Platelet – Leukocyte Interactions: Multiple Links Between Inflammation, Blood Coagulation and Vascular Risk

The aim of this review is to summarize the contribution of platelets and leukocytes and their interactions in inflammation and blood coagulation and its possible relevance in the pathogenesis of thrombosis. There is some evidence of an association between infection/inflammation and thrombosis. This is likely a bidirectional relationship. The presence of a thrombus may serve as a nidus of infection. Vascular injury indeed promotes platelet and leukocyte activation and thrombus formation and the thrombus and its components facilitate adherence of bacteria to the vessel wall. Alternatively, an infection and the associated inflammation can trigger platelet and leukocyte activation and thrombus formation. In either case platelets and leukocytes co-localize and interact in the area of vascular injury, at sites of inflammation and/or at sites of thrombosis. Following vascular injury, the subendothelial tissue, a thrombogenic surface, becomes available for interaction with these blood cells. Tissue factor, found not only in media and adventitia of the vascular wall, but also on activated platelets and leukocytes, triggers blood coagulation. Vascular-blood cell interactions, mediated by the release of preformed components of the endothelium, is modulated by both cell adhesion and production of soluble stimulatory or inhibitory molecules that alter cell function: adhesion molecules regulate cell-cell contact and facilitate the modulation of biochemical pathways relevant to inflammatory and/or thrombotic processes

The night of randomized clinical trials where all patients are black: a need to estimate variability in treatment effects

In the Sixties, the few anti-thrombotic drugs available were administered following several criteria including tradition of the "School", preference of the doctor in charge, pressure of pharmaceutical companies [...

Ultra-processed food consumption and its correlates among Italian children, adolescents and adults from the Italian Nutrition & Health Survey (INHES) cohort study.

AbstractObjective:To assess ultra-processed food (UPF) consumption and its socio-demographic, psychosocial and behavioural correlates in a general population of Italian children, adolescents and adults.Design:Cross-sectional telephone-based surveySetting:Italy, 2010–2013.Participants:In total, 9078 participants (5–97 years) from the Italian Nutrition & Health Survey. Dietary intakes were collected by a 1-d 24-h dietary recall. UPF was defined by the NOVA classification and expressed as percentage of total energies.Results:Average energy intake from UPF (95 % CI) was 17·3 % (17·1 %, 17·6 %) among adults and 25·9 % (24·8 %, 27·0 %) in children/adolescents. Top sources of UPF were processed meats (32·5 %) and bread substitutes (16·7 %). Among adults, age (β = −3·10; 95 % CI (−4·40, −1·80) for >65 years v. 20–40 years; βs are dimensionless) and residing in Southern Italy (β = −0·73; 95 % CI (−1·32, −0·14) v. Northern) inversely associated with UPF. Screen view during meals was directly linked to UPF, as well as poor self-rated health (β = 5·32; 95 % CI (2·66, 7·99)), adverse life events (β = 2·33; 95 % CI (1·48, 3·18)) and low sleep quality (β = 2·34; 95 % CI (1·45, 3·23)). Boys consumed two-point percent more UPF of the total energy than girls (β = 2·01; 95 % CI (0·20, 3·82)). For all ages, a Mediterranean diet was inversely associated with UPF (β = −4·86; 95 % CI (−5·53, −4·20) for good v. poor adherence in adults and (β = −5·08; 95 % CI (−8·38, −1·77) for kids).Conclusions:UPF contributes a modest proportion of energy to the diets of Italian adults while being one-quarter of the total energies in children/adolescents. UPF was associated with several psychosocial factors and eating behaviours. Increased adherence to Mediterranean diet would possibly result in lower UPF consumption

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity. Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (I-M) and erythema (I-E) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of >= 2. The patient's dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes. ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG >= 2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (I-M,I-T0 and I-E,I-T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p<0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with I-M,I-T0 >= 99 to be associated with RTOG >= 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959. ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG >= 2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

Association of nutritional glycaemic indices with global DNA methylation patterns: results from the Moli-sani cohort

Background: High dietary glycaemic index (GI) and load (GL) have been associated with increased risk of various cardiometabolic conditions. Among the molecular potential mechanisms underlying this relationship, DNA methylation has been studied, but a direct link between high GI and/or GL of diet and global DNA methylation levels has not been proved yet. We analyzed the associations between GI and GL and global DNA methylation patterns within an Italian population. Results: Genomic DNA methylation (5mC) and hydroxymethylation (5hmC) levels were measured in 1080 buffy coat samples from participants of the Moli-sani study (mean(SD) = 54.9(11.5) years; 52% women) via ELISA. A 188-item Food Frequency Questionnaire was used to assess food intake and dietary GI and GL for each participant were calculated. Multiple linear regressions were used to investigate the associations between dietary GI and GL and global 5mC and 5hmC levels, as well as the proportion of effect explained by metabolic and inflammatory markers. We found negative associations of GI with both 5mC (β (SE) = - 0.073 (0.027), p = 0.007) and 5hmC (- 0.084 (0.030), p = 0.006), and of GL with 5mC (- 0.14 (0.060), p = 0.014). Circulating biomarkers did not explain the above-mentioned associations. Gender interaction analyses revealed a significant association of the gender-x-GL interaction with 5mC levels, with men showing an inverse association three times as negative as in women (interaction β (SE) = - 0.16 (0.06), p = 0.005). Conclusions: Our findings suggest that global DNA methylation and hydroxymethylation patterns represent a biomarker of carbohydrate intake. Based on the differential association of GL with 5mC between men and women, further gender-based separate approaches are warranted

