Sex-specific differences in myocardial glucose metabolic rate in non-diabetic, pre-diabetic and type 2 diabetic subjects.

BACKGROUND: Evidence has shown that women with type 2 diabetes (T2DM) have a higher excess risk for cardiovascular disease (CVD) than men with T2DM. Subjects with either T2DM or prediabetes exhibit myocardial insulin resistance, but it is still unsettled whether sex-related differences in myocardial insulin resistance occur in diabetic and prediabetic subjects. METHODS: We aimed to evaluate sex-related differences in myocardial glucose metabolic rate (MRGlu), assessed using dynamic PET with 18F-FDG combined with euglycemic-hyperinsulinemic clamp, in subjects with normal glucose tolerance (NGT; n = 20), prediabetes (n = 11), and T2DM (n = 26). RESULTS: Women with prediabetes or T2DM exhibited greater relative differences in myocardial MRGlu than men with prediabetes or T2DM when compared with their NGT counterparts. As compared with women with NGT, those with prediabetes exhibited an age-adjusted 35% lower myocardial MRGlu value (P = 0.04) and women with T2DM a 74% lower value (P = 0.006), respectively. Conversely, as compared with men with NGT, men with T2DM exhibited a 40% lower myocardial MRGlu value (P = 0.004), while no significant difference was observed between men with NGT and prediabetes. The statistical test for interaction between sex and glucose tolerance on myocardial MRGlu (P < 0.0001) was significant suggesting a sex-specific association. CONCLUSIONS: Our data suggest that deterioration of glucose homeostasis in women is associated with a greater impairment in myocardial glucose metabolism as compared with men. The sex-specific myocardial insulin resistance could be an important factor responsible for the greater effect of T2DM on the excess risk of cardiovascular disease in women than in men.

Machine learning pipeline to analyze clinical and proteomics data: experiences on a prostate cancer case.

Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry requires platforms to identify and quantify proteins (or peptides). Clinical information can be related to mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists in studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed at studying and classifying proteomics data enriched using clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training, and (iii) models ensembling. We report the experience of applying such a pipeline to prostate-related diseases. Models have been trained on several biological datasets. We report experimental results about two datasets that result from the integration of clinical and mass spectrometry-based data in the contexts of serum and urine analysis. The pipeline receives input data from blood analytes, tissue samples, proteomic analysis, and urine biomarkers. It then trains different models for feature selection, classification and voting. The presented pipeline has been applied on two datasets obtained in a 2 years research project which aimed to extract hidden information from mass spectrometry, serum, and urine samples from hundreds of patients. We report results on analyzing prostate datasets serum with 143 samples, including 79 PCa and 84 BPH patients, and an urine dataset with 121 samples, including 67 PCa and 54 BPH patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both serum (AUC=0.87, Accuracy=0.83, F1=0.81, Sensitivity=0.84, Specificity=0.81) and urine (AUC=0.88, Accuracy=0.83, F1=0.83, Sensitivity=0.85, Specificity=0.80) datasets showed good predictive performances. We made the pipeline code available on GitHub and we are confident that it will be successfully adopted in similar clinical setups.

Quantitative analysis of visually normal EEG reveals spectral power abnormalities in temporal lobe epilepsy.

