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At least 109 records · Page 6Linked to original sources
PubMed2026

Identification of Immune Response-Related Proteomic Biomarkers in Moyamoya Disease Using Serum Olink Proteomics.

Moyamoya disease, a rare chronic cerebrovascular disorder, requires invasive digital subtraction angiography (DSA) for diagnosis. This study employed high-throughput proteomics to identify plasma biomarkers for Moyamoya disease diagnosis. We conducted immunopanel analysis using the Olink platform to evaluate 92 immune-related proteins in plasma samples from 88 Moyamoya disease patients and 88 healthy controls. Key proteins were identified through differential expression analysis, GO, and KEGG enrichment analysis. A diagnostic model was constructed using LASSO regression, Boruta algorithm, and machine learning models including random forest and XGBoost. Validation of these proteins was performed using GEO external data sets, followed by prediction of potential therapeutic drugs and molecular docking validation through pharmacogenomic databases. A total of 44 differentially expressed proteins were identified through the Olink immunopanel, with 12 downregulated and 32 upregulated. GO and KEGG analyses revealed significant enrichment of these proteins in innate immune responses and signaling pathways such as NF-kB and MAPK. Through LASSO, random forest, and protein under-area analysis, four potential biomarkers for Moyamoya disease (MGMT, SIT1, PRDX1, TRAF2) were identified. A diagnostic model using these proteins showed the highest AUC value with the XGBoost model. Additionally, TRAF2 and PRDX1 exhibited significant expression differences in Moyamoya disease patients within the GEO data set. Our study revealed the immune landscape of Moyamoya disease, identified four biomarkers, and established a variety of diagnostic models.

Humans↗
PubMed2004

The effect of esthetic consultation methods on acceptance of diastema-closure treatment plan: a pilot study.

BACKGROUND: The authors conducted a study to determine which of four consultation methods helped patients best understand a proposed treatment plan for maxillary anterior diastema closure. METHODS: The authors presented 24 subjects with four types of consultation in random order: before-and-after photographs of other patients, diagnostic models with wax setups, resin-based composite/esthetic preview/mock-ups and computer-imaging simulations. After viewing each method, the authors asked the subjects about treatment acceptability. At the end of the demonstrations of all four methods, the authors asked the subjects which consultation method helped them best understand the proposed treatment plan. RESULTS: A total of 87.5 percent of the subjects indicated that they would accept the proposed treatment plan after they were shown the computer-imaging simulation, 50 percent said they would after they were shown the resin-based composite/esthetic preview/mock-up, 41.7 percent said they would after they were shown photographs of other patients, and 25 percent said they would after they were shown diagnostic models with wax setups. When asked which method helped them best understand the proposed treatment, 54.2 percent of the subjects selected computer-imaging simulation, 33.3 percent selected resin-based composite/esthetic preview/mock-ups, and 12.5 percent selected before-and-after photographs of other patients. None of the subjects selected diagnostic models with wax setups. A chi2 test for goodness of fit indicated that these differences were statistically significant. CONCLUSION: Subjects preferred computer-imaging simulation to the other three consultation methods, and they indicated that computer-imaging simulation provided a better understanding of the proposed treatment plan for diastema closure. CLINICAL IMPLICATIONS: The use of computer-imaging simulation enhances the patient's understanding of a proposed treatment plan concerning maxillary anterior diastema closure.

Adolescent↗
PubMed2026

Evaluation of a cornea-specialized large language model for diagnostic and management accuracy in complex corneal cases.

