Specific Context Research Articles

ENHANCING SOFT SKILLS IN TEACHING ENGLISH: A STUDY UTILIZING PADLET APPLICATION AMONG STUDENTS

This research aims to examine the efficacy of utilizing the Padlet application to enhance soft skills in English as a Foreign Language (EFL) education among students at International Information Technology University. Soft skills, such as communication, collaboration, critical thinking, and creativity, are crucial competencies for students navigating the globalized workforce. Technology integration in education has become increasingly important, and Padlet provides a versatile platform for collaborative learning and content sharing. This study adopts a quantitative approach, employing a pre/post-observation design to evaluate the influence of Padlet usage on the development of soft skills among EFL students. Participants are randomly assigned to either an experimental group, engaging with Padlet activities, or a control group receiving traditional instruction. Validated instrument, including performance evaluations, are used to measure soft skills before and after the intervention. Quantitative data analysis techniques are utilized to compare mean scores between groups and assess the effectiveness of Padlet in fostering soft skills. Expected outcomes encompass improvements in communication clarity, increased collaboration in group tasks, enhanced critical thinking through information synthesis, and greater creativity in idea generation. The findings of this study hold implications for EFL educators seeking innovative approaches to skill development and for institutions aiming to effectively integrate technology into language education. By exploring the role of Padlet in enhancing soft skills within the specific context of EFL education at International Information Technology University, this research contributes to a deeper understanding of technology-supported learning and its impact on student outcomes.

Autophagy-associated biomarkers ULK2, UVRAG, and miRNAs miR-21, miR-126, and miR-374: Prognostic significance in glioma patients.

As the pioneering study from Pakistan, our research distinctly focuses on validating the roles of autophagy-associated genes and MicroRNAs (miRs) in the unique context of our population for glioma prognosis. The study delves into the nuanced interplay of autophagy within a miR-modulated environment, prompting an exploration of its potential impact on glioma development and survival. Employing real-time PCR (qPCR), we meticulously assessed the expression profiles of autophagy genes and miRs in glioma tissues, complemented by immunohistochemistry on Formalin-fixed paraffin-embedded tissues from the same patients. Our comprehensive statistical analyses, including the data normality hypothesis Shapiro-Wilk test, the Mann-Whitney U-test, Spearman correlation test, and Kaplan-Meier survival analysis, were tailored to unravel the intricate associations specific to low- and high-grade glioma within our population. Clinicopathological analysis revealed a predominance of male patients (66%) with a median age of 35 years. Glioblastoma (32%) and Astrocytoma (36%) were the most prevalent histopathological subtypes. Molecular analysis showed significant correlations between prognostic markers (Ki-67, IDH-1, p53) and clinicopathological factors, including age, histological type, radiotherapy, and chemotherapy. In high-grade glioma, increased expression of AKT and miR-21, coupled with reduced ULK2 and LC3 expression was distinctly observed. While correlation analysis identified a strong positive correlation between ULK2 and UVRAG, PTEN, miR-7, and miR-100 in low-grade glioma, unveiling distinctive molecular signatures unique to our study. Furthermore, a moderate positive correlation emerged between ULK2 and mTOR, miR-7, miR-30, miR-100, miR-204, and miR-374, also between miR-21 and miR-126. Similarly, a positive correlation appeared between ULK2 and AKT, LC3, PI3K, PTEN, ULK1, VPS34, mTOR, Beclin1, UVRAG, miR-7 and miR-374. AKT positively correlated with LC3, PI3K, PTEN, ULK1, VPS34, mTOR, Beclin1, UVRAG, miR-7, miR-30, miR-204, miR-374, miR-126 and miR-21 weakly correlated with AKT and miR-30 in high-grade glioma, providing further insights into the autophagy pathway within our population. The enrichment analysis for miR-21, miR-126, and miR-374 showed MAPK pathway as a common pathway along with Ras, PI3K, and mTOR pathway. The low ULK2, UVRAG, and miR-374 expression group exhibited significantly poor overall survival in glioma, while miR-21 over-expression indicated a poor prognosis in glioma patients, validating it in our population. This study provides comprehensive insights into the molecular landscape of gliomas, highlighting the dysregulation of autophagy genes ULK2, and UVRAG and the associated miR-21, miR-126 and miR-374 as potential prognostic biomarkers and emphasizing their unique significance in shaping survival outcomes in gliomas within the specific context of the Pakistani population.

