In this work,a rapid and label-free sensing platform was designed for visual detection of p53 gene.The rolling circle amplification(RCA)process of the assay platform was activated by p53 gene to produce long DNA-wires,which could bound with berberine hydrochloride(BBH)and further enhanced its fluorescence.This method showed high sensitivity with a low detection limit of 5.63 pmol/L,and high specificity toward p53 gene over other interference materials,even for single-base mutation gene.The method could realize the visual detection of targets under the illumination of a UV lamp.In addition,the designed fluorescence detection platform was successfully applied to p53 gene analysis in 10% fetal bovine serum samples,and the relative standard deviation and the recoveries were 0.1% -1.2% and 99.5% -104.7%,respectively.This approach had satisfactory characteristics,such as low cost,label-free,rapidity,high sensitivity,good selectivity and anti-interference ability,and reliable detection capability for complex practical samples,demonstrating a promising prospect in the diagnosis and treatment of diseases,especially for cancer.

OBJECTIVES: To report the development, validation, and findings of the Multi-dimensional Attention Rating Scale (MARS), a self-report tool crafted to evaluate six-dimension attention levels. METHODS: The MARS was developed based on Classical Test Theory (CTT). Totally 202 highly educated healthy adult participants were recruited for reliability and validity tests. Reliability was measured using Cronbach's alpha and test-retest reliability. Structural validity was explored using principal component analysis. Criterion validity was analyzed by correlating MARS scores with the Toronto Hospital Alertness Test (THAT), the Attentional Control Scale (ACS), and the Attention Network Test (ANT). RESULTS: The MARS comprises 12 items spanning six distinct dimensions of attention: focused attention, sustained attention, shifting attention, selective attention, divided attention, and response inhibition.As assessed by six experts, the content validation index (CVI) was 0.95, the Cronbach's alpha for the MARS was 0.78, and the test-retest reliability was 0.81. Four factors were identified (cumulative variance contribution rate 68.79%). The total score of MARS was correlated positively with THAT (r = 0.60, P < 0.01) and ACS (r = 0.78, P < 0.01) and negatively with ANT's reaction time for alerting (r = -0.31, P = 0.049). CONCLUSIONS The MARS can reliably and validly assess six-dimension attention levels in real-world settings and is expected to be a new tool for assessing multi-dimensional attention impairments in different mental disorders.
Humans ; Adult ; Male ; Attention/physiology* ; Female ; Middle Aged ; Reproducibility of Results ; Young Adult ; Psychometrics

In recent years, the study of single-cell transcriptome sequencing technology in the heterogeneity of astrocytes (astrocytes) after spinal cord injury (SCI) has provided new perspectives on post-traumatic nerve regeneration and repair. To provide a review on the research progress of single-cell sequencing technology in astrocytes after spinal cord injury (SCI), and to more comprehensively and deeply elaborate the application of single-cell sequencing technology in the field of astrocytes after SCI. Single-cell sequencing technology can analyse the transcriptomes of individual cells in a high-throughput manner, thus revealing fine differences in cell types and states. By using single-cell sequencing technology, the heterogeneity of astrocytes after SCI and their association with nerve regeneration and repair were revealed. In conclusion, the application of single-cell sequencing technology provides an important tool to reveal the heterogeneity of astrocytes after SCI, to further explore the mechanisms of astrocytes in SCI, and to develop intervention strategies targeting their regulatory mechanisms in order to improve the therapeutic efficacy of SCI. The discovery of changes in astrocyte transcriptome dynamics has improved researchers' understanding of spinal cord injury lesion progression and provided new insights into the treatment of spinal cord injury at different time points. To date, all of these findings need to be validated by more basic research and sufficient clinical trials. In the future, single-cell sequencing technology, through interdisciplinary collaboration with bioinformatics, computer science, tissue engineering, and clinical medicine, is expected to open a new window for the treatment of spinal cord injury.
Spinal Cord Injuries/metabolism* ; Astrocytes/cytology* ; Single-Cell Analysis/methods* ; Humans ; Animals ; Transcriptome ; Nerve Regeneration

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.

Schizophrenia (SZ) stands as a severe psychiatric disorder. This study applied diffusion tensor imaging (DTI) data in conjunction with graph neural networks to distinguish SZ patients from normal controls (NCs) and showcases the superior performance of a graph neural network integrating combined fractional anisotropy and fiber number brain network features, achieving an accuracy of 73.79% in distinguishing SZ patients from NCs. Beyond mere discrimination, our study delved deeper into the advantages of utilizing white matter brain network features for identifying SZ patients through interpretable model analysis and gene expression analysis. These analyses uncovered intricate interrelationships between brain imaging markers and genetic biomarkers, providing novel insights into the neuropathological basis of SZ. In summary, our findings underscore the potential of graph neural networks applied to multimodal DTI data for enhancing SZ detection through an integrated analysis of neuroimaging and genetic features.
Humans ; Schizophrenia/pathology* ; Diffusion Tensor Imaging/methods* ; Male ; Female ; Adult ; Brain/metabolism* ; Young Adult ; Middle Aged ; White Matter/pathology* ; Gene Expression ; Nerve Net/diagnostic imaging* ; Graph Neural Networks

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.

Objective: To develop a simple digital chylous plasma device and validate its ability to accurately, standardly, and non-destructively determine chylous plasma in blood banks and clinical transfusions in hospitals. Methods: A digital chylous plasma assessment device was designed and manufactured. This device was used to measure the chylous degrees of chylous plasma samples before freezing, after freeze-thawing, before viral inactivation, and after viral inactivation. The measured chylosity index values were categorized according to the requirements specified in Appendix A of the Chinese national standard GB 18469-2001 "Quality Requirements for Whole Blood and Blood Components". This process established a digital standard for chylous plasma, enabling the identification of severe, moderate and mild chylous plasma, and non-chylous plasma. Results: The initial simple product of the digital chylous assessment device was successfully designed and manufactured. There was no significant difference in the degree of chylous plasma between pre-freezing 468.11±217.73 lux and post-thawing 538.91±273.39 lux of chylous plasma (P>0.05), or between pre-viral inactivation 858.33±387.79 lux and post-viral inactivation 928.33±166.51 lux of chylous plasma (P>0.05). The median of chylous degree values for plasma chylous index grades 0 to 6 were 45 lux, 250 lux, 620 lux, 835 lux, 1 130 lux, 1 390 lux, and 1 700 lux, respectively. The defined cutoff values/ranges for the chylous degree values corresponding to plasma chylous index grade 0 to 6 were ≤125 lux, 126-465 lux, 466-740 lux, 741-1 000 lux, 1 001-1 233 lux, 1 234-1 560 lux, and ≥1 561 lux. Conclusion: This study successfully developed the initial product of the digital chylous device and established digital standards for classifying chylous plasma. The device demonstrates the potential to meet the needs for assessment of chylous plasma in both blood banks and clinical transfusions in hospitals, thereby promoting the development and application of standardized, non-destructive chylous plasma assessment technology.