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The production of reactive oxygen species (ROS) is a normal consequence of the aerobic cell metabolism. Despite their high and potentially detrimental reactivity with various biomolecules, the endogenous production of ROS is a vital part of physiological, immunological, and molecular processes that contribute to fitness. The role of ROS in host-parasite interactions is frequently defined by their contribution to innate immunity as effectors, promoting parasite death during infections. In vertebrates, ROS and antioxidant system enzymes, such as superoxide dismutase (SOD) are also involved in acquired immune memory, where they are responsible for T-cell signalling, activation, proliferation, and viability. Based on recent findings, ROS are now also assumed to play a role in immune priming, i.e., a form of memory in invertebrates. In this study, the potential involvement of Cu,Zn SODs in immunity of the red flour beetle is described for the first time, applying an approach that combines an gene characterisation with an immune priming experiment using the Gram-positive entomopathogen . We identified an unusually high number of three different transcripts for extracellular SOD and found that priming leads to a fine-tuned modulation of SOD expression, highlighting the potential of physiological co-adaptations for immune phenotypes.

Typical 2-Cys peroxiredoxins (2-Cys Prdxs) are proteins with antioxidant properties belonging to the thioredoxin peroxidase family. With their peroxidase activity, they contribute to the homeostatic control of reactive oxygen species (ROS) and, therefore, participate in various physiological functions, such as cell proliferation, differentiation, and apoptosis. Although Prdxs have been shown to be potential biomarkers for monitoring aquatic environments, minimal scientific attention has been devoted to describing their molecular architecture and function in marine invertebrates. Our study aims to clarify the protective role against stress induced by exposure to metals (Cu, Zn, and Cd) of three Prdxs (Prdx2, Prdx3, and Prdx4) in the solitary ascidian Ciona robusta, an invertebrate chordate. Here, we report a detailed pre- and post-translational regulation of the three Prdx isoforms. Data on intestinal mRNA expression, provided by qRT-PCR analyses, show a generalized increase for Prdx2, -3, and -4, which is correlated to metal accumulation. Furthermore, the increase in tissue enzyme activity observed after Zn exposure is slower than that observed with Cu and Cd. The obtained results increase our knowledge of the evolution of anti-stress proteins in invertebrates and emphasize the importance of the synthesis of Prdxs as an efficient way to face adverse environmental conditions.

Glutathione peroxidases (GPxs) form a broad family of antioxidant proteins essential for maintaining redox homeostasis in eukaryotic cells. In this study, we used an integrative approach that combines bioinformatics, molecular biology, and biochemistry to investigate the role of GPxs in reactive oxygen species detoxification in the unicellular eukaryotic model organism Tetrahymena thermophila. Both phylogenetic and mechanistic empirical model analyses provided indications about the evolutionary relationships among the GPXs of Tetrahymena and the orthologous enzymes of phylogenetically related species. In-silico gene characterization and text mining were used to predict the functional relationships between GPxs and other physiologically-relevant processes. The GPx genes contain conserved transcriptional regulatory elements in the promoter region, which suggest that transcription is under tight control of specialized signaling pathways. The bioinformatic findings were next experimentally validated by studying the time course of gene transcription and enzymatic activity after copper (Cu) exposure. Results emphasize the role of GPxs in the detoxification pathways that, by complex regulation of GPx gene expression, enable Tethraymena to survive in high Cu concentrations and the associated redox environment.

ObjectivesWe sought to examine in individuals with SARS-CoV-2 infection whether risk for thrombotic and thromboembolic events (TTE) is modified by presence of a diabetes diagnosis. Furthermore, we analysed whether differential risk for TTEs exists in type 1 diabetes mellitus (T1DM) versus type 2 diabetes mellitus (T2DM).DesignRetrospective case–control study.SettingThe December 2020 version of the Cerner Real-World Data COVID-19 database is a deidentified, nationwide database containing electronic medical record (EMR) data from 87 US-based health systems.ParticipantsWe analysed EMR data for 322 482 patients >17 years old with suspected or confirmed SARS-CoV-2 infection who received care between December 2019 and mid-September 2020. Of these, 2750 had T1DM; 57 811 had T2DM; and 261 921 did not have diabetes.OutcomeTTE, defined as presence of a diagnosis code for myocardial infarction, thrombotic stroke, pulmonary embolism, deep vein thrombosis or other TTE.ResultsOdds of TTE were substantially higher in patients with T1DM (adjusted OR (AOR) 2.23 (1.93–2.59)) and T2DM (AOR 1.52 (1.46–1.58)) versus no diabetes. Among patients with diabetes, odds of TTE were lower in T2DM versus T1DM (AOR 0.84 (0.72–0.98)).ConclusionsRisk of TTE during COVID-19 illness is substantially higher in patients with diabetes. Further, risk for TTEs is higher in those with T1DM versus T2DM. Confirmation of increased diabetes-associated clotting risk in future studies may warrant incorporation of diabetes status into SARS-CoV-2 infection treatment algorithms.

