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Objective To explore the causal relationship between family resilience and mental health in military personnel population. Methods A total of 204 military personnel were recruited from an army unit stationed in Western China with cluster convenience sampling. Family Resilience Scale (FRS) and Symptom Checklist 90 (SCL-90) were used to survey them twice, in an interval of 4 months. Amos 26.0 was applied to construct a cross-lag model and analyze the data. Results After controlling mental symptoms from the first survey, family resilience in the first measure significantly predicted mental symptoms in the second measure (β=-0.14, P < 0.05). After controlling for family resilience from the first survey, mental symptoms in the first measure significantly predicted family resilience in the second measure (β=-0.13, P < 0.05). Conclusion The relationship between family resilience and mental health is mutually causal in military personnel, and one predicts the other one. Our findings highlight the key dimensions of the relationship between the two.

Purpose: Radical surgery, the first‐line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. Materials and Methods: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5‐fold cross‐validation to assess the performance and predictive power of the comparative models. Results: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. Conclusions: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies. [ABSTRACT FROM AUTHOR]

This study proposed a new magnetic abrasive finishing (MAF) method, in which a 6-axis robot with a magnetic machining tool was used to polish the inner surfaces of curved tubes. We have also developed a magnetic machining tool jig, which can be fixed at the front of the 6-axis robot, rotating freely and suitable for polishing the inner surfaces of curved tubes. In this study, we focused on investigating the machining parameters in the initial machining stage and precision finishing stage. Based on the characteristics of machining parameters, a multi-stage MAF process was conducted to obtain an inner surface with high quality and high efficiency. The experimental results showed that both the roughness Ra and Rz of inner surface in the initial machining stage significantly decreased with the increase in the mixed magnetic abrasives, to as low as less than 20 nm Ra in the precision finishing stage when the machining parameters were appropriately adjusted. In addition, the roughness Ra of inner surface could be further reduced to less than 10 nm Ra in the multi-stage MAF process. Finally, the magnetic flux density cloud map and the magnetic field line distribution map were analyzed in Ansys Maxwell. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) is a prevalent malignancy characterized by significant morbidity and mortality, yet its underlying pathogenesis remains elusive. The etiology of GC is multifaceted, involving the activation of oncogenes and the inactivation of antioncogenes. The ubiquitin-proteasome system (UPS), responsible for protein degradation and the regulation of physiological and pathological processes, emerges as a pivotal player in GC development. Specifically, the F-box protein (FBP), an integral component of the SKP1-Cullin1-F-box protein (SCF) E3 ligase complex within the UPS, has garnered attention for its prominent role in carcinogenesis, tumor progression, and drug resistance. Dysregulation of several FBPs has recently been observed in GC, underscoring their significance in disease progression. This comprehensive review aims to elucidate the distinctive characteristics of FBPs involved in GC, encompassing their impact on cell proliferation, apoptosis, invasive metastasis, and chemoresistance. Furthermore, we delve into the emerging role of FBPs as downstream target proteins of non-coding RNAs(ncRNAs) in the regulation of gastric carcinogenesis, outlining the potential utility of FBPs as direct therapeutic targets or advanced therapies for GC., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)