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Results for “External validity”

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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↗

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↗

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↗

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↗

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↗

PubMed2026

Identification of Drug-resistant Cell Subpopulations in Colorectal Cancer Through Single-cell Analysis and Exploration of Potential Therapeutic Strategies.

INTRODUCTION: The therapeutic efficacy of Colorectal Cancer (CRC) is often compromised by resistance to the standard chemotherapy agent oxaliplatin. METHODS: This study obtained single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Differentially Expressed Genes (DEGs) between resistant and sensitive epithelial subpopulations were identified, followed by enrichment analysis. Pseudotemporal trajectory and cell-cell communication were analyzed using Monocle2 and CellChat, respectively. The candidate drug was predicted by Connectivity Map (cMAP) analysis. External validation included assessment of the EpC2 signature in an oxaliplatin-resistant cell line dataset (GSE76092), survival analysis using The Cancer Genome Atlas (TCGA) cohorts, and re-analysis of the GSE179784 dataset to assess the reproducibility of EpC2-like subpopulations and their DNA Damage Repair (DDR) scores. RESULTS: Cell subpopulations were divided into 10 clusters. Among them, epithelial cells comprised 5 subpopulations, with EPC2 identified as a potential oxaliplatin-resistant subset. DEGs were enriched in the TNF and IL-17 pathways. External validation confirmed the enrichment of EpC2 in resistant cell lines and its association with poor survival. Pseudotemporal trajectory revealed that epithelial cells underwent state transitions, forming two distinct branches. The resistant group exhibited enrichment in RNA splicing and NF-κB pathways. Cell-cell communication analysis revealed interactions involving MDK- NCL and PPIA-BSG. Dasatinib was predicted as a candidate drug. DISCUSSION: We identified an oxaliplatin-resistant subpopulation of Epithelial Cells (EpC2) in CRC, elucidated its multi-layered resistance mechanisms, and integrated multi- omics and cMAP database analyses to predict a potential intervention drug. CONCLUSION: This study provided potential therapeutic possibilities for oxaliplatin resistance, contributing to CRC treatment.

Humans↗

PubMed1993

A Pathfinder analysis of pedagogical knowledge structures: a follow-up investigation.

This study is an extension of an earlier investigation of undergraduate students' acquisition of key pedagogical concepts in a physical education teaching methodology course. In that study, Pathfinder, a method for eliciting associative memory networks, was used to describe and compare the pedagogical knowledge structures of students to that of the course instructor. After the course, students' pedagogical knowledge structures corresponded more closely with that of the instructor, and students who corresponded the closest performed better in the course. The results raised an interesting issue regarding the acquisition of knowledge in undergraduate students. Did students acquire a generalizable body of pedagogical knowledge applicable beyond the context of the teaching methodology course or a highly contextualized reflection of their course instructor's knowledge base? In the present study the external validity of the pedagogical knowledge base was examined by using Pathfinder to compare the knowledge structures of students from the initial investigation with knowledge structures of five experienced teacher educators from five different teacher education programs. The findings indicated that students' knowledge structures became significantly more correspondent with that of the experienced teachers' structures from the beginning to the end of the course. Also, students' correspondence with teacher educators' structures following instruction was found to be significantly correlated with academic and teaching performance. The findings point to the external validity of the domain of knowledge under study and the robustness of Pathfinder for capturing pedagogical knowledge.

Adult↗

PubMed1985

Attrition in prevention research.

Selective attrition can detract from the internal and external validity of longitudinal research. Four tests of selective attrition applicable to longitudinal prevention research were conducted on data bases from two recent studies. These tests assessed (1) differences between dropouts and stayers in terms of pretest indices of primary outcome variables (substance use), (2) differences in change scores for dropouts and stayers, (3) differences in rates of attrition among experimental conditions, and (4) differences in pretest indices for dropouts among conditions. Results of these analyses indicate that cigarette smokers, alcohol drinkers, and marijuana users are more likely to drop out than nonusers, limiting the external validity of both studies. For one project, differential rates of attrition among conditions suggested a possible attrition artifact which will interfere with interpretation of outcome results, possibly masking true program effectiveness. Recommendations for standardizing reports of attrition and for avoiding attrition through second efforts are made.

