Blaschkoid melanotic cutaneous lupus erythematosus with "melanocytic nests".

Melanotic cutaneous lupus erythematosus (LE) is a newly described clinical variant of chronic cutaneous LE, presenting with localized or diffuse brownish or grayish macular and reticulated pigmentation in the absence of erythema, scaling, atrophy, scarring, or telangiectasia. The diagnosis is based upon histopathology, which demonstrates the characteristic features of LE with an interface vacuolar dermatitis with melanophages, and a superficial and deep, perivascular and periadnexal lymphocytic infiltrate with mucin deposition. Herein, we describe a case of a 61-year-old White male presenting with melanotic cutaneous LE with a blaschkoid distribution on his face in which the histopathological phenomenon of "true melanocytic nests" in the setting of a lichenoid pattern was seen. We want to highlight how nests of cellular aggregates at the dermoepidermal junction labeling with melanocytic markers may occur in the setting of an interface tissue reaction. This benign reactional pattern may mimic atypical melanocytic proliferations, especially on sun-damaged skin. Clinicopathological correlation and careful microscopic examination using a panel of multiple melanocytic markers is crucial for making an accurate final diagnosis. All the cases of melanotic cutaneous LE reported in the literature are also reviewed.

Primary Cutaneous Spindle Cell B-Cell Follicle Center Lymphoma Presenting as Long-Standing Plaque of Cicatricial Alopecia: A Case Report With a Comprehensive Review of the Literature.

ABSTRACT: Primary cutaneous spindle B-cell lymphoma is an uncommon subtype of cutaneous lymphoma characterized by a distinct spindled cytology of neoplastic B cells. Despite sharing clinical, histopathological, and phenotypical similarities with primary cutaneous follicle center lymphoma, an indolent form of B-cell lymphoma, it also exhibits certain features akin to primary cutaneous diffuse large B-cell lymphoma. Notably, in rare instances, a more aggressive clinical course has been observed. This report details a rare case of primary cutaneous spindle cell B-cell follicle center lymphoma, manifested as a prolonged solitary plaque of cicatricial alopecia. In addition, we provide a comprehensive review of existing cases documented in the literature.

Purification of an Acidic Polysaccharide with Anticoagulant Activity from the Marine Sponge Sarcotragus spinosulus.

Thromboembolic conditions are the most common cause of death in developed countries. Anticoagulant therapy is the treatment of choice, and heparinoids and warfarin are the most adopted drugs. Sulphated polysaccharides extracted from marine organisms have been demonstrated to be effective alternatives, blocking thrombus formation by inhibiting some factors involved in the coagulation cascade. In this study, four acidic glycan fractions from the marine sponge Sarcotragus spinosulus were purified by anion-exchange chromatography, and their anticoagulant properties were investigated through APTT and PT assays and compared with both standard glycosaminoglycans and holothurian sulphated polysaccharides. Moreover, their topographic localization was assessed through histological analysis, and their cytocompatibility was tested on a human fibroblast cell line. A positive correlation between the amount of acid glycans and the inhibitory effect towards both the intrinsic and extrinsic coagulation pathways was observed. The most effective anticoagulant activity was shown by a highly charged fraction, which accounted for almost half (about 40%) of the total hexuronate-containing polysaccharides. Its preliminary structural characterization, performed through infrared spectroscopy and nuclear magnetic resonance, suggested that it may consist of a fucosylated chondroitin sulphate, whose unique structure may be responsible for the anticoagulant activity reported herein for the first time.

Exploring Aesthetic Perception in Impaired Aging: A Multimodal Brain-Computer Interface Study.

