Technology – ASSET

The Relationship Between Barometric Pressure Changes and Weather-Related Headaches

admin — Sat, 27 Dec 2025 18:03:04 +0000

Weather-related headaches have been reported more frequently during rainy season, particularly in fall and winter. Previous researcher suggested that changes in barometric pressure can cause headaches, dizziness, or join pain, this condition is known as meteropathy. Approximately 30% of the global population or 2.4 billion people experience weather-related physical symptoms. Although meteoropathy is not life-threatening, it can significantly affect daily functioning quality of life. Barometric pressure is the pressure caused by the weight of the air. Change in barometric pressure can affect the middle ear, an air-filled cavity connected to the back of the nose through Eustachian tube. Under normal conditions, this tube equalizes internal and external air pressure. However, when barometric pressure changes rapidly, the eustachian tube may not open properly. This can create imbalances of pressure, placing stress on the eardrum and disturbance in inner-ear fluids balance. These changes can interfere with signals sent to the brain, potentially causing headaches, dizziness, or even fainting. To investigate the relationship between barometric pressure and headaches, and weather-related headaches, long-term barometric data were analyzed using NASA Earth observation datasets. The data recorded on September 2025 reveal a significant drop in barometric pressure between September 26 and 27, with a decrease of approximately 9 hPa within one day. Previous research indicates that pressure drops as small as 5hPa can increase the likelihood of meteoropathy symptoms, suggesting that this rapid decline represented a high-risk condition for weather-related headaches. Further analysis involved comparing cumulative distribution function graphs of Seattle, Boston and Miami. Each line on the graph represents a season (spring, summer, fall, or winter), and the vertical line at x = -5 hPa marks the threshold at which weather-related symptoms are more likely to occur. In Seattle, the probability of experiencing a pressure drop of 5 hPa is only about 4% in summer but increases dramatically to about 22% in winter. Boston shows a similar but slightly higher pattern, while Miami remains below 5% across all seasons, indicating a lower likelihood of weather-related symptoms. The difference is likely due to Miami more stable climate, whereas Seattle and Boston are more frequently affected by strong storm system. In conclusion, the data strongly suggested a clear relationship between barometric pressure change and weather-related headaches. Large barometric pressure changes occur much more often during fall and winters, especially in cities like Seattle and Boston. These frequent pressure changes explain why weather-related headaches are more common during colder seasons. Understanding these patterns can help explain how weather is related to human health and why some regions experience more symptoms than others.

Authors List :

Konomi Karube

Presenting Author :

Konomi Karube

Affiliations :

Lake Washington High School

Email :

ko.karube@outlook.com

Key Words (5 Words Maximum) :

Headache, Barometric Pressure, Weather, Meteropathy

Role of scavenger receptor CD36 in microparticle-mediated in Decompression Sickness neuropathology

admin — Fri, 26 Dec 2025 09:18:26 +0000

Blood-borne microparticles (MPs) play a role in several forms of brain injury, but how they interact with the vasculature and con-tribute to neuroinﬂammation is unknown. The scavenger receptor CD36 is expressed across various cell types and regulates inﬂammation, vascular function, and innate immunity. We hypothesized that CD36 mediates MPs-induced neuroinﬂammatory responses in a murine model of decompression sickness (DCS). Wild-type mice subjected to decompression and naïve mice injected with MPs from decompressed mice exhibited a 2.2 ± 0.5-fold elevation in perivascular MPs deposition, 2.8 ± 0.6-fold elevation of inﬂammatory MPs in blood and 2.4 ± 0.4-fold in cervical lymph nodes, 2.7 ± 0.6-fold increase in neutrophil activation,2.0 ± 0.3-fold increased glymphatic ﬂow, 3.1 ± 0.4-fold increased leakage of six megadalton dextran at the blood-brain barrier, and a doubling of inﬂammatory proteins in brain. These events failed to occur in CD36 knockout mice and those conditionally deﬁcient in endothelial CD36 (FLOX). We conclude that inﬂammatory MPs interact with endothelial CD36 to mediate neuroinﬂammatory responses and vascular injury in DCS

Authors List :

