Diverse Set Research Articles

Phylogenomic and phenotypic analyses highlight the diversity of antibiotic resistance and virulence in both human and non-human Acinetobacter baumannii.

Acinetobacter baumannii is a Gram-negative, opportunistic pathogen that causes infections in the immunocompromised. With a high incidence of muti-drug resistance, carbapenem-resistant A. baumannii is designated as a priority 1 pathogen by the WHO. The current literature has expertly characterized clinical isolates of A. baumannii. As the challenge of these infections has recently been classified as a One Health issue, we set out to explore the diversity of isolates from human and non-clinical sources, such as agricultural surface water, urban streams, various effluents from wastewater treatment plants, and food (tank milk); and, importantly, these isolates came from a wide geographic distribution. Phylogenomic analysis considering almost 200 isolates showed that our diverse set is well-differentiated from the main international clones of A. baumannii. We discovered novel sequence types in both hospital and non-clinical settings and five strains that overexpress the resistance-nodulation-division efflux pump adeIJK without changes in susceptibility reflected by this overexpression. Furthermore, we detected a bla ADC-79 in a non-human isolate despite its sensitivity to all antibiotics. There was no significant differentiation between the virulence profiles of clinical and non-clinical isolates in the Galleria mellonella insect model of virulence, suggesting that virulence is neither dependent on geographic origin nor isolation source. The detection of antibiotic resistance and virulence genes in non-human strains suggests that these isolates may act as a genetic reservoir for clinical strains. This endorses the notion that in order to combat multi-drug-resistant infection caused by A. baumannii, a One Health approach is required, and a deeper understanding of non-clinical strains must be achieved.IMPORTANCEThe global crisis of antibiotic resistance is a silent one. More and more bacteria are becoming resistant to all antibiotics available for treatment, leaving no options remaining. This includes Acinetobacter baumannii. This Gram-negative, opportunistic pathogen shows a high frequency of multi-drug resistance, and many strains are resistant to the last-resort drugs carbapenem and colistin. Research has focused on strains of clinical origin, but there is a knowledge gap regarding virulence traits, particularly how A. baumannii became the notorious pathogen of today. Antibiotic resistance and virulence genes have been detected in strains from animals and environmental locations such as grass and soil. As such, A. baumannii is a One Health concern, which includes the health of humans, animals, and the environment. Thus, in order to truly combat the antibiotic resistance crisis, we need to understand the antibiotic resistance and virulence gene reservoirs of this pathogen under the One Health continuum.

Systemic Evaluation of PV Self-Consumption Optimization Using Electric Vehicles

The shift to electric transportation is crucial to fighting climate change. However, Germany’s goal of 15 million electric vehicles (EVs) by 2030 remains distant. Therefore, enhancing their economic viability is essential to promoting EV adoption. One promising option to increase the economics for the user is PV self-consumption optimization using smart charging EVs. Yet, more research is needed to explore the use case’s impacts on the German/European energy systems. Therefore, PV self-consumption optimization using EVs is integrated into an energy system model, assessing its impact on the energy system in 2030. For this purpose, the use case is modeled for different groups of people—personas—which are defined in a way that creates a diverse set of personas reflecting the distribution of different statistical values within Germany. The modified (dis)charging profiles are then aggregated and integrated into the energy system model. With a high implementation of PV self-consumption optimization in Germany in 2030, a positive system effect (with a system cost reduction of 53 million EUR/a) can be observed with a lower need for further storage and less curtailment of renewable energies (RES). Furthermore, the market values for RES increase by 0.7%, which fosters the integration of RES.

Portfolio analysis of global tobacco control research funding at the National Cancer Institute, 2000-2019.