OBJECTIVE: To compare quantitative spectral parameters of visually-normal EEG between Mesial Temporal Lobe Epilepsy (MTLE) patients and healthy controls (HC). METHOD: We enrolled 26 MTLE patients and 26 HC. From each recording we calculated total power of all frequency bands and determined alpha-theta (ATR) and alpha-delta (ADR) power ratios in different brain regions. Group-wise differences between spectral parameters were investigated (p < 0.05). To test for associations between spectral-power and cognitive status, we evaluated correlations between neuropsychological tests and quantitative EEG (qEEG) metrics. RESULTS: In all comparisons, ATR and ADR were significantly decreased in MTLE patients compared to HC, particularly over the hemisphere ipsilateral to epileptic activity. A positive correlation was seen in MTLE patients between ATR in ipsilateral temporal lobe, and results of neuropsychological tests of auditory verbal learning (RAVLT and RAVLT-D), short term verbal memory (Digit span backwards), and executive function (Weigl's sorting test). ADR values in the contralateral posterior region correlated positively with RAVLT-D and Digit span backwards tests. DISCUSSION: Results confirmed that the power spectrum of qEEG is shifted towards lower frequencies in MTLE patients compared to HC. CONCLUSION: Of note, our results were found in visually-normal recordings, providing further evidence of the value of qEEG for longitudinal monitoring of MTLE patients over time. Exploratory analysis of associations between qEEG and neuropsychological data suggest this could be useful for investigating effects of antiseizure medications on cognitive integrity in patients.

The Omicron XBB.1 Variant and Its Descendants: Genomic Mutations, Rapid Dissemination and Notable Characteristics.

The SARS-CoV-2 virus, which is a major threat to human health, has undergone many mutations during the replication process due to errors in the replication steps and modifications in the structure of viral proteins. The XBB variant was identified for the first time in Singapore in the fall of 2022. It was then detected in other countries, including the United States, Canada, and the United Kingdom. We study the impact of sequence changes on spike protein structure on the subvariants of XBB, with particular attention to the velocity of variant diffusion and virus activity with respect to its diffusion. We examine the structural and functional distinctions of the variants in three different conformations: (i) spike glycoprotein in complex with ACE2 (1-up state), (ii) spike glycoprotein (closed-1 state), and (iii) S protein (open-1 state). We also estimate the affinity binding between the spike protein and ACE2. The market binding affinity observed in specific variants raises questions about the efficacy of current vaccines in preparing the immune system for virus variant recognition. This work may be useful in devising strategies to manage the ongoing COVID-19 pandemic. To stay ahead of the virus evolution, further research and surveillance should be carried out to adjust public health measures accordingly.

Strategies and Trends in COVID-19 Vaccination Delivery: What We Learn and What We May Use for the Future.

Vaccination has been the most effective way to control the outbreak of the COVID-19 pandemic. The numbers and types of vaccines have reached considerable proportions, even if the question of vaccine procedures and frequency still needs to be resolved. We have come to learn the necessity of defining vaccination distribution strategies with regard to COVID-19 that could be used for any future pandemics of similar gravity. In fact, vaccine monitoring implies the existence of a strategy that should be measurable in terms of input and output, based on a mathematical model, including death rates, the spread of infections, symptoms, hospitalization, and so on. This paper addresses the issue of vaccine diffusion and strategies for monitoring the pandemic. It provides a description of the importance and take up of vaccines and the links between procedures and the containment of COVID-19 variants, as well as the long-term effects. Finally, the paper focuses on the global scenario in a world undergoing profound social and political change, with particular attention on current and future health provision. This contribution would represent an example of vaccination experiences, which can be useful in other pandemic or epidemiological contexts.

Impaired insulin-stimulated myocardial glucose metabolic rate is associated with reduced estimated myocardial energetic efficiency in subjects with different degrees of glucose tolerance.

