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 investigate the incidence rate,clinical characteristics,and prognosis of neonatal stroke in Shenzhen,China.Methods Led by Shenzhen Children's Hospital,the Shenzhen Neonatal Data Collaboration Network organized 21 institutions to collect 36 cases of neonatal stroke from January 2020 to December 2022.The incidence,clinical characteristics,treatment,and prognosis of neonatal stroke in Shenzhen were analyzed.Results The incidence rate of neonatal stroke in 21 hospitals from 2020 to 2022 was 1/15 137,1/6 060,and 1/7 704,respectively.Ischemic stroke accounted for 75％(27/36);boys accounted for 64％(23/36).Among the 36 neonates,31(86％)had disease onset within 3 days after birth,and 19(53％)had convulsion as the initial presentation.Cerebral MRI showed that 22 neonates(61％)had left cerebral infarction and 13(36％)had basal ganglia infarction.Magnetic resonance angiography was performed for 12 neonates,among whom 9(75％)had involvement of the middle cerebral artery.Electroencephalography was performed for 29 neonates,with sharp waves in 21 neonates(72％)and seizures in 10 neonates(34％).Symptomatic/supportive treatment varied across different hospitals.Neonatal Behavioral Neurological Assessment was performed for 12 neonates(33％,12/36),with a mean score of(32±4)points.The prognosis of 27 neonates was followed up to around 12 months of age,with 44％(12/27)of the neonates having a good prognosis.Conclusions Ischemic stroke is the main type of neonatal stroke,often with convulsions as the initial presentation,involvement of the middle cerebral artery,sharp waves on electroencephalography,and a relatively low neurodevelopment score.Symptomatic/supportive treatment is the main treatment method,and some neonates tend to have a poor prognosis.

Objective To establish reference intervals for serum soluble transferrin receptor(sTfR)and sTfR/log ser-um ferritin index(sTfR/lgSF)in apparently healthy adults in the Wuhan area,so as to provide reference for clinical diagno-sis and treatment of iron deficiency and iron-deficiency anemia.Methods A total of 273 individuals from the Wuhan Aisa General Hospital,including health examination participants and blood donors,were selected to measure sTfR,other iron metabolism indicators and high sensitivity C-reactive protein(hsCRP).The sTfR/lgSF values were calculated and reference intervals for sTfR and sTfR/lgSF were established using the percentile method(P2.5 to P97.5).Spearman correlation anal-ysis was used to evaluate the relationships between sTfR,sTfR/lgSF,and other iron metabolism indicators,as well as hsCRP.Results The sTfR levels(M,mg/L)between males and females(1.01 vs 1.07)were not statistically significant(P＞0.05),but the sTfR/lgSF levels if males were significantly lower than those in females(0.45 vs 0.62)(P＜0.05).There was no significant difference in sTfR(M,mg/L)and sTfR/lgSF(M)among different age groups,with values of 1.07 vs 1.02 vs 1.00 and 0.52 vs 0.53 vs 0.51,respectively(P＞0.05).The reference interval for STfR was(0.72-1.68)mg/L,the sTfR/lgSF reference interval was(0.31-0.88)for males,and(0.37-1.19)for females.Correlation analysis showed no correlation between sTfR,sTfR/lgSF and hsCRP(r=0.043,P＞0.05;r=-0.064,P＞0.05),while serum ferritin(SF),serum iron(SI),transferrin saturation(TSAT)were correlated with hsCRP(r=0.128,P＜0.05;r=-0.195,P＜0.01;r=-0.173,P＜0.01).There was no correlation between sTfR and SF(r=-0.115,P＞0.05),while sTfR/lgSF was significantly correlated with and SF(r=-0.685,P＜0.01).Conclusion Preliminary reference intervals for serum sTfR and sTfR/lgSF in apparently healthy adults in the Wuhan has been established.sTfR and sTfR/lgSF are not affected by inflammatory factors and are significant for identifying iron deficiency in anemia patients with elevated serum ferritin caused by inflammation.

Sucrose synthase plays a crucial role in the plant sugar metabolism pathway by catalyzing the production of uridine diphosphate (UDP)-glucose, which serves as a bioactive glycosyl donor for various metabolic processes. In this study, a sucrose synthase gene named CtSus was cloned from Cistanche tubulosa, a traditional Chinese medicine known for its rich content of diverse glycosides, based on transcriptome analysis results. The open reading frame of CtSus was 2 418 bp long encoding 805 amino acids. Sequence analysis revealed conserved domains associated with the plant sucrose synthase family at both the N-terminal and C-terminal regions of CtSus protein. Phylogenetic analysis demonstrated that CtSus shares a close evolutionary relationship with sucrose synthases from other plants within the same order. Functional identification of CtSus was performed through whole-cell catalysis coupling with UGT71BD1, a characterized glycosyltransferase enzyme. The results showed that the introduction of CtSus significantly enhanced the conversion rate of glycosylation catalyzed by UGT71BD1. The soluble expression of CtSus in Escherichia coli was further achieved using the pCold^TM TF expression vector. In vitro enzymatic assay indicated the activity of CtSus to catalyze the formation of UDP-glucose in the presence of sucrose and UDP. Real-time quantitative-polymerase chain reaction results showed that the expression patterns of CtSus gene in different parts of C. tubulosa and the suspension cell cultures of C. tubulosa under drought stress correlated with the accumulation patterns observed for phenylethanol glycosides, respectively. Furthermore, key amino acids and their interactions between enzyme and substrate were explored based on protein structure prediction and molecular docking results. Overall, our findings identify a sucrose synthase CtSus responsible for the supply of the active glycosyl donor UDP-glucose during the biosynthesis of glycoside products in C. tubulosa and have also provided a gene element that can be utilized in engineering strain construction for glycoside products production.

