Scientific Community Research Articles

Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning.

High entropy alloys and amorphous metallic alloys represent two distinct classes of advanced alloy materials, each with unique structural characteristics. Their emergence has garnered considerable interest across the materials science and engineering communities, driven by their promising properties, including exceptional strength. However, their extensive compositional diversity poses substantial challenges for systematic exploration, as traditional experimental approaches and high-throughput calculations struggle to efficiently navigate this vast space. While the recent development in data-driven materials discovery could potentially help, such efforts are hindered by the scarcity of comprehensive data and the lack of robust predictive tools that can effectively link alloy composition with specific properties. To address these challenges, we have deployed a machine-learning-based workflow for feature selection and statistical analysis to afford predictive models that accelerate the data-driven discovery and optimization of these advanced materials. Our methodology is validated through two case studies: (i) a regression analysis of the bulk modulus, and (ii) a classification analysis based on glass-forming ability. The Bayesian-optimized regression model trained for the prediction of bulk modulus achieved an R2 of 0.969, an mean absolute error (MAE) of 3.958 GPa, and an root mean square error (RMSE) of 5.411 GPa, while our classification model for predicting glass-forming ability achieved an F1-score of 0.91, an area-under-the-curve of the receiver-operating-characteristic curve of 0.98, and an accuracy of 0.91. Furthermore, by leveraging a wide array of chemical data from diverse literature sources, we have successfully predicted a broad range of properties. This success underscores the efficacy of our modeling approach and emphasizes the importance of a comprehensive feature analysis and judicious feature selection strategy over a mere reliance on complex modeling techniques.

Pathways for Strengthening Lived Experience Leadership for Transformative Systems Change: Reflections on Research and Collective Change Strategies.

The Activating Lived Experience Leadership (ALEL) project was a South Australian participatory action research project that aimed to improve the ways lived experience is recognised, valued and integrated across mental health and social sector systems. ALEL was completed during 2019-2021, where it engaged 182 participants in generating community action and research knowledge. Our paper discusses the project's processes of building a collective partnership among lived experience leaders and other leaders from within the sector, so that the actions and strategies identified through research could be implemented by systems-level impact. We describe the collaborative process and key learnings that resulted in eight key action areas for transformative systems change in South Australia. The project invited a diverse range of self-identified lived experience and other leaders to be involved in a PAR process featuring formal qualitative research (focus groups, surveys and interviews) as well as community development activities (leaders' summit meetings, consultations, training and community of practice meetings). These processes were used to help us describe the purpose, achievements and potential of lived experience leadership. Project priorities and systems-level analysis was also undertaken with lived experience sector leaders and project advisors across two leaders' summit meetings, integrating research outcomes with sector planning to define high-level actions and a vision for transformational change. Participatory action research as informed by systems change and collective impact strategies assisted the project to generate detailed findings about the experiences and complexities of lived experience leadership, and collective responses of how systems could better support, be accountable to and leverage lived experience perspectives, experience and peer-work approaches. Systems change to define, value and embed lived experience leadership benefits from collective efforts in both formal research and sector development activities. These can be used to generate foundational understandings and guidance for working together in genuine ways for transforming mental health and social sector systems, experience and outcomes. Members of lived experience communities codesigned the project, and contributed to project governance and the development of all findings and project reports.

Development and validation of a droplet digital PCR method for quantifying lentiviral vector infectious titer

Lentiviruses, with their high transduction efficiency and gene expression levels, are widely used as gene delivery vectors in the development of chimeric antigen receptor T cells (CAR-T) and other genetically modified cell therapies. Accurate determination of the lentiviral vector infectious titer is essential to ensure effective transduction and product consistency. In this study, we developed an efficient method for lentiviral vector titration based on digital droplet polymerase chain reaction (ddPCR) technology, enabling absolute quantification of the target gene. Benzonase treatment of non-transduced plasmids substantially shortened the experimental period, reducing cell culture duration from 10-14 days to 3 days. The method was rigorously validated by assessing specificity, working range, limit of quantification, precision, accuracy, and robustness. This study demonstrates the feasibility of combining enzymatic digestion with ddPCR to quantify lentiviral vector infectious titer and provides a detailed and readily adaptable methodology for the scientific community.

