The role of dark adaptation in understanding early AMD

The main aim of the paper is to discuss current knowledge on how Age Related Macular Degeneration (AMD) affects Dark Adaptation (DA). The paper is divided into three parts. Firstly, we outline some of the molecular mechanisms that control DA. Secondly, we review the psychophysical issues and the corresponding analytical techniques. Finally, we characterise the link between slowed DA and the morphological abnormalities in early AMD.Historically, DA has been regarded as too cumbersome for widespread clinical application. Yet the technique is extremely useful; it is widely accepted that the psychophysically obtained slope of the second rod-mediated phase of the dark adaptation function is an accurate assay of photoreceptor pigment regeneration kinetics. Technological developments have prompted new ways of generating the DA curve, but analytical problems remain. A simple potential solution to these, based on the application of a novel fast mathematical algorithm, is presented. This allows the calculation of the parameters of the DA curve in real time.Improving current management of AMD will depend on identifying a satisfactory endpoint for evaluating future therapeutic strategies. This must be implemented before the onset of severe disease. Morphological changes progress too slowly to act as a satisfactory endpoint for new therapies whereas functional changes, such as those seen in DA, may have more potential in this regard. It is important to recognise, however, that the functional changes are not confined to rods and that building a mathematical model of the DA curve enables the separation of rod and cone dysfunction and allows more versatility in terms of the range of disease severity that can be monitored. Examples are presented that show how analysing the DA curve into its constituent components can improve our understanding of the morphological changes in early AMD.     

新化合物Elixir-X对肝切及辐射所致衰老小鼠的抗衰老作用

目的 探讨蜂巢提取物Elixir-X对自然衰老及辐射衰老小鼠的影响，为Elixir-X的进一步研发提供药理学基础。方法 采用小鼠肝脏切除术建立自然衰老小鼠模型，将其分为空白对照组、Elixir-X组、白藜芦醇组及模型组，连续给药7 d后，检测小鼠70%肝脏切除后的再生过程中，衰老细胞、肝脏功能恢复与肝脏再生的变化，观察Elixir-X对自然衰老的影响；采用60CO辐射法建立辐射衰老小鼠模型，将其分为模型组、空白对照组、白藜芦醇组、Elixir-X高、低剂量组，通过检测小鼠肠隐窝干细胞再生、小肠绒毛结构以及衰老细胞的变化，对比不同剂量Elixir-X对辐射后衰老的影响。结果 在肝脏切除术的小鼠模型中，Elixir-X组的肝功能水平明显低于模型组的肝脏自然再生小鼠，显著促进了肝功能恢复（P＜0.05）；同时与模型组相比，Elixir-X组清除了肝脏切除后再生过程中产生的衰老细胞（P＜0.05），Ki-67的着色斑块明显增加，促进肝脏的再生，且肝血窦的扫描电镜影像结果中Elixir-X组肝血窦再生加快。60CO辐照小鼠模型中，与模型组相比，Elixir-X组有效促进辐照后肝脏功能恢复（P＜0.05），Elixir-X高剂量组体重也恢复到正常水平，与空白对照组无显著差异；与模型组相比，Elixir-X低剂量组小肠的β-Gal着色细胞减少，Elixir-X高剂量组甚至能更有效地减少衰老细胞（P＜0.05）；小肠HE染色的结果中，Elixir-X组小肠绒毛结构得到恢复；Ki-67的IHC染色中，与模型组相比，Elixir-X组着色板块更多，且着色区域都在肠隐窝干细胞位置。结论 Elixir-X能有效清除肝脏切除术引起的自然衰老与辐射引起的损伤性衰老细胞，对衰老具有显著地抑制作用。     

Assessment of a novel deep learning-based marker-less motion capture system for gait study

BackgroundMarker-less systems based on digital video cameras and deep learning for gait analysis could have a deep impact in clinical routine. A recently developed system has shown promising results in terms of joint center position but has not been yet evaluated in terms of gait outcomes.Research questionHow does this novel marker-less system compare to a marker-based reference system in terms of clinically relevant gait parameters?MethodsThe deep learning method behind the developed marker-less system was trained on a dedicated dataset consisting of forty-one asymptomatic and pathological subjects each performing ten walking trials. The system could estimate the three-dimensional position of seventeen joint centers or keypoints (e.g., neck, shoulders, hip, knee, and ankles). We evaluated the marker-less system against a marker-based system in terms of differences in joint position (Euclidean distance), detection of gait events (e.g., heel strike and toe-off), spatiotemporal parameters (e.g., step length, time), kinematic parameters (e.g., hip and knee extension-flexion), and inter-trial reliability for kinematic parameters.ResultsThe marker-less system was able to estimate the three-dimensional position of joint centers with a mean difference of 13.1 mm (SD = 10.2 mm). 99% of the estimated gait events were estimated within 10 ms of the corresponding reference values. Estimated spatiotemporal parameters showed zero bias. The mean and standard deviation of the differences of the estimated kinematic parameters varied by parameter (for example, the mean and standard deviation for knee extension flexion angle were −3.0° and 2.7°). Inter-trial reliability of the measured parameters was similar to that of the marker-based references.SignificanceThe developed marker-less system can measure the spatiotemporal parameters within the range of the minimum detectable changes obtained using the marker-based reference system. Moreover, except for hip extension flexion, the system showed promising results in terms of several kinematic parameters.     