Src-family kinases mediate an outside-in signal necessary for β2 integrins to achieve full activation and sustain firm adhesion of polymorphonuclear leucocytes tethered on E-selectin

In cell suspensions subjected to high-shear rotatory motion, human PMN (polymorphonuclear cells) adhered to E-selectin-expressing CHO (Chinese-hamster ovary) cells (CHO-E), and formed homotypic aggregates when challenged by E-selectin–IgG fusion protein, by a mechanism that involved β2 integrins. Both heterotypic and homotypic PMN adhesion was accompanied by tyrosine phosphorylation of a 110 kDa protein (P110). This event was prevented by blocking anti-(β2 integrin) antibodies and by inhibitors of Src-family kinases, suggesting that it was part of an ‘outside-in’ signalling that was initiated by integrin engagement. Interestingly, Src-family kinase inhibitors prevented β2-integrin-mediated (i) homotypic PMN adhesion triggered by E-selectin–IgG, (ii) heterotypic CHO-E/PMN adhesion in mixed-cell suspensions, and (iii) firm adhesion of PMN to CHO-E monolayers under physiological flow. Similarly to PMN treated with Src-family kinase inhibitors, PMN from hck−/−fgr−/− and hck−/−fgr−/−lyn−/− mice showed significant impairment of β2-integrin-mediated adhesion to CHO-E. Moreover, the expression of β2 integrin activation epitopes at the sites of cell–cell contact in CHO-E/PMN conjugates was abolished by Src-family kinase inhibitors. One component of P110 was identified as the FAK (focal adhesion kinase) Pyk2 (proline-rich tyrosine kinase 2), which was phosphorylated in a β2 integrin- and Src-family-kinase-dependent manner. Thus, Src-family kinases, and perhaps Pyk2, mediate a signal necessary for β2 integrin function in PMN tethered by E-selectin

Plasma fibrinogen levels and all-cause and cause-specific mortality in an Italian adult population: results from the Moli-sani study

Epidemiological data on the association between fibrinogen levels and mortality are scarse and controversial. Longitudinal analyses were performed, separately by sex, on 17,689 individuals from the Moli-sani study [53% women, ≥35 years, free from cardiovascular disease (CVD) or cancer at enrolment], to evaluate the association between plasma fibrinogen and all-cause and cause-specific mortality. Over a median follow-up of 11.2 years, 1,058 deaths (34.7% CVD, 36.3% cancer) were ascertained. Both in the lowest (1.12-2.64 g/L) and highest (≥3.62 g/L) fibrinogen quintiles, women had an increased all-cause mortality hazard, when compared with third quintile (2.97-3.23 g/L). Dose-response analyses showed a U-shaped relationship in women (P overall <0.0001; P non-linear association <0.0001), but a positive linear association for all-cause mortality in men (P overall 0.0038; P non-linear association 0.76). Similar trends for a U-shaped association were observed for CVD mortality, while no association was observed with cancer deaths. A U-shaped association of fibrinogen levels with other-cause mortality was also found in both sexes. This study shows that not only higher but also lower fibrinogen levels represent hazard for mortality when compared to normal levels; U-shaped curves being prevalently observed in women

Identifying brain tumor patients’ subtypes based on pre-diagnostic history and clinical characteristics: a pilot hierarchical clustering and association analysis

IntroductionCentral nervous system (CNS) tumors are severe health conditions with increasing incidence in the last years. Different biological, environmental and clinical factors are thought to have an important role in their epidemiology, which however remains unclear.ObjectiveThe aim of this pilot study was to identify CNS tumor patients’ subtypes based on this information and to test associations with tumor malignancy.Methods90 patients with suspected diagnosis of CNS tumor were recruited by the Neurosurgery Unit of IRCCS Neuromed. Patients underwent anamnestic and clinical assessment, to ascertain known or suspected risk factors including lifestyle, socioeconomic, clinical and psychometric characteristics. We applied a hierarchical clustering analysis to these exposures to identify potential groups of patients with a similar risk pattern and tested whether these clusters associated with brain tumor malignancy.ResultsOut of 67 patients with a confirmed CNS tumor diagnosis, we identified 28 non-malignant and 39 malignant tumor cases. These subtypes showed significant differences in terms of gender (with men more frequently presenting a diagnosis of cancer; p = 6.0 ×10−3) and yearly household income (with non-malignant tumor patients more frequently earning ≥25k Euros/year; p = 3.4×10−3). Cluster analysis revealed the presence of two clusters of patients: one (N=41) with more professionally active, educated, wealthier and healthier patients, and the other one with mostly retired and less healthy men, with a higher frequency of smokers, personal history of cardiovascular disease and cancer familiarity, a mostly sedentary lifestyle and generally lower income, education and cognitive performance. The former cluster showed a protective association with the malignancy of the disease, with a 74 (14-93) % reduction in the prevalent risk of CNS malignant tumors, compared to the other cluster (p=0.026).DiscussionThese preliminary data suggest that patients’ profiling through unsupervised machine learning approaches may somehow help predicting the risk of being affected by a malignant form. If confirmed by further analyses in larger independent cohorts, these findings may be useful to create potential intelligent ranking systems for treatment priority, overcoming the lack of histopathological information and molecular diagnosis of the tumor, which are typically not available until the time of surgery

Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PurposeRadiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity.Methods and materialsOne hundred twenty-nine patients treated for adjuvant whole-breast radiotherapy were evaluated. Two spectrophotometer variables, i.e. the melanin (IM) and erythema (IE) indices, were used to quantitatively assess the skin physical changes. Measurements were performed at 4-time intervals: before RT, at the end of RT and 1 and 6 months after the end of RT. Together with clinical covariates, melanin and erythema indices were correlated with skin toxicity, evaluated using the Radiation Therapy Oncology Group (RTOG) guidelines. Binary group classes were labeled according to a RTOG cut-off score of ≥ 2. The patient’s dataset was randomly split into a training and testing set used for model development/validation and testing (75%/25% split). A 5-times repeated holdout cross-validation was performed. Three supervised machine learning models, including support vector machine (SVM), classification and regression tree analysis (CART) and logistic regression (LR), were employed for modeling and skin toxicity prediction purposes.ResultsThirty-four (26.4%) patients presented with adverse skin effects (RTOG ≥2) at the end of treatment. The two spectrophotometric variables at the beginning of RT (IM,T0 and IE,T0), together with the volumes of breast (PTV2) and boost surgical cavity (PTV1), the body mass index (BMI) and the dose fractionation scheme (FRAC) were found significantly associated with the RTOG score groups (p&lt;0.05) in univariate analysis. The diagnostic performances measured by the area-under-curve (AUC) were 0.816, 0.734, 0.714, 0.691 and 0.664 for IM, IE, PTV2, PTV1 and BMI, respectively. Classification performances reported precision, recall and F1-values greater than 0.8 for all models. The SVM classifier using the RBF kernel had the best performance, with accuracy, precision, recall and F-score equal to 89.8%, 88.7%, 98.6% and 93.3%, respectively. CART analysis classified patients with IM,T0 ≥ 99 to be associated with RTOG ≥ 2 toxicity; subsequently, PTV1 and PTV2 played a significant role in increasing the classification rate. The CART model provided a very high diagnostic performance of AUC=0.959.ConclusionsSpectrophotometry is an objective and reliable tool able to assess radiation induced skin tissue injury. Using a machine learning approach, we were able to predict grade RTOG ≥2 skin toxicity in patients undergoing breast RT. This approach may prove useful for treatment management aiming to improve patient quality of life

Clonal Characterization of Rat Muscle Satellite Cells: Proliferation, Metabolism and Differentiation Define an Intrinsic Heterogeneity

Satellite cells (SCs) represent a distinct lineage of myogenic progenitors responsible for the postnatal growth, repair and maintenance of skeletal muscle. Distinguished on the basis of their unique position in mature skeletal muscle, SCs were considered unipotent stem cells with the ability of generating a unique specialized phenotype. Subsequently, it was demonstrated in mice that opposite differentiation towards osteogenic and adipogenic pathways was also possible. Even though the pool of SCs is accepted as the major, and possibly the only, source of myonuclei in postnatal muscle, it is likely that SCs are not all multipotent stem cells and evidences for diversities within the myogenic compartment have been described both in vitro and in vivo. Here, by isolating single fibers from rat flexor digitorum brevis (FDB) muscle we were able to identify and clonally characterize two main subpopulations of SCs: the low proliferative clones (LPC) present in major proportion (∼75%) and the high proliferative clones (HPC), present instead in minor amount (∼25%). LPC spontaneously generate myotubes whilst HPC differentiate into adipocytes even though they may skip the adipogenic program if co-cultured with LPC. LPC and HPC differ also for mitochondrial membrane potential (ΔΨm), ATP balance and Reactive Oxygen Species (ROS) generation underlying diversities in metabolism that precede differentiation. Notably, SCs heterogeneity is retained in vivo. SCs may therefore be comprised of two distinct, though not irreversibly committed, populations of cells distinguishable for prominent differences in basal biological features such as proliferation, metabolism and differentiation. By these means, novel insights on SCs heterogeneity are provided and evidences for biological readouts potentially relevant for diagnostic purposes described