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Green growth has emerged as a strategy for addressing environmental concerns while also promoting economic development. This study assesses the impact of renewable energy technologies and policies on green growth in the built environment. It investigates 20 developed and 20 developing countries from 2010 to 2021. Panel data estimators such as generalized least squares and generalized method of moments are employed. The results reveal that the contribution of renewable energy sectors to green growth varies between developed and developing countries. In developed countries, solar, wind, and biomass capacities have facilitated green growth, while hydroelectric capacities have not. By contrast, in developing countries, wind capacity has not been effective, while other sectors show a positive contribution. The study also confirms the criticality of judicious renewable energy policies in stimulating investment and technological innovation required for a sustainable built environment.

Due to the increasing emission of greenhouse gases and global warming, the development of renewable energy has become very important. The availability of fossil fuels and the low cost of their extraction compared to renewable energy projects reduce the motivation of countries, especially countries that have abundant natural resources, to develop this technology. Renewable energy deployment has become crucial in response to rising greenhouse gas emissions and global warming. Policies supporting renewable energy play a significant role in this. This study examines the effect of such policies on the deployment of renewable energy technologies, considering the role of natural resources. Two groups of countries were analysed: 20 oil developed countries and 20 oil developing countries. Given the availability of data and the achievement of balanced panels to evaluate short-term and long-term relationships between variables, in current research Data from 2010 to 2020 was used, and various panel data estimators such as Feasible Generalized Least Squares and Generalized Method of Moments were employed. The Quantile estimator was also used to assess the accuracy of the results. The findings suggest that renewable energy policies consistently lead to increased deployment of renewable energy technologies, regardless of a country's group. Of course, this positive effect is different according to the level of development in countries. Due to the higher efficiency of renewable energy policy, developed oil countries have more capacity to support renewable energy projects than oil developing countries. The abundance of natural resources in oil developed countries did not negatively impact renewable energy capacity, but in oil developing countries, the \" resource curse \" hindered the development of installed renewable energy.

Neuroinflammation is the innate and adaptive immune responses that are initiated toward a variety of harmful insults (such as infection, ischemia, stress, and trauma) through the release of inflammatory mediators (such as cytokines, chemokines, and reactive oxygen species) by various immune cells (like microglia, astrocytes, peripherally derived immune cells, and endothelial cells) [2]. Neuroinflammation in the initial stage is mainly beneficial and protective; however, evidence from both clinical and experimental studies indicates that prolonged or excessive inflammation is a pivotal pathological driver of several neurological disorders, such as cerebrovascular diseases (CVD), traumatic brain and spinal cord injuries, neurodegenerative diseases, epilepsy, multiple sclerosis (MS), psychological disorders, and chronic pain. Based on the evidence of a potential correlation between stress-induced inflammation and epilepsy, Espinosa-Garcia and colleagues have described the importance of early interventions for both acute and chronic stress in the improvement of diagnosis, therapy, and outcomes for patients with epilepsy, particularly for subjects with psychiatric comorbidities [10]. [...]modulation of inflammatory processes and mediators represent relevant potential targets for the treatment of epilepsy [11]. In this issue, Aboghazale and colleagues evaluated the electrophysiological alterations of rat brain after traumatic brain injury and have shown the occurrence of both SD and SD-induced depression of cortical activity. [...]their findings revealed that while the occurrence of SD following closed brain trauma led to enhanced oxidative stress (elevated reactive oxygen species), traumatic brains exhibited a decreased antioxidant defense (downregulation of mRNA expression of antioxidant enzymes in response to oxidative stress) [29].

In addressing the current COVID-19 pandemic and evaluating the measures taken by global leaders so far, it is crucial to trace back the circumstances influencing the emergence of the crisis that the world is presently facing. Could it be that the failure to act in a timely manner dates way back to when first concerns about climate change and its inevitable threat to human health came up? Multiple lines of evidence suggest that the large-scale and rapid environmental changes in the last few decades may be implicated in the emergence of COVID-19 pandemic by increasing the potential risk of the occurrence and the spread of zoonotic diseases, worsening food security, and weakening the human immune system. As we are facing progressive climatic change, a failure to act accordingly could inevitably lead to further, more frequent confrontations with newly emerging diseases.

