“COVID-19”Search All-EnPress Publisher LLC.

Nov 24, 2025

Adaptive smart biomaterials to overcome metastatic tumor microenvironment heterogeneity: Theranostic strategies and clinical translation roadmaps

Over 90% of cancer-related mortality worldwide is due to metastatic disease since the dynamic tumor microenvironment poses huge challenges in preventing the spread of metastatic cancer. Introducing the advent of advance biomaterials and their swift evolution, this review highlights the great potential of innovative biomaterials to proficiently tackle the metastatic tumor environment. Focusing on four distinct categories of biomaterials systems, action mechanism of biomaterials utilized in anti-tumor therapy is explained in detail: 1. Nanoplatforms sensitive to biochemical cues including pH, redox, and enzymes are known as stimuli-responsive nanoplatforms that react according their environment, 2. Smart nanoplatforms changing their morphology to penetrate impermeable physical barriers at tumor site, 3. Ingenious biomaterial participating in tumor normalization, and 4. Nanoplatforms with real-time theranostic capabilities due to innate feedback-loop mechanism. Ingeniously structured biomaterials with extensive evidence in preclinical efficacy encourage their inclusion in metastatic tumor therapy however, their utilization in medical settings is prevented due to various challenges; impractical manufacturing cost, regulatory and safety issues as well as large-scale production are major challenges restraining their widespread use. A concrete framework is proposed in this review to accelerate the biomaterial structure standardization process, following the GMP and other regulatory guidelines with the aim of implementing biomaterial-based tumor diagnostics and therapies. Since incorporating advancing technologies in tumor therapy such AI-driven, autonomous biomaterial structure or patient-specific tumor models would enable confront the proliferating metastatic tumor cases.

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Jun 16, 2025

An inquiry into the psychological repercussions considering COVID-19 pandemic

The COVID-19 pandemic has instigated global lockdowns, profoundly altering daily life and resulting in widespread closures, except for essential services like healthcare and grocery stores. This scenario has notably intensified mental health challenges, particularly among children and adolescents. Influenced by a myriad of factors including developmental stages, educational backgrounds, existing psychiatric disorders, and socioeconomic status, the pandemic’s impact extends beyond the immediate health crisis. This paper critically examines the multifaceted effects of the pandemic on mental and physical health across various age groups. It highlights the increased incidence of stress, anxiety, and depression, underscoring the pandemic’s deep psychological footprint. Additionally, the paper explores the societal implications, from altered family dynamics and educational disruptions due to the shift to online learning, to workplace transformations. These changes have led to a mix of adaptive responses and adverse effects, including heightened domestic tensions and mental health issues. The paper also delves into the ethical challenges faced by medical professionals during this crisis, balancing urgent patient care with ongoing medical research and mental health considerations. This analysis aims to provide a comprehensive understanding of the COVID-19 pandemic’s extensive impact on health and society, emphasizing the importance of addressing mental health as a crucial component of the response strategy.

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Feb 5, 2025

The linkage of government assistance programs towards digital and financial well-being of small household income families in Urban Area: Lesson from COVID-19 pandemic

The COVID-19 pandemic and the Movement Control Order (MCO) have had a negative impact on the financial well-being and digital well-being of B40 or small household income families in urban areas. In responding to the crisis, the government had launched multiple assistance programs to assist the group. Hence, the study intends to examine the relationship of government assistance towards digital and financial well-being of the group. Utilizing survey, the study involved 417 respondents living at the Public Housing Project (PPR) residential areas in Kuala Lumpur—the capital of Malaysia. The data was analyzed using Smart PLS and the key findings were (1) government assistance programs significantly affect the digital well-being and financial well-being of B40 urban families. However, there is no significant relationship between financial well-being and digital well-being. Besides, the Importance-Performance Analysis (IPA) indicated that the importance of government assistance programs towards digital well-being performance is higher than financial well-being. The assistance programs offered by the government plays essential role to ease the digital and financial constraints of B40 urban families during a difficult time. Reforms to the government assistance programs, the empowerment of digital aid packages, and the enhancement of social security net initiatives for B40 urban families are necessary to enhance the group’s preparedness and resilience in the face of new shocks in the future.

