Browse Articles

Cyclic assisted cloning of arbitrary unknown single-particle states in amplitude damping channel

In this paper, two conclusive three-party cyclic assisted cloning protocols in amplitude damping (AD) channel are put forward that, respectively clone three arbitrary unknown single-qubit states and single-qutrit states with the help of a state preparer. Each of our protocols includes three consecutive stages: quantum channel preparation, cyclic quantum teleportation (CQT), and multi-party assisted cloning. The first stage of each protocol proposes the detailed processes of sharing a pure entangled quantum state as a component of a quantum channel in AD channel via entanglement compensation. In second stage, a three-party CQT is presented where three unknown single-qubit states (or single-qutrit states) are reconstructed simultaneously in three different places, respectively, by introducing auxiliary qubits and performing appropriate operations. In the third stage, the state preparer Victor performs one multi-qubit measurement (or one unitary transformation and one multi-qutrit measurement) and informs the three communicators of his outcome, three distinct unknown single-qubit states or their orthogonal complement states (or single-qutrit states) are cloned simultaneously and with probability at three separate locations,respectively. Furthermore, we extend the above protocols from two aspects: (i) the extension to the case of (N+1) participants; (ii) extension to the case of d-dimensional unknown single-qudit state cycle-assisted cloning.

Circular RNA RORβ regulates TGFβR1 in alcohol-induced fibroblast-to-myofibroblast differentiation

Defining metabolic abnormalities in acute human traumatic brain injury with cerebral microdialysis and multimodality monitoring

Objective We aimed to compare the prevalence and multimodal associations of mitochondrial dysfunction as defined by published cerebral-microdialysis-based criteria versus our novel multimodality-monitoring-based criteria in acute traumatic brain injury patients. Methods We retrospectively analyzed neurocritical care monitoring data from 619 acute traumatic brain injury patients. Monitoring modalities included cerebral microdialysis, intracranial pressure, brain tissue oxygenation, cerebral perfusion pressure, and the pressure reactivity index. The cerebral-microdialysis-based criteria we compared combine an elevated lactate/pyruvate ratio (25 or 30) with raised concentrations of lactate (2.5 mM) or pyruvate (70 μM or 120 μM). Our multimodality-monitoring-based criteria comprise a consistent lactate/pyruvate ratio > 25 with intracranial pressure ≤ 20 mmHg, brain tissue oxygenation ≥ 15 mmHg, a pressure reactivity index ≤ 0.3, and cerebral glucose ≥ 1.0 mM. Results Across 592 analyzable patients, a lactate/pyruvate ratio > 25 was common, with a median prevalence of 48.9% (41.5% with consistency) and a U-shaped, bimodal distribution. A lactate/pyruvate ratio > 25 was associated with lower glucose and higher glycerol, and when accompanied by high pyruvate (> 120 μM), this derangement was further distinguished by higher glutamate and cerebral perfusion pressure. Using multimodal criteria on a cohort of 268 patients, consistent mitochondrial dysfunction was identified in 25.7% to 41.0% of patients, often in the absence of other physiological derangements. Conclusions Many acute traumatic brain injury patients constantly demonstrate neurometabolic derangements, among which clinical mitochondrial dysfunction is highly prevalent despite normal cerebral pressure, oxygenation, and perfusion. There is necessity for targeted, neurometabolic therapies in neurocritical care that address this abnormality.

Research on the inversion model of soil moisture content based on a novel ReMPDI index in mining areas

Improving adherence to physical activity in treatment-resistant depression: Protocol for a pilot randomized controlled trial of a remotely delivered program

