Using high-energy proton to image the region of interest can directly obtain the accurate estimation of the proton stopping power of the lesions,which is of great significance to reduce the range uncertainty in proton therapy.As a fundamental function of proton computed tomography(CT),radiographic imaging plays a crucial role in assisting clinical positioning.The study develops a compact proton CT detector based on an active array pixel CMOS chip in Monte-Carlo simulation toolkit Geant4,and evaluates the radiographic imaging capability of the system using 180 MeV protons.The angles of tracks are successfully reconstructed.CTP404,CTP528,and the CTP515 of specific materials are used for simulation,obtaining the spatial and density resolutions,and measuring the proton relative stopping power(RSP).The image signal-to-noise ratio is improved when using 2° proton scattering angle cut-off value.The spatial resolution is 3-4 lp/cm measured using CTP528 module.The density resolution is better than 0.05 g/cm3,and the RSP resolution is within 5%when CTP404 module is used.Through the imaging of CTP515 phantom of specific material,it is demonstrated that the system has potential for imaging common human tissues.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.

Periarticular fracture of the shoulder is a common type of fractures in the elderly. Postoperative adverse events such as internal fixation failure, humeral head ischemic necrosis and upper limb dysfunction occur frequently, which seriously endangers the exercise and health of the elderly. Compared with the fracture with normal bone mass, the osteoporotic periarticular fracture of the shoulder is complicated with slow healing and poor rehabilitation, so the clinical management becomes more difficult. At present, there is no targeted guideline or consensus for this type of fracture in China. In such context, experts from Youth Osteoporosis Group of Chinese Orthopedic Association, Orthopedic Expert Committee of Geriatrics Branch of Chinese Association of Gerontology and Geriatrics, Osteoporosis Group of Youth Committee of Chinese Association of Orthopedic Surgeons and Osteoporosis Committee of Shanghai Association of Chinese Integrative Medicine developed the Chinese expert consensus on the diagnosis and treatment of osteoporotic periarticular fracture of the shoulder in the elderly ( version 2023). Nine recommendations were put forward from the aspects of diagnosis, treatment strategies and rehabilitation of osteoporotic periarticular fracture of the shoulder, hoping to promote the standardized, systematic and personalized diagnosis and treatment concept and improve functional outcomes and quality of life in elderly patients with osteoporotic periarticular fracture of the shoulder.

Powered by big data and artificial intelligence, the research and clinical application of treatment planning automation for radiation therapy are rapidly growing. The application and supervision of planning automation systems necessitate careful consideration of different levels of automation, as well as the context for use. For autonomous vehicles, the levels of automation have been defined at home and abroad. Nevertheless, no such definitions exist for radiotherapy planning automation. To promote and standardize the development of radiotherapy planning automation and initiate discussion within the community, we developed this recommendation with reference to the taxonomy of driving automation for vehicles and divided the radiotherapy planning automation into six levels (level 1 to 6).

