Research In Field Research Articles

Mapping the Global Spread of T. indotineae: An Update on Antifungal Resistance, Mutations, and Strategies for Effective Management.

The global spread of Trichophyton indotineae presents a pressing challenge in dermatophytosis management. This systematic review explores the current landscape of T. indotineae infections, emphasizing resistance patterns, susceptibility testing, mutational analysis, and management strategies. A literature search was conducted in November 2023 using Embase, PubMed, Scopus, and Web of Science databases. Inclusion criteria covered clinical trials, observational studies, case series, or case reports with T. indotineae diagnosis through molecular methods. Reports on resistance mechanisms, antifungal susceptibility testing, and management were used for data extraction. A total of 1148 articles were identified through the systematic search process, with 45 meeting the inclusion criteria. The global spread of T. indotineae is evident, with cases reported in numerous new countries in 2023. Tentative epidemiological cut-off values (ECOFFs) suggested by several groups provide insights into the likelihood of clinical resistance. The presence of specific mutations, particularly Phe397Leu, correlate with higher minimum inhibitory concentrations (MICs), indicating potential clinical resistance. Azole resistance has also been reported and investigated in T. indotineae, and is a growing concern. Nevertheless, itraconazole continues to be an alternative therapy. Recommendations for management include oral or combination therapies and individualized approaches based on mutational analysis and susceptibility testing. Trichophyton indotineae poses a complex clinical scenario, necessitating enhanced surveillance, improved diagnostics, and cautious antifungal use. The absence of established clinical breakpoints for dermatophytes underscores the need for further research in this challenging field.

Isolation of Myenteric and Submucosal Plexus from Mouse Gastrointestinal Tract and Subsequent Co-Culture with Small Intestinal Organoids

Intestinal homeostasis results from the proper interplay among epithelial cells, the enteric nervous system (ENS), interstitial cells of Cajal (ICCs), smooth muscle cells, the immune system, and the microbiota. The disruption of this balance underpins the onset of gastrointestinal-related diseases. The scarcity of models replicating the intricate interplay between the ENS and the intestinal epithelium highlights the imperative for developing novel methods. We have pioneered a sophisticated tridimensional in vitro technique, coculturing small intestinal organoids with myenteric and submucosal neurons. Notably, we have made significant advances in (1) refining the isolation technique for culturing the myenteric plexus, (2) enhancing the isolation of the submucosal plexus—both yielding mixed cultures of enteric neurons and glial cells from both plexuses, and (3) subsequently co-culturing myenteric and submucosal neurons with small intestinal organoids. This co-culture system establishes neural innervations with intestinal organoids, allowing for the investigation of regulatory interactions in the context of gastrointestinal diseases. Furthermore, we have developed a method for microinjecting the luminal space of small intestinal organoids with fluorescently labeled compounds. This technique possesses broad applicability such as the assessment of intestinal permeability, transcytosis, and immunocytochemical and immunofluorescence applications. This microinjection method could be extended to alternative experimental setups, incorporating bacterial species, or applying treatments to study ENS-small intestinal epithelium interactions. Therefore, this technique serves as a valuable tool for evaluating the intricate interplay between neuronal and intestinal epithelial cells (IECs) and shows great potential for drug screening, gene editing, the development of novel therapies, the modeling of infectious diseases, and significant advances in regenerative medicine. The co-culture establishment process spans twelve days, making it a powerful asset for comprehensive research in this critical field.

Circular economy in supply chain management: a framework for database tool development to enhance sustainability

ABSTRACT The study surveys circular economy (CE) theoretical building blocks, conceptualisation, and practical implementation within the supply chain. A narrative review synthesises the knowledge and maps research in the CE field. There are six major building blocks: waste management, industrial ecology, bioeconomy, cradle to cradle, green supply chain management, and product-service system. The paper examines their role and their overlapping activity within the supply chain. This study also generates a database containing 43 circular strategies, based on different stages in the supply chain. Each strategy is marked by its contribution towards CE, such as resource conservation, narrowing the loop, slowing the loop, or closing the loop. The database can assist actors to implement possible circular actions and policy makers to formulate supportive legislation. This study contributes to the contention of CE and its implementation.

