Repetitive transcranial magnetic stimulation (rTMS) is widely recognized as an effective and noninvasive neuromodulation for treating depression, and has been applied in a wide range of clinical settings. However, previous studies often reported inconsistent antidepressant effects that may be due to differences in the rTMS protocols such as coil placement. The typical simulation site for rTMS depression protocol is the left dorsolateral prefrontal cortex (DLPFC). Targeting the exact site of the left DLPFC while considering individual brain structure has been challenging due to the non-invasive nature of rTMS. Several researchers have applied various targeting methods to overcome the abovementioned issue. Most of the previous studies have applied the “5-cm rule” or the “international 10-20 system,” which is easily applicable. Recently, researchers started to apply a neuronavigation system that targets the stimulation site based on neuroimaging of each individual. Pros and cons of targeting methods have been discussed in terms of validity and reliability of targeting stimulation sites, differences in treatment responses, as well as considerations of individual characteristics. Therefore, the current review focuses on the targeting methods of stimulation site and the treatment effects of depression in previous studies. For discussion, we divided neuronavigation methods into using fixed coordinates and using individualized targets. Furthermore, the limitations of each targeting method are discussed that may potentially contribute to the development of the optimal rTMS protocol for depression treatment.

Objectives: Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be actively used in actual clinical settings. Ensuring reliability is essential to disseminating technologies, necessitating a wide range of research and subsequent social consensus on requirements for trustworthy AI. Methods: This review divided the requirements for trustworthy medical AI into explainability, fairness, privacy protection, and robustness, investigated research trends in the literature on AI in healthcare, and explored the criteria for trustworthy AI in the medical field. Results: Explainability provides a basis for determining whether healthcare providers would refer to the output of an AI model, which requires the further development of explainable AI technology, evaluation methods, and user interfaces. For AI fairness, the primary task is to identify evaluation metrics optimized for the medical field. As for privacy and robustness, further development of technologies is needed, especially in defending training data or AI algorithms against adversarial attacks. Conclusions In the future, detailed standards need to be established according to the issues that medical AI would solve or the clinical field where medical AI would be used. Furthermore, these criteria should be reflected in AI-related regulations, such as AI development guidelines and approval processes for medical devices.

Streptococcus dysgalactiae subspecies equisimilis (SDSE) is an emerging pathogen in animals and humans. Herein, we describe two clinical swine cases of SDSE infection presenting with lameness, neurological signs, or sudden death. Pathological examination indicated suppurative arthritis, encephalitis, and multifocal abscesses in kidney and heart. The β-hemolytic colonies obtained from joint samples of each case were identified as SDSE. The two isolates had low minimum inhibitory concentrations for β-lactams, and they presented the same virulence gene profile (slo⁻/sagA⁺/pSTKP8⁺). Molecular analysis by multilocus sequence typing identified the SDSE isolates from cases 1 and 2 as sequence types 315 and 252, respectively.
Abscess ; Animals ; Arthritis, Infectious ; Death, Sudden ; Encephalitis ; Heart ; Humans ; Joints ; Kidney ; Microbial Sensitivity Tests ; Multilocus Sequence Typing ; Streptococcus ; Swine ; Virulence