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Jan 21, 2025

E-learning and campus recruitment: Transforming arts and science students amid Covid-19

The COVID-19 pandemic has fundamentally transformed the global education landscape, compelling institutions to adopt e-learning as an essential tool to sustain academic activities. This research examines the critical impact of e-learning on arts and science college students in Coimbatore, with an emphasis on its influence on their readiness for campus recruitment. Using a survey of 300 students, this study investigates their perceptions of online education, highlighting both its advantages, such as flexibility and accessibility, and its challenges, including engagement barriers and technical limitations. Data was collected through structured questionnaires and analyzed using statistical methods to draw meaningful insights. The research also explores the efficacy of online assessments in recruitment processes and assesses students’ awareness of available e-learning platforms and courses. The urgency of this study lies in addressing the pressing need to optimize digital education models as institutions globally transition toward blended learning post-pandemic. The findings underline the dual potential and limitations of e-learning, concluding with actionable recommendations to enhance its effectiveness, particularly in preparing students for competitive employment opportunities.

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Jan 15, 2025

Agritourism as a catalyst for sustainable rural development: Innovations, challenges, and policy perspectives in the post-COVID-19 era

This study investigates the dynamic landscape of agritourism in Thailand, emphasizing innovations, challenges, and policy implications in the post-COVID-19 era. Employing a qualitative approach, including a comprehensive literature review and semi-structured interviews with stakeholders, the research identifies key agritourism models, such as immersive learning experiences, technology-driven agritourism, and unconventional practices like salt and coconut plantations. Findings reveal that agritourism has adapted to shifting market demands through diversification, technological integration, and a heightened focus on sustainability. Notably, technology adoption in precision farming and hydroponics enhances resource efficiency and visitor engagement, while initiatives like rice paddy field tourism and highland agritourism showcase the cultural and ecological richness of rural landscapes. The study underscores the critical role of policy frameworks, infrastructure development, and community empowerment in fostering sustainable agritourism practices. Key policy recommendations include targeted subsidies, capacity-building programs, and harmonized regulatory frameworks to address challenges such as financial constraints, regulatory ambiguities, and inadequate infrastructure. This research contributes to the broader discourse on sustainable tourism and rural development, aligning agritourism with the United Nations Sustainable Development Goals (SDGs). By synthesizing insights on innovation, resilience, and sustainability, this study offers a comprehensive roadmap for policymakers, practitioners, and academics to leverage agritourism as a vehicle for rural revitalization and global sustainability. Future research directions are proposed to explore the long-term impacts of technological integration, community empowerment, and resilience strategies in agritourism.

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Jan 14, 2025

An insight from machine learning perspective for COVID-19 survival prediction in Malaysia based on demographic factor

Malaysia reported its first imported COVID-19 case on 23 January 2020, which marked the country's first confirmed positive case. The first case in Malaysia was from eight close contacts in Johor. The global health landscape has been significantly impacted by the COVID-19 pandemic, with mortality or survival being critical outcomes of interest. This study aims to predict COVID-19 survival occurrences in Malaysia by utilizing machine learning approaches based on demographic factors. The dataset used in this study comprises demographic information of 2,151,315 COVID-19 patients, including nationality, regions, age groups, gender, medical history, vaccine brands, and the number of vaccine doses received between 2020 and 2022. Four machine learning algorithms, namely Logistic Regression, Naïve Bayes, Support Vector Machine, and Artificial Neural Network were employed to assess the relationship between demographic factors and COVID-19 survival. To evaluate the model performance, the datasets are categorized into imbalanced and balanced (down-sampling). The results indicate that the balanced dataset (down-sampling) outperforms the imbalanced dataset in terms of overall accuracy, sensitivity, specificity, precision, and Area Under the Curve (AUC). Based on the analysis, the Artificial Neural Network (ANN) classifier exhibited the highest performance with a specificity 95.2% on a balanced dataset. The model excels in accurately identifying survivors, thereby minimizing false mortality predictions and is selected as the best model for predicting COVID-19 survival. Its capacity to process larger sample sizes, combined with numerous interconnected nodes, enables it to identify complex patterns and extract meaningful insights from diverse datasets, such as demographic factors. Additionally, the optimization of parameters, including the number of layers, learning rate, and activation functions, significantly contributed to its superior accuracy. The study identifies that those of chronic diseases, male, and aged 45 and above as the notable factors associated with lower survival rates among COVID-19 patients. The findings underscore the importance of completing the vaccination series by obtaining at least the second dose, as the first dose alone may not offer sufficient protection. In conclusion, this study successfully achieves its objectives by identifying the optimal dataset configuration and predictive model for forecasting COVID-19 survival based on demographic factors. This network could serve as a benchmark model classifier, offering a valuable tool to predict and promote vaccinations, as well as optimize the general healthcare system during the pandemic outbreak. The study not only contributes to the theoretical understanding of effective COVID-19 prediction but also emphasizes the practical implications of integrating advanced machine learning techniques into pandemic management strategies. Future research can build upon these findings by exploring additional machine learning techniques and considering geographical and environmental factors to further enhance the accuracy of long-term predictions.

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