In the field of neuroscience, brain-computer interfaces (BCIs) are used to connect the human brain with external devices, providing insights into the neural mechanisms underlying cognitive processes, including aesthetic perception. Non-invasive BCIs, such as EEG and fNIRS, are critical for studying central nervous system activity and understanding how individuals with cognitive deficits process and respond to aesthetic stimuli. This study assessed twenty participants who were divided into control and impaired aging (AI) groups based on MMSE scores. EEG and fNIRS were used to measure their neurophysiological responses to aesthetic stimuli that varied in pleasantness and dynamism. Significant differences were identified between the groups in P300 amplitude and late positive potential (LPP), with controls showing greater reactivity. AI subjects showed an increase in oxyhemoglobin in response to pleasurable stimuli, suggesting hemodynamic compensation. This study highlights the effectiveness of multimodal BCIs in identifying the neural basis of aesthetic appreciation and impaired aging. Despite its limitations, such as sample size and the subjective nature of aesthetic appreciation, this research lays the groundwork for cognitive rehabilitation tailored to aesthetic perception, improving the comprehension of cognitive disorders through integrated BCI methodologies.

A Serious Game for the Assessment of Visuomotor Adaptation Capabilities during Locomotion Tasks Employing an Embodied Avatar in Virtual Reality.

The study of visuomotor adaptation (VMA) capabilities has been encompassed in various experimental protocols aimed at investigating human motor control strategies and/or cognitive functions. VMA-oriented frameworks can have clinical applications, primarily in the investigation and assessment of neuromotor impairments caused by conditions such as Parkinson's disease or post-stroke, which affect the lives of tens of thousands of people worldwide. Therefore, they can enhance the understanding of the specific mechanisms of such neuromotor disorders, thus being a potential biomarker for recovery, with the aim of being integrated with conventional rehabilitative programs. Virtual Reality (VR) can be entailed in a framework targeting VMA since it allows the development of visual perturbations in a more customizable and realistic way. Moreover, as has been demonstrated in previous works, a serious game (SG) can further increase engagement thanks to the use of full-body embodied avatars. Most studies implementing VMA frameworks have focused on upper limb tasks and have utilized a cursor as visual feedback for the user. Hence, there is a paucity in the literature about VMA-oriented frameworks targeting locomotion tasks. In this article, the authors present the design, development, and testing of an SG-based framework that addresses VMA in a locomotion activity by controlling a full-body moving avatar in a custom VR environment. This workflow includes a set of metrics to quantitatively assess the participants' performance. Thirteen healthy children were recruited to evaluate the framework. Several quantitative comparisons and analyses were run to validate the different types of introduced visuomotor perturbations and to evaluate the ability of the proposed metrics to describe the difficulty caused by such perturbations. During the experimental sessions, it emerged that the system is safe, easy to use, and practical in a clinical setting. Despite the limited sample size, which represents the main limitation of the study and can be compensated for with future recruitment, the authors claim the potential of this framework as a useful instrument for quantitatively assessing either motor or cognitive impairments. The proposed feature-based approach gives several objective parameters as additional biomarkers that can integrate the conventional clinical scores. Future studies might investigate the relation between the proposed biomarkers and the clinical scores for specific disorders such as Parkinson's disease and cerebral palsy.

The Scent of Monogamy: Self-Reported Olfactory Function Predicts Sexual Well-Being and Infidelity in an Italian Population.

Previous studies have shown that olfactory function plays an essential role in the bonding of a romantic relationship. Body odors, in particular, seem involved in both mate choices and other intimate behaviors. Our sense of smell is also crucial to detect possible pathogen threats, by activating a suitable disgust reaction. Previous studies have shown that disgust sensitivity is negatively related to sociosexuality, and disgust generally inhibits our sexual drive. In the present study, we explored the possible relation between olfactory function, pathogen disgust sensitivity, sociosexuality, sexual well-being, and infidelity through a web survey. Our exploratory analyses found that, in a large Italian sample (N = 1107), among those in a stable relationship, self-reported olfactory function predicted sexual well-being (p < .05) and negatively predicted infidelity (p < .05) when controlling for other relevant sociodemographics variables. Moreover, the relation between self-reported olfactory function and sexual well-being was mediated by pathogen disgust sensitivity. Although significant, these results must be interpreted with caution, because the effect sizes were small.

Liver Stiffness in Obese Hypothyroid Patients Taking Levothyroxine.

