Scientific Reports最新文献

Nature-based Solutions (NbS) are increasingly recognized as effective measures for mitigating flood risks and enhancing climate change adaptation. However, evaluating their efficacy in delivering flood risk reduction ecosystem service (FRR-ESS) is usually limited by reliance on qualitative, expert-based "quick-scan" scoring methods. While already challenging for present-day evaluations, this limitation becomes even more significant when addressing future climate scenarios, introducing deep uncertainties in the evaluation. The present study introduces a model-based framework to quantify FRR-ESS provided by coastal NbS, which integrates expert-based assessments with quantitative results from an eco-hydro-morphodynamic numerical model. The model enables a comparative evaluation of individual and combined effects of NbS following a Building Blocks approach. By integrating habitat map change prediction in the evaluation, NbS flood reduction response to present and future storm scenarios (i.e. wave climate and sea level rise) are investigated. The methodology is applied to a Mediterranean coastal lagoon in Sicily (Italy), and can be easily adapted to diverse coastal ecosystems. Our findings underscore the significant role of coastal habitats in reducing flood risk and highlight the importance of integrating physically-based modelling into FRR-ESS evaluation. This approach provides a robust and flexible tool for policymakers and stakeholders to make informed decisions that support both ecological sustainability and disaster risk reduction.

Despite its adoption and benefits, robotic surgeries can impose additional mental workload on surgeons. Validated questionnaires mostly administered at the end of procedures may not accurately capture the dynamic nature of mental workload over an entire procedure. Hence, we sought to determine if electroencephalogram (EEG) based neural activities in different brain regions can measure variations in expert surgeons' mental workload intraoperatively. EEG data was collected from five different surgeons performing 13 robotic-assisted urological procedures. Data analysis focused on three surgery phases (before, critical, and after). After performing each phase, surgeons provided a rating of their perceived mental workload. A linear mixed effects model was applied to explore the impact of the study phases on the relative spectral band power of EEG signals. The relative theta band power in the frontal brain region was highest during the critical portions of the procedure (p < 0.05). As the subjective ratings increased, the relative frontal theta band power increased (p < 0.001) while the relative parietal alpha band power decreased across all phases. We show that EEG signals can distinguish intraoperative workload in robotic surgeries. This has several applications including predicting risk factors for increased case complexity and surgical education.

RNA binding proteins (RBPs) are key factors regulating post-transcriptional events. Categorized as RBPs, RBM24 expression levels have been shown to be a prognosis-related pivotal gene in oral squamous cell carcinoma. We analyzed the binding targets and regulated post-transcriptional events of RBM24 in RBM24-overexpressing cell lines and controls using iRIP-seq and RNA-seq. RBM24-overexpressing cells showed significant changes in gene expression, which are involved in biological pathways related to apoptosis and immune inflammation. RBM24-regulated genes that undergo alternative splicing are primarily engaged in biological processes related to DNA damage repair and RNA metabolism. More notably, we found that RBM24 binds to lncRNAs in addition to pre-mRNAs. These results indicated that RBM24 could play a key role in cancer progression by finding clinical therapeutic targets for Oral squamous cell carcinoma.

Tumor heterogeneity is predicted to confer inferior clinical outcomes with precision-based strategies, however, modeling heterogeneity in a manner that still represents the tumor of origin remains a formidable challenge. Sequencing technologies are limited in their ability to identify rare subclonal populations and predict response to treatments for patients. Patient-derived organotypic cultures have significantly improved the modeling of cancer biology by faithfully representing the molecular features of primary malignant tissues. Patient-derived cancer organoid (PCO) cultures contain subclonal populations with the potential to recapitulate heterogeneity, although treatment response assessments commonly ignore diversity in the molecular profile or treatment response. Here, we demonstrate the advantage of evaluating individual PCO heterogeneity to enhance the sensitivity of these assays for predicting clinical response. Additionally, organoid subcultures identify subclonal populations with altered treatment response. Finally, dose escalation studies of PCOs to targeted anti-EGFR therapy are utilized which reveal divergent pathway expression when compared to pretreatment cultures. Overall, these studies demonstrate the importance of population-based organoid response assessments, the use of PCOs to identify molecular heterogeneity not observed with bulk tumor sequencing, and PCO heterogeneity for understanding therapeutic resistance mechanisms.

