FitDevo: accurate inference of single-cell developmental potential using sample-specific gene weight.

The quantification of developmental potential is critical for determining developmental stages and identifying essential molecular signatures in single-cell studies. Here, we present FitDevo, a novel method for inferring developmental potential using scRNA-seq data. The main idea of FitDevo is first to generate sample-specific gene weight (SSGW) and then infer developmental potential by calculating the correlation between SSGW and gene expression. SSGW is generated using a generalized linear model that combines sample-specific information and gene weight learned from a training dataset covering scRNA-seq data of 17 previously published datasets. We have rigorously validated FitDevo's effectiveness using a testing dataset with scRNA-seq data from 28 existing datasets and have also demonstrated its superiority over current methods. Furthermore, FitDevo's broad application scope has been illustrated using three practical scenarios: deconvolution analysis of epidermis, spatial transcriptomic data analysis of hearts and intestines, and developmental potential analysis of breast cancer. The source code and related data are available at https://github.com/jumphone/fitdevo.

Integrative Analyses of scRNA-seq, Bulk mRNA-seq, and DNA Methylation Profiling in Depressed Suicide Brain Tissues.

BACKGROUND: Suicidal behaviors have become a serious public health concern globally due to the economic and human cost of suicidal behavior to individuals, families, communities, and society. However, the underlying etiology and biological mechanism of suicidal behavior remains poorly understood. METHODS: We collected different single omic data, including single-cell RNA sequencing (scRNA-seq), bulk mRNA-seq, DNA methylation microarrays from the cortex of Major Depressive Disorder (MDD) in suicide subjects' studies, as well as fluoxetine-treated rats brains. We matched subject IDs that overlapped between the transcriptome dataset and the methylation dataset. The differential expression genes and differentially methylated regions were calculated with a 2-group comparison analysis. Cross-omics analysis was performed to calculate the correlation between the methylated and transcript levels of differentially methylated CpG sites and mapped transcripts. Additionally, we performed a deconvolution analysis for bulk mRNA-seq and DNA methylation profiling with scRNA-seq as the reference profiles. RESULTS: Difference in cell type proportions among 7 cell types. Meanwhile, our analysis of single-cell sequence from the antidepressant-treated rats found that drug-specific differential expression genes were enriched into biological pathways, including ion channels and glutamatergic receptors. CONCLUSIONS: This study identified some important dysregulated genes influenced by DNA methylation in 2 brain regions of depression and suicide patients. Interestingly, we found that oligodendrocyte precursor cells (OPCs) have the most contributors for cell-type proportions related to differential expression genes and methylated sites in suicidal behavior.

Beta-Blocker Separation on Phosphodiester Stationary Phases-The Application of Intelligent Peak Deconvolution Analysis.

Beta-blockers are a class of medications predominantly used to manage abnormal heart rhythms. They are also widely used to treat high blood pressure. From the liquid chromatography separation point of view, beta-blockers are interesting molecules due to their hydrophobic-hydrophilic properties. Thus, the study aimed to investigate the beta-blocker separation selectivity on four phosphodiester stationary phases in reversed-phase liquid chromatography (RP LC) and hydrophilic interactions liquid chromatography (HILIC). On tested stationary phases, beta-blockers provide retention in both chromatographic systems, RP LC and HILIC. Additionally, it was found that cation-exchange mechanisms have a significant contribution to retention. Separations were enhanced by applying ChromSword software for gradient optimization and Intelligent Peak Deconvolution Analysis to separate unseparated peaks digitally.

Thermal Decomposition of Maya Blue: Extraction of Indigo Thermal Decomposition Steps from a Multistep Heterogeneous Reaction Using a Kinetic Deconvolution Analysis.

