We have developed a non-invasive photoacoustic ophthalmoscopy (PAOM) for in vivo retinal imaging. PAOM detects the photoacoustic signal induced by pulsed laser light shined onto the retina. By using a stationary ultrasonic transducer in contact with the eyelids and scanning only the laser light across the retina, PAOM provides volumetric imaging of the retinal micro-vasculature and retinal pigment epithelium at a high speed. For B-scan frames containing 256 A-lines, the current PAOM has a frame rate of 93 Hz, which is comparable with state-of-the-art commercial spectral-domain optical coherence tomography (SD-OCT). By integrating PAOM with SD-OCT, we further achieved OCT-guided PAOM, which can provide multi-modal retinal imaging simultaneously. The capabilities of this novel technology were demonstrated by imaging both the microanatomy and microvasculature of the rat retina in vivo.

Diagnostics is the key in screening and treatment of cancer. As an emerging tool in precision medicine, metabolic analysis detects end products of pathways, and thus is more distal than proteomic/genetic analysis. However, metabolic analysis is far from ideal in clinical diagnosis due to the sample complexity and metabolite abundance in patient specimens. A further challenge is real‐time and accurate tracking of treatment effect, e.g., radiotherapy. Here, Pd–Au synthetic alloys are reported for mass‐spectrometry‐based metabolic fingerprinting and analysis, toward medulloblastoma diagnosis and radiotherapy evaluation. A core–shell structure is designed using magnetic core particles to support Pd–Au alloys on the surface. Optimized synthetic alloys enhance the laser desorption/ionization efficacy and achieve direct detection of 100 nL of biofluids in seconds. Medulloblastoma patients are differentiated from healthy controls with average diagnostic sensitivity of 94.0%, specificity of 85.7%, and accuracy of 89.9%, by machine learning of metabolic fingerprinting. Furthermore, the radiotherapy process of patients is monitored and a preliminary panel of serum metabolite biomarkers is identified with gradual changes. This work will lead to the application‐driven development of novel materials with tailored structural design and establishment of new protocols for precision medicine in near future.

PurposeTo achieve automatic diabetic retinopathy (DR) detection in retinal fundus photographs through the use of a deep transfer learning approach using the Inception-v3 network.MethodsA total of 19,233 eye fundus color numerical images were retrospectively obtained from 5278 adult patients presenting for DR screening. The 8816 images passed image-quality review and were graded as no apparent DR (1374 images), mild nonproliferative DR (NPDR) (2152 images), moderate NPDR (2370 images), severe NPDR (1984 images), and proliferative DR (PDR) (936 images) by eight retinal experts according to the International Clinical Diabetic Retinopathy severity scale. After image preprocessing, 7935 DR images were selected from the above categories as a training dataset, while the rest of the images were used as validation dataset. We introduced a 10-fold cross-validation strategy to assess and optimize our model. We also selected the publicly independent Messidor-2 dataset to test the performance of our model. For discrimination between no referral (no apparent DR and mild NPDR) and referral (moderate NPDR, severe NPDR, and PDR), we also computed prediction accuracy, sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and κ value.ResultsThe proposed approach achieved a high classification accuracy of 93.49% (95% confidence interval [CI], 93.13%–93.85%), with a 96.93% sensitivity (95% CI, 96.35%–97.51%) and a 93.45% specificity (95% CI, 93.12%–93.79%), while the AUC was up to 0.9905 (95% CI, 0.9887–0.9923) on the independent test dataset. The κ value of our best model was 0.919, while the three experts had κ values of 0.906, 0.931, and 0.914, independently.ConclusionsThis approach could automatically detect DR with excellent sensitivity, accuracy, and specificity and could aid in making a referral recommendation for further evaluation and treatment with high reliability.Translational RelevanceThis approach has great value in early DR screening using retinal fundus photographs.

Purpose To evaluate the diagnostic performance of corneal confocal microscopy (CCM) in assessing corneal nerve parameters in patients with diabetic peripheral neuropathy (DPN). Methods Studies in the literature that focused on CCM and DPN were retrieved by searching PubMed, Excerpt Medica Database (EMBASE) and China National Knowledge Infrastructure (CNKI) databases. RevMan V.5.3 software was used for the meta-analysis. The results are presented as weighted mean difference (WMD) with a corresponding 95% CI. Results 13 studies with a total of 1680 participants were included in the meta-analysis. The pooled results showed that the corneal nerve fibre density, nerve branch density and nerve fibre length were significantly reduced (all p<0.00001) in the patients with DPN compared with healthy controls ((WMD=−18.07, 95% CI −21.93 to −14.20), (WMD=−25.35, 95% CI −30.96 to −19.74) and (WMD=−6.37, 95% CI −7.44 to −5.30)) and compared with the diabetic patients without DPN ((WMD=−8.83, 95% CI −11.49 to −6.17), (WMD=−13.54, 95% CI −20.41 to −6.66) and (WMD=−4.19, 95% CI −5.35 to −3.04)), respectively. No significant difference was found in the corneal nerve fibre tortuosity coefficient between diabetic patients with DPN and healthy controls ( p=0.80) or diabetic patients without DPN ( p=0.61). Conclusions This meta-analysis suggested that CCM may be valuable for detecting and assessing early nerve damage in DPN patients.

