Sensitive Measure Research Articles

Lung Infection Detection via CT Images and Transfer Learning Techniques in Deep Learning

Healthcare systems are battling the global coronavirus epidemic with limited resources, requiring early diagnosis and enhanced tools for pandemic prevention. The computer can aid in diagnosis via computed tomography images like PCR. Deep learning and machine learning are popular methods, and their main contributions are COVID-19 detection and prediction. This work aimed to develop an AI-based early detection strategy for COVID-19 based on computed tomography images. The model was trained and tested using a dataset that includes CT images. The SARS-COV-2 dataset contains 2482 CT images of 210 patients from publicly available sources. The modified model demonstrated encouraging outcomes by greatly enhancing the sensitivity measure (95.82±1.75), which is an essential criterion for accurately detecting instances of COVID-19 infection. In addition, the model generated higher values for the accuracy metric (91.67±1.68), the specificity (88.08±3.72), the precision metric (87.51±3.27), the F1_score (91.43±1.55), and the area under the curve (91.98±1.55). Deep learning techniques significantly facilitate the early detection of COVID-19. Its use has the potential to improve clinical doctors' readiness and the management of pandemics.

Intrasubject variability of sustained attention is associated with elevated self-reported attention deficits in women with a fragile X premutation allele.

Women with a fragile X premutation (PM) self-report higher rates of attention difficulties than women without a PM; however, results of studies using objective measures of attention are inconsistent. The present study assessed whether intrasubject variability during a sustained attention task better predicted functional outcomes in women with a PM than the previously published standard reaction time and accuracy variables. We analyzed continuous performance test, a computerized measure of sustained attention, and the Conners' Adult Attention-Deficit/Hyperactivity Disorder Rating Scale Report (CAARS) data from 273 women with a PM and 175 women without a PM aged 18-50 years. Separate analyses using Pearson correlations and independent t tests were performed on the full range of coefficient of variation (CV) of reaction time scores and the subset of scores that showed higher variability. Performance variability of sustained attention measured by the continuous performance test was associated with functional outcomes measured by the CAARS in women with a PM but not women without a PM. Specifically, the CV in those with higher variability was correlated with two CAARS subscale scores (p = .006). Independent t tests showed significant differences in CV between CAARS scores dichotomized for the presence of subclinical symptoms for two subscales (p ≤ .001-.007). Correlation between the full range of CV scores and the CAARS Inattention/Memory Problems subscale approached significance (p = .012). Findings highlight the importance of including intrasubject variability in analyzing attention in clinical populations as a more sensitive objective measure associated with reported symptoms and to assist in predicting functional outcomes. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A Multimodal Approach to Measuring Listening Effort: A Systematic Review on the Effects of Auditory Task Demand on Physiological Measures and Their Relationship.

Listening effort involves the mental effort required to perceive an auditory stimulus, for example in noisy environments. Prolonged increased listening effort, for example due to impaired hearing ability, may increase risk of health complications. It is therefore important to identify valid and sensitive measures of listening effort. Physiological measures have been shown to be sensitive to auditory task demand manipulations and are considered to reflect changes in listening effort. Such measures include pupil dilation, alpha power, skin conductance level, and heart rate variability. The aim of the current systematic review was to provide an overview of studies to listening effort that used multiple physiological measures. The two main questions were: (1) what is the effect of changes in auditory task demand on simultaneously acquired physiological measures from various modalities? and (2) what is the relationship between the responses in these physiological measures? Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, relevant articles were sought in PubMed, PsycInfo, and Web of Science and by examining the references of included articles. Search iterations with different combinations of psychophysiological measures were performed in conjunction with listening effort-related search terms. Quality was assessed using the Appraisal Tool for Cross-Sectional Studies. A total of 297 articles were identified from three databases, of which 27 were included. One additional article was identified from reference lists. Of the total 28 included articles, 16 included an analysis regarding the relationship between the physiological measures. The overall quality of the included studies was reasonable. The included studies showed that most of the physiological measures either show no effect to auditory task demand manipulations or a consistent effect in the expected direction. For example, pupil dilation increased, pre-ejection period decreased, and skin conductance level increased with increasing auditory task demand. Most of the relationships between the responses of these physiological measures were nonsignificant or weak. The physiological measures varied in their sensitivity to auditory task demand manipulations. One of the identified knowledge gaps was that the included studies mostly used tasks with high-performance levels, resulting in an underrepresentation of the physiological changes at lower performance levels. This makes it difficult to capture how the physiological responses behave across the full psychometric curve. Our results support the Framework for Understanding Effortful Listening and the need for a multimodal approach to listening effort. We furthermore discuss focus points for future studies.

