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.

Humans ; Multiple Myeloma/genetics* ; Neoplasm, Residual/diagnosis* ; Flow Cytometry ; High-Throughput Nucleotide Sequencing

Objectives: To investigated the safety and efficacy of treating patients with acute non-ST-segment elevation myocardial infarction (NSTEMI) and elevated levels of N-terminal pro-hormone B-type natriuretic peptide (NT-proBNP) with levosimendan within 24 hours of first medical contact (FMC). Methods: This multicenter, open-label, block-randomized controlled trial (NCT03189901) investigated the safety and efficacy of levosimendan as an early management strategy of acute heart failure (EMS-AHF) for patients with NSTEMI and high NT-proBNP levels. This study included 255 patients with NSTEMI and elevated NT-proBNP levels, including 142 males and 113 females with a median age of 65 (58-70) years, and were admitted in the emergency or outpatient departments at 14 medical centers in China between October 2017 and October 2021. The patients were randomly divided into a levosimendan group (n=129) and a control group (n=126). The primary outcome measure was NT-proBNP levels on day 3 of treatment and changes in the NT-proBNP levels from baseline on day 5 after randomization. The secondary outcome measures included the proportion of patients with more than 30% reduction in NT-proBNP levels from baseline, major adverse cardiovascular events (MACE) during hospitalization and at 6 months after hospitalization, safety during the treatment, and health economics indices. The measurement data parameters between groups were compared using the t-test or the non-parametric test. The count data parameters were compared between groups using the χ² test. Results: On day 3, the NT-proBNP levels in the levosimendan group were lower than the control group but were statistically insignificant [866 (455, 1 960) vs. 1 118 (459, 2 417) ng/L, Z=-1.25,P=0.21]. However, on day 5, changes in the NT-proBNP levels from baseline in the levosimendan group were significantly higher than the control group [67.6% (33.8%,82.5%)vs.54.8% (7.3%,77.9%), Z=-2.14, P=0.03]. There were no significant differences in the proportion of patients with more than 30% reduction in the NT-proBNP levels on day 5 between the levosimendan and the control groups [77.5% (100/129) vs. 69.0% (87/126), χ²=2.34, P=0.13]. Furthermore, incidences of MACE did not show any significant differences between the two groups during hospitalization [4.7% (6/129) vs. 7.1% (9/126), χ²=0.72, P=0.40] and at 6 months [14.7% (19/129) vs. 12.7% (16/126), χ²=0.22, P=0.64]. Four cardiac deaths were reported in the control group during hospitalization [0 (0/129) vs. 3.2% (4/126), P=0.06]. However, 6-month survival rates were comparable between the two groups (log-rank test, P=0.18). Moreover, adverse events or serious adverse events such as shock, ventricular fibrillation, and ventricular tachycardia were not reported in both the groups during levosimendan treatment (days 0-1). The total cost of hospitalization [34 591.00(15 527.46,59 324.80) vs. 37 144.65(16 066.90,63 919.00)yuan, Z=-0.26, P=0.80] and the total length of hospitalization [9 (8, 12) vs. 10 (7, 13) days, Z=0.72, P=0.72] were lower for patients in the levosimendan group compared to those in the control group, but did not show statistically significant differences. Conclusions: Early administration of levosimendan reduced NT-proBNP levels in NSTEMI patients with elevated NT-proBNP and did not increase the total cost and length of hospitalization, but did not significantly improve MACE during hospitalization or at 6 months.
Male ; Female ; Humans ; Aged ; Natriuretic Peptide, Brain ; Simendan/therapeutic use* ; Non-ST Elevated Myocardial Infarction ; Heart Failure/drug therapy* ; Peptide Fragments ; Arrhythmias, Cardiac ; Biomarkers ; Prognosis

