traumatic brain injury: a prehospital HEMS cohort study
Abstract Hypotension is a well-established predictor of poor outcomes and increased mortality in patients with traumatic brain injury (TBI). Even a single episode of low systolic blood pressure (SBP) during the prehospital phase is associated with a worse prognosis. To minimise secondary brain injury, early identification and management of hypotension are critical. While international guidelines increasingly recommend higher SBP thresholds in TBI care, structured prehospital data remain limited in the German emergency care context. This study aimed to quantify the prevalence of hypotension on hospital arrival among adult TBI patients transported by helicopter emergency medical services (HEMS) and to identify high-risk subgroups. This retrospective cohort study analysed ADAC Luftrettung mission data from 2017 to 2021. Adults (≥ 18 years) with documented TBI in the mission record were included. Hypotension was defined as SBP < 90 mmHg in line with current German guidance. Two time points were assessed: SBP at initial HEMS contact (“initial hypotension”) and SBP on hospital arrival (“hypotension on hospital arrival”). TBI severity was classified by Glasgow Coma Scale (GCS), and injury patterns were recorded as isolated TBI, multiple injuries (non-polytrauma; “Mehrfachverletzung”), or polytrauma. A multivariable logistic regression was used to identify independent predictors of hypotension on hospital arrival. A total of 20,756 patients were included (67.7% male; median age 55.0 years). Hypotension on hospital arrival occurred in 3.4% of patients overall and in 35.5% of those with initial hypotension. Initial hypotension was the strongest predictor of hypotension on hospital arrival (OR 13.82, 95% CI 11.47–16.65). Severe TBI (OR 4.26, 95% CI 3.41–5.32) and polytrauma (OR 3.08, 95% CI 2.44–3.90) were additional independent predictors. Initial hypotension identifies a high-risk subgroup of adult TBI patients transported by HEMS who are substantially more likely to be hypotensive at hospital arrival, particularly those with severe TBI and polytrauma. These findings support prioritising early haemodynamic stabilisation in this population and provide a basis for future outcome-linked studies in the German setting.
Design of an in-pipe inspection robotic system (IPIRS) with YOLOv8–LSTM integration for real-time in-pipe navigation
Abstract Conventional methods are still labor-intensive, hazardous, and have limited coverage, even though reliable pipeline inspection is crucial for oil, gas, and water distribution networks, emphasize key challenges of pipeline inspection and automated navigation. Although robotic systems have advanced with mechanisms like helical motion and multi-joint telescopic designs, the problem of obtaining dependable adaptability in curved or varying-diameter pipes has not been solved. Additionally, there are still gaps in precise navigation and predictive decision-making under complicated pipeline conditions because of the limited integration of deep learning and computer vision into real-time autonomous platforms. To provide real-time navigation and predictive analysis, this work introduces an improved In-Pipe Inspection Robotic System (IPIRs) that integrates robotic simulation and deep learning. The system’s strong performance in navigation and visual object detection using YOLOv8 is demonstrated by its mean average accuracy (mAP (0.5)) of 97.9% and F1 score of 0.95. In addition to spatial scanning, the long short-term memory (LSTM) model analyzes temporal and group-action IMU data. The mean square error (MSE) of 0.00037 and mean absolute error (MAE) of 0.00581 test show that the model ensures accurate motor voltage prediction for smooth navigation and object detection in curved pipelines. The modular robot design, developed using the Robot Operating System (ROS) and evaluated using Gazebo and Rviz simulations, demonstrated its ability to automatically navigate and recognize objects in pipes with diameters ranging from 100 to 150 mm. The results demonstrate how the YOLOv8–LSTM architecture enhances inspection accuracy and enables predictive maintenance.
