Background At present, high level of depression is a serious problem in medical staff and may affect their immune function. The role of psychological resilience between depression and immunity cannot be ignored. However, it is still lack of research report in this area. Objective To explore the mediating effect of psychological resilience on the association between depression and humoral immunological biomarkers in medical staff. Methods A total of 108 medical staff from a tertiary hospital in Henan Province were selected using stratified cluster sampling from September 2022 to December 2022. The Connor-Davidson Resilience Scale and Patient Health Questionnaire-9 were used to evaluate their psychological resilience and depression. Serum immunoglobulin (Ig) M (IgM), IgG, IgA, complement 3 (C3), and complement 4 (C4) were detected in fasting venous blood samples. Mann-Whitney U test, Kruskal-Wallis H test, independent-samples t-test, and One-way ANOVA were used for comparisons among different demographic groups. Spearman correlation was used to evaluate correlations among measured variables. PROCESS plug-in was used to verify potential mediating effect of psychological resilience on the relationship between depression and humoral immunological biomarkers. Results The M (P₂₅, P₇₅) score of psychological resilience was 65.50 (53.25, 75.00) in the participating medical staff. The ratios of low, medium, and high levels of psychological resilience were 2.78% (3/108), 51.85% (56/108), and 45.37% (49/108), respectively. The M (P₂₅, P₇₅) score of depression was 6.00 (2.00, 8.00). The positive rate of depression was 61.11% (66/108). The correlation analysis results showed that psychological resilience was negatively correlated with depression and serum complement C3 (r=−0.416 and −0.309, P＜0.01), positively correlated with serum IgG and serum IgA (r=0.302 and 0.517, P＜0.01); optimism, self-improvement, and resilience were negatively correlated with depression (r=−0.387, −0.446, and −0.312, P<0.01), positively correlated with IgG (r=0.194, 0.284, and 0.239, P<0.05), and positively correlated with IgA (r=0.377, 0.378, and 0.444, P<0.01), respectively; resilience was negatively correlated with C3 (r=−0.304, P<0.01), and depression was negatively correlated with serum IgG and serum IgA (r=−0.516 and −0.522, P＜0.01), positively correlated with serum complement C3 (r=0.195, P＜0.05). The mediating effect test showed that psychological resilience showed mediating effects on the relationship between depression and serum IgA and serum complement C3, with mediating effect values of −0.148 (95%CI: −0.051, −0.012) and 0.111 (95%CI: 0.001, 0.010), and their mediating effect ratios were 28.30% and 56.92%. Conclusion The mental health status of the target medical staff is not optimistic. Depression is associated with changes in some humoral immunological biomarkers. Psychological resilience can mediate the correlations between depression and humoral immunological biomarkers. The managers should take measures to improve the levels of psychological resilience and promote the physical and mental health of medical staff.

Prostate cancer is the most prevalent malignant tumor among men, ranking first in incidence and second in mortality globally. Novel hormone therapies (NHT) targeting the androgen receptor (AR) pathway have become the standard of care for metastatic prostate cancer. This review offers a comprehensive overview of NHT, including abiraterone, enzalutamide, apalutamide, darolutamide, and rezvilutamide, which have demonstrated efficacy in delaying disease progression and improving patient survival and quality of life. Nevertheless, resistance to NHT remains a critical challenge. The mechanisms underlying resistance are complex, involving AR gene amplification, mutations, splice variants, increased intratumoral androgens, and AR-independent pathways such as the glucocorticoid receptor, neuroendocrine differentiation, DNA repair defects, autophagy, immune evasion, and activation of alternative signaling pathways. This review discusses these resistance mechanisms and examines strategies to counteract them, including sequential treatment with novel AR-targeted drugs, chemotherapy, poly ADP-ribose polymerase inhibitors, radionuclide therapy, bipolar androgen therapy, and approaches targeting specific resistance pathways. Future research should prioritize elucidating the molecular basis of NHT resistance, optimizing existing therapeutic strategies, and developing more effective combination regimens. Additionally, advanced sequencing technologies and resistance research models should be leveraged to identify novel therapeutic targets and improve drug delivery efficiencies. These advancements hold the potential to overcome NHT resistance and significantly enhance the management and prognosis of patients with advanced prostate cancer.