Intelligent Environment using Extended Reality and Machine Learning to Improve Healthcare

Intelligent Environments (IEs) represent a paradigm shift in the way we interact with our surroundings, leveraging Ambient Intelligence (AmI) to create responsive and adaptive spaces that seamlessly integrate technology into the built environment. By harnessing sensors, actuators, and artificial intelligence, IEs have the potential to enhance human experience, improve efficiency, and promote sustainability across various domains, including smart homes, workplaces, cities, and healthcare facilities. This paper provides an overview of IE concepts, discusses key technologies and applications, explores design considerations, and outlines future directions for research and development in this dynamic field. This paper focuses on the study of the potential use of Extended Reality technologies in order to promote Green Health as well as their use in Smart Hospitals, one of the variants of Smart Environments, incorporating Machine Learning and Computer Vision, as a tool to support and improve healthcare, both from the point of view of the health professional and the patient, through a literature review and analysis of the current situation. Resulting in the elaboration of a conceptual model with the suggestion of technologies that can be used to achieve this scenario selected for their potential, and then the development of prototypes of parts of the conceptual model for Extended Reality Headsets as concept validation.

Effects of Multidimensional Carbon-Based Nanomaterials on the Low-Carbon and High-Performance Cementitious Composites: A Critical Review

Cementitious composites are ubiquitous in construction, and more and more research is focused on improving mechanical properties and environmental effects. However, the jury is still out on which material can achieve low-carbon and high-performance cementitious composites. This article compares the mechanical and environmental performance of zero-dimensional fullerenes, one-dimensional carbon nanotubes (CNTs), two-dimensional graphene oxide (GO), and three-dimensional nano-graphite platelets (NGPs) on cementitious composites. The literature review shows that two-dimensional (2D) GO has the best mechanical and environmental performance, followed by 3D NGPs, 1D CNTs, and 0D fullerenes. Specifically, GO stands out for its lower energy consumption (120–140 MJ/kg) and CO2 emissions (0.17 kg/kg). When the optimal dosage (0.01–0.05 wt%) of GO is selected, due to its high specific surface area and strong adhesion to the matrix, the compressive strength of the cementitious composites is improved by nearly 50%. This study will help engineers and researchers better utilize carbon-based nanomaterials and provide guidance and direction for future research in related fields.

Flavonoids as Potential Natural Compounds for the Prevention and Treatment of Eczema.

Eczema is a systemic autoimmune disease characterized by inflammation and skin manifestation with a range of comorbidities that include physical and psychological disorders. Despite recent advancements in understanding the mechanisms involved in atopic dermatitis, current marketed products have shown varying results with more side effects. The present ob-jective of the research studies is to develop new agents for eczema that cut down the cost of the novel drugs available and also improve the efficacy with the least adverse effects. Natural compounds and medicinal plants have been traditionally used since ancient civilizations. Now-adays, research in the herbal field is at its peak. One such natural compound, flavonoid, was found to be beneficial for the treatment of eczema. This review describes the use of certain flavonoid products to prepare preparations suitable for the treatment of prophylaxis or eczema. This is especially true for prophylaxis or atopic eczema treatment. These compounds exhibit anti-inflammatory, anti-inflammatory, anti-inflammatory, and anti-inflammatory properties and are, therefore, used in treatments to prevent allergies, inflammation, and irritation to the skin. We also dock the flavonoid derivatives used with the protein associated with the inhibi-tion of eczema for better lead optimization. These preparations appear to be used for cosmetic, dermatological, or herbal remedies as a local application.

On the Low-Pressure Hysteresis (LPH) in Gas Sorption Isotherms of Porous Carbons.

