Tag: nevin manimala

Vet Dermatol. 2024 Jun 18. doi: 10.1111/vde.13276. Online ahead of print.

ABSTRACT

BACKGROUND: Limited information exists about the correlation between clinical history and positive serum (SAT) and intradermal allergen test (IDAT) results in atopic dogs.

OBJECTIVES: To evaluate the correlation between clinical history and SAT/IDAT results in atopic dogs.

ANIMALS: Twenty-nine client-owned dogs with nonseasonal atopic dermatitis with or without seasonal exacerbation were enrolled.

MATERIALS AND METHODS: IDAT, SAT (immunoglobulin (Ig)M antibody capture enzyme-linked immunosorbent assay [MacELISA] with bromelain CCD inhibitor) and clinical information collected in a questionnaire regarding seasonal variations in pruritus affecting the dogs were performed on the same day. Two independent investigators (Inv A and Inv B) recorded IDAT results.

RESULTS: The kappa coefficients agreement for positive IDAT scores between Inv A and B was substantial. The agreement between IDAT and SAT results at different ELISA absorbance units (EAU) cut-offs (>79 and ≥300) was slight and fair for both investigators, respectively. A higher agreement was observed between IDAT and SAT (≥300 EAU) than between IDAT and SAT (>79 EAU) with the exception of mite and flea allergens. There was a statistically significant association between clinical history and positive IDAT results for seasonal allergens (Inv A and Inv B, p = 0.016). There was no significance between positive SAT results and clinical history. Five (IDAT) and 12 of 13 (SAT) atopic dogs without clinical seasonal exacerbation showed positive results for seasonal allergens.

CONCLUSIONS AND CLINICAL RELEVANCE: The agreement between IDAT and SAT ≥300 EAU results was fair and the agreement between IDAT and SAT >79 EAU results was slight for all allergens. Only positive IDAT results significantly correlated with clinical history.

PMID:38887975 | DOI:10.1111/vde.13276

CNS Neurosci Ther. 2024 Jun;30(6):e14810. doi: 10.1111/cns.14810.

ABSTRACT

AIMS: To study the changes in cortical thickness and subcortical gray matter structures in children with complete spinal cord injury (CSCI), reveal the possible causes of dysfunction beyond sensory motor dysfunction after CSCI, and provide a possible neural basis for corresponding functional intervention training.

METHODS: Thirty-seven pediatric CSCI patients and 34 age-, gender-matched healthy children as healthy controls (HCs) were recruited. The 3D high-resolution T1-weighted structural images of all subjects were obtained using a 3.0 Tesla MRI system. Statistical differences between pediatric CSCI patients and HCs in cortical thickness and volumes of subcortical gray matter structures were evaluated. Then, correlation analyses were performed to analyze the correlation between the imaging indicators and clinical characteristics.

RESULTS: Compared with HCs, pediatric CSCI patients showed decreased cortical thickness in the right precentral gyrus, superior temporal gyrus, and posterior segment of the lateral sulcus, while increased cortical thickness in the right lingual gyrus and inferior occipital gyrus. The volume of the right thalamus in pediatric CSCI patients was significantly smaller than that in HCs. No significant correlation was found between the imaging indicators and the injury duration, sensory scores, and motor scores of pediatric CSCI patients.

CONCLUSIONS: These findings demonstrated that the brain structural reorganizations of pediatric CSCI occurred not only in sensory motor areas but also in cognitive and visual related brain regions, which may suggest that the visual processing, cognitive abnormalities, and related early intervention therapy also deserve greater attention beyond sensory motor rehabilitation training in pediatric CSCI patients.

PMID:38887969 | DOI:10.1111/cns.14810

Ann Clin Transl Neurol. 2024 Jun 18. doi: 10.1002/acn3.52128. Online ahead of print.

ABSTRACT

OBJECTIVE: High-risk types of diffuse gliomas in adults include isocitrate dehydrogenase (IDH) wild-type glioblastomas and grade 4 astrocytomas. Achieving noninvasive prediction of high-risk molecular subtypes of gliomas is important for personalized and precise diagnosis and treatment.

METHODS: We retrospectively collected data from 116 patients diagnosed with adult diffuse gliomas. Multiple high-risk molecular markers were tested, and various habitat models and whole-tumor models were constructed based on preoperative routine and diffusion kurtosis imaging (DKI) sequences to predict high-risk molecular subtypes of gliomas. Feature selection and model construction utilized Least absolute shrinkage and selection operator (LASSO) and support vector machine (SVM). Finally, the Wilcoxon rank-sum test was employed to explore the correlation between habitat quantitative features (intra-tumor heterogeneity score，ITH score) and heterogeneity, as well as high-risk molecular subtypes.

