Search

Polyphenols are a class of natural compounds that act as antioxidants, neutralising harmful free radicals that would damage cells and increase the risk of diseases such as cancer, diabetes and heart disease. They also reduce inflammation, which is thought to be at the root of many chronic diseases. We are investigating the photoprotective effects of punicalagin, a type of polyphenolic compound mainly found in pomegranates, against UVA-induced damage in human skin fibroblasts. Punicalagin increases cell viability and reduces the high levels of ROS generated by photooxidative stress through its ability to modulate the Nrf2 transcriptional pathway. Interestingly, activation of the Nrf2 pathway results in an increase in reduced glutathione, NADH, and subsequently protects mitochondrial respiratory capacity. Integrating molecular and imaging approaches, our results demonstrate a potential cytoprotective effect of punicalagin against UVA-induced skin damage through an anti-apoptotic mechanism.

Aims: Improving the composition of circulating fatty acids (FA) leads to a reduction in cardiovascular diseases (CVD) in high-risk individuals. The membrane fluidity of red blood cells (RBC), which reflects circulating FA status, may be a valid biomarker of cardiovascular (CV) risk in type 2 diabetes (T2D). Methods: Red blood cell membrane fluidity, quantified as general polarization (GP), was assessed in 234 subjects with T2D, 86 with prior major CVD. Based on GP distribution, a cut-off of.445 was used to divide the study cohort into two groups: the first with higher GP, called GEL, and the second, defined as lower GP (LGP). Lipidomic analysis was performed to evaluate FA composition of RBC membranes. Results: Although with comparable CV risk factors, the LGP group had a greater percentage of patients with major CVD than the GEL group (40% vs 24%, respectively, p <.05). Moreover, in a logistic regression analysis, a lower GP value was independently associated with the presence of macrovascular complications. Lipidomic analysis showed a clear shift of LGP membranes towards a pro-inflammatory condition due to higher content of arachidonic acid and increased omega 6/omega 3 index. Conclusions: Increased membrane fluidity is associated with a higher CV risk in subjects with T2D. If confirmed in prospective studies, membrane fluidity could be a new biomarker for residual CV risk assessment in T2D.

Chewing is essential in regulating metabolism and initiating digestion. Various methods have been used to examine chewing, including analyzing chewing sounds and using piezoelectric sensors to detect muscle contractions. However, these methods struggle to distinguish chewing from other movements. Electromyography (EMG) has proven to be an accurate solution, although it requires sensors attached to the skin. Existing EMG devices focus on detecting the act of chewing or classifying foods and do not provide self-awareness of chewing habits. We developed a non-invasive device that evaluates a personalized chewing style by analyzing various aspects, like chewing time, cycle time, work rate, number of chews and work. It was tested in a case study comparing the chewing pattern of smokers and non-smokers, as smoking can alter chewing habits. Previous studies have shown that smokers exhibit reduced chewing speed, but other aspects of chewing were overlooked. The goal of this study is to present the device and provide additional insights into the effects of smoking on chewing patterns by considering multiple chewing features. Statistical analysis revealed significant differences, as non-smokers had more chews and higher work values, indicating more efficient chewing. The device provides valuable insights into personalized chewing profiles and could modify unhealthy chewing habits.

