bluuebunny/crossref_metadata_2025_split huggingface dataset:
Beyond nimodipine: advanced neuroprotection strategies for aneurysmal subarachnoid hemorrhage vasospasm and delayed cerebral ischemia
Authors: Luzzi Sabino , Bektaşoğlu Pınar Kuru , Doğruel Yücel , Güngor Abuzer 
Published: July 2024

Abstract
The clinical management of aneurysmal subarachnoid hemorrhage (SAH)-associated vasospasm remains a challenge in neurosurgical practice, with its prevention and treatment having a major impact on neurological outcome. While considered a mainstay, nimodipine is burdened by some non-negligible limitations that make it still a suboptimal candidate of pharmacotherapy for SAH. This narrative review aims to provide an update on the pharmacodynamics, pharmacokinetics, overall evidence, and strength of recommendation of nimodipine alternative drugs for aneurysmal SAH-associated vasospasm and delayed cerebral ischemia. A PRISMA literature search was performed in the PubMed/Medline, Web of Science, ClinicalTrials.gov, and PubChem databases using a combination of the MeSH terms “medical therapy,” “management,” “cerebral vasospasm,” “subarachnoid hemorrhage,” and “delayed cerebral ischemia.” Collected articles were reviewed for typology and relevance prior to final inclusion. A total of 346 articles were initially collected. The identification, screening, eligibility, and inclusion process resulted in the selection of 59 studies. Nicardipine and cilostazol, which have longer half-lives than nimodipine, had robust evidence of efficacy and safety. Eicosapentaenoic acid, dapsone and clazosentan showed a good balance between effectiveness and favorable pharmacokinetics. Combinations between different drug classes have been studied to a very limited extent. Nicardipine, cilostazol, Rho-kinase inhibitors, and clazosentan proved their better pharmacokinetic profiles compared with nimodipine without prejudice with effective and safe neuroprotective role. However, the number of trials conducted is significantly lower than for nimodipine. Aneurysmal SAH-associated vasospasm remains an area of ongoing preclinical and clinical research where the search for new drugs or associations is critical.
Advanced methods for treating gemfibrozil and carbamazepine in wastewater: a review
Authors: Sivanesan Jothivel , Rameshwar Sankar Sudharsan , Sivaprakash Baskaran , Rajamohan Natarajan , Osman Ahmed I. , Al-Muhtaseb Ala’a H. 
Published: July 2024

AbstractThe contamination of ecosystems by pharmaceuticals and personal care products represents a significant threat to public health, necessitating innovative approaches to clean wastewater before release into aquatic environments. Here, we review the emerging strategies and methods for the remediation of gemfibrozil and carbamazepine, emphasizing toxicological impacts, advanced oxidation processes, membrane-based removal techniques, and the underlying mechanisms driving these removal processes. We found that engineered composites with strong electron transfer capabilities can enhance the removal efficiency as they boost the generation of highly oxidative radicals. For instance, a nano zero-valent ion incorporated carbon–nitrogen composite removes 100% of gemfibrozil within 60 min. Similarly, a ruthenium perovskite-based heterogeneous catalyst achieved 100% elimination of carbamazepine in 7.5 min.
An In Silico Modelling Approach to Predict Hemodynamic Outcomes in Diabetic and Hypertensive Kidney Disease
Authors: Wang Ning , Benemerito Ivan , Sourbron Steven P , Marzo Alberto 
Published: July 2024

