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Maternal Gestational Diabetes and Autism Spectrum Disorder in Offspring: Risk Factors, Mechanisms, and Pediatric Implications
No full textIntroduction: Gestational diabetes (GDM) complicates around 14% of pregnancies globally. While GDM’s physiological effects are often transient, its long-lasting effects on the mother and the child are significant. Studies show a heightened chance of autism occurring in offspring subjected to gestational diabetes in utero.
Methods: A thorough search of literature was performed across PubMed, SCOPUS, and ProQuest, identifying 35 relevant studies published between 2012 and 2024. This review focuses on exploring the impact of GDM on the offspring’s chances of developing autism. It aims to explore the factors influencing this relationship, such as the timing of GDM onset, the presence of coexisting complications, and the condition’s underlying mechanisms.
Results: The findings demonstrate that gestational diabetes can significantly increase the risk of autism. Key factors influencing this relationship include the timing of diagnosis, maternal glucose management, and treatment strategies during pregnancy. Potential mechanisms include increased fetal exposure to inflammation, oxidative stress, and immune dysregulation.
Conclusion: The findings highlight the importance of early and effective GDM management and its pediatric implications for improving neurodevelopmental outcomes in offspring during early childhood
Quantifying PM2.5 Pollution and its Mortality Impacts in Islamabad: An Integrated Approach with Observed and Remote Sensing Data Along with HYSPLIT and AirQ+ Models
No full textParticulate matter causes serious impacts on the atomsphere and human health. In recent time, it has become a crucial concern for the future of our health and environment. The study employed an integrated approach using observed and remote sensing data, HYSPLIT backward trajectory analysis, and AirQ+ models to quantify PM2.5 pollution and assess its mortality impacts in Islamabad in past three years (2020 to 2022). Our results showed that winters in all three years were significantly more polluted than summers. 22nd December 2021 emerged as the most polluted day with 198.96 mg/m3 PM2.5 concentration. Overall, the daily average (123.6 mg/m3) and the average annual mean (33.98 mg/m3) for 2020 to 2022 exceeded the standards promulgated by the National and US-EPA’s National Ambient Air Quality Standards (NAAQS). The potential source of the pollution was noticed from eastern side of Islamabad i.e., western India, with the help of the HYSPLIT backward trajectories. The study identified a significant correlation of PM2.5 with meteorological (temperature, dew point temperature, relative humidity, and wind speed) and chemical (SO2, NO2 and CO) factors. Moreover, at a concentration of 46.51µg/m3, the attributable proportion (AP) for all-cause mortality was 25%, while ischemic heart disease (IHD) mortality had an AP of 37%, and stroke mortality was 45%, all higher in winter than summer. Thus, our findings underscore the severe health impacts of PM2.5 and proved Islamabad as one of the polluted cities in the country. High mortality rates associated with PM2.5 exposure emphasize the urgent need for stringent air quality regulations, targeted health interventions, and public awareness campaigns. Future efforts should focus on integrating advanced data collection methods and implementing sustainable policies to mitigate pollution and protect public health
Patient Satisfaction with Laboratory Services: Current Trends and Strategies for Quality Improvement
No full textObjectives patient satisfaction with the quality of laboratory services is a pivotal dimension in ensuring health care quality because it can affect the accuracy of diagnostic results, clinical decision-making, and overall trust in the health system. This review analyzes several critical factors that shape the perceptions of patients, including turnaround time, test accuracy, physician communication, easy access to services, and efficient delivery. The paper then considers trends such as automation, artificial intelligence, telemedicine and digital health integration, and how these new systems are affecting laboratory service quality and the patient experience. Additionally, this review explores mechanisms for quality improvement with a focus on accreditation and continuous quality management systems, ongoing professional education, and mechanisms to integrate patient feedback into systems level reform. Although substantial progress has been made, challenges, including workforce shortages, regulatory variability, and inequities in access to high-quality laboratory services, remain. Evidence-based interventions and patient-centered approaches are used to address these issues with the goal of improving service delivery and healthcare outcomes. This paper offers two important messages to those who influence and guide national policy, healthcare decisions, and development of clinical laboratories across the nation as we strive to change practice to meet new demand
A Comprehensive Review on Breast Cancer Detection and Using Machine Learning Techniques: Methods, and Challenges Ahead
