Analysis Platform Research Articles

Abstract A021: Analytical platform to validate microRNA cancer biomarkers

Abstract We developed and tested a novel assay to quantify and validate microRNA (miRNA) cancer biomarkers in liquid biopsies. The assay is amplification-free, exploits a commercially available nanopore-array device for detection (MinION from Oxford Nanopore Technologies), uses total RNA isolated from biospecimen as the miRNA source, and proprietary osmylated oligonucleotides, complementary to the target miRNA, as probes. The assay exploits a bracketing experimental protocol to quantify miRNA, and yields miRNA copy numbers (copies) per 1microLiter of isolated total RNA, with an unprecedented accuracy of approximately +/- 20% across all concentrations. For development purposes and as a control/reference the combined serum of healthy men (# H6914 from Sigma-Aldrich) was used. During a period of 4 years different lots of H6914 yielded, within experimental error, comparable miRNA copies for let-7b, miR-16, miR-21, miR-15b, miR-375 and miR-141. Serum samples from healthy individuals and cancer patients were also tested. Once data were normalized to the same total RNA content, miRNA copies appeared to belong to one of two groups. The first group included miR-21, miR-375 and miR-141 copies in H6914 and in healthy samples. The second group included miR-21, miR-375 and miR-141 copies in cancer samples exhibiting an approximate 1.8-fold overexpression compared to the first group. miR-15b copies in H6914, healthy and cancer samples were all in the first group. The two groups exhibited zero data overlap which is unprecedented in the miRNA/cancer literature. Zero data overlap was the result of (i) the assay’s bracketing approach with a protocol-defined accuracy of approximately +/-20%, and (ii) the normalization of miRNA copies to the same total RNA content. Notably, these two groups included both serum and urine samples from cancer patients and healthy individuals. All subjects were deselected regarding age, sex, and ethnicity, while patients were selected for breast, prostate or pancreatic cancer, Stage I or II, pretreatment. To the best of our knowledge, this is the first-time agreement between serum and urine miRNA copies is reported and leads to the suggestion that a urine sample may replace a blood draw for the tested miRNAs. Not only clear discrimination between healthy and cancer samples was possible, but this analytical platform may be used to validate tentative cancer/disease biomarkers when miRNA overexpression between cancer/disease and healthy is equal or higher than 1.8-fold. Non-Coding RNA 2024, 10(4), 42; https://doi.org/10.3390/ncrna10040042 Citation Format: Anastassia Kanavarioti, M. Hassaan Rehman, Salma Qureshi, Aleena Rafiq, Madiha Sultan. Analytical platform to validate microRNA cancer biomarkers [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A021.

The Evolution of Data Visualization Tools and Their Impact on Business Decision-Making : A Historical Analysis

This article examines the transformative evolution of data visualization tools and their profound impact on business decision-making processes. Through a comprehensive analysis of historical developments, theoretical frameworks, and technological advancements, the article explores how visualization tools have progressed from basic manual charts to sophisticated AI-powered analytical platforms. The investigation encompasses the cognitive foundations of visual information processing, the emergence of first-generation Business Intelligence tools, and the current landscape of modern visualization platforms. Particular attention is given to the integration of artificial intelligence in data visualization, including automated generation, pattern recognition, and predictive analytics capabilities. The article presents both quantitative and qualitative assessments of these tools' impact on organizational decision-making, highlighting significant improvements in decision speed, accuracy, and user engagement. The article reveal substantial benefits in terms of ROI, decision-making efficiency, and organizational adoption patterns. The article concludes by examining emerging trends, including the potential of extended reality technologies and edge computing, while acknowledging current limitations and providing recommendations for future research directions in this rapidly evolving field.