Objective: To investigate the effects of exosomes from human adipose-derived mesenchymal stem cells (ADSCs) on inflammatory response of mouse RAW264.7 cells and wound healing of full-thickness skin defects in mice. Methods: The experimental research methods were adopted. The discarded adipose tissue was collected from 3 female patients (aged 10-25 years) who underwent abdominal surgery in the First Affiliated Hospital of Air Force Medical University. ADSCs were extracted from the adipose tissue by collagenase Ⅰ digestion and identified with flow cytometry. Exosomes were extracted from the human ADSCs by differential ultracentrifugation, the morphology of the exosomes was observed by transmission electron microscopy, the particle diameter of the exosomes was detected by nanoparticle tracking analyzer, and the protein expressions of CD9, CD63, tumor susceptibility gene 101 (TSG101), and β-actin were detected by Western blotting. The human ADSCs exosomes (ADSCs-Exos) and RAW264.7 cells were co-cultured for 12 h, and the uptake of RAW264.7 cells for human ADSCs-Exos was observed. The RAW264.7 cells were divided into phosphate buffer solution (PBS) group stimulated with PBS for suitable time, endotoxin/lipopolysaccharide (LPS) stimulation 2 h group, LPS stimulation 4 h group, LPS stimulation 6 h group, LPS stimulation 12 h group, and LPS stimulation 24 h group stimulated with LPS for corresponding time, with 3 wells in each group, and the mRNA expressions of interleukin 1β (IL-1β), tumor necrosis factor α (TNF-α), IL-6, and IL-10 were detected by real-time fluorescence quantitative reverse transcription polymerase chain reaction (RT-PCR) method. The RAW264.7 cells were divided into PBS group, LPS alone group, and LPS+ADSCs-Exos group, with 3 wells in each group, which were dealt correspondingly for the time screened out in the previous experiment, the mRNA expressions of IL-1β, TNF-α, IL-6, IL-10, trasforming growth factor β (TGF-β,) and vascular endothelial growth factor (VEGF) were detected by real time fluorescence quantitative RT-PCR method, and the protein expressions of inducible nitric oxide synthase (iNOS) and arginase 1 (Arg1) were detected by Western blotting. Twenty-four 8-week-old male BALB/c mice were divided into PBS group and ADSCs-Exos group according to the random number table, with 12 mice in each group, and a full-thickness skin defect wound with area of 1 cm×1 cm was inflicted on the back of each mouse. Immediately after injury, the wounds of mice in the two groups were dealt correspondingly. On post injury day (PID) 1, the concentration of IL-1β and TNF-α in serum were detected by enzyme-linked immunosorbent assay, and the mRNA expressions of IL-1β, TNF-α, and IL-6 were detected by real time fluorescence quantitative RT-PCR method. On PID 3, 6, 9, 12, and 15, the wound healing was observed and the wound non-healing rate was calculated. On PID 15, the defect length of skin accessory and collagen volume fraction (CVF) were detected by hematoxylin eosin staining and Masson staining, respectively, the CD31 expression and neovascularization were detected by immunohistochemistry, and the ratio of Ki67 positive cells, the ratio of iNOS and Arg1 double positive cells, and the ratio of iNOS positive cells to Arg1 positive cells and their fluorescence intensities were detected by immunofluorescence method. The number of samples in animal experiments was 6. Data were statistically analyzed with analysis of variance for repeated measurement, one-way analysis of variance, and independent sample t test. Results: At 12 h of culture, the cells exhibited a typical spindle shape, which were verified as ADSCs with flow cytometry. The exosomes with a vesicular structure and particle diameters of 29-178 nm, were positively expressed CD9, CD63, and TSG101 and negatively expressed β-actin. After 12 h of co-culture, the human ADSCs-Exos were endocytosed into the cytoplasm by RAW264.7 cells. The mRNA expressions of IL-1β, TNF-α, IL-6, and IL-10 of RAW264.7 cells in LPS stimulation 2 h group, LPS stimulation 4 h group, LPS stimulation 6 h group, LPS stimulation 12 h group, and LPS stimulation 24 h group were significantly higher than those in PBS group (with t) values of 39.10, 14.55, 28.80, 4.74, 48.80, 22.97, 13.25, 36.34, 23.12, 18.71, 29.19, 41.08, 11.68, 18.06, 8.54, 43.45, 62.31, 22.52, 21.51, and 37.13, respectively, P<0.01). The stimulation 12 h with significant expressions of all the inflammatory factors was selected as the time point in the following experiment. After stimulation of 12 h, the mRNA expressions of IL-1β, TNF-α, IL-6, and IL-10 of RAW264.7 cells in LPS alone group were significantly higher than those in PBS group (with t values of 44.20, 51.26, 14.71, and 8.54, respectively, P<0.01); the mRNA expressions of IL-1β, TNF-α, and IL-6 of RAW264.7 cells in LPS+ADSCs-Exos group were significantly lower than those in LPS alone group (with t values of 22.89, 25.51, and 8.03, respectively, P<0.01), while the mRNA expressions of IL-10, TGF-β, and VEGF were significantly higher than those in LPS alone group (with t values of 9.89, 13.12, and 7.14, respectively, P<0.01). After stimulation of 12 h, the protein expression of iNOS of RAW264.7 cells in LPS alone group was significantly higher than that in PBS group and LPS+ADSCs-Exos group, respectively (with t values of 11.20 and 5.06, respectively, P<0.05 or P<0.01), and the protein expression of Arg1 was significantly lower than that in LPS+ADSCs-Exos group (t=15.01, P<0.01). On PID 1, the serum concentrations of IL-1β and TNF-α and the mRNA expressions of IL-1β, TNF-α, and IL-6 in wound tissue of mice in ADSCs-Exos group were significantly those in lower than PBS group (with t values of 15.44, 12.24, 9.24, 7.12, and 10.62, respectively, P<0.01). On PID 3, 6, 9, 12, and 15 d, the wound non-healing rates of mice in ADSCs-Exos group were (73.2±4.1)%, (53.8±3.8)%, (42.1±5.1)%, (24.1±2.8)%, and 0, which were significantly lower than (82.5±3.8)%, (71.2±4.6)%, (52.9±4.1)%, (41.5±3.6)%, and (14.8±2.5)% in PBS group, respectively (with t values of 4.77, 8.93, 5.54, 7.63, and 7.59, respectively, P<0.01). On PID 15, the defect length of skin accessory in wounds of mice in PBS group was significantly longer than that in ADSCs-Exos group (t=9.50, P<0.01), and the CVF was significantly lower than that in ADSCs-Exos group (t=9.15, P<0.01). On PID 15, the CD31 expression and the number of new blood vessels (t=12.99, P<0.01), in wound tissue of mice in ADSCs-Exos group were significantly more than those in PBS group, and the ratio of Ki67 positive cells was significantly higher than that in PBS group (t=7.52, P<0.01). On PID 15, the ratio of iNOS and Arg1 double positive cells in wound tissue of mice in PBS group was (12.33±1.97)%, which was significantly higher than (1.78±0.29)% in ADSCs-Exos group (t=13.04, P<0.01), the ratio of iNOS positive cells and the fluorescence intensity of iNOS were obviously higher than those of ADSCs-Exos group, and the ratio of Arg1 positive cells and the fluorescence intensity of Arg1 were obviously lower than those of ADSCs-Exos group. Conclusions: The human ADSCs-Exos can alleviate inflammatory response of mouse RAW264.7 cells, decrease macrophage infiltration and secretion of the pro-inflammatory cytokines, increase the secretion of anti-inflammatory cytokines to promote neovascularization and cell proliferation in full-thickness skin defect wounds of mice, hence accelerating wound healing.
Animals ; Exosomes ; Female ; Humans ; Male ; Mesenchymal Stem Cells ; Mice ; Skin ; Vascular Endothelial Growth Factor A ; Wound Healing