Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of L‐4‐Thiazolylalanine (Protinol™) on skin barrier strength and skin protection

Skin barrier properties are critical for maintaining epidermal water content, protecting from environmental factors and providing the first line of defense against pathogens. In this study, we investigated the non‐proteinogenic amino acid L‐4‐Thiazolylalanine (L4) as a potential active ingredient in skin protection and barrier strength.
Disentangling the Link Between Mindfulness and Dissociation in PTSD: The Mediating Role of Attention and Emotional Acceptance

ABSTRACT Introduction A number of studies have investigated the relationship between mindfulness and dissociation and suggested that mindfulness-based interventions could be effective in the treatment of dissociative symptoms. A recent study in healthy volunteers found that attention and emotional acceptance mediates this relationship. However, no study has yet been performed among a clinical sample to assess this association. Method We recruited 90 patients (76 women) suffering from Posttraumatic Stress Disorder (PTSD). They completed self-report questionnaires to measure PTSD, dissociation, emotion regulation difficulties, childhood trauma, mindfulness abilities and cognitive abilities. Results We found that mindfulness abilities, emotional difficulties, dissociation and attention-concentration were all related to each other. Using a step-by-step approach and bootstrapping techniques, we found a significant indirect effect of mindfulness abilities on dissociation through non-acceptance (confidence interval 95%=-.14 to −.01) and attentional difficulties (confidence interval 95%=-.23 to −.05). Conclusion Patients with higher levels of dissociative symptoms have less capacity for mindfulness. Our results support Bishop et al.’s model proposing that attention and emotional acceptance are the two active components of mindfulness. To extend our findings, clinical trials are required to evaluate a causal relationship and the effectiveness of mindfulness-based interventions for patients suffering from dissociation.
Association of retinal nerve layers thickness and brain imaging in healthy young subjects from the i‐Share‐Bordeaux study

Abstract Given the anatomical and functional similarities between the retina and the brain, the retina could be a “window” for viewing brain structures. We investigated the association between retinal nerve fiber layers (peripapillary retinal nerve fiber layer, ppRNFL; macular ganglion cell‐inner plexiform layer, GC‐IPL; and macular ganglion cell complex, GCC), and brain magnetic resonance imaging (MRI) parameters in young health adults. We included 857 students (mean age: 23.3 years, 71.3% women) from the i‐Share study. We used multivariate linear models to study the cross‐sectional association of each retinal nerve layer thickness assessed by spectral‐domain optical coherence tomography (SD‐OCT) with structural (volumes and cortical thickness), and microstructural brain markers, assessed on MRI globally and regionally. Microstructural MRI parameters included diffusion tensor imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). On global brain analysis, thicker ppRNFL, GC‐IPL and GCC were all significantly associated with patterns of diffusion metrics consistent with higher WM microstructural integrity. In regional analyses, after multiple testing corrections, our results suggested significant associations of some retinal nerve layers with brain regional gray matter occipital volumes and with diffusion MRI parameters in a region involved in the visual pathway and in regions containing associative tracts. No associations were found with global volumes or with global or regional cortical thicknesses. Results of this study suggest that some retinal nerve layers may reflect brain structures. Further studies are needed to confirm these results in young subjects.
Novel therapeutic approaches in the management of chronic kidney disease: a narrative review

ABSTRACT Chronic kidney disease (CKD) remains a pathologic entity with constantly rising incidence and high rates of morbidity and mortality, which are associated with serious cardiovascular complications. Moreover, the incidence of end-stage renal disease tends to increase. The epidemiological trends of CKD warrant the development of novel therapeutic approaches aiming to prevent its development or retard its progression through the control of major risk factors: type 2 diabetes mellitus, arterial hypertension, and dyslipidemia. Contemporary therapeutics such as sodium-glucose cotransporter-2 inhibitors and second-generation mineralocorticoid receptor antagonists are utilized in this direction. Additionally, experimental and clinical studies present novel drug categories that could be employed in managing CKD, such as aldosterone synthesis inhibitors or activators guanylate cyclase, while the role of melatonin should be further tested in the clinical setting. Finally, in this patient population, the use of hypolipidemic agents may provide incremental benefits.
Analysis of Automated Clinical Depression Diagnosis in a Chinese Corpus