Background At least 30% of individuals with major depressive disorder do not respond to conventional treatments (i.e., they meet the criteria for treatment-resistant depression [TRD]). Alternative therapeutic modalities are needed. Some studies have reported that physical activity (PA) programs can improve depressed mood and reduce depressive symptoms. However, few studies to date have examined the effects of PA as an adjunct to standard treatment for TRD. The MoveU.HappyU PA program has been shown to improve depressive symptoms in university students. Before a definitive trial testing MoveU.HappyU in TRD can be designed, pilot data is needed. Methods The current study is a single-site, pilot, two-arm randomized controlled trial. It will investigate the feasibility of randomizing 30 adult participants with TRD to: (1) a remotely delivered four-week MoveU.HappyU adjunct to treatment as usual (TAU), or (2) TAU. Acceptability of the PA program will also be assessed. Participants randomized to the PA program will meet weekly with a program trainer to engage in PA counselling and structured PA. They will also be instructed to independently complete 120 minutes of PA per week. The four-week intervention period will be followed by six weeks of observation. Throughout the study, both groups will receive the same digital monitoring via self-report questionnaires and a wearable device, as well as traditional monitoring (i.e., clinical assessments administered by a masked rater). Discussion This pilot study will assess the feasibility of a trial implementing MoveU.HappyU for TRD and generate clinical parameter estimates for larger studies. This line of research highlights the importance of PA programs that integrate personalized PA with PA counselling. It will also influence the development of interventions that are more tailored and effective. Trial registration ClinicalTrials.gov NCT06404320

BamClassifier: a machine learning method for assessing iron deficiency

Abstract Iron deficiency (ID) is a well-known cause of anaemia and could lead to adverse clinical and functional impairments. However, ID is under-diagnosed due to non-specific symptoms, difficulties in interpreting ambiguous assessment outcomes and suboptimal sensitivities of methods in some circumstances. In this study, we present BamClassifier, a machine learning method for assessment of ID. This method proceeds by repeated selection of samples of instances from routine complete blood count data in such a way that each observation is included in exactly one sample. Then, a median-supplement machine learning model built from each sample, and the performance of the model on test instances are aggregated into a bag of predictions from which ID statuses are assigned to samples by way of the highest frequency counts. We show the effectiveness of our method by applying to real datasets obtained from different investigations in Ghana and simulated data as well. Our method obtained perfect area under receiver operating characteristic curve in all experiments and significantly outperformed other well-established methods in terms of accuracy, sensitivity, specificity, precision, and diagnostic odds ratio in all our evaluations. A successful application of the method will permit the study of large collections of samples for ID assessments, save time and cost using complete blood count parameters, while standardizing interpretation of outcomes of such investigations.

Deep learning methods to forecasting human embryo development in time-lapse videos

Background In assisted reproductive technology, evaluating the quality of the embryo is crucial when selecting the most viable embryo for transferring to a woman. Assessment also plays an important role in determining the optimal transfer time, either in the cleavage stage or in the blastocyst stage. Several AI-based tools exist to automate the assessment process. However, none of the existing tools predicts upcoming video frames to assist embryologists in the early assessment of embryos. In this paper, we propose an AI system to forecast the dynamics of embryo morphology over a time period in the future. Methods The AI system is designed to analyze embryo development in the past two hours and predict the morphological changes of the embryo for the next two hours. It utilizes a novel predictive model incorporating Convolutional LSTM layers for recursive forecasting, enabling prediction of future embryo morphology by analyzing prior changes in the video sequence and predicting embryo development up to 23 hours ahead. Results The results demonstrated that the AI system could accurately forecast embryo development at the cleavage stage on day 2 and the blastocyst stage on day 4. The system provided valuable information on the cell division processes on day 2 and the start of the blastocyst stage on day 4. The system focused on specific developmental features effective across both the categories of embryos. The embryos that were transferred to the female, and the embryos that were discarded. However, in the ‘transfer’ category, the forecast had a clearer cell membrane and less distortion as compared to the ‘avoid’ category. Conclusion This study assists in the embryo evaluation process by providing early insights into the quality of the embryo for both the transfer and avoid categories of videos. The embryologists recognize the ability of the forecast to depict the morphological changes of the embryo. Additionally, enhancement in image quality has the potential to make this approach relevant in clinical settings.