BACKGROUND: Alterations in myocardial mechano-energetic efficiency (MEEi), which represents the capability of the left ventricles to convert the chemical energy obtained by oxidative metabolism into mechanical work, have been associated with cardiovascular disease. Although whole-body insulin resistance has been related to impaired myocardial MEEi, it is unknown the relationship between cardiac insulin resistance and MEEi. Aim of this study was to evaluate the relationship between insulin-stimulated myocardial glucose metabolic rate (MrGlu) and myocardial MEEi in subjects having different degrees of glucose tolerance. METHODS: We evaluated insulin-stimulated myocardial MrGlu using cardiac dynamic positron emission tomography (PET) with 18F-Fluorodeoxyglucose (18F-FDG) combined with euglycemic-hyperinsulinemic clamp, and myocardial MEEi in 57 individuals without history of coronary heart disease having different degrees of glucose tolerance. The subjects were stratified into tertiles according to their myocardial MrGlu values. RESULTS: After adjusting for age, gender and BMI, subjects in I tertile showed a decrease in myocardial MEEi (0.31 ± 0.05 vs 0.42 ± 0.14 ml/s*g, P = 0.02), and an increase in myocardial oxygen consumption (MVO2) (10,153 ± 1375 vs 7816 ± 1229 mmHg*bpm, P < 0.0001) as compared with subjects in III tertile. Univariate correlations showed that insulin-stimulated myocardial MrGlu was positively correlated with MEEi and whole-body glucose disposal, and negatively correlated with waist circumference, fasting plasma glucose, HbA1c and MVO2. In a multivariate regression analysis running a model including several CV risk factors, the only variable that remained significantly associated with MEEi was myocardial MrGlu (ß 0.346; P = 0.01). CONCLUSIONS: These data suggest that an impairment in insulin-stimulated myocardial glucose metabolism is an independent contributor of depressed myocardial MEEi in subjects without history of CHD.

REHABS: An Innovative and User-Friendly Device for Rehabilitation.

Rehabilitation is a complex set of interventions involving the assessment, management, and treatment of injuries. It aims to support and facilitate an individual's recovery process by restoring a physiological function, e.g., limb movement, compromised by physical impairments, injuries or diseases to a condition as close to normal as possible. Innovative devices and solutions make the rehabilitation process of patients easier during their daily activities. Devices support physicians and physiotherapists in monitoring and measuring patients' physical improvements during rehabilitation. In this context, we report the design and implementation of a low-cost rehabilitation system, which is a programmable device designed to support tele-rehabilitation of the upper limbs. The proposed system includes a mechanism to acquire and analyze data and signals related to rehabilitation processes.

Metabolic Syndrome Is Associated With Impaired Insulin-Stimulated Myocardial Glucose Metabolic Rate in Individuals With Type 2 Diabetes: A Cardiac Dynamic 18F-FDG-PET Study.

Metabolic syndrome is a condition characterized by a clustering of metabolic abnormalities associated with an increased risk of type 2 diabetes and cardiovascular disease. An impaired insulin-stimulated myocardial glucose metabolism has been shown to be a risk factor for the development of cardiovascular disease in patients with type 2 diabetes. Whether cardiac insulin resistance occurs in subjects with metabolic syndrome remains uncertain. To investigate this issue, we evaluated myocardial glucose metabolic rate using cardiac dynamic 18F-FDG-PET combined with euglycemic-hyperinsulinemic clamp in three groups: a group of normal glucose tolerant individuals without metabolic syndrome (n = 10), a group of individuals with type 2 diabetes and metabolic syndrome (n = 19), and a group of subjects with type 2 diabetes without metabolic syndrome (n = 6). After adjusting for age and gender, individuals with type 2 diabetes and metabolic syndrome exhibited a significant reduction in insulin-stimulated myocardial glucose metabolic rate (10.5 ± 9.04 µmol/min/100 g) as compared with both control subjects (32.9 ± 9.7 µmol/min/100 g; P < 0.0001) and subjects with type 2 diabetes without metabolic syndrome (25.15 ± 4.92 µmol/min/100 g; P = 0.01). Conversely, as compared with control subjects (13.01 ± 8.53 mg/min x Kg FFM), both diabetic individuals with metabolic syndrome (3.06 ± 1.7 mg/min × Kg FFM, P = 0.008) and those without metabolic syndrome (2.91 ± 1.54 mg/min × Kg FFM, P = 0.01) exhibited a significant reduction in whole-body insulin-stimulated glucose disposal, while no difference was observed between the 2 groups of subjects with type 2 diabetes with or without metabolic syndrome. Univariate correlations showed that myocardial glucose metabolism was positively correlated with insulin-stimulated glucose disposal (r = 0.488, P = 0.003), and negatively correlated with the presence of metabolic syndrome (r = -0.743, P < 0.0001) and with its individual components. In conclusion, our data suggest that an impaired myocardial glucose metabolism may represent an early cardio-metabolic defect in individuals with the coexistence of type 2 diabetes and metabolic syndrome, regardless of whole-body insulin resistance.

