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Renjian Kan,1 Haibo Xu2,3 1School of Nursing, Xuzhou Medical University, Xuzhou, People’s Republic of China; 2School of Management, Xuzhou Medical University, Xuzhou, People’s Republic of China; 3Research Center for Psychological Crisis Prevention and Intervention of College Students in Jiangsu Province, Xuzhou Medical University, Xuzhou, People’s Republic of ChinaCorrespondence: Haibo Xu, Email xhb@xzhmu.edu.cnPurpose: To examine the influence of learning adjustment dimensions (motivation, attitude, ability, teaching pattern, and environment) on depressive symptoms in Chinese nursing freshmen, and the mediating role of psychological capital.Methods: A cross-sectional survey was conducted among 471 nursing freshmen at a university in May 2024. The anonymous self-reported questionnaire included the Patient Health Questionnaire, the Learning Adjustment Questionnaire, and the Psychological Capital Questionnaire. Data were analyzed using SPSS v25.0 and PROCESS v4.1 macro, including correlation and mediation analysis.Results: Learning adjustment had a significant direct effect on depressive symptoms among nursing freshmen (β = − 0.565, p < 0.001), and psychological capital partially mediated this relationship (β= − 0.156, 95% Boot CI [− 0.107, − 0.135]), suggesting psychological capital explains part of the link between poor learning adjustment and depressive symptoms.Conclusion: More attention should be paid to the mental health issues of freshmen. Addressing learning adjustment issues and fostering psychological capital among nursing freshmen may alleviate depressive symptoms, which will require relevant educational interventions and mental health support from university educators and counselors.Keywords: depressive symptom, learning adjustment, psychological capital, nursing freshmen

Peng Yu,1– 4 Ranran Kan,1,3 Xiaoyu Meng,1,3 Zhihan Wang,1,3 Yuxi Xiang,1,3 Beibei Mao,1,3 Xuefeng Yu1,3 1Department of Endocrinology, Tongji Hospital, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 2Department of Endocrinology, the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, People’s Republic of China; 3Branch of National Clinical Research Center for Metabolic Diseases, Hubei, People’s Republic of China; 4Key Laboratory for Molecular Diagnosis of Hubei Province, Wuhan, People’s Republic of ChinaCorrespondence: Xuefeng Yu, Department of Internal Medicine, Tongji Hospital, Huazhong University of Science and Technology, No. 1095, Jiefang Road, Wuhan, 430030, People’s Republic of China, Tel +8602783663331, Email xfyu188@163.comBackground: In China, the spectrum of causes for CKD has been changing in recent years, and the proportion of CKD caused by cardiometabolic diseases, such as diabetes and hypertension continues to increase. Thus, predicting CKD based on cardiometabolic risk factors can to a large extent help identify those at increased risk and facilitate the prevention of CKD. In this study, we aimed to develop a nomogram for predicting CKD risk based on cardiometabolic risk factors.Methods: We developed a nomogram for predicting CKD risk by using a subcohort population of the 4C study, which was located in central China. The prediction model was designed by using a logistic regression model, and a backwards procedure based on the Akaike information criterion was applied for variable selection. The performance of the model was evaluated by the concordance index (C-index), and Hosmer‒Lemeshow goodness-of-fit test. The bootstrapping method was applied for internal validation.Results: During the 3-years follow-up, 167 cases of CKD developed. By using univariate and multivariate logistic regression models, the following factors were identified as predictors in the nomogram: age, sex, HbA1c, baseline eGFR, low HDL-C levels, high TC levels and SBP. The bootstrap-corrected C-index for the model was 0.84, which indicated good discrimination ability. The Hosmer‒Lemeshow goodness-of-fit tests yielded chi-square of 13.61 (P=0.192), and the calibration curves demonstrated good consistency between the predicted and observed probabilities, which indicated satisfactory calibration ability.Conclusion: We developed a convenient and practicable nomogram for the 3‑year risk of incident CKD among a population in central China, which may help to identify high-risk individuals for CKD and contribute to the prevention of CKD.Keywords: CKD, cardiometabolic risk factors, nomogram

