Mitochondrial DNA copy number and trimethylamine levels in the blood: New insights on cardiovascular disease biomarkers.

Among cardiovascular disease (CVD) biomarkers, the mitochondrial DNA copy number (mtDNAcn) is a promising candidate. A growing attention has been also dedicated to trimethylamine-N-oxide (TMAO), an oxidative derivative of the gut metabolite trimethylamine (TMA). With the aim to identify biomarkers predictive of CVD, we investigated TMA, TMAO, and mtDNAcn in a population of 389 coronary artery disease (CAD) patients and 151 healthy controls, in association with established risk factors for CVD (sex, age, hypertension, smoking, diabetes, glomerular filtration rate [GFR]) and troponin, an established marker of CAD. MtDNAcn was significantly lower in CAD patients; it correlates with GFR and TMA, but not with TMAO. A biomarker including mtDNAcn, sex, and hypertension (but neither TMA nor TMAO) emerged as a good predictor of CAD. Our findings support the mtDNAcn as a promising plastic biomarker, useful to monitor the exposure to risk factors and the efficacy of preventive interventions for a personalized CAD risk reduction.

A Bayesian approach for monitoring epidemics in presence of undetected cases.

One of the key indicators used in tracking the evolution of an infectious disease is the reproduction number. This quantity is usually computed using the reported number of cases, but ignoring that many more individuals may be infected (e.g. asymptomatic carriers). We develop a Bayesian procedure to quantify the impact of undetected infectious cases on the determination of the effective reproduction number. Our approach is stochastic, data-driven and not relying on any compartmental model. It is applied to the COVID-19 outbreak in eight different countries and all Italian regions, showing that the effect of undetected cases leads to estimates of the effective reproduction numbers larger than those obtained only with the reported cases by factors ranging from two to ten.

Machine learning models predicting multidrug resistant urinary tract infections using "DsaaS".

BACKGROUND: The scope of this work is to build a Machine Learning model able to predict patients risk to contract a multidrug resistant urinary tract infection (MDR UTI) after hospitalization. To achieve this goal, we used different popular Machine Learning tools. Moreover, we integrated an easy-to-use cloud platform, called DSaaS (Data Science as a Service), well suited for hospital structures, where healthcare operators might not have specific competences in using programming languages but still, they do need to analyze data as a continuous process. Moreover, DSaaS allows the validation of data analysis models based on supervised Machine Learning regression and classification algorithms. RESULTS: We used DSaaS on a real antibiotic stewardship dataset to make predictions about antibiotic resistance in the Clinical Pathology Operative Unit of the Principe di Piemonte Hospital in Senigallia, Marche, Italy. Data related to a total of 1486 hospitalized patients with nosocomial urinary tract infection (UTI). Sex, age, age class, ward and time period, were used to predict the onset of a MDR UTI. Machine Learning methods such as Catboost, Support Vector Machine and Neural Networks were utilized to build predictive models. Among the performance evaluators, already implemented in DSaaS, we used accuracy (ACC), area under receiver operating characteristic curve (AUC-ROC), area under Precision-Recall curve (AUC-PRC), F1 score, sensitivity (SEN), specificity and Matthews correlation coefficient (MCC). Catboost exhibited the best predictive results (MCC 0.909; SEN 0.904; F1 score 0.809; AUC-PRC 0.853, AUC-ROC 0.739; ACC 0.717) with the highest value in every metric. CONCLUSIONS: the predictive model built with DSaaS may serve as a useful support tool for physicians treating hospitalized patients with a high risk to acquire MDR UTIs. We obtained these results using only five easy and fast predictors accessible for each patient hospitalization. In future, DSaaS will be enriched with more features like unsupervised Machine Learning techniques, streaming data analysis, distributed calculation and big data storage and management to allow researchers to perform a complete data analysis pipeline. The DSaaS prototype is available as a demo at the following address: https://dsaas-demo.shinyapps.io/Server/.

Gender-Related Differences in Trimethylamine and Oxidative Blood Biomarkers in Cardiovascular Disease Patients.

