Category: Nevin Manimala

Sci Rep. 2024 Feb 15;14(1):3844. doi: 10.1038/s41598-024-53951-y.

ABSTRACT

Diabetic peripheral neuropathy is the most prominent microvascular complication of diabetes mellitus and the leading cause of ulceration, amputation, and extended hospitalization. Evidence regarding the magnitude and factors associated with diabetic peripheral neuropathy is not well documented in Ethiopia, particularly in the study area. A facility-based cross-sectional study was conducted among 293 adult type 2 diabetic patients who were on treatment and follow-up from May to June 31, 2023. To select participants in the study, a systematic random sampling method was utilized. Data were collected using semi-structured questionnaires and medical record reviews. The Michigan Neuropathy Screening Instrument (MNSI) was employed to assess diabetic peripheral neuropathy. To model the association between diabetic peripheral neuropathy and independent variables, binary logistic regression model was used. An adjusted odds ratio with a 95% confidence interval was used to estimate the association and statistical significance was proclaimed at a p-value < 0.05. The magnitude of diabetic peripheral neuropathy was 14.3% (95% CI 10.4-18.0). It was 13.4% (95% CI 8.4-19.1) among males and 15.4% (95% CI 10.1-22.2) among females. Age above 60 years (AOR = 5.06, 95% CI 1.60-15.96), being rural resident (AOR = 2.41; 95% CI 1.15-5.06), duration of diabetes above 5 years (AOR = 2.48, 95% CI 1.16-5.27) and having comorbid hypertension (AOR = 2.56, 95% CI 1.24-5.28) were independently associated with diabetic peripheral neuropathy. One in seven adult type 2 diabetes patients in the study area had diabetic peripheral neuropathy. Factors such as age, place of residence, duration of diabetes, and comorbid hypertension showed positive associations with diabetic peripheral neuropathy. Thus, it is imperative to give special consideration to diabetic patients who are elderly, living in rural areas, experiencing a prolonged duration of diabetes, or dealing with comorbid hypertension.

PMID:38361024 | DOI:10.1038/s41598-024-53951-y

Sci Rep. 2024 Feb 15;14(1):3826. doi: 10.1038/s41598-024-54067-z.

ABSTRACT

In this work, we present GAROM, a new approach for reduced order modeling (ROM) based on generative adversarial networks (GANs). GANs attempt to learn to generate data with the same statistics of the underlying distribution of a dataset, using two neural networks, namely discriminator and generator. While widely applied in many areas of deep learning, little research is done on their application for ROM, i.e. approximating a high-fidelity model with a simpler one. In this work, we combine the GAN and ROM framework, introducing a data-driven generative adversarial model able to learn solutions to parametric differential equations. In the presented methodology, the discriminator is modeled as an autoencoder, extracting relevant features of the input, and a conditioning mechanism is applied to the generator and discriminator networks specifying the differential equation parameters. We show how to apply our methodology for inference, provide experimental evidence of the model generalization, and perform a convergence study of the method.

PMID:38361023 | DOI:10.1038/s41598-024-54067-z

Eur Spine J. 2024 Feb 15. doi: 10.1007/s00586-024-08150-8. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to analyse the effect of diabetes mellitus (DM) on the radiological changes of Magnetic Resonance Imaging (MRI) on the intervertebral discs and paravertebral muscle to investigate the effect of DM on spinal degeneration.

METHODS: This retrospective study initially included 262 patients who underwent treatment between January 2020 and December 2021 because of lumbar disc herniation. Amongst these patients, 98 patients suffered from type 2 diabetes mellitus (T2DM) for more than five years; this is the poorly controlled group (haemoglobin A1c (HbA1c) ≥ 6.5%; BMI: 26.28 ± 3.60; HbA1c: 7.5, IQR = 1.3). Another 164 patients without T2DM are included in the control group. The data collected and analysed include gender, age, smoking, alcohol use, disease course, Charlson Comorbidity Index, BMI, and radiological parameters including disc height, modified Pfirrmann grading scores, percentage of fat infiltration area of paravertebral muscle, and pathological changes of the endplate.

RESULTS: After propensity score-matched analysis, the difference in general data between the control and T2DM groups was eliminated, and 186 patients were analysed. The modified Pfirrmann grading scores showed statistical differences in every lumbar segment, suggesting that the T2DM group suffered from greater disc degeneration at all L1-S1 segments compared with the control group. The disc height from L1/2 to L5/S1 was not statistically different between the two groups. Compared to the T2DM group, the control group had a lower percentage of fat infiltration areas in L4/5 and L5/S1 paravertebral muscle, whereas L1/2 to L3/4 showed no statistical difference. The T2DM group had more pathological changes of cartilage endplate compared with the control group.

