Tool For Identification Research Articles

The Role of Artificial Intelligence in Enhancing Identification of Autism in Children Through Motor Abnormalities

The research reported in this paper provides a systematic validation and illustrations of the potential of the non-invasive, sensor-less, Kinect-based temporal gait signal data in objective quantification and widely applicable objective identification of motor abnormalities in children with autism, a multidimensional neurodevelopmental disorder. This indicator, when verified and validated through more extensive work with a larger and representative sample, has the potential for an innovative and widely applicable objective identification tool to be utilized early on in routine pediatric and family practice for effective and timely referral for further comprehensive clinical and developmental evaluations from developmental teenage or older pediatric care to adulthood. Our findings and results lay the foundation for the next steps and development of predictive measurements and innovative optimal treatment personalization informed by creating innovative age group models that can enhance the everyday life experiences and long-term welfare of individuals with autism throughout various childhood stages, and for further enforceable legal and ethical person-protected international global appropriate use. The demands on healthcare and social services for young and older children with autism spectrum disorder (ASD), a multidimensional neurodevelopmental disorder, are continually increasing and pose substantial challenges for the family, school, medical, and social services. Community-based timely detection and identification of the potential treatment-elicited motor abnormalities, using a non-invasive technology that needs no physical dependency on the participants and trained staff who conduct the optimal data processing, is essential to further inform a timely, effective, and individualized behavioral interventions program pursued collaboratively by the family, early intervention specialists, and the child.

Application of Biofire Filmarray Joint Infection Panel for Rapid Identification of Aetiology in a Necrotizing Fasciitis Case.

Background: Monomicrobial Enterobacterales necrotizing fasciitis is associated with exceedingly high mortality rates. Although effective antimicrobial therapy is an important part of treatment, the traditional microbiological diagnostic methods are not fast enough to meaningfully influence early therapeutic decisions. Methods: Here, we report the application of the BioMérieux Biofire Filmarray Joint Infection Panel (BFJIP) for the rapid detection of the causative agent and susceptibility prediction in such a case. Aside from the BFJIP-based rapid diagnostic approach and culturing, the whole genome sequencing (WGS) of the causative agent was performed using Illumina MiSeq and Oxford Nanopore MinION platforms. Results: The BFJIP indicated the presence of K. pneumoniae, without KPC, VIM, IMP, NDM, OXA-48 carbapenemase genes, and CTX-M-type extended-spectrum beta-lactamases. Based on the WGS data, the isolate belonged to the K1-capsule-type ST23, harboured a pNTUH-2044-like plasmid, and was positive for all the virulence factors associated with this lineage. The conventional susceptibility results were also in accordance with the BFJIP results; the isolate lacked any of these acquired resistance mechanisms. Conclusions: Despite this being the first case of the successful identification of pathogenic bacteria in necrotising fasciitis using this assay, the BFJIP may become a useful tool for rapid identification of pathogens in necrotising fasciitis cases and guiding antimicrobial therapy for better clinical outcomes.

Developmental validation of a novel all-in one assay of X chromosomal multi-insertion/deletion loci for forensic genetics

Multi-insertion/deletion polymorphisms (Multi-InDels), as the novel genetic markers, show great potential in forensic research. Whereas, forensic researchers mainly focus on the multi-InDels on the autosomes, which can provide relatively limited information in some complex paternity cases. In this study, a novel X chromosomal multi-InDel multiplex amplification system was designed, containing 22 multi-InDels and one STR locus on the X chromosome. In order to verify the applicability and effectiveness of the novel system, the sensitivity, species specificity, repeatability, stability, and precision of the panel were verified. The results showed that the novel developed multiplex amplification system could still obtain complete allele profile at 62.5pg of template DNA and showed good tolerance to six common inhibitors. Meanwhile, the analyses of case samples showed that the system performed well on biological samples commonly found in forensic cases. The combined power of discrimination for males and females in the Guizhou Han population was greater than 0.999999999. Besides, the combined mean exclusion chance of these 23 loci exceeded 0.9999 in the trios and the duos. In conclusion, the multiplex amplification system is sensitive, precision, reproducible, and informative, which could be used as a high-efficient tool for complex kinship identification and personal identification.