Adolescent↗

PubMed2026

ceRNA network of lncRNAs and mRNAs in OSF-to-OSCC progression: Diagnostic biomarkers and functional pathways.

BACKGROUND: Oral submucous fibrosis (OSF) is a chronic potentially malignant disorder that can progress to oral squamous cell carcinoma (OSCC). Although dysregulated non-coding RNAs have been implicated in oral carcinogenesis, the competing endogenous RNA (ceRNA)-mediated regulatory mechanisms underlying OSF-to-OSCC progression remain poorly understood. This study aimed to identify candidate regulatory molecules and construct a putative lncRNA-miRNA-mRNA network associated with malignant transformation. METHODS: Publicly available microarray datasets (GSE117973 and GSE125866) were analyzed to identify differentially expressed genes between OSF and OSCC. Differentially expressed transcripts were classified into mRNAs and lncRNAs based on public transcript annotations. Highly correlated lncRNA-mRNA pairs were identified using Pearson correlation analysis and integrated with multiMiR-supported miRNA-mRNA interactions obtained from public databases to construct a putative ceRNA regulatory network. Functional characterization focused on apoptosis, epithelial-mesenchymal transition (EMT), and immune checkpoint-related pathways. Receiver operating characteristic (ROC) analysis was performed to evaluate diagnostic performance, and selected biomarkers were externally validated using The Cancer Genome Atlas (TCGA) OSCC cohort. RESULTS: Integrated transcriptomic analysis identified several dysregulated mRNAs and lncRNAs associated with OSF-to-OSCC progression. Network analysis highlighted TBC1D3B, RREB1, TEAD3, SREBF1, TMEM41B, FOXK2, and KIAA1958 as prominent hub genes within the putative regulatory network. Functional analyses demonstrated significant associations with apoptosis-, EMT-, and immune checkpoint-related genes, suggesting potential involvement in multiple biological processes contributing to malignant transformation. Several hub genes exhibited strong diagnostic performance, with ROC analysis yielding AUC values ranging from 0.891 to 1.000, indicating excellent discrimination between OSF and OSCC samples. External validation using TCGA further supported the relevance of the identified biomarkers in OSCC. CONCLUSIONS: This study provides a comprehensive transcriptomic framework describing putative lncRNA-miRNA-mRNA regulatory interactions associated with OSF progression to OSCC. The identified hub genes and regulatory networks represent candidate biomarkers for early detection and provide a foundation for future mechanistic and experimental validation. As the proposed ceRNA interactions are computationally inferred, further biological validation is required before clinical application.

RNA, Long Noncoding↗

PubMed1991

An empirical subgrouping of Finnish learning-disabled children.

The internal and external validity of a subgrouping of 82 Finnish children with relatively mild learning disabilities and 84 Controls was explored. The sample was selected from a total population of 1607 second grade pupils. Eight neuropsychological measures from four function areas were selected as classification criteria in cluster analysis. Six consistent and clinically meaningful subgroups were derived. These subgroups were designated as follows: (1) Normal, (2) General Language, (3) Visuo-Motor, (4) General Deficiency, (5) Naming, and (6) Mixed. Most of the LDs were clustered in subgroups (2) through (6); and most of the Controls, in subgroup (1). Several internal and external validation procedures indicated at least moderate validity in the subgroups, with the exception of the Mixed subgroup. The five valid subgroups encompassed 82% of the LDs and 90% of the Controls, and moreover, resembled subgroups which previously have been found among English-speaking children. This suggests that language differences exert no significant effect on the types of the emerging subgroups.

Aging↗

PubMed1998

The problem of protocol driven costs in pharmacoeconomic analysis.