PREX proteins level correlation with insulin resistance markers and lipid profile in obese and overweight non-diabetic patients

Metabolic dysregulation and obesity are associated with many metabolic alterations, including impairment of insulin sensitivity and dyslipidemia. Recent studies highlight the key role of phosphatidylinositol 3,4,5-triphosphate-dependent Rac exchange proteins (PREX proteins) in the pathogenesis of obesity, advocating further elucidation of their potential therapeutic implications. The present study aimed to estimate the serum level of PREX proteins and its potential association with insulin resistance markers and plasma lipids level in obese and overweight non-diabetic patients. The study included 30 persons classified as obese, 30 as overweight, and 30 healthy individuals of similar age and gender. The levels of PREX1 and PREX2 were measured using ELISA kits, insulin, fasting glucose, glycosylated hemoglobin and total lipid profile were determined using appropriate photometric kits. HOMA-IR was used as a measure of insulin sensitivity. According to the obtained results, obese non-diabetic patients had higher serum PREX1 level compared to both overweight and normal-weight individuals. PREX1 correlated positively with the markers of insulin resistance and dyslipidemia. PREX2 level was shown to be lower both in obese compared to overweight patients and in overweight compared to normal-weight individuals. PREX2 correlated negatively with the markers of insulin resistance but not with the markers of dyslipidemia. Keywords: dyslipidemia, insulin resistance, obesity, overweight, PREX proteins

Measuring Pain-related Behavioral Inhibition and Behavioral Activation System Responses: Further Validity Evidence for the Pain Responses Scale.

The Pain Responses Scale and its Short Form (PRS-SF) were recently developed to assess the affective, behavioral, and cognitive responses to pain based on the behavioral inhibition system (BIS) and behavioral activation system (BAS) model of chronic pain. The purpose of this study was to provide additional tests of the psychometric properties of the PRS-SF in a new sample of individuals with chronic pain. A sample of Spanish adults (N = 190) with chronic non-cancer pain completed a translated version of the PRS-SF and a battery of questionnaires measuring validity criteria hypothesized the be associated with BIS and BAS activation, including measures of sensitivity to punishment, sensitivity to reward, pain intensity, pain interference, catastrophizing, and pain acceptance. Confirmatory factor analysis supported a 4-factor structure for the PRS-SF assessing despondent, escape, approach, and relaxation responses (S-B χ 2 [5] = 1.49, Comparative Fit Index = 0.99, Non-Normed Fit Index = 0.99, root-mean-square error of approximation = 0.051, Akaike Information Criterion = 4113.66), with marginal internal consistency for 1 scale (relaxation) and adequate to good internal consistency for the others. The pattern of associations found between the PRS-SF Scale scores and the validity criterion supports the validity of the instrument. The results provide additional support for the validity of the 4 PRS-SF Scale scores, and the reliability of 3 of the scales. If these findings are replicated in future research, investigators may wish to administer more items from the original Relaxation Scale when assessing this domain to ensure adequate reliability for this scale. The other items from the PRS-SF assessing despondent, escape, and approach responses appear to provide at least adequate reliability. When used in this way, the PRS-SF may be used to measure BIS and BAS responses to pain to: (1) provide further tests of the BIS-BAS model of chronic pain and/or (2) understand the potential mediating effects of BIS and BAS responses on the effects of psychological pain treatments to help determine which specific responses are most responsible for the benefits of treatment, and, therefore, which responses should be specifically targeted to enhance treatment response.