Mainshock–aftershock seismic fragility assessment of civil structures: A state‐of‐the‐art review

AbstractSequential seismic events occur worldwide, which impose significant threats to the safety and serviceability of Civil infrastructure, especially buildings and bridges. Fragility functions are imperative to support decision‐making tools for potential seismic risk identification and its impact on structural performance during sequential earthquakes. The increasing number of publications shows a notable increase in interest among researchers and the scientific community in this domain. This study presents a systematic review of available resources and techniques for structural performance and fragility evaluation subjected to mainshock–aftershock seismic loading. Efforts have been made to focus on the salient features of various approaches rather than criticizing the mathematical frameworks and associated analysis approaches. Existing knowledge related to the effect of sequential seismic loading on buildings and bridge infrastructures and their fragility estimates is presented concisely. The paper concludes by detailing the opportunities for future developments in the fragility analysis of Civil infrastructure under sequential seismic hazard. This would encourage stakeholders and decision‐makers to put into practice their applications for risk mitigation, recovery planning, and well‐informed decision‐making.

A machine learning tool for early identification of celiac disease autoimmunity

Identifying which patients should undergo serologic screening for celiac disease (CD) may help diagnose patients who otherwise often experience diagnostic delays or remain undiagnosed. Using anonymized outpatient data from the electronic medical records of Maccabi Healthcare Services, we developed and evaluated five machine learning models to classify patients as at-risk for CD autoimmunity prior to first documented diagnosis or positive serum tissue transglutaminase (tTG-IgA). A train set of highly seropositive (tTG-IgA > 10X ULN) cases (n = 677) with likely CD and controls (n = 176,293) with no evidence of CD autoimmunity was used for model development. Input features included demographic information and commonly available laboratory results. The models were then evaluated for discriminative ability as measured by AUC on a distinct set of highly seropositive cases (n = 153) and controls (n = 41,087). The highest performing model was XGBoost (AUC = 0.86), followed by logistic regression (AUC = 0.85), random forest (AUC = 0.83), multilayer perceptron (AUC = 0.80) and decision tree (AUC = 0.77). Contributing features for the XGBoost model for classifying a patient as at-risk for undiagnosed CD autoimmunity included signs of anemia, transaminitis and decreased high-density lipoprotein. This model’s ability to distinguish cases of incident CD autoimmunity from controls shows promise as a potential clinical tool to identify patients with increased risk of having undiagnosed celiac disease in the community, for serologic screening.

Reliability of ameloglyphics in forensic identification: a systematic review.

Teeth are considered as hard tissue analogue to fingerprints, being unique to an individual. The enamel which forms the outer layer of the tooth is formed through a highly dynamic process in which ameloblasts lay down enamel rods in an undulating and intertwining path, which is reflected as a series of enamel rod pattern. The study of these patterns is termed as "Ameloglyphics". These patterns formed on the surface of enamel are called tooth prints each of which comprises the combination of different sub patterns, and are unique for every single tooth of an individual. This characteristic of uniqueness of the tooth print can serve as a significant biometric tool in forensic identification. To assess if ameloglyphics is an accurate and reliable method for forensic identification of individuals. Registration number- CRD42022338138. Data sources- Google Scholar, PubMed, Proquest, Europe PMC, Scopus. Study appraisal and synthesis methods- Studies were assessed for quality with the help of predetermined criteria which categorized the studies into high, medium and low quality with the help of JBI critical appraisal checklist for analytical cross-sectional studies using the key words such as Ameloglyphics, tooth prints and enamel rod end patterns. A total of 475 articles were obtained in the primary search from PubMed, Scopus, Europe PMC, ProQuest, and Google Scholar including gray literature for thorough search of related publications on Ameloglyphics. Following initial search of titles, 433 articles were excluded because they were not related to the objectives of the systematic review and 42 articles were further excluded after abstract reading and removal of duplicates. Finally,18 out of 22 articles which were fulfilling the inclusion criteria of the study were selected for qualitative synthesis after full-text screening and exclusion of review articles. The data obtained from the eighteen included studies were systematically reviewed but a meta-analysis could not be performed due to heterogeneity of the data. Ameloglyphics is a relatively newer technique and a fair number of research are done on its application in identification of individuals. The results of the eighteen studies included in this review suggest that Ameloglyphicsis an useful adjunct for forensic identification. The study protocol can be accessed through the International Prospective Register of Systematic Reviews, the PROSPERO database with the following register number: CRD42022338138.

