Our findings suggest that certain polymorphisms of the factor VII gene may influence the risk of myocardial infarction. It is possible that this effect may be mediated by alterations in factor VII levels.

Introduction: A hypercoagulable condition was described in patients with COVID-19 and proposed as a possible pathogenic mechanism contributing to disease progression and lethality. Aim: We evaluated if in-hospital administration of heparin improved survival in a large cohort of Italian COVID-19 patients. Methods: In a retrospective observational study, 2,574 unselected patients hospitalised in 30 clinical centres in Italy from February 19, 2020 to May 23, 2020 with laboratory-confirmed SARS-CoV-2 infection, were analysed. The primary end-point in a time-to event analysis was in-hospital death, comparing patients who received heparin (low-molecular weight heparin (LMWH) or unfractionated heparin (UFH)) with patients who did not. We used multivariable Cox proportional-hazards regression models with inverse probability for treatment weighting by propensity scores. Results: Out of 2,574 COVID-19 patients, 70.1% received heparin. LMWH was largely the most used formulation (99.5%). Death rates for patients receiving heparin or not were 7.4 and 14.0 per 1,000 person-days, respectively. After adjustment for propensity scores, we found a 40% lower risk of death in patients receiving heparin (HR=0.60; 95%CI: 0.49 to 0.74; E-value=2.04). This association was particularly evident in patients with a higher severity of disease or strong coagulation activation. Conclusions: In-hospital heparin treatment was associated with lower mortality, particularly in severely ill COVID-19 patients and in those with strong coagulation activation. The results from randomised clinical trials are eagerly awaited to provide clear-cut recommendations.

The aim of the study was to characterize the audiological consequences of congenital cytomegalovirus infection (CMV) and to evaluate the outcome of rehabilitation with hearing aids and/or cochlear implant (CI), associated with an adequate speech-language therapy. A retrospective review of data was made from a total of 16 infants, affected by severe to profound hearing loss from congenital CMV infection, referred to a tertiary audiological center for rehabilitation. Audiological evaluation was performed using behavioral audiometry, auditory brainstem responses (ABR) and/or electrocochleography (ECochG). Of the 16 children (median age at diagnosis of hearing loss: 21.33 +/- 0.7 months) with CMV hearing loss, 14 were affected by profound bilateral hearing loss and received a CI, while 2 were affected by bilateral severe hearing loss and received hearing aids. Cochlear implants can provide useful speech comprehension to patients with CMV-related deafness, even if language development is lower when compared to a group of Connexin (Cx) 26+ cochlear-implanted children (eight subjects), matched for age. Congenital CMV infection still represents a serious clinical condition, as well as an important cause of hearing loss in children. More studies have claimed to identify the pathophysiological mechanisms of damage and thus to ensure a better therapeutic approach. Nonetheless, in cases of CMV-deafened babies, the overall outcome of cochlear implantation is good.

The recent increase in digitalization of industrial systems has resulted in a boost in data availability in the industrial environment. This has favored the adoption of machine learning (ML) methodologies for the analysis of data, but not all contexts boast data abundance. When data are scarce or costly to collect, Design of Experiments (DOE) can be used to provide an informative dataset for analysis using ML techniques. This article aims to provide a systematic overview of the literature on the joint application of DOE and ML in product innovation (PI) settings. To this end, a systematic literature review (SLR) of two major scientific databases is conducted, retrieving 388 papers, of which 86 are selected for careful analysis. The results of this review delineate the state of the art and identify the main trends in terms of experimental designs and ML algorithms selected for joint application on PI. The gaps, open problems, and research opportunities are identified, and directions for future research are provided.

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