Findings show TB-related lung lesions can create protected niches for bacteria, helping explain why some patients develop new infections even after treatment. Prevention, early diagnosis, completing treatment as prescribed and appropriate follow-up care remain important in reducing long-term health risks.

Prof. Long Zhang’s group successfully developed a rapid annealing strategy for synthesizing metal–organic framework (MOF) derivatives, which offers significant advantages in terms of time efficiency and energy consumption compared to conventional pyrolysis methods.

Graphene, as a typical two-dimensional material, has demonstrated transformative application potential in fields such as electronic devices, energy storage, and catalysis due to its outstanding carrier mobility, mechanical strength, and excellent thermal conductivity.

MXenes are gaining attention in nanomedicine, with Nb₂CT_x standing out as a particularly promising, non-toxic, and biocompatible candidate. Despite its potential for clinical applications, Nb₂CT_x requires surface stabilization to prevent oxidation and improve stability.

Korean researchers confirmed that underground wireless communication is possible, moving beyond the terrestrial wireless communication they have primarily focused on until now. This opened up a new wireless channel for confirming the survival of buried people in the event of a collapse of an underground facility such as a mine, conducting underground rescue operations, or conducting underground military operations.

Neuroimaging analysis in brain disorders faces a persistent challenge: brain signals are complex and high-dimensional, while high-quality labeled datasets remain limited. This review article systematically examines how self-supervised learning can help address that gap by learning meaningful representations directly from unlabeled neuroimaging data. It covers major methodological families, including contrastive, generative, and hybrid generative-contrastive approaches, and discusses their applications in functional MRI, EEG, and multimodal brain network analysis.

The review argues that self-supervised learning offers more than annotation efficiency. It may enable more transferable and clinically useful representations for disease screening, diagnosis, and prognosis across heterogeneous datasets and disorders. At the same time, interpretability, data heterogeneity, missing modalities, and clinical validation remain major barriers. Future work will likely focus on stronger multimodal fusion, better cross-site generalization, and more clinically adaptable model design.