Tag: nevin manimala

Pharmacogenomics J. 2023 Mar 25. doi: 10.1038/s41397-023-00302-1. Online ahead of print.

ABSTRACT

Previous observational studies reported associations between non-steroidal anti-inflammatory drugs (NSAIDs) and major depressive disorder (MDD), however, these associations are often inconsistent and underlying biological mechanisms are still poorly understood. We conducted a two-sample Mendelian randomisation (MR) study to examine relationships between genetic variants and NSAID target gene expression or DNA methylation (DNAm) using publicly available expression, methylation quantitative trait loci (eQTL or mQTL) data and genetic variant-disease associations from genome-wide association studies (GWAS of MDD). We also assessed drug exposure using gene expression and DNAm levels of NSAID targets as proxies. Genetic variants were robustly adjusted for multiple comparisons related to gene expression, DNAm was used as MR instrumental variables and GWAS statistics of MDD as the outcome. A 1-standard deviation (SD) lower expression of NEU1 in blood was related to lower C-reactive protein (CRP) levels of -0.215 mg/L (95% confidence interval (CI): 0.128-0.426) and a decreased risk of MDD (odds ratio [OR] = 0.806; 95% CI: 0.735-0.885; p = 5.36 × 10^-6). A concordant direction of association was also observed for NEU1 DNAm levels in blood and a risk of MDD (OR = 0.886; 95% CI: 0.836-0.939; p = 4.71 × 10^-5). Further, the genetic variants associated with MDD were mediated by NEU1 expression via DNAm (β = -0.519; 95% CI: -0.717 to -0.320256; p = 3.16 × 10^-7). We did not observe causal relationships between inflammatory genetic marker estimations and MDD risk. Yet, we identified a concordant association of NEU1 messenger RNA and an adverse direction of association of higher NEU1 DNAm with MDD risk. These results warrant increased pharmacovigilance and further in vivo or in vitro studies to investigate NEU1 inhibitors or supplements for MDD.

PMID:36966195 | DOI:10.1038/s41397-023-00302-1

Sci Rep. 2023 Mar 25;13(1):4905. doi: 10.1038/s41598-023-32173-8.

ABSTRACT

In the present study, various statistical and machine learning (ML) techniques were used to understand how device fabrication parameters affect the performance of copper oxide-based resistive switching (RS) devices. In the present case, the data was collected from copper oxide RS devices-based research articles, published between 2008 to 2022. Initially, different patterns present in the data were analyzed by statistical techniques. Then, the classification and regression tree algorithm (CART) and decision tree (DT) ML algorithms were implemented to get the device fabrication guidelines for the continuous and categorical features of copper oxide-based RS devices, respectively. In the next step, the random forest algorithm was found to be suitable for the prediction of continuous-type features as compared to a linear model and artificial neural network (ANN). Moreover, the DT algorithm predicts the performance of categorical-type features very well. The feature importance score was calculated for each continuous and categorical feature by the gradient boosting (GB) algorithm. Finally, the suggested ML guidelines were employed to fabricate the copper oxide-based RS device and demonstrated its non-volatile memory properties. The results of ML algorithms and experimental devices are in good agreement with each other, suggesting the importance of ML techniques for understanding and optimizing memory devices.

PMID:36966189 | DOI:10.1038/s41598-023-32173-8

NPJ Prim Care Respir Med. 2023 Mar 25;33(1):12. doi: 10.1038/s41533-022-00323-6.

ABSTRACT

Poor adherence to asthma preventer medication is associated with life-threatening asthma attacks. The quality and outcomes framework mandated primary care annual asthma review does not include adherence monitoring and the effect of poor adherence on lung function in paediatric primary care patients is unknown. The aim was to investigate the link between inhaled corticosteroid (ICS) adherence and spirometry, fraction of exhaled nitric oxide (FeNO) and asthma control in asthmatic school-age children in this cross-sectional observational study involving three Leicestershire general practices. Children 5-16 years on the practice’s asthma registers, were invited for a routine annual asthma review between August 2018 and August 2019. Prescription and clinical data were extracted from practice databases. Spirometry, bronchodilator reversibility (BDR) and FeNO testing were performed as part of the review. 130 of 205 eligible children (63.4%) attended their review. Mean adherence to ICS was 36.2% (SEM 2.1%) and only 14.6% of children had good adherence (≥75% prescriptions issued). We found no differences in asthma exacerbations in the preceding 12 months between the adherence quartiles. 28.6% of children in the lowest and 5.6% in the highest adherence quartile had BDR ≥ 12% but this was not statistically significant (p = 0.55). A single high FeNO value did not predict adherence to ICS. Adherence to ICS in children with asthma in primary care is poor. The link between adherence to ICS and asthma exacerbations, spirometry and FeNO is complex but knowledge of adherence to ICS is critical in the management of children with asthma.

