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Neuropsychiatric symptoms occur in the majority of patients with Parkinson's disease (PD). It is one of the most common causes of disability. This paper reviews the mind-body exercise methods affecting anxiety symptoms in PD patients at home and abroad, including Tai Chi, Baduanjin, Wuqinxi, and mindfulness yoga, in order to provide inspiration for constructing the intervention program for non-exercise symptoms in PD patients, reduce disability rate and improve quality of life. (神经精神症状(NPS)在帕金森病(PD)患者的病程中常见, 是导致患者残疾的原因之一。本研究就国内外影响PD患者焦虑症状的身心运动方法进行综述, 包括太极拳、八段锦、五禽戏、正念瑜伽, 以期为构建PD患者非运动症状干预方案提供启发, 降低致残率, 提高生存质量。)

AbstractMicrobes have been extensively utilized for their sustainable and scalable properties in synthesizing desired bio-products. However, insufficient knowledge about intracellular metabolism has impeded further microbial applications. The genome-scale metabolic models (GEMs) play a pivotal role in facilitating a global understanding of cellular metabolic mechanisms. These models enable rational modification by exploring metabolic pathways and predicting potential targets in microorganisms, enabling precise cell regulation without experimental costs. Nonetheless, simplified GEM only considers genome information and network stoichiometry while neglecting other important bio-information, such as enzyme functions, thermodynamic properties, and kinetic parameters. Consequently, uncertainties persist particularly when predicting microbial behaviors in complex and fluctuant systems. The advent of the omics era with its massive quantification of genes, proteins, and metabolites under various conditions has led to the flourishing of multi-constrained models and updated algorithms with improved predicting power and broadened dimension. Meanwhile, machine learning (ML) has demonstrated exceptional analytical and predictive capacities when applied to training sets of biological big data. Incorporating the discriminant strength of ML with GEM facilitates mechanistic modeling efficiency and improves predictive accuracy. This paper provides an overview of research innovations in the GEM, including multi-constrained modeling, analytical approaches, and the latest applications of ML, which may contribute comprehensive knowledge toward genetic refinement, strain development, and yield enhancement for a broad range of biomolecules. [ABSTRACT FROM AUTHOR]

Background: Delirium is a common disorder affecting patients' psychiatric illness, characterized by a high rate of underdiagnosis, misdiagnosis, and high risks. However, previous studies frequently excluded patients with psychiatric illness, leading to limited knowledge about risk factors and optimal assessment tools for delirium in psychiatric settings. Objectives: The scoping review was carried out to (1) identify the risk factors associated with delirium in patients with psychiatric illness; (2) synthesize the performance of assessment tools for detecting delirium in patients with psychiatric illness in psychiatric settings. Design: Scoping review. Data sources: PubMed, Web of Science, and Embase were searched to identify primary studies on delirium in psychiatric settings from inception to Dec 2023 inclusive. Two independent reviewers screened eligible studies against inclusion criteria. A narrative synthesis of the included studies was conducted. Results: A final set of 36 articles meeting the inclusion criteria, two main themes were extracted: risk factors associated with delirium in patients with psychiatric illness and assessment tools for detecting delirium in psychiatric settings. The risk factors associated with delirium primarily included advanced age, physical comorbid, types of psychiatric illness, antipsychotics, anticholinergic drug, Electroconvulsive therapy, and the combination of lithium and Electroconvulsive therapy. Delirium Rating Scale-Revised-98, Memorial Delirium Assessment Scale, and Delirium Diagnostic Tool-Provisional might be valuable for delirium assessment in patients with psychiatric illness in psychiatric settings. Conclusions: Delirium diagnosis in psychiatric settings is complex due to the overlapping clinical manifestations between psychiatric illness and delirium, as well as their potential co-occurrence. It is imperative to understand the risk factors and assessment methods related to delirium in this population to address diagnostic delays, establish effective prevention and screening strategies. Future research should focus on designing, implementing, and evaluating interventions that target modifiable risk factors, to prevent and manage delirium in patients with psychiatric illness. What is already known about the topic?: • Delirium is common among individuals living in psychiatric settings and remains clinically under-detected, and it is associated with the length of hospital stay and poor prognoses. • Dementia with behavioral and psychological symptoms of dementia (BPSD) and other psychiatric illnesses has been shown to make delirium more likely. However, despite extensive research on factors associated with delirium, there is a notable lack of studies conducted in psychiatric settings. • Early detection and identification of delirium are crucial in psychiatric settings; however, literature regarding the assessment and identification of delirium has not been synthesized, particularly in the context of comorbid psychiatric illness. What this paper adds?: • Risk factors associated with delirium in patients with psychiatric illness primarily include advanced age, physical comorbid, types of psychiatric illness, antipsychotics, anticholinergic drug, Electroconvulsive therapy (ECT), and the combination of lithium and ECT. • Delirium Rating Scale-Revised-98, Memorial Delirium Assessment Scale, and Delirium Diagnostic Tool-Provisional might hold promising value as assessment tools for delirium in patients with psychiatric illness in psychiatric settings. [ABSTRACT FROM AUTHOR]