Using Google Earth Engine open-source code for land surface temperature estimation from Landsat data in Chuong My district, Hanoi city, Vietnam

Land surface temperature (LST) in association with urbanization has significantly influenced on local climates and terrestrial surface processes. By using multi-temporal Landsat data in the GEE platform, the study has found that there have been changes in land covers due to the main drivers of industrial development, urban and built-up expansion. This study estimated land surface temperature (LST) in Chuong My district from Landsat 5, 7, 8 and 9 thermal infrared sensors, using different surface emissivity sources. The Google Earth Engine (GEE), an advanced earth science data and analysis platform, offers the estimation of LST products, covering the time period from 2000 to 2024 in study site. RF algorithm for land cover classification and mapping is suggested to be applied in Chuong My district, with overall accuracy of land cover mapping in 2024 being 91.9% with Kappa coefficient of 0.89. The period 2000-2010 showed that 327.0 ha of land were converted to land for industrial development, urban and residential land, while the period 2010-2024 continued to witness a continued conversion of land to other land use purposes, with a decrease in agricultural land area (65.7 ha) and an increase in industrial land use (65.2 ha), but the rate of conversion was much lower than the previous period (2000-2024). The periods of 2000-2010 and 2010-2024 showed that there have been significant changes in land surface temperature. Most of the surface temperature in the entire region increased by over 20C, especially in the period 2003-2015 when a total area of 1424 ha had the temperature increased by above 30C. This is a consequence of the process of urbanization and industrialization, which began to take place in 2008, along with the process of changing the land cover status of the study area. Further studies are needed to better understand the thermal conductivity of surface materials and to plan how to reduce LST in such areas.

#PorkandPigs: an online media listening analysis of public perception of the U.S. swine industry

Abstract With the majority of the U.S. swine industry being generally bounded by social licensing, there is a growing need to understand social perspectives to better adapt to consumer demands. Online and social media data are rich datasets that researchers are leveraging to tackle economic and societal challenges. The information that can be gleaned from online media regarding public perceptions makes it an important tool for producers to understand driving topics of interest to the public. This study utilized a prominent online and social media listening and data analytics platform to identify and quantify online and social media mentions related to the U.S. swine industry from January 1, 2020, to December 31, 2023. A general search for online media referencing pork and pigs was designed resulting in a total of 41,093,309 mentions. The search was further queried to specific topics of food and cooking (20,580,068 mentions), price (2,866,548 mentions), welfare (1,615,208 mentions), sustainability (771,819 mentions), and top authors/organizations within the U.S. swine industry (1,430 mentions). The study spanned the geographic locations of the overall U.S. (including U.S. Minor Outlying Islands), the top 5 most populous states, and the top 5 pork producing states as of 2024. Of all mentions, X/Twitter was the largest domain for nearly all mentions related to pork and pigs. Major organizations designed to spread information between the general public and pork producers had a minor impact on the overall conversation and no online media presence in the top 5 most populous states. Net sentiment was overall positive across all four years and all geographies except for major events such as the COVID-19 pandemic, the H1N1 Swine Flu, the passing of California Proposition 12, environmental events, and inflation-associated pork prices. Overall, most media presence had positive net sentiments and with most interest surrounding ways to cook pork and how to keep bacon cheap. Understanding public perceptions of the U.S. swine industry provides the opportunity to make informed decisions on marketing strategies and production practices.

FORMATION OF A CONTROL SYSTEM IN AN AGRO-INDUSTRIAL ENTERPRISE

The article examines the importance of control in the management systems of agro-industrial enterprises, which is critical for ensuring their stability and effectiveness in a dynamic environment. The aim of the article is to analyze, systematize, generalize, and improve the theoretical and methodological foundations for the formation of a control system in an agro-industrial enterprise. It is determined that in the context of constant changes affecting agriculture, the control function becomes particularly significant as it helps adapt enterprises to new challenges and prevents negative consequences. The study presents the elements of the control system in agro-industrial enterprises, which include: planning (establishing goals, defining standards, and developing plans that serve as a basis for further monitoring and control), monitoring (constant collection and analysis of information on the execution of production processes), deviation analysis (evaluating performance results and comparing them with planned indicators), and feedback mechanisms (providing information on the effectiveness of production processes and their results, which is used to update plans and improve subsequent stages of control). Key control tools applicable at all stages of agro-industrial enterprises' activities are presented: planning and forecasting, result analysis and reporting, monitoring and information, auditing and inspections, quality control of products, budgeting, verification and compliance with environmental standards, personnel management, risk management, and technological control. It is found that timely application of control tools helps avoid losses and maintain high product quality. The purpose of control is justified as ensuring compliance of all production processes with established plans and standards to achieve economic efficiency, reduce costs, and improve product quality. The study allowed for the identification of the significance of integrating modern control methods and information technologies, which significantly enhance the effectiveness of management processes. It is noted that the application of modern technologies, such as ERP systems, IoT, and analytical platforms, significantly improves control over all stages of production and supply.