PMID:36966170 | DOI:10.1038/s41533-022-00323-6

Sci Data. 2023 Mar 25;10(1):165. doi: 10.1038/s41597-023-02060-y.

ABSTRACT

This paper presents a fine-grained and multi-sourced dataset for environmental determinants of health collected from England cities. We provide health outcomes of citizens covering physical health (COVID-19 cases, asthma medication expenditure, etc.), mental health (psychological medication expenditure), and life expectancy estimations. We present the corresponding environmental determinants from four perspectives, including basic statistics (population, area, etc.), behavioural environment (availability of tobacco, health-care services, etc.), built environment (road density, street view features, etc.), and natural environment (air quality, temperature, etc.). To reveal regional differences, we extract and integrate massive environment and health indicators from heterogeneous sources into two unified spatial scales, i.e., at the middle layer super output area (MSOA) and the city level, via big data processing and deep learning. Our data holds great promise for diverse audiences, such as public health researchers and urban designers, to further unveil the environmental determinants of health and design methodology for a healthy, sustainable city.

PMID:36966167 | DOI:10.1038/s41597-023-02060-y

J Magn Reson Imaging. 2023 Mar 25. doi: 10.1002/jmri.28695. Online ahead of print.

ABSTRACT

BACKGROUND: Brain metastasis (BM) is a serious neurological complication of cancer of different origins. The value of deep learning (DL) to identify multiple types of primary origins remains unclear.

PURPOSE: To distinguish primary site of BM and identify the best DL models.

STUDY TYPE: Retrospective.

POPULATION: A total of 449 BM derived from 214 patients (49.5% for female, mean age 58 years) (100 from small cell lung cancer [SCLC], 125 from non-small cell lung cancer [NSCLC], 116 from breast cancer [BC], and 108 from gastrointestinal cancer [GIC]) were included.

FIELD STRENGTH/SEQUENCE: A 3-T, T1 turbo spin echo (T1-TSE), T2-TSE, T2FLAIR-TSE, DWI echo-planar imaging (DWI-EPI) and contrast-enhanced T1-TSE (CE T1-TSE).

ASSESSMENT: Lesions were divided into training (n = 285, 153 patients), testing (n = 122, 93 patients), and independent testing cohorts (n = 42, 34 patients). Three-dimensional residual network (3D-ResNet), named 3D ResNet6 and 3D ResNet 18, was proposed for identifying the four origins based on single MRI and combined MRI (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI, CE-T1WI + T2WI + DWI). DL model was used to distinguish lung cancer from non-lung cancer; then SCLC vs. NSCLC for lung cancer classification and BC vs. GIC for non-lung cancer classification was performed. A subjective visual analysis was implemented and compared with DL models. Gradient-weighted class activation mapping (Grad-CAM) was used to visualize the model by heatmaps.

STATISTICAL TESTS: The area under the receiver operating characteristics curve (AUC) assess each classification performance.

RESULTS: 3D ResNet18 with Grad-CAM and AIC showed better performance than 3DResNet6, 3DResNet18 and the radiologist for distinguishing lung cancer from non-lung cancer, SCLC from NSCLC, and BC from GIC. For single MRI sequence, T1WI, DWI, and CE-T1WI performed best for lung cancer vs. non-lung cancer, SCLC vs. NSCLC, and BC vs. GIC classifications. The AUC ranged from 0.675 to 0.876 and from 0.684 to 0.800 regarding the testing and independent testing datasets, respectively. For combined MRI sequences, the combination of CE-T1WI + T2WI + DWI performed better for BC vs. GIC (AUCs of 0.788 and 0.848 on testing and independent testing datasets, respectively), while the combined MRI approach (T1WI + T2-FLAIR + DWI, CE-T1WI + DWI) could not achieve higher AUCs for lung cancer vs. non-lung cancer, SCLC vs. NSCLC. Grad-CAM helped for model visualization by heatmaps that focused on tumor regions.

DATA CONCLUSION: DL models may help to distinguish the origins of BM based on MRI data.

EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

PMID:36965182 | DOI:10.1002/jmri.28695

J Magn Reson Imaging. 2023 Mar 25. doi: 10.1002/jmri.28701. Online ahead of print.

ABSTRACT

BACKGROUND: Diabetic nephropathy (DN) is the main cause of end-stage renal failure. Multiecho Dixon-based imaging utilizes chemical shift for water-fat separation that may be valuable in detecting changes both fat and oxygen content of the kidney from a single dataset.

PURPOSE: To investigate whether multiecho Dixon-based imaging can assess fat and oxygen metabolism of the kidney in a single breath-hold acquisition for patients with type 2 diabetes mellitus (DM).