Nutrition is a cross-cutting sector in medicine, with a huge impact on health, from cardiovascular disease to cancer. Employment of digital medicine in nutrition relies on digital twins: digital replicas of human physiology representing an emergent solution for prevention and treatment of many diseases. In this context, we have already developed a data-driven model of metabolism, called a "Personalized Metabolic Avatar" (PMA), using gated recurrent unit (GRU) neural networks for weight forecasting. However, putting a digital twin into production to make it available for users is a difficult task that as important as model building. Among the principal issues, changes to data sources, models and hyperparameters introduce room for error and overfitting and can lead to abrupt variations in computational time. In this study, we selected the best strategy for deployment in terms of predictive performance and computational time. Several models, such as the Transformer model, recursive neural networks (GRUs and long short-term memory networks) and the statistical SARIMAX model were tested on ten users. PMAs based on GRUs and LSTM showed optimal and stable predictive performances, with the lowest root mean squared errors (0.38 ± 0.16-0.39 ± 0.18) and acceptable computational times of the retraining phase (12.7 ± 1.42 s-13.5 ± 3.60 s) for a production environment. While the Transformer model did not bring a substantial improvement over RNNs in term of predictive performance, it increased the computational time for both forecasting and retraining by 40%. The SARIMAX model showed the worst performance in term of predictive performance, though it had the best computational time. For all the models considered, the extent of the data source was a negligible factor, and a threshold was established for the number of time points needed for a successful prediction.

Seminoma is the most common testicular cancer. Pituitary tumor-transforming gene 1 (PTTG1) is a securin showing oncogenic activity in several tumors. We previously demonstrated that nuclear PTTG1 promotes seminoma tumor invasion through its transcriptional activity on matrix metalloproteinase 2 (MMP-2) and E-cadherin (CDH1). We wondered if specific interactors could affect its subcellular distribution. To this aim, we investigated the PTTG1 interactome in seminoma cell lines showing different PTTG1 nuclear levels correlated with invasive properties. A proteomic approach upon PTTG1 immunoprecipitation uncovered new specific securin interactors. Western blot, confocal microscopy, cytoplasmic/nuclear fractionation, sphere-forming assay, and Atlas database interrogation were performed to validate the proteomic results and to investigate the interplay between PTTG1 and newly uncovered partners. We observed that spectrin beta-chain (SPTBN1) and PTTG1 were cofactors, with SPTBN1 anchoring the securin in the cytoplasm. SPTBN1 downregulation determined PTTG1 nuclear translocation, promoting its invasive capability. Moreover, a PTTG1 deletion mutant lacking SPTBN1 binding was strongly localized in the nucleus. The Atlas database revealed that seminomas that contained higher nuclear PTTG1 levels showed significantly lower SPTBN1 levels in comparison to non-seminomas. In human seminoma specimens, we found a strong PTTG1/SPTBN1 colocalization that decreases in areas with nuclear PTTG1 distribution. Overall, these results suggest that SPTBN1, along with PTTG1, is a potential prognostic factor useful in the clinical management of seminoma.

Autofluorescence microscopy is a promising label-free approach to characterize NADH and FAD metabolites in live cells, with potential applications in clinical practice. Although spectrally resolved lifetime imaging techniques can acquire multiparametric information about the biophysical and biochemical state of the metabolites, these data are evaluated at the whole-cell level, thus providing only limited insights in the activation of metabolic networks at the microscale. To overcome this issue, here we introduce an artificial intelligence-based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows to detect and classify, through an unsupervised learning approach, metabolic clusters, which are regions having almost uniform metabolic properties. This method contextually detects the cellular mitochondrial turnover and the metabolic activation state of intracellular compartments at the pixel level, described by two functions: the cytosolic activation state (CAF) and the mitochondrial activation state (MAF). This method was applied to investigate metabolic changes elicited in the breast cancer cell line MCF-7 by specific inhibitors of glycolysis and electron transport chain, and by the deregulation of a specific mitochondrial enzyme (ACO2) leading to defective aerobic metabolism associated with tumor growth. In this model, mitochondrial fraction undergoes to a 13% increase upon ACO2 overexpression and the MAF function changes abruptly by altering the metabolic state of about the 25% of the mitochondrial pixels.