QLTI-07. COLLABORATIVE RESEARCH SUITE FOR GENOMIC ANALYSIS, DATA REPORTING AND WORKFLOW CUSTOMISATION

Abstract The genomic analysis pipeline involves multiple stages, including data extraction, transformation, and loading (ETL), exploratory data analysis (EDA), machine learning (ML) modeling, and reporting. The ETL stage involves data profiling and quality control measures to ensure data integrity and consistency. The EDA stage employs statistical and visualization methods to understand data distribution, identify patterns, and detect anomalies. Machine learning modelling involves supervised classification techniques, such as logistic regression and support vector machines (SVMs), to predict tumour classification and develop gene signatures. Unsupervised classification techniques, such as K-means clustering and Hierarchical clustering, identify patterns and relationships in genomic data. Quality metrics evaluate data completeness, accuracy, and consistency, ensuring data reliability and validity. The integration of data science techniques into a comprehensive framework can be achieved with the development of Application Dashboards that provide real-time insights into genomic data across every stage of the analysis pipeline. This enables clinical researchers and bioinformaticians to make informed decisions quickly, improving turn-around time and accelerating the discovery of new insights. In contrast to conventional methods of using stand-alone scripts, these dashboards offer a reusable and scalable solution, allowing for easy modification and adaptation to new datasets and analysis pipelines, thereby reducing development effort and increasing productivity. The dashboards constitute a centralized platform for data visualization, exploration, and analysis, facilitating collaboration and communication among research team members. With customizable and interactive visualizations, users can explore genomic data from multiple dimensions and the integration of multiple sources, including genomic and transcriptomic data. The technologies used to power this Research suite include Streamlit for Web App development, Python for data analysis, Opensearch for data storage and other utility libraries for reporting.

CNSC-08. PROGNOSTIC BIOMARKER DISCOVERY THROUGH PROTEOMIC ANALYSIS OF GLIOBLASTOMA MULTIFORME

Abstract BACKGROUND Glioblastoma multiforme (GBM) is an aggressively malignant brain tumor known for its high recurrence rates and dismal survival outcomes. Despite advancements in surgical and therapeutic strategies, the prognosis for GBM patients remains poor. This study applies proteomic analysis to postoperative GBM tumor specimens to identify molecular markers that could enhance prognosis prediction. Through the analysis of protein expression profiles before and after tumor recurrence, we identified significant alterations in protein pathways via Gene Set Enrichment Analysis (GSEA), highlighting potential biomarkers for disease outcome prediction. METHODS We employed Sequential Window Acquisition of All Theoretical Fragment Ion Spectra (SWATH-MS) proteomics to analyze protein samples from both primary and recurrent GBM patients. Gene Set Enrichment Analysis (GSEA) was utilized to compare protein expression profiles and discern significantly altered pathways. RESULTS Analysis revealed 778 differentially expressed proteins (DEPs), with notable alterations in pathways such as glycolysis, interleukin signaling, and MAPK signaling. Proteins such as TOLLIP, ENO1, and ITGA2B emerged as potential prognostic markers. Validation through an online tumor prognostic analysis platform (ToPP), integrated with The Cancer Genome Atlas (TCGA) data, facilitated the development of a prognostic risk score model. This model effectively categorized patients into high and low-risk groups based on their protein expression profiles, enhancing the prediction of patient outcomes. The proteins and pathways identified offer new insights into the molecular dynamics of GBM postoperatively. The association between protein expression profiles and patient prognosis underscores the potential of these markers in developing customized therapeutic strategies. Additionally, the prognostic risk score model serves as a valuable tool for improving prognostic accuracy and could guide personalized treatment plans. CONCLUSION Proteomic profiling of GBM tumors significantly contributes to uncovering the molecular mechanisms driving this aggressive cancer and facilitates the development of dependable prognostic tools. These findings not only affirm the critical role of proteomics in cancer research but also advance personalized medicine strategies in GBM management.

