miR-146b-5p Regulates the Enteric Nervous System Development in Hirschsprung Disease via Targeting RET Pathway.

The objective of the study is to investigate how miR-146b-5p might contribute to the etiology of HSCR. The study investigated the expression levels of miRNA, mRNA, and proteins in colon tissues obtained from the HSCR and control groups. The role of miR-146b-5p in cell proliferation and migration was studied in vitro. The interaction between miR-146b-5p and RET was validated through a dual-luciferase reporter experiment. To assess the impact of miR-146b-5p on the development of the enteric nervous system, zebrafish embryos were micro-injected with either miR-146b-5p mimics or negative control, followed by subsequent evaluation. Compared to the control group, miR-146b-5p expression levels in the spastic region of HSCR were significantly increased. In vitro, miR-146b-5p prevented cell migration and proliferation by targeting RET pathway. In zebrafish, miR-146b-5p negatively regulates the migration of neural crest cells through a reduction in RET expression. Overexpression of miR-146b-5p hinders the development of mature neurons by decreasing RET expression. Additionally, the aberrant phenotypes induced by miR-146b-5p were partially ameliorated when RET mRNA was co-injected. By targeting RET in HSCR patients, aberrant expression of miR-146b-5p may play a unique role in the etiology of the disease and be involved in enteric nervous system development.

Clinical characteristics and influence of postoperative Hirschsprung-associated enterocolitis: retrospective study at a tertiary children's hospital.

PURPOSE: To explore the influence of postoperative Hirschsprung-associated enterocolitis (post-HAEC) on long-term outcomes and to identify risk factors of post-HAEC. METHODS: The medical records of 304 eligible patients diagnosed with Hirschsprung's disease (HSCR) were reviewed. We analyzed the clinical characteristics of post-HAEC and its influence on long-term outcomes. Furthermore, risk factors for early and recurrent HAEC were identified separately. RESULTS: The overall incidence of post-HAEC was 29.9% (91/304). We categorized early HAEC as occurring within postoperative 3 months (n = 39) and recurrent HAEC as occurring ≥ 3 episodes within postoperative 6 months (n = 25). Patients with early HAEC were more likely to experience worse nutritional status, defecation function, and quality of life compared to those with late or no episodes (P < 0.05). Similarly, the adverse influences of recurrent HAEC on these outcomes were also significant (P < 0.05). The risk factors for early HAEC included preoperative undernutrition, long-segment HSCR, and postoperative Grade 3-4 complications within 30 days. For recurrent HAEC, risk factors were preoperative malnutrition, non-parental caregivers, long-segment HSCR, and postoperative Grade 3-4 complications within 30 days. CONCLUSION: Classification of post-HAEC based on the first episode time and frequency was necessary. The earlier or more frequent episodes of post-HAEC have detrimental influences on long-term outcomes. Furthermore, risk factors for early and recurrent HAEC were different.

A Teenager Physical Fitness Evaluation Model Based on 1D-CNN with LSTM and Wearable Running PPG Recordings.

People attach greater importance to the physical health of teenagers because adolescence is a critical period for the healthy development of the human body. With the progress of biosensing technologies and artificial intelligence, it is feasible to apply wearable devices to continuously record teenagers' physiological signals and make analyses based on modern advanced methods. To solve the challenge that traditional methods of monitoring teenagers' physical fitness lack accurate computational models and in-depth data analyses, we propose a novel evaluation model for predicting the physical fitness of teenagers. First, we collected 1024 teenagers' PPGs under the guidance of the proposed three-stage running paradigm. Next, we applied the median filter and wavelet transform to denoise the original signals and obtain HR and SpO2. Then, we used the Pearson correlation coefficient method to finalize the feature set, based on the extracted nine physical features. Finally, we built a 1D-CNN with LSTM model to classify teenagers' physical fitness condition into four levels: excellent, good, medium, and poor, with an accuracy of 98.27% for boys' physical fitness prediction, and 99.26% for girls' physical fitness prediction. The experimental results provide evidence supporting the feasibility of predicting teenagers' physical fitness levels by their running PPG recordings.