CONCLUSIONS: Prolonged uncontrolled hyperglycaemia may contribute to lumbar disc degeneration, fatty infiltration of the paraspinal muscles in the lower lumbar segments, and increased incidence of endplate cartilage pathological changes in patients with degenerative disc disease.

PMID:38361008 | DOI:10.1007/s00586-024-08150-8

Eur J Nutr. 2024 Feb 15. doi: 10.1007/s00394-024-03340-y. Online ahead of print.

ABSTRACT

PURPOSE: Ultra-processed food may play a role in facilitating snacking behavior because of their convenience and low satiety potential. This study aimed to describe the association between consumption of ultra-processed foods and frequency of snacking.

METHODS: We analyzed data from 46,164 participants (≥ 10 years old) in the 2017-2018 Brazilian Household Budget Survey. Dietary data were collected by 24-h dietary recalls over one or two days for each participant. We estimated energy intake, ultra-processed food consumption, and level of snacking. We measured the association between ultra-processed food consumption and level of snacking using multinomial logistic regression, stratified by age group (adolescents, 10-19 years old; adults, 20-64 years old; elders, 65 or older).

RESULTS: We found a statistically significant tendency of increased daily energy intake and consumption of snacks and that ultra-processed food consumption was positively associated with the level of snacking for all age groups. For adolescents, adults, and elders in the highest quintile of ultra-processed food consumption as a share of their entire diet, the relative risk ratio (95% CI) of having more than two snacks per day compared to no snacks was 14.21 (9.09-22.21), 4.44 (3.54-5.57), and 4.21 (2.67-6.64), respectively, when compared to the lowest quintile.

CONCLUSION: Higher consumption of ultra-processed food was associated with snacking behavior, and the strength of this association was stronger among adolescents. Efforts to mitigate ultra-processed food attributes that facilitate snacking should be incorporated into strategies to promote healthier food choices, especially among adolescents.

PMID:38360983 | DOI:10.1007/s00394-024-03340-y

Psychometrika. 2024 Feb 15. doi: 10.1007/s11336-024-09951-y. Online ahead of print.

ABSTRACT

Grade of membership (GoM) models are popular individual-level mixture models for multivariate categorical data. GoM allows each subject to have mixed memberships in multiple extreme latent profiles. Therefore, GoM models have a richer modeling capacity than latent class models that restrict each subject to belong to a single profile. The flexibility of GoM comes at the cost of more challenging identifiability and estimation problems. In this work, we propose a singular value decomposition (SVD)-based spectral approach to GoM analysis with multivariate binary responses. Our approach hinges on the observation that the expectation of the data matrix has a low-rank decomposition under a GoM model. For identifiability, we develop sufficient and almost necessary conditions for a notion of expectation identifiability. For estimation, we extract only a few leading singular vectors of the observed data matrix and exploit the simplex geometry of these vectors to estimate the mixed membership scores and other parameters. We also establish the consistency of our estimator in the double-asymptotic regime where both the number of subjects and the number of items grow to infinity. Our spectral method has a huge computational advantage over Bayesian or likelihood-based methods and is scalable to large-scale and high-dimensional data. Extensive simulation studies demonstrate the superior efficiency and accuracy of our method. We also illustrate our method by applying it to a personality test dataset.

PMID:38360980 | DOI:10.1007/s11336-024-09951-y

Sci Rep. 2024 Feb 15;14(1):3802. doi: 10.1038/s41598-024-54139-0.

