A novel lncRNA associated with the prognosis of patients with colorectal cancer resists apoptosis through the LYN/BCL-2 pathway.

PURPOSE: We found a novel lncRNA named lncAC138150.2 related to the overall survival and staging of patients with colorectal cancer (CRC) by bioinformatic analysis using data from the Cancer Genome Atlas (TCGA), and the study aimed to elucidate the function of lncAC138150.2 and underlying mechanisms. METHODS: Target molecules were knocked down by transfection with antisense oligonucleotides (ASOs), siRNAs, or lentiviruses and overexpressed by transfection with plasmids. The function of lncAC138150.2 was determined using histological, cytological, and molecular biology methods. The underlying mechanism of lncAC138150.2 function was investigated using RNA-seq, bioinformatics analysis, and molecular biology methods. RESULTS: The expression of lncAC138150.2 was increased in colorectal tissues compared with paired normal tissues. The lncAC138150.2 knockdown increased apoptosis but did not change the cell proliferation, cell cycle distribution, or cell migration ability of CRC cells, while lncAC138150.2 overexpression decreased CRC apoptosis. lncAC138150.2 was mainly located in the cell nucleus, and each lncAC138150.2 transcript knockdown increased CRC apoptosis. BCL-2 pathway was significantly altered in apoptosis induced by lncAC138150.2 knockdown, which was alleviated by BAX knockdown. The expression of LYN was significantly decreased with lncAC138150.2 knockdown, LYN knockdown increased CRC apoptosis, and its overexpression completely alleviated CRC apoptosis induced by lncAC138150.2 knockdown. CONCLUSION: lncAC138150.2 significantly inhibited CRC apoptosis and affected the prognosis of patients with CRC, through the LYN/BCL-2 pathway.

Stellate Ganglion Block Therapy for Complex Regional Pain Syndrome: A Systematic Review and Meta-Analysis.

BACKGROUND: Sympathetic ganglion block (SGB) technique is becoming increasingly prevalent in the treatment of complex regional pain syndromes (CRPS). Given the varied reported effectiveness of these techniques and the heterogeneity of treatment regimens, there is an urgent need for consistent and high-quality evidence on the efficacy and safety of such procedures. OBJECTIVES: This study aimed to compare the efficacy of SGB therapy for CRPS-related pain. STUDY DESIGN: A meta-analysis of randomized controlled trials (RCTs). METHODS: PubMed, EMBASE, Web of Science, CINAHL, US National Institutes of Health Clinical Trials Registry, Google Scholar, and Cochrane Library Databases were systematically searched between January 1967 and April 2023. A meta-analysis of the included RCTs on SGB was conducted to evaluate the effectiveness and risk of bias (ROBs) of SGB. RESULTS: After screening 8523 records, 12 RCTs were included in this meta-analysis. Compared with controls, the visual analog pain score decreased by a weighted mean difference (WMD) of -6.24 mm (95% CI, -11.45, -1.03; P = 0.019) in the random-effects model, and the numerical scale score was reduced by a WMD of -1.17 mm (95% CI, -2.42, 0.08; P = 0.067) in the fixed-effects model, indicating a pain relief. The methodological quality of the included RCTs was high, with an average PEDro score of 7.0 (range: 5-9). LIMITATIONS: The number of included trials was limited. CONCLUSIONS: SGB therapy can reduce pain intensity in patients with CRPS with few adverse events. However, owing to the relatively high heterogeneity of the included RCTs, a larger sample of high-quality RCTs is needed to further confirm this conclusion.

Effects of electromagnetic pulses, exosomes inhibition and their coaction on A549 cells.

Mounting literature indicates that electromagnetic pulses (EMP) is the promising modality to treat cancers with advantages such as noninvasiveness and few side-effects, but its appropriate parameters and underlying mechanisms such as its influence on tumor-derived exosomes (TDEs) are largely unknown. This study aimed to elucidate effects of EMP, exosome inhibition and their coaction on A549 lung adenocarcinoma cells. A549 cells were randomly divided into control group, GW4869 group treated by 20 µM GW4869, vehicle group treated by dimethyl sulfoxide, EMP group treated by EMP exposure, and EMPG group treated by EMP exposure combined with 20 µM GW4869. After EMP exposure, cell proliferation was determined by CCK8 assay, cell cycle and apoptosis was detected by flow cytometry, and cell migration was determined by transwell assay. The results showed that EMP or exosomes inhibition did not affect cell proliferation, cell cycle, apoptosis and cell migration (p > 0.05), but cell migration in EMPG group was significantly decreased compared with vehicle group (p < 0.05). We concluded that under the experimental condition, EMP or GW4869 alone had no effects on behaviors of A549 cells, but their coaction could effectively inhibit the migration of A549 cells.