The Influence of Structural Brain Changes on Cognition in the Context of Healthy Aging: Exploring Mediation Effects Through gBAT-The Graphical Brain Association Tool.

The contribution of age-related structural brain changes to the well-established link between aging and cognitive decline is not fully defined. While both age-related regional brain atrophy and cognitive decline have been extensively studied, the specific mediating role of age-related regional brain atrophy on cognitive functions is unclear. This study introduces an open-source software tool with a graphical user interface that streamlines advanced whole-brain mediation analyses, enabling researchers to systematically explore how the brain acts as a mediator in relationships between various behavioral and health outcomes. The tool is showcased by investigating regional brain volume as a mediator to determine the contribution of age-related brain volume loss toward cognition in healthy aging. We analyzed regional brain volumes and cognitive testing data (Montreal Cognitive Assessment [MoCA]) from a cohort of 131 neurologically healthy adult participants (mean age 50 ± 20.8 years, range 20-79, 73% females) drawn from the Aging Brain Cohort Study at the University of South Carolina. Using our open-source tool developed for evaluating brain-behavior associations across the brain and optimized for exploring mediation effects, we conducted a series of mediation analyses using participant age as the predictor variable, total MoCA and MoCA subtest scores as the outcome variables, and regional brain volume as potential mediators. Age-related atrophy within specific anatomical networks was found to mediate the relationship between age and cognition across multiple cognitive domains. Specifically, atrophy in bilateral frontal, parietal, and occipital areas, along with widespread subcortical regions mediated the effect of age on total MoCA scores. Various MoCA subscores were influenced by age through atrophy in distinct brain regions. These involved prefrontal regions, sensorimotor cortex, and parieto-occipital areas for executive function subscores, prefrontal and temporo-occipital regions, along with the caudate nucleus for attention and concentration subscores, frontal and parieto-occipital areas, alongside connecting subcortical areas such as the optic tract for visuospatial subscores and frontoparietal areas for language subscores. Brain-based mediation analysis offers a causal framework for evaluating the mediating role of brain structure on the relationship between age and cognition and provides a more nuanced understanding of cognitive aging than previously possible. By validating the applicability and effectiveness of this approach and making it openly available to the scientific community, we facilitate the exploration of causal mechanisms between variables mediated by the brain.

Biological Activities and Nanoparticle Synthesis of Dioscorea bulbifera and its Mechanistic Action - An Extensive Review.

Dioscorea bulbifera is commonly known as air potato present in the tropical and subtropical regions. It is a perennial climber traditionally used for various therapeutic purposes by traditional healers. This review explores various medicinal uses of D. bulbifera and its active ingredients, as well as describes its nanoparticle synthesis for medical applications. The Google Scholar search engine was used to conduct this comprehensive review along with the databases of the following publishers: Elsevier, Springer, Taylor and Francis, Bentham, and PubMed. D. bulbifera contains several bioactive compounds that are responsible for its pharmacological properties, such as antioxidant, anti-inflammatory, neuroprotective, anticancer, and antidiabetic properties. It is also used as a nutritive functional food. D. bulbifera-mediated nanoparticle synthesis has been established by the scientific communities for various medicinal applications. D. bulbifera contains numerous active ingredients, including diosbulbins, bafoudiosbulbin, β-sitosterol, diosgenin, dioscin, pennogenin, myricetin, quercetin, and stigmasterols with numerous biological activities. In addition, it has a vital role in synthesizing nanoparticles with good pharmacological applications, especially in drug delivery systems. However, its potential characteristic features and functional properties of the active molecules present in this tuber need to be further explored in clinical trials. We suggest that using this edible tuber, we may formulate the valueadded food with good medicinal applications.