STUDY TYPE: Prospective.

SUBJECTS: A total of 40 DM patients with laboratory examination of biochemical parameters and 20 age- and body mass index (BMI)-matched healthy volunteers (controls).

FIELD STRENGTH/SEQUENCE: 3D multiecho Dixon gradient-echo sequence at 3.0 T.

ASSESSMENT: The DM patients were divided into two groups based on urine albumin-to-creatinine ratio (ACR): type 2 diabetes mellitus (DM, 20 patients, ACR < 30 mg/g) and diabetic nephropathy (DN, 20 patients, ACR ≥ 30 mg/g). In all subjects, fat fraction (FF) and relaxation rate (R2*) maps were derived from the Dixon-based imaging dataset, and mean values in manually drawn regions of interest in the cortex and medulla compared among groups. Associations between MRI and biochemical parameters, including β2-microglobulin, were investigated.

STATISTICAL TESTS: Kruskal-Wallis tests, Spearman correlation analysis, and receiver operating characteristic (ROC) curve analysis.

RESULTS: FF and R2* values of the renal cortex and medulla were significantly different among the three groups with control group < DM < DN (FF: control, 1.11± 0.30, 1.10 ± 0.39; DM, 1.52 ± 0.32, 1.57 ± 0.35; DN, 1.99 ± 0.66, 2.21 ± 0.59. R2*: Control, 16.88 ± 0.77, 20.70 ± 0.86; DM, 17.94 ± 0.75, 22.10 ± 1.12; DN, 19.20 ± 1.24, 23.63 ± 1.33). The highest correlation between MRI and biochemical parameters was that between cortex R2* and β2-microglobulin (r = 0.674). A medulla R2* cutoff of 21.41 seconds^-1 resulted in a sensitivity of 80%, a specificity of 85% and achieved the largest area under the ROC curve (AUC) of 0.83 for discriminating DM from the controls. A cortex FF of 1.81% resulted in a sensitivity of 80%, a specificity of 100% and achieved the largest AUC of 0.83 for discriminating DM from DN.

DATA CONCLUSION: Multiecho Dixon-based imaging is feasible for noninvasively distinguishing DN, DM and healthy controls by measuring FF and R2* values.

EVIDENCE LEVEL: 2.

TECHNICAL EFFICACY: Stage 2.

PMID:36965176 | DOI:10.1002/jmri.28701

J Am Geriatr Soc. 2023 Mar 25. doi: 10.1111/jgs.18343. Online ahead of print.

ABSTRACT

BACKGROUND: Studies revealed unidirectional associations between frailty and medication-related problems (MRPs) among older adults. Less is known about the association between frailty and the risk of MRPs. We aimed to assess the bi-directional association between frailty and the risk of MRPs in community-dwelling older adults in five European countries.

METHODS: Participants were 1785 older adults in the population-based Urban Health Centres Europe project. Repeated assessments were collected at baseline and one-year follow-up, including frailty, the risk of MRPs, and covariates. Linear regression analyses were conducted to examine the unidirectional associations. A cross-lagged panel modeling was used to assess bi-directional associations.

RESULTS: The unidirectional association between frailty at baseline and the risk of MRPs at follow-up remained statistically significant after adjusting for covariates (β = 0.10, 95%CI:0.08, 0.13). The association between the risk of MRPs at baseline and frailty at follow-up shows similar trends. The bi-directional association was comparable with reported unidirectional associations, with a stronger effect from frailty at baseline to the risk of MRPs at follow-up than reversed path (Wald test for comparing lagged effects: p < 0.05).

CONCLUSION: This longitudinal study suggests that a cycle may exist where older adults with higher frailty levels are more likely to have a higher risk of MRPs, which in turn contributes to developing a higher level of frailty. Further research is needed to validate our findings and explore underlying pathways.

PMID:36965170 | DOI:10.1111/jgs.18343

J Anim Breed Genet. 2023 Mar 25. doi: 10.1111/jbg.12770. Online ahead of print.

ABSTRACT

This study aimed to find the parsimonious random regression model (RRM) to evaluate the genetic potential for milk yield (MY), fat content (FC), and protein content (PC) in Tunisian Holstein cows. For this purpose, 551,139; 331,654; and 302,396 test day records for MY, FC, and PC were analysed using various RRMs with different Legendre polynomials (LP) orders on additive genetic (AG) and permanent environmental (PE) effects, and different types of residual variances (RV). The statistical analysis was performed in a Bayesian framework with Gibbs sampling, and the model performances were assessed, mainly, on the predictive ability criteria. The study found that the optimal model for evaluating these traits was an RRM with a third LP order and nine classes of heterogeneous RV. In addition, the study found that heritability estimates for MY, FC, and PC ranged from 0.11 to 0.22, 0.11 to 0.17, and 0.12 to 0.18, respectively, indicating that genetic improvement should be accompanied by improvements in the production environment. The study also suggested that new selection rules could be used to modify lactation curves by exploiting the canonical transformation of the random coefficient covariance (RC) matrix or by using the combination of slopes of individual lactation curves and expected daily breeding values.