PURPOSE: To evaluate whether a cornea-specialized large language model (LLM) enhanced with retrieval-augmented generation (RAG) improves clinicians' diagnostic and management accuracy in complex corneal cases compared to a general-purpose GPT-4o model and unaided clinician performance. METHODS: This prospective, randomized, masked evaluation study involved three cornea trainees who each independently reviewed 39 real-world corneal cases under three experimental conditions: unaided, GPT-4o-assisted, and assisted by a cornea-specialized GPT-4o model. The cornea-specialized model was constructed by embedding over 200 publicly available Wikipedia articles into GPT-4o's RAG framework. Participants provided open-ended diagnoses and selected the next-step management options (multiple choice). They were allowed up to three GPT-4o queries per case, and the AI-assisted arms were randomized to minimize bias. Accuracy for both tasks was compared against expert reference standards using McNemar's test. RESULTS: Diagnostic accuracy was 48.7%, 20.5%, and 38.5% unaided, improving to 69.2%, 46.2%, and 59.0% with general GPT-4o (p<0.04). The cornea-specialized GPT-4o further improved accuracy to 71.8%, 48.7%, and 74.4%, with improvements over unaided performance for all clinicians (p<0.01). For next-step decisions, unaided accuracy was 76.9%, 87.2%, and 59.0%. With the specialized model, Ophthalmologist 3 improved to 71.8% (p<0.05), Ophthalmologist 1 remained high at 82.1%, and Ophthalmologist 2 declined to 64.1% (p<0.05). CONCLUSIONS: A cornea-specialized LLM enhanced with RAG improved diagnostic accuracy in complex corneal cases, particularly among clinicians with lower baseline performance. Effects on management accuracy were inconsistent. Future studies should explore the use of open-ended management tasks and examine whether smaller, curated retrieval corpora yield better model performance.

Humans↗
PubMed2001

Expectant management of early pregnancies of unknown location: a prospective evaluation of methods to predict spontaneous resolution of pregnancy.

OBJECTIVE: To assess prospectively the ability of two multiparameter diagnostic models and their individual components to predict the outcome of early pregnancies which could not be identified on transvaginal ultrasound scan. DESIGN: Prospective observational study. SETTING: Dedicated early pregnancy unit in an inner city teaching hospital. POPULATION: Women with a positive urine pregnancy test and clinical suspicion of early pregnancy complications. METHODS: A full medical history, clinical examination and transvaginal ultrasound scan were carried out at the initial visit. When the location of the pregnancy could not be ascertained by ultrasound, serum beta-human chorionic gonadotrophin (beta-hCG) and progesterone levels were measured. All women were managed expectantly until either a normal pregnancy was visualised on scan; the pregnancy resolved spontaneously or intervention was required due to a worsening of clinical symptoms or non-declining beta-hCG levels. MAIN OUTCOME MEASURES: Spontaneous resolution of pregnancy (i.e. cessation of symptoms and decline in serum beta-hCG level to < 20 iu/L) without need for any active intervention. RESULTS: Of the 104 women recruited, 72 (69%) pregnancies resolved spontaneously. Both multiparameter diagnostic models identified resolving pregnancies with positive predictive values > or = 95%. Their performances were not significantly better compared with individual progesterone levels which achieved a positive predictive value of 97% using a cutoff level of 20 nmol/L. CONCLUSION: Serum progesterone measurement alone is as accurate as more complex diagnostic models for the prediction of successful expectant management in pregnancies of unknown location.

Abortion, Spontaneous↗
PubMed1995

A model and diagnostic measures for response time series on tests of concentration: historical background, conceptual framework, and some applications.

Based upon classical hypotheses about accumulating mental fatigue and distraction and its effect on response times, put forward in late 19th and early 20th century papers, a mathematical model is proposed for response times on tests of speed and concentration. The model assumes the random occurrence of very short distractions during information processing. It explains fluctuation and the increasing trend in response times on successive equivalent task units and leads to some simple diagnostic RT measures of speed and concentration as alternatives to the mean RT. A review is given of several experimental applications of the model, with subjects with and without concentration problems, using cancellation and digit addition tasks. The results demonstrate the potential usefulness of the model and the diagnostic measures derived from it. As predicted by the model, prolonged task performance yields an increasing trend in RT mean and variance that can be strongly reduced by giving very short resting periods very frequently, and to a lesser extent also by task alternation, but not by simply motivating the subjects to concentrate. Some practical implications for the administration and scoring of tests of speed and concentration are discussed, as well as limitations of the present results and the complementary relation of our approach to popular ones such as "stage analysis."

Adult↗
PubMed2006

[Noninvasive assessment of liver fibrosis in chronic hepatitis B using a predictive model].

