Precise Identification Research Articles

NMR Phase Error Correction with New Modelling Approaches

Nuclear Magnetic Resonance (NMR) spectroscopy is a highly sensitive analytical technique essential for precise molecular identification and quantification. However, accurate results depend on effective pre-processing to correct for various types of errors. Phase error correction, in particular, is crucial for ensuring the reliability of NMR data. Current methods often rely on a single linear model, which may not adequately address all types of phase errors. As a result, this limitation frequently requires manual intervention, making the process both time-consuming and prone to errors. To address these limitations, we propose three modelling approaches for NMR phase error correction: nonlinear shrinkage, multiple models, and a new optimization function called delta absolute net minimization (DANM). Our comparison of seven methods revealed that nonlinear shrinkage outperformed others in both simulated spectra and a diabetes study, followed by multiple models with DANM. Additionally, our spike-in experiments demonstrated that DANM performed quite well in both single and multiple models. Our nonlinear shrinkage approach is a simple yet effective solution. We provide an open-source R package, NMRphasing, available on CRAN (https://cran.r-project.org/web/packages/NMRphasing/) and on GitHub (https://github.com/ajiangsfu/NMRphasing).

Research on photovoltaic MPPT under complex conditions based on an advanced great wall construction algorithm method.

To mitigate the challenges posed by the non-linear multi-peak power-voltage output characteristics of photovoltaic (PV) systems operating under partial shading conditions, which often lead to suboptimal performance of conventional Maximum Power Point Tracking (MPPT) algorithms, a novel approach was introduced. We introduce the LGWGCA-P&O method, which synergistically combines an modified Great Wall Construction Algorithm (LGWGCA) with the Perturbation and Observation (P&O) technique. The LGWGCA is refined with a positional update mechanism inspired by the Grey Wolf Optimization (GWO) algorithm, optimizing the distribution of solution agents, while a Levy flight strategy is employed to reduce excessive randomness during agent replacement and recombination, thereby accelerating the tracking process. As the algorithm approaches the maximum power point, it seamlessly transitions to the P&O method, leveraging its rapid convergence to ensure precise and efficient power point identification. Experimental results demonstrate that the LGWGCA-P&O method achieves a PV conversion efficiency exceeding 99.96%, significantly minimizing power losses during MPPT. Furthermore, the method enhances convergence speed by approximately 40% compared to traditional GWO-P&O approaches, and it consistently outperforms Particle Swarm Optimization and Cuckoo Search Optimization algorithms under extreme conditions, demonstrating superior computational efficiency and robustness.

Enhancing pancreatic tumor delineation using dual-energy CT-derived extracellular volume fraction map.

Precise identification of pancreatic tumors is challenging for radiotherapy planning due to the anatomical variability of the tumor and poor visualization of the tumor on 3D cross-sectional imaging. Low extracellular volume fraction (ECVf) correlates with poor vasculature uptake and possible necrosis or hypoxia in pancreatic tumors. This work investigates the feasibility of delineating pancreatic tumors using ECVf spatial distribution maps derived from contrast enhanced dual-energy CT (DECT). Data acquired from radiotherapy simulation of 12 pancreatic cancer patients, using a dual source DECT scanner, were analyzed. For each patient, an ECVf distribution of the pancreas was computed from the simultaneously acquired low and high energy DECT series during the late arterial contrast phase combined with the patient's hematocrit level. Volume of interest (VECVf) maps in ECVf distribution of pancreas were identified by applying an appropriate threshold condition and a connected components clustering algorithm. The obtained VECVf was compared with the clinical gross tumor volume (GTV) using the positive predictive value (PPV), Dice similarity coefficient (DSC), mean distance to agreement (MDA) and true positive rate (TPR). As a proof of concept, our hypothetical threshold condition based on the first quartile separation of the ECVf distribution to find VECVf of the pancreas elucidates the tumor volume within the pancreas. Notably, 7 out of 12 cases studied for VECVf matched well with the GTV and the mean PPV of 0.83±0.12. The mean MDA (2.83±1.0) of the cases confirms that VECVf lies within the tolerance for comparing to the pancreatic GTV. For the remaining 5 cases, the VECVf is substantially affected by other compounding factors, e.g., large cysts, dilate ducts, and thus did not align with the GTVs. This work demonstrated the promising application of the ECVf map, derived from contrast enhanced DECT, to help delineate tumor target for RT planning of pancreatic cancer.