Spatio-temporal transformer traffic prediction network based on multi-level causal attention

Traffic prediction is a core technology in intelligent transportation systems with broad application prospects. However, traffic flow data exhibits complex characteristics across both temporal and spatial dimensions, posing challenges for accurate prediction. In this paper, we propose a spatiotemporal Transformer network based on multi-level causal attention (MLCAFormer). We design a multi-level temporal causal attention mechanism that captures complex long- and short-term dependencies from local to global through a hierarchical architecture while strictly adhering to temporal causality. We also present a node-identity-aware spatial attention mechanism, which enhances the model’s ability to distinguish nodes and learn spatial correlations by assigning a unique identity embedding to each node. Moreover, our model integrates several input features, including original traffic flow data, cyclical patterns, and collaborative spatio-temporal embedding. Comprehensive tests on four real-world traffic datasets—METR-LA, PEMS-BAY, PEMS04, and PEMS08—show that our proposed MLCAFormer outperforms current benchmark models.

DFU_DIALNet: Towards reliable and trustworthy diabetic foot ulcer detection with synergistic confluence of Grad-CAM and LIME

Diabetic Foot Ulcer (DFU) is a major complication of diabetes which needs early detection to help in timely treatment for preventing future serious consequences. Due to peripheral neuropathy, high blood glucose levels, and untreated wounds, DFUs can cause the disintegration of the skin and exposing the tissue below it, if not adequately treated. Recently deep learning (DL) has advanced and has shown its ability to automate DFU detection and classification by analysing medical images. The use of DL has been proven to be very useful for healthcare professionals, enabling earlier diagnosis and effective treatment of DFU. However, most of the studies predominantly rely on a single dataset (e.g., DFUC2021 or DFUC2020) without external validation or cross-dataset testing, raising concerns about generalizability and trustworthiness. The aim of this study is to develop a robust, reliable, and transparent DFU detection framework which is not only good performing but also can effectively give attention to the proper region of the images which are crucial for DFU detection. So, to make DFU detection robust, reliable in a single study, we proposed a custom approach, DFU_DIALNet and to enhance transparency and interpret the model decisions in this study, we integrated Grad-CAM and LIME heatmaps to precisely localize ulcer regions. This allows visual verification of the model’s focus and clarifies the decision-making process, thereby increasing the model’s reliability. DFU_DIALNet outperforms all other traditional models with 99.33% accuracy, 99% F1 score, and 100% AUC score, and compared it to other DL models—DenseNet121, MobileNetV2, InceptionV3, EfficientNetB0, ResNet50V2 and VGG16—in the merged dataset of DFUC2021 with our collected 500 images. We have checked our model’s reliability with 2 other popular datasets—-the KDFU and DFUC2020 datasets, where our proposed approach gives the highest accuracy of 95.61% and 99.54%, respectively, compared to other deep learning approaches. Lastly, we have developed a web app using Streamlit to detect DFU efficiently. This study fills the gap between reliable and interpretable systems with a proposed approach to the efficient detection of DFU.

The role of physical acgtivity in life satisfaction and psychological resilience among college students: Mediating effects of self-efficacy and perceived stress

Background With the increasing global attention on mental health issues, especially the psychological stress and life satisfaction problems faced by college students, it has become particularly important to explore how physical activity is associated with college students’ psychological resilience and quality of life through psychological mechanisms. This study aims to examine the association between physical activity on college students’ life satisfaction and psychological resilience, and to investigate the mediating roles of self-efficacy and perceived stress. Methods This study collected data from college students in several universities in China through online questionnaires, using the Body Self-Concept Questionnaire, Life Satisfaction Scale, the Chinese Revised Version of the Connor–Davidson Resilience Scale, General Self-Efficacy Scale, and Perceived Stress Scale to measure each variable. A total of 560 undergraduate students from three universities participated in the survey, reporting on their physical activity, life satisfaction, psychological resilience, self-efficacy, and perceived stress. Results The findings show that physical activity was significantly associated with higher life satisfaction (r = 0.439, p < 0.001) and psychological resilience (r = 0.521, p < 0.001). Both self-efficacy (95% CI = [0.138, 0.255] and [0.245, 0.399]) and perceived stress (95% CI = [0.013, 0.070] and [0.040, 0.134]) played significant mediating roles in these processes. Specifically, physical activity was linked to better mental health and quality of life through its association with higher self-efficacy and lower perceived stress. Discussion and implications This study validates the association between physical activity and improved college students’ life satisfaction and psychological resilience and reveals the mediating mechanisms of self-efficacy and perceived stress. These findings provide a scientific basis for designing exercise and psychological intervention programs for college students, with important practical implications.

