Integrating children's perspectives in policy-making to combat poverty and social exclusion experienced by single-parent families: a transnational comparative approach

This is the final report of a research project that addressed social exclusion and poverty as it relates to single parent families and their children in particular. The rising numbers of single parent families and children throughout the EU and the increased likelihood that these families will live in poverty and experience many different forms of social exclusion in their daily lives brings in sharp focus the need to address the issue as an urgent one in our efforts to eradicate poverty and social exclusion. The focus on the children of single parent families seeks to rectify a long-standing problem in our knowledge and understanding of single parent families and the social problems they face, namely, the fact that little, if anything, is known about how these children experience and understand their lives as members of these families. The research set out to contribute to policy development and the transnational exchange of best practice by adding a much-neglected dimension on single parent families. The project used a cross-national comparative qualitative research design and methods (Mangen 1999) which involved all partners in the design of each research phase including the analysis; partners were England, Cyprus and Greece

Prediction of intraoperative hypotension from the linear extrapolation of mean arterial pressure

International audienceBACKGROUND: Hypotension prediction index (HPI) software is a proprietary machine learning-based algorithm used to predict intraoperative hypotension (IOH). HPI has shown superiority in predicting IOH when compared to the predictive value of changes in mean arterial pressure (ΔMAP) alone. However, the predictive value of ΔMAP alone, with no reference to the absolute level of MAP, is counterintuitive and poor at predicting IOH. A simple linear extrapolation of mean arterial pressure (LepMAP) is closer to the clinical approach. OBJECTIVES: Our primary objective was to investigate whether LepMAP better predicts IOH than ΔMAP alone. DESIGN: Retrospective diagnostic accuracy study. SETTING: Two tertiary University Hospitals between May 2019 and December 2019. PATIENTS: A total of 83 adult patients undergoing high risk non-cardiac surgery. DATA SOURCES: Arterial pressure data were automatically extracted from the anaesthesia data collection software (one value per minute). IOH was defined as MAP \textless 65 mmHg. ANALYSIS: Correlations for repeated measurements and the area under the curve (AUC) from receiver operating characteristics (ROC) were determined for the ability of LepMAP and ΔMAP to predict IOH at 1, 2 and 5 min before its occurrence (A-analysis, using the whole dataset). Data were also analysed after exclusion of MAP values between 65 and 75 mmHg (B-analysis). RESULTS: A total of 24 318 segments of ten minutes duration were analysed. In the A-analysis, ROC AUCs to predict IOH at 1, 2 and 5 min before its occurrence by LepMAP were 0.87 (95% confidence interval, CI, 0.86 to 0.88), 0.81 (95% CI, 0.79 to 0.83) and 0.69 (95% CI, 0.66 to 0.71) and for ΔMAP alone 0.59 (95% CI, 0.57 to 0.62), 0.61 (95% CI, 0.59 to 0.64), 0.57 (95% CI, 0.54 to 0.69), respectively. In the B analysis for LepMAP these were 0.97 (95% CI, 0.9 to 0.98), 0.93 (95% CI, 0.92 to 0.95) and 0.86 (95% CI, 0.84 to 0.88), respectively, and for ΔMAP alone 0.59 (95% CI, 0.53 to 0.58), 0.56 (95% CI, 0.54 to 0.59), 0.54 (95% CI, 0.51 to 0.57), respectively. LepMAP ROC AUCs were significantly higher than ΔMAP ROC AUCs in all cases. CONCLUSIONS: LepMAP provides reliable real-time and continuous prediction of IOH 1 and 2 min before its occurrence. LepMAP offers better discrimination than ΔMAP at 1, 2 and 5 min before its occurrence. Future studies evaluating machine learning algorithms to predict IOH should be compared with LepMAP rather than ΔMAP

DMPK promoter silencing by CRISPRi as a new therapeutic strategy in myotonic dystrophy type 1

"a. IntroductionMyotonic dystrophy type 1 (DM1) is a life threatening disease and causes severe physical and mental disabilities. Unfortunately, there are currently only symptomatic treatments. Therefore, our team aims at elaborating a new curative approach which consists in the DMPK promoter silencing by the CRISPRi system.b. MethodsThe DMPK promoter inhibition capacity of CRISPRi was tested in immortalized myoblasts from DM1 patients. For this purpose, lentiviral particles were produced using CRISPRi plasmids with their own sgRNAs. Next, these myoblasts were transduced and selected with blasticidin. Then, total DMPK mRNA was titrated by RT-qPCR and nuclear DMPK RNA foci were determinated by FISH.c. ResultsSome sgRNAs lead to near 70% inhibition of DMPK transcription as well as foci average particules in DM1 transduced myoblasts.d. ConclusionsThe CRISPRi system is able to efficiently prevent the DMPK mRNA production and foci formation in myoblast.

Mapping the human genetic architecture of COVID-19

The genetic make-up of an individual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-191,2, host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases3–7. They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease