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Colonization by multidrug-resistant (MDR) bacteria and related bloodstream infections (BSI) are associated with a high rate of mortality in patients with hematological malignancies after intensive chemotherapy and allogeneic stem cell transplantation (allo-SCT). In this retrospective study, we analyzed the outcomes of patients colonized with MDR bacteria (primarily carbapenem-resistant klebsiella pneumoniae , KPC), before allo-SCT. We also investigated the feasibility and safety of an antimicrobial de-escalating approach in these patients. Since 2021, 106 patients have been undergoing allo-SCT in our department, and 34 (32%) of them were colonized by MDR bacteria before allo-SCT. In the pre-engraftment period, 84% received an empiric antibiotic therapy (EAT) active against MDR bacteria and 16% were treated with a conventional EAT. The MDR translocation rate was null, and the overall de-escalation rate was 79%, with 75% in patients with fever of unknown origin (FUO). Among the cohort of patients with MDR-positive rectal swabs just before allo-SCT (n = 18), the de-escalation rate was 100%. The all-cause mortality rates at 30 and 100 days for the whole MDR patient population were 6% (2/34) and 12% (4/34), respectively. Day +30 infection-related mortality rate was 3%. In this study, we confirm the safety of the de-escalation approach in patients with previous MDR infection after allo-SCT. This could reduce the exposure time to EAT antibiotics, reducing the selective pressure. [ABSTRACT FROM AUTHOR]

Background: The study of women exposures and child outcomes occurring in the first 1,000 days of life since conception enhances understanding of the relationships between environmental factors, epigenetic changes, and disease development, extending beyond childhood and spanning the entire lifespan. Generation Gemelli is a recently launched case-control study that enrolls mother-newborns pairs in one of the largest university hospitals in Italy, in order to examine the association between maternal environmental exposures and intrauterine growth restriction (IUGR) and the risk of premature birth. The study will also evaluate the association of maternal exposures and the health and growth of infants and children up to 24 months of age. Methods: The study entails the set-up of a case-control study within a birth cohort. With approximately 4,000 annual deliveries, we aim to enroll 140 cases (newborns with IUGR and premature birth) and 280 controls per year, from September 2022. A comprehensive questionnaire will be used to gather information about various types of maternal environmental exposures before and during pregnancy. We will collect biological samples from both mothers and newborns (including vaginal swab, placenta sample, blood, saliva, meconium, and bronchoalveolar lavage fluid) at birth and within the early hours of the newborn's life. We will perform laboratory examinations including dosage of heavy metals and essential elements, investigation of placental distress and fetal brain damage of biomarkers, analysis of microbiota and of DNA methylation profile. We will conduct clinical follow-up assessments in both cases and controls at months 12 and 24 and we will collect anthropometric data, feeding types with particular reference to breastfeeding and its duration, pediatric emergency room visits, hospitalizations, medication usage, known allergies, and neuropsychological development. Discussion: The Generation Gemelli case-control study holds the promise of significantly enhancing our comprehension of how maternal environmental exposures relate to the health of children and the broader population. The study of the exposome will provide insights into the relationships between environmental exposures, epigenetic changes and health outcomes during the first 1000 days of life and onward. [ABSTRACT FROM AUTHOR]