Background and Objectives: Thyroid dysfunction is associated with non-alcoholic fatty liver disease, but its role in the progression of liver damage in obese patients remains unclear. In addition, several case reports have suggested the existence of a levothyroxine-induced liver injury, which has been poorly investigated. Our aim was to verify whether a difference in the prevalence of liver fibrosis exists in a population of obese individuals taking Levothyroxine. Materials and Methods: We conducted a cross-sectional study on a population of 137 obese individuals, of which 49 were on replacement therapy with Levothyroxine. We excluded those who had hypertriglyceridemia and diabetes mellitus. All participants underwent a liver stiffness assessment by transient elastography as well as biochemical measurements. In subjects with liver fibrosis, other cause of liver fibrosis were ruled out. Results: Participants taking Levothyroxine had a higher prevalence of liver fibrosis than those not taking Levothyroxine (30.6% vs. 2.3%; p < 0.001), and these results were obtained after we made an adjustment for age (Exp(B) = 18.9; 95% CI = 4.1−87.4; p < 0.001). The liver stiffness value differed significantly between groups (6.0 ± 3.6 and 5.1 ± 1.2, p = 0.033). Of those subjects taking Levothyroxine, there were no significant differences in the dose of medication (1.21 ± 0.36 vs. 1.07 ± 0.42; p = 0.240) and treatment duration (13.7 ± 7.43 vs. 11.13 ± 6.23; p = 0.380) between those with and without liver fibrosis. Conclusions: We found, for the first time, a greater prevalence of liver fibrosis in obese individuals taking Levothyroxine than in those not taking this medication. This finding needs to be confirmed by longitudinal population studies as well as by cellular studies.

High Mobility Group A (HMGA) proteins: Molecular instigators of breast cancer onset and progression.

Cancer heterogeneity is one of the factors that constitute an obstacle towards an efficient targeting of this multifaceted disease. Molecular information can help in classifying cancer subtypes and in providing clinicians with novel targeted therapeutic opportunities. In this regard, classification of breast cancer into intrinsic subtypes based on molecular profiling represents a valuable prototype. The High Mobility Group A (HMGA) chromatin architectural factors (HMGA1a, HMGA1b, and HMGA2) have a relevant and causal role in breast cancer onset and development, by influencing virtually all cancer hallmarks. The regulation of HMGA expression is under the control of major pathways involved in cell proliferation and survival, as well as in other cancer-related processes, thereby suggesting, for the HMGA members, a high degree of homology and overlapping activities. Despite of this evidence, HMGA proteins display also specific functions. In this review, we provide an overview of (i) the pathways involved in HMGA transcriptional and post-transcriptional regulation, (ii) the utilization of HMGA as molecular markers, and (iii) the biological role of HMGA in the context of breast cancer. We focus on the potential significance of HMGA in governing the onset and development of this tumour, as well as on the potential of these factors as novel specific targets for preventing and treating strategies. The emerging picture is a highly interconnected triad of proteins that could mutually influence each other, either in a competitive or cooperative manner, and that, in our opinion, should be considered as a unified and integrated protein system.

A neural network for glomerulus classification based on histological images of kidney biopsy.

BACKGROUND: Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. RESULTS: We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). CONCLUSIONS: Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.

Modular MA-XRF Scanner Development in the Multi-Analytical Characterisation of a 17th Century Azulejo from Portugal.

A modular X-ray scanning system was developed, to fill in the gap between portable instruments (with a limited analytical area) and mobile instruments (with large analytical areas, and sometimes bulky and difficult to transport). The scanner has been compared to a commercial tabletop instrument, by analysing a Portuguese tile (azulejo) from the 17th century. Complementary techniques were used to achieve a throughout characterisation of the sample in a complete non-destructive approach. The complexity of the acquired X-ray fluorescence (XRF) spectra, due to inherent sample stratigraphy, has been resolved using Monte Carlo simulations, and Raman spectroscopy, as the most suitable technique to complement the analysis of azulejos colours, yielding satisfactory results. The colouring agents were identified as cobalt blue and a Zn-modified Naples-yellow. The stratigraphy of the area under study was partially modelled with Monte Carlo simulations. The scanners performance has been compared by evaluating the images outputs and the global spectrum.