REVIEW: INSTRUMEN KUESIONER PADA PASIEN DIABETES MELITUS

Diabetes mellitus (DM) is a chronic disease with increasing prevalence worldwide, posing major challenges in patient management and monitoring. Questionnaire instruments are an important tool in DM patient evaluation because they allow systematic and measurable data collection regarding various aspects of the disease, including quality of life, self-management, and perceptions of care. This review article provides a comprehensive overview of various questionnaire instruments used for DM patient evaluation, interpretation of questionnaire results, and grouping based on categories. The review results indicate that the selection of an appropriate instrument must take into account the specific context of the patient population and the evaluation goals. Additionally, we group the evaluation instruments into several categories, including the patient's level of knowledge, compliance with treatment, severity of symptoms, fear of needles, mental condition, and improvement in the patient's quality of life. The conclusion of this article emphasizes the importance of questionnaires in DM management, as well as the need for further research to develop more comprehensive and responsive instruments. Questionnaire instruments, when selected and used appropriately, can improve clinical decision-making and the quality of care provided to DM patients.

¿Puede un pescado cambiar de estatus?

This article will try to discuss the inversion of a stereotype associated with a food product - mackerel (Scomber colias) - a fish whose consumption was connoted with popular and poverty contexts, and its revitalization that has taken place especially since 2011, concomitant with the worsening of the economic crisis in Portugal. This process of revitalization and resignification, initially put into practice by renowned Portuguese chefs, also entered the lexicon and practices of nutritionists who try to promote the idea of "healthy eating". Parallel to the economic crisis, another factor contributed to the revitalization of mackerel in Portugal: the imposition of fishing quotas on sardines, a fish recognized as a symbol of national food practices, associated with summer and its festivities. This brief analysis shows us how the process of gourmetisation of mackerel took place in a very specific context - an economic crisis in Portugal - a process that, as we will try to demonstrate, did not last over time.

Upper Bounds of the Third Hankel Determinant for Bi-Univalent Functions in Crescent-Shaped Domains

This paper investigates the third Hankel determinant, denoted H3(1), for functions within the subclass RS∑*(λ) of bi-univalent functions associated with crescent-shaped regions φ⦅z=z+1+z2. The primary aim of this study is to establish upper bounds for H3(1). By analyzing functions within this specific geometric context, we derive precise constraints on the determinant, thereby enhancing our understanding of its behavior. Our results and examples provide valuable insights into the properties of bi-univalent functions in crescent-shaped domains and contribute to the broader theory of analytic functions.

Analysis of the Main Influencing Factors of Plateau Phenomenon in Chinese Normal University Latin Competitors

This article investigates the primary factors contributing to the plateau phenomenon observed in Latin dance competitors from Chinese normal universities. Through a comprehensive analysis of training methodologies, psychological influences, and environmental conditions, the study aims to identify the key elements that hinder progress at intermediate and advanced levels. By examining these factors, the research provides insights into optimizing training programs and support systems to enhance the performance and growth of Latin dance athletes in this specific educational context.

