Robust Validation Research Articles

The psychometric properties of Chinese version of the Gilles de la Tourette syndrome-quality of life scale (GTS-QOL) for children and adolescents

BackgroundGilles de la Tourette syndrome (GTS) is a persistent neurological disorder that profoundly affects the quality of life for afflicted individuals, however, tailored health-related quality of life (QOL) measures for Chinese pediatric GTS patients are lacking. This pioneering study aims to develop a QOL scale for Chinese children with GTS.MethodsA cohort of 1,121 children (aged 6–16 years) diagnosed with GTS participated in scale development. Internal consistency was evaluated using Cronbach’s alpha. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to assess the factor structure of the GTS-QOL-Chinese, examining goodness-of-fit indices, factor loadings of individual items, and covariances between factors. Validity was determined through interscale correlations and comparisons with external measures to establish convergent and discriminant validity.ResultsFollowing reliability and validity analyses, a Chinese version of the 24-item GTS-specific quality of life scale was conclusively established, encompassing five subscales (psychological, compulsive, cognitive, physical, social). The scale demonstrated high internal consistency with a Cronbach’s alpha coefficient of 0.93, and validity was substantiated through interscale correlations ranging from 0.46 to 0.68. EFA identified five factors, explaining 61.26% of the total variation. CFA further confirmed the scale’s robustness, with all goodness-of-fit indicators meeting acceptable criteria (AGFI = 0.86, PCFI = 0.78, GFI = 0.89, CFI = 0.89, RMSEA = 0.07, SRMR = 0.06).ConclusionsThe Chinese version of the GTS-QOL scale has exhibits robust reliability and validity and is valuable for assessing the quality of life of Chinese children and adolescents with GTS. This scale may assist in tailoring interventions and improving patient care.

Computed Tomography-Derived Fractional Flow Reserve: Developing A Gold Standard for Coronary Artery Disease Diagnostics.

In recent years, a new technique called computed tomography-derived fractional flow reserve (CT-FFR) has been developed. CT-FFR overcomes many limitations in the current gold-standard fractional flow reserve (FFR) techniques while maintaining a better concordance with FFR. This technique integrates static coronary CT angiography data with hydrodynamic models, employing algorithms rather than guidewire interventions to compute the FFR. In addition to diagnosing coronary heart disease, CT-FFR has been applied in the preoperative risk assessment of major adverse cardiovascular events (MACEs) in organ transplantation and transcatheter aortic valve replacement (TAVR). Continuous advancements in CT-FFR techniques and algorithms are expanding their applicability to other methodologies. Subsequently, with robust clinical trial validation, CT-FFR can potentially supersede FFR as the primary "gatekeeper" for interventions.

Cross-cultural adaptation and reliability of the inventory of vicarious posttraumatic growth and research of its influencing factors: a cross-sectional study

ObjectiveThe purpose of this study was to translate the Vicarious Posttraumatic Growth Inventory (VPTGI) into Chinese and to assess its reliability and validity in Nurses, Additionally, it explored the correlations between vicarious posttraumatic growth (VPTG), Secondary Traumatic Stress (STS) and demographic variables.MethodsThe Brislin translation model was used to translate the VPTGI into Chinese. Validity analysis involved exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and assessments of convergent validity, discriminant validity, and content validity. Reliability analysis included split-half reliability, internal consistency reliability, and test-retest reliability. Item analysis employed the Critical Ratio Decision Value (CR) method, item-total correlation method, and reliability change method. Single-factor analysis was conducted to examine the relationship between demographic variables and VPTG, while correlation analysis explored the association between STS and VPTG.ResultsThe Chinese version VPTGI demonstrated robust content validity (I-CVI: 0.83-1, S-CVI: 0.97), supported by EFA (KMO: 0.933) and significant Bartlett’s test (p < 0.001). Four factors explained 67.82% variance, CFA confirmed the model fit (χ2/df = 2.255, RMSEA = 0.079, IFI = 0.931, TLI = 0.914, CFI = 0.930, NFI = 0.882). The Chinese version VPTGI demonstrated high internal consistency (Cronbach’s α = 0.951), with dimensions’ Cronbach’s α ranging from 0.806 to 0.912. Overall, nurses demonstrated a moderate to low level of VPTG and a severe level of STS. Furthermore, there was a significant negative correlation between STS and VPTG.ConclusionThe Chinese version of VPTGI demonstrated satisfactory reliability, validity, and factor structure, making it a reliable tool to assess VPTG in Chinese nurses. These findings underscore the importance of promoting VPTG and addressing STS among healthcare professionals. Further research in this area is warranted to better understand and support the psychological well-being of nurses.