This study investigates the origin of low-pressure hysteresis (LPH) in the adsorption and desorption of three different probe molecules: carbon dioxide, nitrogen, and argon, across various adsorption temperatures (from cryogenic to room temperature), and within five different carbon materials: synthetic carbons (pristine and one post-synthetically oxidized) and natural coal. Significant attention is dedicated to elucidating LPH in oxidized samples outgassed at various temperatures (120-350°C). Experimental results show that insufficient outgassing temperature can lead to unreliable data due to artificial LPH and significantly underestimated textural properties, primarily caused by porosity blockage from substances like moisture. Conversely, in samples where heteroatoms have a stabilizing effect on texture, such as natural coal, careful consideration of outgassing temperature is crucial due to the risk of thermal degradation. Other factors contributing to LPH are adsorption temperature, and especially, kinetic limitations at cryogenic temperatures for cellulose-based carbons. Minor factors responsible for LPH are the physical state of the sample (monolith vs powder) and the flexibility of the porous system, both studied by carbon dioxide sorption. This study constitutes an important piece in the evaluation of LPH, providing practical recommendations and underlining the importance of experimental design, with implications for further research in this complex field.

Error Function Optimization to Compare Neural Activity and Train Blended Rhythmic Networks

We present a novel set of quantitative measures for “likeness” (error function) designed to alleviate the time-consuming and subjective nature of manually comparing biological recordings from electrophysiological experiments with the outcomes of their mathematical models. Our innovative “blended” system approach offers an objective, high-throughput, and computationally efficient method for comparing biological and mathematical models. This approach involves using voltage recordings of biological neurons to drive and train mathematical models, facilitating the derivation of the error function for further parameter optimization. Our calibration process incorporates measurements such as action potential (AP) frequency, voltage moving average, voltage envelopes, and the probability of post-synaptic channels. To assess the effectiveness of our method, we utilized the sea slug Melibe leonina swim central pattern generator (CPG) as our model circuit and conducted electrophysiological experiments with TTX to isolate CPG interneurons. During the comparison of biological recordings and mathematically simulated neurons, we performed a grid search of inhibitory and excitatory synapse conductance. Our findings indicate that a weighted sum of simple functions is essential for comprehensively capturing a neuron’s rhythmic activity. Overall, our study suggests that our blended system approach holds promise for enabling objective and high-throughput comparisons between biological and mathematical models, offering significant potential for advancing research in neural circuitry and related fields.

Assessing the Efficacy of ChatGPT Versus Human Researchers in Identifying Relevant Studies on mHealth Interventions for Improving Medication Adherence in Patients With Ischemic Stroke When Conducting Systematic Reviews: Comparative Analysis.

ChatGPT by OpenAI emerged as a potential tool for researchers, aiding in various aspects of research. One such application was the identification of relevant studies in systematic reviews. However, a comprehensive comparison of the efficacy of relevant study identification between human researchers and ChatGPT has not been conducted. This study aims to compare the efficacy of ChatGPT and human researchers in identifying relevant studies on medication adherence improvement using mobile health interventions in patients with ischemic stroke during systematic reviews. This study used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Four electronic databases, including CINAHL Plus with Full Text, Web of Science, PubMed, and MEDLINE, were searched to identify articles published from inception until 2023 using search terms based on MeSH (Medical Subject Headings) terms generated by human researchers versus ChatGPT. The authors independently screened the titles, abstracts, and full text of the studies identified through separate searches conducted by human researchers and ChatGPT. The comparison encompassed several aspects, including the ability to retrieve relevant studies, accuracy, efficiency, limitations, and challenges associated with each method. A total of 6 articles identified through search terms generated by human researchers were included in the final analysis, of which 4 (67%) reported improvements in medication adherence after the intervention. However, 33% (2/6) of the included studies did not clearly state whether medication adherence improved after the intervention. A total of 10 studies were included based on search terms generated by ChatGPT, of which 6 (60%) overlapped with studies identified by human researchers. Regarding the impact of mobile health interventions on medication adherence, most included studies (8/10, 80%) based on search terms generated by ChatGPT reported improvements in medication adherence after the intervention. However, 20% (2/10) of the studies did not clearly state whether medication adherence improved after the intervention. The precision in accurately identifying relevant studies was higher in human researchers (0.86) than in ChatGPT (0.77). This is consistent with the percentage of relevance, where human researchers (9.8%) demonstrated a higher percentage of relevance than ChatGPT (3%). However, when considering the time required for both humans and ChatGPT to identify relevant studies, ChatGPT substantially outperformed human researchers as it took less time to identify relevant studies. Our comparative analysis highlighted the strengths and limitations of both approaches. Ultimately, the choice between human researchers and ChatGPT depends on the specific requirements and objectives of each review, but the collaborative synergy of both approaches holds the potential to advance evidence-based research and decision-making in the health care field.