Effects of 26 weeks of treatment with empagliflozin versus glimepiride on the myocardial glucose metabolic rate in patients with type 2 diabetes: The randomized, open-label, crossover, active-comparator FIORE trial.

AIM: To determine whether treatment with empagliflozin was able to affect the myocardial glucose metabolic rate, as assessed by cardiac dynamic 18 F-fluorodeoxyglucose-positron emission tomography (18 F-FDG-PET) combined with euglycaemic-hyperinsulinaemic clamp compared with glimepiride in patients with type 2 diabetes. MATERIALS AND METHODS: To further investigate the cardioprotective mechanism of sodium-glucose co-transporter-2 inhibitors, we performed a 26-week, randomized, open-label, crossover, active-comparator study to determine the effects of empagliflozin 10 mg versus glimepiride 2 mg daily on the myocardial glucose metabolic rate assessed by cardiac dynamic 18 F-FDG-PET combined with euglycaemic-hyperinsulinaemic clamp in 23 patients with type 2 diabetes. We also measured cardiac geometry and myocardial mechano-energetic efficiency, as well as systolic and diastolic function by echocardiography. RESULTS: Compared with glimepiride, treatment with empagliflozin resulted in a greater reduction in the myocardial glucose metabolic rate from baseline to 26 weeks (adjusted difference -6.07 [-8.59, -3.55] µmol/min/100 g; P < .0001). Moreover, compared with glimepiride, empagliflozin led to significant reductions in left atrial diameter, left ventricular end-systolic and end-diastolic volumes, N-terminal pro b-type natriuretic peptide levels, blood pressure, heart rate, stroke work, and myocardial oxygen consumption estimated by the rate pressure product, and increases in ejection fraction, myocardial mechano-energetic efficiency, red blood cells, and haematocrit and haemoglobin levels. CONCLUSIONS: The present study provides evidence that empagliflozin treatment in subjects with type 2 diabetes without coronary artery disease leads to a significant reduction in the myocardial glucose metabolic rate.

Biopotential Signal Monitoring Systems in Rehabilitation: A Review.

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject's movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

Reduction in Global Myocardial Glucose Metabolism in Subjects With 1-Hour Postload Hyperglycemia and Impaired Glucose Tolerance.

OBJECTIVE: Impaired insulin-stimulated myocardial glucose uptake has occurred in patients with type 2 diabetes with or without coronary artery disease. Whether cardiac insulin resistance is present remains uncertain in subjects at risk for type 2 diabetes, such as individuals with impaired glucose tolerance (IGT) or those with normal glucose tolerance (NGT) and 1-h postload glucose ≥155 mg/dL during an oral glucose tolerance test (NGT 1-h high). This issue was examined in this study. RESEARCH DESIGN AND METHODS: The myocardial metabolic rate of glucose (MRGlu) was measured by using dynamic 18F-fluorodeoxyglucose positron emission tomography combined with a euglycemic-hyperinsulinemic clamp in 30 volunteers without coronary artery disease. Three groups were studied: 1) those with 1-h postload glucose <155 mg/dL (NGT 1-h low) (n = 10), 2) those with NGT 1-h high (n = 10), 3) and those with IGT (n = 10). RESULTS: After adjusting for age, sex, and BMI, both subjects with NGT 1-h high (23.7 ± 6.4 mmol/min/100 mg; P = 0.024) and those with IGT (16.4 ± 6.0 mmol/min/100 mg; P < 0.0001) exhibited a significant reduction in global myocardial MRGlu; this value was 32.8 ± 9.7 mmol/min/100 mg in subjects with NGT 1-h low. Univariate correlations showed that MRGlu was positively correlated with insulin-stimulated whole-body glucose disposal (r = 0.441; P = 0.019) and negatively correlated with 1-h (r = -0.422; P = 0.025) and 2-h (r = -0.374; P = 0.05) postload glucose levels, but not with fasting glucose. CONCLUSIONS: This study shows that myocardial insulin resistance is an early defect that is already detectable in individuals with dysglycemic conditions associated with an increased risk of type 2 diabetes, such as IGT and NGT 1-h high.

