Area Of Research Research Articles

Decoupling Between Brain Activity and Cerebrospinal Fluid Movement in Neurological Disorders.

Recent research has identified a link between the global mean signal of resting-state functional MRI (fMRI) and macro-scale cerebrospinal fluid movement, indicating the potential link between this resting-state dynamic and brain waste clearance. Consistent with this notion, the strength of this coupling has been associated with multiple neurodegenerative disease pathologies, especially the build-up of toxic proteins. This article aimed to review the latest advancements in this research area, emphasizing studies on spontaneous global brain activity that is tightly linked to the global mean resting-state fMRI signal, and aimed to discuss potential mechanisms through which this activity and associated physiological modulations might affect brain waste clearance. The available evidence supports the presence of a highly organized global brain activity that is linked to arousal and memory systems. This global brain dynamic, along with its associated physiological modulations, has the potential to influence brain waste clearance through multiple pathways through multiple pathways. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 3.

Recent Progress for the Synthesis of Pyrrole Derivatives – An Update

Abstract: Pyrrole is a versatile heterocyclic moiety exhibiting a wide range of pharmacological actions with high therapeutic value. The importance of pyrrole in the pharmaceutical field lies in its versatility, selectivity, and biocompatibility, and these properties make it a valuable tool for drug design and development. The pyrrole moiety is a fundamental building block for many biologically active molecules and has gathered significant attention in the fields of medicinal and organic chemistry; hence, its synthesis has been a crucial area for research. There are various conventional as well as modern approaches to acquiring a series of pyrrole scaffolds, with a wide range of attractive features and drawbacks pertaining to each approach. An extensive amount of literature must be studied to compare the best synthetic routes. This article highlights the applications of pyrrole derivatives in various fields, such as drug discovery, material science, and catalysis, and provides an overview of modern synthetic pathways that include metals, nanomaterials, and complex heterogeneous catalysed methods for pyrrole derivatives. Special emphasis is given to the use of green chemistry principles like green solvent-based methods, microwave-aided methods, and solvent-free methods in the synthesis of pyrroles, with the recent developments and prospects in the synthetic and organic chemistry fields. Overall, this review article provides a comprehensive overview of the synthesis of pyrroles and complies with all the possible developments in the synthetic routes for pyrroles within 2015– 2022. Among all, the reactions catalysed by proline, copper oxides, and oxones have been shown to be the most effective synthetic route for pyrrole derivatives at mild reaction conditions and with excellent yields. This information will be helpful for researchers interested in the development of new pyrrole-based compounds. The categorization in this review provides an easy means for the reader to rationally select the best possible synthetic method for pyrrole derivatives.

Bibliometric Analysis to Improve Combined Treatment Strategies for Glioblastoma in America

Introduction: Bibliometric analysis quantitatively examines scientific literature to extract insights. This article has conducted such analysis on glioblastoma multiforme (GBM) treatment articles. GBM, a prevalent brain tumor, is typically treated with surgery, radiation, and chemotherapy. Objectives: The article aimed to bibliometrically analyze articles discussing combined GBM treatment to identify impactful research areas and encourage collaboration. Materials and Methods: The study encompassed a comprehensive search in the Scopus database, spanning articles published from 1974 to 2022. Inclusion criteria encompassed research conducted in the Americas, both clinical and experimental. A total of 772 articles were collected and categorized based on their primary focus on combined treatment approaches. Results: Clinical studies constituted 52% of articles, suggesting a slight dominance. The analysis unveiled key research moments, including a 1998 focus shift and a pivotal 2005 study on temozolomide- radiation combination. Top journals, trends, and authors were identified, with the USA leading in contributions. Discussions: Despite high brain tumor incidence, research distribution discrepancy is concerning. Regional epidemiological studies have been endorsed. The dominance of US and German authors in GBM collaboration has raised equity issues due to budget and GDP disparities limiting Latin American representation. Conclusion: GBM research in the region is dominated by the USA, while contributions from Latin American countries remain limited. The absence of comprehensive epidemiological studies on GBM in Latin America is concerning, considering the evident impact of the disease in the region. This underscores the urgent need for increased research participation and collaboration to advance the understanding and treatment of GBM across Latin American nations.