Background Emerging technologies have demonstrated outstanding potential in improving healthcare, yet their full integration remains a challenge for all medical specialties, including pediatrics. To support the swift implementation of technologies, we identified the current trends through a bibliometric review, and we conducted a survey on Italian pediatricians to gauge educational needs and willingness to integrate technologies into clinical practice. Methods A working group of pediatricians representing various backgrounds designed and coordinated the study. To identify relevant topics for educational strategy development, we focused on virtual reality, telehealth, natural language processing, smartphone applications, robotics, genomics, and artificial intelligence. A bibliometric analysis limited to 2018–2023 was performed to identify trends and emerging applications within each topic. Based on the results, a questionnaire was developed and made available online to all Italian pediatricians. The results were analyzed through descriptive analysis and a multivariable logistic regression to explore associations between technology adoption and sociodemographic characteristics. Results A total of 3,253 publications were found, with Telehealth and Telemedicine having the highest number of publications and Natural Language Processing the lowest. The number of respondents to the online questionnaire was 1,540, predominantly medical doctors with over 20 years of experience working as family pediatricians. Telehealth had the highest level of knowledge (95.2%), followed by smartphone applications (89.1%) and genomics (63.2%). The greatest potential for increased use through education programs was projected for natural language processing (+ 43.1%), artificial intelligence (+ 39.6%), and virtual and mixed reality (+ 38.1%). Female respondents and older individuals were less likely to use emerging technologies. Hospital pediatricians and residents were more likely to use AI. Conclusions We developed a replicable strategy to identify emerging themes in medical technologies relevant to pediatrics and assess the educational needs of pediatricians. A significant gap still exists between current and potential usage of emerging technologies among Italian pediatricians although they showed a positive attitude towards implementing these technologies following specific education programs. The study highlights the need for comprehensive education programs on emerging technologies in pediatrics and recommends addressing gender and age disparities in technology adoption.

Background: A resurgence of pertussis has been observed in several geographic areas in the post-COVID-19 era. Macrolides are the first-choice antibiotics for the treatment of pertussis. Limited data exist on the impact of the early administration of clarithromycin or azithromycin on infants’ pertussis symptoms. Methods: This retrospective cohort study analyzed infants enrolled in an enhanced surveillance program for pertussis at a single Italian clinical reference center between 2015 and 2020. All cases were laboratory-confirmed. This study compared outcomes based on the timing of macrolide antibiotic treatment: early administration (within 7 days of cough onset) versus late administration (8 days or later). Key outcomes included cough duration, symptom frequency, and complication rates. Results: We studied 148 infants with confirmed pertussis. The median duration of coughing was 14 days in infants with early administration and 24 days in those with late administration. The occurrence of symptoms differed for apnea (62.6% for early administration; 84.6% for late administration). In a multivariable Cox model, the duration of the cough was lower in infants receiving antibiotics within 7 days from the beginning of the cough compared with those starting later (HR = 0.36, 95% CI: 0.25–0.53, p < 0.001). Clarithromycin was associated with a shorter duration of coughing (HR = 0.42, 95% CI: 0.19–0.92, p = 0.030) independently from other factors. Regarding the occurrence of symptoms, children receiving antibiotics later were three times more likely to experience apnea compared to those treated early (p = 0.008). Conclusions: Early treatment with clarithromycin or azithromycin for infants with pertussis improves clinical symptoms. Clarithromycin may be more effective than azithromycin in shortening coughing. The early administration of antibiotics may also help prevent the spread of disease during the resurgence of pertussis and should be considered regardless of the laboratory confirmation, while taking into account the potential side effects of an unnecessary therapy.

