BASH-GN: a new machine learning-derived questionnaire for screening obstructive sleep apnea.

PURPOSE: This study aimed to develop a machine learning-based questionnaire (BASH-GN) to classify obstructive sleep apnea (OSA) risk by considering risk factor subtypes. METHODS: Participants who met study inclusion criteria were selected from the Sleep Heart Health Study Visit 1 (SHHS 1) database. Other participants from the Wisconsin Sleep Cohort (WSC) served as an independent test dataset. Participants with an apnea hypopnea index (AHI) ≥ 15/h were considered as high risk for OSA. Potential risk factors were ranked using mutual information between each factor and the AHI, and only the top 50% were selected. We classified the subjects into 2 different groups, low and high phenotype groups, according to their risk scores. We then developed the BASH-GN, a machine learning-based questionnaire that consists of two logistic regression classifiers for the 2 different subtypes of OSA risk prediction. RESULTS: We evaluated the BASH-GN on the SHHS 1 test set (n = 1237) and WSC set (n = 1120) and compared its performance with four commonly used OSA screening questionnaires, the Four-Variable, Epworth Sleepiness Scale, Berlin, and STOP-BANG. The model outperformed these questionnaires on both test sets regarding the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). The model achieved AUROC (SHHS 1: 0.78, WSC: 0.76) and AUPRC (SHHS 1: 0.72, WSC: 0.74), respectively. The questionnaire is available at https://c2ship.org/bash-gn . CONCLUSION: Considering OSA subtypes when evaluating OSA risk may improve the accuracy of OSA screening.

Pilot Study for Correlation of Heart Rate Variability and Dopamine Transporter Brain Imaging in Patients with Parkinsonian Syndrome.

BACKGROUND: Parkinsonian syndrome (PS) is a broad category of neurodegenerative movement disorders that includes Parkinson disease, multiple system atrophy (MSA), progressive supranuclear palsy, and corticobasal degeneration. Parkinson disease (PD) is the second most common neurodegenerative disorder with loss of dopaminergic neurons of the substantia nigra and, thus, dysfunction of the nigrostriatal pathway. In addition to the motor symptoms of bradykinesia, rigidity, tremors, and postural instability, nonmotor symptoms such as autonomic dysregulation (AutD) can also occur. Heart rate variability (HRV) has been used as a measure of AutD and has shown to be prognostic in diseases such as diabetes mellitus and cirrhosis, as well as PD. I-123 ioflupane, a gamma ray-emitting radiopharmaceutical used in single-photon emission computed tomography (SPECT), is used to measure the loss of dopaminergic neurons in PD. Through the combination of SPECT and HRV, we tested the hypothesis that asymmetrically worse left-sided neuronal loss would cause greater AutD. METHODS: 51 patients were enrolled on the day of their standard of care I-123 ioflupane scan for the work-up of possible Parkinsonian syndrome. Demographic information, medical and medication history, and ECG data were collected. HRV metrics were extracted from the ECG data. I-123 ioflupane scans were interpreted by a board-certified nuclear radiologist and quantified by automated software to generate striatal binding ratios (SBRs). Statistical analyses were performed to find correlations between the HRV and SPECT parameters. RESULTS: 32 patients were excluded from the final analysis because of normal scans, prior strokes, cardiac disorders and procedures, or cancer. Abnormal I-123 ioflupane scans were clustered using T-SNE, and one-way ANOVA was performed to compare HRV and SBR parameters. The analysis was repeated after the exclusion of patients taking angiotensin-converting enzyme inhibitors, given the known mechanism on autonomic function. Subsequent analysis showed a significant difference between the high-frequency domains of heart rate variability, asymmetry of the caudate SBR, and putamen-to-caudate SBR. CONCLUSION: Our results support the hypothesis that more imbalanced (specifically worse left-sided) neuronal loss results in greater AutD.

Coupling analysis of heart rate variability and cortical arousal using a deep learning algorithm.

