Exploring the plasticity of the InhA substrate-binding site using new diaryl ether inhibitors

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a worldwide scourge with more than 10 million people affected yearly. Among the proteins essential for the survival of Mtb, InhA has been and is still clinically validated as a therapeutic target. A new family of direct diaryl ether inhibitors, not requiring prior activation by the catalase peroxidase enzyme KatG, has been designed with the ambition of fully occupying the InhA substrate-binding site. Thus, eleven compounds, featuring three pharmacophores within the same molecule, were synthesized. One of them, 5-(((4-(2-hydroxyphenoxy)benzyl)(octyl)amino)methyl)-2-phenoxyphenol (compound 21), showed good inhibitory activity against InhA with IC50 of 0.70 μM. The crystal structure of compound 21 in complex with InhA/NAD+ showed how the molecule fills the substrate-binding site as well as the minor portal of InhA. This study represents a further step towards the design of new inhibitors of InhA

The Prevention of House Dust Mite Allergies in Pediatric Asthma

: This review provides a concise overview of preventive measures against dust mite allergies in pediatric populations, emphasizing the need for a comprehensive and evolving approach. Dust mites, ubiquitous microscopic arachnids, pose a significant threat to children's health, triggering allergies and asthma. Traditional preventive strategies such as regular cleaning, mattress covers, and humidity control are essential but warrant refinement. Empowering children through personalized hygiene education and exploring innovative bedding solutions showcase a forward-thinking paradigm. Collaboration with healthcare professionals and embracing technology-driven solutions ensures a holistic and adaptable approach to safeguarding pediatric health against dust mite-related ailments. This abstract underscores the importance of continually reassessing and innovating preventive measures to create resilient and health-conscious living environments for children

Luminosity determination using Z boson production at the CMS experiment

The measurement of Z boson production is presented as a method to determine the integrated luminosity of CMS data sets. The analysis uses proton-proton collision data, recorded by the CMS experiment at the CERN LHC in 2017 at a center-of-mass energy of 13 TeV. Events with Z bosons decaying into a pair ofmuons are selected. The total number of Z bosons produced in a fiducial volume is determined, together with the identification efficiencies and correlations from the same data set, in small intervals of 20 pb(-1) of integrated luminosity, thus facilitating the efficiency and rate measurement as a function of time and instantaneous luminosity. Using the ratio of the efficiency-corrected numbers of Z bosons, the precisely measured integrated luminosity of one data set is used to determine the luminosity of another. For the first time, a full quantitative uncertainty analysis of the use of Z bosons for the integrated luminosity measurement is performed. The uncertainty in the extrapolation between two data sets, recorded in 2017 at low and high instantaneous luminosity, is less than 0.5%. We show that the Z boson rate measurement constitutes a precise method, complementary to traditional methods, with the potential to improve the measurement of the integrated luminosity

Exploring the Reactivity of Melanins as Photocatalysts for Reductive Dehalogenations

Four melanin polymers prepared from different precursors (5,6-dihydroxyindole, 5,6-dihydroxyindole 2-carboxylic acid, 1,8-dihydroxynaphthalene and dopamine) have been adopted as photocatalysts in the reductive dehalogenation of a series of model α-halogen carbonyl derivatives. The best performing melanin was that obtained from 5,6-dihydroxyindole 2-carboxylic acid, which allowed to dehalogenate efficiently both bromomalonate esters and phenacyl bromide; on the other hand, chloromalonate esters were almost unreactive under the same reaction conditions. Electrochemical and control experiments enabled to elucidate the reaction mechanism, demonstrating the key role of electrons as charge carriers in the observed photocatalytic process

Cleanroom‐Free Direct Laser Micropatterning of Polymers for Organic Electrochemical Transistors in Logic Circuits and Glucose Biosensors

Organic electrochemical transistors (OECTs) are promising devices for bioelectronics, such as biosensors. However, current cleanroom-based microfabrication of OECTs hinders fast prototyping and widespread adoption of this technology for low-volume, low-cost applications. To address this limitation, a versatile and scalable approach for ultrafast laser microfabrication of OECTs is herein reported, where a femtosecond laser to pattern insulating polymers (such as parylene C or polyimide) is first used, exposing the underlying metal electrodes serving as transistor terminals (source, drain, or gate). After the first patterning step, conducting polymers, such as poly(3,4-ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), or semiconducting polymers, are spin-coated on the device surface. Another femtosecond laser patterning step subsequently defines the active polymer area contributing to the OECT performance by disconnecting the channel and gate from the surrounding spin-coated film. The effective OECT width can be defined with high resolution (down to 2 mu m) in less than a second of exposure. Micropatterning the OECT channel area significantly improved the transistor switching performance in the case of PEDOT:PSS-based transistors, speeding up the devices by two orders of magnitude. The utility of this OECT manufacturing approach is demonstrated by fabricating complementary logic (inverters) and glucose biosensors, thereby showing its potential to accelerate OECT research.Ultrafast focused femtosecond laser has been introduced for the direct micropatterning of organic electrochemical transistors (OECTs), providing high resolution (2 mu m), selective cleanroom-free patterning of insulating and conjugated polymer layers while preserving device operation, and high flexibility in device design. The approach has been validated in the fabrication of complementary inverters and glucose biosensors.imag

Critical reappraisal of short-acting bronchodilators for pediatric respiratory diseases

: Short-acting bronchodilators are a class of medications commonly used to treat asthma, chronic obstructive pulmonary disease, and other respiratory conditions. The use of these medications has evolved over time as we have gained a better understanding of their effectiveness and safety in the pediatric population. This comprehensive review synthesizes the current understanding of short-acting β2-agonists and short-acting anticholinergics in children. It addresses indications, contraindications, safety considerations, and highlights areas where further research is needed to guide the most effective use of short-acting bronchodilators