A methodology based on the fuzzy identification for a hybrid method in times series forecasting models

This work presents a hybrid forecasting method, with concept and improvement through the fuzzy set’s theory coupled with the classic time series method. This study also proposed an identification tool that has not been published in the scientific literature for parametric point identification and estimation within the classical time series methodology combined with fuzzy sets theory, which generated a hybrid identification technique in fuzzy time data. In comparison to the forecasting methods consolidated in the literature, the results presented by the proposed method have been considered fully satisfactory due to the minimal error observed.

Optimization of Breeding Tools in Quinoa (Chenopodium quinoa) and Identification of Suitable Breeding Material for NW Europe.

Quinoa (Chenopodium quinoa) cultivation has become increasingly popular in NW Europe but little is known about the performance of contract-free varieties in this region. In this study, we phenotyped 25 quinoa varieties on a single-plant basis in a field trial in Belgium. In addition, we optimized breeding tools such as NIRS (near-infrared reflectance spectroscopy) to estimate the seed crude protein content and a multiplex PCR set to identify true F1 progeny from pair crosses. We identified 14 varieties with sufficiently early maturity, 17 varieties with plant height below 150 cm, 21 large-seeded varieties, four varieties with a crude protein content exceeding 15%, and two low-saponin varieties. A variety of seed colors and plant morphological traits was observed. Seed yield was not correlated with maturity, plant height or saponin content, but was negatively correlated with seed crude protein content. NIRS could accurately predict seed crude protein content with a determination coefficient of 0.94. Our multiplex SSR set could correctly identify the paternity in 77% to 97% of progeny, depending on the pair cross. In conclusion, our study identified various contract-free varieties that may be suitable for cultivation in NW Europe. In addition, our study provides valuable phenotypic information and breeding tools that breeders can harness for breeding efforts in NW European quinoa.

Системный подход к обеспечению качества и безопасности пищевых продуктов на базе квалиметрии рисков

The article introduces a new systematic approach to product safety and quality. It relies on the measures of technological risk management based on the principles of Hazard Analysis and Critical Control Points (HACCP), qualimetric forecasting, and risk qualimetry. The stages and tools of risks identification, prediction, and analysis make it possible to detect faulty produce from field to market. The case study involved a line of flavored dairy products. The experiment demonstrated high efficiency in the field of risk management of safety and quality inconsistencies across the entire range of safety and quality requirements, including the sensory profile. The new approach provides a systematic solution to various safety and quality issues that every food-producing enterprise has to face.

Empirical Comparison and Analysis of Artificial Intelligence-Based Methods for Identifying Phosphorylation Sites of SARS-CoV-2 Infection.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a member of the large coronavirus family with high infectivity and pathogenicity and is the primary pathogen causing the global pandemic of coronavirus disease 2019 (COVID-19). Phosphorylation is a major type of protein post-translational modification that plays an essential role in the process of SARS-CoV-2-host interactions. The precise identification of phosphorylation sites in host cells infected with SARS-CoV-2 will be of great importance to investigate potential antiviral responses and mechanisms and exploit novel targets for therapeutic development. Numerous computational tools have been developed on the basis of phosphoproteomic data generated by mass spectrometry-based experimental techniques, with which phosphorylation sites can be accurately ascertained across the whole SARS-CoV-2-infected proteomes. In this work, we have comprehensively reviewed several major aspects of the construction strategies and availability of these predictors, including benchmark dataset preparation, feature extraction and refinement methods, machine learning algorithms and deep learning architectures, model evaluation approaches and metrics, and publicly available web servers and packages. We have highlighted and compared the prediction performance of each tool on the independent serine/threonine (S/T) and tyrosine (Y) phosphorylation datasets and discussed the overall limitations of current existing predictors. In summary, this review would provide pertinent insights into the exploitation of new powerful phosphorylation site identification tools, facilitate the localization of more suitable target molecules for experimental verification, and contribute to the development of antiviral therapies.

Pengaruh Topografi terhadap Keanekaragaman Gulma dalam Budidaya Talas Beneng (Xanthosoma undipes)

