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PubMed2026

Transcriptome Analysis and Experimental Validation of Palmitoylation- Related Biomarkers in Atherosclerosis.

INTRODUCTION: Protein palmitoylation contributes to membrane localisation, signal transduction, and cell-fate regulation. It is closely associated with lipid metabolic dysfunction, immune inflammation, and vascular remodelling in atherosclerosis (AS). However, key palmitoylation-related transcriptomic markers and their potential causal associations with AS remain incompletely defined. METHODS: The Gene Expression Omnibus (GEO) dataset GSE100927 was used as the training cohort, and GSE43292 was used as an external validation cohort. Differentially expressed genes were identified using limma and intersected with palmitoylation-related genes to obtain palmitoylation-related differentially expressed genes (PRDEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were then performed using clusterProfiler. Two-sample Mendelian randomisation was used to evaluate potential causal relationships between characteristic genes and AS. Feature selection was conducted using random forest and support vector machine recursive feature elimination (SVM-RFE), and the overlapping genes selected by both methods were retained. Receiver operating characteristic (ROC) curves were used to assess diagnostic performance. A five-gene nomogram was constructed, and its clinical utility was evaluated using calibration curves and decision curve analysis (DCA). Gene set variation analysis (GSVA) was applied to compare pathway activity between high- and low-expression groups for each core gene. Single-cell analysis using Seurat and expression-based cell-cell communication analysis using CellChat were conducted with GSE159677, and upstream transcription factors were predicted using NetworkAnalyst. For in vivo validation, an AS model was established in ApoE⁸/⁸ mice fed a high-fat diet, and aortic gene and protein expression were assessed by RT-qPCR and western blotting. RESULTS: In GSE100927, 51 PRDEGs were identified. GO and KEGG enrichment analyses highlighted pathways associated with regulation of monoatomic ion transport, sarcomere and myofibril organisation, and immune inflammation. Mendelian randomisation suggested a potential protective causal association between SLC7A7 and AS. By integrating MR with random forest and SVM-RFE feature selection, we prioritised five core genes: PLCB2, GMIP, NEXN, PLN, and SLC7A7. These genes showed good diagnostic performance in GSE43292. The resulting nomogram was well calibrated and demonstrated stable net benefit in decision curve and clinical impact curve analyses. Single-gene GSVA identified consistently activated pathways across multiple genes, including innate and adaptive immune recognition, calcium signalling and myocardial contraction/cardiomyopathy, extracellular matrix-receptor interaction, cell junction pathways, autophagy-lysosome pathways, and several metabolic programmes. At the single-cell level, PLCB2 and GMIP were predominantly expressed in T cells and macrophages, NEXN and PLN were enriched in vascular smooth muscle cells, and SLC7A7 was mainly expressed in macrophages. CellChat analysis indicated increased signals for immune-related ligand-receptor interactions. In ApoE⁸/⁸ mice fed a high-fat diet, PLCB2, GMIP, and SLC7A7 were upregulated, whereas NEXN and PLN were downregulated; protein-level changes were concordant with the transcriptomic trends. DISCUSSION: These findings indicate that palmitoylation-related dysregulation in AS converges on immune inflammation, calcium signalling/contractile programmes, ECM remodelling, and autophagy-linked metabolism. The five-gene panel is supported by external validation, single-cell localisation to immune and vascular compartments, and concordant results in ApoE⁸/⁸ mice. CONCLUSION: This study identified and validated five palmitoylation-related genes associated with AS. SLC7A7 showed a potential protective causal signal in MR analysis. The enriched pathway patterns linked these genes to immune inflammation, calcium signalling-contraction coupling, ECM remodelling, cell adhesion, and autophagy- associated metabolic reprogramming. The five-gene nomogram showed potential utility for diagnostic classification and decision support, nominating candidate biomarkers and pathway targets for AS molecular subtyping, diagnosis, and mechanistic investigation.

Atherosclerosis (AS)↗

PubMed2003

A classification-based machine learning approach for the analysis of genome-wide expression data.