IntroductionWe prospectively investigated the associations between diabetes-related family conflict, parent engagement in child type 1 diabetes (T1D) care, and child glycated hemoglobin (HbA1c) in 127 families of school-age children who we recruited within the first year of their T1D diagnosis.Research design and methodsParents completed the Diabetes Family Conflict Scale-Revised (DFCS-R) to assess for diabetes-related family conflict and the Diabetes Self-Management Questionnaire-Brief (DSMQ-Brief) to assess parent engagement in child T1D care at the initial study visit (T1) and at 12 (T2) and 27 (T3) months later. We also collected child HbA1c at these time points. Our analyses included Pearson correlations and repeated measures linear mixed models controlling for child age, sex, and T1D duration at T1.ResultsParents’ DFCS-R scores negatively correlated with DSMQ-Brief scores (r=−0.13, p<0.05) and positively correlated with children’s HbA1c (r=0.26, p<0.001). In our linear mixed models, parents’ DSMQ-Brief scores were unchanged at T2 (β=−0.71, 95% CI −1.59 to 0.16) and higher at T3 (β=8.01, 95% CI 6.89 to 9.13) compared with T1, and there was an association between increasing DFCS-R and decreasing DSMQ-Brief scores (β=−0.14, 95% CI −0.21 to −0.06). Child HbA1c values were significantly higher at T2 (β=0.66, 95% CI 0.38 to 0.94) and T3 (β=0.95, 95% CI 0.63 to 1.27) compared with T1, and there was an association between increasing DFCS-R scores and increasing child HbA1c (β=0.04, 95% CI 0.02 to 0.06).ConclusionsIncreasing diabetes-specific family conflict early in T1D may associate with decreasing parent engagement in child T1D care and increasing child HbA1c, suggesting a need to assess and intervene on diabetes-specific family conflict. Trial registration number NCT03698708.

Metallothioneins (MTs) are low-molecular weight sulfur-rich proteins, widely distributed in nature. They play a homeostatic role in the control and detoxification of metal ions. Previous studies indicated that MTs also have the capacity to scavenge reactive oxygen species. This study aimed to investigate the evolution of the protein in the notothenioid fish, evolved under the selective pressure of relatively high oxygen partial pressures, characteristics of cold Antarctic seawaters, and relatively high concentrations of metal ions, Cd and Cu in particular. The cDNA sequences of MT isoforms were characterized in members of the Nototheniidae, Bathydraconidae, Artedidraconidae, and Channichthyidae families. The phylogenetic relationships of MTs from these families and other teleosts were inferred by using Maximum Likelihood and Bayesian methods. The analysis of coding region and untranslated (UTR) sequences indicated the presence of two MT clades, each containing one of the two MT isoforms, MT-1 and MT-2. Our results indicated, for the first time for these proteins, that the evolution of MT genes has been characterized by strong purifying selection, whereas it did not observe any evidence of positive selection. In addition, phylogenetic analysis of the UTRs suggested that functional changes, in particular related to the MT-1 gene expression, had accompanied the duplication event.