Examining the kinetics of solids' thermal decomposition with multiple overlapping steps is of growing interest in many fields, including materials science and engineering. Despite the difficulty of describing the kinetics for complex reaction processes constrained by physico-geometrical features, the kinetic deconvolution analysis (KDA) based on a cumulative kinetic equation is one practical method of obtaining the fundamental information needed to interpret detailed kinetic features. This article reports the application of KDA to thermal decomposition of clay minerals and indigo-clay mineral hybrid compounds, known as Maya blue, from ancient Mayan civilization. Maya blue samples were prepared by heating solid mixtures of indigo and clay minerals (palygorskite and sepiolite), followed by purification. The multistep thermal decomposition processes of the clay minerals and Maya blue samples were analyzed kinetically in a stepwise manner through preliminary kinetic analyses based on a conventional isoconversional method and mathematical peak deconvolution to finally attain the KDA. By comparing the results of KDA for the thermal decomposition processes of the clay minerals and the Maya blue samples, information about the thermal decomposition steps of the indigo incorporated into the Maya blue samples was extracted. The thermal stability of Maya blue samples was interpreted through the kinetic characterization of the extracted indigo decomposition steps.

Resolution of co-eluting isomers of anti-inflammatory drugs conjugated to carbonic anhydrase inhibitors from plasma in liquid chromatography by energy-resolved tandem mass spectrometry.

Rheumatoid arthritis (RA) is a chronic inflammatory disease caused by a faulty autoimmune response. Recently, it was reported that some human carbonic anhydrases (CAs) isoforms are overexpressed in inflamed synovium of RA patients. New CA inhibitors (CAIs) incorporating CA-binding moiety and the cyclooxygenase inhibitor tail (nonsteroidal anti-inflammatory drug [NSAID] type) were studied. The aim of this work is the evaluation of the chemical stability of NSAID - CAI hybrids towards spontaneous or enzymatic hydrolysis by LC-MS/MS. The analytes are isomer pairs of 6- or 7-hydroxycoumarin, their different fragment ions abundances allowed the development of a mathematical tool (LEDA) to distinguish them. LEDA reliability at ng mL-1 level was checked (>90%), being proved the effectiveness in the correct assignment of the isomer present in the sample. The hybrids resulted stable in all tested matrices allowing us to conclude that these compounds reach the target tissues unmodified, opening perspectives for their development in the treatment of inflammation.

Diagnostic performance of different perfusion algorithms for the detection of angiographical spasm.

PURPOSE: To assess the diagnostic utility of different perfusion algorithms for the detection of angiographical terial spasm. METHOD: During a 2-year period, 45 datasets from 29 patients (54.2±10,75y, 20F) with suspected cerebral vasospasm after aneurysmal subarachnoid hemorrhage were included. Volume Perfusion CT (VPCT), Non-enhanced CT (NCT) and angiography were performed within 6hours post-ictus. Perfusion maps were generated using a maximum slope (MS) and a deconvolution-based approach (DC). Two blinded neuroradiologists independently evaluated MS and DC maps regarding vasospasm-related perfusion impairment on a 3-point Likert-scale (0=no impairment, 1=impairment affecting <50%, 2=impairment affecting >50% of vascular territory). A third independent neuroradiologist assessed angiography for presence and severity of arterial narrowing on a 3-point Likert scale (0=no narrowing, 1=narrowing affecting <50%, 2=narrowing affecting>50% of artery diameter). MS and DC perfusion maps were evaluated regarding diagnostic accuracy for angiographical arterial spasm with angiography as reference standard. Correlation analysis of angiography findings with both MS and DC perfusion maps was additionally performed. Furthermor, the agreement between MS and DC and inter-reader agreement was assessed. RESULTS: DC maps yielded significantly higher diagnostic accuracy than MS perfusion maps (DC:AUC=.870; MS:AUC=.805; P=0.007) with higher sensitivity for DC compared to MS (DC:sensitivity=.758; MS:sensitivity=.625). DC maps revealed stronger correlation with angiography than MS (DC: R=.788; MS: R=694;=<0.001). MS and DC showed substantial agreement (Kappa=.626). Regarding inter-reader analysis, (almost) perfect inter-reader agreement was observed for both MS and DC maps (Kappa≥981). CONCLUSION: DC yields significantly higher diagnostic accuracy for the detection of angiographic arterial spasm and higher correlation with angiographic findings compared to MS.