The current state and trends of immunotherapy research in lung cancer: a review and bibliometric analysis

In recent years, the integration of immunotherapy in the treatment of lung cancer has marked a significant evolution in the field. This is evidenced by the surge in the volume of scientific publications, reflecting rapid advances over time. This paper presents a bibliometric analysis of lung cancer and immunotherapy research from January 2012 to December 2022, drawing on the Web of Science literature database and using the citexs data analysis platform to examine the shifts in topic hotspots over the decade. A total of 8,722 publications were retrieved, with annual publication numbers soaring from 79 in 2012 to 2,112 in 2021. The most prolific country in terms of publication volume was China (n = 3,363, 38.56%), with The University of Texas MD Anderson Cancer Center making the most significant institutional contribution (n = 156, 1.79%). Notably, the most productive authors in this domain were Benjamin Besse and Marina Chiara Garassino, who have collectively published 35 articles to date. Predominant research themes include PD1/PDL1, clinical trials, pembrolizumab, nivolumab, and immune checkpoint inhibitors. Moreover, this paper visualizes the analysis of journals, keywords, key genes and targets, and associated diseases, aiming to provide a systematic review and a forward-looking perspective on research in lung cancer and immunotherapy. By exploring current research dynamics and hotspots and identifying areas for improvement, this study seeks to provide valuable insights for future investigations in this burgeoning field.

Google Earth Engine-based Mangrove Mapping and Change Detections for Sustainable Development in Tien Yen District, Quang Ninh Province, Vietnam

Abstract. Vietnam secures a place among the top countries that possess the largest mangrove areas worldwide. The mangrove forests are mainly found in the Northern, Northeast, Central and Southern Delta, providing goods to habitants, and make significant help mitigate global climate change. Despite this, mangroves are severely threatened because of extensive deforestation in Vietnam. Recent advances have utilized remotely sensed imagery to present the spatial and temporal distribution of mangroves. Nevertheless, the approach is limited by imagery availability and computing resources, and difficult to share within the community. Therefore, a shareable Web-based tool that reinforces coastal managers to monitor the changes in mangroves is needed. Recently, Google Earth Engine (GEE) is a cloud-based geospatial analysis platform, which allows users to freely exploit the availability of satellite imagery and harness their computing capacity. This research aims to use GEE to detect mangrove changes, a case study in Tien Yen district, Quang Ninh, over a period from 2010 to 2020. Four supervised classification algorithms, including Random Forest (RF), Support Vector Machine (SVM), Naïve Bayes classifier, and Classification and Regression Trees (CART) have been implemented on GEE platform to select the best algorithm to produce spatial-temporal mangrove maps, then change detection of mangroves is performed. The results showed that RF demonstrated the highest accuracy with overall accuracy and kappa values of 91.04% and 0.75 respectively. The mangrove areas statistically reported by the GEE-based platform are in line with the governmental statistic report annually, marking the capabilities of GEE in natural resource management.

BindCompare: A Novel Integrated Protein-Nucleic Acid Binding Analysis Platform

Abstract Summary Advanced genomic technologies have generated thousands of protein-nucleic acid binding datasets that have the potential to identify testable gene regulatory network (GRNs) models governed by combinatorial associations between factors. Transcription factors (TFs) and RNA binding proteins (RBPs) are nucleic-acid binding proteins regulating gene expression and are key drivers of GRN function. However, the combinatorial mechanisms by which the interactions between specific TFs and RBPs regulate gene expression remain largely unknown. To identify possible combinations of TFs and RBPs that may function together, developing a tool that compares and contrasts the interactions of multiple TFs and RBPs with nucleic acids to identify their common and unique targets is necessary. Therefore, we introduce BindCompare, a user-friendly tool that can be run locally to predict new combinatorial relationships between TFs and RBPs. BindCompare can analyze data from any organism with known annotated genome information and outputs files with detailed genomic locations and gene information for targets for downstream analysis. Overall, BindCompare is a new tool that identifies TFs and RBPs that co-bind to the same DNA and/or RNA loci, generating testable hypotheses about their combinatorial regulation of target genes. Availability and Implementation BindCompare is an open-source package that is available on the Python Packaging Index (PyPI, https://pypi.org/project/bindcompare/) with the source code available on GitHub (https://github.com/pranavmahabs/bindcompare). Complete documentation for the package can be found at both links. Supplementary information Supplementary data are available at Bioinformatics online.

