Automatic Diagnosis of Parkinson's Disease Based on Deep Learning Models and Multimodal Data

Parkinson's disease (PD) is a common age-related neurodegenerative disorder in the aging society. Early diagnosis of PD is particularly important for efficient intervention. Currently, the diagnosis of PD is mainly made by neurologists who assess the abnormalities of the patient's motor system and evaluate the severity according to established criteria, which is highly dependent on the neurologists' expertise and often unsatisfactory. Artificial intelligence provides new potential for automatic and reliable diagnosis of PD based on multimodal data analysis. Some deep learning models have been developed for automatic detection of PD based on diverse biomarkers such as brain imaging images, electroencephalograms, walking postures, speech, handwriting, etc., with promising accuracy. This chapter summarizes the state-of-the-art, technical advancements, unmet research gaps, and future directions of deep learning models for PD detection. It provides a reference for biomedical engineers, data scientists, and health professionals

Hemodynamic changes in progressive cerebral infarction:An observational study based on blood pressure monitoring

Progressive cerebral infarction (PCI) is a common complication in patients with ischemic stroke that leads to poor prognosis. Blood pressure (BP) can indicate post‐stroke hemodynamic changes which play a key role in the development of PCI. The authors aim to investigate the association between BP‐derived hemodynamic parameters and PCI. Clinical data and BP recordings were collected from 80 patients with cerebral infarction, including 40 patients with PCI and 40 patients with non‐progressive cerebral infarction (NPCI). Hemodynamic parameters were calculated from the BP recordings of the first 7 days after admission, including systolic and diastolic BP, mean arterial pressure, and pulse pressure (PP), with the mean values of each group calculated and compared between daytime and nighttime, and between different days. Hemodynamic parameters and circadian BP rhythm patterns were compared between PCI and NPCI groups using t‐test or non‐parametric equivalent for continuous variables, Chi‐squared test or Fisher's exact test for categorical variables, Cox proportional hazards regression analysis and binary logistic regression analysis for potential risk factors. In PCI and NPCI groups, significant decrease of daytime systolic BP appeared on the second and sixth days, respectively. Systolic BP and fibrinogen at admission, daytime systolic BP of the first day, nighttime systolic BP of the third day, PP, and the ratio of abnormal BP circadian rhythms were all higher in the PCI group. PCI and NPCI groups were significantly different in BP circadian rhythm pattern. PCI is associated with higher systolic BP, PP and more abnormal circadian rhythms of BP

Hemodynamic changes in progressive cerebral infarction: An observational study based on blood pressure monitoring.

Progressive cerebral infarction (PCI) is a common complication in patients with ischemic stroke that leads to poor prognosis. Blood pressure (BP) can indicate post-stroke hemodynamic changes which play a key role in the development of PCI. The authors aim to investigate the association between BP-derived hemodynamic parameters and PCI. Clinical data and BP recordings were collected from 80 patients with cerebral infarction, including 40 patients with PCI and 40 patients with non-progressive cerebral infarction (NPCI). Hemodynamic parameters were calculated from the BP recordings of the first 7 days after admission, including systolic and diastolic BP, mean arterial pressure, and pulse pressure (PP), with the mean values of each group calculated and compared between daytime and nighttime, and between different days. Hemodynamic parameters and circadian BP rhythm patterns were compared between PCI and NPCI groups using t-test or non-parametric equivalent for continuous variables, Chi-squared test or Fisher's exact test for categorical variables, Cox proportional hazards regression analysis and binary logistic regression analysis for potential risk factors. In PCI and NPCI groups, significant decrease of daytime systolic BP appeared on the second and sixth days, respectively. Systolic BP and fibrinogen at admission, daytime systolic BP of the first day, nighttime systolic BP of the third day, PP, and the ratio of abnormal BP circadian rhythms were all higher in the PCI group. PCI and NPCI groups were significantly different in BP circadian rhythm pattern. PCI is associated with higher systolic BP, PP and more abnormal circadian rhythms of BP

Hemodynamic changes in progressive cerebral infarction: An observational study based on blood pressure monitoring.

Progressive cerebral infarction (PCI) is a common complication in patients with ischemic stroke that leads to poor prognosis. Blood pressure (BP) can indicate post-stroke hemodynamic changes which play a key role in the development of PCI. The authors aim to investigate the association between BP-derived hemodynamic parameters and PCI. Clinical data and BP recordings were collected from 80 patients with cerebral infarction, including 40 patients with PCI and 40 patients with non-progressive cerebral infarction (NPCI). Hemodynamic parameters were calculated from the BP recordings of the first 7 days after admission, including systolic and diastolic BP, mean arterial pressure, and pulse pressure (PP), with the mean values of each group calculated and compared between daytime and nighttime, and between different days. Hemodynamic parameters and circadian BP rhythm patterns  were compared between PCI and NPCI groups using t-test or non-parametric equivalent for continuous variables, Chi-squared test or Fisher's exact test for categorical variables, Cox proportional hazards regression analysis and binary logistic regression analysis for potential risk factors. In PCI and NPCI groups, significant decrease of daytime systolic BP appeared on the second and sixth days, respectively. Systolic BP and fibrinogen at admission, daytime systolic BP of the first day, nighttime systolic BP of the third day, PP, and the ratio of abnormal BP circadian rhythms were all higher in the PCI group. PCI and NPCI groups were significantly different in BP circadian rhythm pattern. PCI is associated with higher systolic BP, PP and more abnormal circadian rhythms of BP. [Abstract copyright: © 2023 The Authors. The Journal of Clinical Hypertension published by Wiley Periodicals LLC.

