[Clinical efficacy of mild therapeutic hypothermia with different rewarming time on neonatal hypoxic-ischemic encephalopathy: a prospective randomized controlled study]. / æ–°ç”Ÿå„¿ç¼ºæ°§ç¼ºè¡€æ€§è„‘ç— ä¸åŒå¤æ¸©æ—¶é—´äºšä½Žæ¸©æ²»ç–—ä¸´åºŠæ•ˆæžœçš„å‰çž»æ€§éšæœºå¯¹ç §ç ”ç©¶.

OBJECTIVES: To investigate the clinical efficacy of mild therapeutic hypothermia (MTH) with different rewarming time on neonatal hypoxic-ischemic encephalopathy (HIE). METHODS: A prospective study was performed on 101 neonates with HIE who were born and received MTH in Zhongshan Hospital, Xiamen University, from January 2018 to January 2022. These neonates were randomly divided into two groups: MTH1 group (n=50; rewarming for 10 hours at a rate of 0.25°C/h) and MTH2 group (n=51; rewarming for 25 hours at a rate of 0.10°C/h). The clinical features and the clinical efficacy were compared between the two groups. A binary logistic regression analysis was used to identify the factors influencing the occurrence of normal sleep-wake cycle (SWC) on amplitude-integrated electroencephalogram (aEEG) at 25 hours of rewarming. RESULTS: There were no significant differences between the MTH1 and MTH2 groups in gestational age, 5-minute Apgar score, and proportion of neonates with moderate/severe HIE (P>0.05). Compared with the MTH2 group, the MTH1 group tended to have a normal arterial blood pH value at the end of rewarming, a significantly shorter duration of oxygen dependence, a significantly higher proportion of neonates with normal SWC on aEEG at 10 and 25 hours of rewarming, and a significantly higher Neonatal Behavioral Neurological Assessment score on days 5, 12, and 28 after birth (P<0.05), while there was no significant difference in the incidence rate of rewarming-related seizures between the two groups (P>0.05). There were no significant differences between the two groups in the incidence rate of neurological disability at 6 months of age and the score of Bayley Scale of Infant Development at 3 and 6 months of age (P>0.05). The binary logistic regression analysis showed that prolonged rewarming time (25 hours) was not conducive to the occurrence of normal SWC (OR=3.423, 95%CI: 1.237-9.469, P=0.018). CONCLUSIONS: Rewarming for 10 hours has a better short-term clinical efficacy than rewarming for 25 hours. Prolonging rewarming time has limited clinical benefits on neonates with moderate/severe HIE and is not conducive to the occurrence of normal SWC, and therefore, it is not recommended as a routine treatment method.

Heart rate variability for medical decision support systems: A review.

Heart Rate Variability (HRV) is a good predictor of human health because the heart rhythm is modulated by a wide range of physiological processes. This statement embodies both challenges to and opportunities for HRV analysis. Opportunities arise from the wide-ranging applicability of HRV analysis for disease detection. The availability of modern high-quality sensors and the low data rate of heart rate signals make HRV easy to measure, communicate, store, and process. However, there are also significant obstacles that prevent a wider use of this technology. HRV signals are both nonstationary and nonlinear and, to the human eye, they appear noise-like. This makes them difficult to analyze and indeed the analysis findings are difficult to explain. Moreover, it is difficult to discriminate between the influences of different complex physiological processes on the HRV. These difficulties are compounded by the effects of aging and the presence of comorbidities. In this review, we have looked at scientific studies that have addressed these challenges with advanced signal processing and Artificial Intelligence (AI) methods.

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy. Dopaminergic medications can significantly slow down the progression of PD when applied during the early stages. However, these treatments often become less effective with the disease progression. Early diagnosis of PD is crucial for immediate interventions so that the patients can remain self-sufficient for the longest period of time possible. Unfortunately, diagnoses are often late, due to factors such as a global shortage of neurologists skilled in early PD diagnosis. Computer-aided diagnostic (CAD) tools, based on artificial intelligence methods, that can perform automated diagnosis of PD, are gaining attention from healthcare services. In this review, we have identified 63 studies published between January 2011 and July 2021, that proposed deep learning models for an automated diagnosis of PD, using various types of modalities like brain analysis (SPECT, PET, MRI and EEG), and motion symptoms (gait, handwriting, speech and EMG). From these studies, we identify the best performing deep learning model reported for each modality and highlight the current limitations that are hindering the adoption of such CAD tools in healthcare. Finally, we propose new directions to further the studies on deep learning in the automated detection of PD, in the hopes of improving the utility, applicability and impact of such tools to improve early detection of PD globally.