Objective To evaluate the feasibility and accuracy of virtual preoperative planning and 3D-printed templates for pre-contoured plates for the treatment of posterior wall fractures of the acetabulum.Methods A retrospective analysis of 29 patients with posterior acetabular wall fractures treated between August 2017 and March 2021 were divided into 2 groups based on whether to use preoperative virtual planning and 3D printed template.In 3D-printing group,there were 14 patients,includ-ing 10 males and 4 females;aged from 21 to 53 years old;CT-based virtual surgical planning was done using Mimics and 3-Matic software and 3D-printed templates for pre-contoured plates were adopted.In conventional group,there were 15 patients,including 10 males and 5 females;aged from 19 to 55 years old;conventional method of intra-operative contouring to adapt the plate to the fracture region was adopted.Blood loss,surgical time,radiographic quality of reduction,and hip function were compared between groups.Results The difference in operation time and intraoperative blood loss was significant(P＜0.05).Twenty-three patients were followed up from 12 to 30 months,and the fractures in both groups healed with a healing time of 3 to 6 months.At the last follow-up,the Merle d'Aubign-Postel score of the 3D printed group was lower than that of the conven-tional group(P＜0.05),with no significant differences in walking ability,hip mobility and total score(P＞0.05).In 3D printing group,6 cases were excellent,5 cases were good,3 cases were fair;in conventional group,5 cases were excellent,5 cases were good,4 cases were fair,1 case was worse;no significant difference between two groups(P＞0.05).Conclusion Virtual preopera-tive planning and 3D-printed templates for pre-contoured plates can reduce operative time and the blood loss of surgery,im-prove the quality of reduction.This method is efficient,accurate and reliable to treat acetabular posterior wall fractures.

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.

Human brain banks use a standardized protocol to collect,process and store post-mortem human brains and related tissues,along with relevant clinical information,and to provide the tissue samples and data as a resource to foster neuroscience research according to a standardized operating protocols(SOP).Human brain bank serves as the foundation for neuroscience research and the diagnosis of neurological disorders,highlighting the crucial rule of ensuring the consistency of standardized quality for brain tissue samples.The first version of SOP in 2017 was published by the China Human Brain Bank Consortium.As members increases from different regions in China,a revised SOP was drafted by experts from the China Human Brain Bank Consortium to meet the growing demands for neuroscience research.The revised SOP places a strong emphasis on ethical standards,incorporates neuropathological evaluation of brain regions,and provides clarity on spinal cord sampling and pathological assessment.Notable enhancements in this updated version of the SOP include reinforced ethical guidelines,inclusion of matching controls in recruitment,and expansion of brain regions to be sampled for neuropathological evaluation.

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.

Glycogen synthase kinase-3β (GSK-3β) is an important serine/threonine kinase that implicates in multiple cellular processes and links with the neurodegenerative diseases including Alzheimer’s disease (AD). In this study, structure-based virtual screening was performed to search database for compounds targeting GSK-3β from Enamine’s screening collection. Of the top-ranked compounds, 7 primary hits underwent a luminescent kinase assay and a cell assay using human neuroblastoma SH-SY5Y cells expressing Tau repeat domain (TauRD) with pro-aggregant mutation ΔK280. In the kinase assay for these 7 compounds, residual GSK-3β activities ranged from 36.1% to 90.0% were detected at the IC50 of SB-216763. In the cell assay, only compounds VB-030 and VB-037 reduced Tau aggregation in SH-SY5Y cells expressing ΔK280 TauRD-DsRed folding reporter. In SH-SY5Y cells expressing ΔK280 TauRD, neither VB-030 nor VB-037 increased expression of GSK-3α Ser21 or GSK-3β Ser9. Among extracellular signal-regulated kinase (ERK), AKT serine/threonine kinase 1 (AKT), mitogen-activated protein kinase 14 (P38) and mitogenactivated protein kinase 8 (JNK) which modulate Tau phosphorylation, VB-037 attenuated active phosphorylation of P38 Thr180/ Tyr182, whereas VB-030 had no effect on the phosphorylation status of ERK, AKT, P38 or JNK. However, both VB-030 and VB-037 reduced endogenous Tau phosphorylation at Ser202, Thr231, Ser396 and Ser404 in neuronally differentiated SH-SY5Y expressing ΔK280 TauRD. In addition, VB-030 and VB-037 further improved neuronal survival and/or neurite length and branch in mouse hippocampal primary culture under Tau cytotoxicity. Overall, through inhibiting GSK-3β kinase activity and/or p-P38 (Thr180/Tyr182), both compounds may serve as promising candidates to reduce Tau aggregation/cytotoxicity for AD treatment.

