The Value of VR-PVEP in Objective Evaluation of Monocular Refractive Visual Impairment.

OBJECTIVES: To study the virtual reality-pattern visual evoked potential (VR-PVEP) P100 waveform characteristics of monocular visual impairment with different impaired degrees under simultaneous binocular perception and monocular stimulations. METHODS: A total of 55 young volunteers with normal vision (using decimal recording method, far vision ≥0.8 and near vision ≥0.5) were selected to simulate three groups of monocular refractive visual impairment by interpolation method. The sum of near and far vision ≤0.2 was Group A, the severe visual impairment group; the sum of near and far vision <0.8 was Group B, the moderate visual impairment group; and the sum of near and far vision ≥0.8 was Group C, the mild visual impairment group. The volunteers' binocular normal visions were set as the control group. The VR-PVEP P100 peak times measured by simultaneous binocular perception and monocular stimulation were compared at four spatial frequencies 16×16, 24×24, 32×32 and 64×64. RESULTS: In Group A, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 24×24, 32×32 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group B, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 16×16, 24×24 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group C, there was no significant difference between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at all spatial frequencies (P>0.05). There was no significant difference in the P100 peak times measured at all spatial frequencies between simulant visual impairment eyes and simultaneous binocular perception in the control group (P>0.05). CONCLUSIONS: VR-PVEP can be used for visual acuity evaluation of patients with severe and moderate monocular visual impairment, which can reflect the visual impairment degree caused by ametropia. VR-PVEP has application value in the objective evaluation of visual function and forensic clinical identification.

A deep learning-based automatic tool for measuring the lengths of linear scars: forensic applications.

It is important to measure scars in forensic and clinical medicine. In practice, scars are mostly manually measured, and the results are diverse and influenced by various subjective factors. With the development of digital image technology and artificial intelligence, noncontact and automatic photogrammetry has been gradually used in some practical applications. In this article, we propose an automatic method for measuring the length of linear scars based on multiview stereo and deep learning, which combines the 3D reconstruction algorithm of structure from motion and the image segmentation algorithm based on a convolutional neural network. With a few pictures taken by a smart phone, automatic segmentation and measurement of scars can be realized. The reliability of the measurement was first demonstrated through simulation experiments on five artificial scars, giving errors of length <5%. Then, experiment results on 30 clinical scar samples showed that our measurements were in high agreement with manual measurements, with an average error of 3.69%. Our study demonstrates that the application of photogrammetry in scar measurement is effective and that the deep learning technique can realize the automation of scar measurement with high accuracy.

Predicting visual acuity with machine learning in treated ocular trauma patients.

AIM: To predict best-corrected visual acuity (BCVA) by machine learning in patients with ocular trauma who were treated for at least 6mo. METHODS: The internal dataset consisted of 850 patients with 1589 eyes and an average age of 44.29y. The initial visual acuity was 0.99 logMAR. The test dataset consisted of 60 patients with 100 eyes collected while the model was optimized. Four different machine-learning algorithms (Extreme Gradient Boosting, support vector regression, Bayesian ridge, and random forest regressor) were used to predict BCVA, and four algorithms (Extreme Gradient Boosting, support vector machine, logistic regression, and random forest classifier) were used to classify BCVA in patients with ocular trauma after treatment for 6mo or longer. Clinical features were obtained from outpatient records, and ocular parameters were extracted from optical coherence tomography images and fundus photographs. These features were put into different machine-learning models, and the obtained predicted values were compared with the actual BCVA values. The best-performing model and the best variable selected were further evaluated in the test dataset. RESULTS: There was a significant correlation between the predicted and actual values [all Pearson correlation coefficient (PCC)>0.6]. Considering only the data from the traumatic group (group A) into account, the lowest mean absolute error (MAE) and root mean square error (RMSE) were 0.30 and 0.40 logMAR, respectively. In the traumatic and healthy groups (group B), the lowest MAE and RMSE were 0.20 and 0.33 logMAR, respectively. The sensitivity was always higher than the specificity in group A, in contrast to the results in group B. The classification accuracy and precision were above 0.80 in both groups. The MAE, RMSE, and PCC of the test dataset were 0.20, 0.29, and 0.96, respectively. The sensitivity, precision, specificity, and accuracy of the test dataset were 0.83, 0.92, 0.95, and 0.90, respectively. CONCLUSION: Predicting BCVA using machine-learning models in patients with treated ocular trauma is accurate and helpful in the identification of visual dysfunction.

Cortical anatomical variations, gene expression profiles, and clinical phenotypes in patients with schizophrenia.