Research in low- and middle-income countries (LMICs), where the majority of global tobacco users reside, is critical to addressing the global tobacco epidemic. This analysis describes the global tobacco control research portfolio funded by the National Cancer Institute from fiscal years 2000 to 2019. We used the National Institutes of Health Query, View, Report database to identify extramural grants relevant to global tobacco control research. Abstracts were analyzed to describe grant characteristics, including topic areas, tobacco products, countries, and regions of focus. Bibliometric and co-authorship network analyses were performed for publications associated with relevant grants. Of the 93 relevant grants with foreign (non-US) involvement, the majority (83.9%) supported research in upper and lower middle-income countries. The majority of grants (86.0%) focused on cigarettes, with a small subset of grants addressing smokeless tobacco, waterpipe use, or other non-cigarette products. Most grants focused on at least one of the six tobacco control policy measures in the World Health Organization MPOWER package; almost half (48.4%) focused on monitoring tobacco use and around one-third (32.3%) focused on offering tobacco cessation treatment, while other MPOWER measures received less attention in the research portfolio. While most of these grants, and the funding initiatives that supported them, emphasized research in low- and middle-income countries (LMICs), only 3 of 93 grants were awarded directly to LMIC-based institutions. There is a critical need for research to develop and test strategies to adapt, implement, and scale up evidence-based interventions across diverse LMIC settings. This study identified gaps in research activity that should be addressed to strengthen global tobacco control research capacity.

Inverting the deficit model in global mental health: An examination of strengths and assets of community mental health care in Ghana, India, Occupied Palestinian territories, and South Africa.

Global mental health [GMH] scholarship and practice has typically focused on the unmet needs and barriers to mental health in communities, developing biomedical and psychosocial interventions for integration into formal health care platforms in response. In this article, we analyse four diverse settings to disrupt the emphasises on health system weaknesses, treatment gaps and barriers which can perpetuate harmful hierarchies and colonial and medical assumptions, or a 'deficit model'. We draw on the experiential knowledge of community mental health practitioners and researchers working in Ghana, India, the Occupied Palestinian Territory and South Africa to describe key assets existing in 'informal' community mental health care systems and how these are shaped by socio-political contexts. These qualitative case studies emerged from an online mutual learning process convened between 39 academic and community-based collaborators working in 24 countries who interrogated key tenets to inform a social paradigm for global mental health. Bringing together diverse expertise gained from professional practice and research, our sub-group explored the role of Community Mental Health Systems in GMH through comparative country case studies describing the features of community care beyond the health and social care system. We found that the socio-political health determinants of global economic structures in all four countries exert significant influence on local community health systems. We identified that key assets across sites included: family and community care, and support from non-profit organisations and religious and faith-based organisations. Strengthening community assets may promote reciprocal relationships between the formal and informal sectors, providing resources for support and training for communities while communities collaborate in the design and delivery of interventions rooted in localised expertise. This paper highlights the value of informal care, the unique social structures of each local context, and resources within local communities as key existing assets for mental health.

Unveiling the influence of institutional quality on board gender diversity and corporate environmental, social, and governance disputes in China

AbstractThis paper unravels an unprecedented interplay between board gender diversity (BGD) and corporate environmental, social, and governance (ESG) disputes among Chinese A‐share‐listed nonfinancial companies from 2017 to 2021. Framed within a knowledge‐based and sensemaking perspective of institutional frameworks, the research not only illuminates the profound impact of internal (corporate governance ratings) and external (regional institutional development) institutional factors on this intricate relationship but also brings to light a paradigm‐shifting revelation. The study employed a diverse set of empirical tests, ranging from ordinary least squares regression to advanced methods such as the generalized method of moments, two‐stage least squares, and propensity score matching, providing a nuanced and comprehensive analysis. The findings highlight the pivotal role of female directors in significantly mitigating ESG disputes. A mechanism analysis further uncovers that internal and external institutional quality are potent positive moderators in shaping the BGD–ESG dispute dynamic. This research has profound implications, providing valuable insights for stakeholders, policymakers, and scholars, offering a comprehensive understanding of how gender diversity fosters sustainability while unravelling the intricate tapestry of internal and external institutional dynamics that shape ESG outcomes.