Three important areas of data analysis for global gene expression analysis are class discovery, class prediction, and finding dysregulated genes (biomarkers). The clinical application of microarray data will require marker genes whose expression patterns are sufficiently well understood to allow accurate predictions on disease subclass membership. Commonly used methods of analysis include hierarchical clustering algorithms, t-, F-, and Z-tests, and machine learning approaches. We describe an approach called the maximum difference subset (MDSS) algorithm that combines classification algorithms, classical statistics, and elements of machine learning and provides a coherent framework. By integrating prediction accuracy, the MDSS algorithm learns the critical threshold of statistical significance (the alpha or P-value), eliminating the arbitrariness of setting a threshold of statistical significance and minimizing the effect of the normality assumptions. To reduce the false positive rate and to increase external validity of the predictive gene set, a jackknife step is used. This step identifies and removes genes in the initial MDSS with low combined predictive utility. The overall MDSS provides a prediction that is less dependent on an arbitrary study design (sample inclusion or exclusion) and should thus have high external validity. We demonstrate that this approach, unlike other published methods, identifies biomarkers capable of predicting the outcome of anthracycline-cytarabine chemotherapy in cases of acute myeloid leukemia. By incorporating two criteria-statistical significance and predictive utility-the approach learns the significance level relevant for a given data set. The MDSS approach can be used with any test and classifier operator pair.

Acute Disease↗

PubMed1999

Standard cost lists for healthcare in Canada. Issues in validity and inter-provincial consolidation.

A standard cost list is a listing of recommended costs for a selected group of services. Standard costs are used in economic evaluation studies to eliminate that proportion of cost differences between interventions that are due to cost differences between providers. In this article we provide a summary of cost lists for pharmaceutical economic evaluation purposes which have been developed in 2 provinces in Canada-Alberta and Manitoba. We then assess these 2 lists from 2 different viewpoints. First, we developed criteria for the internal and external validity of costs and, in light of these validity criteria, we assessed how the 2 standard cost lists compared with the 'ideal' measure of long run marginal costs. Second, we identified the criteria for the inter-provincial consolidation of standard cost measures (in order to develop a single, consolidated cost list); in light of these criteria, we assessed whether the degree to which the 2 separate lists could be consolidated. The lists achieved a considerable degree of external validity, but fared less well in terms of internal validity. However, these results depend on the 'ideal' measure of cost which is used. The lists, in the forms which were developed, are not easily consolidated into a single list. Further refined cost data would be needed in order to achieve consolidation.

Alberta↗

PubMed1998

What is the scientific meaning of empirically supported therapy?

It is important to define precisely what is and is not meant by "empirically supported treatments," rigorously based on what is actually known about the nature of experimental therapy research. The criteria for empirically supported treatments merely allow conclusions about whether treatments cause any change beyond the causative effect of such factors as placebo or the passage of time. Applied implications are limited, due to external validity and to the fact that applied decisions are influenced by cost-benefit analyses. Creating increasingly effective therapies through between-group designs is best done by controlled trials specifically aimed at basic questions about the nature of psychological problems and the nature of therapeutic change mechanisms. Naturalistic research is important for external validity but is valuable only if it uses scientifically valid methods to address basic knowledge questions.

Empiricism↗

PubMedDate not supplied

[Methods: scales, biological and clinical criteria].

Assessing depressive symptomatology in antidepressant drug trials is limited by the absence of external validators of depression. Moreover, a high level of standardisation is required by regulatory authorities for registering new drugs. Consequently, the methods used are relatively old, and recent improvements have been minor. Methods used to assess new antidepressants appear highly standardised and lack objectivity. Improvements to these methods may emerge from the discovery of external validators of depression on the one hand and of drugs with new mechanisms of action on the other.

Classification↗

PubMed2005

Adaptation of panic-related psychopathology measures to Russian.

The study reports results of adaptation of panic-related psychopathology measures to Russian, including the Anxiety Sensitivity Index (ASI), the Agoraphobic Cognitions Questionnaire (ACQ), and the Mobility Inventory for Agoraphobia (MIA). Psychometric properties (e.g., reliability, factor structure, endorsement) and external validity of the adaptations were evaluated in a representative sample of Moscow residents (N = 390) and Ukrainian undergraduates (N = 492). The adapted ASI was generally equivalent to the original English-language version. The ACQ showed structural equivalency but did not exhibit expected specificity to panic. Finally, the MIA displayed notable structural discrepancies but had external validity comparable to the original.

