Self-propelled bioglass janus nanomotors for dentin hypersensitivity treatment.

Dentin hypersensitivity treatment is not always successful owing to the exfoliation of the blocking layer. Therefore, efficiently delivering a desensitization agent into the dental tubule is critical. Nanomotors are widely used as in vivo drug delivery systems owing to their strong power and good biocompatibility. Herein, we report a kind of self-propelled bioglass Janus nanomotor with a Pt motion unit (nBGs@Pt) for application in dentin hypersensitivity that was prepared via a simple sol-gel method and magnetron sputtering method, with an average size of 290 nm. The Pt layer as the power unit provided the dynamics to deliver the bioglass (desensitization agent). Using hydrogen peroxide as a fuel, the nBGs@Pt could automatically move in different media. In addition, the nBGs@Pt with a mesoporous structure demonstrated good hydroxyapatite formation performance. An in vitro dentin pressure model was used to verify the blocking ability of the nBGs@Pt in dentin tubules. The dynamics of the nBGs@Pt was sufficient to resist the outflow of dentin fluid and movement into the dentin tubules, with a blocking rate of 58.05%. After remineralization, the blocking rate could reach 96.07% and the formation of hydroxyapatite of up to 10 µm or more occurred. It is expected that this study will provide a simple and feasible new strategy for the painless treatment of dentin sensitivity.

What can we learn from a Chinese social media used by glaucoma patients?

PURPOSE: Our study aims to discuss glaucoma patients' needs and Internet habits using big data analysis and Natural Language Processing (NLP) based on deep learning (DL). METHODS: In this retrospective study, we used web crawler technology to crawl glaucoma-related topic posts from the glaucoma bar of Baidu Tieba, China. According to the contents of topic posts, we classified them into posts with seeking medical advice and without seeking medical advice (social support, expressing emotions, sharing knowledge, and others). Word Cloud and frequency statistics were used to analyze the contents and visualize the keywords of topic posts. Two DL models, Bidirectional Long Short-Term Memory (Bi-LSTM) and Bidirectional Encoder Representations from Transformers (BERT), were trained to identify the posts seeking medical advice. The evaluation matrices included: accuracy, F1 value, and the area under the ROC curve (AUC). RESULTS: A total of 10,892 topic posts were included, among them, most were seeking medical advice (N = 7071, 64.91%), and seeking advice regarding symptoms or examination (N = 4913, 45.11%) dominated the majority. The following were searching for social support (N = 2362, 21.69%), expressing emotions (N = 497, 4.56%), and sharing knowledge (N = 527, 4.84%) in sequence. The word cloud analysis results showed that ocular pressure, visual field, examination, and operation were the most frequent words. The accuracy, F1 score, and AUC were 0.891, 0.891, and 0.931 for the BERT model, 0.82, 0.821, and 0.890 for the Bi-LSTM model. CONCLUSION: Social media can help enhance the patient-doctor relationship by providing patients' concerns and cognition about glaucoma in China. NLP can be a powerful tool to reflect patients' focus on diseases. DL models performed well in classifying Chinese medical-related texts, which could play an important role in public health monitoring.

Annotation-efficient learning for OCT segmentation.

Deep learning has been successfully applied to OCT segmentation. However, for data from different manufacturers and imaging protocols, and for different regions of interest (ROIs), it requires laborious and time-consuming data annotation and training, which is undesirable in many scenarios, such as surgical navigation and multi-center clinical trials. Here we propose an annotation-efficient learning method for OCT segmentation that could significantly reduce annotation costs. Leveraging self-supervised generative learning, we train a Transformer-based model to learn the OCT imagery. Then we connect the trained Transformer-based encoder to a CNN-based decoder, to learn the dense pixel-wise prediction in OCT segmentation. These training phases use open-access data and thus incur no annotation costs, and the pre-trained model can be adapted to different data and ROIs without re-training. Based on the greedy approximation for the k-center problem, we also introduce an algorithm for the selective annotation of the target data. We verified our method on publicly-available and private OCT datasets. Compared to the widely-used U-Net model with 100% training data, our method only requires ∼10% of the data for achieving the same segmentation accuracy, and it speeds the training up to ∼3.5 times. Furthermore, our proposed method outperforms other potential strategies that could improve annotation efficiency. We think this emphasis on learning efficiency may help improve the intelligence and application penetration of OCT-based technologies.