Research on weed diversity in talas beneng cultivation areas at various altitudes in Banten is important for providing initial data for weed management. Weed diversity is influenced by internal factors, such as adaptation mechanisms and reproduction, as well as external factors, including environmental conditions, temperature, soil fertility, and altitude. This study was conducted from August to September 2024, observing weed diversity in lowland (343 m a.s.l., Desa Talaga Warna, Serang), midland (615 m a.s.l., Desa Cinyurup, Pandeglang), and highland (761 m a.s.l., Desa Kaduengang, Pandeglang) areas. Sampling involved three plots for each topography, using a purposive sampling method. Field observations were conducted using various weed identification tools, including the PictureThis and Google Lens applications. Data analysis focused on Absolute and Relative Density, Frequency, Dominance, Importance Value Index (IVI), Summed Dominance Ratio (SDR), and Diversity Index. Results indicated that Synedrella nodifora had the highest density in the lowlands, Ageratum conyzoides in the midlands, and Vandellia diffusa in the highlands. The Shannon Diversity Index (H') showed diversity levels of 0.88 (lowland), 1.89 (midland), and 2.68 (highland). These findings suggest that altitude influences weed diversity, with higher elevations supporting greater diversity due to climate and environmental variations. This study highlights the impact of topography on weed communities, providing insights for more targeted weed management in talas beneng cultivation.

Evaluating the Accuracy of the SIL Score for Predicting the Sepsis Mortality in Emergency Department Triages: A Comparative Analysis with NEWS and SOFA.

Background/Objective: Sepsis is a disease with a high mortality rate, which emphasizes the importance of developing tools for the early identification of high-risk patients and to initiate timely treatments to reduce mortality. The SIL score is a scale that uses the shock index and arterial lactate level to identify early on the patients that are at a high risk of in-hospital mortality due to sepsis. The purpose of this study was to validate the SIL score as a tool for estimating the probability of sepsis in-hospital mortality from the triage room in emergency departments. Additionally, the advantages of the SIL score were evaluated in comparison with NEWS and SOFA. Methods: All of the patients with suspected sepsis were prospectively recruited from the triage room in an emergency department. The SIL score, as well as other evaluation scales, were calculated for these patients. The sensitivity, specificity, predictive values, and areas under the curve (AUC) of each scale were assessed to predict mortality. Results: This study included 315 patients. The total mortality of the cohort was 20.4%. Of the total population, 35.5% were in septic shock. The SIL, NEWS, and SOFA scores had similar sensitivities, approximately 60%; however, a higher specificity was documented in the SIL score over the other scales (67%). The SIL score demonstrated superior discriminatory ability compared to the NEWS and SOFA scores (AUC = 0.754, p = 0.01). Conclusions: The SIL score proved to be a useful tool for predicting in-hospital mortality due to sepsis. Its discriminatory ability surpasses that of other evaluated scales. Therefore, the SIL score can be successfully implemented in the triage room of emergency departments to improve the identification and early management of patients with sepsis.

Rapid Sex Identification in Spotted Knifejaw (Oplegnathus punctatus) Using tmem88 Gene Structural Variation Markers.

Spotted knifejaw (Oplegnathus punctatus) is an economically important marine cultured species exhibiting a unique complex sex chromosome system (X1X1X2X2 in females and X1X2Y in males), with males possessing one fewer chromosome (2n = 47) than females (2n = 48) and an abnormally large Y chromosome. Additionally, males demonstrate significant growth advantages over females. Rapid and accurate sex identification is essential for effective culture management, selective breeding, and population control. In this study, we identified a homologous region of the tmem88 gene containing large DNA insertion markers on the X and Y chromosomes through whole-genome sequencing of O. punctatus. The X1 chromosome harbors a 278bp DNA fragment, whereas the Y chromosome contains a 1472bp fragment, resulting in a 1194bp size difference indicative of structural variation in the non-coding region of the tmem88 gene. We developed a rapid detection method based on this variation, utilizing a pair of primers that amplify two distinct bands (278bp and 1472bp) in male (X1X2Y) individuals and a single 278bp band in female (X1X1X2X2) individuals when analyzed by agarose gel electrophoresis. This method enables efficient and accurate sex differentiation in O. punctatus, significantly reducing the time required for identification and enhancing detection efficiency. This study provides a valuable tool for the rapid identification of sex in O. punctatus, facilitating improved breeding strategies and supporting the large-scale production of high-quality fry.