No full textBreast cancer (BC) continues to be a major global health concern, with rising incidence rates each year. Timely identification is essential for enhancing patient outcomes, but conventional diagnostic techniques often fall short in terms of precision and effectiveness. This review explores the role of artificial intelligence (AI) and machine learning in transforming BC detection, with a focus on advancements up to 2024. A thorough review of recent studies was conducted, emphasizing the application of machine learning in BC detection across diverse data sources, including microarray data, medical imaging such as mammography, ultrasound, (Magnetic Resonance Imaging) (MRI), and histopathology, and clinical records. The analysis traces the progression from traditional machine learning methods to sophisticated deep learning frameworks, especially convolutional neural networks (CNNs), and assesses their effectiveness in real-world clinical environments. Advances in AI have led to notable gains in diagnostic accuracy, with deep learning models delivering exceptional performance in experimental studies. Hybrid imaging strategies that integrate multiple imaging modalities with AI algorithms have proven particularly effective, especially in detecting abnormalities in dense breast tissue. Innovations like transfer learning and explainable AI have enhanced the adaptability and transparency of these models. Nevertheless, issues related to data quality, computational demands, and the lack of standardized protocols remain unresolved. Although AI-driven detection systems exhibit considerable potential in research contexts, their broader adoption in clinical practice faces several hurdles. Future progress will depend on overcoming challenges such as data standardization, improving model interpretability, and optimizing computational efficiency. Combining AI technologies with established diagnostic practices offers a promising approach to advancing the accuracy and accessibility of BC detection
Spatial Analysis of Ground Movement Potential, Based on Rock Type and Distance from Active Faults in Ambon City, Indonesia
No full textAmbon City is one of the most landslide-prone areas in Maluku Province. This study aims to analyze the potential for ground motion in Ambon City, Indonesia, by considering rock types and distance from active faults. This research uses geological data and distance from faults, which are then analyzed using the spatial overlay method. The results showed that the area with high potential for land movement disasters is 8,347.14 ha, medium is 12,157.24 ha, and low is 12,069.09 ha. The predicted built-up area affected in the low class is 2,296.23 ha, in the medium class is 1,470.58 ha, and in the high class is 672.36 ha. These findings highlight the need for special attention to areas with high ground motion potential, as well as the importance of effective mitigation strategies to protect infrastructure and communities. This research is expected to provide useful insights for spatial planning and disaster risk management in Ambon City
An Effectiveness Analysis of Tax Administration Digitalization Reducing the Shadow Economy in Indonesia
No full text“Micro, small, and medium enterprises (“MSMEs”)” play an important role in Indonesia’s economy but are often difficult to tax due to their informal operations. To address this, the government is promoting digitalization to curb the shadow economy. This study aims to evaluate the effectiveness of digitizing tax administration in reducing the shadow economy, analyze the challenges and solutions associated with this process, and examine its impact on state revenue and taxpayer compliance within the informal sector. Using a qualitative research approach, data were collected through in-depth interviews and direct observations involving MSME actors and officials from the Directorate General of Taxes “(“DGT”)”. The findings indicate that the implementation of tax digitalization, particularly through the core tax system, can be assessed using the balanced e-government model, which consists of four dimensions: benefits, efficiency, transparency, and participation. According to the Information Systems Success Model (ISSM), the digitalization of tax administration has been fairly effective. Nevertheless, challenges remain for the DGT in executing these digital initiatives. Despite these obstacles, tax digitalization is expected to simplify tax compliance for MSMEs, thereby increasing taxpayer adherence, boosting state revenue, and reducing the shadow economy
A Systematic Review of Firm Sustainability Metrics: Analytical Approaches to Performance and Impact Assessment