A two-step deconvolution-analysis-informed population pharmacodynamic modeling approach for drugs targeting pulsatile endogenous compounds.

Pharmacodynamic modeling of pulsatile endogenous compounds (e.g. growth hormone [GH]) is currently limited to the identification of a low number of pulses. Commonly used pharmacodynamic models are not able to capture the complexity of pulsatile secretion and therefore non-compartmental analyses are performed to extract summary statistics (mean, AUC, Cmax). The aim of this study was to develop a new quantification method that deals with highly variable pulsatile data by using a deconvolution-analysis-informed population pharmacodynamic modeling approach. Pulse frequency and pulse times were obtained by deconvolution analysis of 24 h GH profiles. The estimated pulse times then informed a non-linear mixed effects population pharmacodynamic model in NONMEM V7.3. The population parameter estimates were used to perform simulations that show agonistic and antagonistic drug effects on the secretion of GH. Additionally, a clinical trial simulation shows the application of this method in the quantification of a hypothetical drug effect that inhibits GH secretion. The GH profiles were modeled using a turnover compartment in which the baseline secretion, kout, pulse secretion width, amount at time point 0 and pulse amplitude were estimated as population parameters. Population parameters were estimated with low relative standard errors (ranging from 2 to 5%). Total body water (%) was identified as a covariate for pulse amplitude, baseline secretion and the pulse secretion width following a power relationship. Simulations visualized multiple gradients of a hypothetical drug that influenced the endogenous secretion of GH. The established model was able to fit and quantify the highly variable individual 24 h GH profiles over time. This pharmacodynamic model can be used to quantify drug effects that target other endogenous pulsatile compounds.

Reduced nocturnal ACTH-driven cortisol secretion during critical illness.

Recently, during critical illness, cortisol metabolism was found to be reduced. We hypothesize that such reduced cortisol breakdown may suppress pulsatile ACTH and cortisol secretion via feedback inhibition. To test this hypothesis, nocturnal ACTH and cortisol secretory profiles were constructed by deconvolution analysis from plasma concentration time series in 40 matched critically ill patients and eight healthy controls, excluding diseases or drugs that affect the hypothalamic-pituitary-adrenal axis. Blood was sampled every 10 min between 2100 and 0600 to quantify plasma concentrations of ACTH and (free) cortisol. Approximate entropy, an estimation of process irregularity, cross-approximate entropy, a measure of ACTH-cortisol asynchrony, and ACTH-cortisol dose-response relationships were calculated. Total and free plasma cortisol concentrations were higher at all times in patients than in controls (all P < 0.04). Pulsatile cortisol secretion was 54% lower in patients than in controls (P = 0.005), explained by reduced cortisol burst mass (P = 0.03), whereas cortisol pulse frequency (P = 0.35) and nonpulsatile cortisol secretion (P = 0.80) were unaltered. Pulsatile ACTH secretion was 31% lower in patients than in controls (P = 0.03), again explained by a lower ACTH burst mass (P = 0.02), whereas ACTH pulse frequency (P = 0.50) and nonpulsatile ACTH secretion (P = 0.80) were unchanged. ACTH-cortisol dose response estimates were similar in patients and controls. ACTH and cortisol approximate entropy were higher in patients (P ≤ 0.03), as was ACTH-cortisol cross-approximate entropy (P ≤ 0.001). We conclude that hypercortisolism during critical illness coincided with suppressed pulsatile ACTH and cortisol secretion and a normal ACTH-cortisol dose response. Increased irregularity and asynchrony of the ACTH and cortisol time series supported non-ACTH-dependent mechanisms driving hypercortisolism during critical illness.

