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In observational studies, one of the main difficulties consists in the comparison of treat- ment effects. In fact, receiving a treatment is not a “purely random” event, and there could be relevant differences between treatment groups. Propensity score is a popular tool to account for this source of bias. However, its use requires a careful modelization of the dependence relationships of the treatment on the covariates. In this work, we consider a general setting with multiple treatments and discrete multi-valued outcome. We propose to estimate the propensity score by using Bayesian Networks and, based on this, we develop an inferential methodology to evaluate the treatments effect. The performance of the pro- posed approach have been studied through a simulation study with very promising results.

The “Healthcare workers’ wellbeing [Benessere Operatori]” project is an exploratory longitudinal study assessing healthcare workers’ mental health at three different time points over a 14-month period during the COVID-19 pandemic. We collected socio-demographic and work-related information and assessed the perceived social support, coping strategies, and levels of depression, anxiety, insomnia, anger, burnout, and PTSD symptoms. In total, 325 Italian healthcare workers (i.e. physicians, nurses, other healthcare workers, and clerks) participated in the first initial survey and either the second or third subsequent survey. Participants reported subclinical levels of psychiatric symptoms that remained mostly unchanged across time, except for an increase in stress, depression, state anger, and emotional exhaustion symptoms. Despite subclinical levels, healthcare workers’ distress can adversely affect the quality of care, patient satisfaction, and medical error rates. Therefore, implementing interventions to improve healthcare workers’ wellbeing is required.

Targeted gene editing in hematopoietic stem cells (HSCs) is a promising treatment for several diseases. However, the limited efficiency of homology-directed repair (HDR) in HSCs and the unknown impact of the procedure on clonal composition and dynamics of transplantation have hampered clinical translation. Here, we apply a barcoding strategy to clonal tracking of edited cells (BAR-Seq) and show that editing activates p53, which substantially shrinks the HSC clonal repertoire in hematochimeric mice, although engrafted edited clones preserve multilineage and self-renewing capacity. Transient p53 inhibition restored polyclonal graft composition. We increased HDR efficiency by forcing cell-cycle progression and upregulating components of the HDR machinery through transient expression of the adenovirus 5 E4orf6/7 protein, which recruits the cell-cycle controller E2F on its target genes. Combined E4orf6/7 expression and p53 inhibition resulted in HDR editing efficiencies of up to 50% in the long-term human graft, without perturbing repopulation and self-renewal of edited HSCs. This enhanced protocol should broaden applicability of HSC gene editing and pave its way to clinical translation.

Background: COVID-19 forced healthcare workers to work in unprecedented and critical circumstances, exacerbating already-problematic and stressful working conditions. The “Healthcare workers’ wellbeing (Benessere Operatori)” project aimed at identifying psychological and personal factors, influencing individuals’ responses to the COVID-19 pandemic. Methods: 291 healthcare workers took part in the project by answering an online questionnaire twice (after the first wave of COVID-19 and during the second wave) and completing questions on socio-demographic and work-related information, the Depression Anxiety Stress Scale-21, the Insomnia Severity Index, the Impact of Event Scale-Revised, the State-Trait Anger Expression Inventory-2, the Maslach Burnout Inventory, the Multidimensional Scale of Perceived Social Support, and the Brief Cope. Results: Higher levels of worry, worse working conditions, a previous history of psychiatric illness, being a nurse, older age, and avoidant and emotion-focused coping strategies seem to be risk factors for healthcare workers’ mental health. High levels of perceived social support, the attendance of emergency training, and problem-focused coping strategies play a protective role. Conclusions: An innovative, and more flexible, data mining statistical approach (i.e., a regression trees approach for repeated measures data) allowed us to identify risk factors and derive classification rules that could be helpful to implement targeted interventions for healthcare workers.