Applications of deep learning in Alzheimer’s disease: a systematic literature review of current trends, methodologies, challenges, innovations, and future directions

Alzheimer’s Disease (AD) constitutes a significant global health issue. In the next 40 years, it is expected to affect 106 million people. Although more and more people are getting AD, there are still no effective drugs to treat it. Insightful information about how important it is to find and treat AD quickly. Recently, Deep Learning (DL) techniques have been used more and more to diagnose AD. They claim better accuracy in drug reuse, medication recognition, and labeling. This essay meticulously examines the works that have talked about using DL with Alzheimer’s disease. Some of the methods are Natural Language Processing (NLP), drug reuse, classification, and identification. Concerning these methods, we examine their pros and cons, paying special attention to how easily they can be explained, how safe they are, and how they can be used in medical situations. One important finding is that Convolutional Neural Networks (CNNs) are most often used for AD research and Python is most often used for DL issues. Some security problems, like data protection and model stability, are not looked at enough in the present research, according to us. This study thoroughly examines present methods and also points out areas that need more work, like better data integration and AI systems that can be explained. The findings should help guide more research and speed up the creation of DL-based AD identification tools in the future.

A Machine Learning-Based Prediction Model for Acute Kidney Injury in Patients With Community-Acquired Pneumonia: Multicenter Validation Study.

Acute kidney injury (AKI) is common in patients with community-acquired pneumonia (CAP) and is associated with increased morbidity and mortality. This study aimed to establish and validate predictive models for AKI in hospitalized patients with CAP based on machine learning algorithms. We trained and externally validated 5 machine learning algorithms, including logistic regression, support vector machine, random forest, extreme gradient boosting, and deep forest (DF). Feature selection was conducted using the sliding window forward feature selection technique. Shapley additive explanations and local interpretable model-agnostic explanation techniques were applied to the optimal model for visual interpretation. A total of 6371 patients with CAP met the inclusion criteria. The development of CAP-associated AKI (CAP-AKI) was recognized in 1006 (15.8%) patients. The 11 selected indicators were sex, temperature, breathing rate, diastolic blood pressure, C-reactive protein, albumin, white blood cell, hemoglobin, platelet, blood urea nitrogen, and neutrophil count. The DF model achieved the best area under the receiver operating characteristic curve (AUC) and accuracy in the internal (AUC=0.89, accuracy=0.90) and external validation sets (AUC=0.87, accuracy=0.83). Furthermore, the DF model had the best calibration among all models. In addition, a web-based prediction platform was developed to predict CAP-AKI. The model described in this study is the first multicenter-validated AKI prediction model that accurately predicts CAP-AKI during hospitalization. The web-based prediction platform embedded with the DF model serves as a user-friendly tool for early identification of high-risk patients.

African swine fever virus biotype identification tool.

African swine fever virus biotyping is a recently described classification technique that is based on an isolate's encoded proteome. In short, proteomes are compared and grouped based on unsupervised machine learning. This tool analyzes African swine fever virus (ASFV) genomes and will report their closest matches along with their ASFV biotypes. Here, we describe a standalone implementation that can be freely downloaded and used.

Reliability of wing morphometrics for species identification of human-biting black flies (Diptera: Simuliidae) in Thailand

BackgroundFast and reliable species identification of black flies is essential for research proposes and effective vector control. Besides traditional identification based on morphology, which is usually supplemented with molecular methods, geometric morphometrics (GM) has emerged as a promising tool for identification. Despite its potential, no specific GM techniques have been established for the identification of black fly species.MethodsAdult female black flies collected using human bait, as well as those reared from pupae, were used in this study. Here, landmark-based GM analysis of wings was assessed for the first time to identify human-biting black fly species in Thailand, comparing this approach with the standard morphological identification method and DNA barcoding based on the mitochondrial cytochrome c oxidase subunit I (COI) gene. To explore genetic relationships between species, maximum likelihood (ML) and neighbor-joining (NJ) phylogenetic trees were built. Additionally, three different methods of species delimitation, i.e., assemble species by automatic partitioning (ASAP), generalized mixed yule coalescent (GMYC), and single Poisson tree processes (PTP), were utilized to identify the morphologically defined species. The effectiveness of a COI barcode in identifying black fly species was further examined through the best match (BM) and best close match (BCM) methods.ResultsSeven black fly species, namely Simulium tenebrosum Takaoka, Srisuka & Saeung, 2018 (complex), S. doipuiense Takaoka & Choochote, 2005 (complex), S. nigrogilvum Summers, 1911, S. nodosum Puri, 1933, S. asakoae Takaoka & Davies, 1995, S. chamlongi Takaoka & Suzuki, 1984, and S. umphangense Takaoka, Srisuka & Saeung, 2017 were morphologically identified. Compared with the standard method, the GM analysis based on wing shape showed high success in separating species, achieving an overall accuracy rate of 88.54%. On the other hand, DNA barcoding surpassed wing GM for species identification with a correct identification rate of 98.57%. Species delimitation analyses confirmed the validity of most nominal species, with an exception for S. tenebrosum complex and S. doipuiense complex, being delimited as a single species. Moreover, the analyses unveiled hidden diversity within S. asakoae, indicating the possible existence of up to four putative species.ConclusionsThis study highlights the potential of wing GM as a promising and reliable complementary tool for species identification of human-biting black flies in Thailand.Graphical

