Category: Statistics

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

Sci Rep. 2024 Feb 15;14(1):3848. doi: 10.1038/s41598-024-53053-9.

ABSTRACT

In the Americas, the fall armyworm (Spodoptera frugiperda) exists in two genetically distinct strains, the corn (C) and rice (R) strains. Despite their names, these strains are not associated with host plant preferences but have been shown to vary in pheromone composition and male responses. Recently, S. frugiperda was detected in Africa as an invasive species, but knowledge about variation in strain types, pheromone composition and inter-strain mating of populations of the pest in the continent has not been fully examined. Therefore, this study aimed to investigate variations, if any in the pheromone composition of female moths, male moth responses, and mating between C and R mitotypes of S. frugiperda populations in Kenya, as well as their geographic distribution. Strains (mitotypes) of S. frugiperda were identified using mitochondrial DNA (mtDNA) markers, and their pheromonal composition determined by coupled gas chromatography-mass spectrometric (GC-MS) analysis. Male moth responses to these compounds were evaluated using GC-electroantennographic detection (EAD), electroantennogram (EAG), and wind tunnel assays. Oviposition assays were used to determine whether R and C mitotype moths could mate and produce eggs. The results showed that both the R and C mitotypes were present, and there were no statistically significant differences in their distribution across all sampled locations. Five pheromone compounds including (Z)-7-dodecenyl acetate (Z7-12:OAc), (Z)-7-tetradecenyl acetate (Z7-14:OAc), (Z)-9-tetradecenyl acetate (Z9-14:OAc), (Z)-11-tetradecenyl acetate (Z11-14:OAc) and (Z)-11-hexadecenyl acetate (Z11-16:OAc), were detected in the pheromone glands of female moths of both mitotypes, with Z9-14:OAc being the most abundant. The relative percentage composition of Z9-14:OAc was similar in both mitotypes. However, the R mitotype had a 2.7 times higher relative percentage composition of Z7-12:OAc compared to the C mitotype moth, while the C mitotype moth had a 2.4 times higher relative percentage composition of Z11-16:OAc than the R mitotype moth. Male moths of both mitotypes exhibited similar responses to the pheromone compounds, showing the strongest responses to Z9-14:OAc and Z7-12:OAc in electrophysiological and behavioural assays. There was mating between R and C mitotypes with egg production comparable to mating within the same mitotype. Our results revealed that differences between the two S. frugiperda mitotypes are characterized by female moth pheromone composition rather than male moth responses to the pheromones, and that this does not prevent hybridisation between the mitotypes, which may have implications for their management.

PMID:38360933 | DOI:10.1038/s41598-024-53053-9

Sci Rep. 2024 Feb 15;14(1):3807. doi: 10.1038/s41598-024-52796-9.

ABSTRACT

Dengue fever, a prevalent and rapidly spreading arboviral disease, poses substantial public health and economic challenges in tropical and sub-tropical regions worldwide. Predicting infectious disease outbreaks on a countrywide scale is complex due to spatiotemporal variations in dengue incidence across administrative areas. To address this, we propose a machine learning ensemble model for forecasting the dengue incidence rate (DIR) in Brazil, with a focus on the population under 19 years old. The model integrates spatial and temporal information, providing one-month-ahead DIR estimates at the state level. Comparative analyses with a dummy model and ablation studies demonstrate the ensemble model’s qualitative and quantitative efficacy across the 27 Brazilian Federal Units. Furthermore, we showcase the transferability of this approach to Peru, another Latin American country with differing epidemiological characteristics. This timely forecast system can aid local governments in implementing targeted control measures. The study advances climate services for health by identifying factors triggering dengue outbreaks in Brazil and Peru, emphasizing collaborative efforts with intergovernmental organizations and public health institutions. The innovation lies not only in the algorithms themselves but in their application to a domain marked by data scarcity and operational scalability challenges. We bridge the gap by integrating well-curated ground data with advanced analytical methods, addressing a significant deficiency in current practices. The successful transfer of the model to Peru and its consistent performance during the 2019 outbreak in Brazil showcase its scalability and practical application. While acknowledging limitations in handling extreme values, especially in regions with low DIR, our approach excels where accurate predictions are critical. The study not only contributes to advancing DIR forecasting but also represents a paradigm shift in integrating advanced analytics into public health operational frameworks. This work, driven by a collaborative spirit involving intergovernmental organizations and public health institutions, sets a precedent for interdisciplinary collaboration in addressing global health challenges. It not only enhances our understanding of factors triggering dengue outbreaks but also serves as a template for the effective implementation of advanced analytical methods in public health.

PMID:38360915 | DOI:10.1038/s41598-024-52796-9