Category: Statistics

Nanoscale. 2023 Nov 27. doi: 10.1039/d3nr05569h. Online ahead of print.

ABSTRACT

Plasmonic metasurfaces made of perfectly regular 2D lattices of metallic nanoparticles deposited on surfaces or close to waveguides can exhibit hybridized plasmonic and photonic modes. The latter arise from the excitation of surface or guided modes through the in-plane coherent scattering of periodic arrays. Recently, laser-induced self-organization of random plasmonic metasurfaces has been used to create nanoparticle gratings embedded in protective layers. Despite the broad size distribution and positional disorder of nanoparticles, the resulting nanostructures exhibit strong coupling between plasmonic and photonic modes in transverse electric polarization, leading to dichroism, which is well-reproduced from one laser printing to another. Here, we examine quantitatively the effect of inhomogeneities at the nanoscale on the hybridization between localized plasmonic modes and delocalized guided modes by considering realistic laser-induced self-organized nanoparticle arrays embedded in a two-layer system. By referring to regular samples, we describe the optical mechanisms involved in the hybridization process at characteristic wavelengths, based on far and near field simulations. Two kinds of real samples are considered, featuring different levels of coupling between the plasmonic and photonic modes. The results demonstrate that controlling the statistical properties of plasmonic metasurfaces, such as the nanoparticle size distribution and average position, over areas a few micrometers wide is enough to control in a reproducible manner the hybridization mechanisms and their resulting optical properties. Thus, this study shows that the inherent irregularities of laser-induced self-organized nanostructures are compatible with smart functionalities of nanophotonics, and confirms that laser processing has huge potential for real-world applications.

PMID:38009459 | DOI:10.1039/d3nr05569h

Vet Med Sci. 2023 Nov 27. doi: 10.1002/vms3.1325. Online ahead of print.

ABSTRACT

BACKGROUND: Factors influencing parasitosis in cattle in Bangladesh remain inadequately explored, necessitating a comprehensive investigation for interventions and sustainable livestock farming.

OBJECTIVES: We conducted this study to estimate the prevalence and distribution of gastrointestinal parasites, exploring their intricate relationship with farm management practices across a spectrum of small-, medium-, and large-scale commercial farms.

METHODS: We conducted this study in the Chattogram district of Bangladesh. We collected a total of 189 freshly voided faecal samples from different farms. We recorded the age, breed, milking status, sex, body condition score, and anthelmintic use history of the sampled animals. We processed the samples using the direct smear method, with the identification of one egg per sample being considered positive.

RESULTS: We estimated the prevalence of gastrointestinal parasite infection in large-scale (52.1%), medium-scale (54.5%), and small-scale farms (70.0%), with statistically significant differences (p ≤ 0.05). Both pregnant and lactating cows, as well as indigenous cattle, were more likely to have gastrointestinal parasites (p ≤ 0.05). The predominant parasites across farms of all sizes were trematodes (Paramphistomum spp. and Schistosomas spp.) and protozoa (Balantidium coli and Coccidia spp.).

CONCLUSION: Poor farm management practices, such as no pasture management and inadequate deworming regimens, may contribute to the elevated prevalence and infection load observed on small-scale farms. The increased parasitosis in previously dewormed animals can be attributed to the development of anthelmintic resistance against gastrointestinal parasites. Implementing proper and effective deworming strategies is crucial to preventing gastrointestinal parasitosis and mitigating the risk of anthelmintic resistance.

PMID:38009452 | DOI:10.1002/vms3.1325

J Child Psychol Psychiatry. 2023 Nov 27. doi: 10.1111/jcpp.13919. Online ahead of print.

ABSTRACT

Early identification of autism spectrum disorder (ASD) continues to be a challenge despite universal screening efforts. One explanation is that screening tools have lower sensitivity and specificity than initial studies report when accounting for incomplete follow-up for all children screened. Sheldrick and colleagues used statistical modeling to demonstrate the impact on sensitivity and specificity when assumptions about the diagnostic outcome of children who do not pursue diagnostic evaluation are altered. Crucially, the work of Sheldrick et al. serves as a reminder that autism screening in primary care is just one component of the clinical assessment and should not be conflated with a diagnostic evaluation. Thus, lack of follow-up after a positive screen is a feature, not only a bug when using a screen in a clinical setting. Engaging families in shared decision-making around screening may help encourage follow-up, and thus, screening tool psychometric performance.