Regulatory role of exosomes in colorectal cancer progression and potential as biomarkers.

Colorectal cancer (CRC) remains an enormous challenge to human health worldwide. Unfortunately, the mechanism underlying CRC progression is not well understood. Mounting evidence has confirmed that exosomes play a vital role in CRC progression, which has attracted extensive attention among researchers. In addition to acting as messengers between CRC cells, exosomes also participate in the CRC immunomodulatory process and reshape immune function. As stable message carriers and liquid biopsy option under development, exosomes are promising biomarkers in the diagnosis or treatment of CRC. In this review we have described and analyzed the biogenesis and release of exosomes and current research on the role of exosomes in immune regulation and metastasis of CRC. Moreover, we have discussed candidate exosomal molecules as potential biomarkers to diagnose CRC, predict CRC progression, or determine CRC chemoresistance, and described the significance of exosomes in the immunotherapy of CRC. This review provides insight to further understand the role of exosomes in CRC progression and identify valuable biomarkers that facilitate the clinical management of CRC patients.

Quantitative proteomics reveals effects of environmental radiofrequency electromagnetic fields on embryonic neural stem cells.

The effects of environmental radiofrequency electromagnetic fields (RF-EMF) on embryonic neural stem cells have not been determined, particularly at the proteomic level. This study aims to elucidate the effects of environmental levels of RF-EMF radiation on embryonic neural stem cells. Neuroectodermal stem cells (NE-4C cells) were randomly divided into a sham group and an RF group, which were sham-exposed and continuously exposed to a 1950 MHz RF-EMF at 2 W/kg for 48 h. After exposure, cell proliferation was determined by a Cell Counting Kit-8 (CCK8) assay, the cell cycle distribution and apoptosis were measured by flow cytometry, protein abundance was detected by liquid chromatography-tandem mass spectrometry (LC-MS/MS), and mRNA expression was evaluated by quantitative reverse transcription polymerase chain reaction (qRT-PCR). We did not detect differences in cell proliferation, cell cycle distribution, and apoptosis between the two groups. However, we detected differences in the abundance of 23 proteins between the two groups, and some of these differences were consistent with alterations in transcript levels determined by qRT-PCR (P < 0.05). A bioinformatics analysis indicated that the differentially regulated proteins were mainly enriched in 'localization' in the cellular process category; however, no significant pathway alterations in NE-4C cells were detected. We conclude that under the experimental conditions, low-level RF-EMF exposure was not neurotoxic but could induce minor changes in the abundance of some proteins involved in neurodevelopment or brain function.

Single cell molecular alterations reveal target cells and pathways of conditioned fear memory.

OBJECTIVES: Recent evidence indicates that hippocampus is important for conditioned fear memory (CFM). Though few studies consider the roles of various cell types' contribution to such a process, as well as the accompanying transcriptome changes during this process. The purpose of this study was to explore the transcriptional regulatory genes and the targeted cells that are altered by CFM reconsolidation. METHODS: A fear conditioning experiment was established on adult male C57 mice, after day 3 tone-cued CFM reconsolidation test, hippocampus cells were dissociated. Using single cell RNA sequencing (scRNA-seq) technique, alterations of transcriptional genes expression were detected and cell cluster analysis were performed and compared with those in sham group. RESULTS: Seven non-neuronal and eight neuronal cell clusters (including four known neurons and four newly identified neuronal subtypes) has been explored. Among them, CA subtype 1 has characteristic gene markers of Ttr and Ptgds, which is speculated to be the outcome of acute stress and promotes the production of CFM. The results of KEGG pathway enrichment indicate the differences in the expression of certain molecular protein functional subunits in long-term potentiation (LTP) pathway between two types of neurons (DG and CA1) and astrocytes, thus providing a new transcriptional perspective for the role of hippocampus in the CFM reconsolidation. More importantly, the correlation between the reconsolidation of CFM and neurodegenerative diseases-linked genes is substantiated by the results from cell-cell interactions and KEGG pathway enrichment. Further analysis shows that the reconsolidation of CFM inhibits the risk-factor genes App and ApoE in Alzheimer's Disease (AD) and activates the protective gene Lrp1. CONCLUSIONS: This study reports the transcriptional genes expression changes of hippocampal cells driven by CFM, which confirm the involvement of LTP pathway and suggest the possibility of CFM-like behavior in preventing AD. However, the current research is limited to normal C57 mice, and further studies on AD model mice are needed to prove this preliminary conclusion.