Geolocation of Fixed Emission Sources Produced by Artisan Brick Kilns in the Atmospheric Basin of the City of Juliaca, Peru

Objective: The study objective is to investigate the application of georeferencing systems to identify the geospatial location of fixed sources of atmospheric emissions produced by artisanal brickyards in the air basin of Juliaca city-Peru. Theoretical Framework: Previous studies have shown that artisanal brickyards are a significant source of air pollution in developing urban and on the outskirts. Emissions of fine particles, nitrogen oxides and other pollutants from the burning of traditional fuels, such as firewood and coal contribute to the degradation of air quality and can have adverse effects on human health and the environment. The georeferencing application and geospatial analysis techniques in the atmospheric pollution study have allowed a better understanding of the spatial distribution of emission sources and their impact on the environment. These tools are essential to identify and map the artisanal brickyard locations and to evaluate their contribution to air pollution in urban and on the outskirts. Method: The methodology adopted for this research includes data collection and compilation of existing information; georeferencing of brickyards through the use of geographic information systems (GIS); analysis of geospatial data for the identification of spatial patterns; preparation of thematic and spatial distribution maps; and the interpretation of results. Results and Discussion: The study results highlight the importance of locating the fixed sources of emissions produced by artisanal brick factories in the Juliaca air basin. This precise spatial identification provides a solid basis for the formulation of policies and mitigation strategies aimed at reducing air pollution in the region, in addition to representing fundamental data for the use and exploitation of GIS for environmental protection and modelling, among others. Research Implications: The results can be applied or influence practices in the environmental and forestry engineering field, the ICTs application, modelling and simulation, and territorial planning, among others. Originality/Value: This study contributes to the literature using georeferencing techniques for environmental modelling purposes. The relevance and value of this research are evident in obtaining georeferenced points for the estimation of emission factors for the predictive calculation of volumes, flows and dispersion of pollutants in an air basin.

Semantic Segmentation Based on Geometric Calibration Using AI and AR in Health Care

In the medical field, medical imaging is essential for precise diagnosis, treatment planning, and condition monitoring. The goal of this work is to improve the field of healthcare imaging by investigating the complementary applications of augmented reality (AR) and artificial intelligence (AI) in semantic segmentation. More precise identification and delineation of anatomical structures and abnormalities in medical images is made possible by AI-driven semantic segmentation, opening the door to more precise diagnosis and treatment plans. Modern AI algorithms are employed in the suggested methodology to perform semantic segmentation in a variety of medical imaging modalities, including ultrasound, CT, and MRI scans. The detection of anomalies such as tumours, irregularities in organs, and neurological disorders is made easier by these algorithms. The foundation for integrating augmented reality technologies into the healthcare ecosystem is provided by the segmented medical images. AR enhances the interaction and visualization of segmented medical data in the context of healthcare. Real-time augmented reality overlays help surgeons by improving surgical navigation and precision. Additionally, AR apps help with medical education by offering professionals and students alike an immersive learning environment. AR provides interactive visualizations to help patients understand their medical conditions when they are going for therapy and rehabilitation. The difficulties in integrating these technologies in the healthcare industry are discussed in this paper, with a focus on the significance of data privacy, regulatory compliance, and easy integration with current healthcare systems. The successful deployment of AI technologies necessitates interdisciplinary collaboration among AI developers, healthcare professionals, and AR specialists to ensure compliance with ethical, legal, and clinical standards mandated in the healthcare domain. The proposed Segmentation mask is redefined with geometric calibration and used with U-Net for image segmentation. The segmentation is experimented on Cityscapes and PASCAL VOC 2012 datasets. The experimental results show that proposed semantic segmentation based on geometric calibration yields more accuracy than its counterparts.