A, the synthesis and evaluation of Polyvinyl Alcohol/Silver Nanoparticles (PVA/AgNPs) via electrochemical method: a model test for antioxidant and antibacterial activities

Background: Silver nanoparticles that are artificially created have electrochemical properties that work as antibacterial agents and are widely used in several areas ofresearch. Purpose: This work aimed to generate silver nanoparticles by the electrochemical method, using PVA as a stabilizer, and assess their antioxidant and antibacterial characteristics. Experimental procedures: Silver nanoparticles were synthesized using an electrochemical method, utilizing a stabilized polyvinyl alcohol solution at a concentration of 1 g/L. The validation of the PVA/AgNPs synthesis was performed using a UV-Vis spectrophotometer. The AgNPs were characterized by FT-IR, DLS, Zeta potential, and AFM. The antioxidant capabilities of PVA/AgNPs were assessed utilizing DPPH methods. The antibacterial activity of PVA/AgNPs against Salmonella enterica, Escherichia coli, Staphylococcus aureus, and Bacillus subtilis was assessed using the bacterial growth inhibition method. An assessment was conducted to determine the susceptibility of silver nanoparticles to limit the growth of microorganisms, as well as to determine the lowest concentration required to achieve this inhibitory effect. Results: The results confirmed that the AgNPs were generated by the electrochemical method with the assistance of a PVA stabilizer. The nanosilver had a mean particle size of 53.1 nm. The antioxidant experiments revealed that PVA/AgNPs exhibit more antioxidant activity than ascorbic acid at higher dosages. PVA/AgNPs exhibit substantial antibacterial effectiveness. The minimum inhibitory concentration (MIC) of PVA/AgNPs against E.coli, S.enterica, S.aureus, and B.subtilis was found to be 12.5 μg/mL. Conclusion: The bioactive chemicals found on the surface of AgNPs are responsible for the antioxidant and antibacterial characteristics of electrochemical PVA/AgNPs composites.

OpenHoldem: A Benchmark for Large-Scale Imperfect-Information Game Research.

Owing to the unremitting efforts from a few institutes, researchers have recently made significant progress in designing superhuman artificial intelligence (AI) in no-limit Texas hold'em (NLTH), the primary testbed for large-scale imperfect-information game research. However, it remains challenging for new researchers to study this problem since there are no standard benchmarks for comparing with existing methods, which hinders further developments in this research area. This work presents OpenHoldem, an integrated benchmark for large-scale imperfect-information game research using NLTH. OpenHoldem makes three main contributions to this research direction: 1) a standardized evaluation protocol for thoroughly evaluating different NLTH AIs; 2) four publicly available strong baselines for NLTH AI; and 3) an online testing platform with easy-to-use APIs for public NLTH AI evaluation. We will publicly release OpenHoldem and hope it facilitates further studies on the unsolved theoretical and computational issues in this area and cultivates crucial research problems like opponent modeling and human-computer interactive learning.

Targeting Cervical Cancer Stem Cells by Phytochemicals.

Cervical cancer (CaCx) poses a significant global health challenge, ranking as the fourth most common cancer among women worldwide. Despite the emergence of advanced treatment strategies, recurrence remains a bottleneck in favorable treatment outcomes and contributes to poor prognosis. The chemo- or radio-therapy resistance coupled with frequent relapse of more aggressive tumors are some key components that contribute to CaCx-related mortality. The onset of therapy resistance and relapse are attributed to a small subset of, slow-proliferating Cancer Stem Cells (CSC). These CSCs possess the properties of tumorigenesis, self-renewal, and multi-lineage differentiation potential. Because of slow cycling, these cells maintain themselves in a semi-quiescent stage and protect themselves from different anti-proliferative anti-cancer drugs. Keeping in view recent advances in their phenotypic and functional characterization, the feasibility of targeting CSC and associated stem cell signaling bears a strong translational value. The presence of CSC has been reported in CaCx (CCSC) which remains a forefront area of research. However, we have yet to identify clinically useful leads that can target CCSC. There is compelling evidence that phytochemicals, because of their advantages over synthetic anticancer drugs, could emerge as potential therapeutic leads to target these CCSCs. The present article examined the potential of phytochemicals with reported anti-CSC properties and evaluated their future in preclinical and clinical applications against CaCx.

