Vitamin D Supplementation in Neonatal and Infant MIS-C Following COVID-19 Infection.

To date, the SARS-CoV-2 pandemic still represents a great clinical challenge worldwide, and effective anti-COVID-19 drugs are limited. For this reason, nutritional supplements have been investigated as adjuvant therapeutic approaches in disease management. Among such supplements, vitamin D has gained great interest, due to its immunomodulatory and anti-inflammatory actions both in adult and pediatric populations. Even if there is conflicting evidence about its prevention and/or mitigation effectiveness in SARS-CoV-2 infection, several studies demonstrated a strict correlation between hypovitaminosis D and disease severity in acute COVID-19 and MIS-C (multisystem inflammatory syndrome in children). This narrative review offers a resume of the state of the art about vitamin D's role in immunity and its clinical use in the context of the current pandemic, specially focusing on pediatric manifestations and MIS-C. It seems biologically reasonable that interventions aimed at normalizing circulating vitamin D levels could be beneficial. To help clinicians in establishing the correct prophylaxis and/or supportive therapy with vitamin D, well-designed and adequately statistically powered clinical trials involving both adult and pediatric populations are needed. Moreover, this review will also discuss the few other nutraceuticals evaluated in this context.

Blind instrument response function identification from fluorescence decays.

Time-resolved fluorescence spectroscopy plays a crucial role when studying dynamic properties of complex photochemical systems. Nevertheless, the analysis of measured time decays and the extraction of exponential lifetimes often requires either the experimental assessment or the modeling of the instrument response function (IRF). However, the intrinsic nature of the IRF in the measurement process, which may vary across measurements due to chemical and instrumental factors, jeopardizes the results obtained by reconvolution approaches. In this paper, we introduce a novel methodology, called blind instrument response function identification (BIRFI), which enables the direct estimation of the IRF from the collected data. It capitalizes on the properties of single exponential signals to transform a deconvolution problem into a well-posed system identification problem. To delve into the specifics, we provide a step-by-step description of the BIRFI method and a protocol for its application to fluorescence decays. The performance of BIRFI is evaluated using simulated and time-correlated single-photon counting data. Our results demonstrate that the BIRFI methodology allows an accurate recovery of the IRF, yielding comparable or even superior results compared with those obtained with experimental IRFs when they are used for reconvolution by parametric model fitting.

Superpixels meet essential spectra for fast Raman hyperspectral microimaging.

In the context of spectral unmixing, essential information corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix which are indispensable to reproduce the full data matrix in a convex linear way. Essential information has recently been shown accessible on-the-fly via a decomposition of the measured spectra in the Fourier domain and has opened new perspectives for fast Raman hyperspectral microimaging. In addition, when some spatial prior is available about the sample, such as the existence of homogeneous objects in the image, further acceleration for the data acquisition procedure can be achieved by using superpixels. The expected gain in acquisition time is shown to be around three order of magnitude on simulated and real data with very limited distortions of the estimated spectrum of each object composing the images.

Per-pixel unmixing of spectrally overlapping fluorophores using intra-exposure excitation modulation.

Multilabel fluorescence imaging is essential for the visualization of complex systems, though a major challenge is the limited width of the useable spectral window. Here, we present a new method, exNEEMO, that enables per-pixel quantification of spectrally-overlapping fluorophores based on their light-induced dynamics, in a way that is compatible with a very broad range of timescales over which these dynamics may occur. Our approach makes use of intra-exposure modulation of the excitation light to distinguish the different emitters given their reference responses to this modulation. We use the approach to simultaneously image four green photochromic fluorescent proteins at the full spatial resolution of the imaging.

Assessment of Essential Information in the Fourier Domain to Accelerate Raman Hyperspectral Microimaging.