Parkinson's disease (PD) is an age-related neurodegenerative condition characterized mostly by motor symptoms. Although a wide range of non-motor symptoms (NMS) are frequently experienced by PD patients. One of the important and common NMS is cognitive impairment, which is measured using different cognitive scales. Monitoring cognitive impairment and its decline in PD is essential for patient care and management. In this study, our goal is to identify the most effective cognitive scale in predicting cognitive decline over a 5-year timeframe initializing clinical biomarkers and DAT SPECT. Machine Learning has previously shown superior performance in image and clinical data classification and detection. In this study, we propose to use machine learning with different types of data, such as DAT SPECT and clinical biomarkers, to predict PD-CD based on various cognitive scales. We collected 330 DAT SPECT images and their clinical data in baseline, years 2,3,4, and 5 from Parkinson's Progression Markers Initiative (PPMI). We then designed a 3D Autoencoder to extract deep radiomic features (DF) from DAT SPECT images, and we then concatenated it with 17 clinical features (CF) to predict cognitive decline based on Montreal Cognitive Assessment (MoCA) and The Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS-I). The utilization of MoCA as a cognitive decline scale yielded better performance in various years compared to MDS-UPDRS-I. In year 4, the application of the deep radiomic feature resulted in the highest achievement, with a cross-validation AUC of 89.28, utilizing the gradient boosting classifier. For the MDS-UPDRS-I scale, the highest achievement was obtained by utilizing the deep radiomic feature, resulting in a cross-validation AUC of 81.34 with the random forest classifier. The study findings indicate that the MoCA scale may be a more effective predictor of cognitive decline within 5 years compared to MDS-UPDRS-I. Furthermore, deep radiomic features had better performance compared to sole clinical biomarkers or clinical and deep radiomic combined. These results suggest that using the MoCA score and deep radiomic features extracted from DAT SPECT could be a promising approach for identifying individuals at risk for cognitive decline in four years. Future research is needed to validate these findings and explore their utility in clinical practice.

The demand for green hydrogen as an energy carrier is projected to exceed 350 million tons per year by 2050, driven by the need for sustainable distribution and storage of energy generated from sources. Despite its potential, hydrogen production currently faces challenges related to cost efficiency, compliance, monitoring, and safety. This work proposes Hydrogen 4.0, a cyber–physical approach that leverages Industry 4.0 technologies—including smart sensing, analytics, and the Internet of Things (IoT)—to address these issues in hydrogen energy plants. Such an approach has the potential to enhance efficiency, safety, and compliance through real-time data analysis, predictive maintenance, and optimised resource allocation, ultimately facilitating the adoption of renewable green hydrogen. The following sections break down conventional hydrogen plants into functional blocks and discusses how Industry 4.0 technologies can be applied to each segment. The components, benefits, and application scenarios of Hydrogen 4.0 are discussed while how digitalisation technologies can contribute to the successful integration of sustainable energy solutions in the global energy sector is also addressed.