PMID:38009449 | DOI:10.1111/jcpp.13919

J Alzheimers Dis. 2023;96(3):1317-1327. doi: 10.3233/JAD-230726.

ABSTRACT

BACKGROUND: Single photon emission tomography (SPECT) can detect early changes in brain perfusion to support the diagnosis of dementia. Inflammation is a driver for dementia progression and measures of inflammation may further support dementia diagnosis.

OBJECTIVE: In this study, we assessed whether combining imaging with markers of inflammation improves prediction of the likelihood of Alzheimer’s disease (AD).

METHODS: We analyzed 91 participants datasets (Institutional Ethics Approval 20/NW/0222). AD biomarkers and markers of inflammation were measured in cerebrospinal fluid. Statistical parametric mapping was used to quantify brain perfusion differences in perfusion SPECT images. Logistic regression models were trained to evaluate the ability of imaging and inflammation markers, both individually and combined, to predict AD.

RESULTS: Regional perfusion reduction in the precuneus and medial temporal regions predicted Aβ42 status. Increase in inflammation markers predicted tau and neurodegeneration. Matrix metalloproteneinase-10, a marker of blood-brain barrier regulation, was associated with perfusion reduction in the right temporal lobe. Adenosine deaminase, an enzyme involved in sleep homeostasis and inflammation, was the strongest predictor of neurodegeneration with an odds ratio of 10.3. The area under the receiver operator characteristic curve for the logistic regression model was 0.76 for imaging and 0.76 for inflammation. Combining inflammation and imaging markers yielded an area under the curve of 0.85.

CONCLUSIONS: Study results showed that markers of brain perfusion imaging and markers of inflammation provide complementary information in AD evaluation. Inflammation markers better predict tau status while perfusion imaging measures represent amyloid status. Combining imaging and inflammation improves AD prediction.

PMID:38009439 | DOI:10.3233/JAD-230726

Ann Clin Transl Neurol. 2023 Nov 27. doi: 10.1002/acn3.51947. Online ahead of print.

ABSTRACT

OBJECTIVE: Late-onset GM2 gangliosidosis (LOGG) subtypes late-onset Tay-Sachs (LOTS) and Sandhoff disease (LOSD) are ultra-rare neurodegenerative lysosomal storage disorders presenting with weakness, ataxia, and neuropsychiatric symptoms. Previous studies considered LOTS and LOSD clinically indistinguishable; recent studies have challenged this. We performed a scoping review to ascertain whether imaging and clinical features may differentiate these diseases.

METHODS: We examined MEDLINE/non-MEDLINE databases up to May 2022. Articles reporting brain imaging findings in genetically/enzymatically confirmed LOGG, symptom onset at age ≥ 10 years (or evaluated at least once ≥18 years) were included, yielding 170 LOGG patients (LOTS = 127, LOSD = 43) across 68 papers. We compared LOTS versus LOSD and performed regression analyses. Results were corrected for multiple comparisons.

RESULTS: Age of onset was lower in LOTS versus LOSD (17.9 ± 8.2 vs. 23.9 ± 14.4 years, p = 0.017), although disease duration was similar (p = 0.34). LOTS more commonly had psychosis/bipolar symptoms (35.0% vs. 9.30%, p = 0.011) but less frequent swallowing problems (4.10% vs. 18.60%, p = 0.041). Cerebellar atrophy was more common in LOTS (89.0%) versus LOSD (60.5%), p < 0.0001, with more severe atrophy in LOTS (p = 0.0005). Brainstem atrophy was documented only in LOTS (14.2%). Independent predictors of LOTS versus LOSD (odds ratio [95% confidence interval]) included the presence of psychosis/bipolar symptoms (4.95 [1.59-19.52], p = 0.011), no swallowing symptoms (0.16 [0.036-0.64], p = 0.011), and cerebellar atrophy (5.81 [2.10-17.08], p = 0.0009). Lower age of onset (0.96 [0.93-1.00], p = 0.075) and tremor (2.50 [0.94-7.43], p = 0.078) were marginally statistically significant but felt relevant to include in the model.

