Background: Spontaneous low-frequency oscillations (LFOs) have been widely studied in cerebrovascular disease, but little is known about their role in moyamoya disease (MMD). The objective of this study was to assess the value of spontaneous LFOs in MMD based on wavelet analysis of near-infrared spectroscopy signals.
Methods: Sixty-four consecutive idiopathic adult patients were prospectively enrolled. The regional tissue oxygenation index (TOI) obtained from continuous near-infrared spectroscopy signals. Five frequency intervals of spontaneous LFOs (I, 0.0095-0.02 Hz; II, 0.02-0.06 Hz; III, 0.06-0.15 Hz; IV, 0.15-0.40 Hz; and V, 0.40-2.00 Hz) were extracted based on wavelet analysis. The data were compared between the patients and healthy control groups. Clinical features, cognitive function, and disease progression of MMD were analyzed using TOI and frequency interval data.
Results: Compared with the healthy control group, patients with MMD had a higher cerebral TOI in both hemispheres. Based on wavelet analysis, the spontaneous LFO of TOI was found to be significantly lower for patients with MMD in frequency intervals II to IV than that for the controls. The spontaneous LFO of TOI is also related to the Suzuki stages in intervals II to IV, stroke in interval III, and cognitive impairment in intervals III to Ⅳ.
Conclusions: There were significant differences in spontaneous LFO between patients with MMD and healthy controls. The change in spontaneous LFO in MMD is related to Suzuki stage, cerebral infarction, and cognitive impairment. This might be an effective method for evaluating the severity and monitoring the progression of MMD.
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http://dx.doi.org/10.1016/j.wneu.2022.10.074 | DOI Listing |
Heliyon
January 2025
College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, 310027, China.
Objective And Rationale: Children's clinical pain phenotypes are complex, and there is a lack of objective biological diagnostic markers and cognitive patterns. Detecting physiological signals through wearable devices simplifies disease diagnosis and holds the potential for remote medical applications.
Method And Results: This research established a pain recognition model based on AI skin potential (SP) signal analysis.
Sci Rep
January 2025
UNESCO Centre of Water Law, Policy & Science, University of Dundee, Dundee, UK.
Understanding snow and ice melt dynamics is vital for flood risk assessment and effective water resource management in populated river basins sourced in inaccessible high-mountains. This study provides an AI-enabled hybrid approach integrating glacio-hydrological model outputs (GSM-SOCONT), with different machine learning and deep learning techniques framed as alternative 'computational scenarios, leveraging both physical processes and data-driven insights for enhanced predictive capabilities. The standalone deep learning model (CNN-LSTM), relying solely on meteorological data, outperformed its counterpart machine learning and glacio-hydrological model equivalents.
View Article and Find Full Text PDFMicroscopy (Oxf)
January 2025
The Ultramicroscopy Research Center, Kyushu University, 744 Motooka, Fukuoka 819-0395, Japan.
The precision in electron holography studies on electrostatic and magnetic fields depends on the image quality of an electron hologram. Enhancing the image quality of electron holograms is essential for the comprehensive analysis of weak electromagnetic fields; however, extended electron beam irradiation can lead to undesirable radiation damage and contamination. Recent studies have demonstrated that noise reduction using the wavelet hidden Markov model (WHMM) can improve the precision of phase analysis for limited thin-foiled crystals.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
Department of Radiology, Nanjing Drum Tower Hospital, Clinical College of Nanjing University of Chinese Medicine, No. 321 Zhongshan Road, Nanjing, 210008, China.
Purpose: To evaluate the application of multi-parametric MRI (MP-MRI) combined with radiomics in diagnosing and grading endometrial fibrosis (EF).
Methods: A total of 74 patients with severe endometrial fibrosis (SEF), 41 patients with mild to moderate fibrosis (MMEF) confirmed by hysteroscopy, and 40 healthy women of reproductive age were prospectively enrolled. The enrolled data were randomly stratified and divided into a train set (108 cases: 28 healthy women, 29 with MMEF, and 51 with SEF) and a test set (47 cases: 12 healthy women, 12 MMEF and 23 SEF) at a ratio of 7:3.
Acta Otolaryngol
January 2025
Laboratory of Otoneurology British Hospital, Montevideo, Uruguay.
Background: Gait instability and falls significantly impact life quality and morbi-mortality in elderly populations. Early diagnosis of gait disorders is one of the most effective approaches to minimize severe injuries.
Objective: To find a gait instability pattern in older adults through an image representation of data collected by a single sensor.
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