Inborn errors of the signal transducer and activator of transcription 1 (STAT1) result in four types of immunodeficiency disease with varying degrees of impaired STAT1 function: autosomal recessive (AR) complete STAT1 deficiency, AR partial STAT1 deficiency, autosomal dominant (AD) STAT1 deficiency, and AD STAT1 gain-of-function (STAT1-GOF). Of which, the STAT1-GOF mutations promote a clinical syndrome of immune dysregulation characterized by recurrent infections, especially chronic mucocutaneous candidiasis (CMC) and Talaromyces marneffei infection and predisposition to humoral autoimmunity. STAT1-GOF mutations lead to enhanced phosphorylation of STAT1 (pSTAT1), delayed dephosphorylation, and impaired nuclear dephosphorylation. As a result, the development of T helper (Th) 17 cells is impaired, limiting the production of interleukin (IL)-17, which plays an important role in antifungal immunity. Additionally, mutations can also cause a decrease in the proportion of CD4⁺, CD8⁺, and natural killer (NK) cells. Recent research demonstrated that in the absence of overt infection, STAT-GOF mice can disrupt naïve CD4⁺ T cell homeostasis and promote expansion and differentiation of abnormal T-follicular helper/T-helper 1-like (Tfh/Th1-like) T cells and germinal center-like (GC-like) B cells, and thus reminds us of the complex molecular mechanism of autoimmune disease with/without fungal infection, which may further involve specific clinical treatment including antifungal and anti-autoimmunity therapies. In addition, sex and location of mutation were also associated with the clinical phenotype. Individuals with DNA binding domain (DBD) mutations had a higher prevalence of autoimmunity and aberrant B cell activation. Disrupted CD4⁺ T cell homeostasis occurred sooner and more robustly in females, highlighting the importance of specific treatment to normalize STAT1 expression and restore immune tolerance in patients with STAT1-GOF syndrome. Herein, we provide a comprehensive review of STAT1-GOF aiming to further clarify the regulatory mechanism of cellular and humoral immune deficiency in patients with fungal infection with or without autoimmunity.

Mpox (formerly Monkeypox) is a zoonotic infection caused by Monkeypox virus (MPXV). Since 2022, Mpox epidemics have occurred in many non‐endemic countries and regions, leading the World Health Organization to declare a public health emergency of international concern. With the persistent transmission and evolution of MPXV, symptoms of Mpox have become milder, with some infections being asymptomatic. In addition, MPXV has become more contagious. Therefore, rapid and accurate diagnosis and screening of MPXV is vital to prevent and control MPXV epidemics. Here, we review and summarize the technical details, application scenarios, and the advantages and disadvantages of MPXV‐specific diagnostic methods.

Cancer immunotherapy has emerged as the fourth most prevalent approach to tumor treatment, alongside surgery, radiotherapy, and chemotherapy. After several decades of development, chimeric antigen receptor T (CAR‐T) cell therapy, a promising branch of adoptive T‐cell therapy, has demonstrated superior efficacy and safety in comparison to other cell therapies in the treatment of cancer. At present, CAR‐T cells are predominantly used to treat hematological malignancies, although their application in solid tumors is being readily investigated. Although numerous studies have examined the biomarkers associated with the safety of CAR‐T cell therapy, few have evaluated predictors of CAR‐T cell therapeutic efficacy. Thus, the primary objective of this review article was to provide a comprehensive overview of the factors predicting the efficacy of CAR‐T cell therapy, with a particular focus on biomarkers and their detection methods.

With the development of scientific technology, the transition to the intelligent era of digitalization and automation is an irresistible trend for medical laboratories. Medical diagnosis systems have undergone significant changes as a result of intelligent technologies, such as machine learning, artificial intelligence, and the Internet of Things, from the collection, transmission, and detection of test samples to the review of reports and the provision of clinical feedback. In addition to significantly enhancing the efficiency, consistency, and accuracy of medical laboratory testing, these technologies also assist the improvement of individualized healthcare and medical expert systems, as well as the early detection and treatment of diseases. The future development of medical laboratories will focus on integrating big data and diverse intelligent resources, cooperating more closely with clinical departments, and realizing the effective pathway of patient‐centered care. The purpose of this review is to illustrate the current state of intelligent technology integration in medical laboratories and provide a preliminary discussion about the potential future influences of intelligent technology development on the evolution of medical laboratories.