Assessing the efficacy of myopia control in monocular orthokeratology treated unilateral myopic children.

PURPOSE: To investigate the efficacy of myopia control by comparing the orthokeratology (Ortho-K) treated eyes and the emmetropic contralateral eyes in unilateral myopic children, and to identify the inter-individual influence factors. METHOD: In this retrospective study, 1566 medical records of children wearing Ortho-K lens were reviewed, and 62 children who received monocular Ortho-K lens for more than 1 year were analyzed. The change in axial length (AL) of the Ortho-K eyes and the emmetropic contralateral eyes was recorded. To evaluate the absolute and relative efficacy of myopia control, the intra-bilateral absolute reduction in AL growth (ibARAL) and the intra-bilateral relative reduction in AL growth (ibRRAL) were calculated as main outcomes. Association of the AL elongation, ibARAL and ibRRAL with age, sex and ocular parameters was analyzed by correlation analysis and generalized estimating equation (GEE) analysis. RESULT: The average initial wearing age was 10.76 ± 1.45 (ranged 8.5 to 15.8). The average baseline SER was - 2.15 ± 1.03 (ranged - 5.25 to -1.00) D in the Ortho-K eyes and - 0.01 ± 0.40 (ranged - 0.75 to 0.75) D in the contralateral eyes. At the 1-year follow-up, the average increased AL was significantly less in the Ortho-K eyes (0.07 ± 0.18 mm) than in the fellow eyes (0.48 ± 0.24 mm) (p < 0.001). The mean ibARAL was 0.41 ± 0.30 mm, and the mean ibRRAL was 83.4%±56.3%. In the GEE model, the AL change in Ortho-K eyes (ß = 0.051, p = 0.009, 95%CI: 0.012 to 0.090), the ibARAL (ß= -0.153, p = 0.000, 95%CI: -0.228 to -0.078) and the ibRRAL (ß= -0.196, p = 0.020, 95%CI: -0.361 to -0.030) were independently associated with the spherical equivalent refraction (SER) of the Ortho-K eyes, after adjusting for age, sex, and keratometry. CONCLUSION: In our study, the Ortho-K treatment was efficacious in controlling axial length growth in the monocular orthokeratology treated unilateral myopic eyes. The efficacy increased when the myopia was more severe. In the children from 8 to 16 years old, the effectiveness was independent of age and sex.

Niobium carbide-mediated photothermal therapy for infected wound treatment.

Bacterial infections of the wounds on the skin surface significantly reduce the rate of wound healing, potentially leading to serious systemic infections. Antibiotics are the first-line drugs for the treatment of these infections. However, the misuse and overuse of antibiotics have led to the emergence of bacterial resistance. Therefore, a new antimicrobial strategy is urgently needed. Photothermal therapy (PTT) is a novel efficient therapeutic technique that can produce irreversible cell damage to induce death of bacteria, possessing a great potential in infected wound healing. This work describes the use of a new photothermal agent (PTA) such as niobium carbide (NbC) nanoparticles with outstanding near-infrared (NIR) absorption property. NbC nanoparticles converted NIR laser irradiation energy into localized heat for photothermal treatment. In vitro antimicrobial experiments have revealed that NbC nanoparticles exert excellent antimicrobial effects against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Moreover, NbC nanoparticles accelerated E. coli-infected wound healing process, reduced inflammatory response, and showed good biosafety in vivo. Altogether, NbC nanoparticles represent an efficient PTA for antimicrobial treatment and are a bio-safe material with low toxicity in vivo.

Dynamic analysis of iris changes and a deep learning system for automated angle-closure classification based on AS-OCT videos.