Herbal Medicinal Nanoformulations for Psoriasis Treatment: Current State of Knowledge and Future Directions

Background: Psoriasis is a persistent immune system disorder that influences the skin, leading to red, flaky patches that can be painful and irritated. Objective: Traditional treatments for psoriasis, such as topical creams and oral medications, may be effective but also have potential side effects. Herbal remedies have been used for centuries to treat skin conditions, and advancements in nanotechnology have led to the development of herbal nanoformulations that offer several advantages over traditional herbal remedies, such as efficacy, safety, and targeted delivery. Methods: The studies and reviews published under the title were looked up in several databases (including PubMed, Elsevier, and Google Scholar). Results: Several herbal nanoformulations, including those containing curcumin, aloe vera, and neem, have been shown to exhibit anti-inflammatory and immunomodulatory impacts, which will be useful within the treatment of psoriasis. However, more study is required to decide the efficacy and safety of these details, as well as the optimal dosing, duration of treatment, and potential side effects. Conclusion: Overall, herbal nanoformulations represent a promising area of research for the treatment of psoriasis, and may offer a safe and effective alternative or adjunct therapy to conventional treatments. This review article summarizes the present state of information for the herbal nanoformulations role in the treatment of psoriasis and their future perspectives.

A Rationalized Approach to Design and Discover Novel Non-steroidal Derivatives through Computational Aid for the Treatment of Prostate Cancer.

Prostate cancer is one of the most prevalent cancers in men, leading to the second most common cause of death in men. Despite the availability of multiple treatments, the prevalence of prostate cancer remains high. Steroidal antagonists are associated with poor bioavailability and side effects, while non-steroidal antagonists show serious side effects, such as gynecomastia. Therefore, there is a need for a potential candidate for the treatment of prostate cancer with better bioavailability, good therapeutic effects, and minimal side effects. This current research work focused on identifying a novel non-steroidal androgen receptor antagonist through computational tools, such as docking and in silico ADMET analysis. Molecules were designed based on a literature survey, followed by molecular docking of all designed compounds and ADMET analysis of the hit compounds. A library of 600 non-steroidal derivatives (cis and trans) was designed, and molecular docking was performed in the active site of the androgen receptor (PDBID: 1Z95) using Auto- Dock Vina 1.5.6. Docking studies resulted in 15 potent hits, which were then subjected to ADME analysis using SwissADME. ADME analysis predicted three compounds (SK-79, SK-109, and SK-169) with the best ADME profile and better bioavailability. Toxicity studies using Protox-II were performed on the three best compounds (SK-79, SK-109, and SK-169), which predicted ideal toxicity for these lead compounds. This research work will provide ample opportunities to explore medicinal and computational research areas. It will facilitate the development of novel androgen receptor antagonists in future experimental studies.

A Survey of Automated Data Augmentation for Image Classification: Learning to Compose, Mix, and Generate.

Data augmentation is an effective way to improve the generalization of deep learning models. However, the underlying augmentation methods mainly rely on handcrafted operations, such as flipping and cropping for image data. These augmentation methods are often designed based on human expertise or repeated trials. Meanwhile, automated data augmentation (AutoDA) is a promising research direction that frames the data augmentation process as a learning task and finds the most effective way to augment the data. In this survey, we categorize recent AutoDA methods into the composition-, mixing-, and generation-based approaches and analyze each category in detail. Based on the analysis, we discuss the challenges and future prospects as well as provide guidelines for applying AutoDA methods by considering the dataset, computation effort, and availability of domain-specific transformations. It is hoped that this article can provide a useful list of AutoDA methods and guidelines for data partitioners when deploying AutoDA in practice. The survey can also serve as a reference for further study by researchers in this emerging research area.

