Predictive value of morphological features in patients with autism versus normal controls.
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ABSTRACT:
SUBMITTER: Ozgen H
PROVIDER: S-EPMC3536966 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Calprotectin is a heat stable intracellular protein shed by neutrophils into the intestinal lumen in response to inflammation. Lack of specificity makes its role in the assessment of inflammatory bowel disease uncertain. However, the strength of faecal calprotectin testing may lie in its negative predictive value (NPV) rather than positive predictive value (PPV) of organic intestinal disease.To determine whether a normal faecal calprotectin in new patients with symptoms safely predicts for functional intestinal disease.To determine the predictive values of normal and raised faecal calprotectin by retrospective review of outcomes in consecutive primary care referrals into secondary care. Patients aged 16-60 years (mean age 41 years; 70% female patients) with intestinal symptoms were identified. 500 referrals had a normal faecal calprotectin and 130 had a raised result. 'Fast track' referrals were excluded. Outcome measures were the NPV of a normal faecal calprotectin and PPV of a raised faecal calprotectin.Normal faecal calprotectin had an NPV of 0.964 for excluding symptomatic organic intestinal disease. Significant incidental non-intestinal (3.6%) and intestinal (6.4%) diseases were also identified. Mean follow-up was 4.8 years with no diagnostic revisions. In the raised faecal calprotectin cohort, the PPV for organic intestinal disease was 0.7.A normal faecal calprotectin safely predicts for functional intestinal disease. It may represent a powerful screening tool for excluding organic intestinal disease in primary care. A prospective primary care based study is needed.
| S-EPMC5517244 | biostudies-other
Project description:IntroductionThis study aims to explore the predictive value of CT radiomics and clinical characteristics for treatment response in COVID-19 patients.MethodsData were collected from clinical/auxiliary examinations and follow-ups of COVID-19 patients. Whole lung radiomics feature extraction was performed at baseline chest CT. Radiomics, clinical, and combined features (nomogram) were evaluated for predicting treatment response.ResultsAmong 36 COVID-19 patients, mild, common, severe, and critical disease symptoms were found in 1, 21, 13, and 1 of them, respectively. Twenty-five (1 mild, 18 common, and 6 severe) patients showed a good response to treatment and 11 poor/fair responses. The clinical classification (p = 0.025) and serum creatinine (p = 0.010) on admission and small area emphasis (p = 0.036) from radiomics analysis significantly differed between the two groups. Predictive models were constructed based on the radiomics, clinical features, and nomogram showing an area under the curve of 0.651, 0.836, and 0.869, respectively. The nomogram achieved good calibration.ConclusionThis new, non-invasive, and low-cost prediction model that combines the radiomics and clinical features is useful for identifying COVID-19 patients who may not respond well to treatment.
| S-EPMC10214574 | biostudies-literature
Project description:Although it does not alter the ERCC1 phenotype, the ERCC1 500C>T (rs11615) polymorphism has undergone a myriad of investigations into its role as a marker for nucleotide excision repair (NER) function in different races, diseases and treatment outcomes. The goal of our study was to test the hypothesis that 500C>T is in linkage disequilibrium (LD) with causative alleles, and that these haplotypes are more frequent in Caucasians with melanoma than in healthy Caucasians or African Americans.In this case-control study, we selected race-specific ERCC1 single-nucleotide polymorphism (SNPs), conducted LD analysis with ERCC1 500C>T and compared the frequency of ERCC1 diplotypes in Caucasians with melanoma (n=165), healthy Caucasians (n=150) and healthy African Americans (n=159). The haplotype was further studied using a fusion gene containing multiple ERCC1 SNPs.Large cancer institute in the USA.A total of 165 Caucasian melanoma patients, 159 healthy Caucasian controls and 159 African American healthy controls. Men and women were enrolled in the clinical trial; however, since the screening trial included prostate cancer screening in addition to screening for other cancers, only male controls were available.The outcome measures were melanoma risk in Caucasians, and LD between ERCC1 SNP, N118N and other race-specific allelic variants.When compared to ERCC1 500C>T alone, a race-specific three-SNP variant haplotype in ERCC1 (comprised of rs11615, rs3212950 and rs3212948) was even more frequent in Caucasians with melanoma than in healthy Caucasians (p=0.0034) or African Americans (p<0.0001). A plasmid containing the variant haplotype was not differentially expressed.We demonstrate that ERCC1 500C>T participates in a previously characterised cancer-risk haplotype found more frequently in Caucasians, while LD is weak in African Americans; this haplotype appears to also be related to melanoma. It is therefore likely that ERCC1 500C>T is only a valid NER, disease or treatment outcome marker in Caucasians.