In the context of multivariate curve resolution (MCR) and spectral unmixing, essential information (EI) corresponds to the most linearly dissimilar rows and/or columns of a two-way data matrix. In recent works, the assessment of EI has been revealed to be a very useful practical tool to select the most relevant spectral information before MCR analysis, key features being speed and compression ability. However, the canonical approach relies on the principal component analysis to evaluate the convex hull that encapsulates the data structure in the normalized score space. This implies that the evaluation of the essentiality of each spectrum can only be achieved after all the spectra have been acquired by the instrument. This paper proposes a new approach to extract EI in the Fourier domain (EIFD). Spectral information is transformed into Fourier coefficients, and EI is assessed from a convex hull analysis of the data point cloud in the 2D phasor plots of a few selected harmonics. Because the coordinate system of a phasor plot does not depend on the data themselves, the evaluation of the essentiality of the information carried by each spectrum can be achieved individually and independently from the others. As a result, time-consuming operations like Raman spectral imaging can be significantly accelerated exploiting a chemometric-driven (i.e., based on the EI content of a spectral pixel) procedure for data acquisition and targeted sampling. The usefulness of EIFD is shown by analyzing Raman hyperspectral microimaging data, demonstrating a potential 50-fold acceleration of Raman acquisition.

Kernelizing: A way to increase accuracy in trilinear decomposition analysis of multiexponential signals.

The unmixing of multiexponential decay signals into monoexponential components using soft modelling approaches is a challenging task due to the strong correlation and complete window overlap of the profiles. To solve this problem, slicing methodologies, such as PowerSlicing, tensorize the original data matrix into a three-way data array that can be decomposed based on trilinear models providing unique solutions. Satisfactory results have been reported for different types of data, e.g., nuclear magnetic resonance or time-resolved fluorescence spectra. However, when decay signals are described by only a few sampling (time) points, a significant degradation of the results can be observed in terms of accuracy and precision of the recovered profiles. In this work, we propose a methodology called Kernelizing that provides a more efficient way to tensorize data matrices of multiexponential decays. Kernelizing relies on the invariance of exponential decays, i.e., when convolving a monoexponential decaying function with any positive function of finite width (hereafter called "kernel"), the shape of the decay (determined by the characteristic decay constant) remains unchanged and only the preexponential factor varies. The way preexponential factors are affected across the sample and time modes is linear, and it only depends on the kernel used. Thus, using kernels of different shapes, a set of convolved curves can be obtained for every sample, and a three-way data array generated, for which the modes are sample, time and kernelizing effect. This three-way array can be afterwards analyzed by a trilinear decomposition method, such as PARAFAC-ALS, to resolve the underlying monoexponential profiles. To validate this new approach and assess its performance, we applied Kernelizing to simulated datasets, real time-resolved fluorescence spectra collected on mixtures of fluorophores and fluorescence-lifetime imaging microscopy data. When the measured multiexponential decays feature few sampling points (down to fifteen), more accurate trilinear model estimates are obtained than when using slicing methodologies.

Health-related quality of life and radiological and functional lung changes of patients with COVID-19 Pneumonia 3 and 10 months after discharge.