Frequent cortical arousal is associated with cardiovascular dysfunction among people with sleep-disordered breathing. Changes in heart rate variability (HRV) can represent pathological conditions associated with autonomic nervous system dysfunction. Previous studies showed changes in cardiac activity due to cortical arousals. However, few studies have examined the instantaneous association between cortical arousal and HRV in an ethnically diverse population. In this study, we included 1,069 subjects' full night ECG signals from unattended polysomnography in the Multi-Ethnic Study of Atherosclerosis dataset. An automated deep learning tool was employed to annotate arousal events from ECG signals. The etiology (e.g., respiratory, or spontaneous) of each arousal event was classified through a temporal analysis. Time domain HRVs and mean heart rate were calculated on pre-, intra-, and post-arousal segments of a 25-s period for each arousal event. We observed that heart rate and HRVs increased during the arousal onsets in the intra-arousal segments, regardless of arousal etiology. Furthermore, HRVs response to cortical arousal occurrence differed according to gender and the sleep stages in which arousal occurred. The more intense HRVs variation due to arousal in females can contribute to a potentially stronger association between arousal burden and long-term mortality. The excessive abrupt sympathetic tone elevation in REM caused by arousal may provide insights on the association between sleep and sudden cardiac death.

SNPs of CD14 change the mastitis morbidity of Chinese Holstein.

Gram­negative (GN) bacterial infection is a main cause of bovine mastitis. The cluster of differentiation (CD) 14 gene serves an essential role in GN bacterium­induced innate immune response. CD14 works as a bacterial lipopolysaccharide (LPS) receptor, combines with LPS­liposaccharide binding protein complex, and causes cellular activation. However, the effects of CD14 single nucleotide polymorphisms (SNPs) on morbidity of clinical mastitis remain unclear. In the present study, To investigate the polymorphisms of CD14 gene and its effects on cows' susceptibility to mastitis, polymerase chain reaction­single­strand conformation polymorphism (PCR­SSCP) assay was used to detect SNPs of CD14 gene in 134 Chinese Holsteins. SNPs were identified in PCR products amplified with 3 sets of primers in CD14 exon 2. A total of three SNPs were located in that exon: g.528 A→C (147Ser→Arg) in allele B; g.612 A→G (175Asn→Asp) in allele D; and g.1022 A→G in allele F (synonymous mutation). The SNPs in alleles B and D affected the secondary structure of CD14. A 3­dimensional (3D) structural analysis predicted three potential protein forms with a similar structure and indicated that the changes of the above­mentioned alleles were on the concave surface of the protein. In more detail, 147 Ser→Arg induced a protein kinase C phosphorylation site to move forward, as assessed by the motif analysis. The morbidity rate of AB (mixed type g.528 A/C) and CD (mixed type g.612 A/G) was the highest among all genotypes presented in the current study, and via of tumor necrosis factor­α and interleukin­6 mRNA levels were upregulated in animals of this genotype compared with others. Taken together, the CD14 SNPs identified in the present study, may be closely associated with the morbidity of mastitis.

Mutations in HSP70-2 gene change the susceptibility to clinical mastitis in Chinese Holstein.

To select the molecular markers susceptible to mastitis and reduce the loss induced by mastitis, the PCR-SSCP method was adopted to investigate the correlation between SNPs of the HSP70-2 gene and mastitis in 103 Chinese Holstein. 25 new polymorphisms were detected in this study: 9 SNPs (g.-115 G→A, g.-98 T→C, g.117 C→A, g.156 A→C, g.1743 G→A, g.1746 C→T, g.1761 T→G, g.1770 C→G, g.1877 G→C) were found to be associated with mastitis; 2 key SNPs led to amino-acid changes: g.1770 C→G (590 Asp→Glu), g.1877 G→C (626 Gly→Ala). 626 Gly→Ala affected the protein secondary structure. 3 potential cow HSP70-2 proteins were found in all the 103 individuals, but predicted three-dimensional structures of 3 proteins are the same with each other. It is suggested that 9 SNPs increase the susceptibility to mastitis due to their low polymorphisms and can be used as molecular markers to breed the dairy cows resistant to mastitis.