AbstractEarly diagnosis of kidney disease remains an unmet clinical challenge, preventing timely and effective intervention. Diabetes and hypertension are two main causes of kidney disease, can often appear together, and can only be distinguished by invasive biopsy. In this study, we developed a modelling approach to simulate blood velocity, volumetric flow rate, and pressure wave propagation in arterial networks of ageing, diabetic, and hypertensive virtual populations. The model was validated by comparing our predictions for pressure, volumetric flow rate and waveform-derived indexes with in vivo data on ageing populations from the literature. The model simulated the effects of kidney disease, and was calibrated to align quantitatively with in vivo data on diabetic and hypertensive nephropathy from the literature. Our study identified some potential biomarkers extracted from renal blood flow rate and flow pulsatility. For typical patient age groups, resistive index values were 0.69 (SD 0.05) and 0.74 (SD 0.02) in the early and severe stages of diabetic nephropathy, respectively. Similar trends were observed in the same stages of hypertensive nephropathy, with a range from 0.65 (SD 0.07) to 0.73 (SD 0.05), respectively. Mean renal blood flow rate through a single diseased kidney ranged from 329 (SD 40, early) to 317 (SD 38, severe) ml/min in diabetic nephropathy and 443 (SD 54, early) to 388 (SD 47, severe) ml/min in hypertensive nephropathy, showing potential as a biomarker for early diagnosis of kidney disease. This modelling approach demonstrated its potential application in informing biomarker identification and facilitating the setup of clinical trials.
The impact of ambient temperature and powertrains of SUVs on the environment in Slovakia during the use phase
Authors: Sečkár Michal , Schwarz Marián 
Published: July 2024

AbstractThis study compares different powertrains of sport utility vehicles (SUVs) with respect to ambient temperature and energy mix in Slovakia using the well-to-wheel (WTW) Life Cycle Assessment (LCA) method. Battery electric vehicles (BEV), plug-in hybrid electric vehicles (PHEV), and petrol and diesel vehicles were assessed and compared. The WTW study was conducted in SimaPro software assessing electricity/petrol/diesel production, transport, and use (energy conversion in the vehicle), with impact categories being climate change, particulates, NOx emissions, ionizing radiation, and fossil resource scarcity depending on the season (summer and winter). The results indicate that for Slovak conditions, BEV generally had the lowest environmental impact in both seasons studied. The only exceptions were ionizing radiation, which is clearly caused by the high share of nuclear power in the Slovak energy mix, and NOx emissions, which are caused by the combustion of biomass for electricity generation. The other impact categories were dominated by vehicles with an internal combustion engine. The results of emissions from fuel production are also given for each impact category. The transportation of fuel did not exceed the value of 1% for any impact category or for any powertrain. The conclusions of the study support the global trend in favour of vehicle electrification as an important way to reduce the negative environmental impacts of internal combustion engine vehicles in Slovakia.
Advances in the diagnosis and treatment of acute acquired comitant esotropia
Authors: Guo Shuyang , Zhou Yulian , Xi Sida , Zhao Chen , Wen Wen 
Published: July 2024

AbstractAcute acquired comitant esotropia (AACE) is mainly characterized by sudden onset, accompanied by diplopia, without extraocular muscles paralysis or ocular motility disorders. In recent years, the incidence of AACE has been increasing, researchers have found that this phenomenon may be related to the widespread use of electronic devices and the increase in the number of people working from home during the COVID-19 pandemic. However, its neural mechanisms have not been fully elucidated. This article primarily reviews the latest developments in the diagnosis and treatment of AACE from the perspectives of etiology and treatment methods, aiming to provide direction for future in-depth exploration of the pathogenesis and treatment approaches of this disease.
Impacts of urban-rural integration on landscape patterns and their implications for landscape sustainability: The case of Changsha, China
Authors: He Yanhua , Wen Chubing , Fang Xuening , Sun Xiao 
Published: July 2024

Abstract
Context
Landscape patterns result from complex social-ecological interactions, such as urban-rural integration which is especially notable in China. However, the impacts of urban-rural integration on landscape sustainability remain understudied.

Objectives
Our objectives were to examine the impacts of urban-rural integration on landscape patterns and to explore its implications for landscape sustainability.

Methods
The study area is the Changsha metropolitan area in central China, which has undergone urban-rural integration since 2005. A three-step approach was used to (1) assess the spatiotemporal patterns of urban-rural integration dynamics with a multidimensional indicator system, (2) quantify landscape pattern dynamics using landscape metrics, and (3) explore the main drivers and processes involved in urban-rural integration responsible for the observed changes in landscape patterns through a time-fixed effects model.

Results
Our findings reveal a significant increase in the level of urban-rural integration in the Changsha metropolitan area from 2005 to 2020. Concurrently, the regional landscape has experienced increasing trends characterized by the loss and fragmentation of natural and agricultural patches, as well as heightened connectivity among constructed patches. Urban-rural integration exerts notable impacts on landscape patterns through processes such as societal convergence, economic interaction, technology sharing, and spatial interlinkage, subsequently influencing landscape (un)sustainability.