“Validation of three scales for enhanced suicide prevention in Spanish-speaking populations: Suicidal ideation, stigma, and literacy”

BackgroundEscalating suicide rates in Spain and Latin America underscore the critical need for accurate assessment and prevention tools tailored to these populations. This study aimed to culturally adapt and validate three essential scales: the Suicidal Ideation Attributes Scale (SIDAS), the Suicide Stigma Scale - Short Form (SOSS-SF), and the Suicide Literacy Scale - Short Form (LOSS-SF). MethodA total of 678 participants (including 150 with suicidal ideation) completed an online survey. The scales underwent rigorous back-translation and adaptation by native speakers from Spain and Latin America. SIDAS and SOSS-SF were validated using Confirmatory Factor Analysis (CFA), with criteria including fit indices (e.g., RMSEA, CFI). LOSS-SF underwent validation through Item Response Theory (IRT). Additionally, gender invariance was assessed across all scales. ResultsCFA confirmed the original factor structures of SIDAS and SOSS-SF, demonstrating good fit indices and internal consistency. The analysis of LOSS-SF required excluding two items, resulting in a final Spanish version with 10 items and an unifactorial structure. Gender invariance was established across all scales. ConclusionsThe culturally adapted and validated scales in this study demonstrate robust reliability and validity for assessing suicidal ideation, suicide stigma, and suicide literacy among Spanish-speaking populations. These validated tools have the potential to enhance clinical assessment, inform personalized interventions, monitor public health initiatives, educate communities about suicide, and advance research in suicide prevention and treatment.

LMCD-OR: a large-scale, multilevel categorized diagnostic dataset for oral radiography

In recent years, digital dentistry has increasingly utilized advanced image analysis techniques, such as image classification and disease diagnosis, to improve clinical outcomes. Despite these advances, the lack of comprehensive benchmark datasets is a significant barrier. To address this gap, our research team develop LMCD-OR, a substantial collection of oral radiograph images designed to support extensive artificial intelligence (AI)-driven diagnostics. LMCD-OR comprises 3,818 digital imaging and communications in medicine (DICOM) oral X-ray images from local medical institutions that are meticulously annotated to provide broad category information for both primary dental outpatient services and detailed secondary disease diagnoses. This dataset is engineered to train and validate multiclassification models to improve the precision and scope of oral disease diagnostics. To ensure robust dataset validation, we employ four cutting-edge visual neural network classification models as benchmarks. These models are tested against rigorous performance metrics, demonstrating the ability of the dataset to support advanced image classification and disease diagnosis tasks. LMCD-OR is publicly available at http://dentaldataset.zeroacademy.net.

From greatest simplicity to full power

AbstractWhile language processing services are key assets for the science and technology of language, the possible ways under which they may be made available to the widest range of their end users are critical to support an Open Science policy for this scientific domain. Although providing such processing services under some web-based interface, at large, offers itself as an immediate and cogent response to that challenge, turning this view into an effective access to language processing services is an undertaking deserving a clear conceptual direction and a corresponding robust empirical validation. Based on an extensive overview of major undertakings towards making language processing tools available and on the design principles worked out and implemented in the PORTULAN CLARIN infrastructure, in this paper we advocate for a Research-Infrastructure-as-a-Service (RIaaS) model. This model unleashes accessibility to language processing services in as many web-based interface modalities as the current stage of technological development permits to support, in order to serve as many types of end users as possible, from IT developers to Digital Humanities researchers, and including citizen scientists, teachers, students and digital artists among many others.