Objective This study aimed to develop a predictive model of feeding mother's own milk (MOM) at discharge using social determinants of health (SDOH), maternal and neonatal factors after deliveries at <33 weeks of gestational age (GA), or birth weight <1,500 g. Study Design Secondary analysis of a retrospective cohort in an inner-city hospital before (Epoch-1, 2018–2019) and after (Epoch-2, 2020–2021) implementing a donor human milk (DHM) program. Results Among 986 neonates, 495 were born in Epoch-1 (320 Hispanic White, 142 Non-Hispanic Black, and 33 Other) and 491 in Epoch-2 (327, 137, and 27, respectively). Feeding any MOM was less frequent in infants of non-Hispanic Black mothers than in those of Hispanic mothers (p < 0.05) but did not change with epoch (p = 0.46). Among infants who received any MOM, continued feeding MOM to the time of discharge was less frequent in infants of non-Hispanic Black mothers versus those of Hispanic mothers, 94/237 (40%) versus 339/595 (57%; p < 0.05), respectively. In multivariate analysis including SDOH and maternal variables, the odds of feeding MOM at discharge were lower with SDOH including neighborhoods with higher poverty levels, multiparity, substance use disorder, non-Hispanic Black versus Hispanic and young maternal age and increased with GA but did not change after implementing DHM. The predictive model including SDOH, maternal and early neonatal variables had good discrimination (area under the curve 0.85) and calibration and was internally validated. It showed the odds of feeding MOM at discharge were lower in infants of non-Hispanic Black mothers and with feeding DHM, higher need for respiratory support and later initiation of feeding MOM. Conclusion Feeding MOM at discharge was associated with SDOH, and maternal and neonatal factors but did not change after implementing DHM. Disparity in feeding MOM at discharge was explained by less frequent initiation and shorter duration of feeding MOM but not by later initiation of feeding MOM. Key Points In this cohort study of preterm infants, factors of feeding MOM at discharge included (1) SDOH; (2) postnatal age at initiation of feeding MOM; and (3) maternal and neonatal factors. Feeding MOM at the time of discharge was less frequent in infants of non-Hispanic Black mothers versus those of Hispanic mothers. Disparity in feeding MOM at discharge was explained by less frequent initiation and shorter duration of MOM feeding but not by later postnatal age at initiation of feeding MOM. [ABSTRACT FROM AUTHOR]

Background: Evaluating individual health outcomes does not capture co-morbidities children experience. Purpose: We aimed to describe profiles of child neurodevelopment and anthropometry and identify their predictors. Methods: Using data from 501 mother-child pairs (age 3-years) in the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a prospective cohort study, we developed phenotypic profiles by applying latent profile analysis to twelve neurodevelopmental and anthropometric traits. Using multinomial regression, we evaluated odds of phenotypic profiles based on maternal, sociodemographic, and child level characteristics. Results: For neurodevelopmental outcomes, we identified three profiles characterized by Non-optimal (9%), Typical (49%), and Optimal neurodevelopment (42%). For anthropometric outcomes, we observed three profiles of Low (12%), Average (61%), and Excess Adiposity (27%). When examining joint profiles, few children had both Non-optimal neurodevelopment and Excess Adiposity (2%). Lower household income, lower birthweight, younger gestational age, decreased caregiving environment, greater maternal depressive symptoms, and male sex were associated with increased odds of being in the Non-optimal neurodevelopment profile. Higher pre-pregnancy body mass index was associated with increased odds of being in the Excess Adiposity profile. Conclusions: Phenotypic profiles of child neurodevelopment and adiposity were associated with maternal, sociodemographic, and child level characteristics. Few children had both non-optimal neurodevelopment and excess adiposity. [ABSTRACT FROM AUTHOR]

Quantum computers hold the promise of more efficient combinatorial optimization solvers, which could be game-changing for a broad range of applications. However, a bottleneck for materializing such advantages is that, in order to challenge classical algorithms in practice, mainstream approaches require a number of qubits prohibitively large for near-term hardware. Here we introduce a variational solver for MaxCut problems over m = O ( n k ) binary variables using only n qubits, with tunable k > 1. The number of parameters and circuit depth display mild linear and sublinear scalings in m, respectively. Moreover, we analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature. Altogether, this leads to high quantum-solver performances. For instance, for m = 7000, numerical simulations produce solutions competitive in quality with state-of-the-art classical solvers. In turn, for m = 2000, experiments with n = 17 trapped-ion qubits feature MaxCut approximation ratios estimated to be beyond the hardness threshold 0.941. Our findings offer an interesting heuristics for quantum-inspired solvers as well as a promising route towards solving commercially-relevant problems on near-term quantum devices. Combinatorial optimization problems might be solvable more efficiently with quantum computing, but near-term quantum optimization solvers are limited in qubit size. Here, the authors propose and demonstrate a variational quantum algorithm that polynomially reduces the qubit overhead for applications like MaxCut. [ABSTRACT FROM AUTHOR]