BACKGROUND: To study the association between dynamic iris change and primary angle-closure disease (PACD) with anterior segment optical coherence tomography (AS-OCT) videos and develop an automated deep learning system for angle-closure screening as well as validate its performance. METHODS: A total of 369 AS-OCT videos (19,940 frames)-159 angle-closure subjects and 210 normal controls (two datasets using different AS-OCT capturing devices)-were included. The correlation between iris changes (pupil constriction) and PACD was analyzed based on dynamic clinical parameters (pupil diameter) under the guidance of a senior ophthalmologist. A temporal network was then developed to learn discriminative temporal features from the videos. The datasets were randomly split into training, and test sets and fivefold stratified cross-validation were used to evaluate the performance. RESULTS: For dynamic clinical parameter evaluation, the mean velocity of pupil constriction (VPC) was significantly lower in angle-closure eyes (0.470 mm/s) than in normal eyes (0.571 mm/s) (P < 0.001), as was the acceleration of pupil constriction (APC, 3.512 mm/s2 vs. 5.256 mm/s2; P < 0.001). For our temporal network, the areas under the curve of the system using AS-OCT images, original AS-OCT videos, and aligned AS-OCT videos were 0.766 (95% CI: 0.610-0.923) vs. 0.820 (95% CI: 0.680-0.961) vs. 0.905 (95% CI: 0.802-1.000) (for Casia dataset) and 0.767 (95% CI: 0.620-0.914) vs. 0.837 (95% CI: 0.713-0.961) vs. 0.919 (95% CI: 0.831-1.000) (for Zeiss dataset). CONCLUSIONS: The results showed, comparatively, that the iris of angle-closure eyes stretches less in response to illumination than in normal eyes. Furthermore, the dynamic feature of iris motion could assist in angle-closure classification.

Influence of Grain Orientation and Grain Boundary Features on Local Stress State of Cu-8Al-11Mn Alloy Investigated Using Crystal Plasticity Finite Element Method.

Reducing the local stress in the vicinity of the grain boundaries is a favorable way to improve the super-elastic properties of super-elastic alloys. The crystal plasticity finite element method (CPFEM) was applied in this study to simulate the deformation behavior and local stress of a super-elastic Cu-8Al-11Mn (wt.%) alloy containing single grains with various orientations, columnar grains with different misorientation angles, and tri-crystals with distinct grain boundary morphologies. The results indicated that the stress distribution of single grains presented obvious orientation dependence during deformation. Uniformly distributed stress was observed in grains with orientations of 0° and 90° when more slip systems were activated during deformation. With the increase in the misorientation angles of columnar grains, the stresses in the vicinity of the grain boundaries increased, which was related to the difference in the shear stress of the slip systems in adjacent grains. When the difference in the shear stress of the slip systems in two adjacent grains was large, a local stress concentration formed in the vicinity of the grain boundary. Compared with the triple-junction grain boundaries, the local stresses of the straight and vertical grain boundaries were smaller, which was closely related to the number of activated slip systems on both sides of the grain boundary. The above results were obtained experimentally and could be used to design super-elastic alloys with high performance.

Ten-year Time-trend Analysis of Dyslipidemia Among Adults in Wuhan.

OBJECTIVE: Dyslipidemia is associated with an increased risk of cardiovascular disease, the major cause of death in an aging population. This study aimed to estimate the prevalence of dyslipidemia for the past decade among adults in Wuhan, China. METHODS: We performed a serial cross-sectional study that recruited 705 219 adults from the Health Management Center of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology from 2010 to 2019. The diagnosis of dyslipidemia was based on the 2016 Chinese Guidelines for the Management of Dyslipidemia in Adults. Fixed effects and random effects models were applied to adjust the confounding variables (gender and age). RESULTS: The overall prevalence of dyslipidemia was 33.1% (46.2% in men and 14.7% in women) in 2019. The prevalence of dyslipidemia was significantly increased over 10 years [from 28.6% (95% CI: 28.2%-29.1%) in 2010 to 32.8 % (95% CI:32.6%-33.1%) in 2019;. P-0.001], especially for hypo-high-density lipoprotein cholesterolemia [from 18.4% (95% CI: 18.0%-18.8%) in 2010 to 24.5% (95% CI: 24.3%-24.7%) in 2019; P-0.001]. In 2019, the prevalence of dyslipidemia was higher in participants with comorbidities, including overweight/obesity, hypertension, diabetes, hyperuricemia, or chronic kidney disease, and dyslipidemia was the most significant among participants aged 30-39 years. CONCLUSION: This study demonstrated that dyslipidemia is on the rise in men, and more emphasis should be provided for the screening of dyslipidemia in young males for the primary prevention of cardiovascular and renal diseases.