Machine Learning for Upper Limb Flexion Movement Classification

Introduction: This paper introduces the development and validation of a Machine Learning (ML) program aimed at discerning smooth and jittery arm movements during flexion/extension exercises. Objective: The study compares the efficacy of three classification algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Logistic Regression—in differentiating flexion/extension movements with and without added weight. Method: Using a quasi-experimental design, participants voluntarily performed the exercise under two conditions: with and without a 5-kilogram dumbbell. Meticulous frame-by-frame extraction of movement parameters informed the data collection process. Results: Biomechanical analysis identified key features (minAngle, coefTrajectory, maxJerk, avgAcceleration, and frames) relevant for algorithm training. Post-normalization, KNN, Logistic Regression, and SVM demonstrated robust validation performance through metrics and confusion matrices. Detection of a userdependent data leak prompted a user-specific validation approach. Conclusion: This research amalgamates biomechanics and ML, yielding insights into algorithmic performance for detecting weighted exercises. Robust validation is crucial for ensuring the generalizability of classification models in real-world scenarios.

K-fold matching model for crowd behavioral anomaly detection from discontinuous inputs

Image and associated features detect behavioural anomalies in crowd scenes through matching. Existingmethods use continuous data inputs to track crowd behaviour. As a result, the systemmistakenly assumes normal behaviour during data gaps, leading to false positives or missing detections of small abnormalities. The design of precision-focused matching requires simplified yet robust validation for any number of crowd elements. This article presents a K-fold Matching Model (KFMM) with edge-to-edge correlation. This proposed model identifies the region edges of an input crowd image sequentially. The difference in edges due to crowd misbehaviour activities is validated using random and continuous image features in K number of steps. Considering the complexity of multiple K-steps, the confinement is performed using a neural sigmoid function where the K × K edge matching is reduced. This reduction is based on activity classification i.e. minimum and maximum between two consecutive input sequences. Therefore, as the classification increases, the K is halted with the maximum correlation; contrarily for a low number of classifications, the manifold matching is pursued until the maximum is reached. This correlation count is defined using the neural sigmoid with a linear function for the edges. For the maximum features, this proposed model achieves an 11.21 % high matching ratio, 8.15 % high classifications, 9.71 % high precision, 9.61 % less complexity, and 8.9 % less linear error.

B-123 Prediction of Opioid Use Duration in Post-Surgical Orthopedic Patients Using CYP2D6 Inhibitor Index and Routine Laboratory Markers

Abstract Background Hydrocodone, the most commonly prescribed opioid in the U.S., is typically used for pain management following surgery and is a known potential drug of abuse with non-negligible risk of addiction. Significant variability in patients’ response to hydrocodone therapy have been reported, owing to both iatrogenic and endogenous sources. The objective of this study is to evaluate the feasibility of predicting post-discharge hydrocodone use in patients who have undergone orthopedic surgery using patient demographics, genotyping, concurrently prescribed medications, and routinely collected laboratory test results. Methods Targeted genotyping was performed on each patient with genes known to affect hydrocodone pharmacokinetics or pharmacodynamics, including CYP2D6, CYP3A4, CYP3A5, CYP2B6, COMT, OPRM1, and ABCB1. An inhibition index for CYP2D6 was calculated by ranking concurrently prescribed medications as weak, moderate, or strong inhibitors of enzyme activity. Pain index, average hydrocodone dose per day during hospital stay, and CYP2D6 estimated activity scores based on allele functionality were included as predictors. In addition, a retrospective chart review of electronic hospital records was performed where basic metabolic panel and complete blood count results were screened within 10 days of each patient's surgery date. Two types of classification and decision tree algorithms were tuned and validated as prediction models using 5-fold cross validation, including a ‘Fast and Frugal Decision Tree’ (FFTree) and an extreme gradient boosting machine (xgBoost). Clinical predictive features were ranked according to the Shapley Additive Explanations (SHAP) values using the xgBoost model. Results Out of 79 total patients with hydrocodone use duration information, 25 were categorized as “Short” and 54 categorized as “Long” using a 1-week threshold. FFTree model selected the top three features of CYP2D6 inhibition index, blood sodium level, and blood creatinine level and obtained a sensitivity and specificity of 0.80 and 0.76, respectively. The xgBoost model, using the same three features, achieved a sensitivity and specificity of 0.872 and 0.628, respectively, an AUROC of 0.814, PRAUC of 0.912, net benefit of 0.359, and was well-calibrated with a Brier score of 0.161. Consistent with previously published results, the predictive capacity of all genotypes analyzed were unremarkable and consistently ranked as the least important features by the xgBoost model SHAP values, due to their low predictive value. Conclusions Both the FFTree and xgBoost models were able to adequately classify the patients according to a 1-week stratification criteria with good to excellent classification performance. Patients with larger CYP2D6 inhibition index were more likely to be classified as “Long” duration. Sodium and creatinine level, which may reflect hydration status and renal function, and therefore hydromorphone elimination rate, were key predictors of post-discharge hydrocodone use duration when combined with the CYP2D6 inhibition index. Further refinement of the proposed models and validation of model robustness and stability using external cohorts is necessary. These results may have implications for providers when prescribing hydrocodone for pain management, where the ability to risk-stratify patients according to their predicted probability of developing opioid use disorder is of clinical importance.

