Taishan Medical University

Due to their large scale, long duration, complex geological conditions, and multiple stakeholders, water conservancy engineering projects are subject to diverse, interrelated, and uncertain risk factors that affect the construction timeline. Traditional critical chain buffer calculation methods, such as the cut-and-paste method and the root variance method, typically assume the independence of risks, which limits their effectiveness in addressing schedule delays caused by correlated risk events. To overcome this limitation, this paper proposes a novel critical chain buffer calculation approach that explicitly incorporates risk correlation analysis. A fuzzy DEMATEL-ISM-BN model is employed to systematically identify the interrelationships and influence pathways among schedule risk factors. Bayesian network inference is then used to quantify the overall occurrence probability while accounting for risk correlations. By integrating critical chain management theory, risk impact coefficients are introduced to improve the traditional root variance method, resulting in a buffer calculation model that captures interdependencies among schedule risks. The effectiveness of the proposed model is validated through a case study of the X Pumped Storage Power Station. The results indicate that, compared with conventional methods, the proposed approach significantly enhances the robustness of project schedule planning under correlated risk conditions while appropriately increasing buffer sizes. Consequently, the adaptability and reliability of schedule control are improved. This study provides novel theoretical tools and practical insights for schedule risk management in complex engineering projects.

Epithelial-mesenchymal transition (EMT) represents a critical process that facilitates metastatic dissemination and immune evasion in colorectal cancer (CRC); however, the molecular factors that connect EMT to modifications in the immune microenvironment remain poorly elucidated. In this investigation, we identify AJUBA as an essential regulator that mediates the association between EMT and immune modulation in CRC. By integrating multi-cohort transcriptomic datasets (The Cancer Genome Atlas (TCGA)-CRC, GSE18105, GSE22598, GSE89076, and GSE110224) with single-cell RNA-seq data (GSE132465), we applied machine learning and deep learning methodologies to comprehensively identify EMT-associated genes demonstrating prognostic significance. AJUBA validation was performed at mRNA and protein levels in a cohort of 90 CRC patient samples using quantitative PCR, Western blotting, and immunohistochemical (IHC) approaches. Functional analyses involved siRNA-mediated knockdown experiments, coupled with evaluations of cell proliferation (CCK-8 assay), migration and invasion (transwell assay), clonogenic capacity (colony formation assay), and in vivo tumor growth in xenograft models. Immune infiltration was assessed via ssGSEA and CIBERSORT algorithms, and spatial transcriptomics data (GSE225857) were used to delineate AJUBA expression within tumor microdomains. Across multiple CRC cohorts, AJUBA exhibited marked upregulation and showed distinct enrichment in epithelial cells with activated EMT characteristics. Spatial transcriptomic profiling demonstrated AJUBA colocalization with cancer-associated fibroblasts (CAFs) within immune-excluded niches. Enhanced AJUBA expression exhibited a positive correlation with heightened infiltration of M2 macrophages and activation of VEGF/NOTCH signaling cascades. In vivo, AJUBA knockdown led to suppressed tumor growth, reduced Ki-67 proliferation indices, and diminished M2 macrophage abundance. Clinically, elevated AJUBA expression correlated with advanced nodal metastasis and served as an independent predictor of poor overall survival (OS; HR = 4.809, 95% CI: 2.385-9.695, p < 0.001). AJUBA functions as a key regulator that links EMT to immune modulation, promoting macrophage polarization and facilitating immune evasion in CRC. Through its coupling of EMT activation with proangiogenic signaling, AJUBA represents both a prognostic biomarker and a promising therapeutic target for alleviating immune exclusion in metastatic CRC.

Abstract Disruptions to ER homeostasis trigger the unfolded protein response (UPR) to restore proteostatic balance. While defects in the secretory machinery are known to induce ER stress, it remains unclear whether specific trafficking components directly modulate UPR signaling. Here, we demonstrate that neuronal overexpression of the gap junction protein UNC-9 activates the IRE1-XBP1 arm of the UPR in C. elegans . Genetic deletion of ERGIC2 or ERGIC3 —genes encoding COPII-associated proteins required for UNC-9 transport—suppresses this UPR activation, revealing an unexpected role for these factors beyond cargo trafficking. Mechanistically, ERGIC2 and ERGIC3 interact with the ER chaperone BiP, facilitating its release from IRE1 to enable UPR and alleviate cargo aggregation. Our findings redefine the UPR as a process dynamically regulated by early secretory components and provide novel insights into how cells integrate trafficking demand with stress adaptation, with implications for ER stress-associated diseases such as neurodegeneration.