OBJECTIVE: To compare the mid-term clinical effect of arthroscopic surgery versus conservative treatment on the middle aged early knee osteoarthritis (EKOA) patients, with the hope to provide clinical evidence for their individual therapy. METHODS: A total of 145 middle aged EKOA patients(182 knees) who received arthroscopic surgery or conservative treatment from January 2015 to December 2016 were retrospectively enrolled, including 35 males and 110 females, aged from 47 to 79 years old with an average of (57.6±6.9) years old, and the duration of disease ranged from 6 to 48 months with an average of(14.6±8.9) months. According to treatment method, patients were divided into arthroscopic surgery group (47 patients, 58 knees) and conservative treatment group(98 patients, 124 knees). Before treatment, patients presented with symptoms of knee joint, such as pain, swelling, locking, limited flexion and extension, and weakness, as well as abnormal findings in knee X-ray (without or suspicious joint space narrow, and a few of osteophyte formation) or in knee MRI (injury or degeneration of articular cartilage or meniscus, loose body in the joint cavity and synovial hyperemia edema, etc). Related data were collected, including duration of knee symptoms, presence of meniscus injury, loose body in the joint cavity or mechanical symptoms such as locking, and visual analogue scale (VAS) and Lysholm knee function score before treatment and at the latest follow-up. Statistical analysis was performed to compare the differences in VAS or Lyshilm score before or after treatment between the low groups and within each group. RESULTS: Patients in the two groups were followed up from 60 to 76 months. In the arthroscopic surgery group, the incision healing was good and no surgical complications occurred. There were no significant differences in age, gender, BMI and follow-up time between the two groups(P>0.05). Before treatment, compared with conservative group, duration of symptoms in the arthroscopic group was longer (P<0.001), comorbidity rates of meniscus injury (P<0.001), free body (P=0.001) and mechanical symptoms (P<0.001) were higher, VAS (P<0.001) and Lysholm score (P<0.001) were worse. At the final follow-up, VAS and Lysholm score in either the conservative group or the arthroscopic group were significantly better than before treatment (P<0.05), while no significant differences between the two groups were found. The VAS was (1.5±1.2) scores in the arthroscopic group and (1.6±1.0)scores in the conservative group(P=0.549), and the Lysholm score was (84.9±12.5) scores in the arthroscopic group and (84.2±9.9) scores in the conservative group (P=0.676). CONCLUSION Both arthroscopic surgery and conservative treatment have satisfactory intermediate clinical effect middle- aged patients with EKOA, without statistically differences. However, most of the patients before surgery in the arthroscopic treatment group had mechanical locking symptoms caused by meniscus injury or loose body. Therefore, for the middle-aged EKOA patients with mechanical locking symptoms or without obtaining satisfactory outcome after conservative treatment, arthroscopic surgery may be considered.
Male ; Middle Aged ; Female ; Humans ; Aged ; Osteoarthritis, Knee/surgery* ; Retrospective Studies ; Arthroscopy/methods* ; Treatment Outcome ; Knee Joint/surgery*

Protective effect of Qilong Capsules(QL) on the myocardial fibrosis and blood circulation of rats with coronary heart disease of Qi deficiency and blood stasis type was investigated. Sleep deprivation and coronary artery ligation were used to construct a disease-symptom combination model, and 60 SD rats were divided into sham operation(sham) group, syndrome(S) group, disease and syndrome(M) group and QL group randomly. The treatment group received administration of QL 0.4 g·kg~(-1)·d~(-1). Other groups were given the same amount of normal saline. The disease indexes of each group [left ventricular end diastolic diameter(LVESD), left ventricular end systolic diameter(LVEDD), left ventricular ejection fraction(LVEF), left ventricular axis shortening rate(LVFS), myocardial histopathology, platelet morphology, peripheral blood flow] and syndrome indexes(tongue color, pulse, grip power) were detected. In sham group, cardiomyocytes and myocardial fibers were arranged neatly and densely with clear structures. The tongues' color in sham were light red, and the pulse shape were regular. RGB is a parameter reflected the brightness of the image of the tongue. In the S group, the amplitude and frequency of the animal's pulse increased accompanied by decreasing R,G,B, however, the decreased R,G,B was accompanied by reduced pulse amplitude in M group. And in M group, we observed fuzzy cell morphology, hypertrophied myocytes, disordered arrangement of cardiomyocytes and myocardial fibers, reduced peripheral blood flow and increased collagen volume fraction(CVF). Increased LVESD and LVEDD, and decreased LVEF and LVFS represented cardiac function in S group was significantly lower than that in sham. In QL group, the tongue's color was red and the pulse was smooth. The myocardial fibers of the QL group were arranged neatly and secreted less collagen. It improved the blood circulation in the sole and tail, and reversed the increasing of LVEDD, LVESD and the decreasing of LVEF and LVFS of M group. Platelets in M and S group showed high reactivity, and QL could decrease aggregation risk. In conclusion, Qilong Capsules has an obvious myocardial protective effect on ischemic cardiomyopathy, which may inhibit the degree of myocardial fibrosis and reduce platelet reactivity.
Animals ; Capsules ; Cardiomyopathies/drug therapy* ; Fibrosis ; Myocytes, Cardiac ; Qi ; Rats ; Rats, Sprague-Dawley ; Stroke Volume ; Ventricular Function, Left

Coronary Angiography ; Coronary Disease ; Humans ; Liver ; Non-alcoholic Fatty Liver Disease/complications*