Rapid Estimation of Soil Moisture Using Spectroscopic Sensor

ABSTRACT Soil moisture plays a significant role in the global hydrological cycle in the field of agriculture, environment science, urban planning and construction, forestry and many more. Soil moisture can be determined using various approaches, however some of the limitations in these approaches are longer time duration, labor intensive, require installation and maintenance, expensive, lower resolution, etc. This research work proposes a soil moisture detection method which is portable, economic and time efficient. Spectroscopic sensor enables rapid soil moisture estimation using photodiodes of varying sensitivities and wavelengths across a broad spectrum of light, allowing precise identification and analysis of various samples based on their unique spectral fingerprints. In this study, a relation between sensor output data and soil moisture percentage value obtained from gravimetric method has been established. Using various regression methods, the highest value of correlation coefficient was found in case of polynomial function of order 3. Residual values of all wavelengths were then calculated by comparing the calculated with the expected moisture values obtained using the polynomial function. Result showed that 705 nanometers (nm) wavelength has the least residual value, thus signifying that the polynomial equation of order 3 at 705 nm wavelength perfectly demonstrates the relation between soil moisture and sensor reading. Furthermore, the results were verified using optimization method which showed that 705 nm wavelength has the highest closeness coefficient, thus verifying this model and observations. This model can be helpful in determining the soil moisture instantaneously instead of hours as seen in traditional gravimetric methods.

Development and external validation of a machine learning model to predict diabetic nephropathy in T1DM patients in the real-world.

Studies on machine learning (ML) for the prediction of diabetic nephropathy (DN) in type 1 diabetes mellitus (T1DM) patients are rare. This study focused on the development and external validation of an explainable ML model to predict the risk of DN among individuals with T1DM. This was a retrospective, multicenter study conducted across 19 hospitals in Gansu Province, China (No: 2022-473). In total, 1368 patients were eligible for analysis among 1633 collected T1DM patients from January 2016 to December 2023. Recursive feature elimination using random forest and fivefold cross-validation was conducted to identify key features. Among the 12 initial ML algorithms, the optimal ML model was developed and validated externally in a distinct population, and its predictive outcomes were explained via the SHapley additive exPlanations method, which offered personalized decision insights. Among the 1368 T1DM patients, 324 had DN. The extreme gradient boosting (XGBoost) model, which achieved optimal performance with an AUC of 83% (95% confidence interval [CI]: 76‒89), was selected to predict the risk of DN among T1DM patients. The DN predictive model included variables such as T1DM duration, postprandial glucose (PPG), systolic blood pressure (SBP), glycated hemoglobin (HbA1c), serum creatinine (Scr) and low-density lipoprotein cholesterol (LDL-C). External validation confirmed the reliability of the model, with an AUC of 76% (95% CI: 70‒82). The ML prediction tool has potential for advancing early and precise identification of the risk of DN among T1DM patients. Although successful external validation indicated that the developed model can provide a promising strategy for clinical adoption and help improve patient outcomes through timely and accurate risk assessment, additional prospective data and further validation in diverse populations are necessary.

SDCSN: A Hierarchical parallel Localisation Method for Pipeline Leakage Based on Vibration Signals

Abstract In applying deep learning methods to detecting and localising pipeline leaks, improving the fitness of deep learning methods to leak signals is an important task. We propose a novel detection model called Stacked dilated convolutional shrinkage network (SDCSN). This model incorporates a Stacked Dilated Convolution (SDC) module specifically designed for vibration signals, enabling the extraction of rich multi-scale local features. Moreover, implementing the Residual Shrinkage Building Unit (RSBU) module for noise reduction in the network architecture. Building upon this foundation, we introduce a new concept centred around hierarchical leakage discrimination and parallel prediction positioning. This approach enables accurate assessment of leakage levels and precise identification of multiple leakage points. Finally, the performance of the proposed method is verified in real experiments and the optimal settings for the dilated rate are determined. The results demonstrate a maximum classification accuracy rate reaching 98.94%.

