Cardiometabolic disease is one of the leading causes of morbidity and mortality in adults. In recent years, the prevalence of cardiometabolic disease has increased year by year, becoming a major problem in the global disease burden. The occurrence and development of cardiometabolic risk are affected by many factors. Shift work as an occupational hazard has been widely concerned. Nurses are a typical population for shift work, but few studies have looked at the association between their shift work and cardiometabolic risk. This article reviewed the current situation of cardiometabolic risk and the research progress on the association between shift work and cardiometabolic risk among nurses. The results showed that cardiometabolic risk is prevalent in the nurse population. Shift work is associated with cardiometabolic risks such as metabolic syndrome, cardiovascular disease, diabetes, and dyslipidemia, and is mediated by circadian rhythm disorder, hormone secretion disorder, and stress response. However, the causal relationship between shift work and cardiometabolic risk is still unclear, and further prospective cohort studies should be conducted to improve the understanding of the impact of shift work on cardiometabolic risk in order to improve the cardiometabolic health status of nurses.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Background Coal workers' pneumoconiosis is a serious occupational disease in China. Exhaled volatile organic compounds (VOCs) can serve as the "breath fingerprint" of internal pathological processes, which provides a theoretical basis for exhaled VOCs to be used as potential non-invasive biomarkers for early diagnosis of coal workers' pneumoconiosis. Objective To screen out the characteristic VOCs and important characteristic VOCs of exhaled air in patients with coal workers' pneumoconiosis, and to explore the potential of these VOCs as biomarkers for early non-invasive diagnosis of the disease. Methods In this study, 27 VOCs in the exhaled breath of 22 patients with stage I coal workers' pneumoconiosis, 77 workers exposed to dust, and 92 healthy controls were quantitatively detected by thermal desorption gas chromatography-mass spectrometry (TD-GC-MS). Substances with P<0.05 in univariate analysis and variable importance projection (VIP) >1 in supervised orthogonal partial least squares discriminant analysis (OPLS-DA) model were selected as the characteristic VOCs for early diagnosis of coal workers' pneumoconiosis. Age was included in the LASSO regression model as a covariate to screen out important characteristic VOCs, and the diagnostic performance was evaluated by receiver operating characteristic (ROC) curve. Spearman correlation was further used to explore the correlation between important characteristic VOCs and clinical lung function indicators. Results Through univariate analysis and OPLS-DA modeling, 8 VOCs were selected, including 2-methylpentane, 3-methylpentane, n-hexane, methylcyclopentane, n-heptane, methylcyclohexane, 4-methyl-2-pentanone, and 2-hexanone, in exhaled breath of patients with coal workers' pneumoconiosis. The concentrations of 4 VOCs, including 3-methylpentane, n-hexane, 4-methyl-2-pentanone, and 2-hexanone, showed a decreasing trend with the increase of dust exposure years. By LASSO regression, the important characteristic VOCs of the coal workers' pneumoconiosis group and the dust exposure group were n-hexane, methylcyclohexane and 4-methyl-2-pentanone, and the important characteristic VOCs of the coal workers' pneumoconiosis group and the healthy group were 2-methyl-pentane and 4-methyl-2-pentanone. The ROC analysis showed that the area under the curve (AUC) of n-hexane, methylcyclohexane, and 4-methyl-2-pentanone were 0.969, 0.909, and 0.956, respectively, and the AUC of combined diagnosis was 0.988 and its Youden index was 0.961, suggesting that these results can serve as a valuable reference for further research on early diagnosis. The Correlation analysis found that there was a positive correlation between n-hexane and lung function indicators in the important characteristic VOCs, indicating that it could indirectly reflect the obstruction of lung function ventilation, further proving that important characteristic VOCs have the potential to monitor lung function decline. Conclusion Three important characteristic VOCs selected in this study have the potential to be used as non-invasive biomarkers for early diagnosis and disease monitoring of coal workers' pneumoconiosis, and are worthy of further study and verification.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

The successful development of messenger RNA (mRNA) vaccines provides a new strategy for mRNA-based therapies for the treatment of various diseases. Nanomaterials have attracted much attention in nanomedicine due to their unique physicochemical properties. Therefore, combining mRNA therapy with nanomaterial-based delivery systems to form a unique mRNA delivery system for the treatment of various central nervous system diseases is undoubtedly an innovative new therapeutic strategy. In this review paper, the advances in delivery strategies for mRNA, including lipid carriers, protein mRNA complexes, polymeric carriers, and mixed carriers, were summarized, and their applications in central nervous system diseases therapeutic such as mRNA vaccines and genome editing in combination with mRNA therapy were described.