Objective: To investigate the ability of radiological parameter canal bone ratio (CBR) to assess bone mineral density and to differentiate between patients with primary and multiple osteoporotic vertebral compression fracture (OVCF). Methods: A retrospective analysis was conducted on OVCF patients treated at our hospital. CBR was measured through full-spine x-rays. Patients were categorized into primary and multiple fracture groups. Receiver operating characteristic curve analysis and area under the curve (AUC) calculation were used to assess the ability of parameters to predict osteoporosis and multiple fractures. Predictors of T values were analyzed by multiple linear regression, and independent risk factors for multiple fractures were determined by multiple logistic regression analysis. Results: CBR showed a moderate negative correlation with dual-energy x-ray absorptiometry T values (r = -0.642, p < 0.01). Higher CBR (odds ratio [OR], -6.483; 95% confidence interval [CI], -8.234 to -4.732; p < 0.01) and lower body mass index (OR, 0.054; 95% CI, 0.023–0.086; p < 0.01) were independent risk factors for osteoporosis. Patients with multiple fractures had lower T values (mean ± standard deviation [SD]: -3.76 ± 0.73 vs. -2.83 ± 0.75, p < 0.01) and higher CBR (mean ± SD: 0.54 ± 0.07 vs. 0.46 ± 0.06, p < 0.01). CBR had an AUC of 0.819 in predicting multiple fractures with a threshold of 0.53. T values prediction had an AUC of 0.816 with a threshold of -3.45. CBR > 0.53 was an independent risk factor for multiple fractures (OR, 14.66; 95% CI, 4.97–43.22; p < 0.01). Conclusion CBR is negatively correlated with bone mineral density (BMD) and can be a novel opportunistic BMD assessment method. It is a simple and effective measurement index for predicting multiple fractures, with predictive performance not inferior to T values.

Objective: To investigate the ability of radiological parameter canal bone ratio (CBR) to assess bone mineral density and to differentiate between patients with primary and multiple osteoporotic vertebral compression fracture (OVCF). Methods: A retrospective analysis was conducted on OVCF patients treated at our hospital. CBR was measured through full-spine x-rays. Patients were categorized into primary and multiple fracture groups. Receiver operating characteristic curve analysis and area under the curve (AUC) calculation were used to assess the ability of parameters to predict osteoporosis and multiple fractures. Predictors of T values were analyzed by multiple linear regression, and independent risk factors for multiple fractures were determined by multiple logistic regression analysis. Results: CBR showed a moderate negative correlation with dual-energy x-ray absorptiometry T values (r = -0.642, p < 0.01). Higher CBR (odds ratio [OR], -6.483; 95% confidence interval [CI], -8.234 to -4.732; p < 0.01) and lower body mass index (OR, 0.054; 95% CI, 0.023–0.086; p < 0.01) were independent risk factors for osteoporosis. Patients with multiple fractures had lower T values (mean ± standard deviation [SD]: -3.76 ± 0.73 vs. -2.83 ± 0.75, p < 0.01) and higher CBR (mean ± SD: 0.54 ± 0.07 vs. 0.46 ± 0.06, p < 0.01). CBR had an AUC of 0.819 in predicting multiple fractures with a threshold of 0.53. T values prediction had an AUC of 0.816 with a threshold of -3.45. CBR > 0.53 was an independent risk factor for multiple fractures (OR, 14.66; 95% CI, 4.97–43.22; p < 0.01). Conclusion CBR is negatively correlated with bone mineral density (BMD) and can be a novel opportunistic BMD assessment method. It is a simple and effective measurement index for predicting multiple fractures, with predictive performance not inferior to T values.