Generating Synthesized Ultrasound Biomicroscopy Images from Anterior Segment Optical Coherent Tomography Images by Generative Adversarial Networks for Iridociliary Assessment.

INTRODUCTION: The aim of this study was to investigate the feasibility of generating synthesized ultrasound biomicroscopy (UBM) images from swept-source anterior segment optical coherent tomography (SS-ASOCT) images using a cycle-consistent generative adversarial network framework (CycleGAN) for iridociliary assessment on a cohort presenting for primary angle-closure screening. METHODS: The CycleGAN architecture was adopted to synthesize high-resolution UBM images trained on the SS-ASOCT dataset from the department of ophthalmology, Xinhua Hospital. The performance of the CycleGAN model was further tested in two separate datasets using synthetic UBM images from two different ASOCT modalities (in-distribution and out-of-distribution). We compared the ability of glaucoma specialists to assess the image quality of real and synthetic images. UBM measurements, including anterior chamber, iridociliary parameters, were compared between real and synthetic UBM images. Intra-class correlation coefficients, coefficients of variation, and Bland-Altman plots were used to assess the level of agreement. The Fréchet Inception Distance (FID) was measured to evaluate the quality of the synthetic images. RESULTS: The whole trained dataset included anterior chamber angle images, of which 4037 were obtained by SS-ASOCT and 2206 were obtained by UBM. The image quality of real versus synthetic SS-ASOCT images was similar as assessed by two glaucoma specialists. The Bland-Altman analysis also suggested high consistency between measurements of real and synthetic UBM images. In addition, there was fair to excellent agreement between real and synthetic UBM measurements for the in-distribution dataset (ICC range 0.48-0.97) and the out-of-distribution dataset (ICC range 0.52-0.86). The FID was 21.3 and 24.1 for the synthetic UBM images from the in-distribution and out-of-distribution datasets, respectively. CONCLUSION: We developed a CycleGAN model to translate UBM images from non-contact SS-ASOCT images. The CycleGAN synthetic UBM images showed fair to excellent reproducibility when compared with real UBM images. Our results suggest that the CycleGAN technique is a promising tool to evaluate the iridociliary and anterior chamber in an alternative non-contact method.

Sex disparities and the risk of urolithiasis: a large cross-sectional study.

BACKGROUND: Urolithiasis is one of the most common diseases in urology, with a lifetime prevalence of 14% and is more prevalent in males compared to females. We designed to explore sex disparities in the Chinese population to provide evidence for prevention measures and mechanisms of stone formation. MATERIALS AND METHODS: A total of 98232 Chinese individuals who had undergone a comprehensive examination in 2017 were included. Fully adjusted odds ratios for kidney stones were measured using restricted cubic splines. Multiple imputations was applied for missing values. Propensity score matching was utilised for sensitivity analysis. RESULTS: Among the 98232 included participants, 42762 participants (43.53%) were females and 55470 participants (56.47%) were males. Patients' factors might cast an influence on the development of kidney stone disease distinctly between the two genders. A risk factor for one gender might have no effect on the other gender. The risk for urolithiasis in females continuously rises as ageing, while for males the risk presents a trend to ascend until the age of around 53 and then descend. CONCLUSIONS: Patients' factors might influence the development of kidney stones distinctly between the two genders. As age grew, the risk to develop kidney stones in females continuously ascended, while the risk in males presented a trend to ascend and then descend, which was presumably related to the weakening of the androgen signals.Key messagesWe found that patients' factors might cast an influence on the development of kidney stone disease distinctly between the two sexes.The association between age and urolithiasis presents distinct trends in the two sexesThe results will provide evidence to explore the mechanisms underlying such differences can cast light on potential therapeutic targets and promote the development of tailored therapy strategies in prospect.

Development and Clinical Validation of Semi-Supervised Generative Adversarial Networks for Detection of Retinal Disorders in Optical Coherence Tomography Images Using Small Dataset.

