The sudden arrival of AI (Artificial Intelligence) into people's daily lives all around the world was marked by the introduction of ChatGPT, which was officially released on November 30, 2022. This AI invasion in our lives drew the attention of not only tech enthusiasts but also scholars from diverse fields, as its capacity extends across various fields. Consequently, numerous articles and journals have been discussing ChatGPT, making it a headline for several topics. However, it does not reflect most public opinion about the product. Therefore, this paper investigated the public's opinions on ChatGPT through topic modelling, Vader-based sentiment analysis and SWOT analysis. To gather data for this study, 202905 comments from the Reddit platform were collected between December 2022 and December 2023. The findings reveal that the Reddit community engaged in discussions related to ChatGPT, covering a range of topics including comparisons with traditional search engines, the impacts on software development, job market, and education industry, exploring ChatGPT's responses on entertainment and politics, the responses from Dan, the alter ego of ChatGPT, the ethical usage of user data as well as queries related to the AI-generated images. The sentiment analysis indicates that most people hold positive views towards this innovative technology across these several aspects. However, concerns also arise regarding the potential negative impacts associated with this product. The SWOT analysis of these results highlights both the strengths and pain points, market opportunities and threats associated with ChatGPT. This analysis also serves as a foundation for providing recommendations aimed at the product development and policy implementation in this paper.

Calostoma insigne puffball mushrooms are only found in forests with rich biodiversity in very few countries including Thailand, and their biofunctions remain largely unexplored. This study used the agar disk diffusion assay, the anti-glucosidase assay, and the 3, 4, 5-dimethylthiazol-2-yl-2-5-diphenyltetrazolium bromide (MTT) assay to evaluate the bioactive potential of these endangered puffball mushrooms. Internal transcribed spacer (ITS) gene analysis identified C. insigne, a puffball mushroom with green, globose, and spiny spores. Fourier-transform infrared spectroscopy (FTIR) analysis confirmed the polysaccharide structure while scanning electron microscopy (SEM) revealed a fiber-like network. The ethanolic gelatinous fruiting body extract exhibited 1,1-diphenyl-2-picrylhydrazyl (DPPH)-scavenging capacity (57.96%), a ferric ion-reducing antioxidant power (FRAP) value of 1.73 mg FeSO₄/g, and α-glucosidase inhibition (73.18%). C. insigne cytotoxicity was effective towards HT-29 colon cancer cells using the MTT assay (IC₅₀ of 770.6 µg/mL at 72 h) and also showed antiproliferative capacity (IC₅₀ of 297.1 µg/mL). This puffball mushroom stimulated apoptotic genes and proteins (caspase-3, Bax, and p21) via an intrinsic apoptotic pathway in HT-29 cells. In the laboratory, the medium formula consisting of 20% potato, 2% sucrose, and 0.2% peptone was optimal to increase fungal mycelial biomass (2.74 g DW/100 mL), with propagation at pH 5.0 and 30 °C. Puffball mushrooms are consumed as local foods and also confer several potential health benefits, making them worthy of conservation for sustainable utilization.

University and college laboratories are important places to train professional and technical personnel. Various regulatory departments in colleges and universities still rely on traditional laboratory management in research projects, which are prone to problems such as untimely information and data transmission. The present study aimed to propose a new method to solve the problem of data islands, explicit ownership, conditional sharing, data safety, and efficiency during laboratory data management. Hence, this study aimed to develop a data-centered lab management system that enhances the safety of lab data management and allows the data owners of the labs to control data sharing with other users. The architecture ensures data privacy by binding data ownership with a person using a key management method. To achieve data flow safely, data ownership conversion through the process of authorization and confirmation was introduced. The designed lab management system enables laboratory regulatory departments to receive data in a secure form by using this platform, which could solve data sharing barriers. Finally, the proposed system was applied and run in different server environments by implementing data security registration, authorization, confirmation, and conditional sharing using SM2, SM4, RSA, and AES algorithms. The system was evaluated in terms of the execution time for several lab data with different sizes. The findings of this study indicate that the proposed strategy is safe and efficient for lab data sharing across domains.

Accurate prediction of antibody paratopes is a critical challenge in structure-limited, high-throughput discovery workflows. We present ParaDeep, a lightweight and interpretable deep learning framework for residue-level paratope prediction directly from amino acid sequences. ParaDeep integrates bidirectional long short-term memory networks with one-dimensional convolutional layers to capture both long-range sequence context and local binding motifs. We systematically evaluated 30 model configurations varying in encoding schemes, convolutional kernel sizes, and antibody chain types. In five-fold cross-validation, heavy (H) chain models achieved the highest performance (F1 = 0.856 ± 0.014, MCC = 0.842 ± 0.015), outperforming light (L) chain models (F1 = 0.774 ± 0.023, MCC = 0.772 ± 0.022). On an independent blind test set, ParaDeep attained F1 = 0.723 and MCC = 0.685 for H chains, and F1 = 0.607 and MCC = 0.587 for L chains, representing a 27% MCC improvement over the sequence-based baseline Parapred. Chain-specific modeling revealed that heavy chains provide stronger sequence-based predictive signals, while light chains benefit more from structural context. ParaDeep approaches the performance of state-of-the-art structure-based methods on heavy chains while requiring only sequence input, enabling faster and broader applicability without the computational cost of 3D modeling. Its efficiency and scalability make it well-suited for early-stage antibody discovery, repertoire profiling, and therapeutic design, particularly in the absence of structural data. The implementation is freely available at https://github.com/PiyachatU/ParaDeep , with Python (PyTorch) code and a Google Colab interface for ease of use.

With the growth of university chemistry experiment projects, the corresponding laboratory safety risks are increasing year by year for scientific research personnel, and specialized equipment. However, accident data are not stored systematically for lack of a safety platform to collect accident information, share the causes of accidents, and predict safety risks. To solve these problems, we designed a laboratory accident system to store and share related data, and predict risk levels. In this paper, the majority of chemistry laboratory accidents were manually collected by Python software (version 3.10.11) and were categorized based on their risk level. Moreover, the variable factors that generated risk were analyzed using Spsspro, which facilitates the construction of a meaningful forecasting model of laboratory safety via Stata. It is worth noting that the registered laboratory accident data in the proposed chemistry accident system were based on the data ownership safety architecture. The chemistry accident system can break through data barriers using confirmation and authorization key algorithms to trace non-tampered data sources in a timely manner when an emergency accident happens. Meanwhile, the proposed system can use our designed accident risk model to predict the risk level of any experimental project. It can also be recommended as an appropriate safety education module.