RESULTS: The results showed that the habitat analysis model based on DKI performed remarkably well (with AUC values reaching 0.977 and 0.902 in the training and test sets, respectively). The model’s performance was further enhanced when combined with clinical variables. (The AUC values were 0.994 and 0.920, respectively.) Additionally, we found a close correlation between ITH score and heterogeneity, with statistically significant differences observed between high-risk and non-high-risk molecular subtypes.

INTERPRETATION: The habitat model based on DKI is an ideal means for preoperatively predicting high-risk molecular subtypes of gliomas, holding significant value for noninvasively alerting malignant gliomas and those with malignant transformation potential.

PMID:38887966 | DOI:10.1002/acn3.52128

Genet Epidemiol. 2024 Jun 17. doi: 10.1002/gepi.22577. Online ahead of print.

ABSTRACT

Instrumental variable (IV) analysis has been widely applied in epidemiology to infer causal relationships using observational data. Genetic variants can also be viewed as valid IVs in Mendelian randomization and transcriptome-wide association studies. However, most multivariate IV approaches cannot scale to high-throughput experimental data. Here, we leverage the flexibility of our previous work, a hierarchical model that jointly analyzes marginal summary statistics (hJAM), to a scalable framework (SHA-JAM) that can be applied to a large number of intermediates and a large number of correlated genetic variants-situations often encountered in modern experiments leveraging omic technologies. SHA-JAM aims to estimate the conditional effect for high-dimensional risk factors on an outcome by incorporating estimates from association analyses of single-nucleotide polymorphism (SNP)-intermediate or SNP-gene expression as prior information in a hierarchical model. Results from extensive simulation studies demonstrate that SHA-JAM yields a higher area under the receiver operating characteristics curve (AUC), a lower mean-squared error of the estimates, and a much faster computation speed, compared to an existing approach for similar analyses. In two applied examples for prostate cancer, we investigated metabolite and transcriptome associations, respectively, using summary statistics from a GWAS for prostate cancer with more than 140,000 men and high dimensional publicly available summary data for metabolites and transcriptomes.

PMID:38887957 | DOI:10.1002/gepi.22577

Circ Cardiovasc Qual Outcomes. 2024 Jun 18:e010731. doi: 10.1161/CIRCOUTCOMES.123.010731. Online ahead of print.

ABSTRACT

BACKGROUND: Text messages may enhance physical activity levels in patients with cardiovascular disease, including those enrolled in cardiac rehabilitation. However, the independent and long-term effects of text messages remain uncertain.

METHODS: The VALENTINE study (Virtual Application-supported Environment to Increase Exercise) was a micro-randomized trial that delivered text messages through a smartwatch (Apple Watch or Fitbit Versa) to participants initiating cardiac rehabilitation. Participants were randomized 4× per day over 6-months to receive no text message or a message encouraging low-level physical activity. Text messages were tailored on contextual factors (eg, weather). Our primary outcome was step count 60 minutes following a text message, and we used a centered and weighted least squares mean method to estimate causal effects. Given potential measurement differences between devices determined a priori, data were assessed separately for Apple Watch and Fitbit Versa users over 3 time periods corresponding to the initiation (0-30 days), maintenance (31-120 days), and completion (121-182 days) of cardiac rehabilitation.

RESULTS: One hundred eight participants were included with 70 552 randomizations over 6 months; mean age was 59.5 (SD, 10.7) years with 36 (32.4%) female and 68 (63.0%) Apple Watch participants. For Apple Watch participants, text messages led to a trend in increased step count by 10% in the 60-minutes following a message during days 1 to 30 (95% CI, -1% to +20%), with no effect from days 31 to 120 (+1% [95% CI, -4% to +5%]), and a significant 6% increase during days 121 to 182 (95% CI, +0% to +11%). For Fitbit users, text messages significantly increased step count by 17% (95% CI, +7% to +28%) in the 60-minutes following a message in the first 30 days of the study with no effect subsequently.

CONCLUSIONS: In patients undergoing cardiac rehabilitation, contextually tailored text messages may increase physical activity, but this effect varies over time and by device.

REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04587882.

PMID:38887953 | DOI:10.1161/CIRCOUTCOMES.123.010731

Ann Surg. 2024 Jun 18. doi: 10.1097/SLA.0000000000006416. Online ahead of print.

ABSTRACT

OBJECTIVE: To assess the utility of tumor-intrinsic and cancer-associated fibroblast (CAF) subtypes of pancreatic ductal adenocarcinoma (PDAC) in predicting response to neoadjuvant therapy (NAT) and overall survival.