TL-SDA: A designed toolkit for the deconvolution analysis of thermoluminescence glow curves.

In the present study, a graphical user interface (GUI) toolkit has been developed to analyze the thermoluminescence (TL) glow-curve and evaluate the trapping parameters using TL expression based on the one-trap one-recombination model. The basic idea of the deconvolution analysis in the developed toolkit is based on performing a sequence of successful fits, where the information provided by each fit is used by the next fit until the deconvolution of the entire glow curve approaches an optimum solution. The starting values and ranges of the fitting parameters can be controlled and adjusted to improve the deconvolution analysis of complex structure glow curves. The designed toolkit is also supported by the background-subtraction option to improve the analysis at low irradiation dose levels. The expanded uncertainty at the 95 % confidence level of the fitted trapping parameters is also provided. All the evaluations performed using the designed toolkit are allowed to be extracted into an Excel spreadsheet. The TL-SDA toolkit can be freely downloaded from: TLSDA_v1 - File Exchange - MATLAB Central (https://www.mathworks.com/matlabcentral/fileexchange/154136-tlsda_v1-1).

Deconvolution analysis identified altered hepatic cell landscape in primary sclerosing cholangitis and primary biliary cholangitis.

Introduction: Primary sclerosing cholangitis (PSC) and primary biliary cholangitis (PBC) are characterized by ductular reaction, hepatic inflammation, and liver fibrosis. Hepatic cells are heterogeneous, and functional roles of different hepatic cell phenotypes are still not defined in the pathophysiology of cholangiopathies. Cell deconvolution analysis estimates cell fractions of different cell phenotypes in bulk transcriptome data, and CIBERSORTx is a powerful deconvolution method to estimate cell composition in microarray data. CIBERSORTx performs estimation based on the reference file, which is referred to as signature matrix, and allows users to create custom signature matrix to identify specific phenotypes. In the current study, we created two custom signature matrices using two single cell RNA sequencing data of hepatic cells and performed deconvolution for bulk microarray data of liver tissues including PSC and PBC patients. Methods: Custom signature matrix files were created using single-cell RNA sequencing data downloaded from GSE185477 and GSE115469. Custom signature matrices were validated for their deconvolution performance using validation data sets. Cell composition of each hepatic cell phenotype in the liver, which was identified in custom signature matrices, was calculated by CIBERSORTx and bulk RNA sequencing data of GSE159676. Deconvolution results were validated by analyzing marker expression for the cell phenotype in GSE159676 data. Results: CIBERSORTx and custom signature matrices showed comprehensive performance in estimation of population of various hepatic cell phenotypes. We identified increased population of large cholangiocytes in PSC and PBC livers, which is in agreement with previous studies referred to as ductular reaction, supporting the effectiveness and reliability of deconvolution analysis in this study. Interestingly, we identified decreased population of small cholangiocytes, periportal hepatocytes, and interzonal hepatocytes in PSC and PBC liver tissues compared to healthy livers. Discussion: Although further studies are required to elucidate the roles of these hepatic cell phenotypes in cholestatic liver injury, our approach provides important implications that cell functions may differ depending on phenotypes, even in the same cell type during liver injury. Deconvolution analysis using CIBERSORTx could provide a novel approach for studies of specific hepatic cell phenotypes in liver diseases.

Proton Pump Inhibitors Modulate Gene Expression Profile in Esophageal Mucosa and Microbiome.