Enhanced Surface Properties of TiO2-Based Coatings via Stevia-Assisted Spark Suppression: Insights from Density Functional Theory Calculations

This study investigates the enhancement of surface properties in TiO2-based coatings on the Ti-6Al-4V alloy through micro-arc oxidation (MAO), employing stevia sugar as a novel additive. By incorporating stevia sugar into acetate–glycerophosphate–tetraethoxysilane solutions used in MAO treatment, the porous morphology of TiO2-based oxide layers is regulated. The incorporation of stevia moderates plasma discharge intensity, facilitating the formation of a uniform silicon-rich structure characterized by reduced porosity and pore size. This effect is attributed to the interaction between stevia and tetraethyl orthosilicate (TEOS), which modifies the TEOS hydrolysis process, thereby enhancing structural uniformity and stability while concurrently reducing plasma discharge intensity. Additionally, theoretical calculations offer a valuable understanding of the reactivity and interactions of stevia, TEOS, and their complex during the MAO process, laying the groundwork for further research and optimization in this promising field.

A Scholarly Dialog on Recent Trends in National Institutes of Health's Funding for the Thyroid Field.

Background: The National Institutes of Health (NIH) is the major funding agency for biomedical research in the United States. To initiate a scholarly dialog about research and career development in the thyroid field, here we reviewed recent trends in NIH funding for this area. We used the Research Portfolio Online Reporting Tool database to estimate the level of NIH extramural support during 2013-2022 (number of active grants/year and $amount/year weighed by the total number of active grants/year and $amount/year), provided by the NIH to the thyroid field. We determined that in 2013, the NIH supported ∼140 grants/year, totaling almost $50 million/year, the majority in the form of R01 grants. Within the thyroid field, support was evenly split between thyroid cancer and thyroid hormone metabolism and action subareas. In the subsequent years (2014-2022), the total number of active grants peaked at 150/year ($55 million) in 2014 but progressively decreased to about 100 active grants/year ($30 million) in 2022. This trend occurred while the NIH budget increased from $29 to $46 billion/year. Globally, the number of thyroid-related publications increased by ∼70% during the study period, and the fractional contribution of several countries remained relatively stable, except for China which increased by ∼600%. Remarkably, the fraction of thyroid-related publications in the United States sponsored by the NIH decreased from 5.5% to 3.1% of the global number. Conclusion: These results constitute a very concerning scenario for research and education in the thyroid field. We appeal to the NIH, the professional societies in endocrinology and thyroidology, and all other relevant stakeholders such as thyroid-related professionals and thyroid patients to engage in further discussions to identify the root causes of this trend and implement an action plan to stabilize and eventually reverse this situation.

Using Reference Publication Year Spectroscopy (RPYS) to analyze the research and publication culture in immunology

AbstractReference Publication Year Spectroscopy (RPYS) is a method to reveal the historical roots and landmark papers of a research field based on cited references data. RPYS visualizes how frequently cited references occur in the cited reference lists of papers published in the research field. Previous RPYS studies revealed various historical aspects of the research and publication culture in various fields of study (e.g., economy and philosophy of science). Employing RPYS, we examine a dataset of citing publications in immunology. We identify and analyze 20 cited publications occurring very frequently in the cited reference lists of these citing papers. The 20 publications shed light on the epistemic culture and the development of the field of immunology. Whereas 65% of the publications were contributions to the theoretical and empirical understanding of immunology, 35% were contributions to methodology.

