Leader workaholism and subordinates' psychological distress: The moderating role of justice climate.

Leader workaholism, characterized by an excessive drive to work long hours, is prevalent among organizational leaders. Its impact on subordinates' mental health warrants examination. This study investigated the direct relationship between leader workaholism and subordinates' psychological distress. Drawing on substitutes for leadership theory, it also assessed the buffering effects of procedural, interactional, and distributive justice climates in this relationship. Data from an online survey of 40 leaders and 200 subordinate employees revealed a positive correlation between leader workaholism and subordinates' psychological distress. However, the procedural and interactional justice climates negatively moderated this relationship, whereas the distributive justice climate did not. This disparity may result from the strong link between distributive justice climate and specific, objective outcomes. The study enhances understanding of the adverse effects of leader workaholism on employee psychological health and suggests organizational strategies, such as fostering procedural and interactional justice climates, to mitigate these effects.

Re-tear after arthroscopic rotator cuff repair can be predicted using deep learning algorithm.

The application of artificial intelligence technology in the medical field has become increasingly prevalent, yet there remains significant room for exploration in its deep implementation. Within the field of orthopedics, which integrates closely with AI due to its extensive data requirements, rotator cuff injuries are a commonly encountered condition in joint motion. One of the most severe complications following rotator cuff repair surgery is the recurrence of tears, which has a significant impact on both patients and healthcare professionals. To address this issue, we utilized the innovative EV-GCN algorithm to train a predictive model. We collected medical records of 1,631 patients who underwent rotator cuff repair surgery at a single center over a span of 5 years. In the end, our model successfully predicted postoperative re-tear before the surgery using 62 preoperative variables with an accuracy of 96.93%, and achieved an accuracy of 79.55% on an independent external dataset of 518 cases from other centers. This model outperforms human doctors in predicting outcomes with high accuracy. Through this methodology and research, our aim is to utilize preoperative prediction models to assist in making informed medical decisions during and after surgery, leading to improved treatment effectiveness. This research method and strategy can be applied to other medical fields, and the research findings can assist in making healthcare decisions.

N6-methyladenosine-mediated SH3BP5-AS1 upregulation promotes GEM chemoresistance in pancreatic cancer by activating the Wnt signaling pathway.

BACKGROUND: Pancreatic cancer (PC) is highly malignant. Chemotherapy is the main treatment strategy, especially for patients with advanced PC. However, chemoresistance has always been a frequently encountered bottleneck. Hence, there is an urgent need to enhance the sensitivity of PC to gemcitabine (GEM). RESULTS: We demonstrated that SH3BP5-AS1 was significantly upregulated in GEM-resistant PC and predicted a poorer prognosis. SH3BP5-AS1 stability was regulated by ALKBH5/IGF2BP1-mediated m6A modification. Loss of SH3BP5-AS1 reduced PC cell migration and invasion and enhanced the sensitivity of PC to GEM, as confirmed by gain- and loss-of-function assays in vitro and in vivo. Bioinformatics analysis revealed that SH3BP5-AS1 acted as a ceRNA against miR-139-5p and directly targeted CTBP1, affecting the biological behavior of PC cells. The mechanistic studies revealed that the upregulation of SH3BP5-AS1 increased CTBP1 expression by directly activating the Wnt signaling pathway, promoting GEM resistance. CONCLUSIONS: This study revealed that SH3BP5-AS1 activated Wnt signaling pathway by sponging miR-139-5p, upregulating CTBP1 expression, and contributing to the sensitivity of PC cells to GEM. SH3BP5-AS1 might be a potential target for PC therapy.

N6-methyladenosine-mediated CELF2 regulates CD44 alternative splicing affecting tumorigenesis via ERAD pathway in pancreatic cancer.