A database and interactive identification key for the anatomy of woods of the Ethiopian Afromontane Forest

Summary Identifying wood and wood charcoal from sub-tropical to tropical regions is challenging, due to the high floristic diversity. This is especially true for the Horn of Africa, characterized by a high degree of endemism, for which no wood anatomy atlas is available. Yet, there is a strong need for anatomical descriptions and identification tools, in particular concerning archaeobotanical research. Numerous rock shelters in the Ethiopian highlands provide rich charcoal assemblages. Their study is of utmost interest to decipher past human-vegetation relationships. Many of these sites are located in the Afromontane Forest, the potential natural vegetation of the highlands within the Eastern Afromontane biodiversity hotspot, hosting 483 dicot woody species. Here, we present the first wood database for the species of the Ethiopian Afromontane Forest. It is set up on the online open-access platform Xper3 and includes an interactive identification key. The anatomical features follow the IAWA nomenclature and include additional ones, to improve the identification process. The database comprises wood anatomical descriptions of (1) 68 species from the wood reference collections of Addis Ababa University, Ethiopia, and Goethe University, Frankfurt, Germany; (2) 72 species from InsideWood and (3) 92 genera/species from specialized literature. The database also includes original pictures taken on samples from the reference collections. Our original coded descriptions and pictures are also available in InsideWood.

Morphological Characteristics of the Incisive Papilla in Pediatric Forensic Odontology: A Cross-Sectional Study

Objectives Objectives: Forensic odontology plays a critical role in identification of individuals through dental evidence. The study aims to evaluate the prevalence and morphological characteristics of incisive papilla among children aged 3-6 years and assess its potential as a reliable tool for racial identification. Material and Methods A cross-sectional study was conducted involving 462 pediatric subjects recruited from Sathyabama Dental College and Hospital. Participants were assessed for incisive papilla morphology using Ortman and Tsao’s classification. Results The study sample comprised 262 males (56.7%) and 199 females (43.3%). The most common shape of the incisive papilla was pear (51.5%), followed by rectangular (34.1%). No statistically significant differences in papilla shape were observed between genders (p = 0.103). Conclusion The findings indicate that the pear shape is the predominant morphology of the incisive papilla in the Chennai population, and its shape can serve as a reliable forensic parameter for identification in dentulous individuals without surgical alterations. Further research is warranted to enhance understanding of its applicability in forensic odontology.

Linear and non-linear relationships between red blood cell indices and cardiovascular risk factors: findings from the China National Health Survey

IntroductionCardiovascular diseases (CVDs) are a leading cause of mortality worldwide. Red blood cell indices (RBIs) are associated with CVD risk factors (CRFs) and easy to test, making them useful as a screening tool for early identification of individuals at high risk for CVDs.MethodsData from 31,781 participants in the China National Health Survey conducted from 2012 to 2017 were analyzed. Linear and non-linear relationships between RBIs and CRFs (hyperuricemia, diabetes, dyslipidemia) were assessed using restricted cubic splines. Propensity score weighting was used to balance confounders between RBI groups in the multivariable logistic regression models.ResultsHemoglobin concentration, red blood cell count and hematocrit all showed a significant linear dose-response association with all CRFs (p values < 0.001). Higher RBIs levels were associated with increased risk of hyperuricemia, diabetes, high LDL, high triglycerides, and high total cholesterol, but decreased HDL. For example, compared to the lowest quantile of HGB, the highest quantile had a 26% (13-40%) higher risk for hyperuricemia, a 43% (25-63%) higher risk of diabetes, 87% (61%-1.18 fold) higher risk of high LDL, and 68% (52-85%) higher risk of high triglycerides. Non-linear relationships were revealed between RBIs and most CRFs except uric acid and glucose. Sex differences were observed, with stronger associations between RBIs and hyperuricemia in women but stronger links with high LDL in men.ConclusionsElevated RBIs indicated higher risk of multiple CRFs. These findings suggest incorporating RBIs into CVD screening strategies to facilitate early prevention efforts, with consideration of sex differences.