Recently, research has been conducted to assist in the processing and analysis of histopathological images using machine learning algorithms. In this study, we established machine learning-based algorithms to detect photothrombotic lesions in histological images of photothrombosis-induced rabbit brains. Six machine learning-based algorithms for binary classification were applied, and the accuracies were compared to classify normal tissues and photothrombotic lesions. The lesion classification model consisting of a 3-layered neural network with a rectified linear unit (ReLU) activation function, Xavier initialization, and Adam optimization using datasets with a unit size of $128\times 128$ pixels yielded the highest accuracy (0.975). In the validation using the tested histological images, it was confirmed that the model could identify regions where brain damage occurred due to photochemical ischemic stroke. Through the development of machine learning-based photothrombotic lesion classification models and performance comparisons, we confirmed that machine learning algorithms have the potential to be utilized in histopathology and various medical diagnostic techniques.

Stroke is the second most common cause of death and third most common cause of disability worldwide. Therefore, it is an important disease from a medical standpoint. For this reason, various studies have developed diagnostic and therapeutic techniques for stroke. Among them, developments and applications of optical modalities are being extensively studied. In this article, we explored three important optical modalities for research, diagnostic, and therapeutics for stroke and the brain injuries related to it: (1) photochemical thrombosis to investigate stroke animal models; (2) optical imaging techniques for in vivo preclinical studies on stroke; and (3) optical neurostimulation based therapy for stroke. We believe that an exploration and an analysis of previous studies will help us proceed from research to clinical applications of optical modalities for research, diagnosis, and treatment of stroke.

Objective: Pigs are used in various biomedical research fields because of their anatomical and physiological similarities to humans. While farm pigs are raised outdoors for several months, minipigs are typically raised in indoor barrier facilities for several years. Although numerous studies have investigated the prevalence of major pathogens, including porcine reproductive and respiratory syndrome virus (PRRSV) and porcine circovirus type 2 (PCV2), in farm pigs, similar studies targeting minipigs are lacking. Materials and Methods: We imported 57 minipigs to our institution from three experimental animal production facilities and immediately assessed the serological and antigenic prevalence of PRRSV and PCV2. Results: PRRSV and PCV2 had seroprevalences of 80.7% and 94.7%, respectively, with 0% antigen positivity rates for PRRSV types 1 and 2 and high-pathogenic PRRSV and PCV2. Two factors could account for the high seroprevalence rates: the majority of individuals may have been vaccinated despite official claims from the origin facilities or exposed to PRRSV and PCV2. Preventing microbial infections is crucial for obtaining accurate and reproducible results. Conclusion: As the first investigation of microbial prevalence in minipigs, our findings indicate that minipigs raised in barrier facilities are not necessarily free from vaccines or infections. These results will significantly enhance the credibility of future biomedical research.

Assessment of the safety of transcranial electrical stimulation devices that contact the scalp and apply electrical stimulations to brain tissues is essential for the prevention of unexpected brain damage caused by electromagnetic fields. In particular, safety studies on transcranial alternating current stimulation (tACS) are needed for active applications to treat brain diseases and for the development of medical devices, because there is a lack of research on the safety of tACS, in contrast to transcranial direct current stimulation. In this study, the safety of tACS with selected parameters, i.e., a stimulation intensity of 1.0 to 2.0 mA, a frequency of 10 Hz, and a treatment time of 20 min, was examined at a preclinical stage using small animals (rats). The results of magnetic resonance imaging and histopathological imaging indicated that the conditions applied in this study provided safe tACS without damaging brain tissues or neuronal components in the acute phase. In addition, the temperature did not increase above 41 °C, which is a temperature limitation for contact-type medical devices, even after 20 min of tACS application.

Positive reinforcement and training for health optimization are pivotal for successful studies with monkeys. Potential food inclination is important for studies on crab-eating macaques in laboratory environments, but evaluations remain scarce. We explored crab-eating macaques’ potential food inclination to establish a reward system for future behavioral assessments. Twelve male and three female monkeys underwent a food inclination assessment in which they were offered four food categories—fruits, vegetables, proteins, and nuts. The monkeys exhibited a higher inclination for plant-based foods, particularly fruits and vegetables, over animal-based proteins like chicken and tuna (p < 0.0001), with a notable inclination for nuts (eaten/provided = 100%). Additionally, the consistency of potential food inclination after repeated offerings was investigated, revealing a time-dependent increase in inclination for protein items. Food consumption ratios correlated positively with caloric intake (r = 0.59, p = 0.02), implying that individuals with a regular high caloric intake and increased body weight are more likely to accept food during positive reinforcement training. Our findings suggest fruits, vegetables, protein-rich foods, and nuts can help with health optimization. However, animal-based protein-rich foods initially had a low preference, which may increase over time. Our study can provide guidelines for positive reinforcement training and health optimization.