INTERPRETATION: These data suggest significant differences in symptomatology, disease course, and imaging findings between LOTS and LOSD.

PMID:38009419 | DOI:10.1002/acn3.51947

J Magn Reson Imaging. 2023 Nov 27. doi: 10.1002/jmri.29156. Online ahead of print.

ABSTRACT

BACKGROUND: Blood flow signals may be a confounder in quantifying T₁ values of plaque or thrombus and how to realize black-blood T₁ mapping remains a challenge task.

PURPOSE: To develop a fast and three-dimensional black-blood T₁ mapping technique for quantitative assessment of atherosclerosis and venous thrombosis.

STUDY TYPE: Sequence development and optimization via phantoms and volunteers as well as pilot prospective.

PHANTOM AND SUBJECTS: Numerical simulations, a standard phantom, 8 healthy volunteers (mean age, 22 ± 1 years; 5 males), and 19 patients (mean age, 57 ± 14 years; 13 males) with atherosclerosis or venous thrombosis.

FIELD STRENGTH/SEQUENCE: 3T/inversion recovery spin-echo sequence (IR-SE), magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE), and black-blood prepared MP2RAGE (BB-MP2RAGE).

ASSESSMENT: The black-blood preparation (i.e., delay alternating with nutation for tailored excitation, DANTE) was incorporated into MP2RAGE for black-blood T₁ mapping. The BB-MP2RAGE was optimized numerically based on the Bloch equation, and then the phantom study was performed to verify the accuracy of T₁ mapping by BB-MP2RAGE against IR-SE and MP2RAGE. Preliminary clinical validation was prospectively performed to assess the flow suppression effect and its potential application in plaque and thrombosis identification.

STATISTICAL TESTS: Pearson correlation test, Bland-Altman analysis, paired t-test, and intraclass correlation coefficient. A P value <0.05 indicates a statistically significant difference.

RESULTS: Phantom experiments showed comparable accuracy of T₁ maps by BB-MP2RAGE with IR-SE and MP2RAGE (all r² > 0.99); Compared to MP2RAGE, BB-MP2RAGE effectively nulled the blood flow signals, and had a significant improvement in contrast-to-noise ratio between static tissue and blood (250.5 ± 66.6 vs. 91.9 ± 35.9). BB-MP2RAGE can quantify plaque or thrombus T₁ relaxation time with blood flow signal suppression.

DATA CONCLUSION: Accurate T₁ mapping with sufficient blood flow suppression was achieved by BB-MP2RAGE. BB-MP2RAGE has the potential to quantitatively characterize atherosclerosis and venous thrombosis.

LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 1.

PMID:38009385 | DOI:10.1002/jmri.29156

Zoonoses Public Health. 2023 Nov 27. doi: 10.1111/zph.13098. Online ahead of print.

ABSTRACT

AIMS: A cross-sectional study was carried out to assess the seroprevalence and risk factors associated with Trichinella spp. exposure in wild boar and Iberian domestic pigs from Mediterranean ecosystems of southwestern Spain.

METHODS AND RESULTS: Serum samples from 1360 wild boar and 439 Iberian domestic pigs were obtained during 2015-2020, from regions where Iberian pigs are raised under extensive conditions, hence sharing habitat with wild boar. Seropositivity was found in 7.4% (100/1360; 95% CI: 6.1-8.9) of the wild boar analysed. In this species, the individual seroprevalence ranged from 3.6% (8/223) (hunting season 2016-2017) to 11.4% (37/326) (2018-2019). A significant higher seropositivity was observed during the hunting season 2018-2019 (p < 0.009: OR = 3.07; 95% CI = 1.32-7.18) and one statistically significant cluster was detected within the studied area, in south central Andalusia [Relative Risk (RR) = 2.9; p = 0.037]. Females showed a significantly higher seroprevalence than males (8.7% vs. 5.8%) (p < 0.001: OR = 1.58; 95% CI = 1.08-2.32). No seropositivity to Trichinella spp. was detected in Iberian domestic pigs (0.0%; 95% CI: 0.0-0.9).