Gender differences in the burden of cardiovascular disease (CVD) have been observed worldwide. In this study, plasmatic levels of trimethylamine (TMA) and blood oxidative biomarkers have been evaluated in 358 men (89 controls and 269 CVD patients) and 189 women (64 control and 125 CVD patients). The fluorescence technique was applied to determine erythrocyte membrane fluidity using 1,6-diphenyl-1,3,5-hexatriene (DPH) and Laurdan, while lipid hydroperoxides were assessed by diphenyl-1-pyrenylphosphine (DPPP). Results show that levels of plasmatic TMA were higher in healthy men with respect to healthy women (p = 0.0001). Significantly lower TMA was observed in male CVD patients (0.609 ± 0.104 µM) compared to healthy male controls (0.680 ± 0.118 µM) (p < 0.001), while higher levels of TMA were measured in female CVD patients (0.595 ± 0.115 µM) with respect to female controls (0.529 ± 0.073 µM) (p < 0.001). DPPP was significantly higher in healthy control men than in women (p < 0.001). Male CVD patients displayed a lower value of DPPP (2777 ± 1924) compared to healthy controls (5528 ± 2222) (p < 0.001), while no significant changes were measured in females with or without CVD (p > 0.05). Membrane fluidity was significantly higher (p < 0.001) in the hydrophobic bilayer only in control male subjects. In conclusion, gender differences were observed in blood oxidative biomarkers, and DPPP value might be suggested as a biomarker predictive of CVD only in men.

Visualising 2-simplex formation in metabolic reactions.

Understanding in silico the dynamics of metabolic reactions made by a large number of molecules has led to the development of different tools for visualising molecular interactions. However, most of them are mainly focused on quantitative aspects. We investigate the potentiality of the topological interpretation of the interaction-as-perception at the basis of a multiagent system, to tackle the complexity of visualising the emerging behaviour of a complex system. We model and simulate the glycolysis process as a multiagent system, and we perform topological data analysis of the molecular perceptions graphs, gained during the formation of the enzymatic complexes, to visualise the set of emerging patterns. Identifying expected patterns in terms of simplicial structures allows us to characterise metabolic reactions from a qualitative point of view and conceivably reveal the simulation reactivity trend.

Anti-Inflammatory, Anti-Arthritic and Anti-Nociceptive Activities of Nigella sativa Oil in a Rat Model of Arthritis.

This study investigated the preventive efficacy of the crude oil extracted from Nigella sativa seeds in a rat model of arthritis induced by using complete Freund's adjuvant (CFA). Nigella sativa oil at 1.82 mL/kg or 0.91 mL/kg (corresponding to 1596 and 798 mg/kg, respectively) was orally administered for 25 days from the day of immunization. One immunized group was treated orally with indomethacin (3 mg/kg) as a reference drug. Body weight growth rate, paw swelling, arthritis score, mechanical allodynia, locomotor activity and anxiety-like behavior were observed, and the levels of Interleukin 6 (IL-6), C-reactive protein, albumin and total cholesterol in plasma were measured on days 15 and 25. Nigella sativa oil showed anti-inflammatory, anti-arthritic and anti-nociceptive activities that were significant as compared to untreated arthritic rats but less than indomethacin. These results indicated that Nigella sativa oil significantly attenuated adjuvant-arthritis in rats and the higher dose (1.82 mL/kg) prevented the development of arthritis with an inhibition of 56%.

Topolnogical classifier for detecting the emergence of epileptic seizures.

OBJECTIVE: An innovative method based on topological data analysis is introduced for classifying EEG recordings of patients affected by epilepsy. We construct a topological space from a collection of EEGs signals using Persistent Homology; then, we analyse the space by Persistent entropy, a global topological feature, in order to classify healthy and epileptic signals. RESULTS: The performance of the resulting one-feature-based linear topological classifier is tested by analysing the Physionet dataset. The quality of classification is evaluated in terms of the Area Under Curve (AUC) of the receiver operating characteristic curve. It is shown that the linear topological classifier has an AUC equal to [Formula: see text] while the performance of a classifier based on Sample Entropy has an AUC equal to 62.0%.

A novel neural prosthesis providing long-term electrocorticography recording and cortical stimulation for epilepsy and brain-computer interface.