BACKGROUND: PDAC remains a deadly disease with limited treatment options, and both the tumor as well as the microenvironment play an important role in pathogenesis. Gene expression-based tumor-intrinsic subtypes (classical and basal-like) have been shown to predict outcomes, but tumor microenvironment subtypes are still evolving.

METHODS: RNA-sequencing was performed on 114 deidentified resected PDAC tumors. Clinical data were collected by retrospective chart review. Single sample classifiers (SSCs) were used to determine classical and basal-like subtypes as well as tumor-permissive permCAF and tumor-restraining restCAF subtypes. Survival was analyzed using log-rank test.

RESULTS: Patients who received NAT had an increase in overall survival (OS), with median survival of 27.9 months compared to 20.1 months for those who did not receive NAT, but the difference did not reach statistical significance (HR 0.64, P=0.076). Either tumor-intrinsic or CAF subtypes alone were associated with OS regardless of NAT or no NAT, and patients with classical or restCAF subtype had the best outcomes. When evaluated together, patients with classical-restCAF subtype had the best OS and basal-permCAF the worst OS (P<0.0001). NAT patients with classical-restCAF subtype demonstrated the longest OS compared to the other groups (P=0.00041).

CONCLUSIONS: CAF subtypes have an additive effect over tumor-intrinsic subtypes in predicting survival with or without neoadjuvant FOLFIRINOX in PDAC. Molecular subtyping of both tumor and CAF compartments of PDAC may be important steps in selecting first-line systemic therapy.

PMID:38887930 | DOI:10.1097/SLA.0000000000006416

Head Neck. 2024 Jun 18. doi: 10.1002/hed.27830. Online ahead of print.

ABSTRACT

BACKGROUND: To establish and validate a machine learning model using pretreatment multiparametric magnetic resonance imaging-based radiomics data with clinical data to predict radiation-induced temporal lobe injury (RTLI) in patients with nasopharyngeal carcinoma (NPC) after intensity-modulated radiotherapy (IMRT).

METHODS: Data from 230 patients with NPC who received IMRT (130 with RTLI and 130 without) were randomly divided into the training (n = 161) and validation cohort (n = 69) with a ratio of 7:3. Radiomics features were extracted from pretreatment apparent diffusion coefficient (ADC) map, T2-weighted imaging (T2WI), and CE-T1-weighted imaging (CE-T1WI). T-test, spearman rank correlation, and least absolute shrinkage and selection operator (LASSO) algorithm were employed to identify significant radiomics features. Clinical features were selected with univariate and multivariate analyses. Radiomics and clinical models were constructed using multiple machine learning classifiers, and a clinical-radiomics nomogram that combined clinical with radiomics features was developed. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) were drawn to compare and verify the predictive performances of the clinical model, radiomics model, and clinical-radiomics nomogram.

RESULTS: A total of 5064 radiomics features were extracted, from which 52 radiomics features were selected to construct the radiomics signature. The AUC of the radiomics signature based on multiparametric MRI was 0.980 in the training cohort and 0.969 in the validation cohort, outperforming the radiomics signature only based on T2WI and CE-T1WI (p < 0.05), which highlighted the significance of the DWI sequence in the prediction of temporal lobe injury. The area under the curve (AUC) of the clinical model was 0.895 in the training cohort and 0.905 in the validation cohort. The nomogram, which integrated radiomics and clinical features, demonstrated an impressive AUC value of 0.984 in the validation set; however, no statistically significant difference was observed compared to the radiomics model. The calibration curve and decision curve analysis of the nomogram demonstrated excellent predictive performance and clinical feasibility.

CONCLUSIONS: The clinical-radiomics nomogram, integrating clinical features with radiomics features derived from pretreatment multiparametric MRI, exhibits compelling predictive performance for RTLI in patients diagnosed with NPC.

PMID:38887926 | DOI:10.1002/hed.27830

J Cachexia Sarcopenia Muscle. 2024 Jun 18. doi: 10.1002/jcsm.13509. Online ahead of print.

ABSTRACT

BACKGROUND: Physical activity and metformin pharmacotherapy are associated with improved clinical outcomes in breast and colorectal cancer survivors. Myokines are cytokines secreted from skeletal muscle that may mediate these associations.

METHODS: This hypothesis-generating analysis used biospecimens collected from a multi-centre 2 × 2 factorial randomized design of 116 patients with stage I-III breast and colorectal cancer who were randomized to 12 weeks of (1) aerobic exercise (moderate intensity titrated to 220 min/week); (2) metformin (850 mg daily for 2 weeks and then titrated to 850 mg twice per day); (3) aerobic exercise and metformin; or (4) control. Fourteen myokines were quantified using a multiplex panel. Myokine concentrations were log-transformed, and main effects analyses were conducted using linear mixed-effects regression models. The type I error rate was controlled with the Holm sequential testing procedure.