Pengaruh Penerapan Maqomah terhadap Pengembangan Materi Pembelajaran Al-Qur'an dan Hadits di Madrasah Aliyah

This study addresses the stagnation in the teaching methods of Al-Qur'an and Hadith at Madrasah Aliyyah Al-Falah Nagreg, emphasizing the need for innovation. The objective is to analyze the implementation of the Maqomah method, a systematic approach aimed at enhancing the learning materials of Al-Qur'an and Hadith. Utilizing a qualitative descriptive methodology, the research was conducted through literature reviews, classroom observations, interviews with teachers and students, and document analysis. The design follows a stepwise exploration of the Maqomah method's application and its impact on students’ comprehension. Key findings reveal that the Maqomah method significantly improves students' understanding and internalization of Islamic teachings, contributing to the overall quality of religious education. However, limitations such as time constraints and the specific context of Madrasah Aliyyah Al-Falah may influence the generalizability of the results. Future research should focus on broader applications of the method across different educational institutions and investigate its integration into national Islamic education curricula. The findings of this research provide valuable insights for educators seeking to innovate religious teaching methods in madrasahs and highlight the potential of Maqomah in fostering a deeper engagement with Islamic texts.

Adaptação da lista de verificação de cirurgia segura em um hospital universitário

Introduction: The checklist is a tool that ensures important decisions and actions are taken during surgery. It contains critical items that should not be overlooked in any situation, preventing serious failures in the Surgical Center. Objective: To adapt the safe surgery checklist to the institution’s specific context. Methods: This is an applied research study on technological development. The checklist was used in 24 surgeries: 12 utilized the World Health Organization’s checklist, and the other 12 used lists adapted to the sector’s context. These adaptations were carried out by the researcher at three different stages. After applying the lists, the professionals present in the operating room completed a semi-structured questionnaire. Results: The study involved 72 participants: surgeons, anesthesiologists, residents, nurses, technicians, and nursing assistants. They were involved in the three stages of checklist application: before anesthesia induction, before the surgical incision, and before the patient left the operating room. The results from the pilot test (1st phase) and the adapted checklist (2nd phase) were categorized to better understand the findings. Conclusion: The checklist was adapted based on the suggestions from the participants. It also served as a communication tool, providing technical guidance, preventing process failures, and ensuring effectiveness and efficiency during surgeries. Keywords: Checklist; patient safety; perioperative care; technological development.

Revolutionizing Land Cover Analysis: A Systematic Review of Geospatial Intelligence with Classification and Segmentation

In today’s fast-paced technological landscape, artificial intelligence (AI) is revolutionizing industries, with geospatial analysis standing at the forefront of this transformation. The process of land cover classification, which involves categorizing different types of land surfaces—such as forests, urban areas, water bodies, and agricultural fields—has traditionally been plagued by inconsistencies and inaccuracies. These shortcomings have led to a variety of pressing real-world issues. Misclassified land cover data can result in the inefficient allocation of resources, where critical areas may be overlooked while less urgent regions receive attention. Additionally, the failure to accurately monitor land cover changes can allow illegal activities, such as deforestation, to go unnoticed, resulting in severe environmental degradation. Similarly, unmonitored topographical changes, like unauthorized construction projects, can significantly alter landscapes without regulatory oversight, posing risks to both the environment and public safety. Unchecked forest fires, exacerbated by delayed detection due to poor land cover classification, can spread rapidly, causing widespread damage. Furthermore, inaccurate monitoring of border fences can lead to security vulnerabilities and geopolitical tensions. Collectively, these issues contribute to the escalating challenges of climate change and urbanization, highlighting the critical need for more precise and reliable land cover classification methods. In response to these challenges, our study seeks to explore the potential of advanced machine learning (ML) techniques to revolutionize land cover classification. We leverage publicly available geospatial datasets, specifically EuroSAT and DeepGlobe, which provide comprehensive satellite imagery data across various regions. The focus of our research is on two primary tasks: Image Classification and Semantic Segmentation. Image Classification involves categorizing entire satellite images into specific land cover classes, providing a broad overview of the landscape. In contrast, Semantic Segmentation is a more granular approach that labels each pixel in an image according to its land cover class, offering detailed insights into the spatial distribution of different land types. To conduct a thorough analysis, we evaluate the performance of several cutting-edge models in these tasks. For Semantic Segmentation, we employ Meta AI’s Segment Anything Model (SAM), which is recognized for its ability to segment objects within images with high precision, and the U-Net architecture, a model that has been widely used in medical image analysis and has proven effective in various segmentation tasks. For Image Classification, we use the VGG and ResNet models, both of which are highly regarded in the field of computer vision for their capacity to extract detailed features from images and classify them with high accuracy. The primary objective of our research is to assess how these models perform when applied to land cover classification tasks and to identify their strengths and weaknesses in this specific context. By analyzing their performance, we aim to provide valuable insights that can guide future research efforts and help improve the accuracy and reliability of land cover classification methods. Additionally, our study seeks to highlight new opportunities for enhancing the monitoring and management of land resources, which is crucial for addressing the environmental and urbanization challenges that the world faces today. Our research aspires to contribute to the geospatial field by offering practical recommendations for utilizing machine learning techniques to improve land cover classification. By addressing the limitations of current methods and proposing more effective solutions, we hope to support the development of tools that are capable of accurately monitoring land cover changes and responding to the complex environmental and societal challenges of our time.