OBJECTIVE: To develop a diagnostic model comprising clinical and serum markers for assessing HBV-related liver fibrosis. METHODS: 270 chronic hepatitis B patients were randomly allocated to either an estimation group (195 cases) or a validation group (75 cases). Liver biopsies were done and staging of fibrosis was assessed. Twenty-six common clinical and serum markers were analyzed initially in the estimation group to derive a predictive model to discriminate the stages of fibrosis. The model created was then assessed with ROC analysis. It was also applied to the validation group to test its accuracy. RESULTS: Among 13 variables associated with liver fibrosis selected by univariate analysis, age, gamma glutamyltranspeptidase (GGT), hyaluronic acid (HA), and platelet count (PLT) were identified by multivariate logistic regression analysis as independent factors of fibrosis. A fibrosis index constructed from the above four markers was established. In ROC analysis, the AUC was 0.889 for the estimation group and 0.850 for the validation group for discriminating > or =S3 from < or=S2. Using the optimal cutoff score 3.0, the sensitivity of the index was 90.2%, the specificity 76.1%, and the accuracy was 82%. There was a positive linear relationship between the index scores and the fibrosis stages (r = 0.731, P<0.001). The AUC for identifying > or=S2 was 0.873 with sensitivity/specificity of 79%/82%, cutoff score 2.2; The AUC for identifying S4 was 0.872 with sensitivity/specificity of 83%/75%, cutoff score 5.4. There were no significant differences in diagnostic efficacy in the model between the estimation and the validation group (P>0.05). CONCLUSION: A model for assessment of liver fibrosis was established with easily accessible markers. It appears to be sensitive, accurate and reproducible, suggesting it could be used to assist or replace liver biopsy to detect dynamic changes of HBV-related liver fibrosis.

Adolescent↗
PubMedDate not supplied

Spirochetes: vaccines, animal models and diagnostics.

The detailed characterization of various proteins from spirochetes using molecular biology techniques has made possible new approaches to vaccine and diagnostic development that are described in this session. The importance of animal models was emphasized and illustrated.

Animals↗
PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗
PubMed2006

Posterolateral intertransverse lumbar fusion in a mouse model: surgical anatomy and operative technique.

BACKGROUND CONTEXT: Animal models are frequently used for studying the effect of bone graft substitutes or allogeneic materials on osterolateral lumbar fusion. Transgenic technology in the mouse provides a unique opportunity to further understand the biology of spine fusion. PURPOSE: To describe pertinent lumbar spine anatomy and formulate a surgical protocol for posterolateral fusion in the mouse model. STUDY DESIGN: Diagnostic model: development of an animal model for biologic evaluation of posterolateral spine fusion. METHOD: Ten mice were killed to study relevant lumbar spine anatomy and develop a protocol for lumbar spine fusion. The L4-L6 fusion protocol was validated in 46 mice for ease of exposure, preparation of the posterolateral fusion bed, introduction of bone inductive agents, and perioperative care. RESULTS: Anatomy and surgical technique for posterolateral intertransverse lumbar fusion in the mouse model are described. A paraspinal approach allows exposure of the transverse processes, decortication, and graft placement at the L4-L6 intertransverse fusion site. Decortication alone did not result in fusion, whereas the use of bone graft resulted in satisfactory fusion rates. Perioperative morbidity and mortality rates were low. CONCLUSION: The mouse posterolateral lumbar spine fusion model is reproducible, inexpensive, and has low complication rates. Knowledge of the relevant anatomy and adherence to a well-defined surgical protocol provides a reliable and reproducible experimental spine fusion model.

Animals↗
PubMed2004

Evidence for genetic linkage between a polymorphism in the adenosine 2A receptor and panic disorder.

Data from clinical and behavioral pharmacological studies have implicated adenosine in anxiety behaviors, while genetic studies have suggested that adenosine receptors may be associated with panic disorder. We have undertaken an analysis of several DNA sequence variations in the adenosine 2A receptor (ADORA2A) in a large sample of panic disorder pedigrees. Individuals from 70 panic disorder pedigrees, and 83 child-parent 'trios', were genotyped at five single-nucleotide polymorphisms (SNPs) in and near the ADORA2A gene and were analyzed for genetic linkage and association. Linkage analysis revealed elevated LOD scores for a silent substitution (1083C/T, SNP-4) in the second coding exon. This SNP has been previously reported to be associated with panic disorder. We observed a maximal heterogeneity LOD score of 2.98 (theta=0) under a recessive genetic model and narrow diagnostic model. Other SNPs showed no evidence for linkage. Association tests were not significant for any of the five ADORA2A SNPs. When SNP haplotypes were assessed in the triads with TRANSMIT, one 3-marker haplotype (SNPs 1, 4, 5) was nominally significantly associated with panic disorder (p=0.029). Pairwise estimations of linkage disequilibrium between the SNPs showed strong patterns of linkage disequilibrium across the ADORA2A locus. Analyses carried out by broadening the panic disorder phenotype to include agoraphobia continued to support linkage to ADORA2A. Our findings provide evidence for a susceptibility locus for panic disorder, and possibly including agoraphobia, either within the ADORA2A gene or in a nearby region of chromosome 22, and serves as the first successful candidate gene replication study in panic disorder.