Considering the unfavourable response of breast cancer (BC) to treatment, we assessed the therapeutic potential hesperidin in mice bearing 4T1 BC tumours. Anti‐tumour effects were assessed by measuring pathologic complete response (pCR), survival analysis, immunohistochemistry for E‐cadherin, VEGF, MMP9, MMP2 and Ki‐67, serum measurement of IFNγ and IL‐4, and gene expression analysis of CD105, VEGFa, VEGFR2 and COX2. Survival of tumour‐bearing mice was the highest in mice receiving a combination of hesperidin and doxorubicin (Dox) (80%) compared to the normal saline (43%), hesperidin 5 (54%), 10 (55.5%), 10 (60.5%) and 40 (66%) mg/kg, and 10 mg/kg Dox‐treated (73%) groups ( p  < 0.0001 for all). Compared to the normal saline group, there was a significant elevation in IFNγ level in the animals receiving 20 ( p  = 0.0026) and 40 ( p  < 0.001) mg/kg hesperidin, 10 mg/kg Dox ( p  < 0.001), and combined hesperidin (20 mg/kg) and Dox (10 mg/kg) ( p  < 0.001). A significant reduction in the gene expression of CD 105 ( p  = 0.0106), VEGFa ( p  < 0.0001), VEGFR2 ( p  < 0.0001), and Cox2 ( p  = 0.034) and a significant higher pCR score ( p  = 0.006) were noticed in mice treated with 10 mg/kg Dox + 20 mg/kg hesperidin compared to those treated with 10 mg/kg Dox alone. Immunohistochemical staining showed significant reductions in Ki‐67 ( p  < 0.001) and VEGF ( p  < 0.001) and a significant elevation in E‐cadherin ( p  = 0.005) in the 10 mg/kg Dox + 20 mg/kg treatment group than in 10 mg/kg Dox alone group. Hesperidin can be considered as a potentially suitable anti‐cancer agent for BC that can synergize with other chemotherapeutics.

This study utilizes an image processing method to analyse the grain size of perovskite, CZTS kesterite and antimony chalcogenide (Sb2Se3) thin films at various temperatures using SEM and TEM images. Empirical equations (exponential, Gaussian, power law) were derived from the data, revealing distinct temperature-dependent trends in grain size. Perovskite films exhibit a Gaussian trend, showing extreme sensitivity to temperature. CZTS films follow a double exponential function, with optimal grain size at 300°C. Sb2Se3 films adhere to a power law (~T6), with grain size rapidly increasing at higher temperatures. These temperature-dependent behaviours offer insights into optimizing fabrication processes and enhancing the efficiency of these materials in photovoltaic applications.

NAD(P)H Quinone Dehydrogenase 1 (NQO1) plays a pivotal role in the regulation of neuronal function and synaptic plasticity, cellular adaptation to oxidative stress, neuroinflammatory and degenerative processes, and tumorigenesis in the central nervous system (CNS). Impairment of the NQO1 activity in the CNS can result in abnormal neurotransmitter release and clearance, increased oxidative stress, and aggravated cellular injury/death. Furthermore, it can cause disturbances in neural circuit function and synaptic neurotransmission. The abnormalities of NQO1 enzyme activity have been linked to the pathophysiological mechanisms of multiple neurological disorders, including Parkinson's disease, Alzheimer's disease, epilepsy, multiple sclerosis, cerebrovascular disease, traumatic brain injury, and brain malignancy. NQO1 contributes to various dimensions of tumorigenesis and treatment response in various brain tumors. The precise mechanisms through which abnormalities in NQO1 function contribute to these neurological disorders continue to be a subject of ongoing research. Building upon the existing knowledge, the present study reviews current investigations describing the role of NQO1 dysregulations in various neurological disorders. This study emphasizes the potential of NQO1 as a biomarker in diagnostic and prognostic approaches, as well as its suitability as a target for drug development strategies in neurological disorders.

This study utilizes an image processing method to analyse the grain size of perovskite, CZTS kesterite and antimony chalcogenide (Sb 2 Se 3 ) thin films at various temperatures using SEM and TEM images. Empirical equations (exponential, Gaussian, power law) were derived from the data, revealing distinct temperature-dependent trends in grain size. Perovskite films exhibit a Gaussian trend, showing extreme sensitivity to temperature. CZTS films follow a double exponential function, with optimal grain size at 300°C. Sb 2 Se 3 films adhere to a power law (~T 6 ), with grain size rapidly increasing at higher temperatures. These temperature-dependent behaviours offer insights into optimizing fabrication processes and enhancing the efficiency of these materials in photovoltaic applications.