The Application of Large Language Models in Gastroenterology: A Review of the Literature.

Large language models (LLMs) are transforming the medical landscape by enhancing access to information, diagnostics, treatment customization, and medical education, especially in areas like Gastroenterology. LLMs utilize extensive medical data to improve decision-making, leading to better patient outcomes and personalized medicine. These models are instrumental in interpreting medical literature and synthesizing patient data, facilitating real-time knowledge for physicians and supporting educational pursuits in medicine. Despite their potential, the complete integration of LLMs in real-life remains ongoing, particularly requiring further study and regulation. This review highlights the existing evidence supporting LLMs' use in Gastroenterology, addressing both their potential and limitations. Recent studies demonstrate LLMs' ability to answer questions from physicians and patients accurately. Specific applications in this field, such as colonoscopy, screening for colorectal cancer, and hepatobiliary and inflammatory bowel diseases, underscore LLMs' promise in improving the communication and understanding of complex medical scenarios. Moreover, the review discusses LLMs' efficacy in clinical contexts, providing guideline-based recommendations and supporting decision-making processes. Despite these advancements, challenges such as data completeness, reference suitability, variability in response accuracy, dependency on input phrasing, and a lack of patient-generated questions underscore limitations in reproducibility and generalizability. The effective integration of LLMs into medical practice demands refinement tailored to specific medical contexts and guidelines. Overall, while LLMs hold significant potential in transforming medical practice, ongoing development and contextual training are essential to fully realize their benefits.

Structure of the Nmd4-Upf1 complex supports conservation of the nonsense-mediated mRNA decay pathway between yeast and humans.

The nonsense-mediated mRNA decay (NMD) pathway clears eukaryotic cells of mRNAs containing premature termination codons (PTCs) or normal stop codons located in specific contexts. It therefore plays an important role in gene expression regulation. The precise molecular mechanism of the NMD pathway has long been considered to differ substantially from yeast to metazoa, despite the involvement of universally conserved factors such as the central ATP-dependent RNA-helicase Upf1. Here, we describe the crystal structure of the yeast Upf1 bound to its recently identified but yet uncharacterized partner Nmd4, show that Nmd4 stimulates Upf1 ATPase activity and that this interaction contributes to the elimination of NMD substrates. We also demonstrate that a region of Nmd4 critical for the interaction with Upf1 in yeast is conserved in the metazoan SMG6 protein, another major NMD factor. We show that this conserved region is involved in the interaction of SMG6 with UPF1 and that mutations in this region affect the levels of endogenous human NMD substrates. Our results support the universal conservation of the NMD mechanism in eukaryotes.