PMID:36965122 | DOI:10.1111/jbg.12770

Med Phys. 2023 Mar 25. doi: 10.1002/mp.16390. Online ahead of print.

ABSTRACT

BACKGROUND: Early detection of solid pancreatic masses through contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) is important. But CH-EUS is difficult to learn.

PURPOSE: To design a deep learning-based contrast-enhanced harmonic endoscopic ultrasound diagnosis system (CH-EUS MASTER) for real-time capture and segmentation of solid pancreatic masses and to verify its value in the training of pancreatic mass identification under endoscopic ultrasound.

METHODS: We designed a real-time capture and segmentation model for solid pancreatic masses and then collected 4530 EUS images of pancreatic masses retrospectively, used for training and testing of this model at a ratio of 8:2. The model is loaded into the EUS host computer to establish the CH-EUS MASTER system. A crossover trial was then conducted to evaluate the model’s value in EUS trainee training by successfully conducting two groups of EUS trainees in model learning and trainer-guided training. The intersection over union (IoU) and the time to find the lesion were used to evaluate the model performance metrics, and the Mann-Whitney test was used to compare the IoU and the time to find the lesion in different groups of subjects. Paired t-test was used to compare the effects before and after training. When α≤0.05, it is considered to have a significant statistical difference.

RESULTS: The model test showed that the model successfully captured and segmented the pancreatic solid mass region in 906 EUS images. The real-time capture and segmentation model had a Dice coefficient of 0.763, a recall rate of 0.941, a precision rate of 0.642, and an accuracy of 0.842 (when the threshold is set to 0.5), and the median IoU of all cases was 0.731. For the AI training effect, the average IoU of eight trainees improved from 0.80 to 0.87(95% CI, 0.032∼0.096; P = 0.002). The average time for identifying lesions in the pancreatic body and tail improved from 22.75 seconds to 17.98 seconds (95% CI, 3.664∼5.886; P < 0.01). The average time for identifying lesions in the pancreatic head and uncinate process improved from 34.21 seconds to 25.92 seconds (95% CI, 7.661∼8.913; P < 0.01).

CONCLUSION: CH-EUS MASTER can provide an effect equivalent to trainer guidance in training pancreatic solid mass identification and segmentation under EUS. This article is protected by copyright. All rights reserved.

PMID:36965116 | DOI:10.1002/mp.16390

Cancer Med. 2023 Mar 25. doi: 10.1002/cam4.5838. Online ahead of print.

ABSTRACT

BACKGROUND: Previous studies had explored the diagnostic or prognostic value of NRP-1/CD304 in blastic plasmacytoid dendritic cell neoplasm (BPDCN), acute myeloid leukemia (AML), and B-cell acute lymphoblastic leukemia (B-ALL), whereas the expression and application value of NRP-1/CD304 in other common hematological diseases have not been reported.

METHODS: Bone marrow samples from 297 newly diagnosed patients with various hematological diseases were collected to detect the expression of NRP-1/CD304 by flow cytometry (FCM). The diagnostic efficacy of NRP-1/ CD304-positive diseases was analyzed by receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC) was compared.

RESULTS: In the research cohort, the total positive rate of NRP-1/CD304 was 14.81% (44/297), mainly distributed in BPDCN (100%, 6/6), B-ALL (48.61%, 35/72), and AML (4.48%, 3/67), with statistically significant differences (p < 0.01). Other diseases, such as T-cell acute lymphoblastic leukemia (T-ALL), B-cell non-Hodgkin lymphoma (B-NHL), T/NK-cell lymphoma and plasma cell neoplasms, did not express NRP-1/CD304. The AUC of NRP-1/CD304 was 0.936 (95% CI 0.898-0.973), 0.723 (95% CI 0.646-0.801), and 0.435 (95% CI 0.435) in BPDCN, B-ALL and AML, respectively. Besides, CD304 was commonly expressed in B-ALL with BCR-ABL1 gene rearrangement (p = 0.000), and CD304 expression was positively correlated with CD34 co-expression (p = 0.009) and CD10 co-expression (p = 0.007).

CONCLUSIONS: NRP-1/CD304 is only expressed in BPDCN, B-ALL and AML, but not in other common hematological diseases. This indicates that NRP-1/CD304 has no obvious diagnostic and follow-up study value in hematological diseases other than BPDCN, B-ALL, and AML.

PMID:36965095 | DOI:10.1002/cam4.5838