| S-EPMC3549215 | biostudies-literature
Project description:Many studies are devoted to the design of radiomic models for a prediction task. When no effective model is found, it is often difficult to know whether the radiomic features do not include information relevant to the task or because of insufficient data. We propose a downsampling method to answer that question when considering a classification task into two groups. Using two large patient cohorts, several experimental configurations involving different numbers of patients were created. Univariate or multivariate radiomic models were designed from each configuration. Their performance as reflected by the Youden index (YI) and Area Under the receiver operating characteristic Curve (AUC) was compared to the stable performance obtained with the highest number of patients. A downsampling method is described to predict the YI and AUC achievable with a large number of patients. Using the multivariate models involving machine learning, YI and AUC increased with the number of patients while they decreased for univariate models. The downsampling method better estimated YI and AUC obtained with the largest number of patients than the YI and AUC obtained using the number of available patients and identifies the lack of information relevant to the classification task when no such information exists.
| S-EPMC6883070 | biostudies-literature
Project description:ObjectiveTo determine differences in clinical presentation and disease progression between patients with dementia due to AD with visually normal and abnormal EEG recordings. We hypothesized that patients with normal electroencephalographs (EEGs) are a representation of the heterogeneity of AD. We expected this group to have a phenotype with relatively predominant hippocampal atrophy, memory deficits, and a slower disease progression.MethodsPatients were included based on diagnosis of dementia due to AD, positive amyloid and tau cerebrospinal fluid (CSF) biomarkers, and the availability of EEG recordings. Patients were categorized in groups of normal (N = 208) and abnormal (N = 336) EEG recordings based on visual assessment by experienced neurophysiologists. At baseline demographics, cognitive, MRI, and CSF measures were compared between groups. Cognitive data from follow-up visits were assessed by linear mixed-effects models (LMMs), and corrected for baseline value, sex, age, and educational level, to compare cognitive deterioration over time between groups.ResultsAbout 1 in 4.5 patients with AD dementia had a visually normal EEG and this group showed better overall cognitive performance compared to the abnormal group, where memory was the most prominent affected domain. The normal group showed less global and parietal but similar medial temporal atrophy. Follow-up data showed a slower deterioration on all tested cognitive domains in the normal EEG group.InterpretationPatients with dementia due to AD and visually normal EEG recordings showed a milder clinical presentation and had a milder disease progression compared to patients with an abnormal EEG. These results provide evidence of clinical and biological heterogeneity within AD dementia.
| S-EPMC8108419 | biostudies-literature
Project description:This study was designed to examine morphological features in a large group of children with autism spectrum disorder versus normal controls. Amongst 421 patients and 1,007 controls, 224 matched pairs were created. Prevalence rates and odds ratios were analyzed by conditional regression analysis, McNemar test or paired t-test matched pairs. Morphological abnormalities were significantly more prevalent in patients with autism than in the normal control group and 48 morphological features distinguished patients from controls. Our findings show that morphological features are associated with autism. Exploring potential underlying genetic mechanisms of this association might lead to a better understanding of autism.
| S-EPMC3005119 | biostudies-literature
Project description:ObjectiveIncomplete lupus erythematosus (ILE) involves clinical and/or serologic manifestations consistent with but insufficient for systemic lupus erythematosus (SLE) classification. Because the nature of ILE is poorly understood and no treatment recommendations exist, we examined the clinical manifestations, medication history, and immunologic features in a diverse collection of ILE and SLE patients.MethodsMedical records of subjects enrolled in the Lupus Family Registry and Repository were reviewed for medication history and American College of Rheumatology (ACR) classification criteria to identify ILE patients (3 ACR criteria; n = 440) and SLE patients (≥4 ACR criteria; n = 3,397). Participants completed the Connective Tissue Disease Screening Questionnaire. Anticardiolipin and plasma B lymphocyte stimulator (BLyS) were measured by enzyme-linked immunosorbent assay, antinuclear antibodies (ANAs) by indirect immunofluorescence, and 13 autoantibodies by bead-based assays.ResultsOn average, ILE patients were older than SLE patients (46.2 years versus 42.0 years; P < 0.0001), and fewer ILE patients were African American (23.9% versus 32.2%; P < 0.001). ILE patients exhibited fewer autoantibody specificities than SLE patients (1.3 versus 2.6; P < 0.0001) and were less likely to have ANA titers ≥1:1,080 (10.5% versus 19.5%; P < 0.0001). BLyS levels were intermediate in ILE patients (controls < ILE; P = 0.016; ILE < SLE; P = 0.008). Pericarditis, renal, or neurologic manifestations occurred in 12.5% of ILE patients and were associated with non-European American race/ethnicity (P = 0.012). Hydroxychloroquine use increased over time, but was less frequent in ILE than SLE patients (65.2% versus 83.1%; P < 0.0001).ConclusionAlthough usually characterized by milder symptoms, ILE manifestations may require immunomodulatory treatments. Longitudinal studies are necessary to understand how ILE affects organ damage and future SLE risk, and to delineate molecular pathways unique to ILE.