The conditional effects of party system change on economic growth in Africa

AbstractAfrican party systems vary considerably in their patterns of stability and change over time. This article examines how economic growth has been shaped by such party system fluidity—in particular, the frequency, scope and variety of change—while taking account of the constitutional regime (i.e., level of democracy) within which these party system dynamics occur. To do so, we use Arellano-Bond generalized method of moments models to test the impact of party system fluidity and the level of democracy on economic growth in Africa during the post-independence period (1970–2018). We find, consistent with previous research, that democracy has an uncertain effect on growth. However, party system fluidity is reliably and positively correlated with growth. Its effects are particularly strong whenever an unstable party system coexists with an autocratic regime, which suggests that this specific political context—which has often been associated with democratization—is especially conducive to economic growth.

Multifaceted role of the actin-binding protein WIP: Promotor and inhibitor of tumor progression and dissemination.

Cancer cells depend on actin cytoskeleton reorganization to achieve hallmark malignant functions including abnormal activation, proliferation, migration and invasiveness. (Neural)-Wiskott-Aldrich Syndrome protein ((N-)WASP) binds actin and forms a complex with the WASP-interacting protein (WIP), which plays a critical role in regulating the actin cytoskeleton, through (N)-WASP-dependent and independent functions. Mutations in the WIP gene (WIPF1) lead to severe early onset immunodeficiency in humans and severe autoimmunity and shortened lifespan in mice. This review covers the available evidence about the physiological role of WIP in different tissues and its contribution to human disease, focusing on cancer. In solid tumors overexpression of WIP has mostly been associated with tumor initiation, progression and dissemination through matrix degradation by invadopodia, while a suppressive function has been shown for WIP in certain hematological cancers. Interestingly, a minority of studies suggest a protective role for WIP in specific tumor contexts. These data support the need for further research to fully understand the mechanisms underlying WIP's diverse functions in health and disease and raise important questions for future work.

COMPREHENSIVE ANALYSIS OF HISTORICALAND TRANSLATION DISCOURSE OF ABAI

The purpose of this scientific article is to study the significance of Abai as a historical precedent figure and to analyze his historical and translational discourses from a scientific perspective. The main aim of the research is to uncover the essence of Abai's discourse, conduct a comprehensive analysis, and determine his significant contribution to the historical development of the country and the field of translation studies. The research also aims to assess the level of precedence associated with Abai's name and his writings. This research makes a significant contribution by presenting Abai's translational discourse as a historical-literary discourse, characterized by adherence to the historical context and cultural specificity. Recognizing Abai's work as a product of a historical personality, the research reflects the features and characteristics of the historical era. The study employs a comprehensive approach, including discursive-historical, cognitive-discursive, and pragmatic-communicative methodologies, supplemented by sociological survey methods. This approach allows for a thorough analysis of Abai's discourse, identifying and describing the extralinguistic, cognitive, pragmatic, and linguistic-stylistic components of his historical discourse. Asa result of the research, an in-depth analysis of Abai's translational discourse was conducted, shedding light on its historical nature and determining its level of precedence. The results obtained not only provide valuable insights into Abai's discourse but also significantly contribute to the development of the theory of historical discourse and translation studies. This study contributes to the development of the theory of historical discourse and translation studies, thereby expanding knowledge of cultural and linguistic dynamics in a historical context. The practical significance of the research lies in its ability to deepen the understanding of Abai's contribution to the historical process and the field of translation studies. By studying Abai's discourses, researchers and scholars can gain valuable information about the historical context and the impact of his works on society and the theory of translation.

Gold and Gold Alloys Electropolishing in Choline Chloride-Glycerol Deep Eutectic Solvents

Electropolishing (EP) is a finishing technique used to reduce the roughness of metallic surfaces while enhancing their brightness. This process enables the treatment of complex shaped parts and reduces processing time compared to conventional mechanical polishing methods. Electropolishing of precious metals typically requires the use of electrolytes containing cyanides and other toxic compounds, which reduces its attractiveness from an industrial perspective. Deep Eutectic Solvents (DES) are emerging as a good alternative for the chemical and electrochemical dissolution of valuable metals, like gold, silver and copper. However, their use in the specific context of electrochemical polishing of precious alloys has not been considered so far. This work aims to evaluate the effectiveness of choline chloride-based DES in electropolishing 18K gold alloys. Their performance will be assessed in terms of roughness reduction, brightness enhancement and color preservation, all characteristics associated with the presence of a uniform dissolution.