BACKGROUND: Few studies have evaluated the long-term impact on health-related quality of life (HRQoL) in patients who have been hospitalized for COVID-19 pneumonia. Specific follow-up should be carried out to detect and treat possible pulmonary abnormalities, and the worsening of HRQoL should be estimated to target necessary resources for care of these patients after acute phase. The objective was to know the impact on HRQoL of patients who have been admitted for COVID-19 pneumonia, and to evaluate the clinical-radiological and functional changes of patients who have overcome COVID-19 pneumonia at 3 and 10 months of follow-up. METHODS: Prospective observational study of patients who required hospitalization for COVID-19 pneumonia between April and December 2020. All patients filled out the EuroQol five-dimension (EQ-5D) questionnaire with the EuroQol Visual Analogue Scale (E-VAS) for self-assessment of health status. Respiratory function tests and chest X-ray were carried out at 3 and 10 months of follow-up. RESULTS: 61 patients were included in the study. The need for ventilatory support was associated with anxiety/depression on the EQ-5D scale, as well as patients admitted to the intensive care unit (ICU). The mean EQ-5D and E-VAS index scores decreased with hospitalization time, the number of days spent in intermediate respiratory care unit (IRCU) and the level of dyspnoea at the beginning of the hospitalization period. Pulmonary sequelae were observed in 25 patients (41%) at 3 months and 17 (27.9%) at 10 months. Patients improve their forced vital capacity (FVC) by 196 ml (p = 0.001) at 10 months as well as 9% in diffusing capacity of lung for carbon monoxide (DLCO) (p = 0.001) at 10 months. DLCO was found to be correlated to lymphopenia and time spent in IRCU. Low FVC values were detected 10 months after discharge for subjects exhibiting high levels of dyspnoea at 3 months after discharge. CONCLUSIONS: Hospitalization for COVID-19 pneumonia affects the HRQoL of patients, with greater anxiety/depression in those who were more serious affected and are younger. A significant percentage of patients present fibrotic abnormalities and lung function impairment at the first and second follow-up after discharge.

Class modelling by Soft Independent Modelling of Class Analogy: why, when, how? A tutorial.

This article contains a comprehensive tutorial on classification by means of Soft Independent Modelling of Class Analogy (SIMCA). Such a tutorial was conceived in an attempt to offer pragmatic guidelines for a sensible and correct utilisation of this tool as well as answers to three basic questions: "why employing SIMCA?", "when employing SIMCA?" and "how employing/not employing SIMCA?". With this purpose in mind, the following points are here addressed: i) the mathematical and statistical fundamentals of the SIMCA approach are presented; ii) distinct variants of the original SIMCA algorithm are thoroughly described and compared in two different case-studies; iii) a flowchart outlining how to fine-tune the parameters of a SIMCA model for achieving an optimal performance is provided; iv) figures of merit and graphical tools for SIMCA model assessment are illustrated and v) computational details and rational suggestions about SIMCA model validation are given. Moreover, a novel Matlab toolbox, which encompasses routines and functions for running and contrasting all the aforementioned SIMCA versions is also made available.

SERS Endoscopy for Monitoring Intracellular Drug Dynamics.

Understanding the dynamics and distribution of medicinal drugs in living cells is essential for the design and discovery of treatments. The tools available for revealing this information are, however, extremely limited. Here, we report the application of surface-enhanced Raman scattering (SERS) endoscopy, using plasmonic nanowires as SERS probes, to monitor the intracellular fate and dynamics of a common chemo-drug, doxorubicin, in A549 cancer cells. The unique spatio-temporal resolution of this technique reveals unprecedented information on the mode of action of doxorubicin: its localization in the nucleus, its complexation with medium components, and its intercalation with DNA as a function of time. Notably, we were able to discriminate these factors for the direct administration of doxorubicin or the use of a doxorubicin delivery system. The results reported here show that SERS endoscopy may have an important future role in medicinal chemistry for studying the dynamics and mechanism of action of drugs in cells.

Performance Metrics of the Scoring System for the Diagnosis of the Beckwith-Wiedemann Spectrum (BWSp) and Its Correlation with Cancer Development.

Different scoring systems for the clinical diagnosis of the Beckwith-Wiedemann spectrum (BWSp) have been developed over time, the most recent being the international consensus score. Here we try to validate and provide data on the performance metrics of these scoring systems of the 2018 international consensus and the previous ones, relating them to BWSp features, molecular tests, and the probability of cancer development in a cohort of 831 patients. The consensus scoring system had the best performance (sensitivity 0.85 and specificity 0.43). In our cohort, the diagnostic yield of tests on blood-extracted DNA was low in patients with a low consensus score (~20% with a score = 2), and the score did not correlate with cancer development. We observed hepatoblastoma (HB) in 4.3% of patients with UPD(11)pat and Wilms tumor in 1.9% of patients with isolated lateralized overgrowth (ILO). We validated the efficacy of the currently used consensus score for BWSp clinical diagnosis. Based on our observation, a first-tier analysis of tissue-extracted DNA in patients with <4 points may be considered. We discourage the use of the consensus score value as an indicator of the probability of cancer development. Moreover, we suggest considering cancer screening for negative patients with ILO (risk ~2%) and HB screening for patients with UPD(11)pat (risk ~4%).