Abstract A034: A new international program to accelerate novel therapeutics for brain tumors: presenting the brain tumor research novel therapeutics accelerator

Abstract Each year, we see many journal manuscripts highlighting successful pre-clinical experiments for potential new brain tumor therapies. Only a few of these therapies move forward into successful clinical trials. Indeed, developing a comprehensive translational program covers many aspects, including but not limited to, medicinal chemistry, blood brain barrier penetration, tumor heterogeneity, delivery, commercialization, and navigating the regulatory environment. Because of this, bringing a successful translational project from bench to bedside requires access to a diverse set of cutting-edge expertise to give promising therapies the best chance of succeeding. Recognizing that this journey is not easy to navigate, the Tessa Jowell Brain Cancer Mission has launched an international Brain Tumor Research Novel Therapeutics Accelerator (BTR-NTA). This program is led by an international group of drug development and brain tumor experts who provide independent guidance to researchers to position their therapeutic along a realistic pathway to clinical trials. The BTR-NTA is open to any international academic and industry researchers developing a medicinal product, delivery technology or device for brain tumors. The BTR-NTA welcomes groups at any stage of pre-clinical or clinical work. Academics and smaller biotech’s could benefit from feedback on their pre-clinical studies and plans for commercialization, while larger pharmaceutical companies could benefit from access to brain tumor specific expertise and patient input into clinical trial design. Researchers can apply to BTR-NTA twice a year, and if accepted will go through the following process: 1. Complete an application form covering work to date and future plans under strict confidentiality agreements; 2. Attend an in-person meeting with a tailored group of experts to further discuss their work; and 3. Receive a comprehensive written report from the BTR-NTA committee with feedback on strengths and weaknesses and guidance on next steps. The expert committee dedicates half a day to reviewing the applicants’ work in-person, and extensive time pre- and post-meeting. Throughout the process, applicants receive up to 150 hours of expert input. The multidisciplinary nature of the review committee spans the entirety of the drug development pathway to support longitudinal collaboration and avoid a siloed approach to research. The BTR-NTA was designed after a similar program in another rare disease field yielded impressive results. Thanks to charitable funding, the BTR-NTA is free to academics and at low cost for industry. The BTR-NTA aims to help address barriers for researchers by highlighting potential pitfalls or risks, providing multi-disciplinary feedback on work to date, giving constructive guidance on next steps and strengthening international collaboration in the brain tumor field. The ultimate goal of the BTR-NTA is to enable researchers with therapy for brain tumors to navigate the drug development journey and make it to the clinic in a low-cost and timely way. Citation Format: Nicky Huskens, Charlotte Aitken, Katie Bushby. A new international program to accelerate novel therapeutics for brain tumors: presenting the brain tumor research novel therapeutics accelerator [abstract]. In: Proceedings of the AACR Special Conference on Brain Cancer; 2023 Oct 19-22; Minneapolis, Minnesota. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_1):Abstract nr A034.

Ethical Leadership of Head Teachers and its Impact on Teachers’ Motivation at the Elementary Level

The present study employed a quantitative, descriptive research design to examine the impact of ethical leadership on elementary school teachers’ motivation in public elementary schools of a district in Punjab, Pakistan. It was a quantitative and descriptive study in nature. Considering the ethical guidelines, a questionnaire was administered to 400 randomly selected male and female elementary-level teachers. In developing the survey instrument, relevant literature was reviewed, and expert input was obtained to ensure the appropriateness of the questions for assessing the variables of interest. The confidentiality of the respondents was maintained throughout the data-gathering process. The collected data was statistically analyzed using SPSS software version 20, measuring means, and standard deviations. The results revealed a significant positive association between principals’ ethical leadership style - characterized by integrity, honesty, and considerate treatment of staff – and elevated intrinsic motivation levels among teachers. As educational leaders shape the culture in their institutions, these findings carry important implications. Promoting ethical leadership through enhanced training and modeling desired behaviors can improve teacher retention, performance, and job satisfaction. It can translate to better outcomes for students. Further in-depth inquiries through qualitative and longitudinal research across diverse settings can enrich understanding of this phenomenon and aid the development of context-specific interventions leveraging ethical leadership to motivate teachers.

