Review of the AlGaN/GaN High-Electron-Mobility Transistor-Based Biosensors: Structure, Mechanisms, and Applications.

Due to its excellent material performance, the AlGaN/GaN high-electron-mobility transistor (HEMT) provides a wide platform for biosensing. The high density and mobility of two-dimensional electron gas (2DEG) at the AlGaN/GaN interface induced by the polarization effect and the short distance between the 2DEG channel and the surface can improve the sensitivity of the biosensors. The high thermal and chemical stability can also benefit HEMT-based biosensors' operation under, for example, high temperatures and chemically harsh environments. This makes creating biosensors with excellent sensitivity, selectivity, reliability, and repeatability achievable using commercialized semiconductor materials. To synthesize the recent developments and advantages in this research field, we review the various AlGaN/GaN HEMT-based biosensors' structures, operations mechanisms, and applications. This review will help new researchers to learn the basic information about the topic and aid in the development of next-generation of AlGaN/GaN HEMT-based biosensors.

Suboptimal capability of individual machine learning algorithms in modeling small-scale imbalanced clinical data of local hospital.

In recent years, artificial intelligence (AI) has shown promising applications in various scientific domains, including biochemical analysis research. However, the effectiveness of AI in modeling small-scale, imbalanced datasets remains an open question in such fields. This study explores the capabilities of eight basic AI algorithms, including ridge regression, logistic regression, random forest regression, and others, in modeling a small, imbalanced clinical dataset (total n = 387, class 0 = 27, class 1 = 360) related to the records of the biochemical blood tests from the patients with multiple wasp stings (MWS). Through rigorous evaluation using k-fold cross-validation and comprehensive scoring, we found that none of the models could effectively model the data. Even after fine-tuning the hyperparameters of the best-performing models, the results remained below acceptable thresholds. The study highlights the challenges of applying AI to small-scale datasets with imbalanced groups in biochemical or clinical research and emphasizes the need for novel algorithms tailored to small-scale data. The findings also call for further exploration into techniques such as transfer learning and data augmentation, and they underline the importance of understanding the minimum dataset scale required for effective AI modeling in biochemical contexts.