CONCLUSIONS: Although wild boar play an important role as a reservoir of Trichinella sp. in the Mediterranean ecosystems of southwestern Spain, our results suggest that the wild boar production system does not seem to pose a risk of Trichinella exposure to domestic pigs, despite sharing habitats in these ecosystems.

PMID:38009293 | DOI:10.1111/zph.13098

Int J Biostat. 2023 Nov 27. doi: 10.1515/ijb-2022-0014. Online ahead of print.

ABSTRACT

Incomplete data is a prevalent complication in longitudinal studies due to individuals’ drop-out before intended completion time. Currently available methods via commercial software for analyzing incomplete longitudinal data at best rely on the ignorability of the drop-outs. If the underlying missing mechanism was non-ignorable, potential bias arises in the statistical inferences. To remove the bias when the drop-out is non-ignorable, joint complete-data and drop-out models have been proposed which involve computational difficulties and untestable assumptions. Since the critical ignorability assumption is unverifiable based on the observed part of the sample, some local sensitivity indices have been proposed in the literature. Specifically, Eftekhari Mahabadi (Second-order local sensitivity to non-ignorability in Bayesian inferences. Stat Med 2018;59:55-95) proposed a second-order local sensitivity tool for Bayesian analysis of cross-sectional studies and show its better performance for handling bias compared with the first-order ones. In this paper, we aim to extend this index for the Bayesian sensitivity analysis of normal longitudinal studies with drop-outs. The index is driven based on a selection model for the drop-out mechanism and a Bayesian linear mixed-effect complete-data model. The presented formulas are calculated using the posterior estimation and draws from the simpler ignorable model. The method is illustrated via some simulation studies and sensitivity analysis of a real antidepressant clinical trial data. Overall, the numerical analysis showed that when repeated outcomes are subject to missingness, regression coefficient estimates are nearly approximated well by a linear function in the neighbourhood of MAR model, but there are a considerable amount of second-order sensitivity for the error term and random effect variances in Bayesian linear mixed-effect model framework.

PMID:38009236 | DOI:10.1515/ijb-2022-0014

Demography. 2023 Nov 27:11075209. doi: 10.1215/00703370-11075209. Online ahead of print.

ABSTRACT

In this study, we provide an assessment of data accuracy from the 2020 Census. We compare block-level population totals from a sample of 173 census blocks in California across three sources: (1) the 2020 Census, which has been infused with error to protect respondent confidentiality; (2) the California Neighborhoods Count, the first independent enumeration survey of census blocks; and (3) projections based on the 2010 Census and subsequent American Community Surveys. We find that, on average, total population counts provided by the U.S. Census Bureau at the block level for the 2020 Census are not biased in any consistent direction. However, subpopulation totals defined by age, race, and ethnicity are highly variable. Additionally, we find that inconsistencies across the three sources are amplified in large blocks defined in terms of land area or by total housing units, blocks in suburban areas, and blocks that lack broadband access.

PMID:38009227 | DOI:10.1215/00703370-11075209

Demography. 2023 Nov 27:11077660. doi: 10.1215/00703370-11077660. Online ahead of print.

ABSTRACT

This article documents child suicide rates from 1980 to 2020 in the United States using the National Vital Statistics System Multiple Cause of Death database. After generally declining for decades, suicide rates among children aged 10-17 accelerated from 2011 to 2018 in an unprecedented rise in both duration and magnitude. I consider the role of the illicit opioid crisis in driving this mental health crisis. In August 2010, an abuse-deterrent version of OxyContin was introduced and the original formulation was removed from the market, leading to a shift to illicit opioids and stimulating growth in illicit opioid markets. Areas more exposed to reformulation-as measured by pre-reformulation rates of OxyContin misuse in the National Survey on Drug Use and Health-were more affected by the transition to illicit opioids and experienced sharper growth in child suicide rates. The evidence suggests that children’s illicit opioid use did not increase, implying that the illicit opioid crisis engendered higher suicide propensities by increasing suicidal risk factors for children, such as increasing rates of child neglect and altering household living arrangements. In complementary analyses, I document how living conditions declined for children during this time period.

PMID:38009202 | DOI:10.1215/00703370-11077660