Exploring the Impact of Lesson Study in Vietnamese Primary Education: Perspectives of EFL Lecturers

This qualitative study investigates the incorporation of Lesson Study, a pedagogical approach from Japan, into Vietnamese primary education, with a specific focus on the perspectives of Vietnamese English as a Foreign Language (EFL) lecturers. These educators, though not directly involved in primary education, engage with it through various roles like teacher training and curriculum development. The research is based on in-depth interviews with nine EFL lecturers from two educational institutions in Vietnam. Employing thematic analysis, the study identifies five key themes: collaborative enhancement of teaching practices, navigating cultural and systemic challenges, improved understanding of student learning needs, professional growth and empowerment, and challenges such as resistance to change and time constraints. These themes underscore the potential of Lesson Study in fostering collaborative and reflective teaching practices, as well as professional development. However, they also highlight the obstacles imposed by traditional educational norms and practical limitations. The study emphasizes the necessity of support structures and resources for effective implementation of Lesson Study, advocating for adaptations to align with local educational contexts and cultural values. Contributing to the discourse on professional development in EFL teaching, this research underscores the importance of collaborative, reflective practices in educational innovation. Moreover, it stresses the significance of considering unique cultural and systemic elements in applying pedagogical methods like Lesson Study. Offering specific insights into its application in the Vietnamese primary education context, this study not only enhances understanding of cross-cultural educational transfer but also provides practical guidance for educators and policymakers. It paves the way for further research in diverse educational settings, expanding the international conversation on educational practices.

Rush regression workbench: An integrated open-source application for regression modeling and analysis in healthcare analytics

Regression is widely used in healthcare analytics, whether for examining hospital quality and safety, characterizing patterns of patient volume and healthcare costs, or predicting patient outcomes. Simple linear regression and other basic forms can be conducted with spreadsheet programs and are useful for examining simple linear relationships. However, expert statistical knowledge, computational skills, and specialized tools may be needed to characterize nonlinear relationships and complex interactions, to examine data that fail the assumptions of linear regression, to identify confounding variables and lessen the influence of outliers, and to build and evaluate predictive models. We constructed the Rush Regression Workbench to accomplish these tasks and to automate cautious and sophisticated analyses, provide interpretive outputs, enable reproducible results, and to provide the community with an evolving open-source good containing a diverse set of analyses and a growing library of over 170 preprocessed public healthcare datasets. The Rush Regression Workbench can be accessed via the web or downloaded and used locally.

Development of a novel machine learning-based approach for brain function assessment and integrated software solution

Industry 5.0 emphasizes human-centered solutions. However, bridging the gap between human conditions and engineering systems remains a challenge in this era. Promoting the development of cybernetics applications in the field of rehabilitation is imperative. In the healthcare sector, the evaluation of rehabilitation products’ ergonomics and the dynamic assessment of the human body are of utmost importance. Amid the burgeoning prominence of data-driven product design concepts, ergonomics methods have emerged as indispensable evaluative tools. Nevertheless, the integration of data from multiple sources remains a contemporary challenge; one must extract meaningful information from diverse data sets while enhancing data analysis and fusion techniques. In this study, we collect multimodal neurobiological signals using electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) to devise a robust multimodal rehabilitation data fusion method based on graph convolutional network (GCN), particularly focusing on the motor imagery (MI) task. To achieve a more holistic evaluation, we incorporate granger causality (GC) and brain region adjacency matrix as supplementary features, integrating electrophysiological and hemodynamic perspectives. This integration aims to enhance information complementarity and foster a comprehensive understanding of the neural processes involved in MI. Our results demonstrate the superiority of the multimodal fusion method: it achieves higher average accuracy and improved stability, as indicated by the reduced standard deviation of accuracy. This enhanced performance suggests its potential regarding broader research applications. Building upon our successful findings, we establish an intelligent algorithmic rehabilitation platform based on multimodal neural data. This platform not only holds significant clinical value, facilitating precision and personalized medicine, but also propels the advancement of medical practices, paving the way for a more tailored and effective approach to patient care. Our research makes progresses in the areas of rehabilitation modeling, equipment design, intelligence, and interdisciplinary collaboration through the in-depth study and extension of cybernetic principles and techniques.