OBJECTIVE: Proton pump inhibitors (PPIs) are commonly used to manage children with upper gastrointestinal symptoms and without a formal diagnosis. We investigated the effect of PPIs on esophageal mucosal transcriptome and active microbiota in children with normal esophagi. Furthermore, we examined whether the differences in host esophageal mucosal gene expression were driven by an underlying esophageal epithelial cell type composition. METHODS: Using metatranscriptomics, the host transcriptional and active microbial profiles were captured from 17 esophageal biopsy samples (PPI naïve [PPI-], n = 7; PPI exposed [PPI+], n = 10) collected from children without any endoscopic and histologic abnormalities in their esophagus (normal esophagus). Deconvolution computational analysis was performed with xCell to assess if the observed epithelial gene expression changes were related to the cell type composition in the esophageal samples. RESULTS: The median (IQR) age of our cohort was 14 years (12-16) with female (63%) preponderance. Both groups were similar in terms of their demographics and clinical features. Compared with PPI-, the PPI+ had upregulation of 27 genes including the MUC genes. The cell type composition was similar between the PPI- and PPI+ groups. Prevotella sp and Streptococcus sp were abundant in PPI+ group. CONCLUSIONS: In children with normal esophagus, PPI exposure can be associated with upregulation of esophageal mucosal homeostasis and epithelial cell function genes in a cell-type independent manner, and an altered esophageal microbiome. Additional studies are warranted to validate our findings and to investigate the causal effect of PPIs on the normal esophageal epithelium and microbial communities.

Bioinformatics Tools for Bulk Gene Expression Deconvolution in Diabetic Retinopathy.

Retinal neovascularization is one of the leading causes of vision loss and a hallmark of proliferative diabetic retinopathy (PDR). The immune system is observed to be involved in the pathogenesis of diabetic retinopathy (DR). The specific immune cell type that contributes to retinal neovascularization can be identified via a bioinformatics analysis of RNA sequencing (RNA-seq) data, known as deconvolution analysis. Previous study has identified the infiltration of macrophages in the retina of rats with hypoxia-induced retinal neovascularization and patients with PDR through a deconvolution algorithm, known as CIBERSORTx. Here, we describe the protocols of using CIBERSORTx to perform the deconvolution analysis and downstream analysis of RNA-seq data.

Electrodermal Activity for Measuring Cognitive and Emotional Stress Level.

Stress can lead to harmful conditions in the body, such as anxiety disorders and depression. One of the promising noninvasive methods, which has been widely used in detecting stress and emotion, is electrodermal activity (EDA). EDA has a tonic and phasic component called skin conductance level and skin conductance response (SCR). However, the components of the EDA cannot be directly extracted and need to be deconvolved to obtain it. The EDA signals were collected from 18 healthy subjects that underwent three sessions - Stroop test with increasing stress levels. The EDA signals were then deconvoluted by using continuous deconvolution analysis (CDA) and convex optimization approach to electrodermal activity (cvxEDA). Four features from the result of the deconvolution process were collected, namely sample average, standard deviation, first absolute difference, and normalized first absolute difference. Those features were used as the input of the classification process using the extreme learning machine (ELM). The output of classification was the stress level; mild, moderate, and severe. The visual of the phasic component using cvxEDA is more precise or smoother than the CDA's result. However, both methods could separate SCR from the original skin conductivity raw and indicate the small peaks from the SCR. The classification process results showed that both CDA and cvxEDA methods with 50 hidden layers in ELM had a high accuracy in classifying the stress level, which was 95.56% and 94.45%, respectively. This study developed a stress level classification method using ELM and the statistical features of SCR. The result showed that EDA could classify the stress level with over 94% accuracy. This system could help people monitor their mental health during overworking, leading to anxiety and depression because of untreated stress.

On Phase Identification of Hardened Cement Pastes by Combined Nanoindentation and Mercury Intrusion Method.

The micro-mechanical properties of hardened cement paste can be obtained by nanoindentation. Phases at different locations can generally be determined by using the Gaussian mixture model (GMM) method and the K-means clustering (KM) method. However, there are differences between analysis methods. In this study, pore structure and porosity of hardened cement paste aged three, seven, and 28 days were obtained by mercury intrusion porosimetry (MIP), and their micro-mechanical properties were obtained by the nanoindentation method. A new method, GMM-MIP and KM-MIP, was proposed to determine the phase of hardened cement paste based on the pore structure and nanoindentation results. The results show that GMM-MIP and KM-MIP methods are more reasonable than GMM and KM methods in determining the phase of hardened cement paste. GMM-MIP can be used to obtain reasonable phase distribution. If the micro-mechanical properties of each phase in hardened cement paste do not satisfy the normal distribution, the GMM method has significant defects.