Firm sensitivity to changes in policy uncertainty and its impact on audit pricing

PurposeThis paper aims to examine how a firm’s exposure to economic policy uncertainty affects the auditors’ perceptions of financial reporting risk. Firms that are more sensitive to policy uncertainty are predicted to engage in more earnings management because these firms are more likely to experience greater uncertainty in future operations. Audit fees will reflect this reporting risk. On the other hand, auditors might feel more fee pressure from policy-sensitive firms because firms are more inclined to reduce spending in the face of uncertainty and subsequently charge lower fees.Design/methodology/approachThe author tests my hypothesis using U.S. data on audit fees and client characteristics of public companies between the years 2001 and 2021. The author estimates a standard audit fee model based on the audit fee literature (Hay et al., 2006) while also including the two policy sensitivity measures. This study uses panel data methods that allow time-series analyses, providing a powerful setting to test dynamic audit fee adjustment to improve the understanding of the audit market.FindingsThe results suggest that audit fee is higher for policy-sensitive firms than for policy-neutral firms. These results are robust to various proxies of policy sensitivity and various specifications designed to mitigate the endogeneity concerns. The study provides assurance that on average, auditor pricing reflects client risk adequately, mitigating the concern that auditors give in to fee pressure and compromise audit quality as a result.Research limitations/implicationsWhile the findings from this study should be of value to regulators and academics seeking to understand audit activities amid escalating macroeconomic uncertainty, when interpreting these results, several limitations must be considered. The study does not examine how external auditors evaluate risks tied to policy uncertainty. A comprehensive understanding of how and why external auditors respond to heightened policy uncertainty faced by firms could be better achieved through interviews with external auditors and audit committee members. In addition, while this study posits that auditors adjust their approach in response to changes in policy uncertainty, largely due to potential shifts in the risks of material misstatement, there might be additional factors at play that warrant higher audit fees post a change in policy uncertainty. For instance, specific policy changes may give rise to new risks or modify existing ones, thereby precipitating increased scrutiny of records and procedures as company directors’ demand. These aspects offer potential avenues for future research.Practical implicationsThis study underscores the significant role of policy sensitivity in determining audit fees and audit quality. Policy-sensitive firms present unique complexities and potential risks that require additional effort and vigilance from auditors. Auditors must develop a specialized understanding of sectors prone to policy fluctuations to navigate these unique challenges effectively. In addition, the role of professional standards boards and regulators in establishing guidelines for auditing policy-sensitive firms cannot be understated. Such guidelines could lead to more consistent audit practices and improved audit quality. Finally, by recognizing and effectively responding to the policy sensitivity of client firms, audit firms can mitigate their own risks, strengthen public trust and enhance the reliability of financial reports.Originality/valueFirst, this study adds to an emerging stream of auditing literature that focuses on how audit fees interact with a firm’s external environment by providing evidence of an unexplored implication, a firm-specific policy sensitivity. Second, my main construct, policy sensitivity, provides two distinct advantages over other variables used in prior studies that explore the relationship between audit fees and external firm environments. Third, this study answers the calls for research by De Villiers et al. (2013, p. 3), who identified the cost behavior of audit fees, especially over time, as an area not well understood.

Should We Stop Using Lexical Diversity Measures in Children's Language Sample Analysis?

