Objective:To evaluate the role of NOD-like receptor protein 3 (NLRP3) inflammasome-mediated microglia activation in myocardial ischaemia-reperfusion-induced brain injury in mice.Methods:Fifty-two SPF healthy male wild-type C57BL/6 mice and 52 NLRP3 -/- mice, aged 8-10 weeks, were divided into 4 groups ( n=26 each) using a random number table method: wild type sham operation group (W-S group), wild type myocardial ischemia-reperfusion group (W-IR group), NLRP3 -/- sham operation group (NLRP3 -/--S group), and NLRP3 -/- myocardial ischemia-reperfusion group (NLRP3 -/--IR group). The myocardial ischemia-reperfusion-induced brain injury model was established by ligating the left anterior descending coronary artery for 45 min followed by 24 h of reperfusion in anesthetized mice. The cognitive function was evaluated using the modified Morris water maze test at 24 h of reperfusion. The mice were sacrificed after blood specimens were collected, and brain tissues were obtained for measurement of the blood-brain barrier permeability and water content, for microscopic examination of the pathological changes of brain tissues, and for determination of serum S-100β protein and neuron-specific enolase (NSE) concentrations, contents of interleukin-1 beta (IL-1β), IL-6 and tumor necrosis factor-alpha (TNF-α) in hippocampal tissues (by enzyme-linked immunosorbent assay), expression of NLRP3, apoptosis-associated speck-like protein (ASC), cleaved cysteine aspartate protease 1 (cleaved-caspase-1), gasdermin D (GSDMD), ionized calcium-binding adapter molecule 1 (Iba-1), and occludin in hippocampal tissues (by immunofluorescence and/or Western blot). The apoptosis rate of neurons and density of dendritic spine were calculated. Results:Compared with sham operation group, the escape latency was significantly prolonged, the number of crossing the original platform was decreased, and the time spent in the target quadrant was shortened, the concentrations of serum S-100β protein and NSE were increased, the blood-brain barrier permeability and brain water content were increased, the dendritic spine density in the hippocampal CA1 area was decreased, the contents of IL-1β, IL-6 and TNF-α were increased, the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD and Iba-1 was up-regulated, and the expression of occludin was down-regulated ( P<0.05), and the pathological injury to brain tissues was found in ischemia-reperfusion group. Compared with W-IR group, the escape latency was significantly shortened, the number of crossing the original platform was increased, and the time spent in the target quadrant was prolonged, the concentrations of serum S-100β protein and NSE were decreased, the blood-brain barrier permeability and brain water content were decreased, the dendritic spine density in the hippocampal CA1 area was increased, the contents of IL-1β, IL-6 and TNF-α were decreased, the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD and Iba-1 was down-regulated, and the expression of occludin was up-regulated ( P<0.05), and the pathological injury to brain tissues was alleviated in NLRP3 -/--IR group. Conclusions:NLRP3 inflammasome-mediated microglia activation is involved in myocardial ischaemia-reperfusion-induced brain injury in mice.

Objective:To investigate clinicopathological and molecular genetic characteristics of CD117-positive eosinophilic renal cell tumors (ERCTs) with unusual morphological and immunophenotypic features.Methods:Formalin-fixed, paraffin-embedded tissues from 10 cases (9 cases from Xiangya Hospital, Central South University and 1 case from Bishan Hospital of Chongqing Medical University) of diagnostically challenging CD117-positive ERCTs between January 2017 and October 2024 were collected. Histological reviews were performed on HE-stained sections, followed by immunostaining and whole-exome sequencing (WES).Results:The 10 patients were composed of 4 males and 6 females, with ages ranging from 29 to 57 years, median 49.5 (36.8, 51.8) years. The sizes of tumors ranged from 2.5 to 6.0 cm, median 4.8(2.9,5.2) cm. All 10 ERCTs were composed of variably eosinophilic cells and characterized by prominent morphological features including exclusively eosinophilic (2 cases), focal chromophobe-like (3 cases), prominent nested (2 cases), prominent flocculent cytoplasm (1 case), a collision of renal oncocytoma (RO)/chromophobe renal cell carcinoma (ChRCC) (1 case), and diffusely degenerative atypia (1 case). Immunohistochemically, a subset of 10 tumors variably expressed CK7 (7/10) and vimentin (3/10), while they were all positive for CD117 (10/10), PAX8 (10/10), SDHB (10/10), and FH (10/10) and negative for CAⅨ (10/10) and 2SC (10/10). The Ki-67 proliferation index ranged from 1% to 5%. WES identified a GNAS mutation in one case of the RO/ChRCC collision tumor, while no characteristic mutations of other renal cell tumor types were detected in the remaining 9 cases. The analysis of copy number variations revealed complex karyotypic alterations in 4 tumors, harboring various gain of chromosomes 4, 5, 7, 12, 13, 15, 16, 18, and 22, with 3 cases showing variable loss of chromosomes 1, 2, 6, 10, 13, and 17. These 4 cases were molecularly classified as eosinophilic ChRCC. The remaining 6 cases, including 2 cases with a normal diploid karyotype and 4 cases with slight karyotypic alterations, were molecularly classified as 5 ROs and 1 RO-dominant RO/ChRCC collision tumor. Finally, the original diagnosis was retained in 4 cases and revised in 6 cases.Conclusions:CD117-positive ERCTs with uncertain classification may exhibit various morphological overlaps, non-classic histological features, and aberrant immunophenotypes. Combined immunostaining of CK7, CD117, vimentin, SDHB, FH, and 2SC can greatly help discriminate among these tumors and their mimics. When the diagnosis is challenging based only on morphology and immunohistochemistry, molecular genetic tests may be useful.

