Papaverine and its derivatives radiosensitize solid tumors by inhibiting mitochondrial metabolism.

Tumor hypoxia reduces the effectiveness of radiation therapy by limiting the biologically effective dose. An acute increase in tumor oxygenation before radiation treatment should therefore significantly improve the tumor cell kill after radiation. Efforts to increase oxygen delivery to the tumor have not shown positive clinical results. Here we show that targeting mitochondrial respiration results in a significant reduction of the tumor cells' demand for oxygen, leading to increased tumor oxygenation and radiation response. We identified an activity of the FDA-approved drug papaverine as an inhibitor of mitochondrial complex I. We also provide genetic evidence that papaverine's complex I inhibition is directly responsible for increased oxygenation and enhanced radiation response. Furthermore, we describe derivatives of papaverine that have the potential to become clinical radiosensitizers with potentially fewer side effects. Importantly, this radiosensitizing strategy will not sensitize well-oxygenated normal tissue, thereby increasing the therapeutic index of radiotherapy.

Intelligent diagnostic model for pterygium by combining attention mechanism and MobileNetV2.

AIM: To evaluate the application of an intelligent diagnostic model for pterygium. METHODS: For intelligent diagnosis of pterygium, the attention mechanisms-SENet, ECANet, CBAM, and Self-Attention-were fused with the lightweight MobileNetV2 model structure to construct a tri-classification model. The study used 1220 images of three types of anterior ocular segments of the pterygium provided by the Eye Hospital of Nanjing Medical University. Conventional classification models-VGG16, ResNet50, MobileNetV2, and EfficientNetB7-were trained on the same dataset for comparison. To evaluate model performance in terms of accuracy, Kappa value, test time, sensitivity, specificity, the area under curve (AUC), and visual heat map, 470 test images of the anterior segment of the pterygium were used. RESULTS: The accuracy of the MobileNetV2+Self-Attention model with 281 MB in model size was 92.77%, and the Kappa value of the model was 88.92%. The testing time using the model was 9ms/image in the server and 138ms/image in the local computer. The sensitivity, specificity, and AUC for the diagnosis of pterygium using normal anterior segment images were 99.47%, 100%, and 100%, respectively; using anterior segment images in the observation period were 88.30%, 95.32%, and 96.70%, respectively; and using the anterior segment images in the surgery period were 88.18%, 94.44%, and 97.30%, respectively. CONCLUSION: The developed model is lightweight and can be used not only for detection but also for assessing the severity of pterygium.

Measurement method of tear meniscus height based on deep learning.

Tear meniscus height (TMH) is an important reference parameter in the diagnosis of dry eye disease. However, most traditional methods of measuring TMH are manual or semi-automatic, which causes the measurement of TMH to be prone to the influence of subjective factors, time consuming, and laborious. To solve these problems, a segmentation algorithm based on deep learning and image processing was proposed to realize the automatic measurement of TMH. To accurately segment the tear meniscus region, the segmentation algorithm designed in this study is based on the DeepLabv3 architecture and combines the partial structure of the ResNet50, GoogleNet, and FCN networks for further improvements. A total of 305 ocular surface images were used in this study, which were divided into training and testing sets. The training set was used to train the network model, and the testing set was used to evaluate the model performance. In the experiment, for tear meniscus segmentation, the average intersection over union was 0.896, the dice coefficient was 0.884, and the sensitivity was 0.877. For the central ring of corneal projection ring segmentation, the average intersection over union was 0.932, the dice coefficient was 0.926, and the sensitivity was 0.947. According to the evaluation index comparison, the segmentation model used in this study was superior to the existing model. Finally, the measurement outcome of TMH of the testing set using the proposed method was compared with manual measurement results. All measurement results were directly compared via linear regression; the regression line was y0.98x-0.02, and the overall correlation coefficient was r 20.94. Thus, the proposed method for measuring TMH in this paper is highly consistent with manual measurement and can realize the automatic measurement of TMH and assist clinicians in the diagnosis of dry eye disease.

Artificial intelligence assisted pterygium diagnosis: current status and perspectives.