Science, health and the military dictatorship in Brazil: the Integrated Endemic Disease Program (PIDE) of CNPq (1973-1987)

This article analyzes the Integrated Endemic Disease Program (PIDE), which was established in 1973 by the National Council for Scientific and Technological Development CNPq, financed by the Brazilian Funding Authority for Studies and Projects FINEP. The program was established to finance research on diseases considered strategic to the economic development plans of the military regime (1964-1985). Acknowledged to be a landmark program in the history of Brazilian parasitology, PIDE was set up during a period when the dictatorship was both violently repressing scholars and investing heavily in science and technology (S&T). The article examines the context in which the program was implemented and analyzes what it signified for planners in the S&T field and for the scientists who coordinated it. The contention is that PIDE was an example of how the scientific community managed to use financial and institutional resources available under the S&T policy in the 1970s to advance research on parasitic diseases and update its agenda. This analysis contributes to recent historiography that, based on specific historical cases, reflects on the paradoxical nature of a regime that, in its authoritarian modernization project, simultaneously persecuted scientists and supported science.

An overview on building-integrated photovoltaics: Technological solutions, modeling, and control

The advancement of renewable and sustainable energy generation technologies has been driven by environment-related issues, energy independence, and high costs of fossil fuels. Building-integrated photovoltaic systems have been demonstrated to be a viable technology for the generation of renewable power, with the potential to assist buildings in meeting their energy demands. This work reviews the current status of novel PV technologies, including bifacial solar cells and semi-transparent solar cells. This review discusses the various constructions of PV technologies, recent advances in these products, the influence of key design factors on electrical and thermal performance, and their potential in the design of energy-efficient smart buildings. The attention is focused on bifacial and semi-transparent PV systems, given the high level of interest of the scientific community in their current and potential applications.Focus is also devoted to the analysis of the electrical, optical, and thermal modeling procedures developed for sizing, designing and integrating photovoltaics into larger building simulations. The development of these models has a positive impact on the implementation of next-generation smart buildings. The latest innovative developments and key issues in the application of bifacial PV solutions in buildings are also summarized and analyzed. Special attention is paid to rear side electrical performance, which can be evaluated by means of illuminance/optical backside modeling. Finally, energy management and control of PV-equipped buildings via both model-based and data-driven approaches are discussed, as well as the integration of electric storage systems in a multi-building context.

Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative

BackgroundWhile many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID. MethodsA working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a community challenge aimed at incentivizing the broader scientific community to develop interpretable and accurate methods for identifying patients at risk of developing Long COVID. From August 2022 to December 2022, participants developed Long COVID risk prediction algorithms using the National COVID Cohort Collaborative (N3C) data enclave, a harmonized data repository from over 75 healthcare institutions from across the United States (U.S.). FindingsOver the course of the challenge, 74 teams designed and built 35 Long COVID prediction models using the N3C data enclave. The top 10 teams all scored above a 0.80 Area Under the Receiver Operator Curve (AUROC) with the highest scoring model achieving a mean AUROC of 0.895. Included in the top submission was a visualization dashboard that built timelines for each patient, updating the risk of a patient developing Long COVID in response to clinical events. InterpretationAs a result of L3C, federal reviewers identified multiple machine learning models that can be used to identify patients at risk for developing Long COVID. Many of the teams used approaches in their submissions which can be applied to future clinical prediction questions. FundingResearch reported in this RADx® Rad publication was supported by the National Institutes of Health. Timothy Bergquist, Johanna Loomba, and Emily Pfaff were supported by Axle Subcontract: NCATS-STSS-P00438.