Direct-to-consumer testing (DTCT) refers to commercial laboratory tests initiated by laypersons without the involvement of healthcare professionals. As this market grows in size and variety of products, a clear definition of DTCT to ground the conceptualization of their harms and benefits is needed. We describe how three different modalities of DTCT (home self-testing, self-sampled tests, and direct access tests) present caveats to the traditional testing process ('brain-to-brain loop'), and how this might differ between medical vs. non-medical laboratories. We make recommendations for ways to improve quality and reduce errors with respect to DTCT. The potential benefits and harms of DTCT will invariably depend on the context and situation of individual consumers and the types of tests involved. Importantly, implications for both consumers and the healthcare system should be considered, such as the effects on improving health outcomes and reducing unnecessary testing and use of clinical resources. 'Consumer initiation' must be a central defining characteristic of DTCT, to clearly demarcate the key drawbacks as well as opportunities of this type of testing from a laboratory specialists' perspective. The concept of 'consumer initiated testing' should also help define DTCT regulation, and provide a locus of efforts to support consumers as the main decision-makers in the purchasing and conducting of these tests in the absence of clinician gatekeeping. [ABSTRACT FROM AUTHOR]

AIM This study aimed to identify associations between psychological distress and perceived stress and the educational environment among baccalaureate nursing students. BACKGROUNDBSN students are a high-risk group for mental problems. High attrition rates are noted among students who screen for psychological distress. METHOD A quantitative descriptive design was utilized with a convenience sample of 202 students. Psychological distress was correlated with perceived stress and educational environment. Regression analysis was used to analyze the data. RESULTS Psychological distress was reported in 45 percent of the sample. A significant positive relationship between being married and experiencing reduced psychological distress was found. Perceived stress and educational environment were significantly correlated (p < .05). CONCLUSION Perceived stress from the educational environment plays a role in influential factors of psychological distress. [ABSTRACT FROM AUTHOR]

Context The 2019 American Association of Clinical Endocrinologists guidelines suggested peak GH-cutoffs to glucagon test (GST) of ≤3 and ≤1 µg/L in the diagnosis of permanent GH deficiency (GHD) during the transition phase. Objective The aim of the study was to evaluate the accuracy of GST compared to insulin tolerance test (ITT) in the definition of GHD at adult height achievement. Patients and methods Ninety-seven subjects with childhood-onset GHD (median age, 17.39 years) underwent ITT, GST, and IGF-1 testing; 44 subjects were idiopathic (isolated GHD), 35 moderate organic GHD (0-2 hormone deficiencies) and 18 severe organic GHD (≥3 hormone deficiencies). Results Bland and Altman analysis showed a high consistency of GH peak measures after ITT and GST. Receiver operating characteristic analysis identified 7.3 μg/L as the optimal GH peak cutoff to GST [95% confidence interval (CI) 4.15-8.91; sensitivity 95.7%, specificity 88.2%, positive predictive value (PPV) 88.0%, negative predictive value (NPV) 95.7%] able to correctly classify 91.8% of the entire cohort while 5.8 μg/L was the best GH peak cutoff able to correctly classify 91.4% of moderate organic GHD patients (95% CI 3.16-7.39; sensitivity 96.0%, specificity 80.0%, PPV 92.3%, NPV 88.9%). Patients with ≥3 hormone deficiencies showed a GH peak <5 μg/L at ITT and <5.8 μg/L at GST but 1. The optimal cutoff for IGF-1 was −1.4 SD score (95% CI −1.94 to 0.77; sensitivity 75%, specificity 94%, PPV 91.7%, NPV 81.0%) that correctly classified 85.1% of the study population. Conclusion A GH peak to GST <5.8 μg/L represents an accurate diagnostic cutoff for young adults with childhood-onset GHD and high pretest probability of permanent GHD. [ABSTRACT FROM AUTHOR]

Objectives This study aims to improve the ethical use of machine learning (ML)-based clinical prediction models (CPMs) in shared decision-making for patients with kidney failure on dialysis. We explore factors that inform acceptability, interpretability, and implementation of ML-based CPMs among multiple constituent groups. Materials and Methods We collected and analyzed qualitative data from focus groups with varied end users, including: dialysis support providers (clinical providers and additional dialysis support providers such as dialysis clinic staff and social workers); patients; patients' caregivers (n  = 52). Results Participants were broadly accepting of ML-based CPMs, but with concerns on data sources, factors included in the model, and accuracy. Use was desired in conjunction with providers' views and explanations. Differences among respondent types were minimal overall but most prevalent in discussions of CPM presentation and model use. Discussion and Conclusion Evidence of acceptability of ML-based CPM usage provides support for ethical use, but numerous specific considerations in acceptability, model construction, and model use for shared clinical decision-making must be considered. There are specific steps that could be taken by data scientists and health systems to engender use that is accepted by end users and facilitates trust, but there are also ongoing barriers or challenges in addressing desires for use. This study contributes to emerging literature on interpretability, mechanisms for sharing complexities, including uncertainty regarding the model results, and implications for decision-making. It examines numerous stakeholder groups including providers, patients, and caregivers to provide specific considerations that can influence health system use and provide a basis for future research. [ABSTRACT FROM AUTHOR]