Prior work has identified weaknesses in commonly used indices of lexical diversity in spoken language samples, such as type-token ratio (TTR) due to sample size and elicitation variation, we explored whether TTR and other diversity measures, such as number of different words/100 (NDW), vocabulary diversity (VocD), and the moving average TTR would be more sensitive to child age and clinical status (typically developing [TD] or developmental language disorder [DLD]) if samples were obtained from standardized prompts. We utilized archival data from the norming samples of the Test of Narrative Language and the Edmonton Narrative Norms Instrument. We examined lexical diversity and other linguistic properties of the samples, from a total of 1,048 children, ages 4-11 years; 798 of these were considered TD, whereas 250 were categorized as having a language learning disorder. TTR was the least sensitive to child age or diagnostic group, with good potential to misidentify children with DLD as TD and TD children as having DLD. Growth slopes of NDW were shallow and not very sensitive to diagnostic grouping. The strongest performing measure was VocD. Mean length of utterance, TNW, and verbs/utterance did show both good growth trajectories and ability to distinguish between clinical and typical samples. This study, the largest and best controlled to date, re-affirms that TTR should not be used in clinical decision making with children. A second popular measure, NDW, is not measurably stronger in terms of its psychometric properties. Because the most sensitive measure of lexical diversity, VocD, is unlikely to gain popularity because of reliance on computer-assisted analysis, we suggest alternatives for the appraisal of children's expressive vocabulary skill.

A novel single-loop estimation method for predictive failure probability-based global sensitivity analysis

The existing predictive failure probability (PFP)-based global sensitivity analysis methods may get stuck in practical application due to the large amount of calculation or insufficient precision. Therefore, a novel single-loop estimation method is proposed in this paper to effectively estimate PFP-based global sensitivity measures. Firstly, by introducing standard normal auxiliary variables and equivalent probability transformation, the overall uncertainty of each input variable is decomposed into its inherent uncertainty and distribution parameter uncertainty. Then, Bayes theorem-based formulations are derived for three PFP-based global sensitivity measures, where only the unconditional PFP and the conditional marginal and joint distributions of standard normal auxiliary variables and distribution parameters need to be known. These items can be evaluated simultaneously using only one set of functional calls. Finally, adaptive Kriging model is embedded into the proposed method in place of the time-demanding performance function to further enhance computational efficiency. The effectiveness of the proposed methods is validated through four benchmark reliability problems asnd one engineering problem.

Advancing diagnostic precision: Unveiling sensitization relationships between cat, dog, and horse allergen molecules.

Recent advancements in molecular diagnostics have unveiled a multitude of allergen molecules (AMs) associated with animal sensitizations, revealing significant cross- and co-sensitization patterns among these seemingly distinct allergens. We investigated the sensitization profiles of 120 children, sensitized to at least one of the 14 AMs from cat, dog, or horse using the Alex test, employing correlations and hierarchical clusters to explore relationship between sensitizations. Sensitizations to Fel d 1, Can f 4/5, and Equ c 4 differ from other cat, dog, and horse AM sensitizations, suggesting they may represent genuine sensitizations for their respective animals. High correlations were observed among various AMs, including lipocalins (Can f 1/2/6, Fel d 4/7, and Equ c 1), serum albumins (Fel d 2, Can f 3, and Equ c 3), and uteroglobins (Fel d 1 and Can f_Fd1). Hierarchical clustering of sensitizations identified two similarity clusters and one dissimilarity cluster, providing an estimation of the likelihood of cross-reactivity. Additionally, our method facilitated speculation regarding cross-, co-, or genuine sensitization. Moreover, we noted a potential increase in the number and level of sensitized animal AMs concurrent with increased sensitization to other aeroallergens with advancing age. No significant difference was detected for the presence or absence of various types of allergic comorbidities. Correlations and hierarchical clustering can unveil the extent and magnitude of cross-, co-, and genuine sensitization relationships among animal AMs. These insights can be leveraged to enhance artificial intelligence algorithms, improving diagnostic accuracy through the integration of other measures of sensitization.