Objective: To investigate the ability of radiological parameter canal bone ratio (CBR) to assess bone mineral density and to differentiate between patients with primary and multiple osteoporotic vertebral compression fracture (OVCF). Methods: A retrospective analysis was conducted on OVCF patients treated at our hospital. CBR was measured through full-spine x-rays. Patients were categorized into primary and multiple fracture groups. Receiver operating characteristic curve analysis and area under the curve (AUC) calculation were used to assess the ability of parameters to predict osteoporosis and multiple fractures. Predictors of T values were analyzed by multiple linear regression, and independent risk factors for multiple fractures were determined by multiple logistic regression analysis. Results: CBR showed a moderate negative correlation with dual-energy x-ray absorptiometry T values (r = -0.642, p < 0.01). Higher CBR (odds ratio [OR], -6.483; 95% confidence interval [CI], -8.234 to -4.732; p < 0.01) and lower body mass index (OR, 0.054; 95% CI, 0.023–0.086; p < 0.01) were independent risk factors for osteoporosis. Patients with multiple fractures had lower T values (mean ± standard deviation [SD]: -3.76 ± 0.73 vs. -2.83 ± 0.75, p < 0.01) and higher CBR (mean ± SD: 0.54 ± 0.07 vs. 0.46 ± 0.06, p < 0.01). CBR had an AUC of 0.819 in predicting multiple fractures with a threshold of 0.53. T values prediction had an AUC of 0.816 with a threshold of -3.45. CBR > 0.53 was an independent risk factor for multiple fractures (OR, 14.66; 95% CI, 4.97–43.22; p < 0.01). Conclusion CBR is negatively correlated with bone mineral density (BMD) and can be a novel opportunistic BMD assessment method. It is a simple and effective measurement index for predicting multiple fractures, with predictive performance not inferior to T values.

Objective: To investigate the ability of radiological parameter canal bone ratio (CBR) to assess bone mineral density and to differentiate between patients with primary and multiple osteoporotic vertebral compression fracture (OVCF). Methods: A retrospective analysis was conducted on OVCF patients treated at our hospital. CBR was measured through full-spine x-rays. Patients were categorized into primary and multiple fracture groups. Receiver operating characteristic curve analysis and area under the curve (AUC) calculation were used to assess the ability of parameters to predict osteoporosis and multiple fractures. Predictors of T values were analyzed by multiple linear regression, and independent risk factors for multiple fractures were determined by multiple logistic regression analysis. Results: CBR showed a moderate negative correlation with dual-energy x-ray absorptiometry T values (r = -0.642, p < 0.01). Higher CBR (odds ratio [OR], -6.483; 95% confidence interval [CI], -8.234 to -4.732; p < 0.01) and lower body mass index (OR, 0.054; 95% CI, 0.023–0.086; p < 0.01) were independent risk factors for osteoporosis. Patients with multiple fractures had lower T values (mean ± standard deviation [SD]: -3.76 ± 0.73 vs. -2.83 ± 0.75, p < 0.01) and higher CBR (mean ± SD: 0.54 ± 0.07 vs. 0.46 ± 0.06, p < 0.01). CBR had an AUC of 0.819 in predicting multiple fractures with a threshold of 0.53. T values prediction had an AUC of 0.816 with a threshold of -3.45. CBR > 0.53 was an independent risk factor for multiple fractures (OR, 14.66; 95% CI, 4.97–43.22; p < 0.01). Conclusion CBR is negatively correlated with bone mineral density (BMD) and can be a novel opportunistic BMD assessment method. It is a simple and effective measurement index for predicting multiple fractures, with predictive performance not inferior to T values.

Objective: To investigate the ability of radiological parameter canal bone ratio (CBR) to assess bone mineral density and to differentiate between patients with primary and multiple osteoporotic vertebral compression fracture (OVCF). Methods: A retrospective analysis was conducted on OVCF patients treated at our hospital. CBR was measured through full-spine x-rays. Patients were categorized into primary and multiple fracture groups. Receiver operating characteristic curve analysis and area under the curve (AUC) calculation were used to assess the ability of parameters to predict osteoporosis and multiple fractures. Predictors of T values were analyzed by multiple linear regression, and independent risk factors for multiple fractures were determined by multiple logistic regression analysis. Results: CBR showed a moderate negative correlation with dual-energy x-ray absorptiometry T values (r = -0.642, p < 0.01). Higher CBR (odds ratio [OR], -6.483; 95% confidence interval [CI], -8.234 to -4.732; p < 0.01) and lower body mass index (OR, 0.054; 95% CI, 0.023–0.086; p < 0.01) were independent risk factors for osteoporosis. Patients with multiple fractures had lower T values (mean ± standard deviation [SD]: -3.76 ± 0.73 vs. -2.83 ± 0.75, p < 0.01) and higher CBR (mean ± SD: 0.54 ± 0.07 vs. 0.46 ± 0.06, p < 0.01). CBR had an AUC of 0.819 in predicting multiple fractures with a threshold of 0.53. T values prediction had an AUC of 0.816 with a threshold of -3.45. CBR > 0.53 was an independent risk factor for multiple fractures (OR, 14.66; 95% CI, 4.97–43.22; p < 0.01). Conclusion CBR is negatively correlated with bone mineral density (BMD) and can be a novel opportunistic BMD assessment method. It is a simple and effective measurement index for predicting multiple fractures, with predictive performance not inferior to T values.