| S-EPMC5524597 | biostudies-literature
Project description:PurposeTo explore the value of MRI-based radiomics features in predicting risk in disease progression for nasopharyngeal carcinoma (NPC).Methods199 patients confirmed with NPC were retrospectively included and then divided into training and validation set using a hold-out validation (159: 40). Discriminative radiomic features were selected with a Wilcoxon signed-rank test from tumors and normal masticatory muscles of 37 NPC patients. LASSO Cox regression and Pearson correlation analysis were applied to further confirm the differential expression of the radiomic features in the training set. Using the multiple Cox regression model, we built a radiomic feature-based classifier, Rad-Score. The prognostic and predictive performance of Rad-Score was validated in the validation cohort and illustrated in all included 199 patients.ResultsWe identified 1832 differentially expressed radiomic features between tumors and normal tissue. Rad-Score was built based on one radiomic feature: CET1-w_wavelet.LLH_GLDM_Dependence-Entropy. Rad-Score showed a satisfactory performance to predict disease progression in NPC with an area under the curve (AUC) of 0.604, 0.732, 0.626 in the training, validation, and the combined cohort (all 199 patients included) respectively. Rad-Score improved risk stratification, and disease progression-free survival was significantly different between these groups in every cohort of patients (p = 0.044 or p < 0.01). Combining radiomics and clinical features, higher AUC was achieved of the prediction of 3-year disease progression-free survival (PFS) (AUC, 0.78) and 5-year disease PFS (AUC, 0.73), although there was no statistical difference.ConclusionThe radiomics classifier, Rad-Score, was proven useful for pretreatment prognosis prediction and showed potential in risk stratification for NPC.Supplementary informationThe online version contains supplementary material available at 10.1007/s12672-021-00460-3.
| S-EPMC8683387 | biostudies-literature
Project description:ObjectiveTo assess the predictive value of common measures validated to predict falls in other geriatric populations in patients presenting with suspected Normal Pressure Hydrocephalus (NPH).MethodsOne hundred ninety-five patients over the age of 60 who received the Fall Risk Questionnaire were retrospectively recruited from the CSF Disorders clinic within the departments of Neurosurgery and Neurology. Multiple logistic regression was used to create a model to predict falls for patients with suspected NPH using common measures: Timed Up & Go, Dual Timed Up & Go, 10 Meter Walk, MiniBESTest, 6-Minute Walk, Lower Extremity Function (Mobility), Fall Risk Questionnaire, and Functional Activities Questionnaire.ResultsThe Fall Risk Questionnaire and age were shown to be the best predictors of falls. The model was 95.92% (Positive predictive value: 83.93%) sensitive and 47.92% specific (Negative predictive value: 77.78%).ConclusionPatients presenting with suspected NPH are at an increased fall risk, 75% of the total patients and 89% of patients who responded to a temporary drain of CSF had at least one fall in the past 6 months. The Fall Risk Questionnaire and age were shown to be predictive of falls for patients with suspected NPH. The preliminary evidence indicates measures that have been validated to assess fall risk in other populations may not be valid for patients presenting with suspected NPH.
| S-EPMC7869204 | biostudies-literature
Project description:Autism Spectrum Disorders (ASD) is a spectrum of highly heritable neurodevelopmental disorders in which known mutations contribute to disease risk in 20% of cases. Here, we report the results of the largest blood transcriptome study to date that aims to identify differences in 170 ASD cases and 115 age/sex-matched controls and to evaluate the utility of gene expression profiling as a tool to aid in the diagnosis of ASD. The differentially expressed genes were enriched for the neurotrophin signaling, long-term potentiation/depression, and notch signaling pathways. We developed a 55-gene prediction model, using a cross-validation strategy, on a sample cohort of 66 male ASD cases and 33 age-matched male controls (P1). Subsequently, 104 ASD cases and 82 controls were recruited and used as a validation set (P2). This 55-gene expression signature achieved 68% classification accuracy with the validation cohort (area under the receiver operating characteristic curve (AUC): 0.70 [95% confidence interval [CI]: 0.62-0.77]). Not surprisingly, our prediction model that was built and trained with male samples performed well for males (AUC 0.73, 95% CI 0.65-0.82), but not for female samples (AUC 0.51, 95% CI 0.36-0.67). The 55-gene signature also performed robustly when the prediction model was trained with P2 male samples to classify P1 samples (AUC 0.69, 95% CI 0.58-0.80). Our result suggests that the use of blood expression profiling for ASD detection may be feasible. Further study is required to determine the age at which such a test should be deployed, and what genetic characteristics of ASD can be identified.
| S-EPMC3515554 | biostudies-literature