Strategic performance blueprint: Optimizing business performance through tailoring strategic postures to environmental and institutional contexts

ABSTRACT In a world where one-size-fits-all strategies are increasingly inadequate, this study examines the strategic triad of entrepreneurial orientation (EO), market orientation (MO), and entrepreneurial marketing (EM) and their context-varied effects on organizational performance. Using structural equation modeling and data representing diverse sets of U.S. organizations and industries, we demonstrate that when market growth is slow, supplier power is strong, and competitive intensity is low, EO outperforms MO and EM. Smaller organizations and those with less diverse and smaller networks are better suited for EO. MO functions best in stable markets with significant growth and limited supplier power, as well as for large organizations and those with strong and diversified networks. EM works best for mid-sized organizations and those with weak networks and in environments of high supplier power, intense competition, and high market turbulence. Offered vignettes demonstrate which strategic posture should be chosen by executives in different contexts.

Enhancing Cookie Formulations with Combined Dehydrated Peach: A Machine Learning Approach for Technological Quality Assessment and Optimization.

This study focuses on predicting and optimizing the quality parameters of cookies enriched with dehydrated peach through the application of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models. The purpose of the study is to employ advanced machine learning techniques to understand the intricate relationships between input parameters, such as the presence of dehydrated peach and treatment methods (lyophilization and lyophilization with osmotic pretreatment), and output variables representing various quality aspects of cookies. For each of the 32 outputs, including the parameters of the basic chemical compositions of the cookie samples, selected mineral contents, moisture contents, baking characteristics, color properties, sensorial attributes, and antioxidant properties, separate models were constructed using SVMs and ANNs. Results showcase the efficiency of ANN models in predicting a diverse set of quality parameters with r2 up to 1.000, with SVM models exhibiting slightly higher coefficients of determination for specific variables with r2 reaching 0.981. The sensitivity analysis underscores the pivotal role of dehydrated peach and the positive influence of osmotic pretreatment on specific compositional attributes. Utilizing established Artificial Neural Network models, multi-objective optimization was conducted, revealing optimal formulation and factor values in cookie quality optimization. The optimal quantity of lyophilized peach with osmotic pretreatment for the cookie formulation was identified as 15%.

Higher-order thin layer method as an efficient forward model for calculating dispersion curves of surface and Lamb waves in layered media

The thin layer method (TLM) is an effective semi-discrete numerical tool for analyzing wave motion in stratified media. The TLM can calculate the eigenvalues and eigenvectors for both propagating and decaying waves, which is essential to simulate waves near the source. The eigenvectors are particularly useful in various applications, such as estimating modal contributions, calculating synthetic seismograms, determining Green’s function, analyzing site response, and solving wave amplification problems in earthquake engineering, to name a few. In practice, most engineering applications use linear element-based TLM. However, the overall performance of TLM depends upon the type of element and the number of thin layers. The linear element TLM requires relatively thin sub-layers to obtain an accurate solution, making it computationally expensive and time-consuming. This study explores the potential of using non-linear higher-order TLM (HTLM) for forward modeling. The mathematical framework of TLM is used to formulate the generalized HTLM in determining the multimodal dispersion curve of Rayleigh, Love, and Lamb waves. The convergence and computational efficiency of different orders of HTLM have been investigated on a diverse set of published soil profiles and plate-like structures. Since non-linear interpolation functions better capture the change in displacement and stress between the layers, the findings reveal that the HTLM consistently outperforms the linear element TLM in terms of accuracy. Furthermore, the computational efficiency of different order HTLM is analyzed by comparing the total degrees of freedom and CPU time. Remarkably, higher-order HTLMs exhibit improved accuracy without increasing the computational burden, making them an attractive option for practical applications such as global search-based inversion and full spectrum inversions where fast and economical forward modeling is essential. Therefore, the HTLM will become helpful in seismic wavefield modeling, dynamic soil characterization, non-destructive evaluation methods, wave propagation analysis through waveguides, seismic site response analysis, etc.

