An investigation into the abnormal dynamic connection mechanism of generalized anxiety disorders based on non-homogeneous Markov models.

BACKGROUND: The dynamic and hierarchical nature of the functional brain network. The neural dynamical systems tend to converge to multiple attractors (stable fixed points or dynamical states) in long run. Little is known about how the changes in this brain dynamic "long-term" behavior of the connectivity flow of brain network in generalized anxiety disorder (GAD). METHODS: This study recruited 92 patients with GAD and 77 healthy controls (HC). We applied a reachable probability approach combining a Non-homogeneous Markov model with transition probability to quantify all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level and the stationary probability vector (10-step transition probabilities) to describe the steady state of the system in the long run. A random forest algorithm was conducted to predict the severity of anxiety. RESULTS: The dynamic functional patterns in distributed brain networks had larger possibility to converge in bilateral thalamus, posterior cingulate cortex (PCC), right superior occipital gyrus (SOG) and smaller possibility to converge in bilateral superior temporal gyrus (STG) and right parahippocampal gyrus (PHG) in patients with GAD compared to HC. The abnormal transition probability pattern could predict anxiety severity in patients with GAD. LIMITATIONS: Small samples and subjects taking medications may have influenced our results. Future studies are expected to rule out the potential confounding effects. CONCLUSION: Our results have revealed abnormal dynamic neural communication and integration in emotion regulation in patients with GAD, which give new insights to understand the dynamics of brain function of patients with GAD.

Enhancing Nursing Care for Children with Acute Gastroenteritis: Integrating Pediatric Early Warning Scores with the Situation-Background-Assessment-Recommendation System.

Background: Acute gastroenteritis is a frequently encountered diarrheal illness in children, often self-limiting but occasionally linked to substantial mortality and morbidity, demanding effective approaches for assessment and intervention. While the utilization of the Pediatric Early Warning Score (PEWS) and the Situation-Background-Assessment-Recommendation system (SBAR) in pediatric patient management is recognized as effective, research in this area remains limited. Objective: Our study aimed to investigate the potential impact of PEWS and SBAR systems on the outcomes of pediatric patients with acute gastroenteritis. Methods: We conducted a randomized controlled trial at our hospital, enrolling 124 children aged 3 to 12 years diagnosed with acute gastroenteritis. These participants were randomly assigned to either a control group (62 cases) or an intervention group (62 cases). Different outcomes were assessed, including the frequency and duration of diarrhea and vomiting, the Modified Vesikari Scale (MVS), the Clinical Dehydration Scale (CDS), and follow-up physician visits. We utilized a two-group independent sample t test to compare outcomes between the two groups. Results: Our study resulted in statistically significant findings favoring the intervention group regarding the frequency and duration of diarrhea and vomiting, the MVS, the CDS, and the need for repeat healthcare visits. Conclusions: The integration of PEWS with SBAR appears to offer improved outcomes for children afflicted with acute gastroenteritis.

Magnetic resonance imaging: Proton density fat fraction for assessment of pancreatic fatty infiltration during progression of T2DM bama minipigs.

BACKGROUND: Recent studies have highlighted the correlation between diabetes and pancreatic fat infiltration. However, pancreatic fat content (PFC) is rarely confirmed by pathological results, and a change of PFC during progression of type 2 diabetes (T2DM) is currently controversial. PURPOSE: To evaluate the relationship of MRI-pancreatic proton density fat fraction to serologic changes and histology in an experimental model of diabetes. STUDY TYPE: Prospective animal study. ANIMAL MODEL: Thirteen Bama pigs were randomly assigned to diabetes (n = 7) or control (n = 6) groups. Pigs in the diabetic group received high-fat/high-sugar feed, combined with three doses of streptozotocin injections. FIELD STRENGTH/SEQUENCE: 3.0T, IDEAL-IQ sequence. ASSESSMENT: Starting in the fifth month, biochemical changes were evaluated; all pigs underwent axial MRI with the IDEAL-IQ sequence to measured pancreatic fat fraction (PFF). PFC was measured by the Soxhlet extraction method. Pancreatic fat distribution and pancreas islet morphology were observed by histopathology. STATISTICAL TESTS: A Mann-Whitney U-test, independent-samples t-test, Pearson correlation, Spearman correlation, single-measure intraclass correlation coefficient (ICC) were performed. RESULTS: During the development of T2DM, the PFF, weight, fasting blood glucose (FBG), triglyceride (TG), total cholesterol (TCHO), low-density lipoprotein (LDL), and HOMA-IR (insulin resistance) of the experimental group showed an upward trend; fasting insulin (INS), high-density lipoprotein (HDL), and HOMA-ß showed decreasing trends. At the end of the fifteenth month, FBG (mmol/L) was 18.06 ± 6.03 and 5.06 ± 1.41 (P < 0.001), PFF (%) was 36.52 ± 4.07 and 27.75 ± 3.73 (P = 0.002), INS (mU/L) was 21.59 ± 2.93 and 29.32 ± 3.27 (P = 0.001), HOMA-IR was 16.83 ± 4.22 and 6.70 ± 2.45 (P < 0.001), HOMA-ß was 1.50 ± 0.24 and 2.77 ± 0.45 (P < 0.001), between the experimental and control groups. There were strong and moderate positive correlations between PFF and PFC (r = 0.968, P < 0.001), and FBG (r = 0.657, P = 0.015), and HOMA-IR (r = 0.608, P = 0.028). DATA CONCLUSION: MRI-proton density fat fraction can measure the fat content of the pancreas with great accuracy and repeatability; PFF is a potential biomarker that can reflect the different stages of diabetes development. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1905-1913.