Adult↗

PubMed2004

Prediction of aqueous solubility based on large datasets using several QSPR models utilizing topological structure representation.

Several QSPR models were developed for predicting intrinsic aqueous solubility, S(o). A data set of 5,964 neutral compounds was sub-divided into two classes, aromatic and non-aromatic compounds. Three models were created with different methods on both data sets: two regression models (multiple linear regression and partial least squares) and an artificial neural network model. These models were based on 3343 aromatic and 1674 non-aromatic compounds for training sets; 938 compounds were used in external validation testing. The range in -log S(o) is -1.6 to 10. Topological structure descriptors were used with all models. A genetic algorithm was used for descriptor selection for regression models. For the artificial neural network (ANN) model, descriptor selection was done with a backward elimination process. All models performed well with r2 values ranging 0.72 to 0.84 in external validation testing. The mean absolute errors in validation ranged from 0.44 to 0.80 for the classes of compounds for all the models. These statistical results indicate a sound ANN model. Furthermore, in a comparison with eight other available models, based on predictions using a validation test set (442 compounds), the artificial neural network model presented in this work (CSLogWS) was clearly superior based on both the mean absolute error and the percentage of residuals less than one log unit. In the ANN model both E-State and hydrogen E-State descriptors were found to be important.

Databases, Factual↗

PubMed2025

Distinct immune-metabolic phenotypes underlie poor coronary collateral circulation.

BACKGROUND: Coronary collateral circulation (CCC) significantly impacts myocardial perfusion and clinical outcomes in coronary artery disease patients, yet the underlying molecular heterogeneity remains inadequately characterized. OBJECTIVE: To identify distinct molecular phenotypes in patients with poor CCC, validate these phenotypes using clinical parameters, and evaluate their prognostic implications. METHODS: This study enrolled 149 patients (80 with good CCC and 69 with poor CCC) for high-throughput proteomic profiling. Unsupervised consensus clustering identified molecular subtypes within poor CCC patients, followed by differential expression analysis and KEGG pathway enrichment. Boruta feature selection was implemented, and multiple machine learning algorithms were tested on clinical data, with XGBoost optimization (accuracy 80.0%, F1-score 80.31%) and SHAP value interpretation. External validation was performed using the MIMIC database. Kaplan-Meier analysis and Cox regression models assessed major adverse cardiovascular events (MACE). RESULTS: Two distinct phenotypes emerged among poor CCC patients: Cluster 1 (n = 39, Complement-Driven Vascular Remodeling [CDVR]) and Cluster 2 (n = 30, Immuno-Thrombotic Myocardial Dysfunction [ITMD]). An XGBoost model incorporating fasting glucose, eosinophil percentage, and HbA1c achieved excellent discrimination (AUC > 0.91). External validation confirmed the phenotype-specific clinical patterns. Notably, Cluster 2 demonstrated significantly higher MACE incidence compared to Cluster 1 (Log-rank p < 0.05), with KEGG analysis revealing significant upregulation of platelet activation, diabetic cardiomyopathy, and metabolic pathways in the ITMD phenotype. CONCLUSION: Poor CCC encompasses distinct immune-metabolic phenotypes that can be accurately classified using integrated proteomic-clinical modeling. This classification enables more precise risk stratification and may guide personalized therapeutic strategies for coronary artery disease patients with inadequate collateralization.

