Leaf apoplastic proteome composition in UV-B treated Arabidopsis thaliana mutants impaired in extracellular glutathione degradation

In plants, environmental perturbations often result in oxidative reactions in the apoplastic space, which are counteracted for by enzymatic and non-enzymatic antioxidative systems, including ascorbate and glutathione. The occurrence of the latter and its exact role in the extracellular space are not well documented, however. In Arabidopsis thaliana, the gamma-glutamyl transferase isoform GGT1 bound to the cell wall takes part in the so-called gamma-glutamyl cycle for extracellular glutathione degradation and recovery, and may be implicated in redox sensing and balance. In this work, oxidative conditions were imposed with UV-B radiation and studied in redox altered ggt1 mutants. Elevated UV-B has detrimental effects on plant metabolism, plasma membranes representing a major target for ROS generated by this harmful radiation. The response of ggt1 knockout Arabidopsis leaves to UV-B radiation was assessed by investigating changes in apoplastic protein composition. We then compared the expression changes resulting from the mutation and from the UV-B treatment. Rearrangements occurring in apoplastic protein composition suggest the involvement of hydrogen peroxide, which may ultimately act as a signal. Other important changes related to hormonal effects, cell wall remodeling, and redox activities are also reported. We argue that oxidative stress conditions imposed by UV-B and by disruption of the gamma-glutamyl cycle result in similar stress-induced responses, to some degree at least. Data shown here are associated with the article from Trentin et al. [1]; protein data have been deposited to the PRIDE database [2] with identifier PXD001807

Proteome readjustments in the apoplastic space of Arabidopsis thaliana ggt1 mutant leaves exposed to UV-B radiation

Ultraviolet-B radiation acts as an environmental stimulus, but in high doses it has detrimental effects on plant metabolism. Plasma membranes represent a major target for ROS generated by this harmful radiation. Oxidative reactions occurring in the apoplastic space are counteracted by antioxidative systems mainly involving ascorbate and, to some extent, glutathione. The occurrence of the latter and its exact role in the extracellular space are not well documented, however. In Arabidopsis thaliana, the gamma-glutamyl transferase isoform GGT1 bound to the cell wall takes part in the so-called gamma-glutamyl cycle for extracellular glutathione degradation and recovery, and may be implicated in redox sensing and balance. In this work, oxidative conditions were imposed with UV-B and studied in redox altered ggt1 mutants. The response of ggt1 knockout Arabidopsis leaves to UV-B radiation was assessed by investigating changes in extracellular glutathione and ascorbate content and their redox state, and in apoplastic protein composition. Our results show that, on UV-B exposure, soluble antioxidants respond to the oxidative conditions in both genotypes. Rearrangements occur in their apoplastic protein composition, suggesting an involvement of H2O2, which may ultimately act as a signal. Other important changes relating to hormonal effects, cell wall remodeling, and redox activities are discussed. We argue that oxidative stress conditions imposed by UV-B and disruption of the gamma-glutamyl cycle result in similar stress-induced responses, to some degree at least. Data are available via ProteomeXchange with identifier PXD001807

Phenomenology and Comorbidity of Dysthymic Disorder in 100 Consecutively Referred Children and Adolescents: Beyond DSM-IV

Objective: Diagnostic criteria and nosological boundaries of juvenile dysthymic disorder (DD) are underresearched. Two different sets of diagnostic criteria are still discussed in the DSM-IV, the first giving major weight to somatic and vegetative symptoms and the second, included in the appendix, to more affective and cognitive symptoms. The aim of this study was to describe prototypical symptomatology and comorbidity of DD, according to DSM-IV criteria, in a consecutive series of referred children and adolescents, as a function of age and sex. Method: One hundred inpatients and outpatients (36 children and 64 adolescents, 57 males, 43 females, age range 7 to 18 years, mean age 13.3 years) received a diagnosis of DD without comorbid major depressive disorder (MDD), using historical information, the Diagnostic Interview for Children and Adolescents-Revised (DICA-R), and symptoms ratings according to the DSM-IV criteria. Results: Irritability, low self-esteem, fatigue or loss of energy, depressed mood, guilt, concentration difficulties, anhedonia, and hopelessness were present in more than 50% of subjects. Differences in symptomatic profile between male and female patients were not significant. Anxiety disorders were commonly comorbid with DD, mainly generalized anxiety disorder, simple phobias, and in prepuberal children, separation anxiety disorder. Externalizing disorders were reported in 35% of the patients, with higher prevalence in male patients. Adolescents showed more suicidal thoughts and anhedonia than children. Conclusions: The clinical picture of early-onset DD we found, based entirely on a pure sample without current and past MDD, is not totally congruent with the diagnostic criteria according to DSM-IV. A more precise definition of the clinical picture may help early diagnosis and prevention of superimposed mental disorders

Application of a Machine Learning Technology in the Definition of Metabolically Healthy and Unhealthy Status: A Retrospective Study of 2567 Subjects Suffering from Obesity with or without Metabolic Syndrome.