Machine-learning models for activity class prediction: A comparative study of feature selection and classification algorithms

PurposeMachine-learning (ML) approaches have been repeatedly coupled with raw accelerometry to classify physical activity classes, but the features required to optimize their predictive performance are still unknown. Our aim was to identify appropriate combination of feature subsets and prediction algorithms for activity class prediction from hip-based raw acceleration data.MethodsThe hip-based raw acceleration data collected from 27 participants was split into training (70 %) and validation (30 %) subsets. A total of 206 time- (TD) and frequencydomain (FD) features were extracted from 6-second non-overlapping windows of the signal. Feature selection was done using seven filter-based, two wrapper-based, and one embedded algorithm, and classification was performed with artificial neural network (ANN), support vector machine (SVM), and random forest (RF). For every combination between the feature selection method and the classifiers, the most appropriate feature subsets were found and used for model training within the training set. These models were then validated with the left-out validation set.ResultsThe appropriate number of features for the ANN, SVM, and RF ranged from 20 to 45. Overall, the accuracy of all the three classifiers was higher when trained with feature subsets generated using filter-based methods compared with when they were trained with wrapper-based methods (range: 78.1 %–88 % vs. 66 %–83.5 %). TD features that reflect how signals vary around the mean, how they differ with one another, and how much and how often they change were more frequently selected via the feature selection methods.ConclusionsA subset of TD features from raw accelerometry could be sufficient for ML-based activity classification if properly selected from different axes.     

Apelin-13 inhibits apoptosis and excessive autophagy in cerebral ischemia/reperfusion injury

Apelin-13 is a novel endogenous ligand for an angiotensin-like orphan G-protein coupled receptor, and it may be neuroprotective against cerebral ischemia injury. However, the precise mechanisms of the effects of apelin-13 remain to be elucidated. To investigate the effects of apelin-13 on apoptosis and autophagy in models of cerebral ischemia/reperfusion injury, a rat model was established by middle cerebral artery occlusion. Apelin-13(50 μg/kg) was injected into the right ventricle as a treatment. In addition, an SH-SY5 Y cell model was established by oxygen-glucose deprivation/reperfusion, with cells first cultured in sugar-free medium with 95% N2 and 5% CO2 for 4 hours and then cultured in a normal environment with sugar-containing medium for 5 hours. This SH-SY5 Y cell model was treated with 10–7 M apelin-13 for 5 hours. Results showed that apelin-13 protected against cerebral ischemia/reperfusion injury. Apelin-13 treatment alleviated neuronal apoptosis by increasing the ratio of Bcl-2/Bax and significantly decreasing cleaved caspase-3 expression. In addition, apelin-13 significantly inhibited excessive autophagy by regulating the expression of LC3 B, p62, and Beclin1. Furthermore, the expression of Bcl-2 and the phosphatidylinositol-3-kinase(PI3 K)/Akt/mammalian target of rapamycin(mTOR) pathway was markedly increased. Both LY294002(20 μM) and rapamycin(500 nM), which are inhibitors of the PI3 K/Akt/mTOR pathway, significantly attenuated the inhibition of autophagy and apoptosis caused by apelin-13. In conclusion, the findings of the present study suggest that Bcl-2 upregulation and mTOR signaling pathway activation lead to the inhibition of apoptosis and excessive autophagy. These effects are involved in apelin-13-induced neuroprotection against cerebral ischemia/reperfusion injury, both in vivo and in vitro. The study was approved by the Animal Ethical and Welfare Committee of Jining Medical University, China(approval No. 2018-JS-001) in February 2018.     

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.     

补中益气汤对于肝再生线粒体能量代谢的研究概况＊

肝再生的机制非常复杂，线粒体功能障碍所引起的能量供给不足是影响因素之一，但其机理亟待研究。严重肝损害时肝细胞ATP供应减少、线粒体能量代谢异常，导致肝再生受到抑制。补中益气汤为李东垣所创，其具补中益气、升阳举陷之功，有实验证实补中益气汤具有保护线粒体功能、增加线粒体能量代谢的作用，从而促进肝再生。本文综述补中益气汤总方与其中各类中药对线粒体能量代谢的保护作用，从而为促进肝再生提供新的治疗手段并对改善病人预后有重要意义。