Research of Practices of Formation and Development of Human Capital in Companies of Various Industries on the Russian Market

Almost all global trends create a synergistic effect, which increases the importance of forming and developing human capital to ensure the survival of companies in the market and their confidence in the future, regardless of their size and type of activity. However, despite the unflagging and continued interest of the scientific community in the issues related to human capital, there are still many “white spots” in this area. The aim of this work is to explore the approaches to forming and developing human capital applied by companies doing business in Russia in the current realities and modern conditions, to identify industry specifics and to outline recommendations. To achieve this goal, the authors conducted a questionnaire survey of managers of enterprises from different segments of the economy. As a result of the analysis of the obtained data, the conclusions were made that not all organisations have a generally accepted definition of HR and not everywhere there are procedures for its assessment. The problems related to personnel development, creation of competitive labour conditions for employees, search for potential employees and internal filling of vacancies were also revealed. The methodology of this work is based on a systemic approach and surveying, as well as general scientific methods such as synthesis, analysis, comparison, and generalization. Research results: Despite the fact that, in the opinion of the authors of the study, it did not consider all the problems of human capital formation and its impact on the sustainability of organisations, the obtained results allowed for the identification of the peculiarities of approaches to forming and developing human capital, considering industry specifics, and can be useful to develop practical recommendations for managers of companies aimed at improving this activity. Originality: This study presents, for the first time, a comprehensive overview of the approaches to forming and developing human capital in companies across various industries in the Russian labor market.

Towards achieving inclusive energy in SSA: The role of financial inclusion and governance quality

Energy poverty is a serious impediment to achieving Sustainable Development Goal 7 (SDG-7), thereby compelling policymakers and the scientific community to make collaborative commitments to address energy issues worldwide. While there is growing debate on the prevalent determinants of energy poverty, the empirical scrutiny of the contributing role of financial inclusion and governance quality remains elusive in existing literature. Therefore, this study investigates the impact of financial inclusion and governance quality on energy poverty in 29 sub-Saharan Africa (SSA) spanning 2004–2022. The empirical analysis was accomplished using the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) techniques with the Driscoll-Kraay standard error (DKSE) approach as a robustness check. The empirical outcomes indicate that a well-developed financial system that fosters easy and quick access to credit, especially among marginalized households, leads to the affordability of modern energy resources, thereby ameliorating energy poverty in the region. Additionally, the findings demonstrate the undeniable role of good governance in fostering a sound and inclusive financial system that enables households to access energy resources, ultimately reducing energy poverty in the region. Based on these outcomes, this study proposes important policy insights for the design and implementation of inclusive financial and energy policies.

Photoredox-Catalyzed Carbamoyl Radical-Initiated Dearomative Spirocyclization To Access Spiro-Cyclohexadiene Oxindoles.

The sustainable construction of spirocyclic compounds is important to the scientific community and the pharmaceutical industries. Herein, we demonstrate a carbamoyl radical-initiated intramolecular dearomative spirocyclization to access the spiro-cyclohexadiene oxindoles under visible light irradiation, which constitutes the first example of accessing the I-substituted derivatives that facilitate diversified transformations. Additionally, the scalability, late-stage modification of drugs, and significant antitumor activity of the products demonstrate the novel spirocyclic synthesis platform for expediting drug development.

Lattice metamaterials with controllable mechanical properties inspired by projection of four-dimensional hypercubes

There has been an increasing interest among the material research community in the pursuit of enhancing the designability of mechanical properties. The existing approaches usually resorted to sophisticated algorithms (such as machine learning) for the reverse design of materials with specific properties. Different from these existing approaches, here we propose a new approach to create lattice metamaterials with continuously controllable mechanical properties by continuously adjusting the geometric parameters of a unique cell topology originated from the projection of four-dimensional hypercubes. The cells contain an inner region and an outer region, each with different deformation characteristics. For example, the inner region is a stretching-dominated simple cubic (SC) unit cell, while the outer region is a bending-dominated body-centered cubic (BCC) unit cell. Specifically, both stiffness and strength isotropy can be simultaneously realized. The proposed lattice metamaterial exhibits intriguing feature of dual stress plateaus. These plateaus can be effectively controlled by adjusting the geometric parameters of inner and outer regions, which enables these lattice metamaterials to hold promising application prospects in the energy absorption scenarios, such as vehicle and pedestrian protection. Such lattice metamaterial design can be used to realize the gradient distribution of mechanical properties through continuous transition of cell topology without introduction of inefficient interfaces, providing a new approach for the design of heterogeneous metamaterials used in the scenarios involving non-uniform stress distribution.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