Detection of biomarkers in biosystems plays a key role in advanced biodiagnostics for research and clinical use. Design of new analytical platforms is challenging and in demand, addressing molecular capture and subsequent quantitation. Herein, we developed a label-free electrochemical sensor for CD44 by ligand-protein interaction. We assembled carbon nanotube composites on the electrode to enhance electronic conductivity by 6.2-fold and reduce overpotential with a shift of 77 mV. We conjugated hyaluronic acid (HA) to the surface of carbon nanotubes via electrostatic interaction between HA and poly(diallyldimethylammonium chloride) (PDDA). Consequently, we performed direct electrochemical sensing of CD44 with a dynamic range of 0.01−100 ng/mL and detection limit of 5.94 pg/mL without any postlabeling for amplification, comparable to the best current results. The sensor also displayed high selectivity, reproducibility with relative standard deviation (RSD, n = 5) of 2.57%, and long-term stability for 14 days. We demonstrated applications of the sensor in detection of human serum and cancer cells. Our work guides the development of more sensor types by ligand-protein interactions and contributes to design of interfaces in given biosystems for diagnosis.

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Retinal disease classification is a significant problem in computer-aided diagnosis (CAD) for medical applications. This paper is focused on a 4-class classification problem to automatically detect choroidal neovascularization (CNV), diabetic macular edema (DME), DRUSEN, and NORMAL in optical coherence tomography (OCT) images. The proposed classification algorithm adopted an ensemble of four classification model instances to identify retinal OCT images, each of which was based on an improved residual neural network (ResNet50). The experiment followed a patient-level 10-fold cross-validation process, on development retinal OCT image dataset. The proposed approach achieved 0.973 (95% confidence interval [CI], 0.971-0.975) classification accuracy, 0.963 (95% CI, 0.960-0.966) sensitivity, and 0.985 (95% CI, 0.983-0.987) specificity at the B-scan level, achieving a matching or exceeding performance to that of ophthalmologists with significant clinical experience. Other performance measures used in the study were the area under receiver operating characteristic curve (AUC) and kappa value. The observations of the study implied that multi-ResNet50 ensembling was a useful technique when the availability of medical images was limited. In addition, we performed qualitative evaluation of model predictions, and occlusion testing to understand the decision-making process of our model. The paper provided an analytical discussion on misclassification and pathology regions identified by the occlusion testing also. Finally, we explored the effect of the integration of retinal OCT images and medical history data from patients on model performance.

OBJECT The purpose of this study was to explore the clinical features and outcome of medulloblastoma in Chinese children. The authors analyze the reasons that treatment is abandoned and attempt to provide evidence-based recommendations for improving the prognosis of medulloblastoma in this population. METHODS A total of 67 pediatric cases of newly diagnosed medulloblastoma were included in this study. All of the children were treated at Xinhua Hospital between January 2007 and June 2013. The authors retrospectively analyzed the clinical data, treatment modalities, and outcome. The male-to-female ratio was 2:1, and the patients’ median age at diagnosis was 51.96 months (range 3.96–168.24 months). The median duration of follow-up was 32 months (range 3–70 months). RESULTS At the most recent follow-up date, 31 patients (46%) were alive, 30 (45%) had died, and 6 (9%) had been lost to follow-up. The estimated 3-year overall survival and progression-free survival, based on Kaplan-Meier analysis, were 55.1% ± 6.4% and 45.6% ± 6.7%, respectively. Univariate analysis showed that standard-risk group (p = 0.009), postoperative radiotherapy (RT) combined with chemotherapy (p < 0.001), older age (≥ 3 years) at diagnosis (p = 0.010), gross-total resection (p = 0.012), annual family income higher than $3000 (p = 0.033), and living in urban areas (p = 0.008) were favorable prognostic factors. Multivariate analysis revealed that postoperative RT combined with chemotherapy was an independent prognostic factor (p < 0.001). The treatment abandonment rate in this cohort was 31% (21 of 67 cases). CONCLUSIONS There was a large gap between the outcome of medulloblastoma in Chinese children and the outcome in Western children. Based on our data, treatment abandonment was the major cause of therapeutic failure. Parents’ misunderstanding of medulloblastoma played a major role in abandonment, followed by financial and transportation difficulties. Establishment of multidisciplinary treatment teams could improve the prognosis of medulloblastoma in Chinese children.