Comparison between tension band and cerclage with X-Plate and lag screws in treatment of comminuted patellar fractures

BackgroundComminuted patellar fractures are not rare, and the ideal treatment method remains controversial. The present study was conducted to evaluate effects and compare complications of two different methods used to treat comminuted patellar fractures.MethodsFrom March 2010 to August 2016, 102 cases of 34-C2 or 34-C3 comminuted patellar fractures were treated at our hospital, wherein patients received two different treatments: titanium cable tension band with cerclage method (group A) and intrafragmentary screws with X-shaped plating technique (group B). At follow-ups, articular step-off, range of motion (ROM), Lysholm scores, time of union, and complications were recorded and analyzed. Radiographic and clinical data as well as rate of complications were statistically analyzed.ResultsIn total, 87 patients were included in the final analysis (n = 47 in group A and n = 40 in group B). No significant differences were noted in terms of cost of implant, age, gender, rate of 34-C3 fractures, rate of layered inferior pole fractures, postoperative articular step-off and union time. At 2-year follow-up, average Lysholm scores, ROM and rate of complications were (89.0 ± 4.5), (122°±12°) and (27.7%) in group A and (90.2 ± 3.9), (124°±11°) and (17.5%) in group B, respectively, with no significant differences (p > 0.05). The mean time of surgery in group B was shorter than that in group A with significant difference (p < 0.05).ConclusionsTreatment using the intrafragmentary screws and plate method for amenable comminuted patellar fractures achieved similar complication rate and favorable functional outcomes at the 2-year follow-up, which was comparable to the titanium cable tension band with cerclage method. Thus, the intrafragmentary screws and plate method is effective, safe and convenient for 34-C2/C3 comminuted patellar fractures, especially appropriate for patients with layered fragments.     

Hepatotoxicity of cantharidin is associated with the altered bile acid metabolism

Cantharidin (CTD) is an effective antitumor agent. However, it exhibits significant hepatotoxicity, the mechanism of which remains unclear. In this study, biochemical and histopathological analyses complemented with ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS)-based targeted metabolomic analysis of bile acids (BAs) were employed to investigate CTD-induced hepatotoxicity in rats. Sixteen male and female Sprague–Dawley rats were randomly divided into two groups: control and CTD (1.0 mg/kg) groups. Serum and liver samples were collected after 28 days of intervention. Biochemical, histopathological, and BA metabolomic analyses were performed for all samples. Further, the key biomarkers of CTD-induced hepatotoxicity were identified via multivariate and metabolic pathway analyses. In addition, metabolite–gene–enzyme network and Kyoto Encyclopedia of Genes and Genomes pathway analyses were used to identify the signaling pathways related to CTD-induced hepatotoxicity. The results revealed significantly increased levels of biochemical indices (alanine aminotransferase, aspartate aminotransferase, and total bile acid). Histopathological analysis revealed that the hepatocytes were damaged. Further, 20 endogenous BAs were quantitated via UHPLC-MS/MS, and multivariate and metabolic pathway analyses of BAs revealed that hyocholic acid, cholic acid, and chenodeoxycholic acid were the key biomarkers of CTD-induced hepatotoxicity. Meanwhile, primary and secondary BA biosynthesis and taurine and hypotaurine metabolism were found to be associated with the mechanism by which CTD induced hepatotoxicity in rats. This study provides useful insights for research on the mechanism of CTD-induced hepatotoxicity.     

中国抗癌协会神经内分泌肿瘤诊治指南（2022年版）

神经内分泌肿瘤（neuroendocrine neoplasm,NEN）是一类起源于肽能神经元和神经内分泌细胞,具有神经内分泌分化并表达神经内分泌标志物的少见肿瘤,可发生于全身各处,以肺及胃肠胰NEN（gastroenteropancreatic neuroendocrine neoplasm, GEP-NEN）最常见。国内外研究数据均提示,NEN的发病率在不断上升。美国流行病学调查结果显示,与其他类型肿瘤相比,NEN的发病率上升趋势更为显著。中国抗癌协会神经内分泌肿瘤专委会在现有循证医学证据基础上,结合已有国内外指南和共识,制订了首版中国抗癌协会神经内分泌肿瘤诊治指南,为临床工作者提供参考。     