Reliability of nephrolithometric nomograms in patients treated with minimally invasive percutaneous nephrolithotomy: A precision study.

Objectives: The study aimed to evaluate quality of nephrolithometric nomograms to predict stone-free rates (SFRs) and complication rates (CRs) in case of minimally invasive percutaneous nephrolithotomy (PNL). In the last decade, nomograms have been introduced to estimate the SFRs and CRs of PNL. However, no data are available regarding their reliability in case of utilization of miniaturized devices. Herein we present a prospective multicentric study to evaluate reliability of Guy's stone score (GSS), the stone size, tract length, obstruction, number of involved calyces, and essence of stone (S.T.O.N.E.) nephrolithometry score and Clinical Research Office of the Endourological Society (CROES) score in patients treated with minimally invasive PNL. Methods: We evaluated SFRs and CRs of 222 adult patients treated with miniaturized PNL. Patients were considered stone-free if no residual fragments of any size at post-operative unenhanced computed tomography scan. Patients demographics, SFRs, and CRs were reported and analyzed. Performances of nomograms were evaluated with the area under the curve (AUC). Results: We included 222 patients, the AUCs of GSS, CROES score, and S.T.O.N.E. nephrolithometry score were 0.69 (95% confidence interval [CI] 0.61-0.78), 0.64 (95% CI 0.56-0.73), and 0.62 (95% CI 0.52-0.71), respectively. Regarding SFRs, at multivariate binomial logistic regression, only the GSS had significance with an odds ratio of 0.53 (95% CI 0.31-0.95, p=0.04). We did not find significant correlation with complications, with only a trend for GSS. Conclusion: This is the first study evaluating nomograms in miniaturized PNL. They still show good reliability; however, our data showed lower performances compared to standard PNL. We emphasize the need of further studies to confirm this trend. A dedicated nomogram for minimally invasive PNL may be necessary.

Improvement of pixel classification by the simultaneous use of spectral and spatial information in the framework of spectroscopic imaging.

Hyperspectral imaging technology is developing in a very fast way. We find it today in many analytical developments using different spectroscopies for sample classification purposes. Instrumental developments allow us to acquire more and more data in shorter and shorter periods of time while improving their quality. Therefore, we are going in the right direction as far as the measure is concerned. On the other hand, we can make a more mixed assessment for the hyperspectral imaging data processing. Indeed, the data acquired in spectroscopic imaging have the particularity of encoding both spectral and spatial information. Unfortunately, in chemometrics, almost all classification approaches today only use spectral information from three-dimensional hyperspectral data arrays. To be more precise, an approach encompassing the unfolding/refolding of such arrays is often applied beforehand because the majority of algorithms for analysing these data are not capable of handling them in their original structure. Spatial information is therefore lost during the chemometric exploration. The study of the spectral part of the acquired data array alone is clearly a limitation that we propose to overcome in this work. 2-D Stationary Wavelet Transform will be used in the data preprocessing phase to ensure the joint use of spectral and spatial information. Two spectroscopic datasets will then be used to evaluate the potential of our approach in the context of supervised classification.

Variable-selection ANOVA Simultaneous Component Analysis (VASCA).