Factors associated with unrealized fertility among women approaching the end of reproductive age in sub-Saharan Africa

Background Understanding women’s fertility preferences is essential for addressing reproductive behaviors and family planning needs. In sub-Saharan Africa, fertility rates remain high, yet many women experience unrealized fertility, which is having fewer children than desired. However, the factors influencing unrealized fertility remain underexplored. This study assessed the determinants of unrealized fertility among women approaching the end of their reproductive years in sub-Saharan Africa. Methods A secondary data analysis was conducted using phase eight Demographic and Health Surveys data from 19 sub-Saharan African countries. The weighted sample included 46,408 women aged 40–49 years. A multilevel Poisson regression model with robust variance was used to identify factors associated with unrealized fertility. Adjusted prevalence ratios with 95% confidence intervals (CI) were reported, and variables with a p value <0.05 were considered statistically significant. Results The pooled prevalence of unrealized fertility among women aged 40–49 years was 61.43% (95% CI: 57.63%, 65.24%). Rwanda (37.40; 95% CI: 27.92%, 46.88%) and Sierra Leone (69.34; 95% CI: 60.30%, 78.38%) had the lowest and highest prevalence, respectively. Older maternal age at first birth, being employed, having no children or only children of one sex, and experiencing child death were associated with higher prevalence of unrealized fertility. Conversely, higher maternal education, the use of contraceptives, having both male and female children, and residing in rural areas were associated with lower prevalence of unrealized fertility. Conclusions A large proportion of women nearing the end of their reproductive careers in sub-Saharan Africa have experienced unrealized fertility. Therefore, addressing cultural norms surrounding sex preference and number of children, alongside empowering women through improved access to education, healthcare, and comprehensive sexual and reproductive health services, is critical.

Deciphering the role of the lncRNA TRIBAL in hepatocyte models

We recently reported that the long non-coding RNA TRIBAL /TRIB1AL was required to sustain key hepatocyte functions. Here, we identify HepaRG cells as a model for studying TRIBAL and provide additional validation and functional insights. In contrast to HepG2 and HuH-7 cells, differentiated HepaRG cells showed similarities to primary hepatocytes in response to TRIBAL suppression. TRIBAL suppression was associated with reduced HNF4A and MLXIPL abundance in hepatocytes and HepaRG cells. TRIBAL targeting using a panel of cognate antisense oligonucleotides confirmed specificity. A comparison of TRIBAL -suppressed hepatocyte and HepaRG transcriptomics identified extensive functional overlap. Biological ontologies associated with key hepatic metabolic functions were predicted to be inhibited in both models. Comparative analyses with TRIB1 -suppressed HepaRG cells, a central metabolic regulator vicinal to TRIBAL , also revealed extensive functional congruence with TRIBAL . Interestingly, TRIBAL transduction failed to restore function in TRIBAL -suppressed cells, which may be linked to structural differences, as supported by contrasting RNAse R sensitivities between the endogenous and transduced forms. In summary, these findings support the use of HepaRG cells as an experimental model to study TRIBAL and underscore its importance in regulating key hepatocyte genes essential for metabolic function.