BackgroundInsomnia is a common comorbid symptom in Parkinson’s disease (PD) patients, significantly impairing their quality of life. Acupuncture is widely applied in treating PD insomnia, yet relevant evidence remains fragmented.ObjectiveTo investigate the efficacy of acupuncture in improving PD insomnia through systematic review and meta-analysis, evaluating its clinical effectiveness and safety.MethodsEight electronic databases were searched: PubMed, Cochrane Library, Embase, Web of Science, China National Knowledge Infrastructure (CNKI), VIP Data Platform, Wanfang Data Knowledge Service Platform, and China Biomedical Literature Service System. References from relevant literature and clinical trial registries were manually searched for randomized controlled trials (RCTs) on acupuncture for PD insomnia. Studies were screened against inclusion and exclusion criteria, relevant data extracted, and meta-analysis conducted using RevMan 5.4 software.ResultsEleven studies involving 800 patients were included. Meta-analysis revealed that acupuncture effectively improved PSQI (MD = −2.87, 95% CI: −4.28 to −1.46, p < 0.0001) and PDSS (MD = 7.96, 95% CI: 5.55–10.37, p < 0.00001), demonstrating superior efficacy compared to the control group (MD = 6.64, 95% CI: 3.47–12.69, p < 0.00001).ConclusionAcupuncture effectively improves PSQI and PDSS scores in patients with PD insomnia and exhibits superior efficacy over the control group. However, due to limitations, further details could not be explored.

Abstract Geomorphic processes are shaped by climate changes, tectonic movements and human activities. Investigating these interactions is crucial for understanding climate change and landform dynamics. However, the mechanisms driving landform development in high‐altitude regions such as the Tibetan Plateau (TP), largely unaffected by human or tectonic activities since the Holocene, remain unclear. This study investigated the Puruogangri icefield region on the central Tibetan Plateau (TP), where diverse landforms such as lakes, rivers, sand dunes and glaciers could offer valuable insights for geomorphic research. Using optically stimulated luminescence (OSL) dating, we analysed the Linggo Co delta and its outwash terraces. The results indicate that the lake maintained a higher water level from 6.2 to 3.5 ka, which dropped between 3.5 and 2.5 ka. The outwash terraces were formed during the periods of accelerated glacier melting around 5.0, 1.8 and 0.6 ka, with warm periods leading to the formation of delta foreset deposits and outwash terraces, while the cold periods characterised by reduced glacier meltwater resulted in the topset deposits as the lake levels decreased. These findings reveal that temperature could be the dominant factor influencing fluvial landform development in this region.

IntroductionPrimary healthcare workers (PHCWs) are crucial to the healthcare system, as they directly impact the delivery of essential health services. Their job performance is influenced by various types of organizational commitment, but the effects of these commitments are not fully understood. This study aims to explore how four types of organizational commitment (affective, normative, economic, and opportunity) affect job performance among PHCWs, using Self-Determination Theory to examine motivation internalization as a mediating factor.MethodsA cross-sectional survey of 870 PHCWs from 38 primary healthcare institutions was conducted. Hierarchical regression analysis was used to explore the relationships between commitment types, motivation internalization, and job performance.ResultsAffective and normative commitments positively predicted job performance, with motivation internalization partially mediating this relationship. Opportunity commitment negatively predicted job performance, mediated by reduced motivation internalization. Economic commitment showed no significant effect on either motivation internalization or job performance.DiscussionThe impact of organizational commitment on job performance is shaped by its motivational quality. Strengthening affective and normative commitments through supportive incentive strategies can enhance PHCWs’ performance in primary healthcare settings.

As artificial intelligence (AI) continues to evolve, its integration into medical practice is becoming increasingly prominent, particularly in the field of neuro-oncology. This review examines the application of AI—specifically machine learning (ML) and deep learning (DL)—in the imaging evaluation of brain metastases (BM). A systematic search of PubMed was conducted to identify relevant studies published within the past 5 years. The retrieved literature was categorized and analyzed according to three key clinical tasks: segmentation, differential diagnosis, and prognostic prediction. We first outline the capabilities of AI in the automatic detection and segmentation of BM using advanced imaging techniques. Subsequently, we synthesize evidence on how AI aids in distinguishing BM from other intracranial structures and lesions. Finally, we discuss the emerging role of AI in predicting disease prognosis and the development of new metastatic abnormalities. Current evidence suggests that AI not only enhances diagnostic efficiency and reproducibility but also provides clinically meaningful insights that support personalized treatment planning. Importantly, the integration of AI into neuro-oncological imaging remains at a nascent stage, indicating substantial potential for future growth and refinement in both technical performance and clinical applicability.

ObjectiveThis study aims to screen for motor-cognitive risk syndrome (MCR) and analyze its influencing factors in rural older population in China, providing a reference for developing effective early intervention strategies.MethodsA total of 5,389 rural older adults from 33 villages in Xintai City, Shandong Province, China, were investigated using a convenience sampling method. We collected demographic information, subjective cognitive decline, gait speed, sleep quality, cognitive function, chronic pain, self-care ability, fear of falling, loneliness, nutritional status, depression, activities of daily living and social support. In this study, rural older adults were divided into an MCR group and a healthy control group. Chi-square tests, t-tests and rank sum tests were used to compare the differences in demographic characteristics between the two groups. Multivariate and linear logistic regression analyses was used to explore the factors influencing MCR in the rural older adults.ResultsA total of 3,678 rural older adults were included in this study. The prevalence rate of MCR was 11.66%. The results revealed that chronic pain, age, falls, depression, social support, living conditions, medication types, vision loss, and chronic diseases were influencing factors of MCR in rural older population (p < 0.05).ConclusionThe prevalence rate of MCR in the rural older population is 11.66%, although its associated problems are more serious. Therefore, scientific interventions should be developed for rural older population to improve their motor and cognitive function, prevent dementia, and enhance their health quality of life.