Isolation, identification, and challenges of extracellular vesicles: emerging players in clinical applications.

Extracellular vesicles (EVs) serve as critical mediators of intercellular communication, encompassing exosomes, microvesicles, and apoptotic vesicles that play significant roles in diverse physiological and pathological contexts. Numerous studies have demonstrated that EVs derived from mesenchymal stem cells (MSC-EVs) play a pivotal role in facilitating tissue and organ repair, alleviating inflammation and apoptosis, enhancing the proliferation of endogenous stem cells within tissues and organs, and modulating immune function-these functions have been extensively utilized in clinical applications. The precise classification, isolation, and identification of MSC-EVs are essential for their clinical applications. This article provides a comprehensive overview of the biological properties of EVs, emphasizing both their advantages and limitations in isolation and identification methodologies. Additionally, we summarize the protein markers associated with MSC-EVs, emphasizing their significance in the treatment of various diseases. Finally, this article addresses the current challenges and dilemmas in developing clinical applications for MSC-EVs, aiming to offer valuable insights for future research.

Enhancing Molecular‐Level Biological Monitoring with a Smart Self‐Assembling 19F‐Labeled Probe

AbstractReal‐time monitoring of molecular transformations is crucial for advancements in biotechnology. In this study, we introduce a novel self‐assembling 19F‐labeled nuclear magnetic resonance (NMR) probe that disassembles upon interaction with various nucleotides. This interaction not only activates the 19F signals but also produces distinct signatures for each specific component, thereby enabling precise identification and quantification of molecules in evolving samples. We demonstrate the capability of this probe for real‐time monitoring of adenosine triphosphate (ATP) hydrolysis and screening potential enzyme inhibitors. These applications highlight the probe's significant potential in enzyme analysis, drug development, and disease diagnostics.

Exploring the Role of Artificial Intelligence in Revolutionizing Microbial Diagnostics

Identifying the microorganisms responsible for illnesses and infections is crucial for accurate diagnosis, which is an essential aspect of providing medical care. Artificial intelligence (AI) systems can enhance drug discovery, epidemiological monitoring, prediction of antibiotic resistance, and disease management in the field of microbiological diagnosis. Artificial intelligence (AI) is a branch of science and technology that uses computers to simulate human intelligence. AI has recently shown tremendous promise as a powerful computational tool for the detection and management of bacterial infections. These machines can imitate human thought processes and cognitive capacities. Today, pathogen identification and Antimicrobial Susceptibility Testing (AST) in clinical laboratories often rely on culturing and isolating pathogens. With the rapid advancement of technology, Artificial Intelligence (AI) has become a vital tool for bacterial AST, providing numerous fast and efficient methods for testing drug susceptibility. AI systems offer enhanced diagnostic methods and early detection of antibiotic resistance. Moreover, they can rapidly and accurately identify diseases, including those that are novel or drug-resistant. In addition, AI can be applied to identify cells infected with malaria, detect drug resistance in pathogens like tuberculosis (TB) and HIV, understand and tackle antimicrobial resistance (AMR), and predict outbreaks. The primary goals of applying AI to bacterial diagnosis are the rapid and precise identification of pathogens and the prediction of drug resistance.

Deep-GB: A novel deep learning model for globular protein prediction using CNN-BiLSTM architecture and enhanced PSSM with trisection strategy.