OBJECTIVE: Wireless technology is a novel tool for the transmission of cortical signals. Wireless electrocorticography (ECoG) aims to improve the safety and diagnostic gain of procedures requiring invasive localization of seizure foci and also to provide long-term recording of brain activity for brain-computer interfaces (BCIs). However, no wireless devices aimed at these clinical applications are currently available. The authors present the application of a fully implantable and externally rechargeable neural prosthesis providing wireless ECoG recording and direct cortical stimulation (DCS). Prolonged wireless ECoG monitoring was tested in nonhuman primates by using a custom-made device (the ECoG implantable wireless 16-electrode [ECOGIW-16E] device) containing a 16-contact subdural grid. This is a preliminary step toward large-scale, long-term wireless ECoG recording in humans. METHODS: The authors implanted the ECOGIW-16E device over the left sensorimotor cortex of a nonhuman primate (Macaca fascicularis), recording ECoG signals over a time span of 6 months. Daily electrode impedances were measured, aiming to maintain the impedance values below a threshold of 100 KΩ. Brain mapping was obtained through wireless cortical stimulation at fixed intervals (1, 3, and 6 months). After 6 months, the device was removed. The authors analyzed cortical tissues by using conventional histological and immunohistological investigation to assess whether there was evidence of damage after the long-term implantation of the grid. RESULTS: The implant was well tolerated; no neurological or behavioral consequences were reported in the monkey, which resumed his normal activities within a few hours of the procedure. The signal quality of wireless ECoG remained excellent over the 6-month observation period. Impedance values remained well below the threshold value; the average impedance per contact remains approximately 40 KΩ. Wireless cortical stimulation induced movements of the upper and lower limbs, and elicited fine movements of the digits as well. After the monkey was euthanized, the grid was found to be encapsulated by a newly formed dural sheet. The grid removal was performed easily, and no direct adhesions of the grid to the cortex were found. Conventional histological studies showed no cortical damage in the brain region covered by the grid, except for a single microscopic spot of cortical necrosis (not visible to the naked eye) in a region that had undergone repeated procedures of electrical stimulation. Immunohistological studies of the cortex underlying the grid showed a mild inflammatory process. CONCLUSIONS: This preliminary experience in a nonhuman primate shows that a wireless neuroprosthesis, with related long-term ECoG recording (up to 6 months) and multiple DCSs, was tolerated without sequelae. The authors predict that epilepsy surgery could realize great benefit from this novel prosthesis, providing an extended time span for ECoG recording.

HTR2C Gene Variant and Salivary Cortisol Levels after Endurance Physical Activity: A Pilot Study.

INTRODUCTION: The 5-hydroxytryptamine 2C receptor (HTR2C) rs6318 polymorphism has been associated with increased sensitivity to stress. This study investigated whether the rs6318 genotype modified the cortisol response to endurance physical activity. METHODS: The HTR2C SNP was genotyped in a population of agonistic cyclists, and salivary cortisol levels were measured before and after an endurance competition. RESULTS AND CONCLUSION: Salivary cortisol levels increased after the competition (from 20.72 ± 12.36 ng/mL to 33.80 ± 21.53 ng/mL; p = 3.189 × 10-5). rs6318 C carriers displayed higher baseline cortisol levels compared to G carriers (26.60 ± 9.35 ng/mL vs. 19.50 ± 12.63 ng/mL; p = 0.04). Baseline cortisol levels were able to predict the cortisol response to exercise (ß = -0.846; p = 1.2 × 10-5). Although regression analysis did not identify an association between HTR2C genotype and change in cortisol levels, a secondary analysis in which the population was classified by median cortisol changes suggested that they might be weakly associated, thus warranting further investigation.

Obesity-related genetic polymorphisms and adiposity indices in a young Italian population.

Pediatric obesity develops when a complex biological predisposition collides with an obesogenic environment. To further elucidate the role of genetics in obesity onset, we performed a candidate-gene association study in a young and sportive Italian population by testing the association of functional polymorphisms in ACE (rs4646994), FTO (rs9939609), MC4R (rs17782313) and PPARG (rs1801282) genes with body mass index (BMI) and waist-to-height ratio (WHtR). We also tested the combinations of identified risk genotypes and epistatic interactions among them to determine the existence of cumulative effects in predicting the predisposition to gain weight. Our results confirm a significant direct influence of MC4R rs17782313 and PPARG rs1801282 on body composition, that is, minor allele homozygotes showed significantly higher BMI (rs17782313, ß = 1.258, P = 0.031; rs1801282, ß = 6.689, P = 1.2 × 10-4 ) and WHtR (rs17782313, ß = 0.021, P = 0.005; rs1801282, ß = 0.069, P = 0.003) values. Moreover, by leveraging multifactor dimensionality reduction and general linear model (GLM) approaches we identified an epistatic interaction between ACE and MC4R, where heterozygosity at ACE rs4646994 seems to protect from the unfavorable predisposition to gain weight given by C/C genotype at MC4R rs17782313 (GLM, P = 0.004). In conclusion, to clarify the role of genetics in multifactorial diseases remains a difficult goal, even for the most investigated polymorphisms and in controlled populations. Further studies on epistasis and gene-gene interaction will help to elucidate this complex scenario. © 2017 IUBMB Life, 69(2):98-105, 2017.