Sensitivity and feature importance of climate factors and evaluation of different machine learning models for predicting fire hotspots in Kalimantan, Indonesia

<p>Hotspots as indicators of forest fires capable of quickly monitoring large areas are often predicted using various machine learning methods. However, there is still little research that analyzes the sensitivity and feature importance of each predictor that forms a machine learning prediction model. This study evaluates and compares several machine learning methods to predict hotspots in Kalimantan. Using the most accurate machine learning model, each climate factor used as a predictor is analyzed for its sensitivity and feature importance. Some of the machine learning methods used include random forest, gradient boosting, Bayesian regression, and artificial neural networks. Meanwhile, several measures of sensitivity and feature importance used are variance-based, density-based, and distribution-based sensitivity indices, as well as permutation and Shapley feature importance. Evaluation of the ML model concluded that the Bayesian linear regression model outperformed other ML models, based on RMSE and explained variance score. Meanwhile, tree-based models, such as random forest and gradient boosting, are indicative of overfit. Based on the results of sensitivity analysis and feature importance, the number of dry days is the most important feature for the Bayesian linear regression model in predicting the number of hotspots in Kalimantan.</p>

Comparison between two phonological awareness tasks in Arabic

Phonological Awareness (PA) in deep orthographies like English can be used to predict reading abilities in later grades. PA is a sensitive measure in identifying Arabic speaking children who struggle to read. The main objective of the present study was to compare two matched versions of a sound blending task to determine whether any of the two created versions (i.e., one with short vowels and the other without short vowels) of this task could yield a more accurate measure of PA in children speaking Jordanian Arabic. Data was collected from 20 randomly selected schools in Jordan. A randomly selected sample of 200 participants in grade 4 who were randomly divided into two groups (100 students each) and were tested on word blending, elision, word recognition, and oral reading fluency. Each group of participants was tested using a different version of the word blending task, while the other tasks used to test the two groups were the same. The major finding of the study revealed a significant difference between the two versions of the blending task in favor of the assumption that using a task without short vowels can produce inflated scores (i.e., the blending task without short vowels yielded higher scores); such scores demonstrated weak correlation with the reading measures. The implications of the current study support the presentation of blending tasks with all the constituent segments of the word including short vowels (i.e., presenting the word with consonants, long vowels, and short vowels), which can better contribute to gauging children's PA abilities.

An Intervention Study of Middle School Students’ Self-Regulation Skills Based on Reinforcement Sensitivity Theory

Individuals in adolescence are prone to behavioral problems, which may be attributed to the weak conflict detection and regulation ability of adolescents, therefore, this study, based on the theoretical foundation of reinforcement sensitivity, focuses on exploring whether it is possible to enhance adolescents’ behavioral inhibitory ability, so as to improve the level of self-regulation and self-efficacy of individuals. The study used the experimental method, and 75 junior high school students (40 in the experimental group and 35 in the control group) were finally selected as the research subjects, and the intervention training was provided to the students. The results of the experiment found that: On the measure of reinforcement sensitivity, the reward sensitivity of the experimental group was significantly higher than that of the pre-intervention; the punishment sensitivity was significantly lower than that of the pre-intervention; on the measure of general self-efficacy, the general self-efficacy of the students in the experimental group was significantly higher than that of the pre-intervention; there was no significant difference between the pre- and post-intervention levels of conflict regulation and self-regulation, but both of them had an upward trend. Conclusion: The intervention increased students’ general self-efficacy and confidence; it increased students’ reward sensitivity and decreased students’ punishment sensitivity, but it did not increase students’ conflict regulation; there was an upward trend in students’ self-regulation.

Health-Related Quality of Life Burden Among Adults with Vitiligo: Relationship to Disease Severity and Disease Location.