Globular proteins (GPs) play vital roles in a wide range of biological processes, encompassing enzymatic catalysis and immune responses. Enzymes, among these globular proteins, facilitate biochemical reactions, while others, such as haemoglobin, contribute to essential physiological functions such as oxygen transport. Given the importance of these considerations, accurately identifying Globular proteins is essential. To address the need for precise GP identification, this research introduces an innovative approach that employs a hybrid-based deep learning model called Deep-GP. We generated two datasets based on primary sequences and developed a novel feature descriptor called, Consensus Sequence-based Trisection-Position Specific Scoring Matrix (CST-PSSM). The model training phase involved the application of deep learning techniques, including the bidirectional long short-term memory network (BiLSTM), gated recurrent unit (GRU), and convolutional neural network (CNN). The BiLSTM and CNN were hybridised for ensemble learning. The CST-PSSM-based ensemble model achieved the most accurate predictive outcomes, outperforming other competitive predictors across both training and testing datasets. This demonstrates the potential of harnessing deep learning for precise GB prediction as a robust tool to expedite research, streamline drug discovery, and unveil novel therapeutic targets.

Detecting water-soaked disorder and reddish-pulp disorder in peach fruit using bio-speckle

This study addresses the challenges in nondestructive identifying diseases, particularly water-soaked and reddish-pulp disorders, in peaches during storage and transport. Existing technologies have struggled to detect these diseases during this period, leading to potential food loss and consumer distrust. Biospeckle has emerged as a promising discriminator of the internal state of the fruit by utilizing laser-induced scattered light patterns. Pigment interference is minimized by employing lasers with wavelengths of 532 and 650 nm. This study focuses on the ability of biospeckle to distinguish between healthy and diseased fruit based on characteristic values, specifically the Fujii index and cumulative amplitude (Cum. amp.) at 2–3, 3–4, 4–5, and 6–7 Hz. The t-test results demonstrated significant differences in these values, particularly for water-soaked and reddish-pulp disorders. Biospeckle outperforms other non-destructive methods by identifying the symptoms pre-storage. These results indicate that Cum. amp. at 3–4, 4–5, and 6–7 Hz may be more useful in identifying water-soaked fruit than the Fujii index and Cum. amp. at 2–3 Hz. Red lasers are more effective in detecting reddish-pulp disorders than green lasers, which are hindered by pigment absorption. This finding highlights the potential of biospeckle in precise symptom identification, which is crucial for ensuring food quality and consumer confidence.

Association of METS-IR index with Type 2 Diabetes: A cross-sectional analysis of national health and nutrition examination survey data from 2009 to 2018.

With rising global diabetes prevalence, precise early identification and management of diabetes risk are critical research areas. The metabolic score for insulin resistance (METS-IR), a novel non-insulin-based tool, is gaining attention for quantifying insulin resistance using multiple metabolic parameters. Despite its potential in predicting diabetes and its precursors, evidence on its specific relationship with diabetes is limited, especially in large-scale population validation and mechanistic exploration. This study aims to analyze the association between METS-IR and type 2 diabetes (T2DM) in American adults. We conducted a cross-sectional analysis of the National Health and Nutrition Examination Survey (NHANES) data from 2009 to 2018. Participants aged 20 years and above were included, excluding individuals with missing data on BMI, fasting blood glucose, high-density lipoprotein cholesterol glycated hemoglobin and diabetes status. Logistic regression analysis, subgroup analysis, and restricted cubic spline analysis were used to assess the association between METS-IR and T2DM, controlling for potential confounding factors. After adjusting for age, gender, race, education level, smoking status, drinking habits, depression, physical activity, hypertension, and hyperlipidemia, we found a positive association between METS-IR and the risk of T2DM. Specifically, each unit increase in METS-IR was associated with a 7% increase in the risk of T2DM (OR = 1.07, 95% CI: 1.06, 1.08). Subgroup analysis showed that the association between METS-IR and T2DM incidence was significantly positive in the highest quartile group, particularly among Mexican Americans over 40 years old and those diagnosed with depression, hypertension, or hyperlipidemia. Our study revealed a significant positive association between METS-IR and the prevalence of T2DM, indicating that this relationship persists even after controlling for various confounding factors. Therefore, monitoring METS-IR may provide a valuable tool for the early identification of individuals at risk of glucose metabolism disorders. Further research should focus on the applicability of METS-IR in different populations and its potential impact on clinical practice.