Pregabalin Addiction: A Bibliometric Analysis

Objective: Bibliometric analyses allow determining the scientific research profile, revealing the current situation on the subject, and identifying new research areas. Pregabalin addiction is an addiction that has serious consequences for the individual, family and society. For this reason, taking photographs of studies related to this field can be a guide for researchers. In this regard, the aim of the research is to bibliometrically examine publications addressing the issue of pregabalin addiction.
 Method: The study was conducted in the Web of Science database. The keywords “pregabalin addiction” were used during the screening without any time limit. 
 Results: 150 publications were reached. Their publication dates were between 2003 and 2023, the average age of publication was 4.83, and the annual increase rate was 5%. The most active country was the United States of America, and the journal with the highest number of publications was “Frontiers in Psychiatry.” In addition, “gabapentin, abuse” was the most frequently used, “opioid, drug abuse” were the motor, “withdrawal” was niche, and “pain, substance” were emerging or disappearing themes. Besides, “neuropathic pain” was a basic theme, and “benzodiazepine” showed consistent development. 
 Conclusion: This is the first bibliometric study addressing pregabalin addiction and provided a comprehensive overview of pregabalin addiction over 20 years. From the quantitative perspective, the mean age of the study and the annual growth rate were low, and the number of studies was limited. Qualitative data showed that research topics mostly clustered on pregabalin addiction, addiction potential, abuse and taking with other drugs, and demonstrated a need for studies in addiction prevention, treatment, and rehabilitation.

A Study of Hierarchical Risk Parity in Portfolio Construction

<p style="text-align: justify;"><span style="font-family: 'times new roman', times, serif; font-size: 14pt;">The construction and optimization of a portfolio is a complex process that has been a historically active research area in finance. For portfolios with highly correlated assets, the performance of traditional risk-based asset allocations methods such as the mean-variance (MV) model is limited when numerous assets are correlated. A novel clustering-based asset allocation method, called Hierarchical Risk Parity (HRP), provides an opportunity to mitigate these limitations in portfolio construction. HRP utilizes the hierarchical relationships between the covariance of assets in a portfolio to determine weight distributions, eliminating the need for the inversion of the covariance matrix that is required by most traditional risk-based asset allocation methods. This research examines the viability of Hierarchical Risk Parity (HRP) method in portfolio construction of a US equity portfolio and compares the performances of HRP to traditional asset allocation methods exemplified by the mean-variance (MV) method. The results of this research show that the performance of the HRP method is comparable to the performance of the MV method. Given these findings, HRP provides an advantageous approach for portfolio construction in practical scenarios where correlated assets are present in the portfolios.</span></p>

Drivers and obstacles on refugee entrepreneurship. The host country perspective

Refugee entrepreneurship encompasses the enterprising endeavors undertaken by individuals forced to flee their home countries. This research area has gained scholarly traction due to its profound relevance in the socio-economic perspective. To provide a comprehensive understanding of this phenomenon, a thorough systematic literature review was conducted, drawing upon the Scopus and Web of Science databases. The review scrutinized a corpus of 57 scholarly articles, which were analyzed and categorized into two main themes: the profile of refugees regarding their entrepreneurial intentions, and the contextual factors within the host countries that either facilitate or impede their entrepreneurial pursuits. Refugee entrepreneurship is associated with the necessity-driven perspective, as individuals often embark on entrepreneurial ventures due to the shortage of viable employment opportunities in their host countries. However, there exists a nuanced perspective wherein some refugees leverage their innate entrepreneurial traits, finding resonance between the economic landscapes of their home and host countries. Nevertheless, the inception of entrepreneurial endeavors is invariably accompanied by some obstacles, spanning legal, financial, and socio-economic domains. These obstacles do not deter the entrepreneurial spirit ingrained within many refugees. Instead, they serve as catalysts for resilience and innovation, prompting the formulation of adaptive strategies to surmount barriers and carve out pathways to success, underscoring the economic activity of displaced individuals and exemplifying their capacity to effectuate positive change amidst adversity.