Effects of pulsed electromagnetic fields on tumor cell viability: a meta-analysis of in vitro randomized controlled experiments.

Malignant tumor treatment remains a big challenge till now, and expanding literature indicated that pulsed electromagnetic fields (PEMF) is promising in tumor treatment with the advantage of safety and being economical, but it is still controversial on whether PEMF could affect the tumor cell viability. Therefore, we conducted the meta-analysis to evaluate effects of PEMF on tumor cell viability. The PubMed, EMBASE, Web of Science, and Cochrane Library databases were searched for studies published up to February 2021. Studies on the direct effects of PEMF on tumor cell viability, determined using colorimetric analysis, were included. Two authors extracted the data and completed the quality assessment. A meta-analysis was performed to calculate the absorbance values and 95% confidence intervals (CIs) using random-effects models. Seven studies, including 32 randomized controlled experiments, were analyzed. Compared with the control group, tumor cell viability in the PEMF exposure group was obviously lower (SMD, -0.67; 95% CI: -1.12 to -0.22). The subgroup meta-analysis results showed that PEMF significantly reduced epithelial cancer cell viability (SMD, -0.58; 95% CI: -0.92 to -0.23) but had no influence on stromal tumor cell viability (SMD, -0.93; 95% CI: -0.21 to 0.15). Our study demonstrated that PEMF could inhibit tumor cell proliferation to some extent, but the risk of bias and high heterogeneity (I2 > 75%) weakened the strength of the conclusions drawn from the analysis.

Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligence.

PURPOSE: Abnormalities of the running pattern of choroidal vessel have been reported in eyes with pachychoroid diseases. However, it is difficult for clinicians to judge the running pattern with high reproducibility. Thus, the purpose of this study was to compare the degree of concordance of the running pattern of the choroidal vessels between that determined by artificial intelligence (AI) to that determined by experienced clinicians. METHODS: The running pattern of the choroidal vessels in en face images of Haller's layer of 413 normal and pachychoroid diseased eyes was classified as symmetrical or asymmetrical by human raters and by three supervised machine learning models; the support vector machine (SVM), Xception, and random forest models. The data from the human raters were used as the supervised data. The accuracy rates of the human raters and the certainty of AI's answers were compared using confidence scores (CSs). RESULTS: The choroidal vascular running pattern could be determined by each AI model with an area under the curve better than 0.94. The random forest method was able to discriminate with the highest accuracy among the three AIs. In the CS analyses, the percentage of certainty was highest (66.4%) and that of uncertainty was lowest (6.1%) in the agreement group. On the other hand, the rate of uncertainty was highest (27.3%) in the disagreement group. CONCLUSION: AI algorithm can automatically classify with ambiguous criteria the presence or absence of a symmetrical blood vessel running pattern of the choroid. The classification was as good as that of supervised humans in accuracy and reproducibility.

Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images.

Deep learning is being employed in disease detection and classification based on medical images for clinical decision making. It typically requires large amounts of labelled data; however, the sample size of such medical image datasets is generally small. This study proposes a novel training framework for building deep learning models of disease detection and classification with small datasets. Our approach is based on a hierarchical classification method where the healthy/disease information from the first model is effectively utilized to build subsequent models for classifying the disease into its sub-types via a transfer learning method. To improve accuracy, multiple input datasets were used, and a stacking ensembled method was employed for final classification. To demonstrate the method's performance, a labelled dataset extracted from volumetric ophthalmic optical coherence tomography data for 156 healthy and 798 glaucoma eyes was used, in which glaucoma eyes were further labelled into four sub-types. The average weighted accuracy and Cohen's kappa for three randomized test datasets were 0.839 and 0.809, respectively. Our approach outperformed the flat classification method by 9.7% using smaller training datasets. The results suggest that the framework can perform accurate classification with a small number of medical images.

tDCS Accelerates the Rehabilitation of MCAO-Induced Motor Function Deficits via Neurogenesis Modulated by the Notch1 Signaling Pathway.

