Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

BACKGROUND: There is mounting evidence that asthma might exacerbate depression. We sought to examine candidates for diagnostic genes in patients suffering from asthma and depression. METHODS: Microarray data were downloaded from the Gene Expression Omnibus(GEO) database and used to screen for differential expressed genes(DEGs) in the SA and MDD datasets. A weighted gene co-expression network analysis(WGCNA) was used to identify the co-expression modules of SA and MDD. The least absolute shrinkage and selection operatoes(LASSO) and support vector machine(SVM) were used to determine critical biomarkers. Immune cell infiltration analysis was used to investigate the correlation between immune cell infiltration and common biomarkers of SA and MDD. Finally, validation of these analytical results was accomplished via the use of both in vivo and in vitro studies. RESULTS: The number of DEGs that were included in the MDD dataset was 5177, whereas the asthma dataset had 1634 DEGs. The intersection of DEGs for SA and MDD included 351 genes, the strongest positive modules of SA and MDD was 119 genes, which played a function in immunity. The intersection of DEGs and modular hub genes was 54, following the analysis using machine learning algorithms,three hub genes were identified and employed to formulate a nomogram and for the evaluation of diagnostic effectiveness, which demonstrated a significant diagnostic value (area under the curve from 0.646 to 0.979). Additionally, immunocyte disorder was identified by immune infiltration. In vitro studies have revealed that STK11IP deficiency aggravated the LPS/IFN-γinduced up-regulation in M1 macrophage activation. CONCLUSION: Asthma and MDD pathophysiology may be associated with alterations in inflammatory processes and immune pathways. Additionally, STK11IP may serve as a diagnostic marker for individuals with the two conditions.

Dysfunction of Emotion Regulation in Mild Cognitive Impairment Individuals Combined With Depressive Disorder: A Neural Mechanism Study.

Depression increases the risk of progression from mild cognitive impairment (MCI) to dementia, where impaired emotion regulation is a core symptom of depression. However, the neural mechanisms underlying the decreased emotion regulation in individuals with MCI combined with depressive symptoms are not precise. We assessed the behavioral performance by emotion regulation tasks and recorded event-related electroencephalography (EEG) signals related to emotion regulation tasks simultaneously. EEG analysis, including event-related potential (ERP), event-related spectral perturbation (ERSP), functional connectivity and graph theory, was used to compare the difference between MCI individuals and MCI depressed individuals in behavioral performance, the late positive potential (LPP) amplitudes, neural oscillations and brain networks during the processing of emotional stimuli. We found that MCI depressed individuals have negative preferences and are prone to allocate more attentional resources to negative stimuli. Results suggested that theta and alpha oscillations activity is increased, and gamma oscillations activity is decreased during negative stimulus processing in MCI depressed individuals, thus indicating that the decreased emotion regulation in MCI depressed individuals may be associated with enhanced low-frequency and decreased high-frequency oscillations activity. Functional connectivity analysis revealed a decrease in functional connectivity in the left cerebral hemisphere of the alpha band and an increase in functional connectivity in the right cerebral hemisphere of the alpha band in MCI depressed individuals. Graph theory analysis suggested that global network metrics, including clustering coefficients and disassortative, decreased, while nodal and modular network metrics regarding local nodal efficiency, degree centrality, and betweenness centrality were significantly increased in the frontal lobe and decreased in the parieto-occipital lobe, which was observed in the alpha band, further suggesting that abnormal alpha band network connectivity may be a potential marker of depressive symptoms. Correlational analyses showed that depressive symptoms were closely related to emotion regulation, power oscillations and functional connectivity. In conclusion, the dominant processing of negative stimuli, the increased low-frequency oscillations activity and decreased high-frequency activity, so as the decrease in top-down information processing in the frontal parieto-occipital lobe, results in the abnormality of alpha-band network connectivity. It is suggested that these factors, in turn, contribute to the declined ability of MCI depressed individuals in emotion regulation.

Machine Learning Decomposition of the Anatomy of Neuropsychological Deficit in Alzheimer's Disease and Mild Cognitive Impairment.

Objective: Alzheimer's Disease (AD) is a progressive condition characterized by cognitive decline. AD is often preceded by mild cognitive impairment (MCI), though the diagnosis of both conditions remains a challenge. Early diagnosis of AD, and prediction of MCI progression require data-driven approaches to improve patient selection for treatment. We used a machine learning tool to predict performance in neuropsychological tests in AD and MCI based on functional connectivity using a whole-brain connectome, in an attempt to identify network substrates of cognitive deficits in AD. Methods: Neuropsychological tests, baseline anatomical T1 magnetic resonance imaging (MRI), resting-state functional MRI, and diffusion weighted imaging scans were obtained from 149 MCI, and 85 AD patients; and 140 cognitively unimpaired geriatric participants. A novel machine learning tool, Hollow Tree Super (HoTS) was utilized to extract feature importance from each machine learning model to identify brain regions that were associated with deficit and absence of deficit for 11 neuropsychological tests. Results: 11 models attained an area under the receiver operating curve (AUC-ROC) greater than 0.65, while five models had an AUC-ROC ≥ 0.7. 20 parcels of the Human Connectome Project Multimodal Parcelation Atlas matched to poor performance in at least two neuropsychological tests, while 14 parcels were associated with good performance in at least two tests. At a network level, most parcels predictive of both presence and absence of deficit were affiliated with the Central Executive Network, Default Mode Network, and the Sensorimotor Networks. Segregating predictors by the cognitive domain associated with each test revealed areas of coherent overlap between cognitive domains, with the parcels providing possible markers to screen for cognitive impairment. Conclusion: Approaches such as ours which incorporate whole-brain functional connectivity and harness feature importance in machine learning models may aid in identifying diagnostic and therapeutic targets in AD.