Network pharmacology and molecular docking to explore the potential molecular mechanism of chlorogenic acid treatment of oral squamous cell carcinoma

Oral squamous cell carcinoma (OSCC) is a tumor type with a high mortality rate. Chlorogenic acid, abundant in resources and widely utilized in cancer treatments, has seen limited studies regarding its efficacy against OSCC. This paper investigates chlorogenic acid’s mechanism in treating OSCC, aiming to guide the development of novel drugs. The study employed network pharmacology, molecular docking, and survival analysis methods. Network pharmacological analysis revealed chlorogenic acid targets 23 OSCC-related proteins, including ESR1, MMP2, MMP9, SRC, MAPK8, MAPK1, CDC42, ERBB2, ATM, and BRAF. Molecular docking simulations indicated that the primary target exhibits significant binding capacity with chlorogenic acid, with MMP9 associated with tumor migration and angiogenesis standing out. Survival analysis demonstrated that the downregulation of most primary targets correlates with improved survival rates in OSCC patients. Enrichment analysis of therapeutic targets highlighted the pivotal role of MAPK-ERK and MAPK-JNK signaling pathways in chlorogenic acid’s efficacy against OSCC. This paper predicts chlorogenic acid’s potential targets and proposes its molecular mechanism in treating OSCC, offering a theoretical foundation for its application in OSCC treatment. We used traditional Chinese medicine, a disease pharmacology-related information base, and an analysis platform to predict targets. The Cytoscape 3.9.1 and STING databases were used to address common targets for drugs and diseases, establish networks of protein interaction relationships, and screen core targets. Meastro11.5 was used for molecular docking simulation. R4.2.2 was used for survival analysis and joint target enrichment analysis. Network pharmacological analysis identified chlorogenic acid acting on 23 OSCC targets. Molecular docking simulations revealed a strong binding affinity of chlorogenic acid compounds with these targets, particularly MMP9, essential for tumor migration and angiogenesis. Survival analysis indicated that the downregulation of most core targets was correlated with improved OSCC patient survival. Enrichment analysis of therapeutic targets highlighted the critical roles of the MAPK-ERK and MAPK-JNK signaling pathways in the effectiveness of chlorogenic acid against OSCC. This study predicted the potential targets of chlorogenic acid in OSCC treatment and hypothesized its molecular mechanism, offering a theoretical foundation for its use in OSCC therapy.

Influence of Temperature Variability on the Efficacy of Negative Ions in Removing Particulate Matter and Pollutants: An Experimental Database

Cities globally must make urgent decisions to ensure a sustainable future as rising pollution, particularly PM2.5, poses severe health risks like respiratory and heart diseases. PM2.5’s harmful composition also impacts vegetation and the environment. Immediate government intervention is necessary to mitigate these effects. This study tackles the urgent problem of reducing PM2.5 levels in Medellín’s urban and indoor environments, where pollution presents serious health risks. To explore effective solutions, this research provides new data on the interaction between particulate matter from various pollutants and negative ions under different temperature conditions, offering valuable insights into air quality improvement strategies. Using a high-voltage system, ions bind to pollutants, accelerating their removal. Experiments measured temperature, humidity, formaldehyde, volatile organic compounds, negative ions, and PM2.5 in a 40 cm3 chamber across various conditions. Pollutants tested included cigarette smoke, incense, charcoal, and gasoline at two voltage levels and three temperature ranges. The data, available in CSV format, were based on 36,000 samples and repeated tests for reliability. This resource is designed to support studies investigating particulate matter control in urban and indoor environments, as well as to improve our understanding of negative ion-based air purification processes. The data are publicly available and structured in formats compatible with leading data analysis platforms.