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters.

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan-Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548-49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000-3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141-2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895-2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.

Insulin Resistance and Cancer: In Search for a Causal Link.

Insulin resistance (IR) is a condition which refers to individuals whose cells and tissues become insensitive to the peptide hormone, insulin. Over the recent years, a wealth of data has made it clear that a synergistic relationship exists between IR, type 2 diabetes mellitus, and cancer. Although the underlying mechanism(s) for this association remain unclear, it is well established that hyperinsulinemia, a hallmark of IR, may play a role in tumorigenesis. On the other hand, IR is strongly associated with visceral adiposity dysfunction and systemic inflammation, two conditions which favor the establishment of a pro-tumorigenic environment. Similarly, epigenetic modifications, such as DNA methylation, histone modifications, and non-coding RNA, in IR states, have been often associated with tumorigenesis in numerous types of human cancer. In addition to these observations, it is also broadly accepted that gut microbiota may play an intriguing role in the development of IR-related diseases, including type 2 diabetes and cancer, whereas potential chemopreventive properties have been attributed to some of the most commonly used antidiabetic medications. Herein we provide a concise overview of the most recent literature in this field and discuss how different but interrelated molecular pathways may impact on tumor development.

Phosphodiesterase Type-5 Inhibitor Tadalafil Modulates Steroid Hormones Signaling in a Prostate Cancer Cell Line.

BACKGROUND: The androgen receptor (AR) plays a key role in normal prostate homeostasis and in prostate cancer (PCa) development, while the role of aromatase (Cyp19a1) is still unclear. We evaluated the effects of a treatment with Tadalafil (TAD) on both these proteins. METHODS: Androgen-sensitive human PCa cell line (LnCAP) was incubated with/without TAD (10-6 M) and bicalutamide (BCT) (10-4 M) to evaluate a potential modulation on cell proliferation, protein and mRNA expression of Cyp19a, AR and estrogen receptor-ß (ERß), respectively. RESULTS: TAD increased early AR nuclear translocation (p < 0.05, after 15 min of exposure), and increased AR transcriptional activity (p < 0.05) and protein expression (p < 0.05) after 24 h. Moreover, after 24 h this treatment upregulated Cyp19a1 and ERß mRNA (p < 0.05 and p < 0.005 respectively) and led to an increase in protein expression of both after 48 h (p < 0.05). Interestingly, TAD counteracted Cyp19a1 stimulation induced by BCT (p < 0.05) but did not alter the effect induced by BCT on the AR protein expression. CONCLUSION: We demonstrate for the first time that TAD can significantly modulate AR expression and activity, Cyp19a1 and ERß expression in PCa cells, suggesting a specific effect of these proteins. In addition, TAD potentiates the antiproliferative activity of BCT, opening a new clinical scenario in the treatment of PCa.

A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.

BACKGROUND: The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. METHODS: Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. RESULTS: Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. CONCLUSION: The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

A low-cost vision system based on the analysis of motor features for recognition and severity rating of Parkinson's Disease.

BACKGROUND: Assessment and rating of Parkinson's Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the MDS-UPDRS (Movement Disorder Society - sponsored revision of Unified Parkinson's Disease Rating Scale) that is the most widely used clinical scale for PD rating. However, clinical scales rely on the observation of some subtle motor phenomena that are either difficult to capture with human eyes or could be misclassified. This limitation motivated several researchers to develop intelligent systems based on machine learning algorithms able to automatically recognize the PD. Nevertheless, most of the previous studies investigated the classification between healthy subjects and PD patients without considering the automatic rating of different levels of severity. METHODS: In this context, we implemented a simple and low-cost clinical tool that can extract postural and kinematic features with the Microsoft Kinect v2 sensor in order to classify and rate PD. Thirty participants were enrolled for the purpose of the present study: sixteen PD patients rated according to MDS-UPDRS and fourteen healthy paired subjects. In order to investigate the motor abilities of the upper and lower body, we acquired and analyzed three main motor tasks: (1) gait, (2) finger tapping, and (3) foot tapping. After preliminary feature selection, different classifiers based on Support Vector Machine (SVM) and Artificial Neural Networks (ANN) were trained and evaluated for the best solution. RESULTS: Concerning the gait analysis, results showed that the ANN classifier performed the best by reaching 89.4% of accuracy with only nine features in diagnosis PD and 95.0% of accuracy with only six features in rating PD severity. Regarding the finger and foot tapping analysis, results showed that an SVM using the extracted features was able to classify healthy subjects versus PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). CONCLUSIONS: The results of this study have shown how a low-cost vision-based system can automatically detect subtle phenomena featuring the PD. Our findings suggest that the proposed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Biometric handwriting analysis to support Parkinson's Disease assessment and grading.

