Advances In Research Research Articles

New approach methods on the bench side to accelerate clinical trials during pregnancy.

Pregnant women are therapeutic orphans as they are excluded from clinical drug development and therapeutic trials. We identify limitations in conducting clinical trials and propose two 'New Approach Methods'(NAMs) to overcome them. NAMs have proven invaluable tools in basic and clinical research to understand human health and disease better, elucidate mechanisms, and study the efficacy and toxicity of therapeutics that have not been possible through animal-based methodologies. The lack of humanized experimental models of FMi and drugs that can safely and effectively cross FMi to reduce the risk of adverse pregnancy has hindered progress in the field of reproductive pharmacology. This report discusses two technological advancements in perinatal research and medicine to accelerate clinical trials during pregnancy. (1) We have developed a humanized microphysiologic system, an Organ-on-a-chip (OOC) platform, to study FMi and their utility in pharmacological studies, and (2) use of extracellular vesicles (EVs) as drug delivery vehicles that are immunologically inert and can cross the fetomaternal barriers. We provide an overview of NAMs that can accelerate preclinical trials and develop drugs to cross the feto-maternal barriers to reduce the risk of adverse pregnancy outcomes like preterm birth.

A comprehensive review on the pharmacological role of gut microbiome in neurodegenerative disorders: potential therapeutic targets.

Neurological disorders, including Alzheimer and Parkinson's, pose significant challenges to public health due to their complex etiologies and limited treatment options. Recent advances in research have highlighted the intricate bidirectional communication between the gut microbiome and the central nervous system (CNS), revealing a potential therapeutic avenue for neurological disorders. Thus, this review aims to summarize the current understanding of the pharmacological role of gut microbiome in neurological disorders. Mounting evidence suggests that the gut microbiome plays a crucial role in modulating CNS function through various mechanisms, including the production of neurotransmitters, neuroactive metabolites, and immune system modulation. Dysbiosis, characterized by alterations in gut microbial composition and function, has been observed in many neurological disorders, indicating a potential causative or contributory role. Pharmacological interventions targeting the gut microbiome have emerged as promising therapeutic strategies for neurological disorders. Probiotics, prebiotics, antibiotics, and microbial metabolite-based interventions have shown beneficial effects in animal models and some human studies. These interventions aim to restore microbial homeostasis, enhance microbial diversity, and promote the production of beneficial metabolites. However, several challenges remain, including the need for standardized protocols, identification of specific microbial signatures associated with different neurological disorders, and understanding the precise mechanisms underlying gut-brain communication. Further research is necessary to unravel the intricate interactions between the gut microbiome and the CNS and to develop targeted pharmacological interventions for neurological disorders.

Comparative analysis of midgut bacterial communities in Chikungunya virus-infected and non-infected Aedes aegypti Thai laboratory strain mosquitoes

Chikungunya virus (CHIKV) poses a significant global health threat, re-emerging as a mosquito-transmitted pathogen that caused high fever, rash, and severe arthralgia. In Thailand, a notable CHIKV outbreak in 2019–2020 affected approximately 20,000 cases across 60 provinces, underscoring the need for effective mosquito control protocols. Previous studies have highlighted the role of midgut bacteria in the interaction between mosquito vectors and pathogen infections, demonstrating their ability to protect the insect from invading pathogens. However, research on the midgut bacteria of Aedes (Ae.) aegypti, the primary vector for CHIKV in Thailand remains limited. This study aims to characterize the bacterial communities in laboratory strains of Ae. aegypti, both infected and non-infected with CHIKV. Female mosquitoes from a laboratory strain of Ae. aegypti were exposed to a CHIKV-infected blood meal through membrane feeding, while the control group received a non-infected blood meal. At 7 days post-infection (dpi), mosquito midguts were dissected for 16S rRNA gene sequencing to identify midgut bacteria, and CHIKV presence was confirmed by E1-nested RT-PCR using mosquito carcasses. The study aimed to compare the bacterial communities between CHIKV-infected and non-infected groups. The analysis included 12 midgut bacterial samples, divided into three groups: CHIKV-infected (exposed and infected), non-infected (exposed but not infected), and non-exposed (negative control). Alpha diversity indices and Bray–Curtis dissimilarity matrix revealed significant differences in bacterial profiles among the three groups. The infected group exhibited an increased abundance of bacteria genus Gluconobacter, while Asaia was prevalent in both non-infected and negative control groups. Chryseobacterium was prominent in the negative control group. These findings highlight potential alterations in the distribution and abundance of gut microbiomes in response to CHIKV infection status. This study provides valuable insights into the dynamic relationship between midgut bacteria and CHIKV, underscoring the potential for alterations in bacterial composition depending on infection status. Understanding the relationships between mosquitoes and their microbiota holds promise for developing new methods and tools to enhance existing strategies for disease prevention and control. This research advances our understanding of the circulating bacterial composition, opening possibilities for new approaches in combating mosquito-borne diseases.

Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms

The research used an artificial neural network (ANN) model to examine the rheological properties of hybrid non-Newtonian ferrofluids (HNFFs) composed of Fe-CuO, water, and ethylene glycol. The performance of neural network was optimized using seven regression methods (RMs), namely Group Method of Data Handling (GMDH), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Multiple Linear Regression (MLR). The findings highlighted GMDH method's superior performance when compared to neural networks. R and RMSE values attained by GMDH for the objective function (OF) μnf were 0.99436 and 2.0135, respectively. For the torque function OF, the values were 0.97652 and 4.8952. Margin of difference (MOD) calculations across various algorithms, such as MLP, SVM, RBF, D-Tree, ELM, MLR, and GMDH-Algos revealed significant disparities, indicating GMDH's efficacy. Comparison of R, RMSD, and standard deviation values between GMDH and MLR algorithms further underscored performance discrepancies. Specific parameters for which NSGA II Algo was rated highest among evaluation indices were as follows: a crossover rate of 0.7, a mutation rate of 0.02, a population size of 50, and 500 generations. Post-optimization, optimal values for μnf and torque (To) were determined as 6.595 and 3.543, respectively, with corresponding values for φ, T, and γ obtained as 0.185, 49.372, and 3.163, respectively. This comprehensive analysis sheds light on the effectiveness of various regression methods in modeling the rheological behavior of hybrid non-Newtonian ferrofluids, contributing to advancements in fluid dynamics research.

Design, Perspective, and Challenge of Niobium‐Based Anode Materials for High‐Energy Alkali Metal‐Ion Batteries

AbstractThe rapid advancement of clean energy has aroused an increased demand for advanced energy storage systems. Alkali metal‐ion batteries (AMIBs) including lithium/sodium/potassium‐ion batteries (LIBs/SIBs/PIBs) play a crucial role in these systems, and their development is closely related to the progress of electrode materials. Niobium (Nb)‐based materials exhibit distinctive features such as quasi‐2D framework, high intercalation potential, robust pseudo‐capacitance effect, and minimal volume expansion during cycles. These characteristics render them highly valuable for energy storage applications. However, the low conductivity and limited capacity of Nb‐based electrode materials significantly limit their widespread development in energy storage applications. To address these challenges, various strategies have been explored. This review primarily summarizes the recent advancements in research on Nb‐based anode materials for AMIBs over the past decade. The synthesis strategy, structure design, and material composite of Nb‐based materials are systematically discussed, and the performances of different types of batteries are also well analyzed and compared. Meanwhile, their merits in the fast‐charging realm are then highlighted. Importantly, this review presents the key issues and challenges encountered by Nb‐based anode materials in future energy storage, along with novel concepts and solutions for the research and development of next‐generation Nb‐based anodes.

Investigating the antecedents of mHealth adoption by older adults in a lower-middle income country: The PLS-MGA approach