A Stacked Generalization of 3D Orthogonal Deep Learning Convolutional Neural Networks for Improved Detection of White Matter Hyperintensities in 3D FLAIR Images

BACKGROUND AND PURPOSE:Accurate and reliable detection of white matter hyperintensities and their volume quantification can provide valuable clinical information to assess neurologic disease progression. In this work, a stacked generalization ensemble of orthogonal 3D convolutional neural networks, StackGen-Net, is explored for improving automated detection of white matter hyperintensities in 3D T2-FLAIR images.MATERIALS AND METHODS:Individual convolutional neural networks in StackGen-Net were trained on 2.5D patches from orthogonal reformatting of 3D-FLAIR (n = 21) to yield white matter hyperintensity posteriors. A meta convolutional neural network was trained to learn the functional mapping from orthogonal white matter hyperintensity posteriors to the final white matter hyperintensity prediction. The impact of training data and architecture choices on white matter hyperintensity segmentation performance was systematically evaluated on a test cohort (n = 9). The segmentation performance of StackGen-Net was compared with state-of-the-art convolutional neural network techniques on an independent test cohort from the Alzheimer’s Disease Neuroimaging Initiative-3 (n = 20).RESULTS:StackGen-Net outperformed individual convolutional neural networks in the ensemble and their combination using averaging or majority voting. In a comparison with state-of-the-art white matter hyperintensity segmentation techniques, StackGen-Net achieved a significantly higher Dice score (0.76 [SD, 0.08], F1-lesion (0.74 [SD, 0.13]), and area under precision-recall curve (0.84 [SD, 0.09]), and the lowest absolute volume difference (13.3% [SD, 9.1%]). StackGen-Net performance in Dice scores (median = 0.74) did not significantly differ (P = .22) from interobserver (median = 0.73) variability between 2 experienced neuroradiologists. We found no significant difference (P = .15) in white matter hyperintensity lesion volumes from StackGen-Net predictions and ground truth annotations.CONCLUSIONS:A stacked generalization of convolutional neural networks, utilizing multiplanar lesion information using 2.5D spatial context, greatly improved the segmentation performance of StackGen-Net compared with traditional ensemble techniques and some state-of-the-art deep learning models for 3D-FLAIR.

White matter hyperintensities (WMHs) correspond to pathologic features of axonal degeneration, demyelination, and gliosis observed within cerebral white matter.¹ Clinically, the extent of WMHs in the brain has been associated with cognitive impairment, Alzheimer’s disease and vascular dementia, and increased risk of stroke.^2,3 The detection and quantification of WMH volumes to monitor lesion burden evolution and its correlation with clinical outcomes have been of interest in clinical research.^4,5 Although the extent of WMHs can be visually scored,⁶ the categoric nature of such scoring systems makes quantitative evaluation of disease progression difficult. Manually segmenting WMHs is tedious, prone to inter- and intraobserver variability, and is, in most cases, impractical. Thus, there is an increased interest in developing fast, accurate, and reliable computer-aided automated techniques for WMH segmentation.Convolutional neural network (CNN)-based approaches have been successful in several semantic segmentation tasks in medical imaging.⁷ Recent works have proposed using deep learning–based methods for segmenting WMHs using 2D-FLAIR images.^8-11 More recently, a WMH segmentation challenge¹² was also organized (http://wmh.isi.uu.nl/) to facilitate comparison of automated segmentation of WMHs of presumed vascular origin in 2D multislice T2-FLAIR images. Architectures that used an ensemble of separately trained CNNs showed promising results in this challenge, with 3 of the top 5 winners using ensemble-based techniques.¹²Conventional 2D-FLAIR images are typically acquired with thick slices (3–4 mm) and possible slice gaps. Partial volume effects from a thick slice are likely to affect the detection of smaller lesions, both in-plane and out-of-plane. 3D-FLAIR images, with isotropic resolution, have been shown to achieve higher resolution and contrast-to-noise ratio¹³ and have shown promising results in MS lesion detection using 3D CNNs.¹⁴ Additionally, the isotropic resolution enables viewing and evaluation of the images in multiple planes. This multiplanar reformatting of 3D-FLAIR without the use of interpolating kernels is only possible due to the isotropic nature of the acquisition. Network architectures that use information from the 3 orthogonal views have been explored in recent works for CNN-based segmentation of 3D MR imaging data.¹⁵ The use of data from multiple planes allows more spatial context during training without the computational burden associated with full 3D training.¹⁶ The use of 3 orthogonal views simultaneously mirrors how humans approach this segmentation task.Ensembles of CNNs have been shown to average away the variances in the solution and the choice of model- and configuration-specific behaviors of CNNs.¹⁷ Traditionally, the solutions from these separately trained CNNs are combined by averaging or using a majority consensus. In this work, we propose the use of a stacked generalization framework (StackGen-Net) for combining multiplanar lesion information from 3D CNN ensembles to improve the detection of WMH lesions in 3D-FLAIR. A stacked generalization¹⁸ framework learns to combine solutions from individual CNNs in the ensemble. We systematically evaluated the performance of this framework and compared it with traditional ensemble techniques, such as averaging or majority voting, and state-of-the-art deep learning techniques.     