Abid Bhat1, Stephen Thom1

Presenting Author :

Abid Bhat

Affiliations :

Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States

Email :

abidpharma8088@gmail.com

Key Words (5 Words Maximum) :

decompression; microglia; microparticles; neutrophil activation; scavenger receptor CD36

Artificial Intelligence in Healthcare

admin — Fri, 26 Dec 2025 09:14:59 +0000

Artificial Intelligence (AI) has emerged as a pivotal driver of innovation in healthcare, reshaping diagnostic, therapeutic, and administrative paradigms. Recent systematic reviews highlight AI’s capacity to enhance clinical decision-making, medical imaging interpretation, and personalized treatment planning, thereby reducing diagnostic errors and improving patient outcomes. Advanced methodologies such as machine learning, deep learning, and natural language processing enable the rapid analysis of complex datasets, supporting early disease detection and predictive analytics for patient deterioration, epidemic surveillance, and hospital resource allocation. Beyond clinical applications, AI contributes to operational efficiency by automating administrative workflows including electronic health record management, patient scheduling, and billing, allowing healthcare professionals to devote greater attention to patient-centered care. The integration of AI into wearable devices and remote monitoring systems further facilitates continuous patient observation and proactive health management. Despite these advances, challenges persist. Concerns regarding data privacy, ethical governance, algorithmic bias, and regulatory compliance remain central to responsible AI adoption. Ensuring transparency, explainability, and human oversight is critical to mitigating risks and fostering trust among stakeholders. Overall, AI demonstrates immense potential to revolutionize healthcare by improving efficiency, accuracy, and accessibility of medical services. Sustained interdisciplinary collaboration, coupled with robust ethical frameworks, will be essential to harness AI’s transformative capabilities for building a resilient, equitable, and patient-centered healthcare ecosystem.

Authors List :

Syeda Amatul Raheem Shafiya, Humera Quadriya, Shagufta Tarannum, Fehmida Begum, Majid Mohiuddin

Presenting Author :

Syeda Amatul Raheem Shafiya

Affiliations :

Anwarul Uloom College

Email :

shafia.2106@gmail.com

Key Words (5 Words Maximum) :

Artificial Intelligence, Clinical Translation, Deep Learning, Predictive Modelling

Artificial Intelligence in Medical Research and Clinical Practice

admin — Fri, 26 Dec 2025 07:25:14 +0000

Background: Artificial intelligence (AI), including machine learning and deep learning methods, is increasingly applied in biomedical research and clinical care. The expansion of electronic health records, medical imaging, and molecular datasets has created a need for analytical approaches capable of generating clinically actionable insights, as reported in multiple NEJM-, Lancet-, and JAMA-indexed studies.
Methods: We reviewed peer-reviewed studies evaluating AI applications in diagnostics, prognostic modeling, and translational research, including retrospective analyses, prospective cohorts, and randomized or externally validated investigations. Emphasis was placed on multimodal data integration, validation methodology, and real-world clinical performance.
Results: Across oncology, cardiology, and neurology, AI-based models demonstrated improved performance in disease classification, risk stratification, and outcome prediction compared with conventional approaches in multicenter validation studies. In diagnostic imaging and digital pathology, AI systems achieved clinically relevant accuracy in detecting malignancies, retinal disease, fractures, and histopathologic features, with reduced interobserver variability. AI applications also showed benefit in drug discovery, clinical trial design, and patient selection.
Conclusion: Evidence from high-impact medical literature supports the research and clinical value of AI. Continued emphasis on validation, interpretability, bias mitigation, and regulatory oversight is required for safe and effective integration into clinical practice.