Predicting 30-day hospital readmissions using ClinicalT5 with structured and unstructured electronic health records

Hospital readmission prediction is a crucial area of research due to its impact on healthcare expenditure, patient care quality, and policy formulation. Accurate prediction of patient readmissions within 30 days post-discharge remains a considerable challenging, given the complexity of healthcare data, which includes both structured (e.g., demographic, clinical) and unstructured (e.g., clinical notes, medical images) data. Consequently, there is an increasing need for hybrid approaches that effectively integrate these two data types to enhance all-cause readmission prediction performance. Despite notable advancements in machine learning, existing predictive models often struggle to achieve both high precision and balanced predictions, mainly due to the variability in patients’ outcome and the complex factors influencing readmissions. This study seeks to address these challenges by developing a hybrid predictive model that combines structured data with unstructured text representations derived from ClinicalT5, a transformer-based large language model. The performance of these hybrid models is evaluated against text-only models, such as PubMedBERT, using multiple metrics including accuracy, precision, recall, and AUROC score. The results demonstrate that the hybrid models, which integrate both structured and unstructured data, outperform text-only models trained on the same dataset. Specifically, hybrid models achieve higher precision and balanced recall, reducing false positives and providing more reliable predictions. This research underscores the potential of hybrid data integration, using ClinicalT5, to improve hospital readmission prediction, thereby improving healthcare outcomes through more accurate predictions that can support better clinical decision making and reduce unnecessary readmissions.

KFERQ-selective protein autophagy in Caenorhabditis elegans depends on LMP-1

Mammalian cells exhibit three autophagy mechanisms: macroautophagy, microautophagy (MIA), and chaperone-mediated autophagy (CMA), each employing unique mechanisms for transporting cellular material to the lysosome for degradation. MIA involves the engulfment of proteins via lysosomes/late endosomes through membrane invagination, while CMA directly imports cytosolic proteins into lysosomes, selectively targeting those harboring the KFERQ pentapeptide motif, helped by the chaperone HSC70. Despite the identification of several genetic markers of these pathways, our understanding of the underlying mechanisms, particularly in MIA and CMA, remains limited. To study CMA in vivo we designed a photoactivatable CMA reporter consisting of a plasmid encoding the KFERQ consensus signal for CMA targeting. We generated transgenic C. elegans strains with diverse genetic backgrounds to analyze the role of known molecular components of CMA in mammals. Additionally, we conducted an in-silico analysis of the structural interaction between C. elegans LMP-1 or LMP-2 proteins with the HSP-1 chaperone. Results: Our study shows a significant alteration in the distribution pattern of the KFERQ reporter in muscle cells upon induction of selective autophagy (CMA or MIA). We found that the reporter localized into lysosomes only during starvation, which abrogated in the absence of LMP-1. This study validates CMA in C. elegans and provides the development of a new tool for understanding selective autophagy mechanisms and their potential implications in various organisms.

Observational suspected adverse drug reaction profiles of fluoro-pharmaceuticals and potential mimicry of per- and polyfluoroalkyl substances (PFAS) in the United Kingdom

Aims The aim of this research is to explore the suspected adverse drug reactions (ADRs) of perfluorinated medicines to determine whether side effects commonly associated with per- and poly-fluoroalkyl substances (PFAS) exposure were correlated to the type or number of fluorine atoms in these medications. Methods Thirteen fluorinated drugs and six non-fluorinated (or low fluorinated) comparators were selected after systematic triage. The reported ADR data from the Medicines and Healthcare Products Regulatory Agency’s (MHRA) Yellow Card, and prescribing data from the OpenPrescribing database and the National Health Service Business Service Authority (NHSBSA) over a 5-year period were curated. Prescribing data was used to standardise the ADRs by calculating ADRs/1,000,000 items dispensed for selected system organ classes (SOCs), associated with PFAS exposure, for all 19 drugs. The physiochemical and pharmacological properties of the selected drugs were determined from ChemDraw version 23.1.1, Drug Bank, electronic medicines compendium (EMC) and the chemical database of bioactive molecules with drug-like properties, European Molecular Biology Laboratory (ChEMBL). Results Excluding congenital, familial, and genetic disorders, and endocrine disorders, all other SOCs (n = 5) showed statistical significance (P < .05) for ADRs/1,000,000 items identified across the 13 fluorinated drugs. It was identified that leflunomide was suspected of more ADRs than other comparator medications, which had the highest suspected ADRs/1,000,000 items dispensed (n = 343) and lansoprazole had the lowest (n = 14). Both drugs contain same number of fluorine atoms (n = 3) and similar type of fluorine moiety (trifluoromethyl, -CF3). Conclusion No correlation between the fluorination status of the drugs and the ADRs were found.

