Characterizing brain network alterations in cervical spondylotic myelopathy using static and dynamic functional network connectivity and machine learning.

Background: Cervical spondylotic myelopathy (CSM) is a debilitating condition that affects the cervical spine, leading to neurological impairments. While the neural mechanisms underlying CSM remain poorly understood, changes in brain network connectivity, particularly within the context of static and dynamic functional network connectivity (sFNC and dFNC), may provide valuable insights into disease pathophysiology. This study investigates brain-wide connectivity alterations in CSM patients using both sFNC and dFNC, combined with machine learning approaches, to explore their potential as biomarkers for disease classification and progression.

Characteristics of fecal mycobiota and bacteriota in laying hens during different laying periods.

Mycobiota represents an important component of the gut microbiome in poultry and plays important roles in host nutrition and metabolism. However, the understanding of gut mycobiota in laying hens during the production cycle is limited. The present study aimed to characterize the structure and diversity of fecal mycobiota and bacteriota and examine the interplays between both microbial communities in laying hens during different laying periods.

Impact of postoperative morbidity on the prognosis of patients with hepatocellular carcinoma after laparoscopic liver resection: a multicenter observational study.

The long-term impact of postoperative morbidity following laparoscopic liver resection for hepatocellular carcinoma is unclear. This study aimed to investigate whether the prognosis of hepatocellular carcinoma patients were affected by postoperative morbidity after laparoscopic liver resection. Hepatocellular carcinoma patients who underwent curative-intent laparoscopic liver resection were included.

Data-model interactive Rul prediction of stochastic degradation devices with multiple uncertainty quantification and multi-sensor information fusion.

This paper proposes an improved remaining useful life (RUL) prediction method for stochastic degradation devices monitored by multi-source sensors under data-model interactive framework. Firstly, the interrelationships among sensors are established using k-nearest neighbor (KNN), and the composite health index (CHI) is constructed by aggregating the multi-source sensor information through the graph convolutional network (GCN). Secondly, a stochastic degradation model with triple uncertainty at any initial degradation level is established to improve the matching degree between the stochastic degradation model and the actual degradation process.