Perinatal depression and patterns of attachment: a critical risk factor?

Background. This study aims to verify if the presence and severity of perinatal depression are related to any particular pattern of attachment. Methods. The study started with a screening of a sample of 453 women in their third trimester of pregnancy, who were administered a survey data form, the Edinburgh Postnatal Depression Scale (EPDS) and the Experience in Close Relationship (ECR). A clinical group of subjects with perinatal depression (PND, 89 subjects) was selected and compared with a control group (C), regarding psychopathological variables and attachment patterns. Results. The ECR showed a prevalence of “Fearful-Avoidant” attachment style in PND group (29.2% versus 1.1%, < 0.001); additionally, the EPDS average score increases with the increasing of ECR dimensions (Avoidance and Anxiety). Conclusion. The severity of depression increases proportionally to attachment disorganization; therefore, we consider attachment as both an important risk factor as well as a focus for early psychotherapeutic intervention

Mapping of Aedes albopictus abundance at a local scale in Italy

Given the growing risk of arbovirus outbreaks in Europe, there is a clear need to better describe the distribution of invasive mosquito species such as Aedes albopictus. Current challenges consist in simulating Ae. albopictus abundance, rather than its presence, and mapping its simulated abundance at a local scale to better assess the transmission risk of mosquito-borne pathogens and optimize mosquito control strategy. During 2014–2015, we sampled adult mosquitoes using 72 BG-Sentinel traps per year in the provinces of Belluno and Trento, Italy. We found that the sum of Ae. albopictus females collected during eight trap nights from June to September was positively related to the mean temperature of the warmest quarter and the percentage of artificial areas in a 250 m buffer around the sampling locations. Maps of Ae. albopictus abundance simulated from the most parsimonious model in the study area showed the largest populations in highly artificial areas with the highest summer temperatures, but with a high uncertainty due to the variability of the trapping collections. Vector abundance maps at a local scale should be promoted to support stakeholders and policy-makers in optimizing vector surveillance and control

Influence of temperature on the life-cycle dynamics of Aedes albopictus population established at temperate latitudes: a laboratory experiment

6openInternationalThe mosquito species Aedes albopictus has successfully colonized many areas at temperate latitudes, representing a major public health concern. As mosquito bionomics is critically affected by temperature, we experimentally investigated the influence of different constant rearing temperatures (10, 15, 25, and 30 °C) on the survival rates, fecundity, and developmental times of different life stages of Ae. albopictus using a laboratory colony established from specimens collected in northern Italy. We compared our results with previously published data obtained with subtropical populations. We found that temperate Ae. albopictus immature stages are better adapted to colder temperatures: temperate larvae were able to develop even at 10 °C and at 15 °C, larval survivorship was comparable to the one observed at warmer conditions. Nonetheless, at these lower temperatures, we did not observe any blood-feeding activity. Adult longevity and fecundity were substantially greater at 25 °C with respect to the other tested temperatures. Our findings highlight the ability of Ae. albopictus to quickly adapt to colder environments and provide new important insights on the bionomics of this species at temperate latitudesopenMarini, G.; Manica, M.; Arnoldi, D.; Inama, E.; Rosa', R.; Rizzoli, A.Marini, G.; Manica, M.; Arnoldi, D.; Inama, E.; Rosa', R.; Rizzoli, A

Joint Application of the Target Trial Causal Framework and Machine Learning Modeling to Optimize Antibiotic Therapy: Use Case on Acute Bacterial Skin and Skin Structure Infections due to Methicillin-resistant Staphylococcus aureus

Bacterial infections are responsible for high mortality worldwide. Antimicrobial resistance underlying the infection, and multifaceted patient's clinical status can hamper the correct choice of antibiotic treatment. Randomized clinical trials provide average treatment effect estimates but are not ideal for risk stratification and optimization of therapeutic choice, i.e., individualized treatment effects (ITE). Here, we leverage large-scale electronic health record data, collected from Southern US academic clinics, to emulate a clinical trial, i.e., 'target trial', and develop a machine learning model of mortality prediction and ITE estimation for patients diagnosed with acute bacterial skin and skin structure infection (ABSSSI) due to methicillin-resistant Staphylococcus aureus (MRSA). ABSSSI-MRSA is a challenging condition with reduced treatment options - vancomycin is the preferred choice, but it has non-negligible side effects. First, we use propensity score matching to emulate the trial and create a treatment randomized (vancomycin vs. other antibiotics) dataset. Next, we use this data to train various machine learning methods (including boosted/LASSO logistic regression, support vector machines, and random forest) and choose the best model in terms of area under the receiver characteristic (AUC) through bootstrap validation. Lastly, we use the models to calculate ITE and identify possible averted deaths by therapy change. The out-of-bag tests indicate that SVM and RF are the most accurate, with AUC of 81% and 78%, respectively, but BLR/LASSO is not far behind (76%). By calculating the counterfactuals using the BLR/LASSO, vancomycin increases the risk of death, but it shows a large variation (odds ratio 1.2, 95% range 0.4-3.8) and the contribution to outcome probability is modest. Instead, the RF exhibits stronger changes in ITE, suggesting more complex treatment heterogeneity.Comment: This is the Proceedings of the KDD workshop on Applied Data Science for Healthcare (DSHealth 2022), which was held on Washington D.C, August 14 202

Optimal hydrogen storage in sodium substituted lithium fullerides

A relevant improvement in the hydrogen storage capability of lithium fullerides is obtained by the co-intercalation of a small amount of sodium

Multisensory Training Intervention for Hearing Impaired Children: Preliminary Results of a Pilot Study

This paper examines the influence of the Interactive Multisensory Environment (iMSE) on the training of deaf children in comparison to traditional methods. Over a 7-week duration, two groups of deaf children were evaluated and trained, one utilizing the iMSE (Experimental Group) and the other employing a traditional PC-based method (Control Group). The training encompassed four different thematic categories, each with nine associated sounds. The iMSE offered an immersive and dynamic learning experience, while the PC-based method presented stimuli through a desktop computer. Results indicate that the iMSE yielded positive effects on the training outcomes of deaf children, as evidenced by improved performance and engagement. This research sheds light on the potential benefits of innovative multisensory technology in educational settings for children with hearing impairments, offering insights for future educational interventions