Zinc-finger, MYND-type containing 10 (ZMYND10), or more commonly called BLU, expression is frequently downregulated in nasopharyngeal carcinoma (NPC) and many other tumors due to promoter hypermethylation. Functional evidence shows that the BLU gene inhibits tumor growth in animal assays, but the detailed molecular mechanism responsible for this is still not well understood. In current studies, we find that 93.5% of early-stage primary NPC tumors show downregulated BLU expression. Using a PCR array, overexpression of the BLU gene was correlated to the angiogenesis network in NPC cells. Moreover, expression changes of the MMP family, VEGF and TSP1, were often detected in different stages of NPC, suggesting the possibility that BLU may be directly involved in the microenvironment and anti-angiogenic activity in NPC development. Compared with vector-alone control cells, BLU stable transfectants, derived from poorly-differentiated NPC HONE1 cells, suppress VEGF165, VEGF189 and TSP1 expression at both the RNA and protein levels, and significantly reduce the secreted VEGF protein in these cells, reflecting an unknown regulatory mechanism mediated by the BLU gene in NPC. Cells expressing BLU inhibited cellular invasion, migration and tube formation. These in vitro results were further confirmed by in vivo tumor suppression and a matrigel plug angiogenesis assay in nude mice. Tube-forming ability was clearly inhibited, when the BLU gene is expressed in these cells. Up to 70-90% of injected tumor cells expressing increased exogenous BLU underwent cell death in animal assays. Overexpressed BLU only inhibited VEGF165 expression in differentiated squamous NPC HK1 cells, but also showed an anti-angiogenic effect in the animal assay, revealing a complicated mechanism regulating angiogenesis and the microenvironment in different NPC cell lines. Results of these studies indicate that alteration of BLU gene expression influences anti-angiogenesis pathways and is important for the development of NPC.

Homosexuality is now accepted as a normal variant of human sexuality, but homophobia among healthcare professionals is well documented. Establishment of trustful doctor-patient relationships is impossible in the presence of homophobia. We were interested to examine the extent of homophobia among medical students, the future doctors. This article aims to study attitudes of medical students of the University of Hong Kong towards homosexuality and to identify significant factors affecting their attitudes. A cross-sectional study was performed on students of the University of Hong Kong. Attitudes towards homosexuality were compared between medical and non-medical students. More than 25% of the medical students agreed with the statement "homosexuality is a psychological disorder that requires therapy". More than 15% would avoid any physical contact with homosexuals to minimise the chance of contracting diseases, especially AIDS. Comparison between the medical students and non-medical students showed that the attitudes of the medical students were significantly more homophobic. Factors explaining the difference included the importance of religion in the formation of moral values, gender, experience of working closely with homosexual friends, sexual orientation, experience of the heterosexual sexual act, acquaintance of homosexual friends, and experience of homosexual love relationships. In conclusion, there appeared to be a certain level of homophobia among the medical students. This could possibly be explained by their lack of experience of working closely with homosexual friends. Relevant curriculum modification and development are urgently needed, to provide ongoing opportunities for students to realise their own values that could unconsciously affect their clinical judgements. (Contains 8 tables and 2 figures.)

The deformation and fracture behavior is studied in Sm123 bulk superconductors by comprehensive loading at room temperature. The effects of specimen length on the compressive mechanical properties are discussed.

Introduction Radiologist shortages threaten the sustainability of breast cancer screening programmes. Artificial intelligence (AI) products that can interpret mammograms could mitigate this risk. While previous studies have suggested this technology has accuracy comparable to radiologists most have been limited by using ‘enriched’ datasets and/or not considering the interaction between the algorithm and human readers. This study will address these limitations by comparing the accuracy of a workflow using AI alongside radiologists on a large consecutive cohort of examinations from a breast cancer screening programme. The study will combine the strengths of a large retrospective design with the benefit of prospective data collection. It will test this technology without risk to screening programme participants nor the need to wait for follow-up data. With a sample of 2 years of consecutive screening examinations, it is likely the largest test of this technology to date. The study will help determine whether this technology can safely be introduced into the BreastScreen New South Wales (NSW) population-based screening programme to address radiology workforce risks without compromising cancer detection rates or increasing false-positive recalls.Methods and analysis A retrospective, consecutive cohort of digital mammography screens from 658 207 examinations from BreastScreen NSW will be reinterpreted by the Lunit Insight MMG AI product. The cohort includes 4383 screen-detected and 1171 interval cancers. The results will be compared with radiologist single reading and the AI results will also be used to replace the second reader in a double-reading model. New adjudication reading will be performed where the AI disagrees with the first reader. Recall rates and cancer detection rates of combined AI–radiologist reading will be compared with the rates obtained at the time of screening.Ethics and dissemination This study has ethical approval from the NSW Health Population Health Services Research Ethics Committee (2022/ETH02397). Findings will be published in peer-reviewed journals and presented at conferences. The findings of this evaluation will be provided to programme managers, governance bodies and other stakeholders in Australian breast cancer screening programmes.