Molecular Biology is a key basic professional course for all the students specializing in Biolo-gy,Biotechnology,and Bioengineering.With the promotion of double world-class project and first-class undergraduate construction,the development of English-taught course faces challenges.We started to teach the Molecular Biology course in English at the East China University of Science and Technology since 2019,the construction of Molecular Biology course has been reformed and practiced,including the combination of imagery,vividness and classroom teaching,the combination of advanced,cutting-edge and classical theories,and the comprehensive coverage of the teaching process,which has effectively pro-moted the construction and practice of Molecular Biology course.The Molecular Biology course taught in English greatly increased the students'professional and scientific research ability,international vision and English academic communication ability,comprehensive ability and satisfaction,and teachers'teaching and research ability.This course provides an effective reference for fostering innovative profes-sional first-class undergraduates and the construction of Molecular Biology course.

Objective:To explore the value of contrast enhancement T1 fluid-attenuated inversion recovery sequence (CE-T1FLAIR) based on modulated flip angle technique in refocused imaging with extended echo train (MATRIX) in detecting metastases.Methods:One hundred and seventy-six patients with pathologically diagnosed malignant tumors and brain metastases accepted enhanced 3.0T MRI scan in Department of Medical Imaging, He'nan Provincial Cancer Hospital from October 2023 to February 2024 were enrolled. Lianying's intelligent brain metastasis AI-assisted detection system and sequences of MATRIX CE-T1FLAIR, 3D GRE_fsp CE-T1FLAIR and FSE CE-T1FLAIR were used to detect the brain metastasis lesions, respectively. Length of the lesions was measured according to Lianying's intelligent brain metastasis AI-assisted detection system, and all lesions were divided into 3 categories: <3 mm, 3-10 mm, and >10 mm. Differences in detection rate in brain metastases of different lengths and locations among the 3 sequences were compared.Results:Detection rates of MATRIX CE-T1FLAIR, 3D GRE_fsp CE-T1FLAIR, and FSE CE-T1FLAIR in brain metastases were 99.67%, 90.52%, and 71.02%, which were decreased successively, with significant differences ( P<0.05). Detection rates of MATRIX CE-T1FLAIR, 3D GRE_fsp CE-T1FLAIR and FSE CE-T1FLAIR in brain metastases with length<3 mm (99.24%, 79.95% and 46.45%) or length of 3-10 mm (100%, 98.19% and 87.53%) were decreased successively, with significant differences ( P<0.05). Detection rates of MATRIX CE-T1FLAIR (100%, 80.56% and 64.24%), 3D GRE_fsp CE-T1FLAIR (100%, 97.25% and 76.11%), and FSE CE-T1FLAIR (100%, 91.18% and 70.59%) in metastases at the superficial area of the brain convexity, gray-white matter junction area, and cerebellum were decreased successively, with significant differences ( P<0.05). Detection rates of FSE CE-T1FLAIR in brain metastases in the basal ganglia and brainstem (69.33% and 50%) were significantly lower than those of MATRIX CE-T1FLAIR and 3D GRE_fsp CE-T1FLAIR (97.33% and 92.86%; 88% and 78.57%, P<0.05). Conclusion:MATRIX CE-T1FLAIR sequence is better than 3D GRE_fsp CE-T1FLAIR and FSE CE-T1FLAIR sequences in detecting brain metastases, especially for metastases with length<10 mm and metastases located at the superficial area of the brain convexity, gray-white matter junction area and cerebellum.