Analysing the effect of climate policies on poverty through employment channels

The recently proposed Green Deals and "building back better" plans have affirmed the importance to make green transitions inclusive. This is particularly related to the labour market, which may witness significant changes. Empirically, this issue has until now received limited attention. The links between poverty and climate change are explored mainly through the lenses of climate change adaptation, or via the effects of rising energy prices on the purchasing power of poor households. We aim to address this gap by using results from a simulation of the global energy transition based on the IEA's Energy Technology Perspectives required to meet the 2-degree target, and compare this to the 6-degree baseline scenario. The simulation with a multi-regional input-output model finds that, overall, this transition results in a small net job increase of 0.3% globally, with cross-country heterogeneity. We complement this macro-level analysis with cross-country household data to draw implications of the effects on poverty through labour market outcomes. The few job losses will be concentrated in specific industries (manufacturing, electricity and construction), while new jobs will be created in industries that currently witness relatively in-work poverty rates, such as construction. We show that high in-work poverty in the industries of interest, and especially in middle-income countries, is often associated with low skills and an insufficient reach of social protection mechanisms. We conclude that green transitions must ensure that the jobs created are indeed decent including fair wages, adequate working conditions, sufficient social protection measures, and accessible to the vulnerable and poorest households.publishedVersio

Prioritarian rates of return to antipoverty transfers

A growing impact evaluation literature on antipoverty transfer programmes in low- and middle-income countries measures changes in utilitarian terms, at their unit value. The paper argues that valuing antipoverty transfers is more appropriately done within a framework of prioritarian social welfare functions, as the very presence of these programmes indicates that polities place a greater value on gains and losses among the disadvantaged. The paper applies this framework to the Senior Citizen Grant in Uganda, including survey and experimental work throwing light on social preferences for redistribution. It finds that default utilitarian valuation significantly underestimates the social value of transfer programmes

Mitigating poverty: The patterns of multiple carbon tax and recycling regimes for Peru

Carbon taxes are an economically effective and efficient policy measure to address climate change mitigation. However, they can have severe adverse distributional effects. Recycling parts of the fiscal revenues to vulnerable, lower income households through cash transfers (social assistance) is an option to also overcome associated political difficulties. This paper simulates the distributional impacts of such a combined policy reform in Peru. In a first step, we assess the distributional impacts of varying carbon tax rates. In a second step, we evaluate different scenarios of recycling revenues through existing or expanded transfer schemes towards vulnerable households. The results indicate that a national carbon tax, without compensation, would increase poverty but have no significant impact on inequality. When tax revenues are recycled through transfer schemes, however, poverty would actually decrease. Depending on the amount to be redistributed and the design of the cash transfer scheme, our simulations show a proportional reduction in the poverty headcount of up to around 17%. In addition, the paper underlines how crucial it is to go beyond aggregate measures of poverty to better identify losers from such reform; and assure that the “leave no one behind” principle of the Sustainable Development Goals (SDGs) is addressed

STUDIO PILOTA SULL’ANALISI DEL MICROBIOTA E DEL MICROBIOMA ORALE PRIMA E DOPO L’ESTRAZIONE DEGLI OTTAVI INFERIORI CON L’UTILIZZO DEL PROBIOTICO CURASEPT PREVENT

Obiettivo: L’obiettivo di questo studio è quello di valutare, con uno studio pilota clinico osservazionale, le differenze qualitative e quantitative del microbiota orale di pazienti sottoposti all’estrazione degli ottavi inferiori inclusi o seminclusi, in tre momenti diversi: T0 prima dell’estrazione, T1 alla rimozione delle suture dopo 5 giorni di terapia antibiotica e 10 giorni di terapia antisettica, T2 dopo 1 mese di terapia con probiotici. Materiali e Metodi: Sono stati arruolati 10 pazienti candidati all’estrazione del dente del giudizio inferiore. I soggetti sono stati sottoposti dopo l’intervento a terapia antibiotica, antisettica e probiotica a base di Bifidobacterium lactis HN019 e Kluyveromyces marxianus fragilis B0399. Attraverso il prelievo di tamponi orali prima dell’intervento, al termine della terapia antibiotica e antisettica e al termine della terapia probiotica, è stata effettuata un’analisi del microbioma orale tramite 16S NGS. Risultati: sono state rilevate differenze significative nel microbiota orale tra i dati ottenuti al termine della terapia antibiotica e antisettica e quelli prima dell’intervento (p=0,002) e dopo la terapia probiotica (p=0,019). Conclusioni: l’utilizzo della terapia antibiotica, antisettica e probiotica ha modificato significativamente il microbiota orale. Questo è uno studio pilota eseguito su dieci pazienti, saranno quindi necessari ulteriori studi per chiarire le dinamiche alla base dei risultati ottenuti