Pterygium is a prevalent ocular disease that can cause discomfort and vision impairment. Early and accurate diagnosis is essential for effective management. Recently, artificial intelligence (AI) has shown promising potential in assisting clinicians with pterygium diagnosis. This paper provides an overview of AI-assisted pterygium diagnosis, including the AI techniques used such as machine learning, deep learning, and computer vision. Furthermore, recent studies that have evaluated the diagnostic performance of AI-based systems for pterygium detection, classification and segmentation were summarized. The advantages and limitations of AI-assisted pterygium diagnosis and discuss potential future developments in this field were also analyzed. The review aims to provide insights into the current state-of-the-art of AI and its potential applications in pterygium diagnosis, which may facilitate the development of more efficient and accurate diagnostic tools for this common ocular disease.

Advances in artificial intelligence applications for ocular surface diseases diagnosis.

In recent years, with the rapid development of computer technology, continual optimization of various learning algorithms and architectures, and establishment of numerous large databases, artificial intelligence (AI) has been unprecedentedly developed and applied in the field of ophthalmology. In the past, ophthalmological AI research mainly focused on posterior segment diseases, such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration, retinal vein occlusion, and glaucoma optic neuropathy. Meanwhile, an increasing number of studies have employed AI to diagnose ocular surface diseases. In this review, we summarize the research progress of AI in the diagnosis of several ocular surface diseases, namely keratitis, keratoconus, dry eye, and pterygium. We discuss the limitations and challenges of AI in the diagnosis of ocular surface diseases, as well as prospects for the future.

Classification of dry and wet macular degeneration based on the ConvNeXT model.

Purpose: To assess the value of an automated classification model for dry and wet macular degeneration based on the ConvNeXT model. Methods: A total of 672 fundus images of normal, dry, and wet macular degeneration were collected from the Affiliated Eye Hospital of Nanjing Medical University and the fundus images of dry macular degeneration were expanded. The ConvNeXT three-category model was trained on the original and expanded datasets, and compared to the results of the VGG16, ResNet18, ResNet50, EfficientNetB7, and RegNet three-category models. A total of 289 fundus images were used to test the models, and the classification results of the models on different datasets were compared. The main evaluation indicators were sensitivity, specificity, F1-score, area under the curve (AUC), accuracy, and kappa. Results: Using 289 fundus images, three-category models trained on the original and expanded datasets were assessed. The ConvNeXT model trained on the expanded dataset was the most effective, with a diagnostic accuracy of 96.89%, kappa value of 94.99%, and high diagnostic consistency. The sensitivity, specificity, F1-score, and AUC values for normal fundus images were 100.00, 99.41, 99.59, and 99.80%, respectively. The sensitivity, specificity, F1-score, and AUC values for dry macular degeneration diagnosis were 87.50, 98.76, 90.32, and 97.10%, respectively. The sensitivity, specificity, F1-score, and AUC values for wet macular degeneration diagnosis were 97.52, 97.02, 96.72, and 99.10%, respectively. Conclusion: The ConvNeXT-based category model for dry and wet macular degeneration automatically identified dry and wet macular degeneration, aiding rapid, and accurate clinical diagnosis.

Nanocomposite hydrogels for biomedical applications.

Nanomaterials' unique structures at the nanometer level determine their incredible functions, and based on this, they can be widely used in the field of nanomedicine. However, nanomaterials do possess disadvantages that cannot be ignored, such as burst release, rapid elimination, and poor bioadhesion. Hydrogels are scaffolds with three-dimensional structures, and they exhibit good biocompatibility and drug release capacity. Hydrogels are also associated with disadvantages for biomedical applications such as poor anti-tumor capability, weak bioimaging capability, limited responsiveness, and so on. Incorporating nanomaterials into the 3D hydrogel network through physical or chemical covalent action may be an effective method to avoid their disadvantages. In nanocomposite hydrogel systems, multifunctional nanomaterials often work as the function core, giving the hydrogels a variety of properties (such as photo-thermal conversion, magnetothermal conversion, conductivity, targeting tumor, etc.). While, hydrogels can effectively improve the retention effect of nanomaterials and make the nanoparticles have good plasticity to adapt to various biomedical applications (such as various biosensors). Nanocomposite hydrogel systems have broad application prospects in biomedicine. In this review, we comprehensively summarize and discuss the most recent advances of nanomaterials composite hydrogels in biomedicine, including drug and cell delivery, cancer treatment, tissue regeneration, biosensing, and bioimaging, and we also briefly discussed the current situation of their commoditization in biomedicine.

2D Nanomaterials for Tissue Engineering and Regenerative Nanomedicines: Recent Advances and Future Challenges.