ABSTRACT The adoption of technology and the rising digital divide among the older adult population, predominantly in lower-middle income countries such as Bangladesh, have posed a precarious challenge. Hence, the time has come to navigate this upcoming challenge. Thus, the current research aims to investigate the impact of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR) on the behavioral intention to adopt mHealth (BI). Furthermore, multigroup analysis (MGA) is used to examine BI regarding gender and income groups. Data from 140 older adults in Bangladesh were gathered and later analysed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to estimate the measurement and structural model parameters and produce concurrent bootstrap evaluations. The findings revealed that PE and PU substantially affect the intention to adopt mHealth, whereas PR has small effect on mHealth adoption. Furthermore, the MGA depicts that the effect of PR is statistically significant for older adult males and females. Additionally, the effect of PE on behavioral intention to adopt mHealth between income groups (lower income vs. higher income) is significant. Therefore, this research advances our theoretical and empirical understanding by yielding new scholarly evidence in the domain of mHealth adoption by older adults. Finally, the paper concludes with augmented practical considerations concerning the adoption of mHealth as a technology by older adults, which will assist in alleviating the digital divide for any such lower-middle income country across the globe.

Diversity and host interaction of the gut microbiota in specific pathogen-free pigs

Pigs are widely used as animal models in various studies related to humans. The interaction between the gut microbiota and the host has significant effects on the host’s health and disease status. However, although there have been many studies investigating the pig gut microbiota, the findings have been inconsistent due to variations in rearing conditions. Interactions between the gut microbiota and host have not been fully explored in pigs. Specific pathogen-free (SPF) pigs are ideal non-primate large animals to study the interactions between the gut microbiota and the host. In this study, we performed high-throughput sequencing analysis of the gut microbiota and the gut tissue transcriptome of six SPF pigs to provide a systematic understanding of the composition, function, and spatial distribution of gut microbiota in SPF pigs. We identified significant differences in microbial diversity and functionality among different gastrointestinal tract sites. Metagenomics data analysis revealed significant differences in alpha diversity and beta diversity of microbiota in different gastrointestinal sites of SPF pigs. Additionally, transcriptomic data indicated significant differences in gene expression as well as KEGG and GO functional enrichment between the small intestine and large intestine. Furthermore, by combining microbial metagenomics and host transcriptomics analyses, specific correlations were found between gut microbiota and host genes. These included a negative correlation between the TCN1 gene and Prevotella dentalis, possibly related to bacterial metabolic pathways involving vitamin B12, and a positive correlation between the BDH1 gene and Roseburia hominis, possibly because both are involved in fatty acid metabolism. These findings lay the groundwork for further exploration of the co-evolution between the microbiota and the host, specifically in relation to nutrition, metabolism, and immunity. In conclusion, we have elucidated the diversity of the gut microbiota in SPF pigs and conducted a detailed investigation into the interactions between the gut microbiota and host gene expression. These results contribute to our understanding of the intricate dynamics between the gut microbiota and the host, offering important references for advancements in life science research, bioproduct production, and sustainable development in animal husbandry.

Broadband Ultrafast Nonlinear Optical Limiting at a Porphyrin‐Based 2D Polymer

AbstractUltrafast lasers have significantly contributed to the advancement of research and technology; however, high‐intensity lasers bring potential risks to precision instruments and the human eye. Developing optical limiting (OL) devices capable of reasonably controlling laser energy to an acceptable energy level is imperative. Nevertheless, achieving both exceptional OL performance and broad‐spectrum laser intensity tunability proves highly challenging. In this work, the synthesis of a porphyrin‐based 2D polymer (Por‐2DP) using a template‐assisted self‐coupling polymerization, constructing a 2D framework through the carbon‐carbon single bond coupling of porphyrin monomer is presented. The resultant Por‐2DP demonstrates remarkable broadband nonlinear absorption performance, spanning the visible and near‐infrared spectral regions under an ultrafast pulse laser (35 fs) for the first time. OL thresholds at excitation wavelengths of 515, 800, and 1550 nm are determined to be 1.44, 0.48, and 0.54 mJ cm−2, respectively, surpassing reported 2D materials to date. Additionally, the Por‐2DP exhibits prominent photostability, enabling sustained operation under intense light conditions for a long time, thereby enhancing OL practical applicability. This study not only introduces a novel OL material and device but also promotes the application of innovative 2D organic polymers in NLO field.