16S rRNA gene amplicons and taxonomic classification of oral microbiome

The term microbiota refers to a set of microorganisms, considered as a living ecosystem, undergoing continuous changes in the growth and survival of all its members. The microbiome consists of the set of microorganism genomes. The human microbiota is estimated to contain about 10^14 commensal bacterial cells. The present high-throughput sequencing technology has led to the development of genome-based methods for bacterial classification and for understanding the functional role of the microbiota and its interaction with the host. In this study we explore the capability of a gene-based sequencing method to classify bacteria of the oral microbiome, the second largest microbial community in the human body, after the gut. The method depends on the detection of sequence variants in the bacterial 16S rRNA gene (length ~1500bp), present in all bacterial genomes. This gene includes nine hypervariable regions (V1-V9) that exhibit sequence diversity among different bacterial species. Therefore, the sequence variability of this gene is used to classify bacteria into proper taxonomic groups. The sequencing of one single hypervariable region cannot summarize the entire gene variability of the bacteria. Therefore, at least 2 hypervariable regions are generally studied. In gut studies the V3 and V4 regions are the most commonly analyzed. This could not be the case for oral microbiome studies. Here, we propose a study that investigates all the 9 hypervariable regions (6 amplicons) and how their characterization impacts on the overall taxa classification, at different taxonomic layers. This will permit to show up also the specificity of each hypervariable region (or their combination) to identify bacterial species. We collected 4 buccal swab samples from healthy individuals, and the extracted DNA was sequenced according to the QIAseq 16S/ITS panel handbook on an Illumina MiSeq NGS platform producing ~200,000 paired end reads (276PE) per sample. We carried out the study in two different ways: 1) by combining data from all amplicons of the 16S regions together, 2) by combining data from each amplicon region that was processed individually, in each sample. Amplicon analyses were performed using the Divisive Amplicon Denoising Algorithm (DADA2) that counts the number of amplicon sequence variants (ASVs) in each analyzed sample, reporting their abundance. ASVs were then classified using a pre-trained set for oral bacterial genome sequences (Human Oral Microbiome Database, version 15.1), slightly modified according to DADA2 requirements. The classification efficiency and accuracy (at genus or species layer) of every ASVs belonging to the different hypervariable regions was then ascertained. This analysis highlights the hypervariable regions able to capture the greatest gene variability for oral microbiome. Moreover, the ten most common species of each of the 6 amplicons, were reported for comparison purposes. We identified about 90 genera and more than 200 species; out of 9 identified phyla, Proteobacteria resulted to be the most abundant phylum (~ 56%). Of all the 2600 unique observed ASVs (4 samples), 1147 were successfully classified at the species taxonomic layer (overall classification rate: 44.1%). Overall, 204 different species were recognized with the entire set of combined amplicons, whereas 206 different species were identified by the combined results of single amplicons. The V1-V2 and V2-V3 amplicons recognized the highest number of species compared to the others, about 134 and 135 different species, respectively, of which 101 species in common. All the single regions showed almost the same ten most recurrent species. Moreover, each region resulted to be able to detect specific bacterial species that were not detectable by the other 16S regions. In conclusion, studying all the 9 16S gene regions is ~1.7 times more informative than studying just either one or 2 regions, and some species can be recognized only when studying specific regions. Still it remains doubtful how to treat data from different regions together to estimate the relative abundances of bacterial species within each sample