A Long Short Term Memory Model for Character-based Analysis of DNS Tunneling Detection

DNS tunneling is the attempt to create a hidden tunnel through a domain name service. Such a tunnel would jeopardize the targeted network and open the door for illegal access, control, and data exfiltration. The information security research community showed the variety of techniques that have been proposed to detect the tunnel. The majority of these efforts were relying on machine learning techniques where features of tunneling are considered such as length of DNS query, size, and entropy of the query. However, an additional analysis of the lexical information of the DNS query has been depicted recently and showed remarkable performance. This paper aims to examine the role of Long Short Term Memory (LSTM) model in terms of DNS lexical analysis. Two benchmark datasets related to DNS have been used. In addition, a character mapping mechanism has been used to replace every possible character with an integer number. Consequentially, the mapped representation has been fed into an LSTM model for DNS tunneling detection. Results showed that the proposed method was able to obtain a weighted average F1-score of 98% for both datasets respectively. Such results are competitive in the context of the state of the art and demonstrate the efficacy of the lexical analysis within the DNS tunneling detection task.

DECam Multimessenger Astrophysics Pipeline. I. From Raw Data to Single-exposure Candidates

We introduce a pipeline that performs rapid image subtraction and source selection to detect transients, with a focus on identifying gravitational-wave optical counterparts using the Dark Energy Camera (DECam). In this work, we present the pipeline steps from processing raw data to identification of astrophysical transients on individual exposures. We process DECam data and build difference images using the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Science Pipelines software, and we use flags and principal component analysis to select transients on a per-exposure basis, without associating the results from different exposures. Those candidates will be sent to brokers for further classification and alert distribution. We validate our pipeline using archival exposures that cover various types of objects, and the tested targets include a kilonova (GW170817), supernovae, stellar flares, variable stars (in a resolved galaxy or the Milky Way Bulge), and serendipitous objects. Overall, the data processing produces clean light curves that are comparable with published results, demonstrating the photometric quality of our pipeline. Real transients can be well selected by our pipeline when sufficiently bright (signal-to-noise ratio ≳15). This pipeline is intended to serve as a tool for the broader research community. Although this pipeline is designed for DECam, our method can be easily applied to other instruments and future LSST observations.

The challenges, opportunities and future of comparative physiology in the Global South: perspectives of early-career researchers.

Researchers in the Global South (GS, developing countries) make valuable contributions to the field of comparative physiology, but face economic and scientific disparities and several unique challenges compared with colleagues in the Global North (developed countries). This Perspective highlights some of the challenges, knowledge gaps and disparities in opportunity faced by GS researchers, especially those at early-career stages. We propose collaborative solutions to help address these issues, and advocate for promoting investment and cultural and societal change for a more inclusive research community. Additionally, we highlight the role of GS researchers in contributing expert knowledge on local biodiversity and the environment; this knowledge can help to shape the future of comparative physiology, allowing us to achieve a better understanding of the evolution of physiological mechanisms and to develop innovative solutions to environmental and biomedical challenges. With this Perspective, we hope to highlight the need to foster a more diverse, equitable and inclusive research landscape in comparative physiology; one that empowers GS scientists to address the global challenges associated with biodiversity loss, climate change and environmental pollution.

CVD Atlas: a multi-omics database of cardiovascular disease.