A Bibliometric Analysis of Literature on The Effect of Fasting Exercise on Obesity Index From 2014 to 2023

Fasting exercise can help obese patients reduce the silent index in the obesity index to a certain extent, and can effectively reduce the impact of chronic diseases. There is a lack of specific research on the concretization of fasting exercise methods and their impact on which obesity index. There have been relevant studies on the impact of fasting on obesity index, as well as some studies on the impact of exercise on obesity index. Therefore, analysis of scientific outputs over the past decade is essential to reveal trends, hotspots, and frontier are-as for future research in this field. The methodology use is retrieving Scopus core collection data and search for publications related to fasting exercise research from 2014 to 2023. Bibliometric software VOSviewer and publish of perish.

3D bioprinting for tissue engineering: Stem cells in hydrogels

Surgical limitations require alternative methods of repairing and replacing diseased and damaged tissue. Regenerative medicine is a growing area of research with engineered tissues already being used successfully in patients. However, the demand for such tissues greatly outweighs the supply and a fast and accurate method of production is still required. 3D bioprinting offers precision control as well as the ability to incorporate biological cues and cells directly into the material as it is being fabricated. Having precise control over scaffold morphology and chemistry is a significant step towards controlling cellular behaviour, particularly where undifferentiated cells, i.e., stem cells, are used. This level of control in the early stages of tissue development is crucial in building more complex systems that morphologically and functionally mimic in vivo tissue. Here we review 3D printing hydrogel materials for tissue engineering purposes and the incorporation of cells within them. Hydrogels are ideal materials for cell culture. They are structurally similar to native extracellular matrix, have a high nutrient retention capacity, allow cells to migrate and can be formed under mild conditions. The techniques used to produce these materials, as well as their benefits and limitations, are outlined.

3D printing of hydrogel composite systems: Recent advances in technology for tissue engineering.

Three-dimensional (3D) printing of hydrogels is now an attractive area of research due to its capability to fabricate intricate, complex and highly customizable scaffold structures that can support cell adhesion and promote cell infiltration for tissue engineering. However, pure hydrogels alone lack the necessary mechanical stability and are too easily degraded to be used as printing ink. To overcome this problem, significant progress has been made in the 3D printing of hydrogel composites with improved mechanical performance and biofunctionality. Herein, we provide a brief overview of existing hydrogel composite 3D printing techniques including laser based-3D printing, nozzle based-3D printing, and inkjet printer based-3D printing systems. Based on the type of additives, we will discuss four main hydrogel composite systems in this review: polymer- or hydrogel-hydrogel composites, particle-reinforced hydrogel composites, fiber-reinforced hydrogel composites, and anisotropic filler-reinforced hydrogel composites. Additionally, several emerging potential applications of hydrogel composites in the field of tissue engineering and their accompanying challenges are discussed in parallel.

Nonfactoid Question Answering as Query-Focused Summarization With Graph-Enhanced Multihop Inference.

Nonfactoid question answering (QA) is one of the most extensive yet challenging applications and research areas in natural language processing (NLP). Existing methods fall short of handling the long-distance and complex semantic relations between the question and the document sentences. In this work, we propose a novel query-focused summarization method, namely a graph-enhanced multihop query-focused summarizer (GMQS), to tackle the nonfactoid QA problem. Specifically, we leverage graph-enhanced reasoning techniques to elaborate the multihop inference process in nonfactoid QA. Three types of graphs with different semantic relations, namely semantic relevance, topical coherence, and coreference linking, are constructed for explicitly capturing the question-document and sentence-sentence interrelationships. Relational graph attention network (RGAT) is then developed to aggregate the multirelational information accordingly. In addition, the proposed method can be adapted to both extractive and abstractive applications as well as be mutually enhanced by joint learning. Experimental results show that the proposed method consistently outperforms both existing extractive and abstractive methods on two nonfactoid QA datasets, WikiHow and PubMedQA, and possesses the capability of performing explainable multihop reasoning.