Validation of the prognostic index for spine metastasis (PRISM) for stratifying survival in patients treated with spinal stereotactic body radiation

PurposeThe Prognostic Index for Spinal Metastasis (PRISM) is a scoring system derived from prospective data from a single institution that stratifies patients undergoing spine stereotactic radiosurgery (SSRS) for spinal metastases into subgroups by overall (OS). We sought to further demonstrate its generalizability by performing validation with a large dataset from a second high-volume institution, Mayo Clinic. Methods and materialsEight hundred seventy-nine patients—424 from Mayo Clinic and 455 from MD Anderson Cancer Center (MDACC)—who received SSRS between 2007 and 2019 were identified. Patients were stratified by PRISM criteria, and overall survival (OS) for the PRISM groups for each cohort was compared using Kaplan-Meier estimations and univariate Cox proportional analyses. Model calibration and concordance indices (C-indices) were calculated for each cohort to assess the quality of the scoring system. ResultsPatient and tumor characteristics varied significantly between both cohorts including histology, sex, performance status, and number of organs involved (all P < 0.001). Median OS was 30.3 and 22.1 months for the Mayo and MDACC cohorts, respectively. Kaplan-Meier survival curves revealed robust separation between prognostic groups within both cohorts. The Mayo cohort showed median OS of 57.1, 37.0, 23.7, and 8.8 months for Groups 1, 2, 3, and 4, respectively. Univariate analysis revealed hazard ratios of 3.0 (95 % confidence interval [CI], 1.9–4.9), 5.2 (95 % CI, 3.2–8.3), and 12.9 (95 % CI, 7.8–21.4) for groups 2, 3 and 4, respectively all P < 0.001). The C-indices were 0.69 and 0.66 for the unstratified and stratified scores for the Mayo cohort, and 0.70 and 0.68 for the MDACC cohort, respectively. ConclusionThese data demonstrate robust validation of the PRISM score to stratify OS in patients treated with SSRS by a large external cohort, despite substantial differences among the cohorts. Overall, the PRISM scoring may help guide optimal treatment selection for patients with spine metastases.

Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4 V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4 V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. This study predicts LPBF Ti-6Al-4 V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a 〈001〉 texture forms at low energy and a 〈111〉 texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N0) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N0 with different energy levels will further advance the optimization of LPBF Ti-6Al-4 V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4 V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

Critical Thinking Evaluation Scale: Design and Validation in a Colombian Population

Critical thinking stands out as one of the most important cognitive abilities needed for effective adaptation to a knowledge-based society in the 21st century. Despite its significance, there remains a lack of consensus regarding the conceptual and methodological frameworks for measuring it. This study aimed to design and validate a comprehensive assessment scale for critical thinking. A specification table was constructed based on the critical thinking components agreed upon in the current literature, from which corresponding items were formulated and subsequently validated by expert judges. Following adjustments to the test, 258 Colombian participants completed it. Sample adequacy (KMO), Bartlett’s sphericity, and collinearity were confirmed, and the results underwent exploratory factor analysis. Reliability analysis was conducted using McDonald’s ω, Cronbach’s α, Guttman’s λ6, and Greatest Lower Bound (GLB) statistics. The final test comprised 17 items organized into 2 constituent factors, demonstrating robust content and internal structure validity, as well as high levels of precision and internal consistency (overall GLB of .93). The Critical Thinking Evaluation Scale (CTES) exhibits validity and reliability for use within the Colombian population. Its adaptation for other contexts and countries, both Spanish and English-speaking, is recommended. The Spanish version, along with the validated English version for potential adaptations, and scoring norms are provided in the attached documents.

The Upside of Teachers’ Technostress: Adaptation and Validation of a Techno-eustress Scale

Digital education transformation introduced a new source of stress for higher education teachers (HETs). Research has studied the effects of technostress, or technology-induced stress, focusing on its harmful consequences (techno-distress). Recent literature further suggests that technostress can also benefit individuals and organizations (techno-eustress), contributing to wellbeing, effectiveness, and performance. However, techno-eustress measurements are scarce. This study aimed to adapt and validate the Techno-eustress Scale for HETs. The scale was translated, adapted, and tested in a nationwide sample of 1,107 Portuguese HETs. Both exploratory and confirmatory factor analyses were used. Results led to a brief 5-item Portuguese version of the techno-eustress scale, exhibiting a one-dimensional structure and robust psychometric qualities, evidencing reliability, construct validity, and strict measurement invariance. The study provided a parsimonious techno-eustress assessment that assists future research and practice. Our work extended technostress research and crosses scientific domains such as education, psychology, information systems, and management. This brief measurement underpins positive organizational studies as it opposes the eternalization of techno(di)stress measurements, in a technology-based era when wellbeing, notably for HETs, is a precious asset.

Advancing pharmacogenomic research in US Hmong populations: prevalence of key single nucleotide variations in California Hmong.

In collaboration with the Minnesota Hmong community, we have previously discovered significant differences in allele frequencies for key Single Nucleotide Variations (SNVs) within Very Important Pharmacogenes (VIPs) between Hmong and East Asians. Recognizing the potential clinical implications of these observed differences, we sought to validate these observations in a Hmong cohort residing in California, the state with the largest Hmong population in the US. Robust validation of these differences would affect motivation for clinicians treating individuals who identify as Hmong to consider pharmacogenomic (PGx) testing as a means to improve clinical decision making when using therapeutic agents in this unique population. Guided by California Hmong community leaders and utilizing the basic approach of community-based participatory research, demographic, clinical information and a buccal swab was obtained from Hmong adults residing in California. A commercial PGx testing panel was performed on these samples and specific allele frequencies of interest were compared between California and Minnesota Hmong. Allele frequency differences between California Hmong, East Asians and Europeans, were also compared. Return-of-PGx-results and presentations of group data were made to members of the Hmong along with PGx educational sessions to help interpret the observations. In 118 California Hmong who completed the study, the allele frequencies for SNV's were similar to previous Minnesota Hmong results. Furthermore, out of the 18 SNVs that were not previously reported in Hmong, allele frequencies were statistically different in 38% (7/18) of SNVs comparing California Hmong to East Asians, and in 77.8% (14/18) SNVs comparing California Hmong to Europeans. These results validate the original study's findings that Hmong people living in different US locations have similar allele frequencies for key PGx genes. Further, for many of these PGx genes, their allele frequencies are significantly different compared to either East Asians or Europeans. Clinicians should consider these important differences when prescribing medications for people who identify as Hmong.

Artificial Intelligence in Dry Eye Disease: A Narrative Review.

