Protection against corneal hyperosmolarity with soft-contact-lens wear

Hyperosmotic tear stimulates human corneal nerve endings, activates ocular immune response, and elicits dry-eye symptoms. A soft contact lens (SCL) covers the cornea preventing it from experiencing direct tear evaporation and the resulting blink-periodic salinity increases. For the cornea to experience hyperosmolarity due to tear evaporation, salt must transport across the SCL to the post-lens tear film (PoLTF) bathing the cornea. Consequently, limited salt transport across a SCL potentially protects the ocular surface from hyperosmotic tear. In addition, despite lens-wear discomfort sharing common sensations to dry eye, no correlation is available between measured tear hyperosmolarity and SCL-wear discomfort. Lack of documentation is likely because clinical measurements of tear osmolarity during lens wear do not interrogate the tear osmolarity of the PoLTF that actually overlays the cornea. Rather, tear osmolarity is clinically measured in the tear meniscus. For the first time, we mathematically quantify tear osmolarity in the PoLTF and show that it differs significantly from the clinically measured tear-meniscus osmolarity. We show further that aqueous-deficient dry eye and evaporative dry eye both exacerbate the hyperosmolarity of the PoLTF. Nevertheless, depending on lens salt-transport properties (i.e., diffusivity, partition coefficient, and thickness), a SCL can indeed protect against corneal hyperosmolarity by reducing PoLTF salinity to below that of the ocular surface during no-lens wear. Importantly, PoLTF osmolarity for dry-eye patients can be reduced to that of normal eyes with no-lens wear provided that the lens exhibits a low lens-salt diffusivity. Infrequent blinking increases PoLTF osmolarity consistent with lens-wear discomfort. Judicious design of SCL material salt-transport properties can ameliorate corneal hyperosmolarity. Our results confirm the importance of PoLTF osmolarity during SCL wear and indicate a possible relation between PoLTF osmolarity and contact-lens discomfort.     

后外侧结构重建在新鲜股骨颈骨折人工股骨头置换术中的应用

目的探讨后外侧结构重建对后外侧入路人工股骨头置换术术后早期关节脱位的影响。方法选取2016年9月至2017年8月于我院行后外侧入路初次人工股骨头置换术的股骨颈骨折患者60例,根据术中是否修补关节囊及外旋肌群分为重建组(33例:舌形切开关节囊,术中将关节囊及外旋肌群原位缝合在大转子后方及臀中肌肌腱附着处)和对照组(27例:切除关节囊后,术中未进行外旋肌群修复重建)。比较两组的手术情况及术后近期关节功能情况。结果重建组的手术时间为(45.0±15.3) min,长于对照组的(35.0±12.4) min (P <0.05)。重建组术腔引流量为(200.0±80.0) m L,少于对照组的(420.0±120.6) m L (P <0.05)。重建组的早期脱位率为0.000%(0例),与对照组的7.407%(2例)比较无统计学差异(P>0.05)。重建组术后Harris评分为(92.0±3.4)分,高于对照组的(88.2±5.0)分(P <0.05)。结论在后外侧入路人工股骨头置换过程中行后外侧结构重建能够有效减少术腔引流量,提高髋关节Harris评分,对维持髋关节软组织平衡具有重要意义。     

Machine learning models for Parkinson’s disease detection and stage classification based on spatial-temporal gait parameters

BackgroundParkinson’s disease (PD) is a chronic and progressive neurodegenerative disease with no cure, presenting a challenging diagnosis and management. However, despite a significant number of criteria and guidelines have been proposed to improve the diagnosis of PD and to determine the PD stage, the gold standard for diagnosis and symptoms monitoring of PD is still mainly based on clinical evaluation, which includes several subjective factors. The use of machine learning (ML) algorithms in spatial-temporal gait parameters is an interesting advance with easy interpretation and objective factors that may assist in PD diagnostic and follow up.Research questionThis article studies ML algorithms for: i) distinguish people with PD vs. matched-healthy individuals; and ii) to discriminate PD stages, based on selected spatial-temporal parameters, including variability and asymmetry.MethodsGait data acquired from 63 people with PD with different levels of PD motor symptoms severity, and 63 matched-control group individuals, during self-selected walking speed, was study in the experiments.ResultsIn the PD diagnosis, a classification accuracy of 84.6 %, with a precision of 0.923 and a recall of 0.800, was achieved by the Naïve Bayes algorithm. We found four significant gait features in PD diagnosis: step length, velocity and width, and step width variability. As to the PD stage identification, the Random Forest outperformed the other studied ML algorithms, by reaching an Area Under the ROC curve of 0.786. We found two relevant gait features in identifying the PD stage: stride width variability and step double support time variability.SignificanceThe results showed that the studied ML algorithms have potential both to PD diagnosis and stage identification by analysing gait parameters.     

Registries,Databases and Repositories for Developing Artificial Intelligence in Cancer Care

Modern artificial intelligence techniques have solved some previously intractable problems and produced impressive results in selected medical domains. One of their drawbacks is that they often need very large amounts of data. Pre-existing datasets in the form of national cancer registries, image/genetic depositories and clinical datasets already exist and have been used for research. In theory, the combination of healthcare Big Data with modern, data-hungry artificial intelligence techniques should offer significant opportunities for artificial intelligence development, but this has not yet happened. Here we discuss some of the structural reasons for this, barriers preventing artificial intelligence from making full use of existing datasets, and make suggestions as to enable progress. To do this, we use the framework of the 6Vs of Big Data and the FAIR criteria for data sharing and availability (Findability, Accessibility, Interoperability, and Reuse). We share our experience in navigating these barriers through The Brain Tumour Data Accelerator, a Brain Tumour Charity-supported initiative to integrate fragmented patient data into an enriched dataset. We conclude with some comments as to the limits of such approaches.