Conclusions
The different drivers and processes of urban-rural integration have varying effects on landscape patterns, with complex and intertwined impacts on landscape sustainability. Therefore, integrated landscape governance is imperative for addressing the disadvantages of urban-rural integration while strategically leveraging its advantages in various social-ecological contexts.
Complementarities between algorithmic and human decision-making: The case of antibiotic prescribing
Authors: Ribers Michael Allan , Ullrich Hannes 
Published: July 2024

AbstractArtificial Intelligence has the potential to improve human decisions in complex environments, but its effectiveness can remain limited if humans hold context-specific private information. Using the empirical example of antibiotic prescribing for urinary tract infections, we show that full automation of prescribing fails to improve on physician decisions. Instead, optimally delegating a share of decisions to physicians, where they possess private diagnostic information, effectively utilizes the complementarity between algorithmic and human decisions. Combining physician and algorithmic decisions can achieve a reduction in inefficient overprescribing of antibiotics by 20.3 percent.
Examining the Strength of the Association Between Problem Gambling and Gambling to Escape. A Systematic Review and Meta-Analysis
Authors: Alaba-Ekpo Olukemi , Caudwell Kim M. , Flack Mal 
Published: July 2024

AbstractGambling to escape is often purported to play a central role in the maintenance of problem gambling. However, the strength and consistency this association is unknown. Therefore, the current systematic review and meta-analysis was conducted to explore the association between gambling to escape and problem gambling. The study protocol was registered in the PROSPERO database and protocols were developed and published studies were searched until May 2024. The PRISMA standards were adopted for screening and extraction of relevant data. Twenty-seven studies that measured gambling motives, reasons, or expectancies, related to escape, using validated multiple item measures, were included. Positive associations were found between gambling to escape and problem gambling, and this association remained after controlling for the effect of gambling for financial gain. Additional subgroup analyses were conducted to test the stability of the association by escape measure. Although the magnitude of the positive association changed depending on the scales used, the positive relationship held. The findings support the contention that problem gambling is at least, in part, maintained by the escape afforded by gambling—indicating the importance considering the emotion focused reasons in the design of initiative to reduce problem gambling behaviours.
ConKeD: multiview contrastive descriptor learning for keypoint-based retinal image registration
Authors: Rivas-Villar David , Hervella Álvaro S. , Rouco José , Novo Jorge 
Published: July 2024

Abstract
Retinal image registration is of utmost importance due to its wide applications in medical practice. In this context, we propose ConKeD, a novel deep learning approach to learn descriptors for retinal image registration. In contrast to current registration methods, our approach employs a novel multi-positive multi-negative contrastive learning strategy that enables the utilization of additional information from the available training samples. This makes it possible to learn high-quality descriptors from limited training data. To train and evaluate ConKeD, we combine these descriptors with domain-specific keypoints, particularly blood vessel bifurcations and crossovers, that are detected using a deep neural network. Our experimental results demonstrate the benefits of the novel multi-positive multi-negative strategy, as it outperforms the widely used triplet loss technique (single-positive and single-negative) as well as the single-positive multi-negative alternative. Additionally, the combination of ConKeD with the domain-specific keypoints produces comparable results to the state-of-the-art methods for retinal image registration, while offering important advantages such as avoiding pre-processing, utilizing fewer training samples, and requiring fewer detected keypoints, among others. Therefore, ConKeD shows a promising potential towards facilitating the development and application of deep learning-based methods for retinal image registration.

Graphical abstract
MicroRNA-513b-5p inhibits epithelial mesenchymal transition of colon cancer stem cells through IL-6/STAT3 signaling pathway
Authors: Zhang Zefeng , Sha Weihong 
Published: July 2024

Abstract
Objective
To reveal the mechanisms by which miR-513b-5p inhibits metastasis of colon cancer stem cells (CCSCs) through IL-6/STAT3 in HCT116 cells.