Vitiligo was historically regarded as a cosmetic disorder; however, it is an autoimmune disease. As a visible condition, it affects patient well-being. We assessed the impact of disease severity, lesion location, and body surface area (BSA) affected on patient health-related quality of life (HRQoL). Retrospective data were from the Adelphi Real World Vitiligo Disease Specific Programme: a cross-sectional survey of physicians and their patients with vitiligo (10/2021-07/2022). Patient-reported outcomes were assessed by the Vitiligo-Specific Quality of Life Instrument (VitiQoL), Hospital Anxiety and Depression Scale (HADS), and EQ-5D-5L. The Work Productivity and Impairment Questionnaire (WPAI) questionnaire was used to assess disease-related impairment of daily activities. Data were stratified by physician-reported disease severity, presence/absence of vitiligo on the face, and BSA percentage affected. In total, 1388 patients were included. Mean (SD) VitiQoL, HADS depression, and anxiety scores were higher for those with severe disease [40.5(26.1), 5.2(4.4), and 6.8(4.7)] than those with mild [24.8(18.8), 3.6(3.8), 4.2(3.8)] or moderate [27.1(22.6), 3.8(4.5), 4.3(4.4)] disease. Patients with face affected reported higher VitiQoL [30.0(22.3) versus 23.2(19.3)], and HADS scores [depression, 4.3(4.3) versus 3.2(3.9); anxiety, 5.0(4.3) versus 3.8 (3.9)] than those without. Patients with ≥ 5% BSA affected had higher VitiQoL, depression and anxiety scores [27.9(21.8), 4.0(4.4), and 4.5(4.2)] than those with 0-5% [24.6(19.7), 3.4(3.7), and 4.3(4.1)]. Patients with severe vitiligo, facial lesions, or ≥ 5% BSA reported higher activity impairment. Mean EQ-5D-5L-utility score was approximately 0.9 regardless of disease severity or total BSA affected. These data demonstrate the impact disease severity can have on HRQoL and daily activities for patients with vitiligo. Lesions that are more severe, on the face, or covering a greater BSA are more often associated with poorer outcomes and activity impairment. These data also highlight the potential insensitivity of commonly used HRQoL measures and a need for more sensitive disease-specific measures.

Surgical treatment of brain arteriovenous malformations in patients with hereditary hemorrhagic telangiectasia: a single-center experience

OBJECTIVE Hereditary hemorrhagic telangiectasia (HHT) is an autosomal-dominant disorder characterized by multiple vascular malformations. Brain arteriovenous malformations (bAVMs) are a significant manifestation of HHT. The surgical management of these lesions in patients with HHT remains debated, with limited literature on postoperative outcomes. The goal of this study was to evaluate the safety and efficacy of surgical treatment for bAVMs in patients with HHT and propose a treatment rationale based on a single-center experience. METHODS This retrospective review included 20 patients diagnosed with HHT who underwent resection of 23 bAVMs at the Stanford University Medical Center between January 2007 and September 2023. Data were also collected on bAVMs treated conservatively, with embolization, or with radiosurgery at the authors’ institution, for comparison. RESULTS There were 16 Spetzler-Martin (SM) grade I, 6 SM grade II, and 1 SM grade IV bAVM. Six of the bAVMs presented with neurological symptoms (3 with hemorrhage and 3 with focal neurological deficits), while the rest were detected on routine screening. Complete excision was angiographically confirmed in all patients, with a mean overall hospital stay of 2.1 days and a mean follow-up of 36 months. Postoperative complications were limited to transient mild weakness in 2 patients, 1 of whom also had transient speech deficits, and visual field deficits in 3 patients, 2 of whom improved on long-term follow-up. CONCLUSIONS In this most extensive surgical series published to date, resection of bAVMs in patients with HHT showed favorable outcomes with a low complication rate, suggesting that the benefits of surgery outweigh the risks, especially considering the potential cumulative lifetime risk of hemorrhage. MR arterial spin labeling was found to be the most sensitive noninvasive measure of detecting bAVMs in patients with HHT.

A Machine Learning Approach for Early Identification of Prodromal Parkinson's Disease.