Secondary SOLID Tumors after Allogeneic STEM CELL Transplantation: A CROSS-Sectional Evaluation in 260 Adults at 1-Year Follow-up

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Cladribine and ocrelizumab induce differential miRNA profiles in peripheral blood mononucleated cells from relapsing–remitting multiple sclerosis patients

Background and objectivesMultiple sclerosis (MS) is a chronic, progressive neurological disease characterized by early-stage neuroinflammation, neurodegeneration, and demyelination that involves a spectrum of heterogeneous clinical manifestations in terms of disease course and response to therapy. Even though several disease-modifying therapies (DMTs) are available to prevent MS-related brain damage—acting on the peripheral immune system with an indirect effect on MS lesions—individualizing therapy according to disease characteristics and prognostic factors is still an unmet need. Given that deregulated miRNAs have been proposed as diagnostic tools in neurodegenerative/neuroinflammatory diseases such as MS, we aimed to explore miRNA profiles as potential classifiers of the relapsing–remitting MS (RRMS) patients’ prospects to gain a more effective DMT choice and achieve a preferential drug response.MethodsA total of 25 adult patients with RRMS were enrolled in a cohort study, according to the latest McDonald criteria before (pre-cladribine, pre-CLA; pre-ocrelizumab, pre-OCRE, time T0) and after high-efficacy DMTs, time T1, 6 months post-CLA (n = 10, 7 F and 3 M, age 39.0 ± 7.5) or post-OCRE (n = 15, 10 F and 5 M, age 40.5 ± 10.4) treatment. A total of 15 age- and sex-matched healthy control subjects (9 F and 6 M, age 36.3 ± 3.0) were also selected. By using Agilent microarrays, we analyzed miRNA profiles from peripheral blood mononuclear cells (PBMC). miRNA–target networks were obtained by miRTargetLink, and Pearson’s correlation served to estimate the association between miRNAs and outcome clinical features.ResultsFirst, the miRNA profiles of pre-CLA or pre-OCRE RRMS patients compared to healthy controls identified modulated miRNA patterns (40 and seven miRNAs, respectively). A direct comparison of the two pre-treatment groups at T0 and T1 revealed more pro-inflammatory patterns in the pre-CLA miRNA profiles. Moreover, both DMTs emerged as being capable of reverting some dysregulated miRNAs toward a protective phenotype. Both drug-dependent miRNA profiles and specific miRNAs, such as miR-199a-3p, miR-29b-3p, and miR-151a-3p, emerged as potentially involved in these drug-induced mechanisms. This enabled the selection of miRNAs correlated to clinical features and the related miRNA–mRNA network.DiscussionThese data support the hypothesis of specific deregulated miRNAs as putative biomarkers in RRMS patients’ stratification and DMT drug response

A triple helix model of medical innovation: supply, demand, and technological capabilities in terms of medical subject headings

We develop a model of innovation that enables us to trace the interplay among three key dimensions of the innovation process: (i) demand of and (ii) supply for innovation, and (iii) technological capabilities available to generate innovation in the forms of products, processes, and services. Building on triple helix research, we use entropy statistics to elaborate an indicator of mutual information among these dimensions that can provide indication of reduction of uncertainty. To do so, we focus on the medical context, where uncertainty poses significant challenges to the governance of innovation. We use the Medical Subject Headings (MeSH) of MEDLINE/PubMed to identify publications classified within the categories “Diseases" (C), "Drugs and Chemicals" (D), "Analytic, Diagnostic, and Therapeutic Techniques and Equipment" (E) and use these as knowledge representations of demand, supply, and technological capabilities, respectively. Three case-studies of medical research areas are used as representative 'entry perspectives' of the medical innovation process. These are: (i) human papilloma virus, (ii) RNA interference, and (iii) magnetic resonance imaging. We find statistically significant periods of synergy among demand, supply, and technological capabilities (C-D-E) that point to three-dimensional interactions as a fundamental perspective for the understanding and governance of the uncertainty associated with medical innovation. Among the pairwise configurations in these contexts, the demand-technological capabilities (C-E) provided the strongest link, followed by the supply-demand (D-C) and the supply-technological capabilities (D-E) channels