Unhealthy Lifestyle Is an Important Risk Factor of Idiopathic BPPV.

Background: Benign paroxysmal positional vertigo (BPPV) is a self-limiting and recurrent disease but the cost is considerable. The number of patients with BPPV increased significantly under the quarantine policy in Hangzhou. The unhealthy lifestyle risk factors of BPPV have not yet been investigated. Thus, the objective is to analyze whether an unhealthy lifestyle is a risk factor of BPPV. Methods: One hundred and sixty three patients with idiopathic BPPV aged 22-87 years (BPPV group), and 89 aged 23-92 years sex-matched control subjects (non-BPPV group) were enrolled in this study. All BPPV patients received a definitive diagnosis which excluded secondary BPPV. Non-BPPV cases excluded BPPV, sudden deafness, Meniere's disease, ear or craniofacial surgery, vestibular neuritis, and head trauma history. We obtained a blood lipids profile, serum uric acid, total bilirubin, and related diagnostic information through the electronic medical record system. To get the time of physical activities and recumbent positions, we asked the patient or their family from February 2020 to June 2020, and the rest of the patient's information was acquired by phone or WeChat. Data Analyses: The t-test or chi-squared test, univariate, and multiple logistic regression analyses were performed for the two groups. For each factor, odds ratios were calculated with 95% confidence intervals (CIs). Moreover, test equality of two or more receiver operating characteristic (ROC) analyses were applied to the physical activities, and recumbent position time; area under curve (AUC) measures were calculated with 95% CIs and compared with each other. Results: The BPPV group had unhealthy lifestyles such as poor physical activities, prolonged recumbent position time, and low rate of calcium or VD supplementation in univariate logistic regression analyses (P < 0.05). Poor physical activities and prolonged recumbent position time were independently associated with BPPV in multiple logistic regression models (OR = 18.92, 95% CI: 6.34-56.43, p = 0.00 and OR = 1.15, 95% CI: 1.01-1.33, p < 0.04). In the comparison of ROC curves of recumbent position time and physical activities in identifying BPPV, AUCs were 0.68 (0.61-0.74), and 0.68 (0.63-0.73), respectively. Conclusion: We conclude that poor physical activities and prolonged recumbent position time may be independent risk factors for BPPV patients, but hypertension, hyperuricemia, hyperlipidemia, hemoglobin, diabetes, serum bilirubin, CHD, and CI, may not be.

Effect of simplified Tai Chi exercise on relieving symptoms of patients with mild to moderate Parkinson's disease.

BACKGROUND: Tai Chi, a kind of physical exercise, may act as a non-pharmacologic approach to reducing the symptoms of Parkinson's disease. This study was conducted to investigate the effect of simplified Tai Chi training plus routine exercise on motor and non-motor symptoms in patients with mild to moderate Parkinson's disease in comparison with routine exercise regimen alone. METHODS: Forty-one outpatients and inpatients with Parkinson's disease (PD) were randomized into Tai Chi group (N.=19) and routine exercise group as control group (N.=22) for 12 weeks. The Tai Chi group included both Tai Chi traning and routine exercise. Motor and non-motor functions were assessed. Motor function was evaluated by Unified Parkinson's Disease Rating Scale part III (UPDRS-III) and Berg Balance Scale (BBS). The non-motor symptoms like quality of life, sleep quality, depression and anxiety state, cognitive function were assessed by Parkinson's Disease Questionnaire-39 (PDQ-39), Parkinson's Disease Sleep Scale (PDSS), Hamilton Depression Scale (HAMD), Hamilton Anxiety Scale (HAMA), Montreal Cognitive Assessment (MOCA) respectively. RESULTS: After 12 weeks of intervention, participants in both Tai Chi and routine exercise groups gained effects in UPDRS-III, BBS, PDQ-39, PDSS and HAMD compared to the baseline. However, significant improvements between Tai Chi group and routine exercise group were only found in PDSS (P=0.029) and MoCA (P=0.024). CONCLUSIONS: Tai Chi training plus routine exercise might therefore be an ideal alternative non-pharmacological approach for the motor and non-motor symptoms of PD patients, and especially be more useful for the improvement of sleep quality and cognitive function in Parkinson's disease compared with routine exercise regimen alone.