Primary hypertension is the most common type of hypertension, with a complex and not fully understood pathogenesis. Insulin resistance (IR) is a metabolic abnormality that has been shown to be quite prevalent among patients with hypertension in existing literature. The triglyceride-glucose (TyG) index is a reliable indicator for assessing insulin resistance (IR). This study aims to evaluate the relationship between the TyG index at admission and all-cause mortality (ACM) in patients with severe primary hypertension, and to explore its role in predicting the future all-cause mortality risk in primary hypertension patients. This study employs a retrospective design to categorize all patients into four quartiles based on the TyG index. The Kaplan-Meier (K-M) method was utilized to estimate the survival curves for each group and to compare the survival outcomes across different quartiles. To assess the nonlinear relationship between the TyG index and prognosis, Cox proportional hazards regression models and restricted cubic splines (RCS) were applied, adjusting for potential confounders. Additionally, subgroup analyses were performed to conduct stratified analyses and interaction tests. Kaplan-Meier survival curve analysis showed that patients with higher TyG index levels had higher all-cause mortality rates at 30 days, 60 days, and 90 days post-admission. This indicates that a higher TyG index is associated with an increased risk of death in the short term. Additionally, multivariate Cox proportional hazards regression analysis revealed that an increased TyG index was significantly associated with all-cause mortality at 30 days, 60 days, and 90 days. Meanwhile, RCS analysis indicates that as the TyG index level increases, the hazard ratio (HR) shows a significant upward trend, suggesting a gradual increase in the risk of all-cause mortality. In summary, among patients with primary hypertension in the intensive care unit, elevated TyG levels are associated with an increased risk of short-term mortality.

The emergency department (ED) plays a crucial role in evaluating patients with decreased general condition (DGC). Prompt diagnosis and precise risk stratification enhances clinical care outcomes. This study aimed to reveal which scoring system is better for predicting mortality in patients with DGC. This single-center prospective study was conducted between 1 March 2021 and 1 June 2021 in a tertiary university hospital's ED. All patients presented with DGC were included. The scores assessed upon ED admission for patients include the Hypotension, Oxygen saturation, low Temperature, ECG changes, and Loss of independence, abbreviated as HOTEL score, Rapid Emergency Medicine Score (REMS), and the Worthing Physiological Scoring (WPS). The study aimed to compare the scoring systems to identify which is the best mortality predictor. Independent risk factors for 30-day mortality were analyzed using binary logistic regression and ROC curves to assess the effectiveness of HOTEL, REMS, and WPS scores in predicting mortality. The study was conducted with 137 DGC patients. The median age of patients presented to the ED with DGC was 77 (66.5-87), and 50.4% were male. 52.6% of patients died within one month. Analysis of mortality risk factors revealed that gender, age, day of admission, visit type, home oxygen, urinary catheter use, and speech status were not independent risk factors. For one-month mortality prediction, AUCs were: HOTEL 0.644 (cut-off 1), REMS 0.635 (cut-off 8), WPS 0.547 (cut-off 5). For mechanical ventilation: HOTEL 0.689 (10), REMS 0.790 (10), WPS 0.777 (4). For ICU admission: HOTEL 0.697 (2), REMS 0.770 (9), WPS 0.728 (4). Both the HOTEL and REMS scoring systems have moderate prognostic value in predicting mortality in patients with a DGC. The REMS, and WPS scores are also more helpful in determining the need for intensive care unit admission and mechanical ventilation.

Hospitals are complex systems, and the flow of patients is dynamic and nonlinear in such systems. Network representation allows flow algorithms to observe bottlenecks as candidates for optimisation. To model the dynamic behaviour of the patient flow, we need to consider the variability in arrival rates and service times (length of stay). Previously proposed dynamic flow algorithms mainly focused on arrival and departure rates, inflow and outflow, edges' and vertices' capacity, and routing, with applications mainly in transportation and telecommunication. In hospitals, bottlenecks that emerge from the patients' flow are a result of the vertices (wards) behaviour defined by capacity (beds), number of servers (staff), service time variability, and edges (care pathways) distribution probability. We offer a modified flow algorithm that takes a hospital network, iterates over the patients' arrival rates, and measures the flow with respect to vertices' capacities, servers, service time variability, edge capacity, and distribution probability. The result is a dynamic residual graph to measure the bottlenecks' persistency and severity, identify the root causes of bottlenecks, and wards' dynamic nonlinear behaviour. The algorithm provides a quick holistic view of hospital performance and the analysis of the edges and vertices' behaviour over time.