Dry eye disease (DED) is a multifactorial condition affecting millions worldwide, characterized by discomfort, visual disturbance, and potential damage to the ocular surface. The complexity of its diagnosis and management, driven by the diversity of symptoms and underlying causes, presents significant challenges to clinicians. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering potential solutions to these challenges through its data analysis, pattern recognition, and predictive modeling capabilities. This narrative review explores the role of AI in diagnosing, treating, and managing dry eye disease. AI-driven tools such as machine learning algorithms, imaging technologies, and diagnostic platforms are examined for their ability to enhance diagnostic accuracy, personalize treatment approaches, and optimize patient outcomes. Furthermore, the review addresses the limitations of AI technologies in ophthalmology, including the need for robust clinical validation, data privacy concerns, and the ethical considerations of integrating AI into clinical practice. The findings suggest that while AI holds promise for improving the care of patients with DED, ongoing research and development are crucial to realizing its full potential.

From Voxel to Gene: A Scoping Review on MRI Radiogenomics' Artificial Intelligence Predictions in Adult Gliomas and Glioblastomas-The Promise of Virtual Biopsy?

Gliomas, including the most severe form known as glioblastomas, are primary brain tumors arising from glial cells, with significant impact on adults, particularly men aged 45 to 70. Recent advancements in the WHO (World Health Organization) classification now correlate genetic markers with glioma phenotypes, enhancing diagnostic precision and therapeutic strategies. This scoping review aims to evaluate the current state of deep learning (DL) applications in the genetic characterization of adult gliomas, addressing the potential of these technologies for a reliable virtual biopsy. We reviewed 17 studies, analyzing the evolution of DL algorithms from fully convolutional networks to more advanced architectures (ResNet and DenseNet). The methods involved various validation techniques, including k-fold cross-validation and external dataset validation. Our findings highlight significant variability in reported performance, largely due to small, homogeneous datasets and inconsistent validation methods. Despite promising results, particularly in predicting individual genetic traits, the lack of robust external validation limits the generalizability of these models. Future efforts should focus on developing larger, more diverse datasets and integrating multidisciplinary collaboration to enhance model reliability. This review underscores the potential of DL in advancing glioma characterization, paving the way for more precise, non-invasive diagnostic tools. The development of a robust algorithm capable of predicting the somatic genetics of gliomas or glioblastomas could accelerate the diagnostic process and inform therapeutic decisions more quickly, while maintaining the same level of accuracy as the traditional diagnostic pathway, which involves invasive tumor biopsies.

Artificial intelligence as a tool in drug discovery and development.

The rapidly advancing field of artificial intelligence (AI) has garnered substantial attention for its potential application in drug discovery and development. This opinion review critically examined the feasibility and prospects of integrating AI as a transformative tool in the pharmaceutical industry. AI, encompassing machine learning algorithms, deep learning, and data analytics, offers unprecedented opportunities to streamline and enhance various stages of drug development. This opinion review delved into the current landscape of AI-driven approaches, discussing their utilization in target identification, lead optimization, and predictive modeling of pharmacokinetics and toxicity. We aimed to scrutinize the integration of large-scale omics data, electronic health records, and chemical informatics, highlighting the power of AI in uncovering novel therapeutic targets and accelerating drug repurposing strategies. Despite the considerable potential of AI, the review also addressed inherent challenges, including data privacy concerns, interpretability of AI models, and the need for robust validation in real-world clinical settings. Additionally, we explored ethical considerations surrounding AI-driven decision-making in drug development. This opinion review provided a nuanced perspective on the transformative role of AI in drug discovery by discussing the existing literature and emerging trends, presenting critical insights and addressing potential hurdles. In conclusion, this study aimed to stimulate discourse within the scientific community and guide future endeavors to harness the full potential of AI in drug development.