BACKGROUND: Handwriting represents one of the major symptom in Parkinson's Disease (PD) patients. The computer-aided analysis of the handwriting allows for the identification of promising patterns that might be useful in PD detection and rating. In this study, we propose an innovative set of features extracted by geometrical, dynamical and muscle activation signals acquired during handwriting tasks, and evaluate the contribution of such features in detecting and rating PD by means of artificial neural networks. METHODS: Eleven healthy subjects and twenty-one PD patients were enrolled in this study. Each involved subject was asked to write three different patterns on a graphic tablet while wearing the Myo Armband used to collect the muscle activation signals of the main forearm muscles. We have then extracted several features related to the written pattern, the movement of the pen and the pressure exerted with the pen and the muscle activations. The computed features have been used to classify healthy subjects versus PD patients and to discriminate mild PD patients from moderate PD patients by using an artificial neural network (ANN). RESULTS: After the training and evaluation of different ANN topologies, the obtained results showed that the proposed features have high relevance in PD detection and rating. In particular, we found that our approach both detect and rate (mild and moderate PD) with a classification accuracy higher than 90%. CONCLUSIONS: In this paper we have investigated the representativeness of a set of proposed features related to handwriting tasks in PD detection and rating. In particular, we used an ANN to classify healthy subjects and PD patients (PD detection), and to classify mild and moderate PD patients (PD rating). The implemented and tested methods showed promising results proven by the high level of accuracy, sensitivity and specificity. Such results suggest the usability of the proposed setup in clinical settings to support the medical decision about Parkinson's Disease.

Secretome Analysis of Hypoxia-Induced 3T3-L1 Adipocytes Uncovers Novel Proteins Potentially Involved in Obesity.

In the obese state, as adipose tissue expands, adipocytes become hypoxic and dysfunctional, leading to changes in the pattern of adipocyte-secreted proteins. To better understand the role of hypoxia in the mechanisms linked to obesity, we comparatively analyzed the secretome of murine differentiated 3T3-L1 adipocytes exposed to normoxia or hypoxia for 24 h. Proteins secreted into the culture media were precipitated by trichloroacetic acid and then digested with trypsin. The peptides were labeled with dimethyl labeling and analyzed by reversed phase nanoscale liquid chromatography coupled to a quadrupole Orbitrap mass spectrometer. From a total of 1508 identified proteins, 109 were differentially regulated, of which 108 were genuinely secreted. Factors significantly downregulated in hypoxic conditions included adiponectin, a known adipokine implicated in metabolic processes, as well as thrombospondin-1 and -2, and matrix metalloproteinase-11, all multifunctional proteins involved in extracellular matrix (ECM) homeostasis. Findings were validated by Western blot analysis. Expression studies of the relative genes were performed in parallel experiments in vitro, in differentiated 3T3-L1 adipocytes, and in vivo, in fat tissues from obese versus lean mice. Our observations are compatible with the concept that hypoxia may be an early trigger for both adipose cell dysfunction and ECM remodeling.

The correct renal function evaluation in patients with thyroid dysfunction.