The increasing number of economic evaluations of healthcare interventions and of drug therapies in particular has been well documented. Surveys of the quality of studies have demonstrated that standards of conduct of such studies have not similarly increased. Concerns over the standards have led to increased calls that economic analyses be more closely linked to randomised controlled clinical trials (RCT). Seven potential threats to the external validity of results limit the generalisability of studies based on RCTs. One such threat is the existence of protocol driven costs. There are two main types of protocol driven costs. Protocol prescribed costs arise as a result of resource use mandated by the clinical trial design. Protocol derived costs occur when increased clinical investigations mandated by trial protocols lead to atypical disease management. Methods to control for protocol driven costs within pharmacoeconomic study designs are available. Modelling studies can be based on data within clinical trials combined with observational data representing more typical resource use. The adoption of pragmatic clinical trial designs provide greater external validity though reduced internal validity. Refinements to explanatory clinical trials can also lead to reduced protocol driven costs. The extent that current studies control for such costs is unclear due to the lack of transparency in the reporting of study methods. A review of published studies found little consideration of protocol driven costs although in several studies there was evidence of their existence. Future studies conducted alongside RCTs should explicitly address how the issue of protocol driven costs was handled within the study framework.

Economics, Pharmaceutical↗

PubMed2026

A comparative evaluation of multiple enlarged perivascular space segmentation tools.

BACKGROUND: Enlarged perivascular spaces (ePVS) are a marker of cerebral small vessel disease, potentially reflecting reduced waste clearance. Because manual quantification is unfeasible in large datasets, we developed and evaluated an automated tool. METHODS: Detection Of Regions of Enlarged perivascular Spaces (DORES), a 3D nnU-Net-based deep learning algorithm was developed for ePVS segmentation using T1-weighted and fluid-attenuated inversion recovery magnetic resonance imaging (MRI). DORES was developed in two stages: an initial model trained on 35 manually segmented scans and a final model on 1460 pseudo-labeled sessions from the Vanderbilt Memory and Aging Project (VMAP). A subset of VMAP participants with 3 T brain MRI underwent whole-brain manual ePVS tracing (n = 35, 73 ± 9 years, 51% male) and visual rating (n = 388, 71 ± 8 years, 54% male) by a neuroradiologist. DORES was evaluated and compared against three other segmentation tools using Dice and F1 scores, absolute volume and element differences, correlation, and agreement. External validation used an Alzheimer's Disease Neuroimaging Initiative 3 subset with manual tracings (ADNI3, n = 18, 73 ± 9 years, 67% female). RESULTS: DORES achieved Dice scores of 0.61 ± 0.16 (white matter) and 0.72 ± 0.08 (basal ganglia) in VMAP, with strong correlations and agreement for ePVS count and volume. Performances modestly declined in ADNI3 across algorithms. Scanner-stratified analyses showed stronger correlations for Philips versus Siemens images in the basal ganglia, indicating scanner-dependent differences in measurement consistency. CONCLUSIONS: DORES provides a multimodal nnU-Net-based pipeline for ePVS segmentation in older adults. The model demonstrates robust within-cohort performance and reasonable external validity, though scanner-related effects limit application across sites.

Humans↗

PubMed2026

Systemic Proteome Profiling to Differentiate Primary Glomerular Diseases.