Associations between human blood metabolome and vascular dementia

BackgroundEffective and specific biomarkers are warranted for the management of vascular dementia. We aimed to systematically screen the human blood metabolome to identify potential mediators of vascular dementia via a two-sample Mendelian randomization (MR) design. MethodsWe selected 93 unique blood metabolites from 3 metabolome genome-wide association studies (GWASs) with a total of 147,827 participants of European ancestry. Summary statistics for vascular dementia originated from a European-descent GWAS dataset released by the FinnGen Study, involving 859 cases and 211,300 controls. We applied the inverse-variance weighted MR method in the main analysis to examine the causal roles of blood metabolites in vascular dementia, followed by several sensitivity analyses for robustness validation. ResultsGenetically determined glycoproteins (OR per 1-SD increase, 0.75; 95 % CI, 0.68–0.83, P = 1.08 × 10−8) and O-methylascorbate (OR per 1-SD increase, 0.08; 95 % CI, 0.02–0.32; P = 3.74 × 10−4) levels had negative associations with the risk of vascular dementia, whereas genetically determined total cholesterol (OR per 1-SD increase, 1.77; 95 % CI, 1.32–2.38; P = 1.39 × 10−4) and low-density lipoprotein (LDL) cholesterol (OR per 1-SD increase, 1.94; 95 % CI, 1.48–2.55; P = 1.61 × 10−6) levels had positive associations with the risk of vascular dementia. MR-Egger regression suggested no directional pleiotropy for the identified associations, and sensitivity analyses with different MR models further confirmed these findings. ConclusionGlycoproteins, O-methylascorbate, total cholesterol, and LDL cholesterol might be promising blood markers of vascular dementia, which may provide novel insights into the prevention of vascular dementia. Further studies are warranted to replicate our findings and elucidate the potential mechanistic pathways.

Serum tumor markers: potential indicators for occult lymph node metastasis in clinical T1 − 2N0M0 small cell lung cancer patients

In their letter-to-the-editor entitled “Letter to the Editor: Incidence rate of occult lymph node metastasis in clinical T1 − 2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study”, Prof. Chen et al. provided insightful comments and suggestions on our original study. We appreciate the authors’ feedback and have conducted a preliminary exploration of the predictive value of serum tumor markers (TMs) for occult lymph node metastasis (OLM) in clinical T1 − 2N0M0 (cT1 − 2N0M0) small cell lung cancer (SCLC) patients. The results indicate that neuron-specific enolase (NSE), carbohydrate antigen 125 (CA125), and squamous cell carcinoma antigen (SCC) have potential predictive value for detecting OLM in cT1 − 2N0M0 SCLC patients. Additionally, further exploration and confirmation through prospective, large-scale studies with robust external validation are needed.

AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration

Numerous investigations have shown that the municipal solid waste incineration (MSWI) has become one of the major sources of dioxin (DXN) emissions. Currently, the primary issue that needs to be addressed for DXN emission reduction control is the online measurement of DXN. Data-driven AI algorithms enable real-time DXN concentration measurement, facilitating its control. However, researchers mainly focus on building models for DXN emissions at the stack. This approach does not allow for the construction of models that online measurement of DXN generation and absorption throughout the whole process. To achieve optimal pollution control, models that encompass the whole process are necessary, not just models focused on the stack. Therefore, this article focuses on modeling the whole process of DXN concentrations, including generation, adsorption, and emission. It uses machine learning techniques based on advanced tree-based data-driven deep and broad learning algorithms. The determination of data characteristics at different phases is grounded in the understanding of the DXN mechanism, offering a novel framework for DXN modeling. State-of-the-art tree-based models, including adaptive deep forest regression algorithm based on cross layer full connection, tree broad learning system, fuzzy forest regression, and aid modeling technologies, are applied to handle diverse data characteristics. These characteristics encompass high-dimensional small samples, low-dimensional ultra-small size samples, and medium-dimensional small samples across different phases related to DXN. The most interesting is the robust validation where the proposed a whole process tree-based model for DXN is validated using nearly one year of authentic data on DXN generation, adsorption, and emission phases in an MSWI plant of Beijing. The proposed modeling framework can be used to explore the mechanism characterization and support the pollution reduction optimal control.