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 evaluate the efficacy and safety of Huashi Baidu Granules (HSBD) in treating patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant. METHODS: A single-center retrospective cohort study was conducted during COVID-19 Omicron epidemic in the Mobile Cabin Hospital of Shanghai New International Expo Center from April 1st to May 23rd, 2022. All COVID-19 patients with asymptomatic or mild infection were assigned to the treatment group (HSBD users) and the control group (non-HSBD users). After propensity score matching in a 1:1 ratio, 496 HSBD users of treatment group were matched by propensity score to 496 non-HSBD users. Patients in the treatment group were administrated HSBD (5 g/bag) orally for 1 bag twice a day for 7 consecutive days. Patients in the control group received standard care and routine treatment. The primary outcomes were the negative conversion time of nucleic acid and negative conversion rate at day 7. Secondary outcomes included the hospitalized days, the time of the first nucleic acid negative conversion, and new-onset symptoms in asymptomatic patients. Adverse events (AEs) that occurred during the study were recorded. Further subgroup analysis was conducted in vaccinated (378 HSBD users and 390 non-HSBD users) and unvaccinated patients (118 HSBD users and 106 non-HSBD users). RESULTS: The median negative conversion time of nucleic acid in the treatment group was significantly shortened than the control group [3 days (IQR: 2-5 days) vs. 5 days (IQR: 4-6 days); P<0.01]. The negative conversion rate of nucleic acid in the treatment group were significantly higher than those in the control group at day 7 (91.73% vs. 86.90%, P=0.014). Compared with the control group, the hospitalized days in the treatment group were significantly reduced [10 days (IQR: 8-11 days) vs. 11 days (IQR: 10.25-12 days); P<0.01]. The time of the first nucleic acid negative conversion had significant differences between the treatment and control groups [3 days (IQR: 2-4 days) vs. 5 days (IQR: 4-6 days); P<0.01]. The incidence of new-onset symptoms including cough, pharyngalgia, expectoration and fever in the treatment group were lower than the control group (P<0.05 or P<0.01). In the vaccinated patients, the median negative conversion time and hospitalized days were significantly shorter than the control group after HSDB treatment [3 days (IQR: 2-5 days) vs. 5 days (IQR: 4-6 days), P<0.01; 10 days (IQR: 8-11 days) vs. 11 days (IQR: 10-12 days), P<0.01]. In the unvaccinated patients, HSBD treatment efficiently shorten the median negative conversion time and hospitalized days [4 days (IQR: 2-6 days) vs. 5 days (IQR: 4-7 days), P<0.01; 10.5 days (IQR: 8.75-11 days) vs. 11.0 days (IQR: 10.75-13 days); P<0.01]. No serious AEs were reported during the study. CONCLUSION HSBD treatment significantly shortened the negative conversion time of nuclear acid, the length of hospitalization, and the time of the first nucleic acid negative conversion in patients infected with SARS-COV-2 Omicron variant (Trial registry No. ChiCTR2200060472).

The current study aims to rapidly and comprehensively profile the chemical composition of Cistanche salsa using direct infusion coupled with MS/MS~(ALL)(DI-MS/MS~(ALL)). The C. salsa extract was directly imported into electrospray ionization(ESI) source of quadrupole time-of-flight(Q-TOF) mass spectrometer with an infusion pump at a flow rate of 10 μL·min~(-1). Acquisition program was applied under negative ionization polarity to collect one MS~1 spectrum(m/z 50-1 200), followed by 1 150 MS~2 spectra with precursor isolation window(m/z 1) amongst mass range m/z 50-1 200. After each MS~2 spectrum was matched to its precursor ion, putative identification was conducted through matching mass spectral data with literature and database. A total of 31 components were identified from C. salsa, including 9 phenylethanoid glycosides, 2 iridoids, 4 saccharides, 9 organic acids, and 7 other compounds, similar to those from C. tubulosa and C. deserticola. In conclusion, DI-MS/MS~(ALL), a facile and reliable analytical tool, can be employed for qualitative analysis of chemical constituents in C. salsa. The research offers a promising strategy to achieve rapid chemome profiling of herbal medicine and provides an alternative source of Cistanches Herba.
Chromatography, High Pressure Liquid ; Cistanche ; Drugs, Chinese Herbal ; Glycosides ; Plants, Medicinal ; Tandem Mass Spectrometry