Depression clinical interview corpora are essential for advancing automated depression diagnosis. While previous studies have used written speech material in controlled settings, these materials do not accurately represent spontaneous conversational speech. Additionally, self-reported measures of depression are subject to bias, making the data unreliable for training models for real-world scenarios. This study introduces a new corpus of depression clinical interviews collected directly from a psychiatric hospital, containing 113 recordings with 52 healthy and 61 depressive patients. The subjects were examined using the Montgomery-Asberg Depression Rating Scale (MADRS) in Chinese. Their final diagnosis was based on medical evaluations through a clinical interview conducted by a psychiatry specialist. All interviews were audio-recorded and transcribed verbatim, and annotated by experienced physicians. This dataset is a valuable resource for automated depression detection research and is expected to advance the field of psychology. Baseline models for detecting and predicting depression presence and level were built, and descriptive statistics of audio and text features were calculated. The decision-making process of the model was also investigated and illustrated. To the best of our knowledge, this is the first study to collect a depression clinical interview corpus in Chinese and train machine learning models to diagnose depression patients.
Episodic obsessive-compulsive disorder and bipolar spectrum disorder: new evidence from a large Italian OCD sample

Abstract Objectives Obsessive-Compulsive Disorder (OCD) has been considered to be a chronic illness; however, some authors described a subtype of OCD characterised by symptom-free periods of time: Episodic-OCD (E-OCD). Only few studies focussed on this subtype of the disorder. The objectives of this research were to study the association between the episodic course of the disorder and lifetime psychiatric comorbidities and to investigate socio-demographic and other clinical features correlated to the episodic course. Methods The sample is composed of adult OCD patients. The course was defined episodic when at least one circumscribed symptom-free interval of at least 6 months was present. The sample was divided into two subgroups: Episodic-OCD and Chronic-OCD. Differences between groups were analysed with Student’s t-test, χ2 tests, Fisher test and multivariate logistic regression. Results Data regarding 585 individuals were collected. 14.2% (N = 83) of our sample had an episodic course. Bipolar I comorbid disorder, abrupt onset, lower severity of illness and lower rates of repeating compulsions were associated with the likelihood of having an E-OCD. Conclusions Our findings confirm that a significant proportion of OCD patients have an episodic course and that E-OCD could represent a specific endophenotype. HIGHLIGHTS A significant proportion of OCD patients has an episodic course; Episodic OCD was related to comorbid Bipolar I Disorder; Episodic course was associated with an abrupt onset of OCD; Lower rates of repeating compulsions were associated with Episodic OCD.
Microfluidic Point-of-Care Diagnostics for Multi-Disease Detection Using Optical Techniques: A Review

The lifestyle of modern society is a major contributing factor for the majority of patients suffering from more than one disease. To Screen and diagnose each of those diseases, there is a great need for portable, and economical diagnostic tools, which are highly stipulated to yield rapid and accurate results using a small volume of the samples such as blood, saliva, sweat, etc. Point-of-care Testing (POCT) is one of the approaches to harvest prompt diagnosis of numerous diseases. The Majority of Point-of-Care Devices (POCD) are developed to diagnose one disease within the specimen. On the other hand, multi-disease detection capabilities in the same point-of-care devices are considered to be an efficient candidate to execute the state-of-the-art platform for multi-disease detection. Most of the literature reviews in this field focus on Point-of-Care (POC) devices, their underlying principles of operation, and their potential applications. It is evident from a perusal of the scholarly works that no review articles have been written on multi-disease detection POC devices. A review study analyzing the current level and functionality of multi-disease detection POC devices would be of great use to future researchers and device manufacturers. This review paper is addressing the above gap by focusing on various optical techniques like fluorescence, Absorbance, and Surface Plasmon Resonance (SPR) for multi-disease detection by harnessing the microfluidic-based POC device.
Lower-Limb Non-Parametric Functional Muscle Network: Test-Retest Reliability Analysis

Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects. Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated. The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures. This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.
Facts do not speak for themselves: Community norms, dialog, and evidentiary practices in discussions of COVID-19 on Reddit

The present study sought to explore the distinct discourse norms and evidentiary practices in discussions of COVID-19 in four subcommunities on Reddit. Qualitative analysis found that communities differed in the degree to which they reinforce and augment Reddit’s platform-wide norms for dialog and evidence use. One of the three communities (r/AskTrumpSupporters) differed from the rest by establishing discourse norms for turn-taking between politically opposed users and structuring dialog around authentic questions aimed at understanding alternative points of view. Quantitative analyses revealed that this community significantly differed from the other communities in the proportion of dialogic exchanges and in the use of evidentiary practices (sourcing, source evaluation, and interpretation of evidence). Excerpts of dialog from this community are used to illustrate findings. We conclude with implications for educators interested in preparing youth to critically engage with scientific information they encounter in public discourse.
Fast ray tracing in phase space for optical design