Methodologies of speech analysis for neurodegenerative diseases evaluation.

BACKGROUND AND OBJECTIVE: Neurodegenerative diseases are disorders that affect neurons in the brain resulting in a debilitating condition and progressive degeneration of nerve cells. These diseases involve different aspects among which speech impairment. Vocal signal analysis is used to evaluate this impairment and to discriminate normal from pathological voices. MATERIALS AND METHODS: In this paper, two methods of vocal signal analysis have been proposed to evaluate an anomalous condition in human speech, known as dysarthria, useful to compare pathological and healthy voices. Parkinson and Multiple Sclerosis disease have been considered and patients affected by both pathologies have been enrolled. The methods have been tested on 153 voice signals belonging to: 39 healthy subjects (HS), 60 patients with Parkinson's Disease (PD) and 54 patients with Multiple Sclerosis (MS). Acoustic (F0, jitter, shimmer, NHR) and vowel metric (tVSA, qVSA, FCR) features have been extracted. RESULTS: The results report significant differences in almost all of these features in pathological and healthy voices by performing statistical tests. F0, jitter, shimmer, NHR, tVSA and FCR are statistically significant features thus they can be used as indicators in the diagnosis of dysarthria-related diseases such as in PD and MS. The results suggest that the applied methodologies are efficient and useful in characterizing the different behavior of vocal signal in healthy and pathological subjects. Consequently, they could be a valid support for physicians in disease evaluation and progression monitoring. CONCLUSIONS: The contribution aims to evaluate, support and diagnose the comorbidity in pathological patients verifying the co-occurrence of speech and neurological disorders in the same individual. The proposed solution is studied and implemented to be efficient and low cost following the model of precision medicine to customize clinical practice in disease diagnosis and treatment.

On the reliability of measurements for a stent positioning simulation system.

BACKGROUND AND OBJECTIVE: Computer aided simulations are useful to support the physician in many steps of the surgical activity, but also in pre-surgical patient classification and in post-surgical diagnosis and treatment decisions. At a broader level, computerized technologies and infrastructures permeate every aspect of the medical activity, from patient management to surgery and patients' follow up with outcomes analyses. Radiography assisted surgery is often used in hemodynamic surgery to study and support cardio-circulatory stents positioning with the use of radioscopy coupled with contrast liquid injected into the vessels. Computer based surgery instruments (both software and hardware) are used to support clinicians during interventions, e.g., to reduce radioscopy time exposure, to minimize errors and to estimate tissues and organs dimension. In this paper we present the use of a newly developed system which supports physicians during transcatheter percutaneous coronary interventions. METHODS: This paper presents a Java-based tool which acquires images from angiographic equipment during surgery procedures. An high performance image acquisition module has been used and a stent simulation environment module is available to simulate stent positioning and to measure vessels. Operators may acquire images, perform measurements and simulations on DICOM images. We performed tests off-line on images to validate the reliability of the tool. Real cases and on line tests have been performed by operators showing the robustness of the system to be used in surgery room. The system has been integrated in the surgery room control panel and allows (i) vascular images acquisition, (ii) vessels and coronary measurement and (iii) stent positioning simulations. The tool is an aid for the physician for both measuring tissues or lesions and for defining the stent's geometry and position before its deployment in the patient's vessels. RESULTS: Experiments have been performed on lesions and vessels by different operators using the system and an available commercial system, on both real patient cases and synthetic images designed with a CAD. It has been tested on 76 images extracted from real angiography cases and on 11 synthetic images created by using CAD. Five different operators performed 2128 measurements for the real cases images (for both Cartesio and CAAS tools) and 112 for the synthetic dataset. Results show the efficacy of the system compared with the commercial one by means of several statistical tests. CONCLUSIONS: The proposed system is a reliable tool for hemodynamic surgery and can be used both for decision support in stent positioning procedures and for didactic training of new physicians.