Background. Ischemic stroke carries a high mortality rate and is a leading cause of severe neurological disability. However, the efficacy of current therapeutic options remains limited. Objective. We aimed to investigate the treatment efficacy of transcranial direct current stimulation (tDCS) in motor function rehabilitation after ischemic stroke and explore the underlying mechanisms. Methods. Male Sprague-Dawley rats with epicranial electrodes were used to establish pathogenetic model through temporary right middle cerebral artery occlusion (MCAO). Subsequently, animals were randomly divided into 4 groups: MCAO + tDCS/sham tDCS, Control + tDCS/sham tDCS. Animals in the groups with tDCS underwent 10 days of cathodal tDCS totally (500 µA, 15 minutes, once a day). During and after tDCS treatment, the motor functions of the animals, ischemic damage area, proliferation and differentiation of neural stem cells (NSCs), and distribution, and protein expression of Notch1 signaling molecules were detected. Results. The rehabilitation of MCAO-induced motor function deficits was dramatically accelerated by tDCS treatment. NSC proliferation in the subventricular zone (SVZ) was significantly increased after MCAO surgery, and tDCS treatment promoted this process. Additionally, NSCs probably migrated from the SVZ to the ischemic striatum and then differentiated into neurons and oligodendrocytes after MCAO surgery, both of which processes were accelerated by tDCS treatment. Finally, tDCS treatment inhibited the activation of Notch1 signaling in NSCs in the ischemic striatum, which may be involved in NSC differentiation in the MCAO model. Conclusion. Our results suggest that tDCS may exert therapeutic efficacy after ischemic stroke in a regenerative medical perspective.

Cathodal tDCS exerts neuroprotective effect in rat brain after acute ischemic stroke.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a non-invasive brain modulation technique that has been proved to exert beneficial effects in the acute phase of stroke. To explore the underlying mechanism, we investigated the neuroprotective effects of cathodal tDCS on brain injury caused by middle cerebral artery occlusion (MCAO). RESULTS: We established the MCAO model and sham MCAO model with an epicranial electrode implanted adult male Sprague-Dawley rats, and then they were randomly divided into four groups (MCAO + tDCS, MCAO + sham tDCS (Sham), Control + tDCS and Control + Sham group). In this study, the severity degree of neurological deficit, the morphology of brain damage, the apoptosis, the level of neuron-specific enolase and inflammatory factors, the activation of glial cells was detected. The results showed that cathodal tDCS significantly improved the level of neurological deficit and the brain morphology, reduced the brain damage area and apoptotic index, and increased the number of Nissl body in MCAO rats, compared with MCAO + Sham group. Meanwhile, the high level of NSE, inflammatory factors, Caspase 3 and Bax/Bcl2 ratio in MCAO rats was reduced by cathodal tDCS. Additionally, cathodal tDCS inhibited the activation of astrocyte and microglia induced by MCAO. No difference was found in two Control groups. CONCLUSION: Our results suggested that cathodal tDCS could accelerate the recovery of neurologic deficit and brain damage caused by MCAO. The inhibition of neuroinflammation and apoptosis resulted from cathodal tDCS may be involved in the neuroprotective process.

Structural Characterization of Glaucoma Patients with Low Ocular Blood Flow.

Purpose: There is an unclear relationship between ocular blood flow (OBF) and the structural characteristics of the optic nerve head (ONH) in glaucoma, a multifactorial disease. This study used laser speckle flowgraphy (LSFG) to identify low-OBF glaucoma patients and investigated the ONH in these patients. Materials and Methods: In 533 eyes with glaucoma, we determined confounding factors for LSFG-measured OBF (tissue-area mean blur rate: MT) and corrected MT with a linear mixed-effects model (LMM). Structural ONH data (from fundus stereo photography), OCT data, and clinical characteristics were then compared in patients with corrected MT in the upper and lower quartiles using the LMM. Results: Single regression showed significant correlations between MT and age, spherical equivalent (SE), central corneal thickness (CCT), and a weighted count of retinal ganglion cells (wRGC), but not axial length or systemic blood pressure. Gender also significantly influenced MT; MT was corrected for these correlated factors and also glaucoma type with the LMM. The lower-quartile MT group had a significantly larger cup area and cup-disc area ratio and lower temporal quadrant circumpapillary retinal nerve fiber layer thickness (cpRNFLT) and macular ganglion cell complex (GCC) than the upper-quartile group. Conclusions: Low-OBF glaucoma patients were characterized by a larger cup-disc ratio, and higher susceptibility to damage in the temporal disc and the macular area.

