慢性泪囊炎患者应用鼻内镜下鼻腔泪囊造口术的临床效果及对安全性的影响

目的探讨慢性泪囊炎患者应用鼻内镜下鼻腔泪囊造口术的临床效果及安全性。方法选取2018年1月至2019年12月因慢性泪囊炎于本院接受治疗的46例患者为研究对象,随机分为研究组与对照组,各23例。对照组接受泪囊鼻腔造口治疗,研究组取鼻内镜下鼻腔泪囊造口术治疗,比较两组临床效果、手术指标以及并发症发生情况。结果研究组治疗总有效率高于对照组(P<0.05);研究组术中出血量少于对照组,手术及住院时间均短于对照组(P<0.05);研究组并发症总发生率低于对照组(P<0.05)。结论慢性泪囊炎患者采用鼻内镜下鼻腔泪囊造口术治疗效果显著,并发症较少,安全性较高,值得临床推广应用。     

Pharmacokinetics and Early Tumor Response to Conventional Transarterial Chemoembolization with Sorafenib and Doxorubicin in a VX2 Rabbit Tumor Model

PurposeTo investigate the pharmacokinetics (PK) and early effects of conventional transarterial chemoembolization (TACE) using sorafenib and doxorubicin on tumor necrosis, hypoxia markers, and angiogenesis in a rabbit VX2 liver tumor model.Materials and MethodsVX2 tumor-laden New Zealand White rabbits (N = 16) were divided into 2 groups: 1 group was treated with hepatic arterial administration of ethiodized oil and doxorubicin emulsion (DOX-TACE), and the other group was treated with ethiodized oil, sorafenib, and doxorubicin emulsion (SORA-DOX-TACE). Animals were killed within 3 days of the procedure. Levels of sorafenib and doxorubicin were measured in blood, tumor, and adjacent liver using mass spectrometry. Tumor necrosis was determined by histopathological examination. Intratumoral hypoxia-inducible factor (HIF) 1α, vascular endothelial growth factor (VEGF), and microvessel density (MVD) were determined by immunohistochemistry.ResultsThe median intratumoral concentration of sorafenib in the SORA-DOX-TACE group was 17.7 μg/mL (interquartile range [IQR], 7.42–33.5 μg/mL), and its maximal plasma concentration (C_max) was 0.164 μg/mL (IQR, 0.0798–0.528 μg/mL). The intratumoral concentration and C_max of doxorubicin were similar between the groups: 4.08 μg/mL (IQR, 3.18–4.79 μg/mL) and 0.677 μg/mL (IQR, 0.315–1.23 μg/mL), respectively, in the DOX-TACE group and 1.68 μg/mL (IQR, 0.795–4.08 μg/mL) and 0.298 μg/mL (IQR, 0.241–0.64 μg/mL), respectively, in the SORA-DOX-TACE group. HIF-1α expression was increased in the SORA-DOX-TACE group than in the DOX-TACE group. Tumor volume, tumor necrosis, VEGF expression, and MVD were similar between the 2 groups.ConclusionsThe addition of sorafenib to DOX-TACE delivered to VX2 liver tumors resulted in high intratumoral and low systemic concentrations of sorafenib without altering the PK of doxorubicin.     

微创治疗方法在输尿管上段结石中应用进展

输尿管上段结石为临床中常见泌尿系结石类型之一,如不能及时诊治,可引起重度积水、泌尿系感染,甚至脓毒血症,对患者肾功能、健康造成严重影响。随着微创治疗技术在泌尿系结石中应用,微创治疗方法能降低对患者造成治疗性创伤,降低相关并发症发生率,促进患者康复,了解临床中微创治疗输尿管上段结石方法,对临床中合理治疗输尿管上段结石有重要价值。     

Comparing Radiomics features of tumour and healthy liver tissue in a limited CT dataset: A machine learning study

IntroductionLiver cancer lesions on Computed Tomography (CT) withholds a great amount of data, which is not visible to the radiologists and radiographer. Radiomics features can be extracted from the lesions and used to train Machine Learning (ML) algorithms to predict between tumour and liver tissue. The purpose of this study was to investigate and classify Radiomics features extracted from liver tumours and normal liver tissue in a limited CT dataset.MethodsThe Liver Tumour Segmentation Benchmark (LiTS) dataset consisting of 131 CT scans of the liver with segmentations of tumour tissue and healthy liver was used to extract Radiomic features. Extracted Radiomic features included size, shape, and location extracted with morphological and statistical techniques according to the International Symposium on Biomedical Imaging manual. Relevant features was selected with chi2 correlation and principal component analysis (PCA) with tumour and healthy liver tissue as outcome according to a consensus between three experienced radiologists. Logistic regression, random forest and support vector machine was used to train and validate the dataset with a 10-fold cross-validation method and the Grid Search as hyper-parameter tuning. Performance was evaluated with sensitivity, specificity and accuracy.ResultsThe performance of the ML algorithms achieved sensitivities, specificities and accuracy ranging from 96.30% (95% CI: 81.03%–99.91%) to 100.00% (95% CI: 86.77%–100.00%), 91.30% (95% CI: 71.96%–98.93%) to 100.00% (95% CI: 83.89%–100.00%)and 94.00% (95% CI: 83.45%–98.75%) to 100.00% (95% CI: 92.45%–100.00%), respectively.ConclusionML algorithms classifies Radiomics features extracted from healthy liver and tumour tissue with perfect accuracy. The Radiomics signature allows for a prognostic biomarker for hepatic tumour screening on liver CT.Implications for practiceDifferentiation between tumour and liver tissue with Radiomics ML algorithms have the potential to increase the diagnostic accuracy, assist in the decision-making of supplementary multiphasic enhanced medical imaging, as well as for developing novel prognostic biomarkers for liver cancer patients.     