Authors List :

Mohammed Imran

Presenting Author :

Mohammed Imran

Email :

muhammed_immu@yahoo.com

Key Words (5 Words Maximum) :

Artificial Intelligence, Clinical Decision Support, Translational Medicine

The Relationship Between Barometric Pressure Changes and Weather-Related Headaches

admin — Thu, 25 Dec 2025 23:07:19 +0000

Background: Weather-related headaches have been reported more frequently during rainy season, particularly in fall and winter. Previous researcher suggested that changes in barometric pressure can cause headaches, dizziness, or join pain, this condition is known as meteropathy. Approximately 30% of the global population or 2.4 billion people experience weather-related physical symptoms. Although meteoropathy is not life-threatening, it can significantly affect daily functioning quality of life.
Barometric pressure is the pressure caused by the weight of the air. Change in barometric pressure can affect the middle ear, an air-filled cavity connected to the back of the nose through Eustachian tube. Under normal conditions, this tube equalizes internal and external air pressure. However, when barometric pressure changes rapidly, the eustachian tube may not open properly. This can create imbalances of pressure, placing stress on the eardrum and disturbance in inner-ear fluids balance. These changes can interfere with signals sent to the brain, potentially causing headaches, dizziness, or even fainting.
To investigate the relationship between barometric pressure and headaches, and weather-related headaches, long-term barometric data were analyzed using NASA Earth observation datasets.
Results: The data recorded on September 2025 reveal a significant drop in barometric pressure between September 26 and 27, with a decrease of approximately 9 hPa within one day. Previous research indicates that pressure drops as small as 5hPa can increase the likelihood of meteoropathy symptoms, suggesting that this rapid decline represented a high-risk condition for weather-related headaches.
Further analysis involved comparing cumulative distribution function graphs of Seattle, Boston and Miami. Each line on the graph represents a season (spring, summer, fall, or winter), and the vertical line at x = -5 hPa marks the threshold at which weather-related symptoms are more likely to occur. In Seattle, the probability of experiencing a pressure drop of 5 hPa is only about 4% in summer but increases dramatically to about 22% in winter. Boston shows a similar but slightly higher pattern, while Miami remains below 5% across all seasons, indicating a lower likelihood of weather-related symptoms. The difference is likely due to Miami more stable climate, whereas Seattle and Boston are more frequently affected by strong storm system. In conclusion, the data strongly suggested a clear relationship between barometric pressure change and weather-related headaches. Large barometric pressure changes occur much more often during fall and winters, especially in cities like Seattle and Boston. These frequent pressure changes explain why weather-related headaches are more common during colder seasons. Understanding these patterns can help explain how weather is related to human health and why some regions experience more symptoms than others.

Authors List :

Konomi Karube

Presenting Author :

Konomi Karube

Affiliations :

Lake Washington High School

Email :

ko.karube@outlook.com

Key Words (5 Words Maximum) :

Headache, Barometric Pressure, Weather, Meteropathy

Emerging Genetic Biomarkers for Breast Cancer Diagnosis

admin — Thu, 25 Dec 2025 20:17:35 +0000

Breast cancer is a complex and heterogeneous disease, and early detection is crucial for improving patient outcomes. Recent advances in genomic technology have facilitated the identification of emerging genetic biomarkers that extend beyond traditional markers such as BRCA1 and BRCA2. BRCA1 and BRCA2 have limitations in identifying high risk individuals and predicting treatment responses. This review synthesizes current research on novel candidate change and genetic alternation that demonstrate potential clinical relevance in brief cancer diagnosis and management. This shift is unable more accurate early detection,refined risk satisfaction and the development of high personalized treatment strategies. Emerging research synthesizes a wide array of new candidate genes and molecular signatures with significant potential for clinical integration. Liquid biopsy biomarkers:Circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), offer non- invasive real time monitoring of tumor dynamics. High penetrance genes:Beyond BRCA1/2, genes like PALB2, CHEK2,ATM and PIK3CA, which contribute to inherited suspectibility and Hereditary breast cancer risk.Moderate risk genes:Variants in genes like RAD51C/D and BARD1 my inform risk satisfaction.Gene expressions signatures:Multi gene panels (e.g., oncotype DX, Mamma Print) predict recurrence risk and guide treatment decisions. Epigenetic markers:DNA methylation patterns, microRNAs and histone modifications are emerging as potential diagnostics. Emerging genetics hold significant potential for refining this cancel diagnosis and management and emphasize the importance of integrating genomic data into clinical practice to advance precision medicine increase cancer care.