Blurring the lines to promote access and equity in 2‐ and 4‐year Hispanic serving institutions

AbstractPartnerships between two‐ and four‐year Hispanic Serving Institutions (HSI) can provide high‐quality, in‐depth learning experiences for traditionally underserved students. Blurring the lines between these institutions allows for more seamless transitions for prospective teachers (PTs), and maximum collaboration and program impact. This article contributes to the growing literature on such partnerships and collaborations. We describe how our partnership between Northwest Vista College (2‐year institution) and University of Texas at San Antonio (4‐year institution) supported PTs and their development of mathematics pedagogical content knowledge and skills to facilitate in diverse settings (i.e., mathematics sensemaking, questioning, teaching through problem solving, soft teaching skills). To describe the nature of our partnership, in this article we (1) explain some of the main elements of the partnership between the two‐ and four‐year campuses, (2) review the unique, collaborative nature of the Community Mathematics Project, and (3) discuss outcomes that have resulted among Mathematics Fellows who have gone through the program.

Cross-Cultural Pragmatics: Analysing Speech Acts in Different Cultures

Cross-cultural communication entails understanding the nuanced differences in speech acts across diverse cultural contexts. This study aimed to explore these variations, focusing on pragmatic differences and similarities in speech acts among participants from various cultural backgrounds. Employing qualitative methods, communicative scenarios were devised to elicit speech acts, and interactions were recorded for analysis. The participants' cultural backgrounds were diverse, representing a range of ethnicities, languages, and socio-cultural contexts. Data analysis involved categorizing and comparing speech acts across cultures, identifying patterns, and examining cultural influences on speech act usage. Results indicated significant variations in the frequency, form, and interpretation of speech acts among different cultural groups. Cultural norms, values, and communication styles were found to shape participants' understanding and enactment of speech acts, highlighting the intricate relationship between language and culture in communication. These findings have important implications for intercultural communication, language education, and international business. Understanding cultural differences in speech act usage can enhance communication effectiveness in diverse settings, fostering mutual understanding and reducing misunderstandings. Overall, this study contributes to the growing body of research on cross-cultural pragmatics, emphasizing the need for culturally sensitive approaches to communication in an increasingly globalized world.

Intelligent decision-making in healthcare telemonitoring via forward-backward chaining and IoT

Healthcare telemonitoring has emerged as a promising approach to remotely monitor patients remotely, enabling timely intervention and personalized care. Internet of things (IoT) device-generated patient data necessitates innovative solutions for intelligent healthcare decision-making, as current methods struggle to provide timely, context-aware, and data-driven recommendations, resulting in suboptimal patient care. This study aims to develop an intelligent decision-making framework for healthcare telemonitoring by leveraging forward-backward chaining and IoT technology. The research focuses on a system using forward-backward chaining algorithms to analyze real-time patient data from IoT devices. It utilizes machine learning models to adapt to changing conditions and refine decision-making, demonstrating its ability to provide real-time context-aware recommendations. Temperature, blood pressure, oxygen level, and heart rate measurement errors are 2.01%, 1.74 to 2.13%, 0.61%, and 1.45%, respectively. The success rate of early disease diagnosis using an expert system is 81%, with an average application interface responsiveness time of 4.978 s. The integration of IoT data with intelligent decision-making algorithms in healthcare telemonitoring has the potential to revolutionize patient care. However, future work should focus on scalability and interoperability for diverse healthcare settings.