Combining market surveys and participative approaches to map small ruminant mobility in three selected states in northern Nigeria

In Nigeria, a huge gap in knowledge on livestock mobility and its role on transboundary disease spread exists. As animals move, so do diseases. Therefore, there is a need to understand how livestock movements can contribute to the circulation and maintenance of infectious livestock diseases which can impede the design of particular surveillance and control tactics in the event of outbreaks. Our study aim was to reconstruct small ruminants’ mobility patterns in three selected states in Northern Nigeria for better surveillance and control of small ruminant’s transboundary animal diseases (TADs). To this end, a mixed approach was used to collect data. A market survey, employing structured questionnaires, was administered to 1,065 market traders. Additionally, 20 focus group discussions were conducted with traders and transhumance actors across 10 Local Government Areas (LGAs) spanning three northern Nigerian states: Plateau, Bauchi, and Kano. The respondent movements by type, animal movement, reason for movement was described and summarized. Data collected were used to reconstruct small ruminant mobility networks, whose nodes were LGAs, in the three states of the survey area and with other states in Nigeria and movement mapped. Characteristics of both networks were studied using a complex network approach either separately or combined. Using the two approaches provided a complementary view of small ruminant mobility. The reconstructed networks were connected, highly heterogeneous and had very low density. The networks included LGAs belonging up to 31 states. The presence of hubs increased the risk of disease spread. Gwarzo, Wudil (Kano) and Alkaleri (Bauchi) LGAs received the most sheep and goats, while Jos North (Plateau) and Gwarzo supplied more small ruminants. Bukuru and Alkaleri markets were classified as super-spreaders with a higher probability of detecting virus circulation. Four to six multistate communities were identified. Our findings could support policy choices to identify priority areas for surveillance and disease control in small ruminants.

Evaluation of ISSA proactive leading indicators for safety, health and well-being: Application of multi-criteria decision-making methods based on hesitant fuzzy

Background The Vision Zero (VZ) strategy, adopted by the International Social Security Association (ISSA), is based on seven golden rules and 14 safety, health, and well-being (SHW) indicator called VZ Proactive Leading Indicators (VZPLI). Methods The study evaluates the status of SHW VZPLI in Iran’s Petrochemical Industries by IAM-VZPLI (ISSA Assessment Method for VZPLI) and proposes a new model for assessing these indicators, called EMA-VZPLI (Extended Method for Assessment of VZPLI). EMA-VZPLI uses an integrated method consisting of multi-criteria decision-making(MCDM), the best-worst method(BWM), Decision-Making Trial and Evaluation Laboratory (DEMATEL), and axiomatic design(AD) based on hesitant fuzzy(HF) sets. Results Weight of indicators and their relationships was determined using hesitant fuzzy best-worst method (HFBWM) and Interval-Valued Hesitant Fuzzy DEMATEL(IVHF-DEMATEL) method respectively. The results showed that the C2 indicator (competent leadership) was the most important, while the C10 indicator (Procurement) was the least important. Additionally, the C2 (competent leadership) and C1 (Visible leadership commitment) indicators were dependent, showing the highest degree of dependence. The indicators of the ‘well-being’ aspect in all studied petrochemical companies were worse than those of the safety’ and ‘health’ aspects. Conclusion The results showed a significant difference in ranking of petrochemical companies for VZPLIs in EMA-VZPLI compared to IAM-VZPLI. Therefore, this method could be applied in more accurate assessment of VZPLIs of VZ indicators.