Humans↗

PubMed2026

A CFH- and SPINT2-based prognostic signature for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly malignant tumor with a poor prognosis, and reliable biomarkers for postoperative risk stratification remain limited. This study aimed to develop and validate a CFH- and SPINT2-based prognostic signature to support postoperative risk stratification and inform adjuvant therapy selection in CCA through integrative machine learning and single-cell transcriptomics. METHODS: Differentially expressed genes were screened from GSE26566. Integrative machine learning (least absolute shrinkage and selection operator-Cox, random forest, and univariate Cox regression) was performed in the training cohort (GSE89749; n=115) to construct a risk model, which was externally validated in two independent cohorts: cohort 1 (E-MTAB-6389; n=75) and cohort 2 [The Cancer Genome Atlas Cholangiocarcinoma (TCGA-CHOL) data set; n=36]. Systematic analysis was conducted and included examinations of immune infiltration [via single-sample gene set enrichment analysis (ssGSEA)], pathway enrichment (via hallmark GSEA), cellular localization (via single-cell RNA sequencing), and drug sensitivity (via the Genomics of Drug Sensitivity in Cancer 2 database). RESULTS: Two genes, CFH and SPINT2, were identified and incorporated into a prognostic risk score. High-risk patients in the training cohort had a significantly worse overall survival (log-rank P=0.02). External validation was performed in two independent cohorts. In validation cohort 1, the risk group was an independent prognostic factor [hazard ratio =2.27, 95% confidence interval (CI): 1.18-4.37; P=0.01]. In validation cohort 2, the model demonstrated acceptable discriminative ability (concordance index =0.721; 3-year area under the curve =0.692). The high-risk group exhibited an immunosuppressive microenvironment characterized by increased infiltration of macrophages and myeloid-derived suppressor cells, along with the activation of epithelial-mesenchymal transition, inflammatory response, and NF-κB signaling pathways. Single-cell analysis revealed a cell-type-specific expression pattern: CFH was predominantly expressed in fibroblasts, while SPINT2 was mainly expressed in malignant cells. Drug sensitivity analysis demonstrated that the high-risk group was more sensitive to gemcitabine, cisplatin, poly(ADP-ribose) polymerase (PARP) inhibitors, and mammalian target of rapamycin (mTOR) inhibitors, whereas the low-risk group was more sensitive to lapatinib. CONCLUSIONS: The CFH- and SPINT2-based prognostic signature may serve as an independent biomarker for postoperative risk stratification in CCA. High-risk patients, characterized by fibroblast-derived CFH enrichment and malignant-cell SPINT2 loss, exhibit an immunosuppressive microenvironment and may be more suitable for gemcitabine-based chemotherapy or PARP/mTOR inhibitors, whereas low-risk patients may benefit from less intensive adjuvant strategies or HER2/EGFR-targeted lapatinib. Prospective validation is warranted before clinical implementation.

Cholangiocarcinoma (CCA)↗

PubMed1999

Psychometric properties of the French version of the composite scale of morningness in adults.

The objective of this study was to provide a reliable instrument to measure morningness for upcoming studies in French samples, using the Composite Scale of Morningness (CSM), which has been translated into French. Nursing students (n = 356) completed the questionnaire between February and March 1997. The total score obtained was independent of age and gender, and normally distributed. The reliability was high (Cronbach's alpha = 0.85), and factorial analysis confirmed the unidimensionality of the scale. Evening-type subjects are thought to score under 31, and morning-type subjects are thought to score above 44. As an external validation, morningness was associated, on weekdays and weekends, with early rising times and bedtimes and early peak times of physical and mental performance. In conclusion, we found that the English and the French versions of the Composite Scale of Morningness gave identical results. The scale is reliable and can be used for French-speaking adult samples. Nevertheless, normative data and other external validity criteria are needed.

Adult↗

PubMed2006

Prediction oriented QSAR modelling of EGFR inhibition.

Epidermal Growth Factor Receptor (EGFR) is a high priority target in anticancer drug research. Thousands of very effective EGFR inhibitors have been developed in the last decade. The known inhibitors are originated from a very diverse chemical space but--without exception--all of them act at the Adenosine TriPhosphate (ATP) binding site of the enzyme. We have collected all of the diverse inhibitor structures and the relevant biological data obtained from comparable assays and built prediction oriented Quantitative Structure-Activity Relationship (QSAR) which models the ATP binding pocket's interactive surface from the ligand side. We describe a QSAR method with automatic Variable Subset Selection (VSS) by Genetic Algorithm (GA) and goodness-of-prediction driven QSAR model building, resulting an externally validated EGFR inhibitory model built from pIC50 values of a diverse structural set of 623 EGFR inhibitors. Repeated Trainings/Evaluations (RTE) were used to obtain model fitness values and the effectiveness of VSS is amplified by using predictive ability scores of descriptors. Numerous models were generated by different methods and viable models were collected. Then, intensive RTE were applied to identify ultimate models for external validations. Finally, suitable models were validated by statistical tests. Since we use calculated molecular descriptors in the modeling, these models are suitable for virtual screening for obtaining novel potential EGFR inhibitors.