No full textThis study aims to identify key factors that influence firm sustainability, examining their impacts and other related relationships. Using a Systematic Literature Review (SLR) approach, the research seeks to determine the most effective sustainability metrics applicable across various industrial sectors and to evaluate their impact on firm sustainability. The review was conducted using articles sourced from the Scopus database, selected based on predetermined criteria. Following a screening process, 20 relevant articles were selected and analyzed in detail, considering elements such as research type, year of publication, methodologies, and key findings. The findings indicate that both internal and external organizational factors can influence firm sustainability. This study contributes to the growing body of knowledge on sustainability by offering insights into effective metrics and framework. It is also intended to support companies in developing strategies to enhance long-term sustainability while enriching the theoretical understanding of sustainability in organizational contexts
Financial Literacy, Credit Access, and Financial Decision-making: An Integrated Framework for Micro-enterprises
No full textMicro-level businesses function as essential components of worldwide economic development, particularly in developing nations. This systematic literature analysis investigates the interrelationship between financial literacy, credit access, and financing decisions in micro-scale enterprises through the establishment of an integrated theoretical framework. Through analysis of literature from 2018 to 2024 using the Scopus database, 13 relevant studies were selected from 219 articles after quality assessment. The study integrates the Theory of Planned Behavior as grand theory, with Behavioral Finance Theory, Signaling Theory, and Pecking Order Theory as bridge theories. The findings reveal that financial literacy positively influences both credit access and financial decision-making, with credit access serving as a mediating mechanism. The study contributes theoretically by extending these theories in micro-enterprise contexts and demonstrating how financial literacy functions as a signaling mechanism. This review offers both theoretical advancement through novel integration and practical insights for policymakers to design effective financial literacy programs and credit access mechanisms, supporting micro-enterprise success
Building Accounting Cost Production Using Analytic Network Process and Full Costing Method for Leather Industry Craft in Malang
No full textThis article discusses accounting management for cost production and calculation accounting-based material use for leather crafting industry study case UKM SuyteeStore in Malang. This design application calculation from raw material leather until cost production is influenced by all variables in leather industry calculation. Analytic network process (ANP) is method for decision product with variables from owner leather industry based on sale data in this management application. The result are most productive and profitable for industry before cutting process material. Full costing method in this article development with variable process cutting material leather as one attribute for calculation accounting-based method cutting material is more profitable. This application is built using web programming and results in cost production from influenced attributes. The leather industry begins with the input value in the ANP method andthen calculates Full Costing with attribute values input by the main industry to obtain the most profitable result for the leather industry
The Effect of Beauty Influencers and Social Media Advertising on Purchase Decisions for Skincare Products Through Purchase Intention as a Mediating Variable: A Study on Somethinc Product Consumers in Beji, Bangil, and Pandaan
No full textThe swift expansion of the cosmetics industry in Indonesia can be seen as part of the impact of globalization, where beauty and skincare trends from various countries are quickly influencing consumer preferences in Indonesia. With the development of technology and social media, information about beauty trends from various parts of the world can be easily accessed by Indonesians. The trend of beauty products in Indonesia itself has shown a positive trend in recent years. People’s increasing interest in personal beauty is the main factor that drives the increasing use of beauty products. This research analyzes the impact beauty influencers and social media advertising have on purchasing decisions for skin care products, with purchase intention as a mediating variable. Using a descriptive quantitative method with purposive sampling technique with a population of consumers of Somethinc products in Beji, Bangil, and Pandaan Districts, Pasuruan, East Java, data collection was carried out through distributing questionnaires. The sample size used was 120 respondents. The data obtained were analyzed using statistical programs for social science analysis techniques through SPSS 20.0 software and multivariate analysis techniques through WarPLS 8.0 software. The results showed that beauty influencers have a significant effect on purchasing decisions, while social media advertising has no effect. Purchase interest has a positive and significant effect on purchasing decisions and is able to fully mediate the influence of beauty influencers and partially the influence of social media advertising. Based on the results of this study, Somethinc product companies are expected to be able to increase purchases through consumer purchasing decisions and beauty influencers