Geoarchaeological research on site formation process, paleoenvironment, and human behaviors in the early Holocene of the Gobi Desert, Mongolia

This paper presents a rare example of the multi-proxy investigation results on the prehistoric settlement from vast areas of the Mongolian Gobi Desert, where, during favorable climatic conditions, postglacial hunter-gatherer groups occupied a seasonal lake district. The geoarchaeological research conducted at site FV92, located at the Luulityn Toirom Paleolake, provides insight into the problem of human relations with the changing environment of the Early Holocene, as well as the problem of the site formation process in the Gobi area. Sedimentological studies and luminescence dating of the Luulityn Toirom Lake sediments indicate the presence of the lake and favorable environmental conditions for human settlement in the Early Holocene in the period before 8130 ± 83 BP. Spatial analyses of the artifact distribution, as well as refitting studies of the discovered lithic assemblage, enabled the determination of the site’s formation process. Initially, the site was influenced by fluvial processes, but as the climate dried, it was subsequently affected by aeolian processes. The techno-typological analysis, refitting studies, and microscopic analyses carried out provide the first such detailed insight into the technological behavior and identification of the chaîne opératoire used by the Early Holocene hunter-gatherer communities of the Gobi area. The results confirmed that the lithic technology was mainly based on microblade technology. Microscopic analyses of traces created during tool use indicate butchery activity and the use of plant resources. The studies indicate a high degree of mobility of hunter-gatherer communities living by the lakes, as evidenced by the medium-range transport of raw material brought to the campsite from the surrounding mountainous Altai area.

Resilience enhancement of an urban road network during traffic accidents by optimally dispatching rescue teams

The efficient dispatch of rescue teams (RTs) during traffic accidents is crucial for the rapid restoration of normal operations in the affected urban road network (URN), thereby enhancing the network’s resilience during such events. However, previous studies focusing on optimizing RT dispatch strategies to enhance URN resilience remain limited. To address this gap, this paper develops a mixed-integer linear programming model aimed at optimizing RT dispatch during traffic accidents. The formulated model is solved using the commercial solver (i.e., CPLEX). Numerical experiments conducted on a hypothetical URN demonstrate that the model generates an optimal dispatch scheme. Compared to baseline strategies, the optimized scheme reduces the total objective function values by 27.36% in small-scale cases and 16.28% in large-scale case, respectively. Furthermore, sensitivity analysis reveal that accident severity and destination locations significantly influence the dispatch scheme design. Finally, the paper discusses the impact of several parameters on the model’s solution, showing that its performance is highly sensitive to several critical factors like RT dispatch costs, the maximum allowable delay time, passenger value of time, and vehicle travel speeds.

Study protocol for a randomized controlled clinical trial of a multifaceted cognitive training program using video games in childhood cancer survivors

This randomized controlled trial aims to evaluate the efficacy of a cognitive training program using video games in improving neuropsychological, neurological, immunological, and inflammatory parameters in childhood cancer survivors. This study will recruit 56 patients aged 8–17 years who have completed cancer treatment 1–8 years prior to enrollment. Participants will be randomized to either the video game intervention or waiting group. The primary objectives are analyzing potential changes in neuropsychological tests covering all neurocognitive domains, neuroimaging tests (structural, diffusion, and functional imaging), and immune and inflammatory biomarker levels after video game intervention. The secondary objectives are to define the prevalence of neurocognitive deficits in the study population, analyze psychological and emotional self-perception and parental perception after the intervention, and assess the feasibility of implementing this new intervention methodology. The inclusion criteria comprise specific diagnoses (central nervous system [CNS] cancer, hematologic malignancies, extracranial solid tumors, and nonmalignant hematological diseases requiring allogeneic hematopoietic progenitor transplantation) and treatments (CNS surgery, radiotherapy, intrathecal/intraventricular chemotherapy, neurotoxic systemic chemotherapy, and hematopoietic stem cell transplantation). Patients with active disease, relapse, or prior neurological or psychiatric pathology will be excluded. This study will improve the understanding and management of neurocognitive sequelae in childhood cancer survivors and ultimately enhance their quality of life. Trial identifier: NCT06312969