ABSTRACT

Myocardial perfusion imaging (MPI) is a clinical tool which can assess the heart’s perfusion status, thereby revealing impairments in patients’ cardiac function. Within the MPI modality, the acquired three-dimensional signals are typically represented as a sequence of two-dimensional grayscale tomographic images. Here, we proposed an end-to-end survival training approach for processing gray-scale MPI tomograms to generate a risk score which reflects subsequent time to cardiovascular incidents, including cardiovascular death, non-fatal myocardial infarction, and non-fatal ischemic stroke (collectively known as Major Adverse Cardiovascular Events; MACE) as well as Congestive Heart Failure (CHF). We recruited a total of 1928 patients who had undergone MPI followed by coronary interventions. Among them, 80% (n = 1540) were randomly reserved for the training and 5- fold cross-validation stage, while 20% (n = 388) were set aside for the testing stage. The end-to-end survival training can converge well in generating effective AI models via the fivefold cross-validation approach with 1540 patients. When a candidate model is evaluated using independent images, the model can stratify patients into below-median-risk (n = 194) and above-median-risk (n = 194) groups, the corresponding survival curves of the two groups have significant difference (P < 0.0001). We further stratify the above-median-risk group to the quartile 3 and 4 group (n = 97 each), and the three patient strata, referred to as the high, intermediate and low risk groups respectively, manifest statistically significant difference. Notably, the 5-year cardiovascular incident rate is less than 5% in the low-risk group (accounting for 50% of all patients), while the rate is nearly 40% in the high-risk group (accounting for 25% of all patients). Evaluation of patient subgroups revealed stronger effect size in patients with three blocked arteries (Hazard ratio [HR]: 18.377, 95% CI 3.719-90.801, p < 0.001), followed by those with two blocked vessels at HR 7.484 (95% CI 1.858-30.150; p = 0.005). Regarding stent placement, patients with a single stent displayed a HR of 4.410 (95% CI 1.399-13.904; p = 0.011). Patients with two stents show a HR of 10.699 (95% CI 2.262-50.601; p = 0.003), escalating notably to a HR of 57.446 (95% CI 1.922-1717.207; p = 0.019) for patients with three or more stents, indicating a substantial relationship between the disease severity and the predictive capability of the AI for subsequent cardiovascular inciidents. The success of the MPI AI model in stratifying patients into subgroups with distinct time-to-cardiovascular incidents demonstrated the feasibility of proposed end-to-end survival training approach.

PMID:38360974 | DOI:10.1038/s41598-024-54139-0

Insights Imaging. 2024 Feb 16;15(1):48. doi: 10.1186/s13244-024-01628-5.

ABSTRACT

BACKGROUND: Crohn’s disease (CD) is an inflammatory disease of the gastrointestinal tract. The disease behavior changes over time, and endoscopy is crucial in evaluating and monitoring the course of CD. To reduce the economic burden of patients and alleviate the discomfort associated with ineffective examination, it is necessary to fully understand the location, extent, and severity of intestinal stenosis in patients with CD before endoscopy. This study aimed to utilize imaging features of magnetic resonance enterography (MRE) to evaluate intestinal stenosis in patients with CD and to predict whether endoscopy could be passed.

METHODS: MRE data of patients with CD were collected, while age, gender, disease duration, and laboratory test parameters were also gathered. Two radiologists analyzed the images and assessed whether endoscopy could be passed based on the imaging performance. Imaging features of MRE were analyzed in groups based on endoscopy results.

RESULTS: The readers evaluated the imaging performance for 86 patients to determine if endoscopy could be passed and performed a consistency test (compared between two readers k = 0.812, p = 0.000). In the univariate analysis, statistical differences were observed in the degree of T1WI enhancement, thickness of the intestine wall at the stenosis, and diameter of the upstream intestine between the two groups of whether endoscopy was passed. In multivariate logistic regression, the diameter of the upstream intestine was identified to be an independent factor in predicting whether endoscopy was passed or not (OR = 3.260, p = 0.046).

CONCLUSIONS: The utilization of MRE signs for assessing the passage of an endoscope through the narrow segment revealed that the diameter of the upstream intestine emerged as an independent predictor of endoscopic passage. Before performing an endoscopy, MRE can aid in evaluating the passage of the endoscope.

CRITICAL RELEVANCE STATEMENT: This retrospective study explored the imaging features of MRE to evaluate intestinal stenosis in patients with Crohn’s disease and determined that the diameter of the upstream intestine of the stenotic segment was an independent predictor in assessing endoscopic passage.

KEY POINTS: • Endoscopy is crucial in evaluating and monitoring the course of Crohn’s disease. • The diameter of the upstream intestine of the stenotic segment was an independent predictor in assessing endoscopic passage. • MRE can aid in evaluating the passage of the endoscope in stenotic segments of Crohn’s disease.

PMID:38360968 | DOI:10.1186/s13244-024-01628-5

Nat Commun. 2024 Feb 15;15(1):1404. doi: 10.1038/s41467-024-45674-5.

