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Abstract Objective Expand genetic screening for atypical Type I sialidosis (ST‐1) could address its underdiagnosed in both progressive myoclonic ataxia (PMA) and ataxia patients. To evaluate the potential founder effect of mutation in the population. Methods We enrolled 231 patients with PMA or ataxia from the First Affiliated Hospital of Fujian Medical University. Through Whole Exome Sequencing and Sanger sequencing, we identified the causative gene in patients. Haplotype analysis was employed to explore a potential founder effect of the NEU1 c.544A>G mutation. Results A total of 31 patients from 23 unrelated families were genetically diagnosed with ST‐1. A significant 80.6% of these patients were homozygous for the c.544A>G mutation. We discovered six different NEU1 variants, including two novel mutations: c.951_968del and c.517T>G. The mean age of onset was 18.0 ± 7.1 years. The clinical spectrum of ST‐1 featured ataxia and myoclonus as the most common initial symptoms. Over 40% suffered from controlled generalized tonic–clonic seizures. Mobility and independence varied greatly across the cohort. Cherry‐red spots were rare, occurring in just 9.5% (2/21) of patients. Brain MRIs were typically unremarkable, except for two patients with unusual findings. EEGs showed diffuse paroxysmal activity in 17 patients. The c.544A>G mutation in NEU1 is a founder variant in Fujian, with a unique haplotype prevalent in East Asians. Interpretation ST‐1 should be suspected in patients with PMA or ataxia in Southeast China, even without macular cherry‐red spots and seizures, and the premier test could be a variant screening of the founder variant NEU1 c.544A>G.

Abstract Background The blood–brain barrier serves as a critical interface between the bloodstream and brain tissue, mainly composed of pericytes, neurons, endothelial cells, and tightly connected basal membranes. It plays a pivotal role in safeguarding brain from harmful substances, thus protecting the integrity of the nervous system and preserving overall brain homeostasis. However, this remarkable selective transmission also poses a formidable challenge in the realm of central nervous system diseases treatment, hindering the delivery of large-molecule drugs into the brain. In response to this challenge, many researchers have devoted themselves to developing drug delivery systems capable of breaching the blood–brain barrier. Among these, blood–brain barrier penetrating peptides have emerged as promising candidates. These peptides had the advantages of high biosafety, ease of synthesis, and exceptional penetration efficiency, making them an effective drug delivery solution. While previous studies have developed a few prediction models for blood–brain barrier penetrating peptides, their performance has often been hampered by issue of limited positive data. Results In this study, we present Augur, a novel prediction model using borderline-SMOTE-based data augmentation and machine learning. we extract highly interpretable physicochemical properties of blood–brain barrier penetrating peptides while solving the issues of small sample size and imbalance of positive and negative samples. Experimental results demonstrate the superior prediction performance of Augur with an AUC value of 0.932 on the training set and 0.931 on the independent test set. Conclusions This newly developed Augur model demonstrates superior performance in predicting blood–brain barrier penetrating peptides, offering valuable insights for drug development targeting neurological disorders. This breakthrough may enhance the efficiency of peptide-based drug discovery and pave the way for innovative treatment strategies for central nervous system diseases.

The current study presents the development process and initial validation of the Engagement in Athletic Training Scale (EATS), which was designed to evaluate athletes’ engagement in athletic training. In study 1, item generation and initial content validity of the EATS were achieved. In study 2, the factor structure of the EATS was examined using exploratory factor analysis (EFA) and exploratory structural equation modeling (ESEM). Internal consistency reliabilities of the subscales were examined (N = 460). In study 3, factor structure, discriminant validity, internal consistency reliability, and nomological validity of the EATS were further examined in an independent sample (N = 513). Meanwhile, measurement invariance of the EATS across samples (study 2 and study 3) and genders was evaluated. Overall, results from the 3 rigorous studies provided initial psychometric evidence for the 19-item EATS and suggested that the EATS could be used as a valid and reliable measure to evaluate athletes’ engagement in athletic training.

The stored grain can be regarded as a complex ecosystem, which exist a complex multi-field coupling interaction among the abiotic factors, the biotic factors, and the surrounding environment. In this system, only a few parameters such as temperature can be accurately detected, which causes the system to operate like a “black box” to the outside world. To truly and accurately detect various grain pile parameters, and further study the multi-field coupling interaction in grain piles, a graphical detection system for multi-filed interaction in grain storage based on NMR has been developed. The system consists of an NMR imaging analyzer, a test grain bin, and a data acquisition and processing system, which can accurately detect the grain pile temperature, humidity, and moisture simultaneously, and intuitively and vividly present the cloud map of the temperature field, humidity field, and moisture field in the grain pile through the cloud map drawing software. In this system, the NMR imaging technology was applied to the research field of multi-field coupling interaction in grain piles for the first time, which can provide a new technical means to find out the complex coupling relationship of stored grain ecosystem and further confirm the correctness of relevant theoretical research. The development of this system will be helpful in deepening the theory of multi-field coupling and biological field of grain piles, developed by the industry.