Methods
Sphere formation media and magnetic cell sorting were used to enrich and screen CCSCs. We used a colony formation assay, cell proliferation and viability assays, and a nude mouse transplantation tumor assay to identify CCSCs. ELISA was performed to identify IL-6 in the cell culture medium, and the growth, viability, wound healing, and transwell migration of distinct cell groups were compared to differentiate them. Dual-luciferase reporter assay, RT-PCR, and/or Western Blot analysis were conducted to determine the correlation between them.

Results
CD133+CD44+ HCT116 cells were shown to have higher cloning efficiency, greater proliferation ability and viability, and stronger tumorigenicity. A dual-luciferase reporter assay revealed that miR-513b-5p negatively affected STAT3 expression. RT-PCR and/or Western Blot analysis suggested that miR-513b-5p negatively affected STAT3 and Vimentin, while positively affecting E-cadherin expression. The STAT3 overexpression vector + miR-513b-5p inhibitor cell group had the highest efficiency, greatest proliferation ability and viability, and the highest IL-6 level in the experiments.

Conclusions
Mir-513b-5p inhibited the epithelial-mesenchymal transition (EMT) of CCSCs through IL-6/STAT3. This potential mechanism may provide a new therapeutic target for colon cancer.
Vertiport navigation requirements and multisensor architecture considerations for urban air mobility
Authors: García Crespillo Omar , Zhu Chen , Simonetti Maximilian , Gerbeth Daniel , Lee Young-Hee , Hao Wenhan 
Published: July 2024

AbstractCommunication, Navigation and Surveillance (CNS) technologies are key enablers for future safe operation of drones in urban environments. However, the design of navigation technologies for these new applications is more challenging compared to e.g., civil aviation. On the one hand, the use cases and operations in urban environments are expected to have stringent requirements in terms of accuracy, integrity, continuity and availability. On the other hand, airborne sensors may not be based on high-quality equipment as in civil aviation and solutions need to rely on tighter multisensor solutions, whose safety is difficult to assess. In this work, we first provide some initial navigation requirements related to precision approach operations based on recently proposed vertiport designs. Then, we provide an overview of a possible multisensor navigation architecture solution able to support these types of operations and we comment on the challenges of each of the subsystems. Finally, initial proof of concept for some navigation sensor subsystems is presented based on flight trials performed during the German Aerospace Center (DLR) project HorizonUAM.
Climate policy as social policy? A comprehensive assessment of the economic impact of climate action in the UK
Authors: Sudmant Andrew , Boyle Dom , Higgins‐Lavery Ruaidhri , Gouldson Andy , Boyle Andy , Fulker James , Brogan Jamie 
Published: July 2024

AbstractCo-benefits are central to the case for climate action but are side-lined in many economic analyses. This paper presents an evaluation of three dimensions of the costs and benefits of climate change interventions in six urban regions of the UK. Findings indicate that meeting the UK’s 2033–2037 climate targets could yield £164 billion in total benefits. Notably, only 13% of these benefits are financial, in contrast to the 79% of which are social benefits. These social benefits include improvements in public health, reduced traffic congestion, and increased thermal comfort in homes. These results underscore the need for economic evaluations to expand their scope and move beyond the narrow financial cost–benefit analysis that predominates. Moreover, the magnitude of the social benefits underscores the need for integrating social and climate challenges in policymaking. Concurrently, the results demonstrate the sensitivity of the social benefits of climate actions to the normative aspects of empirical analysis. Determining whether emissions reductions in the transport sector, for example, should be achieved through the deployment of electric cars, expansion of public transport, and/or increases in walking and cycling requires both technical analysis and value-based decision making. Ensuring that decision-making processes are deliberate and transparent in empirical analysis is therefore critical. We conclude by suggesting that institutions such as the UK Climate Change Committee and Scottish Climate Intelligence Service should take the opportunity to be more explicit in the normative decisions embedded in their empirical work to demonstrate best practice for the wider research community.
MicroRNA and renal fibrosis in autosomal dominant polycystic kidney disease: a longitudinal study
Authors: Lai Silvia , Mastroluca Daniela , Perrotta Adolfo Marco , Muscaritoli Maurizio , Lucciola Sara , Felli Maria Pia , Izzo Paolo , Rotondi Silverio , Izzo Sara , Tartaglione Lida , Belli Roberta , Ramaccini Cesarina , Izzo Luciano , De Intinis Claudia , Panebianco Valeria , Mazzaferro Sandro 
Published: July 2024