Monitoring of Ammonium and Nitrate Ions in Soil Using Ion-Sensitive Potentiometric Microsensors

Focusing on the ChemFET (chemical field-effect transistor) technology, the development of a multi-microsensor platform for soil analysis is described in this work. Thus, different FET-based microdevices (i.e., pH-ChemFET pNH4-ISFET and pNO3-ISFET sensors) were realized with the aim of monitoring nitrogen-based ionic species in soil, evidencing quasi-Nernstian detection properties (>50 mV/decade) in appropriate concentration ranges for agricultural applications. Using a specific test bench adapted to important earth samples (mass: ~50 kg), first experiments were done in a lab, mimicking rainy periods as well as nitrogen-based fertilizer inputs. By monitoring pH, pNH4, and pNO3 in an acidic (pH ≈ 4.7) clay-silt soil matrix, different processes associated to the nitrogen cycle were characterized over a fortnight, demonstrating comprehensive results for ammonium nitrate NH4NO3 inputs at different concentrations, water additions, nitrification phenomena, and ammonium NH4+ ion trapping. Even if the ChemFET-based measurement system should be improved according to the soil(electrolyte)/sensor contact, such realizations and results show the ChemFET technology potentials for long-term analysis in soil, paving the way for future “in situ” approaches in the frame of modern farming.

О практике развития некоторых элементов конвергентного образования в технологическом вузе

The article examines the experience of the Faculty of Humanities and its Center for Cognitive Psychology at the University of Technology in involving students in research and applied scientific projects through cooperation and integration of the humanities and technology departments of the University. The ideas of the transformation of the country's educational system and the change of its methodological, pedagogical and technological guidelines in response to modern challenges and risks are analyzed, primarily for the younger generation in need of social adaptation and deeper natural science and humanitarian training for professional and personal growth. The attempts to substantiate the definition of "convergent education" are analyzed. All of them are based on an understanding, to one degree or another, of an interdisciplinary methodological approach to the formation of educational programs, in order to overcome the highly professional training of specialists and avoid formalizing the content of educational material, taking into account the productive interaction of the fundamental component of humanitarian and professional knowledge, the use of modern methods of automation of various types of information activities using information and computer technologies (algorithmization of learning, information modeling). In general, the convergence of education is defined as a process of learning and upbringing, which is a consequence of the implementation of the modern educational paradigm aimed at understanding, rather than memorizing, education for change, the ability to withstand stress, live in a digital world, preserving humanity, sociability and collectivism, ensuring teamwork, preservation and development of the humanitarian and intellectual component "in the context of the interpenetration of sciences and technologies in the implementation of the convergence of real and virtual communications. The scientific and methodological basis for the development of convergent education at the first stage was the development of scientific and pedagogical practices. This can include the management of students' research activities (R&D), involving them in the development and implementation of various projects of scientific laboratories and centers of departments and faculties. The article summarizes some of the experience of the Center for Cognitive Psychology of the Faculty of Social Sciences and Humanities - SGT (until 2022 – ISGT– Institute) of the Federal State Educational Institution of Higher Education "K.G. Razumovsky Moscow State Technical University (PKU)", which was actively involved in the process of cluster formation in the conditions of the university's transformation into an innovative expert–analytical platform, the formation of an expert-educational cluster of excellence "NBICS-technologies of the food industry".

Human Dynamics Research in GIScience: challenges and opportunities

The Symposium on Human Dynamics Research, first organized at the 2015 AAG Meeting in Chicago, celebrated its 10th anniversary at the 2024 AAG Meeting in Honolulu, marking a decade of transformative advancements in the field. Over the past decade, the focus of human dynamics research has shifted from traditional spatial-temporal analyses to sophisticated modeling of human behavior in a hybrid physical-virtual world. This evolving field now examines the intricate interdependencies between physical and digital environments, addressing critical issues such as urban resilience, public health, social equity, and community sustainability. The symposium emphasized the growing importance of interdisciplinary collaboration, advanced data-driven analytical platforms, and innovative theoretical frameworks to better understand human interactions across these spaces. As human dynamics continue to shape global urban systems, these advancements are pivotal for future research and real-world problem-solving, offering novel insights into the interconnectedness of mobility, technology, and societal well-being in a rapidly changing world.