Regenerative medicine has become one of the hottest research topics in medical science that provides a promising way for repairing tissue defects in the human body. Due to their excellent physicochemical properties, the application of 2D nanomaterials in regenerative medicine has gradually developed and has been attracting a wide range of research interests in recent years. In particular, graphene and its derivatives, black phosphorus, and transition metal dichalcogenides are applied in all the aspects of tissue engineering to replace or restore tissues. This review focuses on the latest advances in the application of 2D-nanomaterial-based hydrogels, nanosheets, or scaffolds that are engineered to repair skin, bone, and cartilage tissues. Reviews on other applications, including cardiac muscle regeneration, skeletal muscle repair, nerve regeneration, brain disease treatment, and spinal cord healing are also provided. The challenges and prospects of applications of 2D nanomaterials in regenerative medicine are discussed.

Smart nano-micro platforms for ophthalmological applications: The state-of-the-art and future perspectives.

Smart nano-micro platforms have been extensively applied for diverse biomedical applications, mostly focusing on cancer therapy. In comparison with conventional nanotechnology, the smart nano-micro matrix can exhibit specific response to exogenous or endogenous triggers, and thus can achieve multiple functions e.g. site-specific drug delivery, bio-imaging and detection of bio-molecules. These intriguing techniques have expanded into ophthalmology in recent years, yet few works have been summarized in this field. In this work, we provide the state-of-the-art of diverse nano-micro platforms based on both the conventional materials (e.g. natural or synthetic polymers, lipid nanomaterials, metal and metal oxide nanoparticles) and emerging nanomaterials (e.g. up-conversion nanoparticles, quantum dots and carbon materials) in ophthalmology, with some smart nano/micro platformers highlighted. The common ocular diseases studied in the field of nano-micro systems are firstly introduced, and their therapeutic method and the related drawback in clinic treatment are presented. The recent progress of different materials for diverse ocular applications is then demonstrated, with the representative nano- and micro-systems highlighted in detail. At last, an in-depth discussion on the clinical translation challenges faced in this field and the future direction are provided. This review would allow the researchers to design more smart nanomedicines in a more rational manner for specific ophthalmology applications.

A Dual-modality Smartphone Microendoscope for Quantifying the Physiological and Morphological Properties of Epithelial Tissues.

We report a nonconcurrent dual-modality fiber-optic microendoscope (named SmartME) that integrates quantitative diffuse reflectance spectroscopy (DRS) and high-resolution fluorescence imaging (FLI) into a smartphone platform. The FLI module has a spatial resolution of ~3.5 µm, which allows the determination of the nuclear-cytoplasmic ratio (N/C) of epithelial tissues. The DRS has a spectral resolution of ~2 nm and can measure the total hemoglobin concentration (THC) and scattering properties of epithelial tissues with mean errors of 4.7% and 6.9%, respectively, which are comparable to the errors achieved with a benchtop spectrometer. Our preliminary in vivo studies from a single healthy human subject demonstrate that the SmartME can noninvasively quantify the tissue parameters of normal human oral mucosa tissues, including labial mucosa tissue, gingival tissue, and tongue dorsum tissue. The THCs of the three oral mucosa tissues are significantly different from each other (p ≤ 0.003). The reduced scattering coefficients of the gingival and labial tissues are significantly different from those of the tongue dorsum tissue (p < 0.001) but are not significantly different from each other. The N/Cs for all three tissue types are similar. The SmartME has great potential to be used as a portable, cost-effective, and globally connected tool to quantify the THC and scattering properties of tissues in vivo.

Smartphone based optical spectrometer for diffusive reflectance spectroscopic measurement of hemoglobin.

We report a miniature, visible to near infrared G-Fresnel spectrometer that contains a complete spectrograph system, including the detection hardware and connects with a smartphone through a microUSB port for operational control. The smartphone spectrometer is able to achieve a resolution of ~5 nm in a wavelength range from 400 nm to 1000 nm. We further developed a diffuse reflectance spectroscopy system using the smartphone spectrometer and demonstrated the capability of hemoglobin measurement. Proof of concept studies of tissue phantoms yielded a mean error of 9.2% on hemoglobin concentration measurement, comparable to that obtained with a commercial benchtop spectrometer. The smartphone G-Fresnel spectrometer and the diffuse reflectance spectroscopy system can potentially enable new point-of-care opportunities, such as cancer screening.