Objective: This study aims to investigate the associations between genetic variations of pyroptosis pathway related key genes and adverse events (AEs) of postoperative chemoradiotherapy (CRT) in patients with rectal cancer. Methods: DNA was extracted from the peripheral blood which was collected from 347 patients before CRT. Sequenom MassARRAY was used to detect the genotypes of 43 haplotype-tagging single nucleotide polymorphisms (htSNPs) in eight pyroptosis genes, including absent in melanoma 2 (AIM2), caspase-1 (CASP1), caspase-4(CASP4), caspase-5 (CASP5), caspase-11 (CASP11), gasdermin D (GSDMD), gasdermin E (GSDME) and NLR family pyrin domain containing 3 (NLRP3). The associations between 43 htSNPs and AEs were evaluated by the odd ratios (ORs) and 95% confidence intervals (CIs) by unconditional logistic regression models, adjusted for sex, age, clinical stage, tumor grade, Karnofsky performance status (KPS), surgical procedure, and tumor location. Results: Among the 347 patients with rectal cancer underwent concurrent CRT with capecitabine after surgery, a total of 101(29.1%) occurred grade ≥ 2 leukopenia. rs11226565 (OR=0.41, 95% CI: 0.21-0.79, P=0.008), rs579408(OR=1.54, 95% CI: 1.03-2.29, P=0.034) and rs543923 (OR=0.63, 95% CI: 0.41-0.98, P=0.040) were significantly associated with the occurrence of grade ≥ 2 leukopenia. One hundred and fifty-six (45.0%) had grade ≥ 2 diarrhea, two SNPs were significantly associated with the occurrence of grade ≥ diarrhea, including CASP11 rs10880868 (OR=0.55, 95% CI: 0.33-0.91, P=0.020) and GSDME rs2954558 (OR=1.52, 95% CI: 1.01-2.31, P=0.050). In addition, sixty-six cases (19.0%) developed grade ≥2 dermatitis, three SNPs that significantly associated with the risk of grade ≥2 dermatitis included GSDME rs2237314 (OR=0.36, 95% CI: 0.16-0.83, P=0.017), GSDME rs12540919 (OR=0.52, 95% CI: 0.27-0.99, P=0.045) and NLRP3 rs3806268 (OR=1.51, 95% CI: 1.03-2.22, P=0.037). There was no significant difference in the association between other genetic variations and AEs of rectal cancer patients (all P>0.05). Surgical procedure and tumor location had great impacts on the occurrence of grade ≥2 diarrhea and dermatitis (all P<0.01). Conclusion: The genetic variants of CASP4, CASP11, GSDME and NLRP3 are associated with the occurrence of AEs in patients with rectal cancer who received postoperative CRT, suggesting they may be potential genetic markers in predicting the grade of AEs to achieve individualized treatment of rectal cancer.
Humans ; Pyroptosis ; NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Gasdermins ; Chemoradiotherapy/adverse effects* ; Rectal Neoplasms/surgery* ; Caspases/metabolism* ; Diarrhea/chemically induced* ; Leukopenia/genetics* ; Genetic Variation ; Dermatitis