Research advances in Zein-based nano-delivery systems

Zein is the main vegetable protein from maize. In recent years, Zein has been widely used in pharmaceutical, agriculture, food, environmental protection, and other fields because it has excellent biocompatibility and biosafety. However, there is still a lack of systematic review and research on Zein-based nano-delivery systems. This paper systematically reviews preparation and modification methods of Zein-based nano-delivery systems, based on the basic properties of Zein. It discusses the preparation of Zein nanoparticles and the influencing factors in detail, as well as analyzing the advantages and disadvantages of different preparation methods and summarizing modification methods of Zein nanoparticles. This study provides a new idea for the research of Zein-based nano-delivery system and promotes its application.

A comprehensive investigation of clustering algorithms for User and Entity Behavior Analytics

IntroductionGovernment agencies are now encouraging industries to enhance their security systems to detect and respond proactively to cybersecurity incidents. Consequently, equipping with a security operation center that combines the analytical capabilities of human experts with systems based on Machine Learning (ML) plays a critical role. In this setting, Security Information and Event Management (SIEM) platforms can effectively handle network-related events to trigger cybersecurity alerts. Furthermore, a SIEM may include a User and Entity Behavior Analytics (UEBA) engine that examines the behavior of both users and devices, or entities, within a corporate network.MethodsIn recent literature, several contributions have employed ML algorithms for UEBA, especially those based on the unsupervised learning paradigm, because anomalous behaviors are usually not known in advance. However, to shorten the gap between research advances and practice, it is necessary to comprehensively analyze the effectiveness of these methodologies. This paper proposes a thorough investigation of traditional and emerging clustering algorithms for UEBA, considering multiple application contexts, i.e., different user-entity interaction scenarios.Results and discussionOur study involves three datasets sourced from the existing literature and fifteen clustering algorithms. Among the compared techniques, HDBSCAN and DenMune showed promising performance on the state-of-the-art CERT behavior-related dataset, producing groups with a density very close to the number of users.

Emergence of Quantum Dots as Innovative Tools for Early Diagnosis and Advanced Treatment of Breast Cancer.

Quantum dots (QDs) semiconducting nanomaterials, have garnered attention due to their distinctive properties, including small size, high luminescence, and biocompatibility. In the context of triple-negative breast cancer (TNBC), notorious for its resistance to conventional treatments, QDs exhibit promising potential for enhancing diagnostic imaging and providing targeted therapies. This review underscores recent advancements in the utilization of QDs in imaging techniques, such as fluorescence tomography and magnetic resonance imaging, aiming at the early and precise detection of tumors. Emphasis is placed on the significance of QD design, synthesis and functionalization processes as well as their use in innovative strategies for targeted drug delivery, capitalizing on their ability to selectively deliver therapeutic agents to cancer cells. As the research in this field advances rapidly, this review covers a classification of QDs according to their composition, the characterization techniques than can be used to determine their properties and, subsequently, emphasizes recent findings in the field of TNBC-targeting, highlighting the imperative need to address challenges, like potential toxicity or methodologies standardization. Collectively, the findings explored thus far suggest that QDs could pave the way for early diagnosis and effective therapy of TNBC, representing a significant stride toward precise and personalized strategies in treating TNBC.

The impact of ethical leadership on KM practices and performance

AbstractRecent advancements in leadership research emphasise the influential role of ethical leadership in shaping positive employee behaviour. However, there remains unclear the precise impact of ethical leadership on organisational performance and its interaction with knowledge creation and knowledge networks. Drawing on both the knowledge‐based view and the ethical leadership literature, this study asserts a positive influence of ethical leadership on both organisational performance and knowledge creation. Furthermore, we propose that knowledge creation positively impacts knowledge networks, which, in turn, exert a positive influence on organisational performance. To validate these propositions, we employ structural equation modelling on a dataset comprising 205 Turkish firms. Our study contributes to the (ethical) leadership literature as one of the initial attempts to examine the relationship between ethical leadership and organisational performance while considering the roles of knowledge creation and knowledge networks.

Generation of induced pluripotent stem cells (NIMHi015-A) from a Parkinson’s Disease patient harbouring a homozygous Exon 3 deletion in the PRKN gene

The Parkin (PRKN) gene mutation is prevalent in young-onset Parkinson's disease (PD), typically emerging before age 30, accompanied by early motor symptoms. Induced pluripotent stem cells (iPSCs) were derived from peripheral blood mononuclear cells of a PD patient with an exon 3 deletion in PRKN using Sendai-virus reprogramming. PD diagnosis was confirmed via the Unified Parkinson's Disease Rating Scale (UPDRS). Characterization of the iPSC line ensured self-renewal and pluripotency. This resource serves as a valuable platform for drug screening and elucidating the pathophysiology of this mutation, facilitating advancements in PD research.