Philips Lighting is interested in designing optical systems that produce desired light characteristics, such as luminous intensity, on a target. Ray tracing is one way to relate the photometric properties of the light source to the characteristics of the light at the target. Monte Carlo methods are commonly used for illumination ray tracing, but these are computationally expensive. When light travels through an optical system, the flux and étendue are conserved and hence the luminance is constant along a ray. The goal of ray tracing methods is to find how the luminance, defined as a function on the optical phase space of the source, behaves as a function on the optical phase space of the target. From this, other light characteristics can be derived. Even if the luminance of the source is a well-behaved function, the luminance on the target has discontinuities. These discontinuities occur at the boundaries of regions of rays that have followed a similar path through the optical system. An iterative ray trace process is designed to approximate these boundaries. Then a multilevel B-spline interpolation algorithm is used to interpolate the luminance that is known only on a finite data set. This method creates a function that can be integrated analytically, to obtain the luminous intensity or the illuminance. Results show that the phase space ray trace method can be a significant improvement to the classical Monte Carlo ray tracer.
Investigating the correlation between Xrn1-resistant RNAs and frameshifter pseudoknots

ABSTRACT Xrn1-resistant RNA structures are multifunctional elements employed by an increasing number of RNA viruses. One of such elements is the coremin motif, discovered in plant virus RNAs, of which the structure has been hypothesized to form a yet unelucidated pseudoknot. Recently, the coremin motif was shown to be capable of stalling not only Xrn1, but scanning ribosomes as well. Following that observation, in this study we demonstrate that the coremin motif can promote −1 ribosomal frameshifting, similar to better-characterized viral frameshifting pseudoknots. Since this function was lost in concert with substitutions that were known to disturb Xrn1-resistance, we developed a frameshifting screen for finding novel Xrn1-resistant RNAs by randomizing parts of the coremin motif. This yielded new insights into the coremin motif structure, as Xrn1-resistant variations were identified that more clearly indicate a pseudoknot interaction. In addition, we show that the Xrn1-resistant RNA of Zika virus promotes frameshifting as well, while known −1 programmed ribosomal frameshifting pseudoknots do not stall Xrn1, suggesting that promoting frameshifting is a universal characteristic of Xrn1-resistant RNAs, but that Xrn1-resistance requires more than just a frameshifting pseudoknot.
Connection Probability in Diffusion Tensor Imaging via Anisotropic Gaussian Kernel Smoothing

1. Motivation We present a novel approach of obtaining white fiber anatomical connection probability in diffusion tensor imaging (DTI) via anisotropic Gaussian kernel smoothing. Our approach is compatible to other probabilistic approach such as solving an anisotropic diffusion equation (Batchelor et al., 2001) or Monte-Carlo random walk simulation (Kosh et al., 2002). Our formulation is simpler than solving the diffusion equation and deterministic in a sense that it avoids using Monte-Carlo random walk simulation in constructing the connection probability so the resulting connectivity maps do not change from one computational run to another. As a further usefulness of this new method, the same computational framework can also be used in smoothing any type of data along the white fiber tracks. This poster is based on a technical report Chung et al. (2003). 2. Anisotropic Gaussian kernel smoothing
Increased incidence of thyroid disease in patients with sarcoidosis: a systematic review and meta-analysis

Background Sarcoidosis is a multiple systemic granulomatous disease, and its main pathological feature is non-caseous necrotic epithelial granuloma. The pathogenesis is not fully understood. The prevalence of thyroid disease is likely higher among individuals with sarcoidosis. However, this association still lacks clinical evidence. Objective The aim of this study was to estimate the incidence of thyroid disease in patients with sarcoidosis. Methods A literature search was conducted using PubMed, Web of Science, Embase, and China National Knowledge Infrastructure literature databases. Fixed- or random-effects models were used for analysis according to heterogeneity. The results were subjected to meta-analysis with odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Results In total, six articles were included in this meta-analysis, which involved 2044 sarcoidosis cases and 5652 controls. The studies found that the incidence of thyroid disease in patients with sarcoidosis was significantly increased compared to the controls (OR 3.28, 95% CI 1.83–5.88). Conclusions This systematic review is the first to evaluate the incidence of thyroid disease in sarcoidosis patients, which was increased compared with the controls, suggesting that sarcoidosis patients should be screened for thyroid disease.
REMARKS ON PICARD-LINDEL()F ITERATION PART I