AutoEval: Are Labels Always Necessary for Classifier Accuracy Evaluation?

Understanding model decision under novel test scenarios is central to the community. A common practice is evaluating models on labeled test sets. However, many real-world scenarios see unlabeled test data, rendering the common supervised evaluation protocols infeasible. In this paper, we investigate such an important but under-explored problem, named Automatic model Evaluation (AutoEval). Specifically, given a trained classifier, we aim to estimate its accuracy on various unlabeled test datasets. We construct a meta-dataset: a dataset comprised of datasets (sample sets) created from original images via various transformations such as rotation and background substitution. Correlation studies on the meta-dataset show that classifier accuracy exhibits a strong negative linear relationship with distribution shift (Pearson's Correlation ). This new finding inspires us to formulate AutoEval as a dataset-level regression problem. Specifically, we learn regression models (e.g., a regression neural network) to estimate classifier accuracy from overall feature statistics of a test set. In the experiment, we show that the meta-dataset contains sufficient and diverse sample sets, allowing us to train robust regression models and report reasonable and promising predictions of the classifier accuracy on various test sets. We also provide insights into application scopes, limitations, and potential future directions of AutoEval.

Scripting success: a systematic literature review of the process genre approach for writing in Asian education

The problem and the aim of the study. In the 21st century, the process genre approach has gained prominence among various writing methodologies. Numerous scholars have scrutinized its impact on writing within diverse educational settings. However, the extensive body of research makes it challenging to distil current theories, dependent variables, instruments and teaching methods for writing in Asian education. By doing so, it aims to uncover contemporary theories, effective practices, and relevant pedagogical techniques, contributing valuable insights to enhance writing education in this context. Research methods. The primary methodology employed in this study is the systematic literature review method, renowned for its rigorous approach to gathering, evaluating, and synthesizing existing research. The process initiates with comprehensive data searching across authoritative academic databases renowned for their scholarly rigour and reliability from 2019 to 2023, specifically Scopus, Wiley, Taylor and Francis, Emerald and Sage. A deliberate preference for these databases over more expansive platforms like Google Scholar underscores the commitment to selecting high-quality and peer-reviewed articles. The review process encompasses several stages, commencing with the meticulous screening of titles and abstracts to ascertain alignment with predefined inclusion and exclusion criteria. Subsequently, the selected articles undergo a thorough examination, enabling a nuanced analysis of their content. The culmination of this methodological rigour culminates in a comprehensive report that distils key findings, ultimately contributing substantively to the scholarly discourse in Asian education writing instruction. Results. Within the confines of this systematic literature review, the researchers have successfully identified and meticulously scrutinised a total of nine pertinent theoretical frameworks that are intricately intertwined with the approach to PG writing. Furthermore, it has been ascertained that a total of 19 scholars have employed 12 distinct dependent variables in their respective research endeavours. The analysis conducted in this study has also yielded a comprehensive categorisation of instruments, which encompasses six distinct categories. This categorisation provides valuable insights into the various methodological approaches that have been utilised by scholars in their research on the topic of PG writing. By means of a rigorous examination of pedagogical and compositional methodologies, this synthesis has unveiled overarching foundational principles, encompassing preparation, modelling, planning, joint construction, independent construction, and revision, thereby encapsulating the comprehensive terrain of PG writing research. Conclusion. In summary, this systematic literature review serves as a highly valuable and indispensable tool for forthcoming scholars who are venturing into the realm of research pertaining to PG studies. The comprehensive findings encapsulate a wide range of interconnected theories and frameworks, a multitude of dependent variables examined, a variety of instruments employed, and intricate observations regarding teaching methodologies employed in the domain of PG writing research. The compilation of essential elements presented herein serves as a valuable resource for scholars seeking to engage in PG writing research. It facilitates the seamless identification and addressing of research gaps, while offering a comprehensive overview of the current research trends within this particular domain.