Optimization Under Uncertainty: Applications to Machine Learning and Waste Management

In this thesis, we deal with optimization problems affected by uncertainty. The first class of problems we analyze aims at separating sets of data points by means of linear and nonlinear classifiers. The classification task is performed according to variants of the Support Vector Machine (SVM) and the uncertainty in real-world data is handled by means of Robust Optimization (RO) techniques. In the case of binary classification, we start by formulating a novel SVM-type model with nonlinear classifiers and perfectly known data points. Secondly, to prevent low accuracies in the classification process due to data perturbations, we construct bounded-by-norm uncertainty sets around the samples. Then, we derive the robust counterpart of the deterministic model thanks to RO strategies. To tackle the problem of multiclass classification, we design a new multiclass Twin Parametric Margin SVM (TPMSVM). We consider the cases of both linear and kernel-induced boundaries and propose two alternatives for the final decision function. Data perturbations are then included in the model and RO techniques are applied to prevent the TPMSVM against the worst possible realization of the uncertainty. All the aforementioned approaches are tested on real-world datasets, showing the advantages of explicitly considering the uncertainty versus deterministic approaches. The second problem we analyze is related to waste collection. Within this application, uncertainty lies in the waste accumulation rate of the network bins. Since information on the empirical distribution of the uncertainty is available, Stochastic Optimization (SO) techniques are applied. We model the waste collection problem as a multi-stage stochastic inventory routing problem, where the decisions are related to the selection of bins to be visited and the corresponding visiting sequence in a predefined time horizon. Given the computational complexity of the model, we solve it through a rolling horizon heuristic approach, and carry out computational experiments on real-data instances. The impact of stochasticity on waste generation is examined through stochastic measures, and the performance of the rolling horizon approach is evaluated. Finally, we discuss some managerial insights.In this thesis, we deal with optimization problems affected by uncertainty. The first class of problems we analyze aims at separating sets of data points by means of linear and nonlinear classifiers. The classification task is performed according to variants of the Support Vector Machine (SVM) and the uncertainty in real-world data is handled by means of Robust Optimization (RO) techniques. In the case of binary classification, we start by formulating a novel SVM-type model with nonlinear classifiers and perfectly known data points. Secondly, to prevent low accuracies in the classification process due to data perturbations, we construct bounded-by-norm uncertainty sets around the samples. Then, we derive the robust counterpart of the deterministic model thanks to RO strategies. To tackle the problem of multiclass classification, we design a new multiclass Twin Parametric Margin SVM (TPMSVM). We consider the cases of both linear and kernel-induced boundaries and propose two alternatives for the final decision function. Data perturbations are then included in the model and RO techniques are applied to prevent the TPMSVM against the worst possible realization of the uncertainty. All the aforementioned approaches are tested on real-world datasets, showing the advantages of explicitly considering the uncertainty versus deterministic approaches. The second problem we analyze is related to waste collection. Within this application, uncertainty lies in the waste accumulation rate of the network bins. Since information on the empirical distribution of the uncertainty is available, Stochastic Optimization (SO) techniques are applied. We model the waste collection problem as a multi-stage stochastic inventory routing problem, where the decisions are related to the selection of bins to be visited and the corresponding visiting sequence in a predefined time horizon. Given the computational complexity of the model, we solve it through a rolling horizon heuristic approach, and carry out computational experiments on real-data instances. The impact of stochasticity on waste generation is examined through stochastic measures, and the performance of the rolling horizon approach is evaluated. Finally, we discuss some managerial insights

An Adapted Questionnaire Tailored for Assessing the Risk of Vitamin D Deficiency in Children That Is Proving Useful in Guiding Clinical Interventions

Background: The identification of vitamin D (VitD) deficiency in pediatric populations is essential for preventive healthcare. We refined and tested the Evaluation of Deficiency Questionnaire (EVIDENCe-Q) for its utility in detecting VitD insufficiency among children. Patients and methods: We enrolled 201 pediatric patients (aged between 3 and 18 years). Clinical evaluation and serum vitamin D levels were assessed in all subjects. The EVIDENCe-Q was updated to incorporate factors influencing VitD biosynthesis, intake, assimilation, and metabolism, with scores spanning from 0 (optimal) to 36 (poor). Results: We established scores for severe deficiency (<10 mg/dL) at 20, deficiency (<20 mg/dL) at 22, and insufficiency (<30 mg/dL) at 28. A score of 20 or greater was determined as the optimal cut-off for distinguishing VitD deficient from sufficient statuses, as evidenced by ROC curve analysis AUC = 0.7066; SE = 0.0841; sensitivity 100%, 95% CI 0.561-1. The most accurate alignment was seen with VitD insufficiency, defined as 25-OH-D3 < 20 ng/mL. Conclusions: This study confirms that the EVIDENCe-Q is a valid instrument for assessing the risk of vitamin D deficiency and insufficiency in children. It offers a practical approach for determining the need for clinical intervention and dietary supplementation of VitD in the pediatric population

Climate, desertification, and local human development: evidence from 1564 regions around the world

How global warming affects human development is a central question for economists as well as social scientists. While most of the literature has focused on the impact of weather on income, less is known on the relationship between climate and local human development. This paper considers shocks in precipitation, temperature, and an original measure of soil aridity to first exploit the association between climate warming and human development, and second, on its dimensions. We show that while precipitations do not have a significant long-term impact on human development growth, variations in temperature and potential evapotranspiration negatively affect two of the three determinants of the Human Development Index, namely life expectancy at birth and education. These results suggest that other climate indicators, such as the potential evapo-transpiration of the soil, should be considered in addition to the standard indicators, when evaluating the localized economic effects of climate change