Identification and immunological characterization of genes associated with ferroptosis in Alzheimer's disease and experimental demonstration.

 The incidence of Alzheimer's disease (AD) is rising globally, yet its treatment and prediction of this condition remain challenging due to the complex pathophysiological mechanisms associated with it. Consequently, the objective of the present study was to analyze and characterize the molecular mechanisms underlying ferroptosis­related genes (FEGs) in the pathogenesis of AD, as well as to construct a prognostic model. The findings will provide new insights for the future diagnosis and treatment of AD. First, the AD dataset GSE33000 from the Gene Expression Omnibus database and the FEGs from FerrDB were obtained. Next, unsupervised cluster analysis was used to obtain the FEGs that were most relevant to AD. Subsequently, enrichment analyses were performed on the FEGs to explore biological functions. Subsequently, the role of these genes in the immune microenvironment was elucidated through CIBERSORT. Then, the optimal machine learning was selected by comparing the performance of different machine learning models. To validate the prediction efficiency, the models were validated using nomograms, calibration curves, decision curve analysis and external datasets. Furthermore, the expression of FEGs between different groups was verified using reverse transcription quantitative PCR and western blot analysis. In AD, alterations in the expression of FEGs affect the aggregation and infiltration of certain immune cells. This indicated that the occurrence of AD is strongly associated with immune infiltration. Finally, the most appropriate machine learning models were selected, and AD diagnostic models and nomograms were built. The present study provided novel insights that enhance understanding with regard to the molecular mechanism of action of FEGs in AD. Moreover, the present study provided biomarkers that may facilitate the diagnosis of AD.

The basic reproductive number estimated from a Mycoplasma conjunctivae outbreak in a dairy goat farm.

Clinicians can evaluate the relevance of an outbreak based on its basic reproductive number (R0). So far there has been no report on the R0 of Mycoplasma conjunctivae which is a major cause of goats' conjunctivitis in Taiwan. The present study sought to investigate an outbreak of infectious keratoconjunctivitis (IKC) by Mycoplasma conjunctivae (MC) in an indoor dairy goat barn. The epidemiological curve was recorded to build a susceptible-infected-recovered model and to estimate the R0 by three methods In the investigated goat barn, 60% (31/55) goats showed degrees of IKC signs. The number of infected animals increased quickly after 15days, but slowed down after 41days. The sick goats began to recover after 30days. The epidemic fully stopped after 81days. The estimated R0 ranged from 1.35 to 4.46. In summary, this is the first MC report in Taiwan, and the first one to estimate the R0 of MC.

Detection of Hb anti-Lepore Hong Kong (NG_000007.3: g.63154_70565dup) in Chinese individuals.

We have identified four Chinese individuals from three unrelated families with raised Hb A2 levels. The anti-Lepore hybrid hemoglobin (Hb) variant was amplified using a pair of primers, 5' to the ß-globin gene Cap site and 3' to the δ-globin gene polyadenylation site (polyA) region, respectively. Direct sequencing of the ßδ fusion products confirmed the anti-Lepore Hong Kong (NG_000007.3: g.63154_70565dup) variant. We found that this anti-Lepore variant is positioned in zone 3 on the capillary electrophoresis system. It may help in differential diagnosis of Hb variants and providing better information in clinical counseling.