MOTIVATION: ANOVA Simultaneous Component Analysis (ASCA) is a popular method for the analysis of multivariate data yielded by designed experiments. Meaningful associations between factors/interactions of the experimental design and measured variables in the dataset are typically identified via significance testing, with permutation tests being the standard go-to choice. However, in settings with large numbers of variables, like omics (genomics, transcriptomics, proteomics and metabolomics) experiments, the 'holistic' testing approach of ASCA (all variables considered) often overlooks statistically significant effects encoded by only a few variables (biomarkers). RESULTS: We hereby propose Variable-selection ASCA (VASCA), a method that generalizes ASCA through variable selection, augmenting its statistical power without inflating the Type-I error risk. The method is evaluated with simulations and with a real dataset from a multi-omic clinical experiment. We show that VASCA is more powerful than both ASCA and the widely adopted false discovery rate controlling procedure; the latter is used as a benchmark for variable selection based on multiple significance testing. We further illustrate the usefulness of VASCA for exploratory data analysis in comparison to the popular partial least squares discriminant analysis method and its sparse counterpart. AVAILABILITY AND IMPLEMENTATION: The code for VASCA is available in the MEDA Toolbox at https://github.com/josecamachop/MEDA-Toolbox (release v1.3). The simulation results and motivating example can be reproduced using the repository at https://github.com/josecamachop/VASCA/tree/v1.0.0 (DOI 10.5281/zenodo.7410623). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bone Molecular Modifications Induced by Diagenesis Followed-Up for 12 Months.

After death, diagenesis takes place. Numerous processes occur concomitantly, which makes it difficult to identify the diagenetic processes. The diagenetic processes refer to all processes (chemical or physical) that modify the skeletal remains. These processes are highly variable depending on the environmental factors (weather, temperature, age, sex, etc.), especially in the early stages. Numerous studies have evaluated bone diagenetic processes over long timescales (~millions of years), but fewer have been done over short timescales (between days and thousands of years). The objective of the study is to assess the early stages of diagenetic processes by Raman microspectroscopy over 12 months. The mineral and organic matrix modifications are monitored through physicochemical parameters. Ribs from six humans were buried in soil. The modifications of bone composition were followed by Raman spectroscopy each month. The decrease in the mineral/organic ratio and carbonate type-B content and the increase in crystallinity reveal that minerals undergo dissolution-recrystallization. The decrease in collagen cross-linking indicates that collagen hydrolysis induces the fragmentation of collagen fibres over 12 months.

Machine-learning-derived predictive score for early estimation of COVID-19 mortality risk in hospitalized patients.

The clinical course of COVID-19 is highly variable. It is therefore essential to predict as early and accurately as possible the severity level of the disease in a COVID-19 patient who is admitted to the hospital. This means identifying the contributing factors of mortality and developing an easy-to-use score that could enable a fast assessment of the mortality risk using only information recorded at the hospitalization. A large database of adult patients with a confirmed diagnosis of COVID-19 (n = 15,628; with 2,846 deceased) admitted to Spanish hospitals between December 2019 and July 2020 was analyzed. By means of multiple machine learning algorithms, we developed models that could accurately predict their mortality. We used the information about classifiers' performance metrics and about importance and coherence among the predictors to define a mortality score that can be easily calculated using a minimal number of mortality predictors and yielded accurate estimates of the patient severity status. The optimal predictive model encompassed five predictors (age, oxygen saturation, platelets, lactate dehydrogenase, and creatinine) and yielded a satisfactory classification of survived and deceased patients (area under the curve: 0.8454 with validation set). These five predictors were additionally used to define a mortality score for COVID-19 patients at their hospitalization. This score is not only easy to calculate but also to interpret since it ranges from zero to eight, along with a linear increase in the mortality risk from 0% to 80%. A simple risk score based on five commonly available clinical variables of adult COVID-19 patients admitted to hospital is able to accurately discriminate their mortality probability, and its interpretation is straightforward and useful.

New strategies for the prevention of respiratory syncytial virus (RSV).