Implementing a Phase II Quality Control Protocol for a High Precision 137Cs Dosimetry Calibration Irradiator.

In medical physics, rigorous quality assurance and quality control protocols are vital for precise dose delivery applications. In many health physics applications, the allowable uncertainty for various processes is often greater than that of medical physics due to looser safety ties. This results in less demand for quality control and uncertainty analyses, since these may not be needed. However, certain applications can benefit from a comprehensive quality control program, as it may yield important insights, such as air kerma monitoring in dosimetry calibrations for environmental and low-dose applications. By implementing a thorough quality control program tailored to specific contexts and needs, uncertainties associated with dose measurements can be quantified with greater accuracy and reliability. This proactive approach not only ensures the integrity of data collected but also enhances understanding of the measured doses. For these reasons, a comprehensive quality control initiative was implemented following documented failures in a 137Cs dosimetry calibration irradiator. This involved systematic charge collection using NIST-traceable ion chambers to observe long-term changes. A Phase I quality control protocol was previously implemented, which employed Shewhart control charts and Nelson's rules to analyze various datasets subgrouped under different conditions. This study addresses the development of a Phase II protocol, which focuses more on uncertainty quantification of systematic errors and irradiator changes, and air kerma precision for dosimetry calibrations. A designed experiment was performed to identify how much systematic errors influence the air kerma. Emphasis was placed on stricter quality assurance protocols, continuous data collection, and additional control charts to monitor short-term changes, such as exponentially weighted moving average control charts. A pre-irradiation control process was implemented to verify that the total air kerma met the measurement quality objective and to show how various uncertainties were applied before calibration. This study indicates how uncertainty is applied given observed air kerma measurements from the irradiator. Ongoing efforts aim to streamline the quality control procedure, ensure consistent data collection, and assess its impact on dosimetry applications.

Scientific evidence and specific context: leveraging large language models for health fact-checking

PurposeThis study aims to evaluate the performance of LLMs with various prompt engineering strategies in the context of health fact-checking.Design/methodology/approachInspired by Dual Process Theory, we introduce two kinds of prompts: Conclusion-first (System 1) and Explanation-first (System 2), and their respective retrieval-augmented variations. We evaluate the performance of these prompts across accuracy, argument elements, common errors and cost-effectiveness. Our study, conducted on two public health fact-checking datasets, categorized 10,212 claims as knowledge, anecdotes and news. To further analyze the reasoning process of LLM, we delve into the argument elements of health fact-checking generated by different prompts, revealing their tendencies in using evidence and contextual qualifiers. We conducted content analysis to identify and compare the common errors across various prompts.FindingsResults indicate that the Conclusion-first prompt performs well in knowledge (89.70%,66.09%), anecdote (79.49%,79.99%) and news (85.61%,85.95%) claims even without retrieval augmentation, proving to be cost-effective. In contrast, the Explanation-first prompt often classifies claims as unknown. However, it significantly boosts accuracy for news claims (87.53%,88.60%) and anecdote claims (87.28%,90.62%) with retrieval augmentation. The Explanation-first prompt is more focused on context specificity and user intent understanding during health fact-checking, showing high potential with retrieval augmentation. Additionally, retrieval-augmented LLMs concentrate more on evidence and context, highlighting the importance of the relevance and safety of retrieved content.Originality/valueThis study offers insights into how a balanced integration could enhance the overall performance of LLMs in critical applications, paving the way for future research on optimizing LLMs for complex cognitive tasks.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2024-0111

Cultural Influences on Consumer Behavior in Rohilkhand :An Empirical Analysis of Traditions, Values, and Social Norms

This study examines cultural factors such as customs, social norms, and values, and their influence on consumer buying behavior in Rohilkhand. It aims to explore how culture affects consumers' purchasing decisions and preferences within this specific context. Data were collected and analyzed from 399 consumers living in Rohilkhand, focusing on key aspects of culture: festivals, social norms that shape community beliefs, personal values, and family that influence their buying behavior.