Thyroid dysfunction induces several renal derangements involving all nephron portions. Furthermore, dysthyroidism is a recognized risk factor associated with the development of chronic kidney disease. Current data, in fact, demonstrate that either subclinical or overt thyroid disease is associated with significant changes in creatinine, estimated glomerular filtration rate, measured glomerular filtration rate and Cystatin C. Herein, we systematically reviewed several relevant studies aiming at the identification of the most sensitive and specific parameter for the correct renal function evaluation in patients with thyroid dysfunction, that are usually treated as outpatients. Our systematic review indicates that estimated glomerular filtration rate, preferably with CKD-EPI equation, appears to be the most reliable and wieldy renal function parameter. Instead, Cystatin C should be better used in the grading of thyroid dysfunction severity.

High-mobility group A1 protein: a new coregulator of peroxisome proliferator-activated receptor-γ-mediated transrepression in the vasculature.

RATIONALE: The nuclear receptor peroxisome proliferator-activated receptor-γ (PPARγ) is an important regulator of gene transcription in vascular cells and mediates the vascular protection observed with antidiabetic glitazones. OBJECTIVE: To determine the molecular mechanism of ligand-dependent transrepression in vascular smooth muscle cells and their impact on the vascular protective actions of PPARγ. METHODS AND RESULTS: Here, we report a molecular pathway in vascular smooth muscle cells by which ligand-activated PPARγ represses transcriptional activation of the matrix-degrading matrix metalloproteinase-9 (MMP-9) gene, a crucial mediator of vascular injury. PPARγ-mediated transrepression of the MMP-9 gene was dependent on the presence of the high-mobility group A1 (HMGA1) protein, a gene highly expressed in vascular smooth muscle cells, newly identified by oligonucleotide array expression analysis. Transrepression of MMP-9 by PPARγ and regulation by HMGA1 required PPARγ SUMOylation at K367. This process was associated with formation of a complex between PPARγ, HMGA1, and the SUMO E2 ligase Ubc9 (ubiquitin-like protein SUMO-1 conjugating enzyme). After PPARγ ligand stimulation, HMGA1 and PPARγ were recruited to the MMP-9 promoter, which facilitated binding of SMRT (silencing mediator of retinoic acid and thyroid hormone receptor), a nuclear corepressor involved in transrepression. The relevance of HMGA1 for vascular PPARγ signaling was underlined by the complete absence of vascular protection through a PPARγ ligand in HMGA1(-/-) mice after arterial wire injury. CONCLUSIONS: The present data suggest that ligand-dependent formation of HMGA1-Ubc9-PPARγ complexes facilitates PPARγ SUMOylation, which results in the prevention of SMRT corepressor clearance and induction of MMP-9 transrepression. These data provide new information on PPARγ-dependent vascular transcriptional regulation and help us to understand the molecular consequences of therapeutic interventions with PPARγ ligands in the vasculature.

Early effects of a hypocaloric, Mediterranean diet on laboratory parameters in obese individuals.

Calorie restriction is a common strategy for weight loss in obese individuals. However, little is known about the impact of moderate hypocaloric diets on obesity-related laboratory parameters in a short-term period. Aim of this study was to evaluate the variation of laboratory biomarkers in obese individuals following a Mediterranean, hypocaloric (1400-1600 Kcal/die) diet. 23 obese, pharmacologically untreated patients were enrolled and subjected to the determination of anthropometric variables and blood collection at baseline, 1 and 4 months after diet initiation. After 4 months of calorie restriction, we observed a significant decrease in body weight and BMI (both P < 0.0001), insulin (P = 0.037), HOMA-IR (P = 0.026), leptin (P = 0.008), and LDH (P = 0.023) and an increase in EGF (P = 0.013). All these parameters, except LDH, varied significantly already at 1 month after diet initiation. Also, lower levels of insulin (P = 0.025), leptin (P = 0.023), and EGF (P = 0.035) were associated with a greater (>5%) weight loss. Collectively, our data support a precocious improvement of insulin and leptin sensitivity after a modest calorie restriction and weight reduction. Moreover, EGF and LDH may represent novel markers of obesity, which deserve further investigations.