The key factors playing a role in the pathogenesis of metabolic alterations observed in many patients with obesity have not been fully characterized. Their identification is crucial, and it would represent a fundamental step towards better management of this urgent public health issue. This aim could be accomplished by exploiting the potential of machine learning (ML) technology. In a single-centre study (n = 2567), we used an ML analysis to cluster patients with metabolically healthy (MHO) or metabolically unhealthy (MUO) obesity, based on several clinical and biochemical variables. The first model provided by ML was able to predict the presence/absence of MHO with an accuracy of 66.67% and 72.15%, respectively, and included the following parameters: HOMA-IR, upper body fat/lower body fat, glycosylated haemoglobin, red blood cells, age, alanine aminotransferase, uric acid, white blood cells, insulin-like growth factor 1 (IGF-1) and gamma-glutamyl transferase. For each of these parameters, ML provided threshold values identifying either MUO or MHO. A second model including IGF-1 zSDS, a surrogate marker of IGF-1 normalized by age and sex, was even more accurate with a 71.84% and 72.3% precision, respectively. Our results demonstrated high IGF-1 levels in MHO patients, thus highlighting a possible role of IGF-1 as a novel metabolic health parameter to effectively predict the development of MUO using ML technology

Uncovering Predictors of Lipid Goal Attainment in Type 2 Diabetes Outpatients Using Logic Learning Machine: Insights from the AMD Annals and AMD Artificial Intelligence Study Group

Identifying and treating lipid abnormalities is crucial for preventing cardiovascular disease in diabetic patients, yet only two-thirds of patients reach recommended cholesterol levels. Elucidating the factors associated with lipid goal attainment represents an unmet clinical need. To address this knowledge gap, we conducted a real-world analysis of the lipid profiles of 11.252 patients from the Annals of the Italian Association of Medical Diabetologists (AMD) database from 2005 to 2019. We used a Logic Learning Machine (LLM) to extract and classify the most relevant variables predicting the achievement of a low-density lipoprotein cholesterol (LDL-C) value lower than 100 mg/dL (2.60 mmol/L) within two years of the start of lipid-lowering therapy. Our analysis showed that 61.4% of the patients achieved the treatment goal. The LLM model demonstrated good predictive performance, with a precision of 0.78, accuracy of 0.69, recall of 0.70, F1 Score of 0.74, and ROC-AUC of 0.79. The most significant predictors of achieving the treatment goal were LDL-C values at the start of lipid-lowering therapy and their reduction after six months. Other predictors of a greater likelihood of reaching the target included high-density lipoprotein cholesterol, albuminuria, and body mass index at baseline, as well as younger age, male sex, more follow-up visits, no therapy discontinuation, higher Q-score, lower blood glucose and HbA1c levels, and the use of anti-hypertensive medication. At baseline, for each LDL-C range analysed, the LLM model also provided the minimum reduction that needs to be achieved by the next six-month visit to increase the likelihood of reaching the therapeutic goal within two years. These findings could serve as a useful tool to inform therapeutic decisions and to encourage further in-depth analysis and testing

Status of the Micro Vertex Detector of the Compressed Baryonic Matter Experiment

The CBM experiment will investigate heavy-ion collisions at beam energies from 8 to 45 AGeV at the future accelerator facility FAIR. The goal of the experiment is to study the QCD phase diagram in the vincinity of the QCD critical point. To do so, CBM aims at measuring rare probes among them open charm. In order to identify those rare and short lived particles despite the rich combinatorial background generated in heavy ion collisions, a micro vertex detector (MVD) providing an unprecedented combination of high rate capability and radiation hardness, very light material budget and excellent granularity is required. In this work, we will discuss the concept of this detector and summarize the status of the R&D

Perspectiva interdisciplinaria para el abordaje de una enfermedad infecciosa: chagas o tripanosomiasis americana

La enfermedad de Chagas, producida por el Trypanosoma cruzi y transmitida por un insecto triatomino, es de gran complejidad. En el control de esta endemia no puede considerarse la enfermedad como un hecho individual y sólo biológico. Entre sus múltiples componentes debe considerarse la relación de los sujetos con el hábitat, los modos de producción, las condiciones culturales, las relaciones sociales y las formas organizativas. Como profesionales del campo de la salud intentamos nuevos enfoques que integran diferentes miradas disciplinares y modos de intervención distintos, donde “el otro” recupere su ser sujeto y no esté convocado a desempeñar un mero rol de paciente. Posiciones que implican favorecer procesos participativos, escuchar a los propios protagonistas (mujeres con Chagas, equipos de salud, referentes comunitarios) recuperar sus peculiares visiones, poner en palabras lo no dicho sobre esta enfermedad silenciosa y silenciada, y develar lo que el Chagas esconde. Constituye una herramienta importante a la hora de pensar propuestas de trabajo