Treatment of colorectal cancer by traditional Chinese medicine: prevention and treatment mechanisms

Colorectal cancer (CRC) is a significant global health burden, with high morbidity and mortality rates. It is often diagnosed at middle to advanced stage, affecting approximately 35% of patients at the time of diagnosis. Currently, chemotherapy has been used to improve patient prognosis and increase overall survival. However, chemotherapy can also have cytotoxic effects and lead to adverse reactions, such as inhibiting bone marrow hematopoiesis, causing digestive dysfunction, hand-foot syndrome, and even life-threatening conditions. In response to these adverse effects, researchers have proposed using Traditional Chinese Medicine (TCM) as an option to treat cancer. TCM research focuses on prescriptions, herbs, and components, which form essential components of the current research in Chinese medicine. The study and implementation of TCM prescriptions and herbs demonstrate its distinctive holistic approach to therapy, characterized by applying multi-component and multi-target treatment. TMC components have advantages in developing new drugs as they consist of single ingredients, require smaller medication dosages, have a precise measure of pharmacodynamic effects, and have a clear mechanism of action compared to TCM prescriptions and herbs. However, further research is still needed to determine whether TMC components can fully substitute the therapeutic efficacy of TCM prescriptions. This paper presents a comprehensive analysis of the research advancements made in TCM prescriptions, herbs, and components. The findings of this study can serve as a theoretical basis for researchers who are interested in exploring the potential of TCM for the treatment of colorectal cancer.

Does firm-created social media communication develop brand evangelists? Role of perceived values and customer experience

PurposeThe social media communication of luxury service firms remains largely unexplored. This study explores the influence of firm-created social media communication (FCSMC) on predicting brand evangelism (BEM) via perceived values, including functional value (FV), emotional value (EV) and social value (SV), by embedding the direct and moderating influence of customer experience (CX) on brand evangelism in the luxury hotel sector.Design/methodology/approachThis study recruited 405 regular travelers to participate in an online survey. Following meticulous data curation, the empirical analysis was performed on 363 responses using structural equation modeling.FindingsThe findings revealed that FCSMC substantially impacted perceived values, including FV, EV and SV, as well as BEM. Likewise, perceived values, including FV and EV, were positively associated with BEM. In addition, this study revealed that CX exhibited significant predictive capability with its direct and moderating effects on BEM in the luxury hotel sector.Originality/valueThis original research advances the uses and gratifications theory and attribution theory. It provides novel theoretical insights and practical recommendations for the luxury hotel sector.

A survey on imbalanced learning: latest research, applications and future directions

Imbalanced learning constitutes one of the most formidable challenges within data mining and machine learning. Despite continuous research advancement over the past decades, learning from data with an imbalanced class distribution remains a compelling research area. Imbalanced class distributions commonly constrain the practical utility of machine learning and even deep learning models in tangible applications. Numerous recent studies have made substantial progress in the field of imbalanced learning, deepening our understanding of its nature while concurrently unearthing new challenges. Given the field’s rapid evolution, this paper aims to encapsulate the recent breakthroughs in imbalanced learning by providing an in-depth review of extant strategies to confront this issue. Unlike most surveys that primarily address classification tasks in machine learning, we also delve into techniques addressing regression tasks and facets of deep long-tail learning. Furthermore, we explore real-world applications of imbalanced learning, devising a broad spectrum of research applications from management science to engineering, and lastly, discuss newly-emerging issues and challenges necessitating further exploration in the realm of imbalanced learning.

Stem cell therapies: a new era in the treatment of multiple sclerosis

Multiple Sclerosis (MS) is an immune-mediated condition that persistently harms the central nervous system. While existing treatments can slow its course, a cure remains elusive. Stem cell therapy has gained attention as a promising approach, offering new perspectives with its regenerative and immunomodulatory properties. This article reviews the application of stem cells in MS, encompassing various stem cell types, therapeutic potential mechanisms, preclinical explorations, clinical research advancements, safety profiles of clinical applications, as well as limitations and challenges, aiming to provide new insights into the treatment research for MS.