Enzyme Inhibitors↗

PubMed2005

The high prevalence of bipolar spectrum disorders in young adults with recurrent depression: toward an innovative diagnostic framework.

BACKGROUND: Young adults with early-onset major depressive disorder (MDD) may be at high risk of progression to bipolar disorder. Although hypomanic symptoms are common in young people with depression, many do not reach the strict DSM-IV and ICD-10 criteria for hypomania. We used an emerging innovative framework for bipolar spectrum to evaluate this question. METHODS: Consecutive referrals to a psychiatric outpatient clinic at a university health service were assessed for recurrent episodes of depression. DSM-IV diagnoses were based on a SCID-1 interview. We used two approaches to delineate bipolar spectrum. The first focused on bipolar spectrum disorder (BSD, as defined by Ghaemi et al. [Can. J. Psychiatry 47 (2002) 125]), and the second on a symptoms perspective based on MDD with a history of hypomanic symptoms, using a 15-point hypomanic symptoms checklist with a cut-off > or =8 or more symptoms (modified from J. Affect. Disord. 73 (2003) 39 and J. Affect. Disord. 73 (2003) 73). Data were also obtained on family history of affective disorder, course and number of episodes of depression, symptom severity, psychosocial functioning, suicidality and deliberate self-harm, and drug and alcohol use. RESULTS: High rates of bipolar and bipolar spectrum disorder were identified. Under DSM-IV, 14 subjects (16.1%) had bipolar affective disorder and 73 subjects (83.9%) had recurrent MDD. Depending on the method used to diagnose bipolar spectrum, between 47.1% and 77.0% of the total cohort could be so diagnosed. Hypomanic symptom counts, irrespective of duration, yielded the highest estimates for bipolar spectrum. High rates of pharmacological hypomania were also identified: 12 subjects (16.4%) with recurrent MDD group reported this, and all could be diagnosed with bipolar spectrum. LIMITATIONS: The reliability of using the 15-point hypomanic scale for the diagnostic assignments was not tested. All subjects were recruited from a university health service and, given the affluence of their parents, findings may not generalise to other populations. Most importantly, because bipolar family history and pharmacological hypomania were part of the diagnostic criteria of the BSD group, they could not be used as external validators for Ghaemi's BSD construct. CONCLUSIONS: Bipolar disorders emerge as extremely common in this cohort of young adults with recurrent depression. Antidepressant-induced hypomania and high scores on a hypomanic symptoms checklist help to identify patients who are likely to have a bipolar spectrum illness, but who do not meet DSM-IV criteria for bipolar disorder. This is a preliminary study, and further evidence from external validating strategies are needed to verify the bipolar status of these patients in a larger and unselected cohort representing a broader socio-economic demographic profile.

Adult↗

PubMed2026

Exploration and experimental verification of triaptosis-related prognostic genes and cells in gastric cancer.