Supported silver nanoparticles over Metronidazole modified-single walled carbon nanotubes for overcoming anti-microbial properties, anti-cancer effect, and apoptosis induction on human gastric cancer cell line

One of the most important goals of current research in the medical field is to identify treatments for antimicrobial resistance (AMR) and cancer. The latest developments in nanotechnology have provided opportunities to develop novel nanomaterials that can overcome the limitations of current treatments. In this regard, carbon nanotubes (CNTs) are considered potential nano-tools with attractive implications. The analysis of MTZ/SWCNT/AgNPs in this study was done using different techniques, including SEM, TEM, FTIR, XRD, and EDX. Antibacterial activity was assessed for MTN/SWCNT/AgNPs against Escherichia coli, Pseudomonas aeruginosa, Enterococcus faecalis, and Staphylococcus aureus. The effects of MTZ/SWCNTs/AgNPs on mitochondrial and apoptotic activities in human gastric cancer (AGS) were evaluated. Overall, new CNTs were produced efficiently. MTZ/SWCNTs/AgNPs are considered efficient antibacterial and anticancer agents for biological applications.

Quantum-Enhanced Representation Learning: A Quanvolutional Autoencoder Approach against DDoS Threats

Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.

Research on the Influencing Factors of DT of SMEs from TOE Perspective

Purpose: This paper is to examine how the DT of SMEs is affected and driven, and provide theoretical and practical implications for the DT of SMEs. Design/methodology/approach: Starting from the TOE framework, combined with the resource-based view and contingency theory, this study identifies the antecedents that affect the DT of SMEs from the three dimensions of technology, organization, and environment. Under the framework of TOE, digital technology infrastructure, employee skills, management support and environment uncertainty will be fully discussed. With the data from 221 SMEs from China analyzed by the fs_QCA 3.0, the driving paths for high DT are divided into different conditions configurations. Findings: The DT of SMEs is not the result of a single factor driven, but rather the result of the synergistic effect of the enterprise. There are multiple resource allocation configurations that drive the DT of SMEs, indicating the complexity and synergy of the factors affecting the DT of SMEs, which can prove the DT of SMEs has the characteristic of “multiple concurrency”. Research limitations/implications: DT is a very complex phenomenon, and there are many factors that affect the DT of SMEs. Based on the TOE framework, this paper selects four factors, and many possible factors are not included in the model. Besides, for QCA, a case-based research method with a quantitative approach, structured questionnaire surveys often result in a lack of detailed understanding of the research subjects and the inability to delve into all case enterprises. Originality/value: The findings will enrich the research in the DT field, and deepen the rational understanding of the complex interaction nature of multiple factors behind the successful DT of SMEs and provide path guidance for SMEs to realize DT. The research conclusions of this study provide valuable insights for SMEs to promote DT by combining possible antecedent configurations.

Similarity Measurement for Graph Data: An Improved Centrality and Geometric Perspective-Based Approach

How to make a precise similarity measurement for graph data is considered as highly recommended research in many fields. Hereinto, the so-named graph data is the coalition of patterns and edges that connect patterns. By taking both of pattern information and edge information into consideration, this paper introduces an improved centrality and geometric perspective-based approach to measure the similarity between any two graph data. Once these two graph data are projected into a plane, the pattern distance can be calculated by Euclid metric. With the help of the area composed by length of each edge and angle that constructed by the positive X-axis and the edge, the area-based edge distance is computed. To get better measurement, position-based edge distance is used to modify the edge distance. Up to now, the global distance between any two graph data can be determined by combining the above mentioned two distance results. Finally, the letter dataset is applied for experiment to examine the proposed similarity approach. The experimental results show that the proposed approach captures the similarity of graph data commendably and gets a tradeoff between time and precision.

Steering sp‐Carbon Content in Graphdiynes for Enhanced Two‐Electron Oxygen Reduction to Hydrogen Peroxide