An Innovative Framework for Bioimage Annotation and Studies.

The collection and analysis of clinical data are needed to investigate diseases and to define medical protocols and treatments. Bioimages, medical annotations and patient history are clinical data acquired and studied to perform a correct diagnosis and to propose an appropriate therapy. Currently, hospital departments manage these data using legacy systems which do not often allow data integration among different departments or health structures. Thus, in many cases clinical information sharing and exchange are difficult to implement. This is also the case for biomedical images for which data integration or data overlapping is usually not available. Image annotations and comparison can be crucial for physicians in many case studies. In this paper, a general purpose framework for bioimage management and annotations is proposed. Moreover, a simple-to-use information system has been developed to integrate clinical and diagnosis codes. The framework allows physicians (1) to integrate DICOM images from different platforms and (2) to report notes and highlights directly on images, thus offering, among the others, to query and compare similar clinical cases. This contribution is the result of a framework aimed to support oncologists in managing DICOM images and clinical data from different departments. Data integration is performed using a here-proposed XML-based module also utilized to trace temporal changes in image annotations.

A framework for the atrial fibrillation prediction in electrophysiological studies.

BACKGROUND AND OBJECTIVE: Cardiac arrhythmias are disorders in terms of speed or rhythm in the heart's electrical system. Atrial fibrillation (AFib) is the most common sustained arrhythmia that affects a large number of persons. Electrophysiologic study (EPS) procedures are used to study fibrillation in patients; they consist of inducing a controlled fibrillation in surgical room to analyze electrical heart reactions or to decide for implanting medical devices (i.e., pacemaker). Nevertheless, the spontaneous induction may generate an undesired AFib, which may induce risk for patient and thus a critical issue for physicians. We study the unexpected AFib onset, aiming to identify signal patterns occurring in time interval preceding an event of spontaneous (i.e., not inducted) fibrillation. Profiling such signal patterns allowed to design and implement an AFib prediction algorithm able to early identify a spontaneous fibrillation. The objective is to increase the reliability of EPS procedures. METHODS: We gathered data signals collected by a General Electric Healthcare's CardioLab electrophysiology recording system (i.e., a polygraph). We extracted superficial and intracavitary cardiac signals regarding 50 different patients studied at the University Magna Graecia Cardiology Department. By studying waveform (i.e., amplitude and energy) of intracavitary signals before the onset of the arrhythmia, we were able to define patterns related to AFib onsets that are side effects of an inducted fibrillation. RESULTS: A framework for atrial fibrillation prediction during electrophysiological studies has been developed. It includes a prediction algorithm to alert an upcoming AFib onset. Tests have been performed on an intracavitary cardiac signals data set, related to patients studied in electrophysiological room. Also, results have been validated by the clinicians, proving that the framework can be useful in case of integration with the polygraph, helping physicians in managing and controlling of patient status during EPS.

AutoSPET: an SPM plugin to automatize neuroimages PET analysis.

Neuroimaging analysis supports clinicians in the diagnosis of neurological diseases by extracting information from digital images. Due to the large number of images generated by new devices (e.g. PET ones), there is a lot of effort in defining computer-based tools to analyze and classify (brain) radiological images. Statistical tools, such as SPM (for Statistical Parametric Mapping), are largely used by physicians for image analysis. Nevertheless, large datasets analysis requires repetitive steps, due to the lack of automatic procedures. E.g. SPM requires human intervention during long and complex steps.We here present a tool, called AutoSPET (for Automatic SPM analysis for PET images), which allows to perform SPM analyses on large sets of PET images. It works as a meta-component orchestrating interactions with SPM, Matlab and with SPM plugins via a unified user interface. AutoSPET has been tested with real clinical datasets and it is publicly available as an official SPM plugin on the SPM website.