KEY POINTS: Plasma proteome profiling identified distinct signatures across biopsy-proven primary glomerular disease subtypes. An elastic net model using 93 proteins classified primary glomerular disease subtypes and controls, with external validation. Integrating proteomics with machine learning yields biologically interpretable insights in primary glomerular diseases. BACKGROUND: Primary GN is a heterogeneous group of kidney disorders where understanding of their pathophysiology remains incomplete. Despite the diagnostic potential of high-throughput proteomics, constrained proteomic depth and a reliance on binary comparisons have left the feasibility of using systemic signatures to differentiate multiple GN subtypes largely unexplored. METHODS: To identify protein signatures that noninvasively differentiate major primary glomerular disease subtypes and provide mechanistic insights, we performed large-scale systemic proteome profiling of 5416 plasma proteins via Olink Explore HT in a discovery cohort ( n =147) and an external validation cohort ( n =85) of Korean participants (mean age, 41±13 years; 46% female). The study population included patients with four GN subtypes-focal segmental glomerulosclerosis, IgA nephropathy, minimal change disease, and membranous nephropathy-alongside healthy controls. We developed a machine learning (ML) model using logistic regression with elastic net regularization to classify disease groups based on proteomic profiles and evaluated its performance in the independent validation cohort. RESULTS: Plasma proteome profiles were distinct among disease subtypes, emerging as a significant source of data variation independent of conventional markers such as eGFR or proteinuria levels. The ML model performed robustly in both the discovery and validation cohorts, achieving an area under the receiver operating characteristic curve >0.8 for differentiating minimal change disease, membranous nephropathy, and IgA nephropathy. The model, even without clinical information, correctly identified 93% of minimal change disease cases (14 of 15) and 63% of IgA nephropathy cases (20 of 32), but its performance was limited for focal segmental glomerulosclerosis, with only 21% of cases (three of 14) correctly classified. Functional analysis of key proteins highlighted distinct biologic pathways, such as hemostasis in minimal change disease. CONCLUSIONS: We identified distinct systemic proteome signatures for primary glomerular diseases, where disease subtype served as a major determinant of proteomic variance alongside conventional clinical markers. ML models demonstrated robust discriminatory performance for minimal change disease, membranous nephropathy, and IgA nephropathy, underscoring the potential for proteome-based classification.

Humans↗

PubMed2000

Ethnic representation in a sample of the literature of applied psychology.

A number of authors have raised concerns over the external validity of psychological research. This study examined the extent to which empirical articles include human participants from diverse ethnic backgrounds. Articles published over a 5-year period in 14 selected journals representing 3 applied sub-disciplines of psychology were examined. Of the 2,536 articles coded, only 61% indicated the ethnicity of the participants. For those articles, the ethnic compositions approximated U.S. Census estimates, with the exception of an overrepresentation of African Americans and an underrepresentation of Hispanic Americans. The results imply that although the field is apparently adequately recruiting English speakers, representation of non-English speakers should be increased. To further enhance the external validity of psychological research, ethnicity of participants should be not only specified but also analyzed in relation to the results of a study.

Authorship↗

PubMed2000

[What is the evidence with regard to the effectiveness of orlistat?].

BACKGROUND: Obesity is worldwide one of today's most important medical and public health problems. Orlistat (Xenical) is a relatively new drug in the pharmacological treatment of obesity which partially blocks fat absorption. The following article analyses the available evidence of orlistat's effectiveness in the treatment of obese patients. METHODS: Three randomised controlled trials investigating the effect of orlistat in the treatment of obesity were identified by systematic Medline search. The internal and external validity of these studies was assessed using systematic criteria. RESULTS: All three studies consistently demonstrate a treatment benefit of orlistat compared to placebo. Patients treated with orlistat lost an average of 3.4 kg more than patients taking placebo while on a hypocaloric diet. Simultaneously, control of cardiovascular risk factors improved independently of the observed weight loss. Up to 40% of all patients experienced gastrointestinal side effects which were generally well tolerated. The studies prove that treatment with orlistat can result in a moderate weight reduction. However, the results of the studies cannot be easily generalised to obese patients in a primary care setting, due to limitations concerning the studies' internal and external validity. CONCLUSIONS: Based on these studies orlistat is an efficient pharmacological treatment for obesity in patients adhering to a hypocaloric diet. Studies demonstrating orlistat's effectiveness in a primary care setting are so far lacking. From a public health perspective there is a need for a randomised controlled trial showing not only orlistat's effectiveness on surrogate markers in a primary care setting but, ideally, a reduction in obesity-related mortality and morbidity.

Anti-Obesity Agents↗