Authors List :

Aamna Sultana, Humera Quadriya, Fehmida Begum, Shagufta Tarannum, Majid Mohiuddin

Presenting Author :

Aamna Sultana

Affiliations :

Anwarul Uloom College

Email :

aamnasultana2@gmail.com

Key Words (5 Words Maximum) :

Breast Cancer; BRCA1,2; Liquid biopsy biomarkers: Circulating tumor DNA (ctDNA); circulating tumor cells (CTCs)

Integrative Bioengineering: Multiscale Control and Functional Design in Programmable Morphogenetic Agents (Xenobots)

admin — Thu, 25 Dec 2025 20:12:15 +0000

The emergence of synthetic biological machines necessitates a departure from classical reductionist frameworks toward an integrative paradigm combining developmental biology, information theory, and robotics. This study evaluates the transition from passive cellular assemblies to autonomous, programmable living systems, characterized by the development of “Xenobots”—millimeter-scale bio-machines derived from Xenopus laevis progenitors. We analyze the synthesis of these organisms through the lens of computational morphogenetics and multiscale control. By repurposing pluripotent contractile and epithelial cells, these constructs are engineered to execute specific kinematic tasks. We examine the principles of self-assembly and functional plasticity, arguing that organizational control mechanisms are substrate-independent and can be modeled using algorithmic frameworks. Xenobots demonstrate unique emergent properties, including autonomous locomotion, collective behavior, and robust self-repair following physical trauma. Unlike traditional soft robotics, these biological agents offer complete biodegradability and metabolic efficiency. The synthesis of these systems challenges the conventional dichotomy between “living organisms” and “engineered machines,” suggesting that biological media can serve as a programmable interface for complex task execution. The convergence of developmental biology and bioengineering provides a robust framework for designing adaptive, biodegradable tools for targeted drug delivery and regenerative medicine. By leveraging the inherent intelligence of cellular collectives, this integrative approach opens new frontiers in the creation of biocompatible machines capable of operating within complex physiological environments.

Authors List :

Shaza asim khan, Humera Quadriya, Shagufta Tarannum, Fehmida Begum, Majid Mohiuddin

Presenting Author :

Shaza asim khan

Affiliations :

Anwarul Uloom College

Email :

Khanshazaasim@gmail.com

Key Words (5 Words Maximum) :

Xenobots, Synthetic Biology, Bioengineering, Morphogenesis, Programmable Organisms, Soft Robotics.

From Passive Generation to Autonomous Agency: A Comprehensive Survey of Agentic AI Architectures

admin — Thu, 25 Dec 2025 20:11:01 +0000

The evolution of Artificial Intelligence (AI) from passive generative models to autonomous, goal-driven entities marks a paradigm shift toward Agentic AI. This survey synthesizes recent advancements in agentic architectures, offering a comprehensive analysis of frameworks that transcend traditional Large Language Models (LLMs) by incorporating autonomy, contextual reasoning, tool integration, and environmental feedback loops. Drawing on over 50 state-of-the-art implementations, we classify agentic systems into three principal lineages: symbolic, neural, and hybrid. Symbolic agents rely on persistent state and algorithmic planning, while neural agents leverage stochastic generation and prompt-driven orchestration; hybrid models blend these modalities to enhance adaptability and scalability. A prominent trend identified is the emergence of collaborative agent ecosystems, wherein multiple specialized agents coordinate to execute complex, multi-step workflows—ranging from scientific discovery to software engineering. These systems demonstrate increasing competence in long-horizon planning, dynamic tool selection, and inter-agent communication. However, the transition from sandboxed experimentation to enterprise-grade deployment is hindered by unresolved challenges in reliability, safety, and evaluation metrics. We highlight the need for standardized benchmarks, robust interpretability frameworks, and fail-safe mechanisms to ensure trustworthy operation in real-world environments. This review positions Agentic AI as a foundational layer for next-generation intelligent systems, with implications for autonomous research assistants, adaptive robotics, and scalable decision-making platforms. By bridging conceptual gaps between reactive generation and proactive agency, this work contributes to the theoretical and practical scaffolding required for building resilient, collaborative, and context-aware AI agents

Authors List :

Kubra Fatima, Humera Quadriya, Fehmida Begum, Shagufta Tarannum, Majid Mohiuddin

Presenting Author :