Genetic predisposition to coffee consumption and the association with the early risk of atherosclerosis
Abstract The cardiovascular effects of coffee consumption remain debated, particularly regarding early-stage subclinical atherosclerosis. This study investigated the association between coffee intake, genetic predisposition, and the risk of subclinical coronary and carotid atherosclerosis in 24,835 participants from the Swedish CArdioPulmonary bioImage Study (SCAPIS). Coffee intake was assessed via self-reported questionnaires. Atherosclerosis was assessed via segment involvement score (SIS), coronary artery calcium score (CACS) and carotid plaque. Observational analysis showed no significant association between coffee consumption and SIS, CACS, or carotid plaques. However, both one-sample and two-sample (SCAPIS and UK Biobank) Mendelian randomization (MR) analyses showed an association between genetic predisposition to higher coffee consumption and increased SIS. Stratification analyses further explored differences in genetic associations across varying coffee consumption levels. Among individuals consuming coffee more than twice daily, two coffee consumption-associated single nucleotide polymorphisms (SNPs) in AHR and CYP1A1 / CYP1A2 were correlated with SIS. Integrative metabolomics and proteomics analyses identified lipid-related metabolites (triglycerides, phospholipids, free cholesterol) and inflammation-related proteins (DLK1, IL1RL2, CCL17) associated with the genetic proxy of coffee consumption. These findings suggest that genetically influenced coffee consumption may be associated with coronary atherosclerosis risk in frequent coffee drinkers, although the underlying biological basis remains to be clarified.
Evaluation of the service quality and diagnostic performance of syphilis self-test kits purchased from E-commerce platforms in China
Abstract Syphilis self-testing (SST) facilitates screening accessibility among key populations (such as men who have sex with men, female sex workers, etc.). SST kits are readily available on several prominent e-commerce platforms in China. This study aims to compare the differences of diagnostic performance between kits obtained online or based on facilities, which provided evidence for the expanding application of SST in China. Utilizing the HONcode standard, we developed a quality assessment scale to evaluate the service quality of online SST kits. Furthermore, we assessed the diagnostic performance of them compared with facility-based kits, using TPPA as the gold standard. The online sales data showed that the after-sales reviews for SST kits accounted for only 5.5% of HIV self-testing kits and 17% of HIV/syphilis dual-testing kits. Quality assessment results indicated scale scores ranging from 31 to 44 for these SST kits, reflecting satisfactory service quality. The sensitivity and specificity of eight online kits were 94.85%-100% and 98.90%-100%, respectively, and the nine facility-based testing kits were 91.91%-100% and 97.81%-100%, respectively. The online SST kits demonstrated satisfactory quality and diagnostic performance, which provide evidence for promoting the utilization among key populations in China.
A sample model for applying feature selection and machine learning techniques to estimate and manage crayfish populations
Current status survey and risk factor analysis of metabolic dysfunction-associated steatotic liver disease among adolescents in Hainan Province
Targeting of CD28 and CD38 as a potential novel therapeutic strategy for peripheral T-cell lymphomas
A low-latency deep learning framework for volcanic ash cloud nowcasting using geostationary satellite imagery
Abstract Rapid assessment of hazardous aerosol dispersion is critical for emergency response, yet operational dispersion workflows can exhibit end-to-end latency that is incompatible with the first minutes of decision-making. This study develops and validates a deep learning approach for near-real-time nowcasting of volcanic ash dispersion from geostationary observations. The model was trained on an archive of volcanic ash satellite imagery from EUMETSAT’s SEVIRI instrument (Ash RGB composite) and achieved a structural similarity index of 0.88 for 15-minute next-frame forecasts. The complete edge workflow, including data download and inference, runs in under five seconds on an NVIDIA Jetson AGX Orin. To illustrate how the same nowcasting pipeline can be used for hypothetical scenario exploration across particulate sources, a pixel-based event-injection algorithm is introduced to overlay synthetic plumes of varying sizes into real-time satellite frames before inference. Scenario demonstrations parameterized by nuclear-yield-inspired sizes (10 kt to 100 Mt) are presented at urban (Paris, London, Berlin), national (Iberian Peninsula), and continental (Europe-wide) scales. These scenario outputs are intended as illustrative, low-latency visualizations of kinematic transport patterns in the SEVIRI observation space, as validated predictions of nuclear plume morphology. The primary contribution is a fast, low-cost volcanic ash nowcasting system, complemented by a generalizable injection framework for rapid scenario visualization on edge computing.