PURPOSE: To develop and test semi-supervised generative adversarial networks (GANs) that detect retinal disorders on optical coherence tomography (OCT) images using a small-labeled dataset. METHODS: From a public database, we randomly chose a small supervised dataset with 400 OCT images (100 choroidal neovascularization, 100 diabetic macular edema, 100 drusen, and 100 normal) and assigned all other OCT images to unsupervised dataset (107,912 images without labeling). We adopted a semi-supervised GAN and a supervised deep learning (DL) model for automatically detecting retinal disorders from OCT images. The performance of the 2 models was compared in 3 testing datasets with different OCT devices. The evaluation metrics included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curves. RESULTS: The local validation dataset included 1000 images with 250 from each category. The independent clinical dataset included 366 OCT images using Cirrus OCT Shanghai Shibei Hospital and 511 OCT images using RTVue OCT from Xinhua Hospital respectively. The semi-supervised GANs classifier achieved better accuracy than supervised DL model (0.91 vs 0.86 for local cell validation dataset, 0.91 vs 0.86 in the Shanghai Shibei Hospital testing dataset, and 0.93 vs 0.92 in Xinhua Hospital testing dataset). For detecting urgent referrals (choroidal neo-vascularization and diabetic macular edema) from nonurgent referrals (drusen and normal) on OCT images, the semi-supervised GANs classifier also achieved better area under the receiver operating characteristic curves than supervised DL model (0.99 vs 0.97, 0.97 vs 0.96, and 0.99 vs 0.99, respectively). CONCLUSIONS: A semi-supervised GAN can achieve better performance than that of a supervised DL model when the labeled dataset is limited. The current study offers utility to various research and clinical studies using DL with relatively small datasets. Semi-supervised GANs can detect retinal disorders from OCT images using relatively small dataset.

Comparison of iridolenticular contact area under different light brightness between post primary acute angle closure and cataract eyes.

BACKGROUND: To study the iridolenticular contact area (ILCA) under different light conditions in acute primary angle closure (APAC). METHODS: This cross-sectional, observational study involved 22 unilateral APAC patients and 59 cataract patients (59 eyes). Images of the APAC eyes, fellow eyes and cataract eyes were collected by anterior segment optical coherence tomography (ASOCT) under different light conditions respectively. The ILCA, anterior chamber width (ACW), anterior chamber area (ACA), lens vault (LV), angle opening distance at 750 µm (AOD750), trabecular iris space area at 750 µm (TISA750) and iris area at 750 µm (IA750) were measured using Image J software. RESULTS: The ILCA of cataract eyes were significantly larger than APAC eyes (4.424 ± 1.208 vs 4.049 ± 2.725mm2, P = 0.034) and fellow eyes (4.424 ± 1.208 vs 3.651 ± 1.629 mm2, P = 0.008) under dark condition. Under dark condition, ILCA of APAC eyes was negatively correlated with AOD750 (r = -0.444, P = 0.038), TISA750 (r = -0.498, P = 0.018). The ILCA of cataract eyes under dark condition was significantly greater than under bright condition (4.424 ± 1.208 vs 2.526 ± 0.992 mm2, P < 0.001). CONCLUSIONS: This study showed that ILCA in both APAC eye and fellow eye were smaller than cataract eye. Future study should focus on both the contact area and force at the interface of lens and iris with larger sample size.

Sex Disparities in the Association of Serum Uric Acid With Kidney Stone: A Cross-Sectional Study in China.