Parkinson's disease (PD) affects approximately 6 million people worldwide. Data analysis of early PD symptoms using machine learning (ML) models may provide an inexpensive, non-invasive, and simple method for the remote diagnosis of early PD. The aim of this project was to analyze voice, computer keystrokes, spiral drawings, and gait data involving PD patients and controls available in public databases using ML models and identify early PD characteristics that are more pronounced than others. An ML model was developed using Random Forest to analyze existing clinical data for PD patients, prodromal PD patients with REM (rapid eye movement) sleep behavior disorder (RBD) symptoms, and non-PD healthy controls. We reviewed and collected data from the UCI (University of California Irvine) Machine Learning Repository, PPMI (Parkinson's Progression Markers Initiative), and Kaggle databases. ML analysis was carried out on voice samples in PD and RBD patients, computer keystroke data, spiral drawings, and gait datasets. The ML prediction model developed may be helpful in improving risk prediction for PD, enabling early intervention and resource prioritization. The ML study suggests that voice analysis is the most robust test, followed by computer keystroke data, spiral drawings, and gait analysis, in that order. Voice is affected even in RBD patients, revealing that it is a sensitive and early measure of prodromal PD. The low accuracy of the analysis indicates that several PD-positive samples may remain undetected and unclassified. Combining all four features, that is, voice analysis, computer keystroke data, spiral drawings, and gait analysis, may improve the overall accuracy.

Early Cortical Microstructural Changes in Aging Are Linked to Vulnerability to Alzheimer’s Disease Pathology

BackgroundEarly identification of Alzheimer’s disease (AD) risk is critical for improving treatment success. Cortical thickness is a macrostructural measure used to assess neurodegeneration in AD. However, cortical microstructural changes appear to precede macrostructural atrophy and may improve early risk identification. Currently, whether cortical microstructural changes in aging are linked to vulnerability to AD pathophysiology remains unclear in nonclinical populations, who are precisely the target for early risk identification. MethodsIn 194 adults, we calculated magnetic resonance imaging–derived maps of changes in cortical mean diffusivity (microstructure) and cortical thickness (macrostructure) over 5 to 6 years (mean age: time 1 = 61.82 years; time 2 = 67.48 years). Episodic memory was assessed using 3 well-established tests. We obtained positron emission tomography–derived maps of AD pathology deposition (amyloid-β, tau) and neurotransmitter receptors (cholinergic, glutamatergic) implicated in AD pathophysiology. Spatial correlational analyses were used to compare pattern similarity among maps. ResultsSpatial patterns of cortical macrostructural changes resembled patterns of cortical organization sensitive to age-related processes (r = −0.31, p < .05), whereas microstructural changes resembled the patterns of tau deposition in AD (r = 0.39, p = .038). Individuals with patterns of microstructural changes that more closely resembled stereotypical tau deposition exhibited greater memory decline (β = 0.22, p = .029). Microstructural changes and AD pathology deposition were enriched in areas with greater densities of cholinergic and glutamatergic receptors (ps < .05). ConclusionsPatterns of cortical microstructural changes were more AD-like than patterns of macrostructural changes, which appeared to reflect more general aging processes. Microstructural changes may better inform early risk prediction efforts as a sensitive measure of vulnerability to pathological processes prior to overt atrophy and cognitive decline.

Psychometric evaluation of the perfectionism scale's characteristics regarding physical appearance in patients seeking rhinoplasty surgery

IntroductionThis study aimed to investigate the psychometric characteristics of the perfectionism scale regarding physical appearance in patients seeking rhinoplasty. MethodsThis cross-sectional study involved 250 individuals seeking rhinoplasty in Kermanshah beauty clinics. Participants were purposively selected. Tools included perfectionism, social comparison, and body image acceptance scales. Divergence and convergence were assessed using various scales. Data analysis was performed using SPSS version 25 and LISREL. ResultsThe obtained value for the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.83, indicating that the sample adequacy was desirable, and the data in this study had the potential for factor analysis. The significance level for Bartlett's test of sphericity was also less than 0.0001, demonstrating the appropriateness of factor analysis for identifying the structure (factor model). Furthermore, the Cronbach's alpha-coefficient for the entire scale was 0.853, indicating acceptable questionnaire reliability. Internal consistency among the perfectionism subscales was confirmed. Additionally, the correlation between perfectionism subscales and measures of interindividual sensitivity and body appearance acceptance was confirmed (p < 0.001). Results supported the convergent validity of perfectionism with appearance acceptance and the divergent validity of perfectionism with interindividual sensitivity. ConclusionThis questionnaire, a self-report tool for measuring perfectionism traits in individuals seeking rhinoplasty, is recommended for use alongside interviews and observations for a thorough assessment. However, results may be influenced by individual biases. Despite this, the acceptable validity and reliability of this questionnaire make it suitable for research.