Cardiovascular disease (CVD) is the leading cause of illness and death worldwide. Numerous studies have been conducted into the underlying mechanisms and molecular characteristics of CVD using various omics approaches. However, there is still a need for comprehensive resources on CVD. To fill this gap, we present the CVD Atlas, accessed at https://ngdc.cncb.ac.cn/cvd. This database compiles knowledge and information from manual curation, large-scale data analysis, and existing databases, utilizing multi-omics data to understand CVDs comprehensively. The current version of CVD Atlas contains 215,333 associations gathered from 308 publications, 652 datasetsand 7 databases. It covers 190 diseases and 44 traits across multiple omics levels. Additionally, it provides an interactive knowledge graph that integrates disease-gene associations and two types of analysis tools, offering an engaging way to query and display relationships. CVD Atlas also features a user-friendly web interface that allows users to easily browse, search, and download all association information, research metadata, and annotation details. In conclusion, CVD Atlas is a valuable resource that enhances the accessibility and utility of knowledge and information related to CVD, benefiting human health and CVD research communities.

Sarcopenia in general practice: Towards improving muscle health screening, assessment and management in Australia.

An estimated one in five Australians aged 60 years and older have sarcopenia, marked by progressive and accelerated loss in muscle mass, strength and function. Sarcopenia is associated with considerable healthcare costs and a myriad of adverse health outcomes, including increased risk of death. Despite its clinical importance, muscle health is often overlooked in routine clinical practice, hindering diagnosis and treatment. In July 2023, eight representatives from Australia's primary care and research communities convened to discuss barriers to sarcopenia screening, assessment and management within routine clinical practice. Solutions were proposed to improve the implementation of muscle health assessment and management in general practice. This article summarises the key discussions and outcomes from this meeting. Strategies to improve the implementation of muscle health assessment and management in general practice include (1) improving public awareness; (2) professional education; (3) provision of tools and resources; (4) advocacy and policy; and (5) increasing collaborative efforts between healthcare professionals, professional societies, universities, electronic medical record software vendors and the government.

A comparative study on biodegradation of low density polyethylene bags by a Rhizopus arrhizus SLNEA1 strain in batch and continuous cultures.

Biodegradation poses a challenge for environmentalists and scientific community, offering a potential solution to the plastic waste problem. This study aims to investigate the biological degradation of low-density polyethylene (LDPE) bags by a fungus in both batch and continuous cultures, with the goal of identifying an eco-friendly and cost-effective waste management strategy. The fungal strain Rhizopus arrhizus SLNEA1, isolated from a landfill located in northeastern Algeria, was tested for its capability to degrade LDPE films and utilize them as a sole carbon source in batch (α-LDPE) and continuous (γ-LDPE) cultures. The results indicated a higher rate of weight loss for γ-LDPE (29.74%) compared to α-LDPE (23.77%). The biodegradation effect was examined using scanning electron microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS) and Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) to evaluate morphological and chemical changes in LDPE samples, highlighting alterations of LDPE films through cracks, veins and holes under SEM and chemical transformation and appearance of new functional groups in the FTIR data. Rhizopus arrhizus SLNEA1 demonstrated the ability to break down and utilize LDPE films as a carbon source. This isolate shows promise for LDPE biodegradation applications, which may be leveraged for the development of future plastic degradation systems involving fungi.

Mesenchymal Stem Cell Therapy: Therapeutic Opportunities and Challenges for Diabetic Kidney Disease

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), which severely affects the quality of patients’ lives. However, the current therapeutic approaches can only postpone its progression to ESRD. It is therefore imperative to develop a novel therapeutic strategy for renal injury in DKD, with the objective of restoring renal function and reversing the process of ESRD. In recent years, the potential of mesenchymal stem cell (MSC) therapy for DKD has garnered increasing attention within the scientific community. Preclinical research on MSC therapy has yielded promising results, and the safety of MSC treatment in vivo has been substantiated in clinical studies. An increasing body of evidence suggests that MSC therapy has significant potential for the treatment of DKD. This article reviews the existing research on MSCs and their derived exosomes in treating DKD and analyzes the underlying mechanism of MSC-based therapy for DKD. Additionally, we discuss the potential of combining MSC therapy with conventional pharmacological treatments, along with the constraints and prospects of MSC therapy for DKD. We hope this review can provide a precise and comprehensive understanding of MSCs for the treatment of DKD.