One of the critical challenges posed by the spread of Lithium-ion Batteries (LIBs) within Electric Vehicles (EVs) is the real-time estimation of their State-of-Health (SOH), commonly regarded as the leading indicator of EV aging. However, SOH estimation is still challenging due to the electrochemical complexity of LIBs. This work proposes a novel, computationally-inexpensive, and chemically agnostic Machine Learning (ML) procedure for onboard real-time SOH estimation. The proposed methodology requires a narrow time window of voltage, current, and State-of-Charge battery data, collected while driving the vehicle. We defined a Simulink-based EV simulator, modeling a specific real-world EV, and we utilized it to generate a synthetic dataset by simulating multiple driving sessions of the EV to compensate for the lack of large-scale publicly available EV monitoring data. Then, we examined three feature extraction methods and three ML regression models, estimating the battery pack’s SOH. We conducted a thorough comparison of the proposed feature extraction methods and ML models, training the ML model with processed synthetic data and inferring over real driving session monitoring data from the corresponding real-world EV model. The best synthetic-trained ML model achieves an MAE of 0.27% and 5.08%, and an RMSE of 0.37% and 5.92% over synthetic and real test data, respectively. Finally, we implemented transfer learning over the ML models, employing a portion of the available real data, reaching the lowest MAE of 1.97%, and an RMSE of 2.56% over the remaining real test set.

Abstract Quantum computers hold the promise of more efficient combinatorial optimization solvers, which could be game-changing for a broad range of applications. However, a bottleneck for materializing such advantages is that, in order to challenge classical algorithms in practice, mainstream approaches require a number of qubits prohibitively large for near-term hardware. Here we introduce a variational solver for MaxCut problems over $$m={{\mathcal{O}}}({n}^{k})$$ m = O ( n k ) binary variables using only n qubits, with tunable k > 1. The number of parameters and circuit depth display mild linear and sublinear scalings in m, respectively. Moreover, we analytically prove that the specific qubit-efficient encoding brings in a super-polynomial mitigation of barren plateaus as a built-in feature. Altogether, this leads to high quantum-solver performances. For instance, for m = 7000, numerical simulations produce solutions competitive in quality with state-of-the-art classical solvers. In turn, for m = 2000, experiments with n = 17 trapped-ion qubits feature MaxCut approximation ratios estimated to be beyond the hardness threshold 0.941. Our findings offer an interesting heuristics for quantum-inspired solvers as well as a promising route towards solving commercially-relevant problems on near-term quantum devices.

Colonization by multidrug-resistant (MDR) bacteria and related bloodstream infections (BSI) are associated with a high rate of mortality in patients with hematological malignancies after intensive chemotherapy and allogeneic stem cell transplantation (allo-SCT). In this retrospective study, we analyzed the outcomes of patients colonized with MDR bacteria (primarily carbapenem-resistant klebsiella pneumoniae, KPC), before allo-SCT. We also investigated the feasibility and safety of an antimicrobial de-escalating approach in these patients. Since 2021, 106 patients have been undergoing allo-SCT in our department, and 34 (32%) of them were colonized by MDR bacteria before allo-SCT. In the pre-engraftment period, 84% received an empiric antibiotic therapy (EAT) active against MDR bacteria and 16% were treated with a conventional EAT. The MDR translocation rate was null, and the overall de-escalation rate was 79%, with 75% in patients with fever of unknown origin (FUO). Among the cohort of patients with MDR-positive rectal swabs just before allo-SCT (n = 18), the de-escalation rate was 100%. The all-cause mortality rates at 30 and 100 days for the whole MDR patient population were 6% (2/34) and 12% (4/34), respectively. Day +30 infection-related mortality rate was 3%. In this study, we confirm the safety of the de-escalation approach in patients with previous MDR infection after allo-SCT. This could reduce the exposure time to EAT antibiotics, reducing the selective pressure.