Background: COVID-19 forced healthcare workers to work in unprecedented and critical circumstances, exacerbating already-problematic and stressful working conditions. The “Healthcare workers’ wellbeing (Benessere Operatori)” project aimed at identifying psychological and personal factors, influencing individuals’ responses to the COVID-19 pandemic. Methods: 291 healthcare workers took part in the project by answering an online questionnaire twice (after the first wave of COVID-19 and during the second wave) and completing questions on socio-demographic and work-related information, the Depression Anxiety Stress Scale-21, the Insomnia Severity Index, the Impact of Event Scale-Revised, the State-Trait Anger Expression Inventory-2, the Maslach Burnout Inventory, the Multidimensional Scale of Perceived Social Support, and the Brief Cope. Results: Higher levels of worry, worse working conditions, a previous history of psychiatric illness, being a nurse, older age, and avoidant and emotion-focused coping strategies seem to be risk factors for healthcare workers’ mental health. High levels of perceived social support, the attendance of emergency training, and problem-focused coping strategies play a protective role. Conclusions: An innovative, and more flexible, data mining statistical approach (i.e., a regression trees approach for repeated measures data) allowed us to identify risk factors and derive classification rules that could be helpful to implement targeted interventions for healthcare workers.

Objective: Referring to Leventhal's common-sense model, this observational cross-sectional study aimed at investigating the relationship between illness mental representations, coping mechanisms and psychological distress in a sample of women with gestational trophoblastic disease (GTD). Methods: Thirty-eight women diagnosed with GTD (18 with hydatidiform mole; 20 with gestational trophoblastic neoplasia) were asked to complete the Illness Perception Questionnaire—Revised, the Coping Orientation to the Problems Experienced, the State-Trait Anxiety Inventory—Form Y and the Beck Depression Inventory—Short Form. Demographic and clinical information was collected through a self-report questionnaire. Results: The sample did not report significant symptomatic distress in relation to GTD. Correlation analysis showed that the Emotional representations subscale of the Illness Perception Questionnaire—Revised was significantly associated with both state anxiety and depression; avoidant coping significantly and positively correlated with anxiety and depression, as well as with illness emotional representations. Mediation analysis revealed significant indirect effects of avoidant coping on both anxiety and depression through the mediation of emotional representations. Conclusion: Avoidant coping could lead women to develop emotional representations of illness characterised by negative affects, which in turn enhance distress levels. Results underline the importance to promote adaptive coping strategies, along with accurate illness perceptions, to foster better psychological adjustment to GTD.

In observational studies evaluating the treatment effect on a given out- come, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to esti- mate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.

In observational studies evaluating the treatment effect on a given out- come, the treated and untreated subjects may be highly unbalanced in their observed covariates, and these differences can lead to biased estimates of treatment effects. Propensity score is popular tool to reduce this bias. In this work we propose to esti- mate the propensity score by using Bayesian Networks as alternative to conventional logistic regression. Based on it, we develop an inferential methodology to evaluate the treatment effect. In simulation study, our proposed approach resulted in the best performance.

As the COVID-19 pandemic is spreading around the world, increasing evidence highlights the role of cardiometabolic risk factors in determining the susceptibility to the disease. The fragmented data collected during the initial emergency limited the possibility of investigating the effect of highly correlated covariates and of modeling the interplay between risk factors and medication. The present study is based on comprehensive monitoring of 576 COVID-19 patients. Different statistical approaches were applied to gain a comprehensive insight in terms of both the identification of risk factors and the analysis of dependency structure among clinical and demographic characteristics. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus enters host cells by binding to the angiotensinconverting enzyme 2 (ACE2), but whether or not renin-angiotensin-aldosterone system inhibitors (RAASi) would be beneficial to COVID-19 cases remains controversial. The survival tree approach was applied to define a multilayer risk stratification and better profile patient survival with respect to drug regimens, showing a significant protective effect of RAASi with a reduced risk of in-hospital death. Bayesian networks were estimated, to uncover complex interrelationships and confounding effects. The results confirmed the role of RAASi in reducing the risk of death in COVID-19 patients. De novo treatment with RAASi in patients hospitalized with COVID-19 should be prospectively investigated in a randomized controlled trial to ascertain the extent of risk reduction for in-hospital death in COVID-19.