Tunable Activated Esters Enable Lysine-Selective Protein Labeling and Profiling.

Lysine residues on protein surfaces are abundant and often found in enzyme active sites, making them critical targets for studying undruggable proteins. However, the varied microenvironment surrounding lysine residues results in a wide range of pKa values, complicating site-specific covalent binding. In this study, we address the challenges posed by the diverse reactivity of amino side chains by modulating the amide reaction activity of heteroaromatic activated esters. By fine-tuning the type, position, and number of heteroatoms, we successfully rationalized the regulation of their amide reaction activity, leading to the design of probes for selective lysine labeling within the proteome for profiling purposes. Systematic optimization of these esters' reactivity and selectivity has yielded a series of effective probes suitable for both in vitro and cellular applications. These findings significantly enhance our understanding of protein functions and mechanisms, facilitated by the precise identification and analysis of protein labeling and profiling.

Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction

A critical component of bus network adjustment is the accurate prediction of potential risks, such as the likelihood of complaints from passengers. Traditional simulation methods, however, face limitations in identifying passengers and understanding how their travel patterns may change. To address this issue, a pre-evaluation method has been developed, leveraging the spatial distribution of bus networks and the spatio-temporal behavior of passengers. The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. First, we explore the actual passengers’ origin and destination (OD) stop from bus card (or passenger Code) payment data and biometric recognition data, with the OD as one of the main input parameters. Second, a digital bus network model is constructed to represent the logical and spatial relationships between routes and stops. Upon inputting bus line adjustment parameters, these relationships allow for the precise and automatic identification of the affected areas, as well as the calculation of accessible paths of each OD pair. Third, the factors influencing passengers’ path selection are analyzed, and a predictive model is built to estimate post-adjustment path choices. A genetic algorithm is employed to optimize the model’s weights. Finally, various metrics, such as changes in travel routes and ride times, are analyzed by integrating passenger profiles. The proposed method was tested on the case of the Guangzhou 543 route adjustment. Results show that the accuracy of the number of predicted trips after adjustment is 89.6%, and the predicted flow of each associated bus line is also consistent with the actual situation. The main reason for the error is that the path selection has a certain level of irrationality, which stems from the fact that the proportion of passengers who choose the minimum cost path for direct travel is about 65%, while the proportion of one-transfer passengers is only about 50%. Overall, the proposed algorithm can quantitatively analyze the impact of rigid travel groups, occasional travel groups, elderly groups, and other groups that are prone to making complaints in response to bus line adjustment.

Metabolic Acidity/H2O2Dual-Cascade-Activatable Molecular Imaging Platform toward Metastatic Breast Tumor Malignancy.

Fluorescence imaging in the second near-infrared window (NIR-II) is crucial for accurate tumor diagnosis, offering superior resolution and penetration capabilities. Current NIR-II probes are limited by either being "always on" or responding to one stimulus, leading to low signal-to-noise ratios and potential false positives. We introduced a dual-lock-controlled probe, HN-PBA, activated by both H2O2 and tumor acidic environment. This dual response ensures bright fluorescence at tumor sites, leading to higher tumor-to-normal tissue ratios (T/NT) compared to conventional "always on" probes and probes activated only by H2O2. This strategy allows precise tumor identification and removal of primary and metastatic tumors, achieving superior T/NT ratios (24.3/6.4 for orthotopic and lung metastasis, respectively). Our probe also effectively detected lung metastatic foci as small as ≤ 0.7 mm and showed the capability for accurate lesion localization in clinical breast cancer specimens. This dual-stimuli-responsive strategy could aid future diagnostic probe design.

