Research on the damage and wound repair of cornea by GO and ZnO.

OBJECTIVE: The widespread use of nanoparticles in recent years has increased the risk of ocular exposure. zinc oxide (ZnO) is widely used in the field of cosmetics because of its unique chemical properties. The application of graphene oxide (GO) as an emerging nanomaterial in the field of eye drops is also gradually emerging. Currently, research on ZnO and GO eye exposure mainly focuses on application or toxicity to optic nerve cells. There's less study on corneal wound healing effects. and the previous research hasn't compared ZnO and GO corneal toxicity. METHODS: We systematically established a complete chain study of in vitro and in vivo experiments and mouse corneal injury model, and comprehensively evaluated the ocular safety and toxicity of ZnO and GO. RESULTS: We found that 50 ug/mL GO and 0.5 ug/mL ZnO can reduce human corneal epithelial cells (HCEpiC) viability in a concentration-dependent manner. Short-term repeated exposure to ZnO can cause sterile inflammation of the cornea with concentration-dependence, while GO have not been significantly altered. 50 ug/mL ZnO could significantly delay the healing of corneal wounds, while GO did not change wound healing. CONCLUSION: The toxic effect of ZnO is higher than that of GO. Inflammatory signal transduction, oxidative stress and apopnano zitosis are involved in the ocular toxicity injury process of nanoparticles. Research can provide a judgement basis for people's eye health and eye protection risk control.

A new ChatGPT-empowered, easy-to-use machine learning paradigm for environmental science.

The quantity and complexity of environmental data show exponential growth in recent years. High-quality big data analysis is critical for performing a sophisticated characterization of the complex network of environmental pollution. Machine learning (ML) has been employed as a powerful tool for decoupling the complexities of environmental big data based on its remarkable fitting ability. Yet, due to the knowledge gap across different subjects, ML concepts and algorithms have not been well-popularized among researchers in environmental sustainability. In this context, we introduce a new research paradigm-"ChatGPT + ML + Environment", providing an unprecedented chance for environmental researchers to reduce the difficulty of using ML models. For instance, each step involved in applying ML models to environmental sustainability, including data preparation, model selection and construction, model training and evaluation, and hyper-parameter optimization, can be easily performed with guidance from ChatGPT. We also discuss the challenges and limitations of using this research paradigm in the field of environmental sustainability. Furthermore, we highlight the importance of "secondary training" for future application of "ChatGPT + ML + Environment".

Combining hyperspectral imaging techniques with deep learning to aid in early pathological diagnosis of melanoma.

BACKGROUND: Cutaneous melanoma, an exceedingly aggressive form of skin cancer, holds the top rank in both malignancy and mortality among skin cancers. In early stages, distinguishing malignant melanomas from benign pigmented nevi pathologically becomes a significant challenge due to their indistinguishable traits. Traditional skin histological examination techniques, largely reliant on light microscopic imagery, offer constrained information and yield low-contrast results, underscoring the necessity for swift and effective early diagnostic methodologies. As a non-contact, non-ionizing, and label-free imaging tool, hyperspectral imaging offers potential in assisting pathologists with identification procedures sans contrast agents. METHODS: This investigation leverages hyperspectral cameras to ascertain the optical properties and to capture the spectral features of malignant melanoma and pigmented nevus tissues, intending to facilitate early pathological diagnostic applications. We further enhance the diagnostic process by integrating transfer learning with deep convolutional networks to classify melanomas and pigmented nevi in hyperspectral pathology images. The study encompasses pathological sections from 50 melanoma and 50 pigmented nevus patients. To accurately represent the spectral variances between different tissues, we employed reflectance calibration, highlighting that the most distinctive spectral differences emerged within the 500-675 nm band range. RESULTS: The classification accuracy of pigmented tumors and pigmented nevi was 89% for one-dimensional sample data and 98% for two-dimensional sample data. CONCLUSIONS: Our findings have the potential to expedite pathological diagnoses, enhance diagnostic precision, and offer novel research perspectives in differentiating melanoma and nevus.