Change detection in remote sensing imagery is vital for Earth monitoring but faces challenges such as background complexity and pseudo-changes. Effective interaction between bitemporal images is crucial for accurate change information extraction. This paper presents a multistage interaction network designed for effective change detection, incorporating interaction at the image, feature, and decision levels. At the image level, change information is directly extracted from intensity changes, mitigating potential change information loss during feature extraction. Instead of separately extracting features from bitemporal images, the feature-level interaction jointly extracts features from bitemporal images. By enhancing relevance to spatial variant information and shared semantic channels, the network excels in overcoming background complexity and pseudo-changes. The decision-level interaction combines image-level and feature-level interactions, producing multiscale feature differences for precise change prediction. Extensive experiments demonstrate the superior performance of our method compared to existing approaches, establishing it as a robust solution for remote sensing image change detection.

This article primarily focuses on the localization and extraction of multiple moving objects in images taken from a moving camera platform, such as image sequences captured by drones. The positions of moving objects in the images are influenced by both the camera’s motion and the movement of the objects themselves, while the background position in the images is related to the camera’s motion. The main objective of this article was to extract all moving objects from the background in an image. We first constructed a motion feature space containing motion distance and direction, to map the trajectories of feature points. Subsequently, we employed a clustering algorithm based on trajectory distinctiveness to differentiate between moving objects and the background, as well as feature points corresponding to different moving objects. The pixels between the feature points were then designated as source points. Within local regions, complete moving objects were segmented by identifying these pixels. We validated the algorithm on some sequences in the Video Verification of Identity (VIVID) program database and compared it with relevant algorithms. The experimental results demonstrated that, in the test sequences when the feature point trajectories exceed 10 frames, there was a significant difference in the feature space between the feature points on the moving objects and those on the background. Correctly classified frames with feature points accounted for 67% of the total frames.The positions of the moving objects in the images were accurately localized, with an average IOU value of 0.76 and an average contour accuracy of 0.57. This indicated that our algorithm effectively localized and segmented the moving objects in images captured by moving cameras.

Infrared small target detection plays a crucial role in maritime security. However, detecting small targets within heavy sea clutter environments remains challenging. Existing methods often fail to deliver satisfactory performance in the presence of substantial clutter interference. This paper analyzes the spatial–temporal appearance characteristics of small targets and sea clutter. Based on this analysis, we propose a novel detection method based on the appearance stable isotropy measure (ASIM). First, the original images are processed using the Top-Hat transformation to obtain the salient regions. Next, a preliminary threshold operation is employed to extract the candidate targets from these salient regions, forming a candidate target array image. Third, to distinguish between small targets and sea clutter, we introduce two characteristics: the gradient histogram equalization measure (GHEM) and the local optical flow consistency measure (LOFCM). GHEM evaluates the isotropy of the candidate targets by examining their gradient histogram equalization, while LOFCM assesses their appearance stability based on local optical flow consistency. To effectively combine the complementary information provided by GHEM and LOFCM, we propose ASIM as a fusion characteristic, which can effectively enhance the real target. Finally, a threshold operation is applied to determine the final targets. Experimental results demonstrate that our proposed method exhibits superior comprehensive performance compared to baseline methods.

Diabetic retinopathy is not just the most common complication of diabetes but also the leading cause of adult blindness. Currently, doctors determine the cause of diabetic retinopathy primarily by diagnosing fundus images. Large-scale manual screening is difficult to achieve for retinal health screen. In this paper, we proposed an improved U-net network for segmenting retinal vessels. Firstly, due to the lack of retinal data, pre-processing of the raw data is required. The data processed by grayscale transformation, normalization, CLAHE, gamma transformation. Data augmentation can prevent overfitting in the training process. Secondly, the basic network structure model U-net is built, and the Bi-FPN network is fused based on U-net. Datasets from a public challenge are used to evaluate the performance of the proposed method, which is able to detect vessel SP of 0.8604, SE of 0.9767, ACC of 0.9651, and AUC of 0.9787.