BACKGROUND: Alternative splicing (AS) of genes has been found to affect gene stability, and its abnormal regulation can lead to tumorigenesis. CELF2 is a vital splicing factor to participate in mRNA alternative splicing. Its downregulation has been confirmed to promote the occurrence and development of pancreatic cancer (PC). However, the regulatory role and mechanisms in PC has not been elucidated. RESULTS: CELF2 was downregulated in PC tissues, which affected tumor TNM stage and tumor size, and low expression of CELF2 indicated a poor prognosis of PC. In vivo and in vitro experiments showed that abnormal expression of CELF2 affected the stemness, apoptosis, and proliferation of PC cells. Furthmore, we also found that CELF2 was targeted by ALKBH5 for m6A modification, leading to CELF2 degradation by YTHDF2. Bioinformatic analysis of AS model based on the TCGA database indicated that CELF2 could target CD44 to form different spliceosomes, thereby affecting the biological behavior of PC cells. The conversion of CD44s to CD44V is the key to tumorigenesis. Transcriptomic analysis was conducted to reveal the mechanism of CELF2-mediated CD44 AS in PC. We found that CELF2-mediated splicing of CD44 led to changes in the level of endoplasmic reticulum stress, further regulating the endoplasmic reticulum-associated degradation (ERAD) signaling pathway, thereby affecting apoptosis and cell stemness. In addition, ERAD signaling pathway inhibitor, EerI, could effectively reverse the effect of CD44 on tumors. CONCLUSIONS: This study indicates that N6-methyladenosine-mediated CELF2 promotes AS of CD44, affecting the ERAD pathway and regulating the biological behavior of PC cells. CELF2 is expected to be a new target for targeted-drug development.

MAMF-GCN: Multi-scale adaptive multi-channel fusion deep graph convolutional network for predicting mental disorder.

PURPOSE: Existing diagnoses of mental disorders rely on symptoms, patient descriptions, and scales, which are not objective enough. We attempt to explore an objective diagnostic method on fMRI data. Graph neural networks (GNN) have been paid more attention recently because of their advantages in processing unstructured relational data, especially for fMRI data. However, how to deeply embed and well-integrate with different modalities and scales on GNN is still a challenge. Instead of reaching a high degree of fusion, existing GCN methods simply combine image and non-image data. Most graph convolutional network (GCN) models use shallow structures, making it challenging to learn about potential information. Furthermore, current graph construction approaches usually use a single specific brain atlas, limiting the analysis and results. METHOD: In this paper, a multi-scale adaptive multi-channel fusion deep graph convolutional network based on an attention mechanism (MAMF-GCN) is proposed to better integrate features of modalities and different atlas by exploiting multi-channel correlation. An encoder automatically combines one channel with non-imaging data to generate similarity weights between subjects using a similarity perception mechanism. Other channels generate multi-scale imaging features of fMRI data after processing in the different atlas. Multi-modal information is fused using an adaptive convolution module that applies a deep graph convolutional network (GCN) to extract information from richer hidden layers. RESULTS: To demonstrate the effectiveness of our approach, we evaluate the performance of the proposed method on the Autism Brain Imaging Data Exchange (ABIDE) dataset and the Major Depressive Disorder (MDD) dataset. The experimental result shows that the proposed method outperforms many state-of-the-art methods in node classification performance. An extensive group of experiments on two disease prediction tasks demonstrates that the performance of the proposed MAMF-GCN on MDD/ABIDE dataset is improved by 3.37%-39.83% and 12.59%-32.92%, respectively. Moreover, our proposed method has also shown very effective performance in real-life clinical diagnosis. The comprehensive experiments demonstrate that our method is effective for node classification with brain disorders diagnosis. CONCLUSION: The proposed MAMF-GCN method simultaneously extracts specific and common embeddings from the topology composed of multi-scale imaging features, phenotypic information, and their combinations, then learning adaptive embedding weights by attention mechanism, which can capture and fuse the multi-scale essential embeddings to improve the classification performance of brain disorder diagnosis.