改良三点式重睑成形术的临床应用研究

目的:探讨改良三点式重睑术的临床应用疗效。方法:2012年6月-2019年6月,共165例单睑患者采用了改良三点式重睑术,沿术前标记线将三点处皮肤切开,剪刀将切口下唇的眼轮匝肌适当去除,同时将切口与切口在皮下层打通,剪除切口与切口之间的眼轮匝肌,6-0可吸收线挂睑板前筋膜或提上睑肌腱膜及切口下唇皮下组织缝合,三点切口各缝1针。再用6-0单丝尼龙线按照常规重睑线缝合方法挂切口下唇皮肤、睑板前筋膜或提上睑肌腱膜及切口上唇皮肤缝合打结,三点切口各缝1针。伴内眦赘皮者同时行内眦赘皮矫正术。结果:152例患者获得随访,随访患者大部分获得了比较满意的重睑,睁眼重睑流畅、自然,闭眼刀口痕迹不明显。2例患者出现内侧重睑线变浅,1例患者出现外侧重睑线变浅,所有患者均未出现重睑消失。5例患者双侧重睑线有轻度不对称。患者总体满意率为94.7%(144/152)。结论:改良三点式重睑术具有创伤小、并发症少、效果逼真、不易脱落、手术痕迹不明显等优点,值得推广应用。     

Myeloid-derived suppressor cell (MDSC) key genes analysis in rat anti-CD28-induced immune tolerance kidney transplantation

BackgroundIn the field of transplantation, inducing immune tolerance in recipients is of great importance. Blocking co-stimulatory molecule using anti-CD28 antibody could induce tolerance in a rat kidney transplantation model. Myeloid-derived suppressor cells (MDSCs) reveals strong immune suppressive abilities in kidney transplantation. Here we analyzed key genes of MDSCs leading to transplant tolerance in this model.MethodsMicroarray data of rat gene expression profiles under accession number {"type":"entrez-geo","attrs":{"text":"GSE28545","term_id":"28545"}}GSE28545 in the Gene Expression Omnibus (GEO) database were analyzed. Running the LIMMA package in R language, the differentially expressed genes (DEGs) were found. Enrichment analysis of the DEGs was conducted in the Database for Annotation, Visualization and Integrated Discovery (DAVID) database to explore gene ontology (GO) annotation and their Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Their protein-protein interactions (PPIs) were provided by STRING database and was visualized in Cytoscape. Hub genes were carried out by CytoHubba.ResultsThree hundred and thirty-eight DEGs were exported, including 27 upregulated and 311 downregulated genes. The functions and KEGG pathways of the DEGs were assessed and the PPI network was constructed based on the string interactions of the DEGs. The network was visualized in Cytoscape; the entire PPI network consisted of 192 nodes and 469 edges. Zap70, Cdc42, Stat1, Stat4, Ccl5 and Cxcr3 were among the hub genes.ConclusionsThese key genes, corresponding proteins and their functions may provide valuable background for both basic and clinical research and could be the direction of future studies in immune tolerance, especially those examining immunocyte-induced tolerance.