Tag: nevin manimala

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

J Sep Sci. 2023 Mar 25:e2200985. doi: 10.1002/jssc.202200985. Online ahead of print.

ABSTRACT

Astragali Radix is widely used because of its dual use in medicine and food, and its quality evaluation is of great importance. In this study, a pseudo-targeted metabolomics approach based on scheduled multiple reaction monitoring was developed, and a total of 114 compounds with good linearity, sensitivity and reproducibility were selected for relative quantification, and the chemical differences between Astragali Radix of different growth patterns were further compared by chemometric analysis. With the help of multivariate and univariate analysis, 26 differential compounds between wild/semi-wild Astragali Radix and cultivated Astragali Radix were determined. Then 5 marker compounds were screened out by lasso regression, and further verified by systematic clustering, random forest, support vector machine, and logistic regression. In addition, malonyl-substituted flavonoids showed relatively higher content in wild/semi-wild Astragali Radix. Thus, the malonyl-substitution was the characteristic for flavonoids in wild/semi-wild Astragali Radix. In conclusion, the application of pseudo-targeted metabolomics and various statistical methods could offer multi-dimensional information for the holistic quality evaluation of Astragali Radix. This article is protected by copyright. All rights reserved.

PMID:36965089 | DOI:10.1002/jssc.202200985

Cancer Med. 2023 Mar 25. doi: 10.1002/cam4.5842. Online ahead of print.

ABSTRACT

BACKGROUND: The first HPV-vaccine eligible cohorts in the Netherlands will enter the cervical screening program in 2023. However, a substantial number of young women already have had a cervical sample taken before entry into the regular screening program. This study was initiated to explore early effects of HPV vaccination on detection of cytological abnormalities in cervical samples of women younger than the screening age.

METHODS: Results of cervical samples were obtained from the Dutch National Pathology Databank (PALGA) and were linked to the women’s HPV vaccination status from the national vaccination registry (Praeventis) (N = 42,171). Occurrence of low-grade and high-grade squamous intraepithelial lesions or worse (LSIL and HSIL+) and high-risk HPV positive tests (hrHPV) in the first cervical sample were compared between vaccinated and unvaccinated women by multivariable logistic regression analysis, corrected for age at cervical sampling and age of vaccination (12/13 years, ≥ = 14 years).

RESULTS: For fully vaccinated women (three- or two-dose schedule), statistically significant reductions were seen for all outcomes compared to unvaccinated women (hrHPV: adjusted OR, 0.70, 95% CI, 0.63-0.79; LSIL: 0.70, 0.61-0.80; HSIL+: 0.39, 0.30-0.51).

CONCLUSIONS: By linking nation-wide registries on pathology and vaccination, we show significant beneficial early effects of HPV-vaccination on LSIL, HSIL+, CIN3/AIS/carcinoma and hrHPV detection in young women upto 24 years of age who have a cervical sample taken before entry into the cervical cancer screening program.

PMID:36965085 | DOI:10.1002/cam4.5842

J Physiol. 2023 Mar 25. doi: 10.1113/JP283986. Online ahead of print.

ABSTRACT

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) elegantly probes excitability and connectivity of the human brain. However, TMS-EEG signals inevitably also contain sensory evoked responses caused by TMS-associated auditory and somatosensory inputs, constituting a substantial confounding factor. Here we applied our recently established optimized SHAM protocol (Gordon et al., Neuroimage 2021:118708) to disentangle TMS-EEG responses caused by TMS vs. sensory input. One unresolved question is whether these responses superimpose without relevant interaction, a requirement for their disaggregation by the optimized SHAM approach. We applied in 20 healthy subjects a pharmacological intervention using a single oral dose of 20 mg of diazepam, a positive modulator of GABAA receptors. Diazepam decreased the amplitudes of the P60 and P150 components specifically in the ACTIVE TMS and/or the ACTIVE TMS minus SHAM conditions but not in the SHAM condition, pointing to a response caused by TMS. In contrast, diazepam suppressed the amplitude of the N100 component indiscriminately in the ACTIVE TMS and SHAM conditions but not in the ACTIVE TMS minus SHAM condition, pointing to a response caused by sensory input. Moreover, diazepam suppressed the beta-band response observed in the motor cortex specifically after ACTIVE TMS and ACTIVE TMS minus SHAM. These findings demonstrate lack of interaction of TMS-EEG responses caused by TMS vs. sensory input and validate optimized SHAM-controlled TMS-EEG as an appropriate approach to untangle these TMS-EEG responses. This gain of knowledge will enable the proficient use of TMS-EEG to probe physiology of human cortex. KEY POINTS: Optimized SHAM disentangles TMS-EEG responses caused by TMS vs. sensory input Diazepam modulates differentially TMS-EEG responses caused by TMS vs. sensory input Diazepam modulation of P60 and P150 indicate TMS-EEG responses caused by TMS Diazepam modulation of N100 indicate a TMS-EEG response caused by sensory input Abstract figure legend a. Representation of the TMS target on the scalp (marked as red “x”) indicating the left primary motor cortex (around the location of the C3 electrode). b. Representation of the SHAM TMS condition, which involved the delivery of auditory (masking nose and sham coil) and somatosensory stimuli (scalp electrical stimulation) of equivalent intensity compared to the ACTIVE TMS. To the right, topographical plots display the results from the statistical comparison between responses post vs. pre diazepam intake, using cluster-based dependent samples t-tests (electrodes that composed the significant clusters in cyan). Below, time course plot of the EEG responses to the stimuli before (green) and after (purple) the intake of diazepam. Plotted signal corresponds to the average across all significant electrodes, displayed in the topographical plots above. Shaded gray areas indicate the time windows of significant difference between the EEG responses. c. Representation of the ACTIVE TMS condition, which, in addition to auditory (masking noise and real coil) and somatosensory stimuli (scalp electrical stimulation and real coil), involved the direct activation of the underlying cortex. Time course plot of EEG responses and topographical plots as in “b”. d. By subtracting the individual EEG responses to sensory stimuli (SHAM) from the response to TMS (ACTIVE) we obtain the EEG response attributed solely to the direct cortical activation by TMS. Time course plot of EEG responses and topographical plots as in “b”. This article is protected by copyright. All rights reserved.

PMID:36965075 | DOI:10.1113/JP283986