Objective:To evaluate the role of NOD-like receptor protein 3 (NLRP3) inflammasome-mediated microglia activation in myocardial ischaemia-reperfusion-induced brain injury in mice.Methods:Fifty-two SPF healthy male wild-type C57BL/6 mice and 52 NLRP3 -/- mice, aged 8-10 weeks, were divided into 4 groups ( n=26 each) using a random number table method: wild type sham operation group (W-S group), wild type myocardial ischemia-reperfusion group (W-IR group), NLRP3 -/- sham operation group (NLRP3 -/--S group), and NLRP3 -/- myocardial ischemia-reperfusion group (NLRP3 -/--IR group). The myocardial ischemia-reperfusion-induced brain injury model was established by ligating the left anterior descending coronary artery for 45 min followed by 24 h of reperfusion in anesthetized mice. The cognitive function was evaluated using the modified Morris water maze test at 24 h of reperfusion. The mice were sacrificed after blood specimens were collected, and brain tissues were obtained for measurement of the blood-brain barrier permeability and water content, for microscopic examination of the pathological changes of brain tissues, and for determination of serum S-100β protein and neuron-specific enolase (NSE) concentrations, contents of interleukin-1 beta (IL-1β), IL-6 and tumor necrosis factor-alpha (TNF-α) in hippocampal tissues (by enzyme-linked immunosorbent assay), expression of NLRP3, apoptosis-associated speck-like protein (ASC), cleaved cysteine aspartate protease 1 (cleaved-caspase-1), gasdermin D (GSDMD), ionized calcium-binding adapter molecule 1 (Iba-1), and occludin in hippocampal tissues (by immunofluorescence and/or Western blot). The apoptosis rate of neurons and density of dendritic spine were calculated. Results:Compared with sham operation group, the escape latency was significantly prolonged, the number of crossing the original platform was decreased, and the time spent in the target quadrant was shortened, the concentrations of serum S-100β protein and NSE were increased, the blood-brain barrier permeability and brain water content were increased, the dendritic spine density in the hippocampal CA1 area was decreased, the contents of IL-1β, IL-6 and TNF-α were increased, the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD and Iba-1 was up-regulated, and the expression of occludin was down-regulated ( P<0.05), and the pathological injury to brain tissues was found in ischemia-reperfusion group. Compared with W-IR group, the escape latency was significantly shortened, the number of crossing the original platform was increased, and the time spent in the target quadrant was prolonged, the concentrations of serum S-100β protein and NSE were decreased, the blood-brain barrier permeability and brain water content were decreased, the dendritic spine density in the hippocampal CA1 area was increased, the contents of IL-1β, IL-6 and TNF-α were decreased, the expression of NLRP3, ASC, cleaved-caspase-1, GSDMD and Iba-1 was down-regulated, and the expression of occludin was up-regulated ( P<0.05), and the pathological injury to brain tissues was alleviated in NLRP3 -/--IR group. Conclusions:NLRP3 inflammasome-mediated microglia activation is involved in myocardial ischaemia-reperfusion-induced brain injury in mice.

Objective:To investigate clinicopathological and molecular genetic characteristics of CD117-positive eosinophilic renal cell tumors (ERCTs) with unusual morphological and immunophenotypic features.Methods:Formalin-fixed, paraffin-embedded tissues from 10 cases (9 cases from Xiangya Hospital, Central South University and 1 case from Bishan Hospital of Chongqing Medical University) of diagnostically challenging CD117-positive ERCTs between January 2017 and October 2024 were collected. Histological reviews were performed on HE-stained sections, followed by immunostaining and whole-exome sequencing (WES).Results:The 10 patients were composed of 4 males and 6 females, with ages ranging from 29 to 57 years, median 49.5 (36.8, 51.8) years. The sizes of tumors ranged from 2.5 to 6.0 cm, median 4.8(2.9,5.2) cm. All 10 ERCTs were composed of variably eosinophilic cells and characterized by prominent morphological features including exclusively eosinophilic (2 cases), focal chromophobe-like (3 cases), prominent nested (2 cases), prominent flocculent cytoplasm (1 case), a collision of renal oncocytoma (RO)/chromophobe renal cell carcinoma (ChRCC) (1 case), and diffusely degenerative atypia (1 case). Immunohistochemically, a subset of 10 tumors variably expressed CK7 (7/10) and vimentin (3/10), while they were all positive for CD117 (10/10), PAX8 (10/10), SDHB (10/10), and FH (10/10) and negative for CAⅨ (10/10) and 2SC (10/10). The Ki-67 proliferation index ranged from 1% to 5%. WES identified a GNAS mutation in one case of the RO/ChRCC collision tumor, while no characteristic mutations of other renal cell tumor types were detected in the remaining 9 cases. The analysis of copy number variations revealed complex karyotypic alterations in 4 tumors, harboring various gain of chromosomes 4, 5, 7, 12, 13, 15, 16, 18, and 22, with 3 cases showing variable loss of chromosomes 1, 2, 6, 10, 13, and 17. These 4 cases were molecularly classified as eosinophilic ChRCC. The remaining 6 cases, including 2 cases with a normal diploid karyotype and 4 cases with slight karyotypic alterations, were molecularly classified as 5 ROs and 1 RO-dominant RO/ChRCC collision tumor. Finally, the original diagnosis was retained in 4 cases and revised in 6 cases.Conclusions:CD117-positive ERCTs with uncertain classification may exhibit various morphological overlaps, non-classic histological features, and aberrant immunophenotypes. Combined immunostaining of CK7, CD117, vimentin, SDHB, FH, and 2SC can greatly help discriminate among these tumors and their mimics. When the diagnosis is challenging based only on morphology and immunohistochemistry, molecular genetic tests may be useful.

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.

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.