A recent presidential advisory from the American Heart Association (AHA) has introduced the term cardiovascular-kidney-metabolic (CKM) syndrome to describe the complex interplay among health conditions linking heart, kidney, and metabolism. The aim of our study was to compare the prevalence of concurrent CKM syndrome components before and during the COVID-19 pandemic and identify associated risk factors. We conducted a study utilizing data from a real-world population obtained from a primary care database. The study cohort comprised a closed group followed over a 6-year period (2017-2022). A total of 81,051 individuals were included: 32,650 in the pre-pandemic period and 48,401 in the 2020-2022 triennium. After propensity-score matching for sex, age, and BMI, the study included 30,511 participants for each period. 3554 individuals were diagnosed with type 2 diabetes in the pre-pandemic period, compared to 7430 during the pandemic. Hypertension, dyslipidemia, and obesity displayed significant increases in prevalence during the pandemic, and prediabetes had a particularly sharp rise of 170%. Age-stratified analyses revealed a higher burden of CKM conditions with advancing age. Our findings indicate a substantial increase in the prevalence of CKM syndrome during the COVID-19 pandemic, with nearly half of the patients exhibiting one or more CKM syndrome components.

Interpersonal emotion regulation entails a wide array of strategies aimed at influencing others' emotions. Despite its importance for successful social interactions, only a few studies have evaluated interpersonal emotion regulation in children. In detail, the study of developmental changes in the use of emotion regulation strategies overlooked age, gender and cultural differences across different emotions. To address this gap, the present study used the serious game Emodiscovery, a simulation game targeted at 8-10-year-olds, which measures the strategies selected by children to improve the emotions of anger, sadness, and fear displayed by 3D virtual characters. One-hundred British (M = 9.10 years; 39% girls) and 108 Spanish (M = 9.04, 44% girls) 8-10-year-olds played 3 different levels or scenarios of the game. In each level, the character displayed a negative emotion (i.e., sadness, anger, and fear, respectively) and children were first asked to indicate what emotion the character was feeling and afterward to interact three times with the character to improve his or her mood. In each interaction, 4 possible regulation strategies (2 adaptive and 2 maladaptive) were displayed for children to select. Results showed that in the scenario where the character was displaying sadness, 8-year-olds chose significantly less adaptive strategies than 10-years-olds. In the scenario where the character was angry, boys who accurately recognized the emotion of anger chose more adaptive strategies than those who did not recognize the emotion. For the scenario depicting fear, boys chose less adaptive strategies than girls. The obtained results highlight the importance of looking at specific emotions when researching interpersonal emotion regulation. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Background: The aim of this study was to investigate how the systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), taken individually and combined, are associated with overall survival (OS) in patients surgically treated for malignant salivary gland tumors (MSGTs). Methods: A retrospective analysis of 74 cases following surgery at our department between January 2011 and June 2018 was performed. The Receiver Operating Characteristic (ROC) curve was used to calculate the optimal cutoff values for SII, SIRI, PLR, and NLR. Survival curves of different groups at 1–3–5 years were estimated using the Kaplan–Meier method. Results: The optimal thresholds with the highest sensitivity and specificity were 3.95 for NLR, 187.6 for PLR, 917.585 for SII, and 2.045 for SIRI. The ROC curves revealed that the best combination with AUC = 0.884 was SII + SIRI. The estimated 5-year OS probability in patients with SII+ SIRI scores of 0, 1, and 2 was 96%, 87.5% and 12.5%, respectively (p < 0.001). Conclusion: SII+ SIRI can independently predict the OS of patients after MSGT surgery. The prognostic score system based on SII+ SIRI may be good clinical practice as a reference for clinical decision-making.

The core principles of the evolutionary theories of emotions declare that affective states represent crucial drives for action selection in the environment and regulated the behavior and adaptation of natural agents in ancestrally recurrent situations. While many different studies used autonomous artificial agents to simulate emotional responses and the way these patterns can affect decision-making, few are the approaches that tried to analyze the evolutionary emergence of affective behaviors directly from the specific adaptive problems posed by the ancestral environment. A model of the evolution of affective behaviors is presented using simulated artificial agents equipped with neural networks and physically inspired on the architecture of the iCub humanoid robot. We use genetic algorithms to train populations of virtual robots across generations, and investigate the spontaneous emergence of basic emotional behaviors in different experimental conditions. In particular, we focus on studying the emotion of fear, therefore the environment explored by the artificial agents can contain stimuli that are safe or dangerous to pick. The simulated task is based on classical conditioning and the agents are asked to learn a strategy to recognize whether the environment is safe or represents a threat to their lives and select the correct action to perform in absence of any visual cues. The simulated agents have special input units in their neural structure whose activation keep track of their actual “sensations” based on the outcome of past behavior. We train five different neural network architectures and then test the best ranked individuals comparing their performances and analyzing the unit activations in each individual’s life cycle. We show that the agents, regardless of the presence of recurrent connections, spontaneously evolved the ability to cope with potentially dangerous environment by collecting information about the environment and then switching their behavior to a genetically selected pattern in order to maximize the possible reward. We also prove the determinant presence of an internal time perception unit for the robots to achieve the highest performance and survivability across all conditions.