VO2max index has a significant impact on overall health. Its estimation through wearables notifies the user of his level of fitness but cannot provide a detailed analysis of the time intervals in which heartbeat dynamics are changed and/or fatigue is emerging. Here, we developed a multiple modality biosignal processing method to investigate running sessions to characterize in real time heartbeat dynamics in response to external energy demand. We isolated dynamic regimes whose fraction increases with the VO2max and with the emergence of neuromuscular fatigue. This analysis can be extremely valuable by providing personalized feedback about the user's fitness level improvement that can be realized by developing personalized exercise plans aimed to target a contextual increase in the dynamic regime fraction related to VO2max increase, at the expense of the dynamic regime fraction related to the emergence of fatigue. These strategies can ultimately result in the reduction in cardiovascular risk.

In this work, we will investigate if red blood cell (RBC) membrane fluidity, influenced by several hyperglycemia-induced pathways, could provide a complementary index of HbA1c to monitor the development of type 2 diabetes mellitus (T2DM)-related macroangiopathic complications such as Peripheral Artery Disease (PAD). The contextual liquid crystalline (LC) domain spatial organization in the membrane was analysed to investigate the phase dynamics of the transition. Twenty-seven patients with long-duration T2DM were recruited and classified in DM, including 12 non-PAD patients, and DM + PAD, including 15 patients in any stage of PAD. Mean values of RBC generalized polarization (GP), representative of membrane fluidity, together with spatial organization of LC domains were compared between the two groups; p-values < 0.05 were considered statistically significant. Although comparable for anthropometric characteristics, duration of diabetes, and HbA1c, RBC membranes of PAD patients were found to be significantly more fluid (GP: 0.501 +/- 0.026) than non-PAD patients (GP: 0.519 +/- 0.007). These alterations were shown to be triggered by changes in both LC microdomain composition and distribution. We found a decrease in Feret diameter from 0.245 +/- 0.281 mu m in DM to 0.183 +/- 0.124 mu m in DM + PAD, and an increase in circularity. Altered RBC membrane fluidity is correlated to a spatial reconfiguration of LC domains, which, by possibly altering metabolic function, are associated with the development of T2DM-related macroangiopathic complications.

Simple Summary Seminoma represents one of the most common neoplasms in Caucasian males between 15 and 40 years old. The molecular pathways underlying its clinical behavior are far from being understood yet. We previously demonstrated that nuclear Pituitary-tumor transforming-gene 1 (PTTG1), overexpressed in several neoplasms, promotes invasiveness through its transcriptional target matrix-metalloproteinase-2 (MMP2). PTTG1 sustains the migratory and invasive properties of cancer cells through the induction of the epithelial-to-mesenchymal transition (EMT). E-Cadherin (E-CAD) repression is the first step of EMT. Therefore, we investigated the role of PTTG1 in EMT in human seminoma using an in vitro and in vivo model and through Atlas database interrogation. Our data showed a PTTG1-mediated E-CAD transcriptional repression through Zinc finger E-box binding homeobox 1 (ZEB1), a master regulator of the EMT process. Our data provide insights into the molecular characterization of seminoma, promoting PTTG1 as a prognostic marker useful in human seminoma clinical management. (1) Background: PTTG1 sustains the EMT process and the invasiveness of several neoplasms. We previously showed the role of nuclear PTTG1 in promoting invasiveness, through its transcriptional target MMP2, in seminoma in vitro models. Here, we investigated the key players involved in PTTG1-mediated EMT in human seminoma. (2) Methods: Two seminoma cell lines and four human seminoma tumor specimens were used. E-Cadherin gene regulation was investigated using Western blot, real-time PCR, and luciferase assay. Immunoprecipitation, ChIP, RE-ChIP, and confocal microscopy analysis were performed to evaluate the interplay between PTTG1 and ZEB1. Matrigel invasion and spheroid formation assays were applied to functionally investigate PTTG1 involvement in the EMT of seminoma cell lines. RNA depletion and overexpression experiments were performed to verify the role of PTTG1/ZEB1 in E-Cadherin repression and seminom