BACKGROUND AND AIMS: Urolithiasis is characterized by high rates of prevalence and recurrence. Hyperuricemia is related to various diseases. We hope to determine the association between serum uric acid (UA) level and kidney stone (KS). METHODS: In this population-based cross-sectional study, a total of 82,017 Chinese individuals who underwent a comprehensive examination in 2017 were included. The KS was diagnosed based on ultrasonography examination outcomes. Fully adjusted odds ratio (OR) for KS, and mean difference between the two groups were applied to determine the association of UA level with KS. RESULTS: Among the 82,017 participants included in this study (aged 18~99 years), 9,435 participants (11.5%) are diagnosed with KS. A proportion of 56.3% of individuals is male. The mean UA level of overall participants is 341.77 µmol/L. The participants with KS report higher UA level than the participants without KS [mean UA level 369.91 vs. 338.11 µmol/L; mean difference (MD), 31.96 (95% CI, 29.61~34.28) µmol/L]. In men, the OR for KS significantly increases from 330 µmol/L UA level. Every 50 µmol/L elevation of UA level increases the risk of KS formation by about 10.7% above the UA level of 330 µmol/L in men. The subgroup analysis for male is consistent with the overall result except for the participants presenting underweight [adjusted OR, 1.035 (0.875~1.217); MD, -5.57 (-16.45~11.37)], low cholesterol [adjusted OR, 1.088 (0.938~1.261); MD, 8.18 (-7.93~24.68)] or high estimated glomerular filtration rate (eGFR) [adjusted OR, 1.044 (0.983~1.108); MD, 5.61 (-1.84~13.36)]. However, no significant association is observed in women between UA and KS either in all female participants or in female subgroups. CONCLUSION: Among Chinese adults, UA level is associated with KS in a dose-response manner in men but not in women. However, the association becomes considerably weak in male participants with malnutrition status.

Detection of shallow anterior chamber depth from two-dimensional anterior segment photographs using deep learning.

BACKGROUND: The purpose of this study was to implement and evaluate a deep learning (DL) approach for automatically detecting shallow anterior chamber depth (ACD) from two-dimensional (2D) overview anterior segment photographs. METHODS: We trained a DL model using a dataset of anterior segment photographs collected from Shanghai Aier Eye Hospital from June 2018 to December 2019. A Pentacam HR system was used to capture a 2D overview eye image and measure the ACD. Shallow ACD was defined as ACD less than 2.4 mm. The DL model was evaluated by a five-fold cross-validation test in a hold-out testing dataset. We also evaluated the DL model by testing it against two glaucoma specialists. The performance of the DL model was calculated by metrics, including accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: A total of 3753 photographs (1720 shallow AC and 2033 deep AC images) were assigned to the training dataset, and 1302 photographs (509 shallow AC and 793 deep AC images) were held out for two internal testing datasets. In detecting shallow ACD in the internal hold-out testing dataset, the DL model achieved an AUC of 0.86 (95% CI, 0.83-0.90) with 80% sensitivity and 79% specificity. In the same testing dataset, the DL model also achieved better performance than the two glaucoma specialists (accuracy of 80% vs. accuracy of 74 and 69%). CONCLUSIONS: We proposed a high-performing DL model to automatically detect shallow ACD from overview anterior segment photographs. Our DL model has potential applications in detecting and monitoring shallow ACD in the real world. TRIAL REGISTRATION: http://clinicaltrials.gov , NCT04340635 , retrospectively registered on 29 March 2020.

Semi-supervised generative adversarial networks for closed-angle detection on anterior segment optical coherence tomography images: an empirical study with a small training dataset.

BACKGROUND: Semi-supervised learning algorithms can leverage an unlabeled dataset when labeling is limited or expensive to obtain. In the current study, we developed and evaluated a semi-supervised generative adversarial networks (GANs) model that detects closed-angle on anterior segment optical coherence tomography (AS-OCT) images using a small labeled dataset. METHODS: In this cross-sectional study, a semi-supervised GANs model was developed for automatic closed-angle detection training on a small labeled and large unsupervised training dataset collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong (JSIEC). The closed-angle was defined as iris-trabecular contact beyond the scleral spur in AS-OCT images. We further developed two supervised deep learning (DL) models training on the same supervised dataset and the whole dataset separately. The semi-supervised GANs model and supervised DL models' performance were compared on two independent testing datasets from JSIEC (515 images) and the Department of Ophthalmology (84 images), National University Health System, respectively. The diagnostic performance was assessed by evaluation matrices, including the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS: For closed-angle detection using clinician grading of AS-OCT imaging as the reference standard, the semi-supervised GANs model showed comparable performance, with AUCs of 0.97 (95% CI, 0.96-0.99) and 0.98 (95% CI, 0.94-1.00), compared with the supervised DL model (using the whole dataset) [AUCs of 0.97 (95% CI, 0.96-0.99), and 0.97 (95% CI, 0.94-1.00)]. When training on the same small supervised dataset, the semi-supervised GANs achieved performance at least as well as, if not better than, the supervised DL model [AUCs of 0.90 (95% CI: 0.84-0.96), and 0.92 (95% CI, 0.86-0.97)]. CONCLUSIONS: The semi-supervised GANs method achieves diagnostic performance at least as good as a supervised DL model when trained on small labeled datasets. Further development of semi-supervised learning methods could be useful within clinical and research settings. TRIAL REGISTRATION NUMBER: ChiCTR2000037892.