BACKGROUND AND HYPOTHESIS: Schizophrenia (SZ) patients display significant structural brain abnormalities; nevertheless, the genetic mechanisms regulating cortical anatomical variations and their correlation with the disease phenotype are still ambiguous. STUDY DESIGN: We characterized anatomical variation using a surface-based method derived from structural magnetic resonance imaging of patients with SZ and age- and sex-matched healthy controls (HCs). Partial least-squares regression was performed across cortex regions between anatomical variation and average transcriptional profiles of SZ risk genes and all qualified genes from the Allen Human Brain Atlas. The morphological features of each brain region were correlated to symptomology variables in patients with SZ using partial correlation analysis. STUDY RESULTS: A total of 203 SZ and 201 HCs were included in the final analysis. We observed significant variation of 55 regions of cortical thickness, 23 regions of volume, 7 regions of area, and 55 regions of local gyrification index (LGI) between SZ and HC groups. Expression profiles of 4 SZ risk genes and 96 genes from all qualified genes showed a correlation to anatomical variability, however, after multiple comparisons, the correlations were no longer significant. LGI variability in multiple frontal subregions was associated with specific symptoms of SZ, whereas cognitive function involving attention/vigilance was linked to LGI variability across nine brain regions. CONCLUSIONS: Cortical anatomical variation of patients with schizophrenia is associated with gene transcriptome profiles as well as clinical phenotypes.

Research Progress of Age Estimation in the Living by Knee Joint MRI.

Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice, medicine, archaeology, etc. As a non-invasive evaluation method of the epiphyseal development stage, MRI is widely used in living age estimation. In recent years, the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation, which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI, introduces the current research trends, and future application trend.

The value of PMCTA in the diagnosis of coronary atherosclerosis in isolated human hearts.

Postmortem computed tomography (PMCT) has a limited value in investigating coronary artery disease, despite several obvious advantages over the conventional autopsy. To address this issue, postmortem computed tomography angiography (PMCTA) has been introduced into various studies, where it has been used to investigate natural and unnatural deaths involving vascular damage, occlusion, or other pathologies of the vascular system. To investigate the application value of PMCTA in the diagnosis of coronary artery stenosis in ex situ hearts, the water-based contrast media were injected into isolated hearts, scaned, and finally compared with gold standards (autopsy and histology findings of the coronary artery). This study involved 16 subjects from the Academy of Forensic Science who were suspected to have died of sudden death without traumatic injuries. Unenhanced PMCT was performed first, followed by PMCTA using a water-based contrast agent, injected into the coronary arteries of isolated hearts using a self-designed angiography device. The image data were reconstructed into three-dimensional (3D) angiography images using software in the angiography facility. The 3D images were recorded and evaluated by two radiologists and then statistically analysed. The results of PMCTA were consistent with the gold standards for the diagnosis of coronary artery stenosis (P > 0.05). However, water-based contrast media can only be used to examine the pathological changes of blood vessels, which may have limitations in the diagnosis of causes of death such as myocardial oedema. PMCTA can be used as a new method to evaluate the degree of coronary atherosclerosis in addition to traditional autopsy. The 3D reconstruction technique reveals the coronary artery lesions more objectively and vividly and provides the opportunity to re-read the data at any time. Key points: The methods and parameters for coronary angiography in isolated human hearts were standardized based on the previous researcher.PMCTA in isolated human hearts is including the 3D reconstruction technique that reveals the coronary artery lesions more objectively and vividly, and provides the opportunity to re-read the data at anytime.PMCTA could only be used to examine the pathological changes of blood vessels, which might have limitations for the diagnosis of causes of death.PMCTA in isolated human hearts can be viewed as an auxiliary method for establishing the cause of death, which can provide an assessment of degree and extent of arterial stenosis and accurately help determine the abnormal location.

Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

OBJECTIVES: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application. METHODS: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. RESULTS: The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. CONCLUSIONS: In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.

Effects of methanol and formic acid on CRYB, ALDH2, and ATP5A1 of RGCs.

OBJECTIVE: To explore the toxicity of methanol and its metabolite, formic acid on αB-crystallin(CRYB), aldehyde dehydrogenase (ALDH2), and ATPsynthase (ATP5A1) of rat retinal ganglion cells (RGCs). METHODS: RGCs are cultured in vitro in a toxic environment with 15/30/60 mM methanol or formic acid, respectively. Then, the morphological changes of RGCs and protein and mRNA levels of ALDH2, ATP5A1, and CRYB in rat RGCs were evaluated. RESULTS: 1) Compared to the toxicity of 15 mM formic acid on RGCs, 30 mM of formic acid environment significantly promoted apoptosis, and cell death occurred in the 60-mM formic acid group 24 h later. The toxicity of methanol for inducing apoptosis was not as obvious as formic acid. 2) In the 15-mM group, the level of CRYB protein was down-regulated after stimulating with both methanol and formic acid for 48 h, and ATP5A1 protein level decreased significantly with formic but not methanol. No change in ALDH2 was observed in methanol or formic acid. With a prolonged duration (>7 d) or high concentration (>30 mM) stimulation, cells treated with both methanol and formic acid showed severe apoptosis, rendering it challenging to collect a sufficient number of cells for protein detection. 3) In the 48-h group, no significant effect was detected on the mRNA of CRYB, ATP5A1, and ALDH2 by both 15/30 mM formic acid and 15 mM methanol. Conversely, 30 mM methanol had a significant up-regulation effect on the expression of the three genes, while no significant effect was observed in the 7-d groups. CONCLUSIONS: Formic acid exerted stronger toxicity on CRYB, ATP5A1, and ALDH2 than methanol and played a regulatory role at the translation level, while the effect of methanol is still uncertain, needing additional investigation.