Development of predictive computational models of metabolism through mechanistic models is complex and resource demanding, and their personalization remains challenging. Data-driven models of human metabolism would constitute a reliable, fast, and continuously updating model for predictive analytics. Wearable devices, such as smart bands and impedance balances, allow the real time and remote monitoring of physiological parameters, providing for a flux of data carrying information on user metabolism. Here, we developed a data-driven model of end-user metabolism, the Personalized Metabolic Avatar (PMA), to estimate its personalized reactions to diets. PMA consists of a gated recurrent unit (GRU) deep learning model trained to forecast personalized weight variations according to macronutrient composition and daily energy balance. The model can perform simulations and evaluation of diet plans, allowing the definition of tailored goals for achieving ideal weight. This approach can provide the correct clues to empower citizens with scientific knowledge, augmenting their self-awareness with the aim to achieve long-lasting results in pursuing a healthy lifestyle.

2-deoxy-2-fluorine-(18F)fluoro-D-glucose Positron Emission Tomography/Computed Tomography (18F-FDG-PET/CT) is widely used in oncology mainly for diagnosis and staging of various cancer types, including lung cancer, which is the most common cancer worldwide. Since histopathologic subtypes of lung cancer show different degree of 18F-FDG uptake, to date there are some diagnostic limits and uncertainties, hindering an 18F-FDG-PET-driven classification of histologic subtypes of lung cancers. On the other hand, since activated macrophages, neutrophils, fibroblasts and granulation tissues also show an increased 18F-FDG activity, infectious and/or inflammatory processes and post-surgical and post-radiation changes may cause false-positive results, especially for lymph-nodes assessment. Here we propose a model-free, machine-learning based algorithm for the automated classification of adenocarcinoma, the most common type of lung cancer, and other types of tumors. Input for the algorithm are dynamic acquisitions of PET data (dPET), providing for a spatially and temporally resolved characterization of the uptake kinetic. The algorithm consists in a trained Random Forest classifier which, relying contextually on several spatial and temporal features of 18F-FDG uptake, generates as an outcome probability maps allowing to distinguish adenocarcinoma from other lung histotype and to identify metastatic lymph-nodes, ultimately increasing the specificity of the technique. Its performance, evaluated on a dPET dataset of 19 patients affected by primary lung cancer, provides a probability 0.943 ± 0.090 for the detection of adenocarcinoma. The use of this algorithm will guarantee an automatic and more accurate localization and discrimination of tumors, also providing a powerful tool for detecting at which extent tumor has spread beyond a primary tumor into lymphatic system.

The thermo-mechanical properties of streptavidin-conjugated gold nanospheres, adhered to a surface via complex molecular chains, are investigated by two-color infrared asynchronous optical sampling pump-probe spectroscopy. Nanospheres with different surface densities have been deposited and exposed to a plasma treatment to modify their polymer binding chains. The aim is to monitor their optical response in complex chemical environments that may be experienced in, e.g., photothermal therapy or drug delivery applications. By applying unsupervised learning techniques to the spectroscopic traces, we identify their thermo-mechanical response variation. This variation discriminates nanospheres in different chemical environments or different surface densities. Such discrimination is not evident based on a standard analysis of the spectroscopic traces. This kind of analysis is important, given the widespread application of conjugated gold nanospheres in medicine and biology.

Free fatty acids are essential structural components of the cell, and their intracellular distribution and effects on membrane organelles have crucial roles in regulating the metabolism, development, and cell cycle of most cell types. Here we engineered novel fluorescent, polarity-sensitive fatty acid derivatives, with the fatty acid aliphatic chain of increasing length (from 12 to 18 carbons). As in the laurdan probe, the lipophilic acyl tail is connected to the environmentally sensitive dimethylaminonaphthalene moiety. The fluorescence lifetime imaging analysis allowed us to monitor the intracellular distribution of the free fatty acids within the cell, and to simultaneously examine how the fluidity and the microviscosity of the membrane environment influence their localization. Each of these probes can thus be used to investigate the membrane fluidity regulation of the correspondent fatty acid intracellular distribution. We observed that, in PC-12 cells, fluorescent sensitive fatty acid derivatives with increased chain length compartmentalize more preferentially in the fluid regions, characterized by a low microviscosity. Moreover, fatty acid derivatives with the longest chain compartmentalize in lipid droplets and lysosomes with characteristic lifetimes, thus making these probes a promising tool for monitoring lipophagy and related events.