Microminipig: A suitable animal model to estimate oral absorption of sustained-release formulation in humans.

We investigated the gastrointestinal absorption characteristics of oral sustained-release formulations in microminipigs, dogs, and monkeys in order to clarify the similarities in absorption properties between these animals and humans. Time profiles of oral absorption of nifedipine and valproic acid were calculated from the plasma concentration-time profiles of the drugs by a deconvolution method. The curves for both drugs in microminipigs were close to or slightly higher than those in humans, whereas those in monkeys were lower. Furthermore, the plasma concentration-time profiles of the drugs were subjected to non-compartmental analysis. The fractions of a dose absorbed into the portal vein (FaFg) in microminipigs ranged from 50 to 100% of the human values, whereas those in monkeys were less than half the human values. In addition, the other absorption-related parameters for the sustained-release formulation in microminipigs, as well as monkeys, were comparable to those in humans. In conclusion, the oral absorption properties of microminipigs and humans were similar regarding the sustained-release formulations. Therefore, microminipig is a suitable animal model to estimate the oral absorption of sustained-release formulations in humans.

Skin Conductance as a Viable Alternative for Closing the Deep Brain Stimulation Loop in Neuropsychiatric Disorders.

Markers from local field potentials, neurochemicals, skin conductance, and hormone concentrations have been proposed as a means of closing the loop in Deep Brain Stimulation (DBS) therapy for treating neuropsychiatric and movement disorders. Developing a closed-loop DBS controller based on peripheral signals would require: (i) the recovery of a biomarker from the source neural stimuli underlying the peripheral signal variations; (ii) the estimation of an unobserved brain or central nervous system related state variable from the biomarker. The state variable is application-specific. It is emotion-related in the case of depression or post-traumatic stress disorder, and movement-related for Parkinson's or essential tremor. We present a method for closing the DBS loop in neuropsychiatric disorders based on the estimation of sympathetic arousal from skin conductance measurements. We deconvolve skin conductance via an optimization formulation utilizing sparse recovery and obtain neural impulses from sympathetic nerve fibers stimulating the sweat glands. We perform this deconvolution via a two-step coordinate descent procedure that recovers the sparse neural stimuli and estimates physiological system parameters simultaneously. We next relate an unobserved sympathetic arousal state to the probability that these neural impulses occur and use Bayesian filtering within an Expectation-Maximization framework for estimation. We evaluate our method on a publicly available data-set examining the effect of different types of stress on peripheral signal changes including body temperature, skin conductance and heart rate. A high degree of arousal is estimated during cognitive tasks, as are much lower levels during relaxation. The results demonstrate the ability to decode psychological arousal from neural activity underlying skin conductance signal variations. The complete pipeline from recovering neural stimuli to decoding an emotion-related brain state using skin conductance presents a promising methodology for the ultimate realization of a closed-loop DBS controller. Closed-loop DBS treatment would additionally help reduce unnecessary power consumption and improve therapeutic gains.

Pilot study: an acute bout of high intensity interval exercise increases 12.5 h GH secretion.