Information that matters: Exploring information needs of people affected by algorithmic decisions

Every AI system that makes decisions about people has a group of stakeholders that are personally affected by these decisions. However, explanations of AI systems rarely address the information needs of this stakeholder group, who often are AI novices. This creates a gap between conveyed information and information that matters to those who are impacted by the system’s decisions, such as domain experts and decision subjects. To address this, we present the “XAI Novice Question Bank”, an extension of the XAI Question Bank (Liao et al., 2020) containing a catalog of information needs from AI novices in two use cases: employment prediction and health monitoring. The catalog covers the categories of data, system context, system usage, and system specifications. We gathered information needs through task based interviews where participants asked questions about two AI systems to decide on their adoption and received verbal explanations in response. Our analysis showed that participants’ confidence increased after receiving explanations but that their understanding faced challenges. These included difficulties in locating information and in assessing their own understanding, as well as attempts to outsource understanding. Additionally, participants’ prior perceptions of the systems’ risks and benefits influenced their information needs. Participants who perceived high risks sought explanations about the intentions behind a system’s deployment, while those who perceived low risks rather asked about the system’s operation. Our work aims to support the inclusion of AI novices in explainability efforts by highlighting their information needs, aims, and challenges. We summarize our findings as five key implications that can inform the design of future explanations for lay stakeholder audiences.

Context-specific variation in life history traits and behavior of Aedes aegypti mosquitoes.

Aedes aegypti, the vector for dengue, chikungunya, yellow fever, and Zika, poses a growing global epidemiological risk. Despite extensive research on Ae. aegypti's life history traits and behavior, critical knowledge gaps persist, particularly in integrating these findings across varied experimental contexts. The plasticity of Ae. aegypti's traits throughout its life cycle allows dynamic responses to environmental changes, yet understanding these variations within heterogeneous study designs remains challenging. A critical aspect often overlooked is the impact of using lab-adapted lines of Ae. aegypti, which may have evolved under laboratory conditions, potentially altering their life history traits and behavioral responses compared to wild populations. Therefore, incorporating field-derived populations in experimental designs is essential to capture the natural variability and adaptability of Ae. aegypti. The relationship between larval growing conditions and adult traits and behavior is significantly influenced by the specific context in which mosquitoes are studied. Laboratory conditions may not replicate the ecological complexities faced by wild populations, leading to discrepancies in observed traits and behavior. These discrepancies highlight the need for ecologically relevant experimental conditions, allowing mosquito traits and behavior to reflect field distributions. One effective approach is semi-field studies involving field-collected mosquitoes housed for fewer generations in the lab under ecologically relevant conditions. This growing trend provides researchers with the desired control over experimental conditions while maintaining the genetic diversity of field populations. By focusing on variations in life history traits and behavioral plasticity within these varied contexts, this review highlights the intricate relationship between larval growing conditions and adult traits and behavior. It underscores the significance of transstadial effects and the necessity of adopting study designs and reporting practices that acknowledge plasticity in adult traits and behavior, considering variations due to larval rearing conditions. Embracing such approaches paves the way for a comprehensive understanding of contextual variations in mosquito life history traits and behavior. This integrated perspective enables the synthesis of research findings across laboratory, semi-field, and field-based investigations, which is crucial for devising targeted intervention strategies tailored to specific ecological contexts to combat the health threat posed by this formidable disease vector effectively.