BACKGROUND: Triaptosis is a recently characterized form of programmed cell death with unclear implications in cancer. This study aimed to investigate the prognostic significance and biological relevance of triaptosis in gastric cancer (GC). METHODS: Transcriptomic and clinical data from TCGA-STAD and GSE62254, and single-cell RNA sequencing data from GSE183904 were analyzed. Triaptosis-related gene (TRG) scores were calculated using single-sample gene set enrichment analysis. Differentially expressed genes identified in TRG-score and GC-versus-normal comparisons underwent functional enrichment, Cox regression, and least absolute shrinkage and selection operator regression to develop an externally validated signature. Immune profiles, pathway activity, somatic mutations, tumor mutational burden (TMB), predicted drug sensitivity, and clinical features were compared by risk group. Single-cell analyses assessed TRG activity, prognostic gene expression, cell-cell communication, and pseudotime. Reverse transcription-quantitative PCR and Western blotting assessed mRNA expression and protein levels, respectively. RESULTS: A TRG-based prognostic model comprising ASPN, GRB14, and VTN was developed and externally validated, effectively distinguishing patients into two distinct risk groups with notably different survival outcomes. mRNA expression of all three genes and their protein levels were significantly higher in SGC-7901 cells than in GES-1 cells. High-risk patients had higher stromal scores and distinct immune profiles; 15 immune cell types differed between groups. Single-cell analysis revealed fibroblasts and pericytes among high-TRG-active cell types. Prognostic genes were significantly overexpressed in fibroblasts, which also showed high TRG activity. Fibroblasts demonstrated enhanced communication with pericytes, whereas tumor-derived fibroblasts showed weaker communication with macrophages, indicating immune microenvironment remodeling. CONCLUSION: The three-gene prognostic signature predicted GC prognosis and was associated with distinct immune and genomic features, suggesting potential value for risk stratification and personalized treatment.

Humans↗

PubMed2025

Stratifying Lung Adenocarcinoma Risk with Multi-ancestry Polygenic Risk Scores in East Asian Never-Smokers.

BACKGROUND: Lung adenocarcinoma (LUAD) in never-smokers is a major public health burden, especially among East Asian women. Polygenic risk scores (PRSs) are promising for risk stratification but are primarily developed in European-ancestry populations. We aimed to develop and validate single- and multi-ancestry PRSs for East Asian never-smokers to improve LUAD risk prediction. METHODS: PRSs were developed using genome-wide association study summary statistics from East Asian (8,002 cases; 20,782 controls) and European (2,058 cases; 5,575 controls) populations. Single-ancestry models included PRS-25, PRS-CT, and LDpred2; multi-ancestry models included LDpred2+PRS-EUR128, PRS-CSx, and CT-SLEB. Performance was evaluated in independent East Asian data from the Female Lung Cancer Consortium (FLCCA) and externally validated in the Nanjing Lung Cancer Cohort (NJLCC). We assessed predictive accuracy via AUC, with 10-year and (age 30-80) absolute risks estimates. RESULTS: The best multi-ancestry PRS, using East Asian and European data via CT-SLEB (clumping and thresholding, super learning, empirical Bayes), outperformed the best East Asian-only PRS (LDpred2; AUC=0.629, 95% CI:0.618,0.641), achieving an AUC of 0.640 (95% CI:0.629,0.653) and odds ratio of 1.71 (95% CI:1.61,1.82) per SD increase. NJLCC Validation confirmed robust performance (AUC =0.649, 95% CI: 0.623, 0.676). The top 20% PRS group had a 3.92-fold higher LUAD risk than the bottom 20%. Further, the top 5% PRS group reached a 6.69% lifetime absolute risk. Notably, this group reached the average population 10-year LUAD risk at age 50 (0.42%) by age 41, nine years earlier. CONCLUSIONS: Multi-ancestry PRS approaches enhance LUAD risk stratification in East Asian never-smokers, with consistent external validation, suggesting future clinical utility.

East Asian never smokers↗

PubMed2006

Development of clinical guidelines: methodological and practical issues.

The key elements for developing a clinical guideline are (a) guidelines are developed by multidisciplinary groups, (b) they are based on a systematic review of the scientific evidence, and (c) recommendations are explicitly linked to the supporting evidence and graded according to the strength of that evidence. Besides reporting the statistical strength of the randomised controlled trial results, it is necessary to consider the strength of the evidence, the methodological quality of the studies and the external validity by applying a "considered judgement" to the whole amount of the data. The Scottish Intercollegiate Guidelines Network (SIGN) process for developing guidelines is based on 4 steps: (a) methodological evaluation, (b) synthesis of evidence, (c) considered judgement and (d) grading system. The judgement on grading of recommendations is made on the basis of an (objective) assessment of the study design and quality, and a (perhaps more subjective) judgement of the consistency, clinical relevance and external validity of the evidence. The SPREAD group decided to adopt this methodology starting from the 3rd edition (2003); however, it was agreed to integrate the principles of the SIGN [4] with the statistical considerations on alpha and beta error size suggested by the Centre for Evidence-Based Medicine methodology [6], to give a more comprehensive evaluation of the available evidence. By being the product of a multidisciplinary approach, being explicit and providing information on the way agreement has been reached or on the reasons of disagreement, the SPREAD guidelines seem to fulfil the needs for shared guidelines, and to avoid the concerns related to pitfalls in the transparency of the process and in the reaching of a consensus.