Optimized 3D-2D CNN for automatic mineral classification in hyperspectral images

Abstract Mineral classification using hyperspectral imaging represents an essential field of research improving the understanding of geological compositions. This study presents an advancedmethodology that uses an optimized 3D-2D CNNmodel for automatic mineral identification and classification. Our approach includes such crucial steps as using the Diagnostic Absorption Band (DAB) selection technique to selectively extract bands that contain the absorption features of minerals for classification in the Cuprite zone. Focusing on the Cuprite dataset, our study successfully identified the following minerals: alunite, calcite, chalcedony, halloysite, kaolinite,montmorillonite,muscovite, and nontronite. The Cuprite dataset results with an overall accuracy rate of 95.73%underscore the effectiveness of our approach and a significant improvement over the benchmarks established by related studies. Specifically, ASMLP achieved a 94.67%accuracy rate, followed by 3D CNN at 93.86%, SAI-MLP at 91.03%, RNN at 89.09%, SPE-MLP at 85.53%, and SAMat 83.31 %. Beyond the precise identification of specific minerals, ourmethodology proves its versatility for broader applications in hyperspectral image analysis. The optimized 3D-2D CNNmodel excels in terms of mineral identification and sets a new standard for robust feature extraction and classification.

N6-methyladenosine regulators in hepatocellular carcinoma: investigating the precise definition and clinical applications of biomarkers

BackgroundAccurately identifying effective biomarkers and translating them into clinical practice have significant implications for improving clinical outcomes in hepatocellular carcinoma (HCC). In this study, our objective is to explore appropriate methods to improve the accuracy of biomarker identification and investigate their clinical value.MethodsConcentrating on the N6-methyladenosine (m6A) modification regulators, we utilized dozens of multi-omics HCC datasets to analyze the expression patterns and genetic features of m6A regulators. Through the integration of big data analysis with function experiments, we have redefined the biological roles of m6A regulators in HCC. Based on the key regulators, we constructed m6A risk models and explored their clinical value in estimating prognosis and guiding personalized therapy for HCC.ResultsMost m6A regulators exhibit abnormal expression in HCC, and their expression is influenced by copy number variations (CNV) and DNA methylation. Large-scale data analysis has revealed the biological roles of many key m6A regulators, and these findings are well consistent with experimental results. The m6A risk models offer significant prognostic value. Moreover, they assist in reassessing the therapeutic potential of drugs such as sorafenib, gemcitabine, CTLA4 and PD1 blockers in HCC.ConclusionsOur findings suggest that the mutual validation of big data analysis and functional experiments may facilitate the precise identification and definition of biomarkers, and our m6A risk models may have the potential to guide personalized chemotherapy, targeted treatment, and immunotherapy decisions in HCC.

Clinical diagnosis and management of wheat and buckwheat allergy: application and prospects of allergen component diagnostics

Wheat and buckwheat allergies are common food allergies that significantly impact patients' quality of life and health. Wheat allergy encompasses various forms, including wheat food allergy, exercise-induced allergic reactions (WDEIA), baker's occupational asthma/allergy, and contact urticaria. IgE-mediated allergic reactions involve sensitization to stable wheat allergens such as ω-5 gliadin and gluten. Although buckwheat allergy is less common, it is gaining attention in certain regions. Allergen component diagnostic technologies, by detecting specific allergen components [e.g., ω-5 gliadin, gliadins (α, β, γ), and Tri a 14], offer precise allergen source identification, aiding in the optimization of diagnosis and management processes. Oral challenge tests are considered the gold standard for diagnosing wheat allergy, and combining skin prick tests with specific IgE measurements can enhance diagnostic accuracy. While avoidance of allergens remains the primary management strategy, research into immunotherapy is ongoing. Future research should focus on a deeper understanding of the structural and immunological characteristics of wheat and buckwheat allergens to develop more accurate diagnostic tools and treatment methods, thereby improving allergy management and patient quality of life. This article provides a detailed interpretation of the Molecular Allergology User's Guide 2.0 (MAUG 2.0) published by the European Academy of Allergy and Clinical Immunology (EAACI) and recent research advances on wheat and buckwheat allergies, highlighting the crucial role of allergen component diagnostics in optimizing food allergy diagnosis and treatment processes, supporting clinicians in accurately identifying common allergens and their cross-reactivity, and formulating more personalized treatment plans for patients.