RESULTS: Randomization to exercise increased leukaemia inhibitory factor (1.26 pg/mL, 95% confidence interval [CI]: 0.69, 1.84; adjusted P = 0.001) and interleukin-15 (2.23 pg/mL, 95% CI: 0.87, 3.60; adjusted P = 0.013) compared with randomization to no exercise. Randomization to metformin decreased apelin (-2.69 pg/mL, 95% CI: -4.31, -1.07; adjusted P = 0.014) and interleukin-15 (-1.74 pg/mL, 95% CI: -2.79, -0.69; adjusted P = 0.013) compared with randomization to no metformin. Metformin decreased myostatin, irisin, oncostatin M, fibroblast growth factor 21 and osteocrin; however, these changes were not statistically significant after correction for multiple comparisons.

CONCLUSIONS: This pilot study demonstrates that randomization to exercise and metformin elicit unique effects on myokine concentrations in cancer patients. This hypothesis-generating observation warrants further basic, translational and clinical investigation and replication.

PMID:38887915 | DOI:10.1002/jcsm.13509

Biostatistics. 2024 Jun 17:kxae017. doi: 10.1093/biostatistics/kxae017. Online ahead of print.

ABSTRACT

Although transcriptomics data is typically used to analyze mature spliced mRNA, recent attention has focused on jointly investigating spliced and unspliced (or precursor-) mRNA, which can be used to study gene regulation and changes in gene expression production. Nonetheless, most methods for spliced/unspliced inference (such as RNA velocity tools) focus on individual samples, and rarely allow comparisons between groups of samples (e.g. healthy vs. diseased). Furthermore, this kind of inference is challenging, because spliced and unspliced mRNA abundance is characterized by a high degree of quantification uncertainty, due to the prevalence of multi-mapping reads, ie reads compatible with multiple transcripts (or genes), and/or with both their spliced and unspliced versions. Here, we present DifferentialRegulation, a Bayesian hierarchical method to discover changes between experimental conditions with respect to the relative abundance of unspliced mRNA (over the total mRNA). We model the quantification uncertainty via a latent variable approach, where reads are allocated to their gene/transcript of origin, and to the respective splice version. We designed several benchmarks where our approach shows good performance, in terms of sensitivity and error control, vs. state-of-the-art competitors. Importantly, our tool is flexible, and works with both bulk and single-cell RNA-sequencing data. DifferentialRegulation is distributed as a Bioconductor R package.

PMID:38887902 | DOI:10.1093/biostatistics/kxae017

EuroIntervention. 2024 Jun 17;20(12):e760-e769. doi: 10.4244/EIJ-D-23-01068.

ABSTRACT

BACKGROUND: Cardiac fibrosis plays a major pathophysiological role in any form of chronic heart disease, and high levels are associated with poor outcome. Diffuse and focal cardiac fibrosis are different subtypes, which have different pathomechanisms and prognostic implications. The total fibrosis burden in endomyocardial biopsy tissue was recently proved to play an independent prognostic role in aortic stenosis patients after transcatheter aortic valve implantation (TAVI).

AIMS: Here, for the first time, we aim to assess the specific impact of different fibrosis subtypes on sudden cardiac death (SCD) as a primary reason for cardiovascular mortality after TAVI.

METHODS: The fibrosis pattern was assessed histologically in the left ventricular biopsies obtained during TAVI interventions in 161 patients, who received a structured follow-up thereafter.

RESULTS: Receiver operating characteristic analyses, performed 6, 12, 24 and 48 months after TAVI, showed diffuse, but not focal, fibrosis as a significant predictor for SCD at all timepoints, with the highest area under the curve at the first time point and a decrease in its SCD predictivity over time. In both multivariate Cox proportional hazards and Fine-Gray competing risk models, including both fibrosis subtypes, as well as age, sex and ejection fraction, high diffuse fibrosis remained statistically significant. Accordingly, it represents an independent SCD predictor, most importantly for the occurrence of early events.

CONCLUSIONS: The burden of diffuse cardiac fibrosis plays an important and independent prognostic role regarding SCD early after TAVI. Therefore, the histological evaluation of fibrosis topography has value as a prognostic tool for TAVI patients and may help to tailor individualised approaches to optimise their postinterventional management.

PMID:38887885 | DOI:10.4244/EIJ-D-23-01068