Aiming at the problems of missing important features, inconspicuous details and unclear textures in the fusion of multimodal medical images, this paper proposes a method of computed tomography (CT) image and magnetic resonance imaging (MRI) image fusion using generative adversarial network (GAN) and convolutional neural network (CNN) under image enhancement. The generator aimed at high-frequency feature images and used double discriminators to target the fusion images after inverse transform; Then high-frequency feature images were fused by trained GAN model, and low-frequency feature images were fused by CNN pre-training model based on transfer learning. Experimental results showed that, compared with the current advanced fusion algorithm, the proposed method had more abundant texture details and clearer contour edge information in subjective representation. In the evaluation of objective indicators, Q ^AB/F, information entropy (IE), spatial frequency (SF), structural similarity (SSIM), mutual information (MI) and visual information fidelity for fusion (VIFF) were 2.0%, 6.3%, 7.0%, 5.5%, 9.0% and 3.3% higher than the best test results, respectively. The fused image can be effectively applied to medical diagnosis to further improve the diagnostic efficiency.
Image Processing, Computer-Assisted/methods* ; Neural Networks, Computer ; Tomography, X-Ray Computed ; Magnetic Resonance Imaging/methods* ; Algorithms

Objective: Schizophrenia (SCZ) is one of the most common and severe mental disorders. Modified electroconvulsive therapy (MECT) is the most effective therapy for all kinds of SCZ, and the underlying molecular mechanism remains unclear. This study is aim to detect the molecule mechanism by constructing the transcriptome dataset from SCZ patients treated with MECT and health controls (HCs). Methods: Transcriptome sequencing was performed on blood samples of 8 SCZ (BECT: before MECT; AECT: after MECT) and 8 HCs, weighted gene co-expression network analysis (WGCNA) was used to cluster the different expression genes, enrichment and protein-protein interaction (PPI) enrichment analysis were used to detect the related pathways. Results: Three gene modules (black, blue and turquoise) were significantly associated with MECT, enrichment analysis found that the long-term potentiation pathway was associated with MECT. PPI enrichment p-value of black, blue, turquoise module are 0.00127, <1×10-16 and 1.09×10-13, respectively. At the same time, EP300 is a key node in the PPI for genes in black module, which got from the transcriptome sequencing data. Conclusion It is suggested that the long-term potentiation pathways were associated with biological mechanism of MECT.

Objective: Schizophrenia (SCZ) is one of the most common and severe mental disorders. Modified electroconvulsive therapy (MECT) is the most effective therapy for all kinds of SCZ, and the underlying molecular mechanism remains unclear. This study is aim to detect the molecule mechanism by constructing the transcriptome dataset from SCZ patients treated with MECT and health controls (HCs). Methods: Transcriptome sequencing was performed on blood samples of 8 SCZ (BECT: before MECT; AECT: after MECT) and 8 HCs, weighted gene co-expression network analysis (WGCNA) was used to cluster the different expression genes, enrichment and protein-protein interaction (PPI) enrichment analysis were used to detect the related pathways. Results: Three gene modules (black, blue and turquoise) were significantly associated with MECT, enrichment analysis found that the long-term potentiation pathway was associated with MECT. PPI enrichment p-value of black, blue, turquoise module are 0.00127, <1×10-16 and 1.09×10-13, respectively. At the same time, EP300 is a key node in the PPI for genes in black module, which got from the transcriptome sequencing data. Conclusion It is suggested that the long-term potentiation pathways were associated with biological mechanism of MECT.