Detecting and quantifying quantum entanglement remain significant challenges in the noisy intermediate-scale quantum (NISQ) era. This study presents the implementation of quantum support vector machines (QSVMs) on IBM quantum devices to identify and classify entangled states. By employing quantum variational circuits, the proposed framework achieves a runtime complexity of [Formula: see text], where N is the number of qubits, t is the number of iterations, and ε is the acceptable error margin. We investigate various quantum circuits with multiple blocks and obtain the accuracy of QSVM as measures of expressibility and entangling capability. Our results demonstrate that the QSVM framework achieves over 90% accuracy in distinguishing entangled states, despite hardware noise such as decoherence and gate errors. Benchmarks across superconducting qubit platforms (e.g., IBM Perth, Lagos, and Nairobi) highlight the robustness of the model. Furthermore, the QSVM framework effectively classifies two-qubit states and extends its predictive capabilities to three-qubit entangled states. This work marks a significant advancement in quantum machine learning for entanglement detection.

In recent years imaging flow cytometry (IFC) is gaining increasing attention as it combines the characteristics of conventional flow cytometry with optical microscopy techniques, allowing for high-throughput, multi-parameter screening of single cell populations. In the field of biology, the always increasing demand for high content morphological and spatial information led to the development of systems for volumetric imaging. However, current 3D IFC systems are often limited by the incompatibility with available microfluidic devices or by instrumental complexity that might lead to optical misalignment or mechanical instabilities in day-by-day operation. To this end, here we demonstrate the importance of advancing the laser fabrication technique by reporting on a fully integrated optofluidic platform composed of a borosilicate glass chip encompassing reconfigurable integrated photonic circuits for patterned light generation, bonded to a fused silica glass chip incorporating cylindrical hollow lenses, for light-sheet illumination, perfectly aligned to a microchannel where the sample under investigation flows. The system is capable of high-resolution imaging flow cytometry by implementing structured light sheet microscopy in a heterogeneously integrated platform with unprecedented stability. All the components are realized by femtosecond laser irradiation followed by chemical etching. The extreme level of integration permitted by the advanced optimization of the laser fabrication technique allowed the reduction of the assembled components and the absence of moving parts, thus ensuring durable alignment as well as mechanical and thermal stability both in short and long-term operation of the device, for the automated fluorescence signal acquisition during the sample flow.

Obesity represents a major global public-health problem during childhood and adolescence. The genetic contribution to obesity and its consequences is well-established. Variation in glucocorticoid (GC)-sensitivity can be partly explained by polymorphisms in GC receptor (GR) gene where NR3C1; Bcl1 rs41423247 and NR3C1 rs6198 single nucleotide polymorphisms (SNPs) have been linked to higher and lower GC sensitivity, respectively. We aimed to explore the potential association between the GR gene SNPs and risk of obesity in a cohort of Egyptian children. We included 100 pre-pubertal children; 60 obese children and 40 age-and sex-matched normal-weight controls. Bcl1 rs41423247 SNP was genotyped using PCR-restriction fragment length polymorphism technique and NR3C1 rs6198 SNP was genotyped using Real-time-PCR.In Bcl1 rs41423247, obese children had more frequent CG, GG genotypes and G allele compared to healthy controls (P = 0.039, 0.019 and 0.007 respectively). Moreover, insulin resistance was significantly higher in combined CG + GG group compared to CC group. On the contrary, no significant differences were found in genotypes, alleles frequencies or insulin resistance between obese and non-obese children in NR3C1 rs6198. GR Bcl1 rs41423247 gene polymorphism may play a role in genetic susceptibility to obesity that can be a future targeted therapy for obesity.