Utilizing Machine Learning to Identify Biomarkers of Endoplasmic Reticulum Stress and Analyze Immune Cell Infiltration in Parkinson's Disease.

The neurodegenerative disorder known as Parkinson's disease (PD) affects many people. The objective of this investigation was to examine the relationship between immune system infiltration, ATP-binding cassette transporter subfamily A member 7 (ABCA7) and TBL2 as well as potential therapeutic targets for the identification of PD associated to endoplasmic reticulum (ER) stress. First, we obtained PD data through GEO and divided it into two sets: a training set (GSE8397) plus a set for validation (GSE7621). Functional enrichment analysis was performed on a set of DEGs that overlapped with genes involved in endoplasmic reticulum stress. To identify genes of PD linked with endoplasmic reticulum stress, we employed random forest (RF) along with the least absolute shrinkage and selection operator (LASSO) logistic regression. Spearman's rank correlation analysis was then used to find associations among diagnostic markers with immune cell penetration. A grand total of 2 stress-related endoplasmic reticulum signature transcripts were identified. ABCA7 and TBL2 were shown to have diagnostic potential for PD and immune infiltrating cells have a role in the etiology of the disease. Additionally, resting CD4 memory, plasma cells, and NK cells overall exhibited positive associations with ABCA7, whereas triggered macrophages, T cells with active CD4 memory, activating NK cells, T cells with activated CD4 naive, engaged NK cells, and neutrophils all had adverse interactions with ABCA7. Overall, ABCA7 together with TBL2 have diagnostic utility for PD, and several types of immune cells, especially macrophages, may be involved in the development and progression of the disease.

Study on the Correlation between Blood Urea Nitrogen, Creatinine Level, Proteinuria and Parkinson's Disease.

Introduction: Parkinson's disease (PD) is related to renal insufficiency. The purpose of this study was to explore the correlation between PD and blood urea nitrogen, creatinine, and proteinuria. Methods: The case-control study method was adopted in this study. In total, 200 patients with PD who were hospitalized in the Department of Neurology of the Second Affiliated Hospital of Anhui Medical University were selected as the PD group, and 110 healthy patients during the same period were selected as the control group. The differences in clinical data and laboratory results between the two groups were compared. Logistic regression analysis, ROC curve, and Spearman correlation analysis were used to determine the correlation between PD and blood urea nitrogen, creatinine, and urine protein. Results: The levels of cholesterol (TC), triglycerides (TG), low-density lipoprotein (LDL-C), and apolipoprotein B in the PD group were lower than those in the control group. The levels of creatinine, urea nitrogen, and proteinuria in the PD group were higher than those in the control group. Multivariate logistic regression analysis showed that elevated blood urea nitrogen, creatinine, and urine protein levels were risk factors for PD, and elevated LDL-C levels were protective factors for PD. The blood urea nitrogen level of patients with PD was positively correlated with the course of PD, Hoehn-Yahr staging, and UPDRS exercise score (r = 0.309, 0.434, and 0.540, respectively; P < 0.01). Serum creatinine level was positively correlated with the course of PD, Hoehn-Yahr staging, and UPDRS exercise score (r = 0.139, 0.320, and 0.290, respectively; P < 0.01). Conclusion: Blood urea nitrogen, creatinine levels, and proteinuria can be regarded as the onset of PD and a biomarker of disease progression.

Does the HyperCP evidence for the decay Sigma+ -->pmu+mu- indicate a light pseudoscalar Higgs boson?

The HyperCP Collaboration has observed three events for the decay Sigma+ -->p mu+mu- which may be interpreted as a new particle of mass 214.3 MeV. However, existing data from kaon and B-meson decays provide stringent constraints on the construction of models that support this interpretation. In this Letter we show that the "HyperCP particle" can be identified with the light pseudoscalar Higgs boson in the next-to-minimal supersymmetric standard model, the A10. In this model there are regions of parameter space where the A10 can satisfy all the existing constraints from kaon and B-meson decays and mediate Sigma+ -->p mu+mu- at a level consistent with the HyperCP observation.