Breast cancer remains a worldwide public health issue. LncRNA and autophagy respectively or simultaneously, get involved in cellular and molecular processes of many different cancers, including genesis, metastasis, and deterioration of breast cancer and other malignant tumors. In this review, relevant studies have been summarized, and we have found that lncRNA-mediated autophagy in luminal A breast cancer, luminal B breast cancer, HER-2 positive breast cancer, and basal-like breast cancer may play an important role in mediating drug resistance sensitivity. LncRNAs target genes and affect different signaling pathways to a complex network, which attenuates the occurrence and development of primary breast cancer by coordinating autophagy. Abnormal expression of LncRNA may lead to dysregulation of autophagy, resulting in tumor genesis, expansion, and resistance to anti-tumor therapy. Targeting specific lncRNAs for autophagy regulation may conduct as a bio-marker for reliable diagnosis and prognosis treatment of breast cancer or provide a promising therapeutic strategy.

Previous studies reported the early diagnostic values of plasma Epstein-Barr virus (EBV)-DNA. The present study aimed to assess the relationship between the concentration of plasma EBV-DNA and the number of CD8⁺PD-1⁺(programmed cell death-1,PD-1) and regulatory T (Treg) cells in patients with nasopharyngeal carcinoma (NPC) who were treated with intensity-modulated radiotherapy (IMRT). This study included 37 patients treated with IMRT. Peripheral blood samples were collected two times for each patient, before radiation therapy and 1 week after the treatment. Further, the numbers of CD4⁺, Treg, CD8⁺, and CD8⁺PD1⁺ cells were determined by flow cytometry. The changes after IMRT were determined by comparing the numbers of neutrophils, lymphocytes, CD4⁺, Treg, CD8⁺, CD8⁺PD1⁺ cells, and the concentration of plasma EBV-DNA between pretreatment and post-treatment groups. IMRT could reduce the expression level of PD-1 and the number of Treg cells. The concentration of plasma EBV-DNA and the expression level of CD8⁺PD-1⁺ were closely associated with the occurrence and development of NPC. Thus, EBV-DNA can be used as an important marker for early diagnosis, and IMRT can strongly reduce the copies of EBV-DNA. This study showed that IMRT could reverse T-cell exhaustion and reduce the copies of EBV-DNA. In clinical practice, plasma EBV-DNA is a sensitive biomarker for diagnosis, prognosis, and evaluation of clinical efficacy.

Purpose: To identify the key mental health and improvement factors in hospital administrators working from home during COVID-19 normalization prevention and control.Methods: The survey was conducted from May to June 2023, and the practical experiences of 33 hospital administrators were collected using purposive sampling. The study examined a set of mental health factor systems. The relationship structure between the factors was constructed using the Decision-making Trial and Evaluation Laboratory (DEMATEL) method. Finally, the structure was transformed using the influence weight of each factor via the DEMATEL-based Analytic Network Process.Results: Regarding influence weight, the key mental health factors of hospital administrators are mainly “lack of coordination,” “time management issues,” and “work-life imbalances.” The influential network relation map shows that improvements can be made by addressing “improper guidelines,” “laziness due to being at home,” and “job insecurity” because they are the main sources of influence. The reliability level of the results for the network structure and weight was 98.79% (i.e., the gap was 1.12% < 5%).Conclusion: The network analysis model based on DEMATEL proposed in this study can evaluate the mental health factors of hospital administrators during the pandemic period from a multidimensional and multidirectional perspective and may help improve mental health problems and provide suggestions for hospital administrators.

Objectives. Papillary thyroid carcinoma (PTC) is the most common endocrine system malignant thyroid cancer, and patients with lymph node metastasis typically exhibit poor prognosis. MicroRNAs (miRNAs) can act as either oncogenes or tumor suppressors in PTC. This study was aimed at using PTC transcriptome data obtained from The Cancer Genome Atlas (TCGA) to identify differentially expressed, survival‐related miRNAs and target genes. Methods. We analyzed the TCGA datasets to identify differentially expressed mRNAs/miRNAs in 493 PTC patients with stage I_II group (stages I and II) versus stage III_IV group (stages III and IV) according to TNM staging. The Kaplan‐Meier survival analysis, the Cox regression analysis, and the log‐rank test were performed to investigate survival‐related miRNAs. Results. We identified 36 significantly differentially expressed miRNAs in the stage I_II group versus the stage III_IV group, in which 31 were upregulated and only 5 were downregulated (i.e., hsa‐miR‐891a‐5p, hsa‐miR‐892a, hsa‐miR‐888‐5p, hsa‐miR‐891b, and hsa‐miR‐892b). Additionally, five signature miRNAs (hsa‐miR‐206, hsa‐miR‐299‐3p, hsa‐miR‐299‐5p, hsa‐miR‐496, and hsa‐miR‐509‐3‐5p) were associated with the overall survival of PTC patients. We also found that LMX1B, whose expression was inversely correlated with hsa‐miR‐206 expression, was a putative target gene of hsa‐miR‐206 and LMX1B was likely to serve as a tumor suppressor in PTC. Conclusion. hsa‐miR‐206b might be involved in promoting TNM staging in PTC via targeting of LMX1B.

In Smart City and Vehicle-to-Everything (V2X) systems, acquiring pedestrians' accurate locations is crucial to traffic safety. Current systems adopt cameras and wireless sensors to detect and estimate people's locations via sensor fusion. Standard fusion algorithms, however, become inapplicable when multi-modal data is not associated. For example, pedestrians are out of the camera field of view, or data from camera modality is missing. To address this challenge and produce more accurate location estimations for pedestrians, we propose a Generative Adversarial Network (GAN) architecture. During training, it learns the underlying linkage between pedestrians' camera-phone data correspondences. During inference, it generates refined position estimations based only on pedestrians' phone data that consists of GPS, IMU and FTM. Results show that our GAN produces 3D coordinates at 1 to 2 meter localization error across 5 different outdoor scenes. We further show that the proposed model supports self-learning. The generated coordinates can be associated with pedestrian's bounding box coordinates to obtain additional camera-phone data correspondences. This allows automatic data collection during inference. After fine-tuning on the expanded dataset, localization accuracy is improved by up to 26%.