EEG functional connectivity contributes to outcome prediction of postanoxic coma

ObjectiveTo investigate the additional value of EEG functional connectivity features, in addition to non-coupling EEG features, for outcome prediction of comatose patients after cardiac arrest.MethodsProspective, multicenter cohort study. Coherence, phase locking value, and mutual information were calculated in 19-channel EEGs at 12 h, 24 h and 48 h after cardiac arrest. Three sets of machine learning classification models were trained and validated with functional connectivity, EEG non-coupling features, and a combination of these. Neurological outcome was assessed at six months and categorized as “good” (Cerebral Performance Category [CPC] 1–2) or “poor” (CPC 3–5).ResultsWe included 594 patients (46% good outcome). A sensitivity of 51% (95% CI: 34–56%) at 100% specificity in predicting poor outcome was achieved by the best functional connectivity-based classifier at 12 h after cardiac arrest, while the best non-coupling-based model reached a sensitivity of 32% (0–54%) at 100% specificity using data at 12 h and 48 h. Combination of both sets of features achieved a sensitivity of 73% (50–77%) at 100% specificity.ConclusionFunctional connectivity measures improve EEG based prediction models for poor outcome of postanoxic coma.SignificanceFunctional connectivity features derived from early EEG hold potential to improve outcome prediction of coma after cardiac arrest.     

Selective Laser Melting of Al-Based Matrix Composites with Al2O3 Reinforcement: Features and Advantages

Aluminum matrix composites (AMC) are of great interest and importance as high-performance materials with enhanced mechanical properties. Al₂O₃ is a commonly used reinforcement in AMCs fabricated by means of various technological methods, including casting and sintering. Selective laser melting (SLM) is a suitable modern method of the fabrication of net-shape fully dense parts from AMC with alumina. The main results, achievements, and difficulties of SLM applied to AMCs with alumina are discussed in this review and compared with conventional methods. It was shown that the initial powder preparation, namely the particle size distribution, sphericity, and thorough mixing, affected the final microstructure and properties of SLMed materials drastically. The distribution of reinforcing particles tends to consolidate the near-melting pool-edges process because of pushing by the liquid–solid interface during the solidification process that is a common problem of various fabrication methods. The achievement of an homogeneous distribution was shown to be possible through both the thorough mixing of the initial powders and the precise optimization of SLM parameters. The strength of the AMCs fabricated by the SLM method was relatively low compared with materials produced by conventional methods, while for superior relative densities of more than 99%, hardness and tribological properties were obtained, making SLM a promising method for the Al-based matrix composites with Al₂O₃.     

Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson's disease

ObjectivesDespite its use in determining nigrostriatal degeneration, the lack of a consistent interpretation of nigrosome 1 susceptibility map-weighted imaging (SMwI) limits its generalized applicability. To implement and evaluate a diagnostic algorithm based on convolutional neural networks for interpreting nigrosome 1 SMwI for determining nigrostriatal degeneration in idiopathic Parkinson's disease (IPD).MethodsIn this retrospective study, we enrolled 267 IPD patients and 160 control subjects (125 patients with drug-induced parkinsonism and 35 healthy subjects) at our institute, and 24 IPD patients and 27 control subjects at three other institutes on approval of the local institutional review boards. Dopamine transporter imaging served as the reference standard for the presence or absence of abnormalities of nigrosome 1 on SMwI. Diagnostic performance was compared between visual assessment by an experienced neuroradiologist and the developed deep learning-based diagnostic algorithm in both internal and external datasets using a bootstrapping method with 10000 re-samples by the “pROC” package of R (version 1.16.2).ResultsThe area under the receiver operating characteristics curve (AUC) (95% confidence interval [CI]) per participant by the bootstrap method was not significantly different between visual assessment and the deep learning-based algorithm (internal validation, .9622 [0.8912–1.0000] versus 0.9534 [0.8779-0.9956], P = .1511; external validation, 0.9367 [0.8843-0.9802] versus 0.9208 [0.8634-0.9693], P = .6267), indicative of a comparable performance to visual assessment.ConclusionsOur deep learning-based algorithm for assessing abnormalities of nigrosome 1 on SMwI was found to have a comparable performance to that of an experienced neuroradiologist.