Reliability, Sensitivity, and Specificity Measures of the Comprehensive Assessment of Accentedness and Intelligibility (CAAI) Test Battery

Reliability, Sensitivity, and Specificity Measures of the Comprehensive Assessment of Accentedness and Intelligibility (CAAI) Test Battery

UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings

Measures of physical growth, such as weight and height have long been the predominant outcomes for monitoring child health and evaluating interventional outcomes in public health studies, including those that may impact neurodevelopment. While physical growth generally reflects overall health and nutritional status, it lacks sensitivity and specificity to brain growth and developing cognitive skills and abilities. Psychometric tools, e.g., the Bayley Scales of Infant and Toddler Development, may afford more direct assessment of cognitive development but they require language translation, cultural adaptation, and population norming. Further, they are not always reliable predictors of future outcomes when assessed within the first 12–18 months of a child’s life. Neuroimaging may provide more objective, sensitive, and predictive measures of neurodevelopment but tools such as magnetic resonance (MR) imaging are not readily available in many low and middle-income countries (LMICs). MRI systems that operate at lower magnetic fields (< 100mT) may offer increased accessibility, but their use for global health studies remains nascent. The UNITY project is envisaged as a global partnership to advance neuroimaging in global health studies. Here we describe the UNITY project, its goals, methods, operating procedures, and expected outcomes in characterizing neurodevelopment in sub-Saharan Africa and South Asia.

Free-Breathing Functional Pulmonary Proton MRI: A Novel Approach Using Voxel-Wise Lung Ventilation (VOLVE) Assessment in Healthy Volunteers and Patients With Chronic Obstructive Pulmonary Disease.

In respiratory medicine, there is a need for sensitive measures of regional lung function that can be performed using standard imaging technology, without the need for inhaled or intravenous contrast agents. To describe VOxel-wise Lung VEntilation (VOLVE), a new method for quantifying regional lung ventilation (V) and perfusion (Q) using free-breathing proton MRI, and to evaluate VOLVE in healthy never-smokers, healthy people with smoking history, and people with chronic obstructive pulmonary disease (COPD). Prospective pilot. Twelve healthy never-smoker participants (age 30.3 ± 12.5 years, five male), four healthy participants with smoking history (>10 pack-years) (age 42.5 ± 18.3 years, one male), and 12 participants with COPD (age 62.8 ± 11.1 years, seven male). Single-slice free-breathing two-dimensional fast field echo sequence at 3 T. A novel postprocessing was developed to evaluate the MR signal changes in the lung parenchyma using a linear regression-based approach, which makes use of all the data in the time series for maximum sensitivity. V/Q-weighted maps were produced by computing the cross-correlation, lag and gradient between the respiratory/cardiac phase time course and lung parenchyma signal time courses. A comparison of histogram median and skewness values and spirometry was performed. Kruskal-Wallis tests with Dunn's multiple comparison tests to compare VOLVE metrics between groups; Spearman correlation to assess the correlation between MRI and spirometry-derived parameters; and Bland-Altman analysis and coefficient of variation to evaluate repeatability were used. A P-value <0.05 was considered significant. Significant differences between the groups were found for ventilation between healthy never-smoker and COPD groups (median XCCV, LagV, and GradV) and perfusion (median XCCQ, LagQ, and GradQ). Minimal bias and no significant differences between intravisit scans were found (P range = 0.12-0.97). This preliminary study showed that VOLVE has potential to provide metrics of function quantification. 2 TECHNICAL EFFICACY: Stage 1.