Application of Natural Language Processing to the Development of Sports Biomechanics in China: A Literature Review of Journal Abstracts in Chinese Between 1980 and 2022

This paper aims to explore the field of sports biomechanics in China between 1980 and 2022 in terms of key developments, hot research topics, integration with other disciplines in kinesiology, and future trends by using text mining and natural language processing to analyze abstracts published in Chinese journals. Over 1,400 research paper abstracts were selected and processed, focusing on specific terms, significance, word-cloud analysis, co-occurrence, and network analysis. Results showed that sports biomechanics research focused on sports technical analysis, application of sports biomechanical principles to athletic training, and sport-injury prevention and rehabilitation. The research areas are multidimensional but well balanced with other disciplines such as physical education, sports training, and motor skill acquisition. Integration with fields like biomedical engineering, computer software and applications, and medical aspects of specific environments suggesting sports biomechanics has a promising future as it continues to develop as a discipline interwoven with other disciplines.

Recent Advances in Nanocarrier-based Approaches to Atopic Dermatitis and Emerging Trends in Drug Development and Design.

Atopic dermatitis (AD), commonly known as Eczema, is a non-communicable skin condition that tends to become chronic. The deteriorating immunological abnormalities are marked by mild to severe erythema, severe itching, and recurrent eczematous lesions. Different pharmacological approaches are used to treat AD. The problem with commercial topical preparations lies in the limitation of skin atrophy, systemic side effects, and burning sensation that decreases patient compliance. The carrier-based system promises to eliminate these shortcomings; thus, a novel approach to treating AD is required. Liposomes, microemulsions, solid lipid nanoparticles (SLNs), nanoemulsions, etc., have been developed recently to address this ailment. Despite extensive research in the development method and various techniques, it has been challenging to demonstrate the commercial feasibility of these carrier- based systems, which illustrates a gap among the different research areas. Further, different soft wares and other tools have proliferated among biochemists as part of a cooperative approach to drug discovery. It is crucial in designing, developing, and analyzing processes in the pharmaceutical industry and is widely used to reduce costs, accelerate the development of biologically innovative active ingredients, and shorten the development time. This review sheds light on the compilation of extensive efforts to combat this disease, the product development processes, commercial products along with patents in this regard, numerous options for each step of computer-aided drug design, including in silico pharmacokinetics, pharmacodynamics, and toxicity screening or predictions that are important in finding the drug-like compounds.

Robust Visual Question Answering: Datasets, Methods, and Future Challenges.

Visual question answering requires a system to provide an accurate natural language answer given an image and a natural language question. However, it is widely recognized that previous generic VQA methods often tend to memorize biases present in the training data rather than learning proper behaviors, such as grounding images before predicting answers. Therefore, these methods usually achieve high in-distribution but poor out-of-distribution performance. In recent years, various datasets and debiasing methods have been proposed to evaluate and enhance the VQA robustness, respectively. This paper provides the first comprehensive survey focused on this emerging fashion. Specifically, we first provide an overview of the development process of datasets from in-distribution and out-of-distribution perspectives. Then, we examine the evaluation metrics employed by these datasets. Third, we propose a typology that presents the development process, similarities and differences, robustness comparison, and technical features of existing debiasing methods. Furthermore, we analyze and discuss the robustness of representative vision-and-language pre-training models on VQA. Finally, through a thorough review of the available literature and experimental analysis, we discuss the key areas for future research from various viewpoints.

Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here?

Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.