Respiratory Syncytial Virus (RSV) is the main cause of lower respiratory tract infections (LRTIs) in newborns in the first two years of life. RSV disease has a traditional seasonal trend, with an onset and offset, duration and peak. Prematurity, male gender, bronchopulmonary dysplasia (BPD), critical congenital cardiovascular disorders (CCHD), neuromuscular diseases, congenital and inherited airways anatomical anomalies are the main risk factors for increased severity of this infection. RSV infection is associated with negative long-term respiratory outcomes, with excess of morbidity, resulting in reduced quality of life of the infected children and representing a burden for the healthcare costs and resources. Despite all the efforts, prevention remains, to date, the most effective strategy to reduce RSV-related morbidity. Among the current prevention strategies, strict hygiene, breastfeeding and passive immunization with the monoclonal antibody Palivizumab are the cornerstone. In the next future, it is likely that new possibilities of prevention will add, including use of more potent and longer-acting monoclonal antibodies, implementation of maternal vaccination in pregnancy, and active immunization in children. The purpose of this review is to provide an overview of the main current and future prevention strategies against RSV.

Fetal Brain Damage during Maternal COVID-19: Emerging Hypothesis, Mechanism, and Possible Mitigation through Maternal-Targeted Nutritional Supplementation.

The recent outbreak of the novel Coronavirus (SARS-CoV-2 or CoV-2) pandemic in 2019 and the risk of CoV-2 infection during pregnancy led the scientific community to investigate the potential negative effects of Coronavirus infection on pregnancy outcomes and fetal development. In particular, as CoV-2 neurotropism has been demonstrated in adults, recent studies suggested a possible risk of fetal brain damage and fetal brain development impairment, with consequent psychiatric manifestations in offspring of mothers affected by COronaVIrus Disease (COVID) during pregnancy. Through the understanding of CoV-2's pathogenesis and the pathways responsible for cell damage, along with the available data about neurotropic virus attitudes, different strategies have been suggested to lower the risk of neurologic disease in newborns. In this regard, the role of nutrition in mitigating fetal damages related to oxidative stress and the inflammatory environment during viral infection has been investigated, and arginine, n3PUFA, vitamins B1 and B9, choline, and flavonoids were found to be promising in and out of pregnancy. The aim of this review is to provide an overview of the current knowledge on the mechanism of fetal brain damage and the impact of nutrition in reducing inflammation related to worse neurological outcomes in the context of CoV-2 infections during pregnancy.

Corrigendum: Hyperspectral Video Analysis by Motion and Intensity Preprocessing and Subspace Autoencoding.

[This corrects the article DOI: 10.3389/fchem.2022.818974.].

Expansion microscopy allows high resolution single cell analysis of epigenetic readers.

Interactions between epigenetic readers and histone modifications play a pivotal role in gene expression regulation and aberrations can enact etiopathogenic roles in both developmental and acquired disorders like cancer. Typically, epigenetic interactions are studied by mass spectrometry or chromatin immunoprecipitation sequencing. However, in these methods, spatial information is completely lost. Here, we devise an expansion microscopy based method, termed Expansion Microscopy for Epigenetics or ExEpi, to preserve spatial information and improve resolution. We calculated relative co-localization ratios for two epigenetic readers, lens epithelium derived growth factor (LEDGF) and bromodomain containing protein 4 (BRD4), with marks for heterochromatin (H3K9me3 and H3K27me3) and euchromatin (H3K36me2, H3K36me3 and H3K9/14ac). ExEpi confirmed their preferred epigenetic interactions, showing co-localization for LEDGF with H3K36me3/me2 and for BRD4 with H3K9/14ac. Moreover addition of JQ1, a known BET-inhibitor, abolished BRD4 interaction with H3K9/14ac with an IC50 of 137 nM, indicating ExEpi could serve as a platform for epigenetic drug discovery. Since ExEpi retains spatial information, the nuclear localization of marks and readers was determined, which is one of the main advantages of ExEpi. The heterochromatin mark, H3K9me3, is located in the nuclear rim whereas LEDGF co-localization with H3K36me3 and BRD4 co-localization with H3K9/14ac occur further inside the nucleus.