Germline Predisposition and Copy Number Alteration in Pre-stage Lung Adenocarcinomas Presenting as Ground-Glass Nodules

Objective: Synchronous multiple ground-glass nodules (SM-GGNs) are a distinct entity of lung cancer which has been emerging increasingly in recent years in China. The oncogenesis molecular mechanisms of SM-GGNs remain elusive.Methods: We investigated single nucleotide variations (SNV), insertions and deletions (INDEL), somatic copy number variations (CNV), and germline mutations of 69 SM-GGN samples collected from 31 patients, using target sequencing (TRS) and whole exome sequencing (WES).Results: In the entire cohort, many known driver mutations were found, including EGFR (21.7%), BRAF (14.5%), and KRAS (6%). However, only one out of the 31 patients had the same somatic missense or truncated events within SM-GGNs, indicating the independent origins for almost all of these SM-GGNs. Many germline mutations with a low frequency in the Chinese population, and genes harboring both germline and somatic variations, were discovered in these pre-stage GGNs. These GGNs also bore large segments of copy number gains and/or losses. The CNV segment number tended to be positively correlated with the germline mutations (r = 0.57). The CNV sizes were correlated with the somatic mutations (r = 0.55). A moderate correlation (r = 0.54) was also shown between the somatic and germline mutations.Conclusion: Our data suggests that the precancerous unstable CNVs with potentially predisposing genetic backgrounds may foster the onset of driver mutations and the development of independent SM-GGNs during the local stimulation of mutagens

Case Report: New Application of a Gufoni Maneuver Variation for Apogeotropic Lateral Semicircular Canal Benign Paroxysmal Positional Vertigo

BACKGROUND: Several canalith repositioning procedures (CRPs) such as Gufoni maneuver have been proposed to treat the apogeotropic lateral semicircular canal variant of BPPV (LC-BPPV). The reported success rate varied widely in different studies. Research showed that there was a risk of treatment failure due to insufficient repositioning of the debris. So far, there is insufficient evidence to recommend a preferable CRP for apogeotropic LC-BPPV. CASE DESCRIPTION: A 49-year-old woman and a 48-year-old man diagnosed with apogeotropic LC-BPPV relapse were treated with original Gufoni maneuver for apogeotropic variant but no satisfactory result was obtained. A variation of Gufoni maneuver originally proposed for the geotropic variant was applied to detach otoconia toward the utricle or the non-ampullary arm. Apogeotropic nystagmus was successfully transformed into the geotropic variant. The subsequent Gufoni maneuver was successful. On a 64-year-old male with untreated apogeotropic LC-BPPV, we performed the Gufoni maneuver variation and observed a change in nystagmus direction. In all the three cases, no relapse of vertigo was reported after 1 month. CONCLUSION: The new application of Gufoni maneuver variation may improve the treatment of apogeotropic LC-BPPV. Treatment efficacy and patient-specific optimization such as head rotation angle deserve a large-scale validation and further investigation

A hybrid deep learning approach by integrating extreme gradient boosting‐long short‐term memory with generalized autoregressive conditional heteroscedasticity family models for natural gas load volatility prediction

Abstract Natural gas load forecasting provides decision‐making support for natural gas dispatch and management, pipeline network construction, pricing, and sustainable energy development. To explain the uncertainty and volatility in natural gas load forecasting, this study predicts the natural gas load volatility. As the natural gas load volatility has the time‐series features, along with long‐term memory, volatility aggregation, asymmetry, and nonnormality, this study proposes a natural gas load volatility prediction model by combining generalized autoregressive conditional heteroscedasticity (GARCH) family models, XGBoost algorithm, and long short‐term memory (LSTM) network. The model first takes the GARCH family models parameters of sliding estimation and meteorological factors as the influencing factors of volatility, and then it screens these influencing factors through the extreme gradient boosting (XGBoost) algorithm. Finally, the selected important features are input into the LSTM network to predict the volatility, and the 90% confidence interval of the volatility is calculated. Compared with a variety of single and combined models, the model proposed in this study has an average reduction of 45.404% in the evaluation index of mean squared error. The experimental results show that the model proposed in this study has a good performance and accuracy in predicting the volatility of natural gas load