Objective:To investigate the clinicopathological characteristics of ossifying fibromyxoid tumor (OFMT) with rare fusion subtypes.Methods:Three cases of OFMT with rare fusion subtypes, diagnosed and consulted in the Zhejiang Hospital, Zhejiang Provincial People′s Hospital, Hangzhou, China and Ningbo Clinical Pathology Diagnosis Center, Ningbo, China from January 2016 to December 2024 were collected. Immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and targeted RNA sequencing were performed to analyze the immunohistochemical and molecular genetic characteristics of these OFMT. Literature review was also conducted.Results:All three patients were male, with ages of 50, 74, and 58 years, respectively. The tumors were located in the left foot, left thigh, and left lumbar region, respectively, and all presented as slowly growing, painless masses in the skin or subcutaneous tissue. Grossly, the tumors measured 3.5 cm, 6.3 cm, and 5.0 cm in maximum diameter, respectively, with a grayish-white to grayish-yellow, solid, lobulated cut surface. One case exhibited a noticeable myxoid texture. Microscopically, one tumor was located in the superficial dermis, while the other two were in the subcutaneous tissue. The tumors were well-demarcated and showed a lobulated or multinodular growth pattern. None of the cases had a complete surrounding bony shell (only one case had very focal ossification). The tumor cells were monomorphic, short spindle-shaped, oval to epithelioid, and arranged in solid sheets, trabeculae, and small nests within a variably fibromyxoid stroma. Case 1 exhibited abundant pseudorosette-like structures formed by short spindle cells surrounding acellular fibrous stroma. Case 2 showed focal transition of epithelioid tumor cells into fasciculately arranged spindle cells, with extensive stromal hyalinization. Case 3 had a predominantly myxoid stroma with a rich network of thin-walled blood vessels. The tumor cells exhibited mild nuclear atypia with 1-3 mitotic figures per 50 high-power fields. All three cases showed diffuse and strong expression of CD10. Two of the three cases showed nuclear expression of TFE3, while one case showed diffuse and strong expression of desmin and S-100. Targeted RNA sequencing revealed PHF1 (ex12)::TFE3 (ex7) fusion in two cases and MEAF6 (ex5)::PHF1 (5′UTR) fusion in one case, which were further confirmed by FISH study. All three patients underwent tumor resection. Two showed no recurrence during follow-up periods of 98 months and 15 months, respectively, while one experienced local recurrence at 12 months postoperatively.Conclusions:OFMT with rare fusion subtypes often exhibits atypical histological and immunophenotypic features, and lacks a characteristic bony shell. Incorporating TFE3 into the diagnostic IHC panel greatly aids in screening for the cases with rare PHF1::TFE3 fusions. Familiarity with the histological and immunophenotypic characteristics, and differential diagnostic points of these rare OFMT subtypes, is essential for judicious use of molecular genetic tools in achieving a definitive diagnosis.

Objective:To investigate the clinicopathological characteristics of ossifying fibromyxoid tumor (OFMT) with rare fusion subtypes.Methods:Three cases of OFMT with rare fusion subtypes, diagnosed and consulted in the Zhejiang Hospital, Zhejiang Provincial People′s Hospital, Hangzhou, China and Ningbo Clinical Pathology Diagnosis Center, Ningbo, China from January 2016 to December 2024 were collected. Immunohistochemistry (IHC), fluorescence in situ hybridization (FISH), and targeted RNA sequencing were performed to analyze the immunohistochemical and molecular genetic characteristics of these OFMT. Literature review was also conducted.Results:All three patients were male, with ages of 50, 74, and 58 years, respectively. The tumors were located in the left foot, left thigh, and left lumbar region, respectively, and all presented as slowly growing, painless masses in the skin or subcutaneous tissue. Grossly, the tumors measured 3.5 cm, 6.3 cm, and 5.0 cm in maximum diameter, respectively, with a grayish-white to grayish-yellow, solid, lobulated cut surface. One case exhibited a noticeable myxoid texture. Microscopically, one tumor was located in the superficial dermis, while the other two were in the subcutaneous tissue. The tumors were well-demarcated and showed a lobulated or multinodular growth pattern. None of the cases had a complete surrounding bony shell (only one case had very focal ossification). The tumor cells were monomorphic, short spindle-shaped, oval to epithelioid, and arranged in solid sheets, trabeculae, and small nests within a variably fibromyxoid stroma. Case 1 exhibited abundant pseudorosette-like structures formed by short spindle cells surrounding acellular fibrous stroma. Case 2 showed focal transition of epithelioid tumor cells into fasciculately arranged spindle cells, with extensive stromal hyalinization. Case 3 had a predominantly myxoid stroma with a rich network of thin-walled blood vessels. The tumor cells exhibited mild nuclear atypia with 1-3 mitotic figures per 50 high-power fields. All three cases showed diffuse and strong expression of CD10. Two of the three cases showed nuclear expression of TFE3, while one case showed diffuse and strong expression of desmin and S-100. Targeted RNA sequencing revealed PHF1 (ex12)::TFE3 (ex7) fusion in two cases and MEAF6 (ex5)::PHF1 (5′UTR) fusion in one case, which were further confirmed by FISH study. All three patients underwent tumor resection. Two showed no recurrence during follow-up periods of 98 months and 15 months, respectively, while one experienced local recurrence at 12 months postoperatively.Conclusions:OFMT with rare fusion subtypes often exhibits atypical histological and immunophenotypic features, and lacks a characteristic bony shell. Incorporating TFE3 into the diagnostic IHC panel greatly aids in screening for the cases with rare PHF1::TFE3 fusions. Familiarity with the histological and immunophenotypic characteristics, and differential diagnostic points of these rare OFMT subtypes, is essential for judicious use of molecular genetic tools in achieving a definitive diagnosis.