Background: A high level of glycosylated haemoglobin (HbA1c), which is a nonenzymatic glycosylation product, is correlated with an increased risk of developing microangiopathic complications in Diabetes Mellitus (DM). Erythrocyte membrane fluidity could provide a complementary index to monitor the development of complications since it is influenced by several hyperglycaemia-induced pathways and other independent risk factors. Materials and methods: 15 healthy controls and 33 patients with long-duration (≥20 years) type 1 Diabetes Mellitus (T1DM) were recruited. Diabetic subjects were classified into two groups: T1DM, constituted by 14 nonretinopathic patients, and T1DM + RD, constituted by 19 patients in any stage of diabetic retinopathy. Red blood cells (RBC) were incubated with the fluorescent Laurdan probe and median values of Generalized Polarization (GP), representative of membrane fluidity, were compared between the two groups. Baseline characteristics among groups have been compared with Student's t test or ANOVA. Values of P <.05 were considered statistically significant. Results: All the participants were comparable for age, Body Mass Index (BMI), creatinine and lipid profile. The duration of diabetes was similar for T1DM (34.4 ± 7.8 years) and T1DM + RD (32.8 ± 7.5 years) subjects as well as values of HbA1c: (55.6 ± 8.1) mmol/mol for T1DM and (61.2 ± 11.0) mmol/mol for T1DM + RD, respectively. Erythrocyte plasmatic membranes of RD patients were found to be more fluid (GP: 0.40 ± 0.04) than non-RD patients (GP: 0.43 ± 0.03) with a statistically significant difference (P =.035). Conclusions: Altered erythrocyte membrane fluidity may therefore represent a marker of retinopathy in T1DM patients as a result of post-translational modifications of multifactorial aetiology (nonenzymatic glycosylation of proteins, generation of reactive oxygen speci

Solvatochromic probes undergo an emission shift when the hydration level of the membrane environment increases and are commonly used to distinguish between solid-ordered and liquid-disordered phases in artificial membrane bilayers. This emission shift is currently limited in unraveling the broad spectrum of membrane phases of natural cell membranes and their spatial organization. Spectrally resolved fluorescence lifetime imaging can provide pixel-resolved multiparametric information about the biophysical state of the membranes, like membrane hydration, microviscosity and the partition coefficient of the probe. Here, we introduce a clustering based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows us to classify through an unsupervised learning approach multiple membrane phases with sub-micrometric resolution. This method extends the spectrum of detectable membrane phases allowing to dissect and characterize up to six different phases, and to study real-time phase transitions in cultured cells and tissues undergoing different treatments. We applied this method to investigate membrane remodeling induced by high glucose on PC-12 neuronal cells, associated with the development of diabetic neuropathy. Due to its wide applicability, this method provides a new paradigm in the analysis of environmentally sensitive fluorescent probes.

Solvatochromic probes undergo an emission shift when the hydration level of the membrane environment increases and are commonly used to distinguish between solid-ordered and liquid-disordered phases in artificial membrane bilayers. This emission shift is currently limited in unraveling the broad spectrum of membrane phases of natural cell membranes and their spatial organization. Spectrally resolved fluorescence lifetime imaging can provide pixel-resolved multiparametric information about the biophysical state of the membranes, like membrane hydration, microviscosity and the partition coefficient of the probe. Here, we introduce a clustering based analysis that, leveraging the multiparametric content of spectrally resolved lifetime images, allows us to classify through an unsupervised learning approach multiple membrane phases with sub-micrometric resolution. This method extends the spectrum of detectable membrane phases allowing to dissect and characterize up to six different phases, and to study real-time phase transitions in cultured cells and tissues undergoing different treatments. We applied this method to investigate membrane remodeling induced by high glucose on PC-12 neuronal cells, associated with the development of diabetic neuropathy. Due to its wide applicability, this method provides a new paradigm in the analysis of environmentally sensitive fluorescent probes.