Female↗
PubMed2005

Outlier recognition in crystal-structure least-squares modelling by diagnostic techniques based on leverage analysis.

The identification of the actual outliers in a least-squares crystal-structure model refinement and their subsequent elimination from the data set is a non-trivial task that has to be carried out carefully when a high level of accuracy of the estimates is required. One of the most suitable tools for detecting the influence of each data entry on the regression is the identification of ;leverage points'. On the other hand, the recognition of the actual statistical outliers is effectively possible by using some diagnostics as a function of the leverage, such as Cook's distance, DFFITS and FVARATIO. The evaluation of these estimators makes it possible to achieve a reliable identification of the outliers and the elimination of those that impair the least-squares fit. In this paper, a procedure for filtering data points based on this kind of analysis for crystallographic X-ray data is presented and discussed.

Journal Article↗
PubMed2026

Serum Olink Proteomics Reveals Novel Biomarkers for Early Diagnosis of Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is a highly prevalent malignant tumor in China, and early diagnosis critically affects the prognosis. Current imaging and pathological biopsy techniques have limitations, including high invasiveness and limited accessibility, while the insufficient sensitivity of serum biomarkers (such as AFP) restricts their use in early screening. In this study, using the Olink proteomics platform based on the proximity extension assay (PEA), we screened for hepatocellular carcinoma-related differentially expressed proteins (DEPs) and constructed a multiprotein diagnostic model. In the discovery cohort, we included 15 patients with newly diagnosed HCCs and 16 healthy controls. DEPs were identified using Olink, and their diagnostic performance was analyzed to identify the candidate biomarkers. In an independent validation cohort, including 116 HCC patients (50 early stage, 66 late stage) and 83 healthy controls, we further validated the expression levels and diagnostic performance of identified proteins&#x2500;C1QA and GFER. The C1QA and GFER expression levels were significantly higher in the serum of patients with early and late HCC stages compared to healthy controls. By constructing a multiprotein diagnostic model, we identified C1QA, GFER, and AFP as the optimal diagnostic combination, demonstrating a combined diagnostic AUC of 0.92 and 0.99 for early-stage and advanced-stage HCC, respectively.

Humans↗
PubMed1989

Quantification of periodontal attachment at single-rooted teeth.

The measurement process of attachment loss has been criticized in recent years. Problems with clinical interpretation, precision of the measurement, and statistical manipulation of the obtained data, are some of the problems associated with the present methodology. The purpose of the present study was to propose an alternative measurement process which addresses some of the existing problems by estimating the lost attachment surface area (LAS) and the remaining attachment surface area (RAS) from a combination of clinical measurements. The results show that a linear combination of several sources of clinical information can be used to predict RAS and LAS. A diagnostic model for LAS (R2 = 81.5%) predicts the square root of LAS with information obtained from bucco-lingual attachment level measurements, the radiographic lost attachment area, the gingivitis index and the radiographic tooth length. This model increases the precision of the estimate of LAS by a factor of 1.86 when compared to the estimate of LAS using only attachment level measurements. A diagnostic model for RAS (R2 = 75.5%) predicts the square root of RAS with the information obtained from the remaining radiographic attachment area, the gingivitis index and the mobility index. Both linear inference models are constructed with measurements of anatomical landmarks to avoid the discrepancy between anatomical and clinical measurements in the produced estimates. It is concluded that modeling of periodontal data provides a simple, inexpensive, and precise diagnostic tool for predicting the lost and the remaining periodontal attachment of single-rooted teeth. Measurement processes of this type could provide a convincing basis for the evaluation of clinical decisions and research questions.

Cuspid↗
PubMed1998

[Hormonal islet cell tumors. Proposition of a diagnostic-therapeutic model].

Islet cell tumors make a serious therapeutic problem due to their specific clinical presentation and the necessity of applying a variety of multidisciplinary diagnostic and therapeutic methods. The authors present their own algorithm for diagnosing and treatment of islet-cell tumors worked out basing on many-year experience.

Adenoma, Islet Cell↗