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.

Objective:To analyze the changes in plasma endothelin-1 (ET-1) concentrations in the patients with acute respiratory distress syndrome (ARDS).Methods:Fourteen patients with ARDS induced by trauma, 8 males and 6 females, aged 19-80 yr, were studied. The severity of ARDS was graded according to the Berlin definition of ARDS after admission to intensive care unit (ICU). Venous blood samples were obtained on 1st, 3rd and 5th days after admission to ICU, the plasma ET-1 concentrations were measured by radioimmunoassay, the pulmonary vascular permeability index (PVPI) was determined by PiCCO technique, and multiple organ dysfunction (MOD) score and lung injury score (LIS) were assessed. Spearman correlation of plasma ET-1 concentrations with MOD score, LIS and PVPI was analyzed.Results:MOD score, LIS, PVPI and plasma ET-1 concentrations were significantly decreased in mild ARDS patients ( n=5) as compared with moderate ARDS patients ( n=9, P<0.05). The plasma ET-1 concentration was positively correlated with MOD score, LIS and PVPI ( r=0.69, 0.76, 0.62, P<0.001). Conclusions:Plasma ET-1 concentrations can reflect the pulmonary vascular permeability and even the severity of the disease in the early stage of ARDS, so it is necessary to carry out dynamic monitoring in the patients.

Objective: To compare the clinical characteristics of different types of human adenovirus (HAdV) infection in hospitalized children with acute respiratory infection in Beijing, and to clarify the clinical necessity of adenovirus typing. Methods: In a cross-sectional study, 9 022 respiratory tract specimens collected from hospitalized children with acute respiratory infection from November 2017 to October 2019 in Affiliated Children's Hospital, Capital Institute of Pediatrics were screened for HAdV by direct immunofluorescence (DFA) and (or) nucleic acid detection. Then the Penton base, Hexon and Fiber gene of HAdV were amplified from HAdV positive specimens to confirm their HAdV types by phylogenetic tree construction. Clinical data such as laboratory results and imaging data were analyzed for children with predominate type HAdV infection using t, U, or χ² test. Results: There were 392 cases (4.34%) positive for HAdV among 9 022 specimens from hospitalized children with acute respiratory infection. Among those 205 cases who were successfully typed, 131 were male and 74 were female, age of 22.6 (6.7, 52.5) months,102 cases (49.76%) were positive for HAdV-3 and 86 cases (41.95%), HAdV-7, respectively, while 17 cases were confirmed as HAdV-1, 2, 4, 6, 14 or 21. In comparison of clinical characteristics between the predominate HAdV type 7 and 3 infection, significant differences were shown in proportions of children with wheezing (10 cases (11.63%) vs. 25 cases (24.51%)), white blood cell count >15 ×10⁹/L (4 cases (4.65%) vs.14 cases (13.73%)), white blood cell count <5×10⁹/L (26 cases (30.23%) vs.11 cases (10.78%)), procalcitonin level>0.5 mg/L (43 cases (50.00%) vs. 29 cases (28.43%)), multilobar infiltration (45 cases (52.33%) vs.38 cases (37.25%)), pleural effusion (23 cases (26.74%) vs. 10 cases (9.80%)), and severe adenovirus pneumonia (7 cases (8.14%) vs. 2 cases (1.96%)) with χ²=5.11, 4.44, 11.16, 9.19, 4.30, 9.25, 3.91 and P=0.024, 0.035, 0.001, 0.002, 0.038, 0.002, 0.048, respectively, and also in length of hospital stay (11 (8, 15) vs. 7 (5, 13) d, Z=3.73, P<0.001). Conclusions: HAdV-3 and 7 were the predominate types of HAdV infection in hospitalized children with acute respiratory tract infection in Beijing. Compared with HAdV-3 infection, HAdV-7 infection caused more obvious inflammatory reaction, more severe pulmonary symptoms, longer length of hospital stay, suggesting the clinical necessity of further typing of HAdVs.
Adenovirus Infections, Human/epidemiology* ; Adenoviruses, Human/genetics* ; Beijing/epidemiology* ; Child ; Child, Hospitalized ; Cross-Sectional Studies ; Female ; Humans ; Infant ; Male ; Phylogeny ; Respiratory Tract Infections/epidemiology*

In recent years, the real-world studies (RWS) have attracted extensive attention, and the real-world evidence (RWE) has been accepted to support the drug development in China and abroad. However, there is still a lack of standards for the evaluation of the quality of RWE. It is necessary to formulate a quality evaluation and reporting specification for RWE especially in traditional Chinese medicine (TCM). To this end, under the guidance of China Association of Chinese Medicine, the Quality Evaluation and Reporting Specification for Real-World Evidence of Traditional Chinese Medicine (QUERST) Group, including 24 experts (clinical epidemiologists, clinicians, pharmacologists, ethical reviewer and statisticians), was established to develop the specification. This specification contains the listing of classification of RWS design and RWE, the general principles and methods of RWE quality evaluation (26 tools or scales), 25 types of bias in RWS, the special considerations in evaluating the quality of RWE of TCM, and the 19 reporting standards of RWE. This specification aims to propose the quality evaluation principles and key points of RWE, and provide guidance for the proper use of RWE in the development of TCM new drugs.
Medicine, Chinese Traditional ; China