Pine bark extracts were tested as valid natural alternatives to common phenolic antioxidants to be deposited onto solid matrices. An analytical method for determining both deposition-efficacy and maintenance of antioxidant activity on fabrics surfaces was developed and successfully applied to phenolic antioxidants and pine tannins onto cotton fabric samples. Accumulated data suggest that common phenolic antioxidants and tannin contribute to an elevated antioxidant activity on fabric. A known source of odour is autoxidation of residual sebum on fabric, leading to the generation of malodourous compounds. Therefore, antioxidants hold the promise for mitigating malodour on fabrics, a top unmet consumer need around the globe.

Background: A high level of glycosylated haemoglobin (HbA1c), which is a nonenzymatic glycosylation product, is correlated with an increased risk of developing microangiopathic complications in Diabetes Mellitus (DM). Erythrocyte membrane fluidity could provide a complementary index to monitor the development of complications since it is influenced by several hyperglycaemia-induced pathways and other independent risk factors. Materials and methods: 15 healthy controls and 33 patients with long-duration (≥20 years) type 1 Diabetes Mellitus (T1DM) were recruited. Diabetic subjects were classified into two groups: T1DM, constituted by 14 nonretinopathic patients, and T1DM + RD, constituted by 19 patients in any stage of diabetic retinopathy. Red blood cells (RBC) were incubated with the fluorescent Laurdan probe and median values of Generalized Polarization (GP), representative of membrane fluidity, were compared between the two groups. Baseline characteristics among groups have been compared with Student's t test or ANOVA. Values of P <.05 were considered statistically significant. Results: All the participants were comparable for age, Body Mass Index (BMI), creatinine and lipid profile. The duration of diabetes was similar for T1DM (34.4 ± 7.8 years) and T1DM + RD (32.8 ± 7.5 years) subjects as well as values of HbA1c: (55.6 ± 8.1) mmol/mol for T1DM and (61.2 ± 11.0) mmol/mol for T1DM + RD, respectively. Erythrocyte plasmatic membranes of RD patients were found to be more fluid (GP: 0.40 ± 0.04) than non-RD patients (GP: 0.43 ± 0.03) with a statistically significant difference (P =.035). Conclusions: Altered erythrocyte membrane fluidity may therefore represent a marker of retinopathy in T1DM patients as a result of post-translational modifications of multifactorial aetiology (nonenzymatic glycosylation of proteins, generation of reactive oxygen speci

The chemical composition of Acacia catechu, Schinopsis balansae, and Acacia mearnsii proanthocyanidins has been determined using a novel analytical approach that rests on the concerted use of quantitative 31P NMR and two-dimensional heteronuclear NMR spectroscopy. This approach has offered significant detailed information regarding the structure and purity of these complex and often elusive proanthocyanidins. More specifically, rings A, B, and C of their flavan-3-ol units show well-defined and resolved absorbance regions in both the quantitative 31P NMR and HSQC spectra. By integrating each of these regions in the 31P NMR spectra, it is possible to identify the oxygenation patterns of the flavan-3-ol units. At the same time it is possible to acquire a fingerprint of the proanthocyanidin sample and evaluate its purity via the HSQC information. This analytical approach is suitable for both the purified natural product proanthocyanidins and their commercial analogues. Overall, this effort demonstrates the power of the concerted use of these two NMR techniques for the structural elucidation of natural products containing labile hydroxy protons and a carbon framework that can be traced out via HSQC.