Although prior research has identified multiple risk factors for diabetic ketoacidosis (DKA), clinicians continue to lack clinic-ready models to predict dangerous and costly episodes of DKA. We asked whether we could apply deep learning, specifically the use of a long short-term memory (LSTM) model, to accurately predict the 180-day risk of DKA-related hospitalization for youth with type 1 diabetes (T1D). We aimed to describe the development of an LSTM model to predict the 180-day risk of DKA-related hospitalization for youth with T1D. We used 17 consecutive calendar quarters of clinical data (January 10, 2016, to March 18, 2020) for 1745 youths aged 8 to 18 years with T1D from a pediatric diabetes clinic network in the Midwestern United States. The input data included demographics, discrete clinical observations (laboratory results, vital signs, anthropometric measures, diagnosis, and procedure codes), medications, visit counts by type of encounter, number of historic DKA episodes, number of days since last DKA admission, patient-reported outcomes (answers to clinic intake questions), and data features derived from diabetes- and nondiabetes-related clinical notes via natural language processing. We trained the model using input data from quarters 1 to 7 (n=1377), validated it using input from quarters 3 to 9 in a partial out-of-sample (OOS-P; n=1505) cohort, and further validated it in a full out-of-sample (OOS-F; n=354) cohort with input from quarters 10 to 15. DKA admissions occurred at a rate of 5% per 180-days in both out-of-sample cohorts. In the OOS-P and OOS-F cohorts, the median age was 13.7 (IQR 11.3-15.8) years and 13.1 (IQR 10.7-15.5) years; median glycated hemoglobin levels at enrollment were 8.6% (IQR 7.6%-9.8%) and 8.1% (IQR 6.9%-9.5%); recall was 33% (26/80) and 50% (9/18) for the top-ranked 5% of youth with T1D; and 14.15% (213/1505) and 12.7% (45/354) had prior DKA admissions (after the T1D diagnosis), respectively. For lists rank ordered by the probability of hospitalization, precision increased from 33% to 56% to 100% for positions 1 to 80, 1 to 25, and 1 to 10 in the OOS-P cohort and from 50% to 60% to 80% for positions 1 to 18, 1 to 10, and 1 to 5 in the OOS-F cohort, respectively. The proposed LSTM model for predicting 180-day DKA-related hospitalization was valid in this sample. Future research should evaluate model validity in multiple populations and settings to account for health inequities that may be present in different segments of the population (eg, racially or socioeconomically diverse cohorts). Rank ordering youth by probability of DKA-related hospitalization will allow clinics to identify the most at-risk youth. The clinical implication of this is that clinics may then create and evaluate novel preventive interventions based on available resources.

Background:Clinicians currently lack an effective means for identifying youth with type 1 diabetes (T1D) who are at risk for experiencing glycemic deterioration between diabetes clinic visits. As a result, their ability to identify youth who may optimally benefit from targeted interventions designed to address rising glycemic levels is limited. Although electronic health records (EHR)–based risk predictions have been used to forecast health outcomes in T1D, no study has investigated the potential for using EHR data to identify youth with T1D who will experience a clinically significant rise in glycated hemoglobin (HbA1c) ≥0.3% (approximately 3 mmol/mol) between diabetes clinic visits.Objective:We aimed to evaluate the feasibility of using routinely collected EHR data to develop a machine learning model to predict 90-day unit-change in HbA1c (in % units) in youth (aged 9‐18 y) with T1D. We assessed our model’s ability to augment clinical decision-making by identifying a percent change cut point that optimized identification of youth who would experience a clinically significant rise in HbA1c.Methods:From a cohort of 2757 youth with T1D who received care from a network of pediatric diabetes clinics in the Midwestern United States (January 2012-August 2017), we identified 1743 youth with 9643 HbA1c observation windows (ie, 2 HbA1c measurements separated by 70‐110 d, approximating the 90-day time interval between routine diabetes clinic visits). We used up to 5 years of youths’ longitudinal EHR data to transform 17,466 features (demographics, laboratory results, vital signs, anthropometric measures, medications, diagnosis codes, procedure codes, and free-text data) for model training. We performed 3-fold cross-validation to train random forest regression models to predict 90-day unit-change in HbA1c(%).Results:Across all 3 folds of our cross-validation model, the average root-mean-square error was 0.88 (95% CI 0.85‐0.90). Predicted HbA1c(%) strongly correlated with true HbA1c(%) (r=0.79; 95% CI 0.78‐0.80). The top 10 features impacting model predictions included postal code, various metrics related to HbA1c, and the frequency of a diagnosis code indicating difficulty with treatment engagement. At a clinically significant percent rise threshold of ≥0.3% (approximately 3 mmol/mol), our model’s positive predictive value was 60.3%, indicating a 1.5-fold enrichment (relative to the observed frequency that youth experienced this outcome [3928/9643, 40.7%]). Model sensitivity and positive predictive value improved when thresholds for clinical significance included smaller changes in HbA1c, whereas specificity and negative predictive value improved when thresholds required larger changes in HbA1c.Conclusions:Routinely collected EHR data can be used to create an ML model for predicting unit-change in HbA1c between diabetes clinic visits among youth with T1D. Future work will focus on optimizing model performance and validating the model in additional cohorts and in other diabetes clinics.