Photothermal treatment of oropharyngeal cancer with carbon-defective silicon carbide.

Oral squamous carcinoma (OSCC) is a clinical common tumor with high recurrence rate and low 5 year survival rate. In this work, photothermal antitumor treatment has been performed to treat OSCC by taking anti-wound infection into consideration. By introducing C defects, we have successfully converted the semi-conductive SiC into metallic carbon-defective silicon carbide (SiC1-x), and endowed it with the near infrared absorption property for photothermal therapy (PTT). The results revealed that SiC1-x mediated PTT treatment could remove solid OSCC tumor in a biosafe way, showing low hematotoxicity, cytotoxicity and tissue toxicity. Moreover, the low invasion of PTT treatment could not only prevent the invasion of bacteria, but also realize an antibacterial effect on the wound, both of which are important for oral surgery. SiC1-x could be excreted from the body post treatment, which thus reduces the long-term potential toxicity. On the whole, this study provided a promising way to treat OSCC in an effective and safe way.

Determinants of peripapillary retinal nerve fiber layer's grayscale value in normal eyes by spectral domain optical coherence tomography.

To determine and evaluate the distribution, variation, and determinants of peripapillary retinal nerve fiber layer (pRNFL) grayscale value with spectral-domain optical coherence tomography (SD-OCT) in normal eyes. In this cross-sectional study, three hundred ninety-seven normal eyes from 397 healthy Chinese adults aged 18-80 were consecutively recruited from a tertiary eye care center. An SD-OCT instrument took pRNFL imaging. We used a customized software to measure pRNFL parameters, including thickness and grayscale value. Univariable and multiple linear regression analyses were performed to examine the relationship between pRNFL grayscale value with ocular (e.g., axial length [A.L.], spherical equivalent [S.E.], intraocular pressure [IOP]), and systemic (e.g., age, sex) factors. A total of 397 eyes from 397 healthy subjects were included in the final analysis with mean (± SD) age 44.63 ± 16.43 years (range 18-80 years) and 196 (49.4%) males. The mean average of pRNFL grayscale value and thickness 164.82 ± 5.69 and 106.68 ± 8.89 µm, respectively. pRNFL grayscale value in nasal sectors (163.26 ± 9.31) was significantly lower comparing those in all other five sectors (all with p < 0.001)]. In multivariable analysis, average pRNFL grayscale value was independently correlated to older age (ß = - 0.053, p = 0.002), longer axial length (ß = - 0.664, p = 0.003), lower RPE grayscale value (ß = 0.372, p < 0.001) and lower ImageQ (ß = 0.658, p < 0.001). In this study, we provided normative SD-OCT data on the pRNFL grayscale value profile in nonglaucomatous eyes. Lower average pRNFL grayscale value was independently correlated to older age, longer axial length, lower RPE grayscale value, and lower ImageQ. These determinants should be considered when interpreting pRNFL grayscale value in glaucoma assessment.

Assessment of Generative Adversarial Networks for Synthetic Anterior Segment Optical Coherence Tomography Images in Closed-Angle Detection.

