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This study aimed to elucidate the alterations in the prefrontal cortex’s functional connectivity and network topology in narcolepsy patients using functional near-infrared spectroscopy (fNIRS). Twelve narcolepsy-diagnosed patients from Guangxi Zhuang Autonomous Region’s People’s Hospital Sleep Medicine Department and 11 matched healthy controls underwent resting fNIRS scans. Functional connectivity and graph theory analyses were employed to assess the prefrontal cortex network’s properties and their correlation with clinical features. Results indicated increased functional connectivity in these adolescent and young adult patients with narcolepsy, with significant variations in metrics like average degree centrality and node efficiency, particularly in the left middle frontal gyrus. These alterations showed correlations with clinical symptoms, including depression and sleep efficiency. However, the significance of these findings was reduced post False Discovery Rate adjustment, suggesting a larger sample size is needed for validation. In conclusion, the study offers initial observations that alterations in the prefrontal cortex’s functional connectivity may potentially act as a neurobiological indicator of narcolepsy, warranting further investigation with a larger cohort to substantiate these findings and understand the underlying mechanisms.

Personalized recommendation algorithms, designed to cater to users’ individualized preferences and enhance the efficiency of Internet information services, have inadvertently precipitated significant legal risks, notably the potential for user privacy breaches. This development necessitates prompt and appropriate legal responses. This paper presents an approach utilizing cosine similarity and Pearson correlation to establish a set of nearest neighbors, facilitating the identification and querying of the closest users based on similarity metrics. Subsequently, we implement a collaborative filtering recommendation algorithm tailored to these personalized settings. We thoroughly evaluate the legal risks associated with the application of customized recommendation algorithms, supplemented by detailed case analyses. The Pareto chart of risk crisis factors reveals that the frequency of issues related to policy legislation and technical concerns are 165 and 95, respectively, cumulatively accounting for 46.68% of the probability share—nearly half of all identified factors. This highlights the critical need for focused attention in these areas. Furthermore, our analysis calculates the probability and severity of data leakage across various paths within the recommendation process. Results indicate severities of 0.029, 0.0333, 0.0409, 0.0447, 0.0481, and 0.165, with the most significant vulnerability appearing in the final recommendation phase to the user, underscoring the imperative to prioritize data privacy in this module. In response, we propose modifications to the existing models to incorporate principles of differential privacy, aiming to mitigate these legal risks and enhance the security and reliability of personalized recommendation algorithms. This initiative is essential for aligning technological advancements with stringent privacy standards and legal requirements.

This paper proposes a legal ontology model for personal information protection to extract structured information on the high-frequency elements of common privacy leakage and data security cases as key information of judicial documents. The prediction model of relevant sentences based on the CART decision tree is used to predict the outcome of the defendant’s sentence. The application scenarios of the legal ontology model of personal information protection in judicial guarantee are explored to guarantee the fairness of adjudication of personal information protection cases in terms of reliability of legal counseling, accuracy of sentencing assistance, and categorization of cases. The results show that 113 sample cases were retrieved by keyword search, excluding duplicate cases, with Beijing having the highest number of 42. With the development of the big data era, there has been a significant increase in personal information protection cases after 2019. The legal ontology model constructed in this paper has only 56 instances of classification error, the Macro-F1-Score and Micro-F1-Score indicator values are above 70%, and the utility values of personal information protection-related cases are all higher than 0.5. Therefore, the legal ontology model for personal information protection constructed in this paper is accurate in classification and sentencing prediction, with high reliability, and realizes the path of the big data era.

Emerging evidence indicates that transfer RNA (tRNA)‐derived small RNAs (tsRNAs), originated from tRNA with high abundance RNA modifications, play an important role in many complex physiological and pathological processes. However, the biological functions and regulatory mechanisms of modified tsRNAs in cancer remain poorly understood. Here, it is screened for and confirmed the presence of a novel m7G‐modified tsRNA, m7G‐3′‐tiRNA LysTTT (mtiRL), in a variety of chemical carcinogenesis models by combining small RNA sequencing with an m7G small RNA‐modified chip. Moreover, it is found that mtiRL, catalyzed by the tRNA m7G‐modifying enzyme mettl1, promotes bladder cancer (BC) malignancy in vitro and in vivo. Mechanistically, mtiRL is found to specifically bind the oncoprotein Annexin A2 (ANXA2) to promote its Tyr24 phosphorylation by enhancing the interactions between ANXA2 and Yes proto‐oncogene 1 (Yes1), leading to ANXA2 activation and increased p‐ANXA2‐Y24 nuclear localization in BC cells. Together, these findings define a critical role for mtiRL and suggest that targeting this novel m7G‐modified tsRNA can be an efficient way for to treat BC. [ABSTRACT FROM AUTHOR]

Background: To date, most existing models for predicting neuromyelitis optica spectrum disorder (NMOSD) are based primarily on clinical characteristics. Blood‐based NMOSD severity and prognostic predictive immune‐ and inflammation‐related biomarkers are needed. We aimed to investigate the associations between plasma inflammatory biomarkers and relapse and attack severity in NMOSD. Methods: This two‐step, single‐center prospective cohort study included discovery and validation cohorts. We quantified 92 plasma inflammatory proteins by using Olink's proximity extension assay and identified differentially expressed proteins in the relapse group (relapse within 1 year of follow‐up) and severe attack group. To define a new molecular prognostic model, we calculated the risk score of each patient based on the key protein signatures and validated the results in the validation cohort. Results: The relapse prediction model, including FGF‐23, DNER, GDNF, and SLAMF1, predicted the 1‐year relapse risk. The severe attack prediction model, including PD‐L1 and MCP‐2, predicted the severe clinical attack risk. Both the relapse and severe attack prediction models demonstrated good discriminative ability and high accuracy in the validation cohort. Conclusions: Our discovered biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression. [ABSTRACT FROM AUTHOR]