ObjectivesThe present study aimed to examine the psychometric properties of the Chinese version of the Career Adapt-Abilities Scale-Short Form (CAAS-SF) among a sample of Chinese elite athletes.MethodsA sample of Chinese elite athletes (n = 770) was invited to participate in this study. First, the factor structure of the Chinese version of the CAAS-SF was examined, and six measurement models (CFA, H-CFA, B-CFA, ESEM, H-ESEM, and B-ESEM) were constructed and compared. Second, the internal consistency reliability of the Chinese version of the CAAS-SF was examined. Finally, structural equation modeling (SEM) was employed to assess the nomological validity of the Chinese version of the CAAS-SF.ResultsThe results showed that the hierarchical ESEM (H-ESEM) model best represented the factor structure of the CAAS-SF among Chinese elite athletes. It suggests that the higher-order factor of career adaptability explains the four distinctive but interrelated specific factors of concern, control, curiosity, and confidence. Cronbach's alpha coefficients (0.84–0.90), composite reliability (0.81–0.96), and coefficient omega hierarchical (0.855–0.94) of the Chinese version of the CAAS-SF were larger than the cutoff values, which suggest satisfactory reliability. The results of the SEM revealed that the higher-order factor of career adaptability was positively associated with career decision self-efficacy (β = 0.676, p < 0.001). This result is consistent with previous findings (r = 0.65, p < 0.01) and provided support for the nomological validity of the CAAS-SF among Chinese elite athletes.ConclusionThe findings of the present study indicated that the Chinese version of the CAAS-SF displayed satisfactory reliability and validity and could be used to assess the career adaptability of Chinese elite athletes. In addition, the total score of the CAAS-SF is suggested to be used in future research and practical works.

BackgroundThe activation of YAP/TAZ transcriptional co-activators, downstream effectors of the Hippo/YAP pathway, is commonly observed in human cancers, promoting tumor growth and invasion. The aim of this study was to use machine learning models and molecular map based on the Hippo/YAP pathway to explore the prognosis, immune microenvironment and therapeutic regimen of patients with lower grade glioma (LGG).MethodsSW1783 and SW1088 cell lines were used as in vitro models for LGG, and the cell viability of the XMU-MP-1 (a small molecule inhibitor of the Hippo signaling pathway) treated group was evaluated using a Cell Counting Kit-8 (CCK-8). Univariate Cox analysis on 19 Hippo/YAP pathway related genes (HPRGs) was performed to identify 16 HPRGs that exhibited significant prognostic value in meta cohort. Consensus clustering algorithm was used to classify the meta cohort into three molecular subtypes associated with Hippo/YAP Pathway activation profiles. The Hippo/YAP pathway’s potential for guiding therapeutic interventions was also investigated by evaluating the efficacy of small molecule inhibitors. Finally, a composite machine learning models was used to predict individual patients’ survival risk profiles and the Hippo/YAP pathway status.ResultsThe findings showed that XMU-MP-1 significantly enhanced the proliferation of LGG cells. Different Hippo/YAP Pathway activation profiles were associated with different prognostic and clinical features. The immune scores of subtype B were dominated by MDSC and Treg cells, which are known to have immunosuppressive effects. Gene Set Variation Analysis (GSVA) indicated that subtypes B with a poor prognosis exhibited decreased propanoate metabolic activity and suppressed Hippo pathway signaling. Subtype B had the lowest IC50 value, indicating sensitivity to drugs that target the Hippo/YAP pathway. Finally, the random forest tree model predicted the Hippo/YAP pathway status in patients with different survival risk profiles.ConclusionsThis study demonstrates the significance of the Hippo/YAP pathway in predicting the prognosis of patients with LGG. The different Hippo/YAP Pathway activation profiles associated with different prognostic and clinical features suggest the potential for personalized treatments.

Lung adenocarcinoma is a malignancy with a low overall survival and a poor prognosis. Studies have shown that lung adenocarcinoma progression relates to locus-specific/global changes in histone modifications. To explore the relationship between histone modification and gene expression changes, we focused on 11 histone modifications and quantitatively analyzed their influences on gene expression. We found that, among the studied histone modifications, H3K79me2 displayed the greatest impact on gene expression regulation. Based on the Shannon entropy, 867 genes with differential H3K79me2 levels during tumorigenesis were identified. Enrichment analyses showed that these genes were involved in 16 common cancer pathways and 11 tumors and were target-regulated by trans-regulatory elements, such as Tp53 and WT1. Then, an open-source computational framework was presented (https://github.com/zlq-imu/Identification-of-potential-LUND-driver-genes). Twelve potential driver genes were extracted from the genes with differential H3K79me2 levels during tumorigenesis. The expression levels of these potential driver genes were significantly increased/decreased in tumor cells, as assayed by RT–qPCR. A risk score model comprising these driver genes was further constructed, and this model was strongly negatively associated with the overall survival of patients in different datasets. The proportional hazards assumption and outlier test indicated that this model could robustly distinguish patients with different survival rates. Immune analyses and responses to immunotherapeutic and chemotherapeutic agents showed that patients in the high and low-risk groups may have distinct tendencies for clinical selection. Finally, the regions with clear H3K79me2 signal changes on these driver genes were accurately identified. Our research may offer potential molecular biomarkers for lung adenocarcinoma treatment.