The paper discusses Picard-Lindel6f iteration for systems of autonomous linear equations on finite intervals, as well as its numerical variants. Most of the discussion is under a model assumption which roughly says that the coupling terms are of moderate size compared with the slow time scales in the problem. It is shown that the speed of convergence is quite independent of the step sizes already for very large time steps. This makes it possible to design strategies in which the mesh gets gradually refined during the iteration in such a way that the iteration error stays essentially on the level of discretization error. Subject classifications AMS: 65L05, 65F10.
Geolocation prediction from STR genotyping: a pilot study in five geographically distinct global populations

Abstract Background Traditional CE-based STR profiles are highly useful for the purpose of individualisation. However, they do not give any additional information without the presence of the reference sample for comparison. Aim To assess the usability of STR-based genotypes for the prediction of an individual’s geolocation. Subjects and Methods Genotype data from five geographically distinct populations, i.e. Caucasian, Hispanic, Asian, Estonian, and Bahrainian, were collected from the published literature. Results A significant difference (p < 0.05) in the observed genotypes was found between these populations. D1S1656 and SE33 showed substantial differences in their genotype frequencies across the tested populations. SE33, D12S391, D21S11, D19S433, D18S51, and D1S1656 were found to have the highest occurrence of “unique genotype’s” in different populations. In addition, D12S391 and D13S317 exhibited distinct population-specific “most frequent genotypes. ” Conclusions Three different prediction models have been proposed for genotype to geolocation prediction, i.e. (i) use of unique genotypes of a population, (ii) use of the most frequent genotype, and (iii) a combinatorial approach of unique and most frequent genotypes. These models could aid the investigating agencies in cases where no reference sample is available for comparison of the profile.
Tobit quantile regression of left-censored longitudinal data with informative observation times

: In many longitudinal studies, longitudinal responses are subject to left-censoring and may be correlated with observation times. In this article, we propose a Tobit quantile regression model for the analysis of left-censored longitudinal data with informative observation times and with the longitudinal responses allowed to depend on the past observation history. Estimating equation approaches are developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. A modiﬁed Majorize-Minimize algorithm is proposed to compute the proposed estimators. Simulation studies show that the proposed estimators perform well. An application to a data set from an AIDS clinical trial study is provided.
Seasonal influence on respiratory tract infection severity including COVID‐19 quantified through Markov Chain modeling

Respiratory tract infections (RTIs) are a burden to global health, but their characterization is complicated by the influence of seasonality on incidence and severity. The Re‐BCG‐CoV‐19 trial (NCT04379336) assessed BCG (re)vaccination for protection from coronavirus disease 2019 (COVID‐19) and recorded 958 RTIs in 574 individuals followed over 1 year. We characterized the probability of RTI occurrence and severity using a Markov model with health scores (HSs) for four states of symptom severity. Covariate analysis on the transition probability between HSs explored the influence of demographics, medical history, severe acute respiratory syndrome‐coronavirus 2 (SARS‐CoV‐2), or influenza vaccinations, which became available during the trial, SARS‐CoV‐2 serology, and epidemiology‐informed seasonal influence of infection pressure represented as regional COVID‐19 pandemic waves, as well as BCG (re)vaccination. The infection pressure reflecting the pandemic waves increased the risk of RTI symptom development, whereas the presence of SARS‐CoV‐2 antibodies protected against RTI symptom development and increased the probability of symptom relief. Higher probability of symptom relief was also found in participants with African ethnicity and with male biological gender. SARS‐CoV‐2 or influenza vaccination reduced the probability of transitioning from mild to healthy symptoms. Model diagnostics over calendar‐time indicated that COVID‐19 cases were under‐reported during the first wave by an estimated 2.76‐fold. This trial was performed during the initial phase of the COVID‐19 pandemic in South Africa and the results reflect that situation. Using this unique clinical dataset of prospectively studied RTIs over the course of 1 year, our Markov Chain model was able to capture risk factors for RTI development and severity, including epidemiology‐informed infection pressure.