Kubra Fatima

Affiliations :

Anwarul Uloom College

Email :

kayrafatima7722@gmail.com

Key Words (5 Words Maximum) :

Agentic AI, Autonomous Agents, Symbolic Reasoning, Neural Architectures, Tool-Use, Multi-Agent Systems, Safety in AI, AI Evaluation Metrics

Adverse Drug Reactions and Pharmacogenomics

admin — Thu, 25 Dec 2025 20:02:04 +0000

Adverse drug reactions (ADRs) are unintended, harmful responses to medications occurring at normal therapeutic doses and represent a significant challenge in clinical practice and public health. They contribute substantially to patient morbidity, mortality, prolonged hospital stays, and increased healthcare costs worldwide. ADRs may arise due to predictable, dose-dependent effects related to a drug’s pharmacological action (Type A reactions) or unpredictable, dose-independent effects such as hypersensitivity or idiosyncratic responses (Type B reactions). Several factors influence the occurrence of ADRs, including age, genetic predisposition, comorbidities, polypharmacy, and drug–drug interactions. In vulnerable populations such as children, the elderly, and patients with chronic diseases, the risk of ADRs is particularly high. Effective identification, assessment, and prevention of ADRs are therefore critical components of patient safety. Pharmacovigilance systems play a central role in monitoring ADRs through spontaneous reporting, post-marketing surveillance, and clinical studies, enabling early detection of safety signals and regulatory interventions. Healthcare professionals have a vital responsibility to recognize, document, and report suspected ADRs to improve drug safety profiles. Advances in pharmacogenomics and data-driven monitoring tools offer promising strategies for predicting and minimizing ADRs in the future. Overall, understanding ADRs is essential for optimizing therapeutic outcomes, enhancing patient safety, and promoting rational drug use in modern healthcare systems.

Authors List :

Ayesha fatima, Humera Quadriya, Shagufta Tarannum, Fehmida Begum, Majid Mohiuddin

Presenting Author :

Ayesha fatima

Affiliations :

Anwarul Uloom College

Email :

ayeshafatimaaa37@gmail.com

Key Words (5 Words Maximum) :

ADR, Drug, Morbidity, Risk Factor

Integration of Bioinformatics and Statistical Tools in Biomedical Research

admin — Thu, 25 Dec 2025 20:00:19 +0000

The continuous and rapid growth of high-throughput biological data has made the integration of bioinformatics and statistical tools essential in biomedical research. It is deemed essential because of its applicability across several domains like genetics, microbiology, and drug screening for computational and statistical methodologies for data analysis, sequence alignment, and predictive modeling. Bioinformatics tools such as sequence alignment algorithms, genomic databases, and molecular modeling platforms make handling efficient and interpretation of large-scale biological datasets is sped up while maintaining accuracy during handling. Data validation, hypothesis testing, pattern recognition, and model evaluation is executed flawlessly. In genetics it is often utilized in gene identification and annotation, variant and mutation analysis, Genome-wide association studies (GWAS), and similarly in microbiology it is used in microbial genome sequencing, species identification and classification. In drug screening bioinformatics handles and interprets the biological data and statistical tools to analyze the large data and find meaningful patterns so the researchers can identify the potential drug targets, predict how the drug might interact with proteins and analyze the screening results. With determination researchers are quickly adapting to advanced machine learning and AI integration for personalized medicine and precision healthcare while setting up the foundation for real-time genomic and clinical data analysis and automated drug discovery pipelines. In conclusion, integration of bioinformatics and statistical tools is indispensable in modern biomedical research which enables comprehensive analysis across genetics, microbiology, and drug discovery, driving innovation in diagnostics, therapeutics, and personalized medicine.

Authors List :

Adiva Baig, Humera Quadriya, Fehmida Begum, Shagufta Tarannum, Majid Mohiuddin

Presenting Author :

Adiva Baig

Affiliations :

Anwarul Uloom College

Email :

adivabaig@gmail.com

Key Words (5 Words Maximum) :

Artificial intelligence, Bioinformatics, Genome-wide association studies (GWAS), Machine learning, Statistical analysis