Optical Coherence Tomography-Based Deep-Learning Models for Classifying Normal and Age-Related Macular Degeneration and Exudative and Non-Exudative Age-Related Macular Degeneration Changes.

INTRODUCTION: The use of optical coherence tomography (OCT) images is increasing in the medical treatment of age-related macular degeneration (AMD), and thus, the amount of data requiring analysis is increasing. Advances in machine-learning techniques may facilitate processing of large amounts of medical image data. Among deep-learning methods, convolution neural networks (CNNs) show superior image recognition ability. This study aimed to build deep-learning models that could distinguish AMD from healthy OCT scans and to distinguish AMD with and without exudative changes without using a segmentation algorithm. METHODS: This was a cross-sectional observational clinical study. A total of 1621 spectral domain (SD)-OCT images of patients with AMD and a healthy control group were studied. The first CNN model was trained and validated using 1382 AMD images and 239 normal images. The second transfer-learning model was trained and validated with 721 AMD images with exudative changes and 661 AMD images without any exudate. The attention area of the CNN was described as a heat map by class activation mapping (CAM). In the second model, which classified images into AMD with or without exudative changes, we compared the learning stabilization of models using or not using transfer learning. RESULTS: Using the first CNN model, we could classify AMD and normal OCT images with 100% sensitivity, 91.8% specificity, and 99.0% accuracy. In the second, transfer-learning model, we could classify AMD as having or not having exudative changes, with 98.4% sensitivity, 88.3% specificity, and 93.9% accuracy. CAM successfully described the heat-map area on the OCT images. Including the transfer-learning model in the second model resulted in faster stabilization than when the transfer-learning model was not included. CONCLUSION: Two computational deep-learning models were developed and evaluated here; both models showed good performance. Automation of the interpretation process by using deep-learning models can save time and improve efficiency. TRIAL REGISTRATION: No15073.

A safety study of 500 µA cathodal transcranial direct current stimulation in rat.

BACKGROUND: Transcranial direct current stimulation (tDCS) is a noninvasive neural control technology that has become a research hotspot. To facilitate further research of tDCS, the biosafety of 500 µA cathodal tDCS, a controversial parameter in rats was evaluated. RESULTS: 24 animals were randomly divided into two groups: a cathodal tDCS group (tDCS, n = 12) and control group (control, n = 12). Animals in the tDCS group received 5 consecutive days of cathodal tDCS (500 µA, 15 min, once per day) followed by a tDCS-free interval of 2 days and 5 additional days of stimulation, totally two treatments of tDCS for a total of 10 days. Computational 3D rat model was adopted to calculate the current density distributions in brain during tDCS treatment. Essential brain functions including motor function and learning and memory ability were evaluated. Additionally, to estimate the neurotoxicity of tDCS, the brain morphology, neurotransmitter levels and cerebral temperature were investigated. Our results showed that the current density inside the brain was less than 20 A/m2 during tDCS treatment in computational model. tDCS did not affect motor functions and learning and memory ability after tDCS treatment. In addition, no significant differences were found for the tDCS group in hematology, serum biochemical markers or the morphology of major organs. Moreover, tDCS treatment had no effect on the brain morphology, neural structures, neurotransmitter levels or cerebral temperature. CONCLUSION: 500 µA cathodal tDCS as performed in the present study was safe for rodents.

Effects of 220 MHz Pulsed Modulated Radiofrequency Field on the Sperm Quality in Rats.

Under some occupational conditions, workers are inevitably exposed to high-intensity radiofrequency (RF) fields. In this study, we investigated the effects of one-month exposure to a 220 MHz pulsed modulated RF field at the power density of 50 W/m² on the sperm quality in male adult rats. The sperm quality was evaluated by measuring the number, abnormality and survival rate of sperm cells. The morphology of testis was examined by hematoxylin-eosin (HE) staining. The levels of secreting factors by Sertoli cells (SCs) and Leydig cells (LCs) were determined by enzyme linked immunosorbent assay (ELISA). The level of cleaved caspase 3 in the testis was detected by immunofluorescence staining. Finally, the expression levels of the apoptosis-related protein (caspase 3, BAX and BCL2) in the testis were assessed by Western blotting. Compared with the sham group, the sperm quality in the RF group decreased significantly. The levels of secreting factors of SCs and the morphology of the testis showed an obvious change after RF exposure. The level of the secreting factor of LCs decreased significantly after RF exposure. The levels of cleaved caspase 3, caspase 3, and the BAX/BCL2 ratio in the testis increased markedly after RF exposure. These data collectively suggested that under the present experimental conditions, 220 MHz pulsed modulated RF exposure could impair sperm quality in rats, and the disruption of the secreting function of LCs and increased apoptosis of testis cells induced by the RF field might be accounted for by this damaging effect.

Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.

This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.

Deep Learning Classification Models Built with Two-step Transfer Learning for Age Related Macular Degeneration Diagnosis.

The objective of this study was to build deep learning models with optical coherence tomography (OCT) images to classify normal and age related macular degeneration (AMD), AMD with fluid, and AMD without any fluid. In this study, 185 normal OCT images from 49 normal subjects, 535 OCT images of AMD with fluid, and 514 OCT mages of AMD without fluid from 120 AMD eyes as training data, while 49 normal images from 25 normal eyes, 188 AMD OCT images with fluid and 154 AMD images without any fluid from 77 AMD eyes as test data, were enrolled. Data augmentation was applied to increase the number of images to build deep learning models. Totally, two classification models were built in two steps. In the first step, a VGG16 model pre-trained on ImageNet dataset was transfer learned to classify normal and AMD, including AMD with fluid and/or without any fluid. Then, in the second step, the fine-tuned model in the first step was transfer learned again to distinguish the images of AMD with fluid from the ones without any fluid. With the first model, normal and AMD OCT images were classified with 0.999 area under receiver operating characteristic curve (AUC), and 99.2% accuracy. With the second model, AMD with the presence of any fluid, and AMD without fluid were classified with 0.992 AUC, and 95.1% accuracy. Compared with a transfer learned VGG16 model pre-trained on ImageNet dataset, to classify the three categories directly, higher classification performance was achieved with our notable approach. Conclusively, two classification models for AMD clinical practice were built with high classification performance, and these models should help improve the early diagnosis and treatment for AMD.

Retinal Thickness Analysis in High Myopia based on Medial Axis Transforms.

This paper presents a retinal thickness analysis method from 3D images acquired by optical coherence tomography (OCT). Given OCT images with segmented boundaries of retinal layers, medial axes of the layers are computed by medial axis transforms (MAT), and thickness is evaluated based on Euclidean distance fields. Since the MAT computes the closest points on the boundary of the layer, it can compute more correct thickness than conventional methods that evaluate Y-axis-aligned thickness. Experimental results show that our method can detect thin-parts around distorted regions, or a clue of high myopia. This is useful for early diagnosis of high myopia and other eye diseases.

Pilot study for three-dimensional assessment of laminar pore structure in patients with glaucoma, as measured with swept source optical coherence tomography.

PURPOSE: To develop a method to quantify, based on swept-source optical coherence tomography (OCT), the 3D structure of the laminar pores in patients with glaucoma. METHODS: This retrospective study examined 160 laminar pores from 8 eyes of 8 cases: 4 normal subjects and 4 open-angle glaucoma (OAG) patients. We reconstructed 3D volume data for a 3 x 3 mm disc, using a method similar to OCT angiography, and segmented the structure of the lamina cribrosa. Then, we manually segmented each laminar pore in sequential C-scan images (>90 slices at 2.6-micron intervals) with VCAT5 (RIKEN, Japan). We compared the control and OAG subjects with the Mann-Whitney U test. Differences were considered significant at p < 0.05. RESULTS: We found that the laminar pores of the OAG patients had a significantly smaller average cross-sectional area, smaller 3D volume (adjusted to the average thickness of the lamina cribrosa), and higher true sphericity, and lower principal value (P1, 2, 3) of the 3D structure data (all: p < 0.0001). The topographic distribution of damaged laminar pores was consistent with the damaged area of the macular map. CONCLUSION: We successfully developed a method to quantify the 3D structure of the laminar pores; providing a useful tool to assess lamina cribrosa-associated risk factors for glaucoma. These findings promise to benefit future investigations into the pathomechanisms of glaucoma.

Comparison of Machine-Learning Classification Models for Glaucoma Management.

This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients' background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.