The purpose of this study was to test the hypothesis that high-intensity interval exercise (HIE) significantly increases growth hormone (GH) secretion to a greater extent than moderate-intensity continuous exercise (MOD) in young women. Five young, sedentary women (mean ± SD; age: 22.6±1.3 years; BMI: 27.4±3.1 kg/m2 ) were tested during the early follicular phase of their menstrual cycle on three occasions. For each visit, participants reported to the laboratory at 1700 h, exercised from 1730-1800 h, and remained in the laboratory until 0700 h the following morning. The exercise component consisted of either 30-min of moderate-intensity continuous cycling at 50% of measured peak power (MOD), four 30-s "all-out" sprints with 4.5 min of active recovery (HIE), or a time-matched sedentary control using a randomized, cross-over design. The overnight GH secretory profile of each trial was determined from 10-min sampling of venous blood from 1730-0600 h, using deconvolution analysis. Deconvolution GH parameters were log transformed prior to statistical analyses. Calculated GH AUC (0-120 min) was significantly greater in HIE than CON (P = 0.04), but HIE was not different from MOD. Total GH secretory rate (ng/mL/12.5 h) was significantly greater in the HIE than the CON (P = 0.05), but MOD was not different from CON or HIE. Nocturnal GH secretion (ng/mL/7.5 h) was not different between the three trials. For these women, in this pilot study, a single bout of HIE was sufficient to increase 12.5 h pulsatile GH secretion. It remains to be determined if regular HIE may contribute to increased daily GH secretion.

The GHRH + arginine stimulated pituitary GH secretion in children and adults with Prader-Willi syndrome shows age- and BMI-dependent and genotype-related differences.

OBJECTIVE: The quantitative and qualitative aspects of the pituitary response in children and adults with Prader-Willi syndrome (PWS) are compared in order to verify the possible age-dependent and genotype-related differences in terms of GH secretion. DESIGN: 29 young subjects (21 males and 8 females) and 65 adults (24 males and 41 females) with PWS were studied. All subjects underwent a standard GH Releasing Hormone (GHRH 1-29, 1 µg/kg as i.v. bolus at 0 minutes)+arginine (0.5 g/kg) test. Peak GH values, standard GH area under the curve (AUC), AUC of the instantaneous secretion rate (ISR), and secretion response analysis (i.e. half-secretion time) were evaluated. A regression analysis was performed to investigate which are the patient characteristics that affect the amplitude and shape of the GH secretion response. RESULTS: Peak GH values and AUCGH were significantly higher in PWS children than in PWS adults, these differences being also significant both in PWS DEL15 (only peak GH value) and PWS UPD15. Moreover, PWS children showed significantly lower half secretion time than PWS adults, this delayed response being present both in PWS DEL15 and PWS UPD15. Significant negative correlations between AUCGH and BMISDS were observed in the two groups (adults and children), as well as in adults and children DEL15, but not in adults and children PWS UPD15. A regression analysis performed on the whole dataset showed that for PWS DEL15 the statistically significant variable explaining GH responsiveness was BMISDS (p<0.0001), while for UPD15 no statistically significant covariate was found. Conversely, when the delay of the secretion response was considered, the regression model yielding the best performances was the one with only age as a regressor (p<0.001). CONCLUSIONS: The quantitative and qualitative analyses of GH responsiveness to GHRH+arginine highlight relevant differences between PWS children and PWS adults and genotype-related traits. The negative influence of BMISDS on GH secretion reinforces the need for an early start of life-long weight management in PWS subjects.

On the Electronic Structures of Au25(SR)18 Clusters Studied by Magnetic Circular Dichroism Spectroscopy.

The first magnetic circular dichroism (MCD) spectra are reported at room temperature for well-defined thiolate-protected Au25 clusters (Au25(SG)18 and Au25(PET)18, where SG and PET denote glutathione and 2-phenylethanethiolate, respectively). MCD essentially corresponds to electronic transitions in the absorption spectrum, so the electronic structures of the Au25 clusters are explored based on a simultaneous deconvolution analysis of both the electronic absorption and MCD spectra, giving enhanced spectral resolution. We then find that the observed MCD responses are entirely interpreted in terms of the Faraday B terms, representing strict nondegeneracies of the excited and ground states of the Au25 clusters that correspond to so-called superatom D and P orbitals, respectively.