The UKMYC5 plate is a 96-well microtiter plate designed by the CRyPTIC Consortium (Comprehensive Resistance Prediction for Tuberculosis: an International Consortium) to enable the measurement of MICs of 14 different antituberculosis (anti-TB) compounds for 30,000 clinical Mycobacterium tuberculosis isolates. Unlike the MYCOTB plate, on which the UKMYC5 plate is based, the UKMYC5 plate includes two new (bedaquiline and delamanid) and two repurposed (clofazimine and linezolid) compounds. UKMYC5 plates were tested by seven laboratories on four continents by use of a panel of 19 external quality assessment (EQA) strains, including H37Rv. To assess the optimal combination of reading method and incubation time, MICs were measured from each plate by two readers, using three methods (mirrored box, microscope, and Vizion digital viewing system), after 7, 10, 14, and 21 days of incubation. In addition, all EQA strains were subjected to whole-genome sequencing and phenotypically characterized by the 7H10/7H11 agar proportion method (APM) and by use of MGIT960 mycobacterial growth indicator tubes. We concluded that the UKMYC5 plate is optimally read using the Vizion system after 14 days of incubation, achieving an interreader agreement of 97.9% and intra- and interlaboratory reproducibility rates of 95.6% and 93.1%, respectively. The mirrored box had a similar reproducibility. Strains classified as resistant by APM, MGIT960, or the presence of mutations known to confer resistance consistently showed elevated MICs compared to those for strains classified as susceptible. Finally, the UKMYC5 plate records intermediate MICs for one strain for which the APM measured MICs close to the applied critical concentration, providing early evidence that the UKMYC5 plate can quantitatively measure the magnitude of resistance to anti-TB compounds that is due to specific genetic variation.

Background: Breast Cancer susceptibility genes 1 and 2 are implicated in hereditary breast and ovarian cancer and women can test for the presence of these genes prior to developing cancer. The goal of this study is to examine psychological distress, quality of life, and active coping mechanisms in a sample of women during the pre-test stage of the genetic counseling process, considering that pre-test distress can be an indicator of post-test distress. We also wanted to identify if subgroups of women, defined based on their health status, were more vulnerable to developing distress during the genetic counseling process. Methods: This study included 181 female participants who accessed a Cancer Genetic Counseling Clinic. The participants were subdivided into three groups on the basis of the presence of a cancer diagnosis: Affected patients, Ex-patients, and Unaffected participants. Following a self-report questionnaire, a battery of tests was administered to examine psychological symptomatology, quality of life, and coping mechanisms. Results: The results confirm that the genetic counseling procedure is not a source of psychological distress. Certain participants were identified as being more vulnerable than others; in the pre-test phase, they reported on average higher levels of distress and lower quality of life. These participants were predominantly Ex-patients and Affected patients, who may be at risk of distress during the counseling process. Conclusions: These findings highlight that individuals who take part in the genetic counseling process are not all the same regarding pre-test psychological distress. Attention should be paid particularly to Ex-patients and Affected patients by the multidisciplinary treating team.

Background: Breast Cancer susceptibility genes 1 and 2 are implicated in hereditary breast and ovarian cancer and women can test for the presence of these genes prior to developing cancer. The goal of this study is to examine psychological distress, quality of life, and active coping mechanisms in a sample of women during the pre-test stage of the genetic counseling process, considering that pre-test distress can be an indicator of post-test distress. We also wanted to identify if subgroups of women, defined based on their health status, were more vulnerable to developing distress during the genetic counseling process. Methods: This study included 181 female participants who accessed a Cancer Genetic Counseling Clinic. The participants were subdivided into three groups on the basis of the presence of a cancer diagnosis: Affected patients, Ex-patients, and Unaffected participants. Following a self-report questionnaire, a battery of tests was administered to examine psychological symptomatology, quality of life, and coping mechanisms. Results: The results confirm that the genetic counseling procedure is not a source of psychological distress. Certain participants were identified as being more vulnerable than others; in the pre-test phase, they reported on average higher levels of distress and lower quality of life. These participants were predominantly Ex-patients and Affected patients, who may be at risk of distress during the counseling process. Conclusions: These findings highlight that individuals who take part in the genetic counseling process are not all the same regarding pre-test psychological distress. Attention should be paid particularly to Ex-patients and Affected patients by the multidisciplinary treating team.