Evidence-Based Medicine↗

PubMed1999

Development of the Quality of Life in Epilepsy Inventory for Adolescents: the QOLIE-AD-48.

PURPOSE: We report the development of an instrument to assess health-related quality of life (HRQOL) in adolescents with epilepsy. METHODS: A sample of 197 English-speaking adolescents (aged 11-17 years) with epilepsy completed a test questionnaire of 88 items. Also included were mastery and self-esteem scales to assess external validity. A parent simultaneously completed an 11-item questionnaire to evaluate the child's HRQOL. Both adolescent and parent questionnaires were repeated in 2-4 weeks. Demographic information and information pertaining to seizures were collected at baseline along with assessment of systemic and neurologic toxicity. RESULTS: The QOLIE-AD-48 contains 48 items in eight subscales: epilepsy impact (12 items), memory/concentration (10), attitudes toward epilepsy (four), physical functioning (five), stigma (six), social support (four), school behavior (four), health perceptions (three), and a total summary score, with higher scores indicating better HRQOL. Internal construct validity was demonstrated in a single-factor solution for the eight dimensions. All correlations were statistically significant at p < 0.05 level. Internal consistency reliability estimated by Cronbach's alpha coefficient was 0.74 for the summary score and ranged from a low of 0.52 (three-item Health Perceptions Scale) to 0.73-0.94 for the other individual scales. Good test-retest reliability was found for the overall measure (0.83). Summary score correlations with the two external validity scales, self-efficacy and self-esteem were 0.65 and 0.54, respectively. Statistically significant differences in summary scores indicating that HRQOL was increasingly better for adolescents as seizure severity decreases (no seizures = 77+/-13, low = 70+/-17, high = 63+/-17) were found among seizure-severity groups. CONCLUSIONS: These data describe the development of a robust instrument to evaluate HRQOL in adolescents with epilepsy. Empiric analyses provide strong evidence that the QOLIE-AD-48 is both a reliable and valid measure for adolescents with epilepsy.

Adolescent↗

PubMed2005

Relationship between chemical structure and the occupational asthma hazard of low molecular weight organic compounds.

AIMS: To investigate quantitatively, relationships between chemical structure and reported occupational asthma hazard for low molecular weight (LMW) organic compounds; to develop and validate a model linking asthma hazard with chemical substructure; and to generate mechanistic hypotheses that might explain the relationships. METHODS: A learning dataset used 78 LMW chemical asthmagens reported in the literature before 1995, and 301 control compounds with recognised occupational exposures and hazards other than respiratory sensitisation. The chemical structures of the asthmagens and control compounds were characterised by the presence of chemical substructure fragments. Odds ratios were calculated for these fragments to determine which were associated with a likelihood of being reported as an occupational asthmagen. Logistic regression modelling was used to identify the independent contribution of these substructures. A post-1995 set of 21 asthmagens and 77 controls were selected to externally validate the model. RESULTS: Nitrogen or oxygen containing functional groups such as isocyanate, amine, acid anhydride, and carbonyl were associated with an occupational asthma hazard, particularly when the functional group was present twice or more in the same molecule. A logistic regression model using only statistically significant independent variables for occupational asthma hazard correctly assigned 90% of the model development set. The external validation showed a sensitivity of 86% and specificity of 99%. CONCLUSIONS: Although a wide variety of chemical structures are associated with occupational asthma, bifunctional reactivity is strongly associated with occupational asthma hazard across a range of chemical substructures. This suggests that chemical cross-linking is an important molecular mechanism leading to the development of occupational asthma. The logistic regression model is freely available on the internet and may offer a useful but inexpensive adjunct to the prediction of occupational asthma hazard.

Asthma↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