Pyrethroid Pesticide Metabolite in Urine and Microelements in Hair of Children Affected by Autism Spectrum Disorders: A Preliminary Investigation.

The number of children affected by Autism Spectrum Disorders (ASD) is dramatically increasing as well as the studies aimed at understanding the risk factors associated with the development of ASD. Since the etiology of ASD is partly genetic and partly environmental, factors (i.e., heavy metals, pesticides) as well as lifestyle seem to have a key role in the development of the disease. ASD and Control (CTR) children, aged 5-12 years, were compared. Gas chromatography coupled with trap mass detector was used to measure the level of 3-PBA, the main pyrethroid metabolite in urine in a group of ASD patients, while optical emission spectrometry analysis was employed to estimate the level of metals and microelements in hair in a different group of ASD children. The presence of 3-PBA in urine seems to be independent of age in ASD children, while a positive correlation between 3-PBA and age was observed in the control group of the same age range. Urine concentration of 3-BPA in ASD children had higher values than in the control group, which were marginally significant (p = 0.054). Mg results were significantly decreased in ASD with respect to controls, while V, S, Zn, and Ca/Mg were marginally increased, without reaching statistical significance. Results of Principal Component (PC) analysis of metals and microelements in hair were not associated with either age or health status. In conclusion, 3-PBA in urine and Mg in hair were changed in ASD children relative to control ones.

Metal and Microelement Biomarkers of Neurodegeneration in Early Life Permethrin-Treated Rats.

Hair is a non-invasive biological material useful in the biomonitoring of trace elements because it is a vehicle for substance excretion from the body, and it permits evaluating long-term metal exposure. Here, hair from an animal model of neurodegeneration, induced by early life permethrin treatment from the sixth to 21th day of life, has been analyzed with the aim to assess if metal and microelement content could be used as biomarkers. A hair trace element assay was performed by the ICP-MS technique in six- and 12-month-old rats. A significant increase of As, Mg, S and Zn was measured in the permethrin-treated group at 12 months compared to six months, while Si and Cu/Zn were decreased. K, Cu/Zn and S were increased in the treated group compared to age-matched controls at six and 12 months, respectively. Cr significantly decreased in the treated group at 12 months. PCA analysis showed both a best difference between treated and age-matched control groups at six months. The present findings support the evidence that the Cu/Zn ratio and K, measured at six months, are the best biomarkers for neurodegeneration. This study supports the use of hair analysis to identify biomarkers of neurodegeneration induced by early life permethrin pesticide exposure.

A fully integrated wireless system for intracranial direct cortical stimulation, real-time electrocorticography data transmission, and smart cage for wireless battery recharge.

Wireless transmission of cortical signals is an essential step to improve the safety of epilepsy procedures requiring seizure focus localization and to provide chronic recording of brain activity for Brain Computer Interface (BCI) applications. Our group developed a fully implantable and externally rechargeable device, able to provide wireless electrocorticographic (ECoG) recording and cortical stimulation (CS). The first prototype of a wireless multi-channel very low power ECoG system was custom-designed to be implanted on non-human primates. The device, named ECOGIW-16E, is housed in a compact hermetically sealed Polyether ether ketone (PEEK) enclosure, allowing seamless battery recharge. ECOGIW-16E is recharged in a wireless fashion using a special cage designed to facilitate the recharge process in monkeys and developed in accordance with guidelines for accommodation of animals by Council of Europe (ETS123). The inductively recharging cage is made up of nylon and provides a thoroughly novel experimental setting on freely moving animals. The combination of wireless cable-free ECoG and external seamless battery recharge solves the problems and shortcomings caused by the presence of cables leaving the skull, providing a safer and easier way to monitor patients and to perform ECoG recording on primates. Data transmission exploits the newly available Medical Implant Communication Service band (MICS): 402-405 MHz. ECOGIW-16E was implanted over the left sensorimotor cortex of a macaca fascicularis to assess the feasibility of wireless ECoG monitoring and brain mapping through CS. With this device, we were able to record the everyday life ECoG signal from a monkey and to deliver focal brain stimulation with movement elicitation.