Abstract
Background
Autosomal dominant polycystic kidney disease (ADPKD) is a hereditary kidney disorder that may progress to kidney failure, accounting for 5–10% of all patients with end-stage kidney disease (ESKD). Clinical data, as well as molecular genetics and advanced imaging techniques have provided surrogate prognostic biomarkers to predict rapid decline in kidney function, nonetheless enhanced tools for assessing prognosis for ADPKD are still needed. The aim of this study was to analyze specific microRNAs involved in the pathogenesis of ADPKD and in the development of renal fibrosis, evaluating their potential role as predictors of renal function loss.

Methods
We evaluated kidney function by estimated glomerular filtration rate (eGFR) in 32 ADPKD patients in different stages of kidney disease at T0 and after a 24-month follow up (T1). Patients were divided into two groups: Rapid disease progression ([RP], n 15) and Non-rapid disease progression ([NRP], n 17), according to the Mayo Clinic classification criteria. At T0, ADPKD patients underwent plasma sampling for quantitative analysis of h-miR-17-5p, h-miR-21-5p and h-miR-199a-5p microRNA expression, using the quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) method and a 3 T magnetic resonance imaging (MRI), using an advanced MRI imaging protocol, for the quantification of total kidney volume (TKV), total perfusion volume (TPV) and total fibrotic volume (TFV).

Results
The expression of h-miR17-5p was higher (p < 0.05) in ADPKD patients with rapid disease progression. h-miR-17-5p, h-miR-21-5p and h-mir-199-5p showed a positive and significant correlation with the eGFR slope (mL/min/1.73 m2/year) (p < 0.05) but not with the eGFR at both T0 and T1. Both total fibrotic volume (cm3) and height-adjusted total fibrotic volume (cm3/m) were positively and significantly correlated to h-miR 21-5p and h-miR 199-5p (p < 0.05), but not to total kidney volume (cm3) and height-adjusted total kidney volume (cm3/m).

Conclusions
The microRNAs we studied were associated with fibrosis and renal damage, suggesting their possible role as biomarkers able to identify ADPKD patients at high risk of disease progression regardless of the degree of kidney function, and therefore suitable for medical therapy, and may help uncovering new molecular mechanisms underlying cystogenesis.

Graphical abstract
Storage of weights and retrieval method (SWARM) approach for neural networks hybridized with conformal prediction to construct the prediction intervals for energy system applications
Authors: Ashraf Waqar Muhammad , Dua Vivek 
Published: July 2024

AbstractThe prediction intervals represent the uncertainty associated with the model-predicted responses that impacts the sequential decision-making analytics. Here in this work, we present a novel model-based data-driven approach to construct the prediction intervals around the model-simulated responses using artificial neural network (ANN) model. The loss function is modified with least mean square error and standard deviation between the model-simulated and actual responses for the online-training mode of ANN model development. The parameters (weights and biases) stored during the model development are extracted and are deployed to construct the prediction intervals with 95% confidence level for the test datasets of the three energy systems-based case studies including: crease recovery angle, energy efficiency cooling & energy efficiency heating and gas turbine power plant & coal power plant which are taken from literature, benchmark datasets and industrial-scale applications, respectively. The developed ANN models present root-mean-squared error of 1.20% and 0.52% on test dataset for energy efficiency cooling and energy efficiency heating, respectively. The width of prediction intervals made by the proposed approach, called as Storage of Weights And Retrieval Method (SWARM), incorporates the information available for each test observation during the model training and the SWARM-based prediction intervals are compared to those of inductive conformal prediction (ICP) technique. It is noted that SWARM technique offers better locally adaptive prediction intervals than those of ICP, highlighting the effectiveness of the SWARM technique for the estimation of prediction intervals for the case studies. This research presents a novel data-driven approach to construct the prediction intervals using the model-based information that can be applied on different real-life applications.