Retrospective Case Analysis on the Effectiveness of Boerhavia Diffusa Mother Tincture in the Management of Essential Hypertension

Background: Hypertension is a prevalent medical condition with significant health implications. This retrospective case record analysis aimed to investigate the efficacy of Boerhaavia diffusa mother tincture in managing hypertension. The study utilized data from diverse healthcare settings affiliated with Sivaraj Homoeopathic Medical College and Research Institute, Salem. Methods: A total of 30 cases meeting the inclusion criteria, comprising both sexes aged between 30 to 75 years, diagnosed with hypertension based on clinical parameters (systolic BP >140 mmHg, diastolic BP >90 mmHg), and presenting common symptoms, were included. Exclusion criteria encompassed cases of secondary hypertension and those with incomplete follow-ups. The efficacy of Boerhaavia diffusa mother tincture was assessed by changes in blood pressure recorded during the treatment period. The study adhered to ethical guidelines, and all patients received Boerhaavia diffusa mother tincture as part of their therapeutic regimen. Results and Discussion: Among the 30 cases analyzed, 6 patients exhibited marked improvement, 10 demonstrated moderate improvement, and 10 showed mild improvement, while 4 cases showed no improvement. Notably, the majority of patients experienced marked symptomatic relief. Despite the positive trend observed in the outcomes, the study has limitations, including a small sample size and the absence of a control group. The lack of detailed information on dosage and treatment duration further restricts the generalizability of the findings. The retrospective nature of the analysis introduces potential biases, and confounding factors such as lifestyle changes were not systematically addressed. Conclusion: This retrospective case record analysis suggests a potential beneficial effect of Boerhaavia diffusa mother tincture in managing hypertension. The majority of patients experienced symptomatic relief, although caution is warranted due to study limitations. Future research, including well-designed randomized controlled trials with larger sample sizes, is needed to establish the efficacy and safety of Boerhaavia diffusa mother tincture in hypertensive patients.

Identification and validation of stable reference genes for expression profiling of target genes in diverse ovine tissues

Quantitative PCR (qPCR) is a widely-used technique for quantifying the expression of target genes across various tissues, as well as under different pathological and physiological conditions. One of the challenges associated with this method is the need to identify optimal reference genes (RGs) that maintain consistent expression levels under diverse experimental settings, thereby ensuring accurate biological interpretation. In this study, we conducted a thorough analysis of 18 candidate RGs (ACTB, BACH1, B2M, GAPDH, HMBS, HPRT1, PGK1, PPIA, PPIB, RPLP0, RPL19, RPS9, RPS15, RPS28, SDHA, TBP, UXT, and YWHAZ) across 10 ovine tissues (muscle, skin, kidney, liver, intestine, rumen, lung, testis, heart, and spleen) obtained from five individual sheep. We aimed to identify genes with stable expression across these tissues. A literature-based survey helped us shortlist candidate genes representing various functional classes from multiple livestock species. We employed four algorithms: geNorm, NormFinder, BestKeeper, and Delta Ct (ΔCt), to rank these genes based on their stability. A consistent trend in the rankings was observed across these different algorithms. RefFinder was then used for a comprehensive ranking, integrating the outputs from the various methods. ACTB, PPIB, BACH1, and B2M emerged as the most stable RGs, while RPS9, RPS15, and PGK1 displayed variable expression. We validated our findings through qPCR analysis of four target genes (ACTN2, CRYAB, DLK1, and TRIM54) in the skin samples from two different sheep breeds. Based on these results, we recommend ACTB, PPIB, BACH1, and B2M as reliable internal control genes for qPCR experiments involving diverse ovine tissues.