ABSTRACT

Multiple emulsions are usually stabilized by amphiphilic molecules that combine the chemical characteristics of the different phases in contact. When one phase is a liquid crystal (LC), the choice of stabilizer also determines its configuration, but conventional wisdom assumes that the orientational order of the LC has no impact on the stabilizer. Here we show that, for the case of amphiphilic polymer stabilizers, this impact can be considerable. The mode of interaction between stabilizer and LC changes if the latter is heated close to its isotropic state, initiating a feedback loop that reverberates on the LC in form of a complete structural rearrangement. We utilize this phenomenon to dynamically tune the configuration of cholesteric LC shells from one with radial helix and spherically symmetric Bragg diffraction to a focal conic domain configuration with highly complex optics. Moreover, we template photonic microparticles from the LC shells by photopolymerizing them into solids, retaining any selected LC-derived structure. Our study places LC emulsions in a new light, calling for a reevaluation of the behavior of stabilizer molecules in contact with long-range ordered phases, while also enabling highly interesting photonic elements with application opportunities across vast fields.

PMID:38360960 | DOI:10.1038/s41467-024-45674-5

Insights Imaging. 2024 Feb 16;15(1):49. doi: 10.1186/s13244-024-01613-y.

ABSTRACT

OBJECTIVE: To evaluate the added benefit of diffusion-weighted imaging (DWI) over clinical parameters in predicting kidney allograft function decline.

METHODS: Data from 97 patients with DWI of the kidney allograft were retrospectively analyzed. The DWI signals were analyzed with both the mono-exponential and bi-exponential models, yielding total apparent diffusion coefficient (ADC_T), true diffusion (D), pseudo-diffusion (D*), and perfusion fraction (fp). Three predictive models were constructed: Model 1 with clinical parameters, Model 2 with DWI parameters, and Model 3 with both clinical and DWI parameters. The predictive capability of each model was compared by calculating the area under the receiver-operating characteristic curve (AUROC).

RESULTS: Forty-five patients experienced kidney allograft function decline during a median follow-up of 98 months. The AUROC for Model 1 gradually decreased with follow-up time > 40 months, whereas Model 2 and Model 3 maintained relatively stable AUROCs. The AUROCs of Model 1 and Model 2 were not statistically significant. Multivariable analysis showed that the Model 3 included cortical D (HR = 3.93, p = 0.001) and cortical fp (HR = 2.85, p = 0.006), in addition to baseline estimated glomerular filtration rate (eGFR) and proteinuria. The AUROCs for Model 3 were significantly higher than those for Model 1 at 60-month (0.91 vs 0.86, p = 0.02) and 84-month (0.90 vs 0.83, p = 0.007) follow-up.

CONCLUSIONS: DWI parameters were comparable to clinical parameters in predicting kidney allograft function decline. Integrating cortical D and fp into the clinical model with baseline eGFR and proteinuria may add prognostic value for long-term allograft function decline.

CRITICAL RELEVANCE STATEMENT: Our findings suggested that cortical D and fp derived from IVIM-DWI increased the performance to predict long-term kidney allograft function decline. This preliminary study provided basis for the utility of multi-b DWI for managing patients with a kidney transplant.

KEY POINTS: • Both clinical and multi-b DWI parameters could predict kidney allograft function decline. • The ability to predict kidney allograft function decline was similar between DWI and clinical parameters. • Cortical D and fp derived from IVIM-DWI increased the performance to predict long-term kidney allograft function decline.

PMID:38360950 | DOI:10.1186/s13244-024-01613-y

Sci Rep. 2024 Feb 15;14(1):3845. doi: 10.1038/s41598-024-54502-1.

ABSTRACT

To assess the image quality parameters of dual-energy computed tomography angiography (DECTA) 40-, and 60 keV virtual monoenergetic images (VMIs) combined with deep learning-based image reconstruction model (DLM) and iterative reconstructions (IR). CT scans of 28 post EVAR patients were enrolled. The 60 s delayed phase of DECTA was evaluated. Objective [noise, contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR)] and subjective (overall image quality and endoleak conspicuity – 3 blinded readers assessment) image quality analyses were performed. The following reconstructions were evaluated: VMI 40, 60 keV VMI; IR VMI 40, 60 keV; DLM VMI 40, 60 keV. The noise level of the DLM VMI images was approximately 50% lower than that of VMI reconstruction. The highest CNR and SNR values were measured in VMI DLM images. The mean CNR in endoleak in 40 keV was accounted for as 1.83 ± 1.2; 2.07 ± 2.02; 3.6 ± 3.26 in VMI, VMI IR, and VMI DLM, respectively. The DLM algorithm significantly reduced noise and increased lesion conspicuity, resulting in higher objective and subjective image quality compared to other reconstruction techniques. The application of DLM algorithms to low-energy VMIs significantly enhances the diagnostic value of DECTA in evaluating endoleaks. DLM reconstructions surpass traditional VMIs and IR in terms of image quality.

PMID:38360941 | DOI:10.1038/s41598-024-54502-1