F-actin and myosin XI play important roles in plant organelle movement. A few myosin XI genes in the genome of Arabidopsis are mainly expressed in mature pollen, which suggests that they may play a crucial role in pollen germination and pollen tube tip growth. In this study, a genetic complementation assay was conducted in a myosin xi-c (myo11c1) myosin xi-e (myo11c2) double mutant, and fluorescence labeling combined with microscopic observation was applied. We found that myosin XI-E (Myo11C2)-green fluorescent protein (GFP) restored the slow pollen tube growth and seed deficiency phenotypes of the myo11c1 myo11c2 double mutant and Myo11C2-GFP partially colocalized with mitochondria, peroxisomes and Golgi stacks. Furthermore, decreased mitochondrial movement and subapical accumulation were detected in my-o11c1 myo11c2 double mutant pollen tubes. Fluorescence recovery after photobleaching experiments showed that the fluorescence recoveries of GFP-RabA4d and AtPRK1-GFP at the pollen tube tip of the my-o11c1 myo11c2 double mutant were lower than those of the wild type were after photobleaching. These results suggest that Myo11C2 may be associated with mitochondria, peroxisomes and Golgi stacks, and play a crucial role in organelle movement and apical accumulation of secretory vesicles in pollen tubes of Arabidopsis thaliana.
The movement of organelles and vesicles in pollen tubes depends on F-actin. However, the molecular mechanism through which plant myosin XI drives the movement of organelles is still controversial, and the relationship between myosin XI and vesicle movement in pollen tubes is also unclear. In this study, we found that the siliques of the myosin xi-b/e mutant were obviously shorter than those of the wild-type (WT) and that the seed set of the mutant was severely deficient. The pollen tube growth of myosin xi-b/e was significantly inhibited both in vitro and in vivo. Fluorescence recovery after photobleaching showed that the velocity of vesicle movement in the pollen tube tip of the myosin xi-b/e mutant was lower than that of the WT. It was also found that peroxisome movement was significantly inhibited in the pollen tubes of the myosin xi-b/e mutant, while the velocities of the Golgi stack and mitochondrial movement decreased relatively less in the pollen tubes of the mutant. The endoplasmic reticulum streaming in the pollen tube shanks was not significantly different between the WT and the myosin xi-b/e mutant. In addition, we found that myosin XI-B-GFP colocalized obviously with vesicles and peroxisomes in the pollen tubes of Arabidopsis. Taken together, these results indicate that myosin XI-B may bind mainly to vesicles and peroxisomes, and drive their movement in pollen tubes. These results also suggest that the mechanism by which myosin XI drives organelle movement in plant cells may be evolutionarily conserved compared with other eukaryotic cells.
Being bedridden is a frequent comorbid condition that leads to a series of complications in clinical practice. The present study aimed to predict bedridden duration of hospitalized patients based on EMR at admission by machine learning. The medical data of 4345 hospitalized patients who were bedridden for at least 24 hours after admission were retrospectively collected. After preprocessing of the data, features for modeling were selected by support vector machine recursive feature elimination. Thereafter, logistic regression, support vector machine, and extreme gradient boosting algorithms were adopted to predict the bedridden duration. The feasibility and efficacy of above models were evaluated by performance indicators. Our results demonstrated that the most important features related to bedridden duration were Charlson Comorbidity Index, age, bedridden duration before admission, mobility capability, and perceptual ability. The extreme gradient boosting algorithm showed the best performance (accuracy, 0.797; area under the curve, 0.841) when compared with support vector machine (accuracy, 0.771; area under the curve, 0.803) and logistic regression (accuracy, 0.765; area under the curve, 0.809) algorithms. Meanwhile, the extreme gradient boosting algorithm had a higher sensitivity (0.856), specificity (0.650), and F1 score (0.858) than that of support vector machine algorithm (0.843, 0.589, and 0.841) and logistic regression (0.852, 0.545, and 0.839), respectively. These findings indicate that machine learning based on EMRs at admission is a feasible avenue to predict the bedridden duration. The extreme gradient boosting algorithm shows great potential for further clinical application.
BackgroundThe association between paroxysmal vertigo and right-to-left shunt (RLS) is rarely reported. This study investigates the prevalence and correlation of RLS in patients with different paroxysmal vertigo diseases.MethodsPatients with paroxysmal vertigo from seven hospitals in China were included in this observational study between 2017 and 2021. Migraine patients within the same period were included for comparison. Demographic data and medical history were collected; contrast transthoracic echocardiography was performed; and the clinical features, Dizziness Handicap Inventory, and incidence of RLS in each group were recorded.ResultsA total of 2,751 patients were enrolled. This study's results demonstrated that the proportion of RLS in patients with benign recurrent vertigo (BRV) and vestibular migraine (VM) was significantly higher than that in patients with benign paroxysmal positional vertigo, Meniere's disease, and vestibular paroxysmia (P < 0.05). No statistical difference was shown between the frequency of RLS in patients with BRV and those with migraine and VM. A positive correlation was shown between the RLS grade and Dizziness Handicap Inventory scores of patients with VM and BRV (P < 0.01) after effectively controlleding the effect of confounding variables.ConclusionsRLS was significantly associated with BRV and VM. RLS may be involved in the pathogeneses of BRV and VM and may serve as a differential reference index for the paroxysmal vertigo.Trial RegistrationCHRS, NCT04939922, registered 14 June 2021- retrospectively registered, https://register.clinicaltrials.gov.
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