Empirical Analysis of a Super-SBM-Based Framework for Wetland Carbon Stock Safety Assessment

With climate change and urbanization expansion, wetlands, which are some of the largest carbon stocks in the world, are facing threats such as shrinking areas and declining carbon sequestration capacities. Wetland carbon stocks are at risk of being transformed into carbon sources, especially those of wetlands with strong land use–natural resource conservation conflict. Moreover, there is a lack of well-established indicators for evaluating the health of wetland carbon stocks. To address this issue, we proposed a novel framework for the safety assessment of wetland carbon stocks using the Super Slack-Based Measure (Super-SBM), and we then conducted an empirical study on the Quanzhou Bay Estuary Wetland (QBEW). This framework integrates the unexpected output indicator (i.e., carbon emissions), the expected output indicators, including the GDP per capita and carbon stock estimates calculated via machine learning (ML)-based remote sensing inversion, and the input indicators, such as environmental governance investigations, climate conditions, socio-economic activities, and resource utilization. The results show that the annual average safety assessment for carbon pools in the QBEW was a meager 0.29 in 2015, signaling a very poor state, likely due to inadequate inputs or excessive unexpected outputs. However, there has been a substantial improvement since then, as evidenced by the fact that all the safety assessments have exceeded the threshold of 1 from 2018 onwards, reflecting a transition to a “weakly effective” status within a safe and acceptable range. Moreover, our investigation employing the Super-SBM model to calculate the “slack variables” yielded valuable insights into optimization strategies. This research advances the field by establishing a safety measurement framework for wetland carbon pools that leverages efficiency assessment methods, thereby offering a quantitative safeguard mechanism that supports the achievement of the “3060” dual-carbon target.

Recent Advancements in Research on DNA Methylation and Testicular Germ Cell Tumors: Unveiling the Intricate Relationship

Testicular germ cell tumors (TGCTs) are the most common type of testicular cancer, with a particularly high incidence in the 15–45-year age category. Although highly treatable, resistance to therapy sometimes occurs, with devastating consequences for the patients. Additionally, the young age at diagnosis and the treatment itself pose a great threat to patients’ fertility. Despite extensive research concerning genetic and environmental risk factors, little is known about TGCT etiology. However, epigenetics has recently come into the spotlight as a major factor in TGCT initiation, progression, and even resistance to treatment. As such, recent studies have been focusing on epigenetic mechanisms, which have revealed their potential in the development of novel, non-invasive biomarkers. As the most studied epigenetic mechanism, DNA methylation was the first revelation in this particular field, and it continues to be a main target of investigations as research into its association with TGCT has contributed to a better understanding of this type of cancer and constantly reveals novel aspects that can be exploited through clinical applications. In addition to biomarker development, DNA methylation holds potential for developing novel treatments based on DNA methyltransferase inhibitors (DNMTis) and may even be of interest for fertility management in cancer survivors. This manuscript is structured as a literature review, which comprehensively explores the pivotal role of DNA methylation in the pathogenesis, progression, and treatment resistance of TGCTs.

Economic and Social Impact of Huanglongbing on the Mexico Citrus Industry: A Review and Future Perspectives

The citrus industry is of great importance in Mexico, with an estimated production value of USD 2.4 billion and the potential to generate up to 28 million employees per year. Approximately 69,000 Mexican families depend on this sector. However, it is under serious threat from the disease known as Huanglongbing (HLB). The rapid spread of this disease has caused significant economic losses, impacted the livelihoods of citrus growers, and led to substantial job losses. Currently, HLB is not under control in Mexico, and as the management strategies applied have proven to be ineffective, yields have not been recovered to pre-HLB levels, and production costs have significantly increased. Therefore, it remains the most destructive citrus disease. This review paper describes the current situation of the disease, its economic and social impact, and the measures adopted for its management in Mexico. Future perspectives highlight advances in research based on new biotechnological tools as an eco-friendly management alternative. Practical suggestions to address HLB are also mentioned in our analysis.