Purpose: To develop generative adversarial networks (GANs) that synthesize realistic anterior segment optical coherence tomography (AS-OCT) images and evaluate deep learning (DL) models that are trained on real and synthetic datasets for detecting angle closure. Methods: The GAN architecture was adopted and trained on the dataset with AS-OCT images collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, synthesizing open- and closed-angle AS-OCT images. A visual Turing test with two glaucoma specialists was performed to assess the image quality of real and synthetic images. DL models, trained on either real or synthetic datasets, were developed. Using the clinicians' grading of the AS-OCT images as the reference standard, we compared the diagnostic performance of open-angle vs. closed-angle detection of DL models and the AS-OCT parameter, defined as a trabecular-iris space area 750 µm anterior to the scleral spur (TISA750), in a small independent validation dataset. Results: The GAN training included 28,643 AS-OCT anterior chamber angle (ACA) images. The real and synthetic datasets for DL model training have an equal distribution of open- and closed-angle images (all with 10,000 images each). The independent validation dataset included 238 open-angle and 243 closed-angle AS-OCT ACA images. The image quality of real versus synthetic AS-OCT images was similar, as assessed by the two glaucoma specialists, except for the scleral spur visibility. For the independent validation dataset, both DL models achieved higher areas under the curve compared with TISA750. Two DL models had areas under the curve of 0.97 (95% confidence interval, 0.96-0.99) and 0.94 (95% confidence interval, 0.92-0.96). Conclusions: The GAN synthetic AS-OCT images appeared to be of good quality, according to the glaucoma specialists. The DL models, trained on all-synthetic AS-OCT images, can achieve high diagnostic performance. Translational Relevance: The GANs can generate realistic AS-OCT images, which can also be used to train DL models.

Detection of Referable Horizontal Strabismus in Children's Primary Gaze Photographs Using Deep Learning.

Purpose: This study implements and demonstrates a deep learning (DL) approach for screening referable horizontal strabismus based on primary gaze photographs using clinical assessments as a reference. The purpose of this study was to develop and evaluate deep learning algorithms that screen referable horizontal strabismus in children's primary gaze photographs. Methods: DL algorithms were developed and trained using primary gaze photographs from two tertiary hospitals of children with primary horizontal strabismus who underwent surgery as well as orthotropic children who underwent routine refractive tests. A total of 7026 images (3829 non-strabismus from 3021 orthoptics [healthy] subjects and 3197 strabismus images from 2772 subjects) were used to develop the DL algorithms. The DL model was evaluated by 5-fold cross-validation and tested on an independent validation data set of 277 images. The diagnostic performance of the DL algorithm was assessed by calculating the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). Results: Using 5-fold cross-validation during training, the average AUCs of the DL models were approximately 0.99. In the external validation data set, the DL algorithm achieved an AUC of 0.99 with a sensitivity of 94.0% and a specificity of 99.3%. The DL algorithm's performance (with an accuracy of 0.95) in diagnosing referable horizontal strabismus was better than that of the resident ophthalmologists (with accuracy ranging from 0.81 to 0.85). Conclusions: We developed and evaluated a DL model to automatically identify referable horizontal strabismus using primary gaze photographs. The diagnostic performance of the DL model is comparable to or better than that of ophthalmologists. Translational Relevance: DL methods that automate the detection of referable horizontal strabismus can facilitate clinical assessment and screening for children at risk of strabismus.

Development and validation of deep learning algorithms for automated eye laterality detection with anterior segment photography.

This paper aimed to develop and validate a deep learning (DL) model for automated detection of the laterality of the eye on anterior segment photographs. Anterior segment photographs for training a DL model were collected with the Scheimpflug anterior segment analyzer. We applied transfer learning and fine-tuning of pre-trained deep convolutional neural networks (InceptionV3, VGG16, MobileNetV2) to develop DL models for determining the eye laterality. Testing datasets, from Scheimpflug and slit-lamp digital camera photography, were employed to test the DL model, and the results were compared with a classification performed by human experts. The performance of the DL model was evaluated by accuracy, sensitivity, specificity, operating characteristic curves, and corresponding area under the curve values. A total of 14,468 photographs were collected for the development of DL models. After training for 100 epochs, the DL models of the InceptionV3 mode achieved the area under the receiver operating characteristic curve of 0.998 (with 95% CI 0.924-0.958) for detecting eye laterality. In the external testing dataset (76 primary gaze photographs taken by a digital camera), the DL model achieves an accuracy of 96.1% (95% CI 91.7%-100%), which is better than an accuracy of 72.3% (95% CI 62.2%-82.4%), 82.8% (95% CI 78.7%-86.9%) and 86.8% (95% CI 82.5%-91.1%) achieved by human graders. Our study demonstrated that this high-performing DL model can be used for automated labeling for the laterality of eyes. Our DL model is useful for managing a large volume of the anterior segment images with a slit-lamp camera in the clinical setting.