AbstractCompared to sp2‐hybridized graphene, graphdiynes (GDYs) composed of sp and sp2 carbon are highly promising as efficient catalysts for electrocatalytic oxygen reduction into oxygen peroxide because of the high catalytic reactivity of the electron‐rich sp‐carbon atoms. The desired catalytic capacity of GDY, such as catalytic selectivity and efficiency, can theoretically be achieved by strategically steering the sp‐carbon contents or the topological arrangement of the acetylenic linkages and aromatic bonds. Herein, we successfully tuned the electrocatalytic activity of GDYs by regulating the sp‐to‐sp2 carbon ratios with different organic monomer precursors. As the active sp‐carbon atoms possess electron‐sufficient π orbitals, they can donate electrons to the lowest unoccupied molecular orbital (LUMO) orbitals of O2 molecules and initiate subsequent O2 reduction, GDY with the high sp‐carbon content of 50 at % exhibits excellent capability of catalyzing O2 reduction into H2O2. It demonstrates exceptional H2O2 selectivity of over 95.0 % and impressive performance in practical H2O2 production, Faraday efficiency (FE) exceeding 99.0 %, and a yield of 83.3 nmol s−1 cm−2. Our work holds significant importance in effectively steering the inherent properties of GDYs by purposefully adjusting the sp‐to‐sp2 carbon ratio and highlights their immense potential for research and applications in catalysis and other fields.

Recent Advances of Metal Fluoride Compounds Cathode Materials for Lithium Ion Batteries: A Review

Abstract As the most successful new energy storage device developed in recent decades, lithium-ion batteries (LIBs) are ubiquitous in the modern society. However, current commercial LIBs comprising mainly intercalated cathode materials are limited by the theoretical energy density which cannot meet the high storing energy demanded by renewable applications. Compared to intercalation-type cathode materials, low-cost conversion-type cathode materials with a high theoretical specific capacity are expected to boost the overall energy of LIBs. Among the different conversion cathode materials, metal fluorides have become a popular research subject for their environmental friendliness, low toxicity, wide voltage range, and high theoretical specific capacity. In this review, we compare the energy storage performance of intercalation and conversion cathode materials based on thermodynamic calculation and summarize the main challenges. The common conversion-type cathode materials are described and their respective reaction mechanisms are discussed. In particular, the structural flaws and corresponding solutions and strategies are described. Finally, we discussed the prospective of metal fluorides and other conversion cathode materials to guide further research in this important field.

TEG and TEC Battery Cooling System

Abstract: In various industries and applications, maintaining optimal operating temperatures for batteries is important to ensure performance, longevity, and safety. Traditional methods of battery cooling often involve complex and energy-intensive systems. In this paper, we propose a Thermoelectric Generator (TEG) and Thermoelectric Cooler (TEC) battery cooling system as an innovative solution to address this challenge. By harnessing the thermoelectric effect, this system offers a compact, efficient, and environmentally friendly approach to regulate battery temperatures. Through a comprehensive review of relevant literature and the design and implementation of our TEG and TEC cooling system, we explore its potential advantages and limitations. Experimental results demonstrate the system's effectiveness in maintaining desired battery temperatures while highlighting opportunities for further optimization. This research contributes to the advancement of battery cooling technology and opens avenues for future research and practical applications in various fields, including electric vehicles, renewable energy systems, and portable electronics.

Faculty members as academic knowledge brokers in Iran's health sector: a social network analysis study

BackgroundInteraction between researchers and policymakers is an essential factor to facilitate the evidence-informed policymaking. One of the effective ways to establish this relationship and promote evidence-informed policymaking is to employ people or organizations that can play the role of knowledge brokers. This study aims to analyze the communication network and interactions between researchers and policymakers in Iran's health sector and identify key people serving as academic knowledge brokers.MethodsThis study was a survey research. Using a census approach, we administered a sociometric survey to faculty members in the health field in top ten Iranian medical universities to construct academic-policymaker network using social network analysis method. Network maps were generated using UCINET and NetDraw software. We used Indegree Centrality, Outdegree Centrality, and Betweenness Centrality indicators to determine knowledge brokers in the network.ResultsThe drawn network had a total of 188 nodes consisting of 94 university faculty members and 94 policymakers at three national, provincial, and university levels. The network comprised a total of 177 links, with 125 connecting to policymakers and 52 to peers. Of 56 faculty members, we identified four knowledge brokers. Six policymakers were identified as key policymakers in the network, too.ConclusionsIt seems that the flow of knowledge produced by research in the health field in Iran is not accomplished well from the producers of research evidence to the users of knowledge. Therefore, it seems necessary to consider incentive and support mechanisms to strengthen the interaction between researchers and policymakers in Iran's health sector.