The genomic landscape of papillary thyroid carcinoma on next-generation sequencing in patients undergoing total thyroidectomy.

Thyroidectomy is increasingly performed for suspected malignancy. This cohort study aimed to identify genetic markers associated with malignancy and determine the molecular landscape of papillary thyroid carcinoma (PTC) through next-generation sequencing (NGS) in patients undergoing total thyroidectomy. Among 116 surgical candidates, 103 patients (age=42.9±13.7years; Male: Female=14:89) with benign or malignant thyroid nodules were eligible. Live thyroid tissue samples harvested intraoperatively with adequate DNA and RNA yield were subjected to NGS on the Illumina NovaSeq 6000 platform for genomic and transcriptomic analysis, respectively. Histopathology comprised 20 malignant (19.4%) and 83 benign (80.6%) cases, including 16 PTC (15.5%) cases. On NGS, single nucleotidepolymorphisms (SNPs) in NTRK1 at NC_000001.11:156879016 on chromosome 1 and ALK at NC_000002.12:29717663 on chromosome 2 were frequent in malignant lesions (p<0.05). A SNP in ALK at NC_000002.12:29193706 was consistently a homozygous alternate alleleacross the cohort. DNA-sequencing of PTC lesions identified recurrentsomatic mutations in BRAF (100%), ALK (56.3%), RET (18.8%), PIK3CA (12.5%), NTRK1 (12.5%), NTRK2 (87.5%), NTRK3(12.5%), NRAS(6.3%), and PTCH1 (31.3%) genes. RNA sequencing revealednovelfusiongenes, including MKRN1-BRAF, RN7SL1-CDH1, IRF2BPL-MED12, MED12-IRF2BPL, CPM-MDM2, and AC005895.3-PDGFRB. Inreceiveroperative characteristics analysis, the AUCs ofALKmutationpredictingrecurrence and metastases were 0.818 and 0.783. This Indian study identified novel somatic mutations and fusion genes in PTC, revealing a distinct genomic landscape with implications in precision diagnostics and personalized therapies. NGS with intraoperative live sampling shows promise in prognostication and therapeutic optimization of advanced/metastatic PTC cases. CTRI/2020/09/027607dt 04/09/2020; REF/2020/08/036119.

Multiplatform metabolomic interlaboratory study of a whole human stool candidate reference material from omnivore and vegan donors.

Human metabolomics has made significant strides in understanding metabolic changes and their implications for human health, with promising applications in diagnostics and treatment, particularly regarding the gut microbiome. However, progress is hampered by issues with data comparability and reproducibility across studies, limiting the translation of these discoveries into practical applications. This study aims to evaluate the fit-for-purpose of a suite of human stool samples as potential candidate reference materials (RMs) and assess the state of the field regarding harmonizing gut metabolomics measurements. An interlaboratory study was conducted with 18 participating institutions. The study allowed for the use of preferred analytical techniques, including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), and nuclear magnetic resonance (NMR). Different laboratories used various methods and analytical platforms to identify the metabolites present in human stool RM samples. The study found a 40% to 70% recurrence in the reported top 20 most abundant metabolites across the four materials. In the full annotation list, the percentage of metabolites reported multiple times after nomenclature standardization was 36% (LC-MS), 58% (GC-MS) and 76% (NMR). Out of 9,300 unique metabolites, only 37 were reported across all three measurement techniques. This collaborative exercise emphasized the broad chemical survey possible with multi-technique approaches. Community engagement is essential for the evaluation and characterization of common materials designed to facilitate comparability and ensure data quality underscoring the value of determining current practices, challenges, and progress of a field through interlaboratory studies.

Automated Diagnosis of Knee Osteoarthritis Using ResNet101 on a DEEP:PHI: Leveraging a No-Code AI Platform for Efficient and Accurate Medical Image Analysis

Background: Knee osteoarthritis (OA) is a prevalent degenerative joint disease significantly impacting global health. Early and accurate diagnosis is crucial for effective management, but traditional methods often rely on subjective assessments. This study evaluates the efficacy of a deep learning model implemented through a no-code AI platform for diagnosing and grading knee OA from plain radiographs. Methods: We utilized the Osteoarthritis Initiative (OAI) dataset, comprising knee X-ray data from 1526 patients. The data were split into training (47.0%), validation (26.5%), and test (26.5%) sets. We employed a ResNet101 model on the DEEP:PHI no-code AI platform for image analysis. The model was trained to classify knee OA into five grades (0–4) based on the Kellgren–Lawrence scale. Results: Our AI model demonstrated high accuracy in distinguishing between different OA grades, with particular strength in early-stage detection. The model achieved optimal performance at 20 epochs, suggesting efficient learning dynamics. Grad-CAM visualizations were used to enhance the interpretability of the model’s decision-making process. Conclusions: This study demonstrates the potential of AI, implemented through a no-code platform, to accurately diagnose and grade knee OA from radiographs. The use of a no-code AI platform such as DEEP:PHI represents a step towards democratizing AI in healthcare, enabling the rapid development and deployment of sophisticated medical AI applications without extensive coding expertise. This approach could significantly enhance the early detection and management of knee OA, potentially improving patient outcomes and streamlining clinical workflows.

Investigating the Mechanism of Banxia Xiexin Decoction in Treating Gastritis and Diabetes Mellitus through Network Pharmacology and Molecular Docking Analysis

Background: Banxia Xiexin decoration (BXD), a complex prescription in Traditional Chinese Medicine (TCM), clinically acts as a treatment for gastritis and diabetes while its mechanism of treatment remains unknown. Objection: This study aimed to explore the common mechanism of BXD in treating gastritis and diabetes based on network pharmacology and molecular docking technology. Methods: The seven Chinese herbal components and drug targets were collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) for gastritis and diabetes using GeneCards, DisGeNET, Comparative Toxicogenomics Database (CTD), and Online Mendelian Inheritance in Man (OMIM) databases. Common drug and disease targets were imported into the STRING data platform for protein-protein interaction (PPI) analysis, and Cytoscape 3.7.2 software for network topology analysis, and core targets were filtered. Results: There were 124 components, 249 targets, 449 targets for gastritis, and 4005 targets for diabetes. After mapping, 83 BXD targets for gastritis and diabetes were obtained, and the targets with high correlation were STAT 3, JUN, TNF, IL-6, etc. More relevant targets were involved in the cancer pathway, AGE-RAGE signaling pathway of diabetic complications, fluid shear stress, and atherosclerosis pathway. Conclusion: This study preliminarily reveals that BXD may play a role in the treatment of gastritis and diabetes mellitus through multi-components, multi-targets, and multi-pathways, and proposes some potential "component-target-pathway" hypotheses in light of previous reports.

Photothermal modulated hotspot for accurate and sensitive SERS detection of pesticides in complicated media

Designing smart surface-enhanced Raman scattering (SERS) nanosensors with superior sensibility, stabilities and repeatability is a challenging and intriguing goal in chemical measurement. Herein, we develop a photothermal modulated SERS platform (PM-SERS) for sensitive and stable analysis of targets by exploiting a stimuli-responsive gold nanoparticles (GNPs) decorated polyvinyl alcohol hydrogel (GPH). Importantly, the proposed GPH facilitates exactly controlling 3D hot-spot regions by modulating surrounding temperature and humidity. The adjustable GPH could not only act as a stable matrix for installing of high-density GNPs, but also as a sorption domain for preconcentration of targeted analyte in hot-spots sites, thus permitting simultaneously modulating 3D nanostructures and seizing targeted analytes in plasmonic regions. Crucially, because of the reversible nanostructures and nanoplasmonic features, GPH can eradicate adsorbates after being exposed to bromide solutions, thereby allowing renewable and reliable on-site SERS detection of complex media. The GPH-based PM-SERS platform exhibits excellent sensitivities and reproducibility for on-demand detection of multiplexed pesticide over a wide linear concentration range (2 × 10-6 to 103 μM), with detection limits as low as hundreds of fM. The GPH-based analytical platform provides tremendous potentials for credible quantification of complicated matrices and on-line process screening.