Anti-interferon-gamma autoantibody (AIGA)-associated adult-onset immunodeficiency (AOID) is an emerging disease that can lead to serious opportunistic infections, which has a history of 20 years since it was first reported in 2004. It’s a hard-detected AOID caused by AIGA. In recent years, there has been an increasing number of reports on the disease, but there is still a lack of consensus on the diagnosis and treatment. We here report a case of a 70-year-old Chinese male who had had AIGA in serum and suffered from recurrent pyothorax. Although his condition improved with antimicrobial therapy each time, his pyothorax frequently relapsed, requiring repeated hospitalizations. A literature review of AIGA-associated AOID was conducted. We searched PubMed, Web of Science, Embase, and the Chinese literature database for manuscripts concerning AIGA. Cases detected with AIGA and met our criteria were included. A total of 502 patients were retrospectively analyzed, with 256 (51.0%) males and 246 (49.0%) females. The majority of patients are from Southeast Asia (98.2%). Lymph node (83.7%) is the most commonly involved organ, followed by the lung (60.6%). Nontuberculous mycobacteria (NTM) was identified as the predominant pathogen reported in 77.49% of the patients. The clinical manifestations are diverse and non-specific for the disease often presenting with multi-organ involvement and multiple infections. Timely identification of patients with AIGA, appropriate diagnosis, and individualized treatment are critical; thus, we propose a reasonable diagnostic criterion and a structured diagnostic and treatment process based on our findings to provide clinicians with comprehensive information for clinical practice.

Neoadjuvant immunotherapy (NIT) has emerged as a transformative treatment strategy across various cancer types. However, due to the significant heterogeneity of tumors, patients exhibit highly variable responses to NIT, making the accurate preoperative identification of those who would benefit a pressing clinical challenge. In recent years, artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has opened new pathways for predicting treatment response. AI-driven approaches have the ability to extract latent features from high-dimensional, multimodal oncological data, facilitating the construction of efficient predictive models that can optimize individualized treatment strategies. In this review, we systematically summarize existing AI-driven computational approaches for NIT response prediction, categorizing them into indirect and direct predictive paradigms. The indirect paradigm predicts clinically validated surrogate biomarkers to infer therapeutic response to NIT. In contrast, the direct paradigm leverages AI to analyze high-throughput data and establish data-driven biomarkers that directly predict clinical endpoints of NIT. Additionally, we categorize existing AI predictive models based on data modalities, spanning radiomics, pathomics, genomics, and multi-omics approaches, each providing distinct insights into tumor characteristics and treatment response. Despite notable progress, current predictive models still face significant challenges, which we broadly classify into biomarker-based and AI-based limitations. We further discuss potential strategies to address these challenges. This review systematically summarizes recent AI-based predictive models for NIT response across cancer types. By offering a structured analysis of current methodologies and challenges, we aim to guide future research and accelerate the integration of AI into precision immunotherapy.

The taxonomy of the genus Neisseria remains confusing, particularly regarding Neisseria mucosa and Neisseria sicca. In 2012, ribosomal multi-locus sequence typing reclassified both as N. mucosa, but data concerning 17 N. sicca strains remain available in GenBank. The continuous progress of high-throughput sequencing has facilitated ready accessibility of whole-genome data, promoting vigorous development of average nucleotide identity (ANI) and high-resolution phylogenetic analysis. Here, we report that a Neisseria isolate, which caused native-valve endocarditis and multiple embolic brain infarcts in a patient with congenital heart disease, was misidentified as N. sicca by VITEK MS. This isolate was reclassified as N. mucosa by ANI blast (ANIb) and by phylogenetic analysis using whole-genome data yielded by the PacBio Sequel and Illumina NovaSeq PE150 platforms. The confusion evident in the GenBank and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) databases suggests that N. mucosa (n = 13) and N. sicca (n = 16) in GenBank should be reclassified using ANIb and high-resolution phylogenetic analysis. The whole-genome data of 30 strains (including the clinical isolate) were compared with the data of 27 type Neisseria strains (including one N. sicca and two N. mucosa type strains) as a genomic index. In total, 25 (8 originally identified as N. mucosa and 17 originally identified as N. sicca) and 7 (1 originally identified as N. sicca and 6 originally identified as N. mucosa) strains were reclassified into the N. mucosa and Neisseria subflava groups, respectively; 1 residual N. mucosa strain was reclassified as Neisseria meningitidis. In conclusion, a combination of ANIb and robust phylogenetic analysis reclassified strains originally identified as N. mucosa and N. sicca into (principally) the N. mucosa group and the N. subflava group. The misclassified N. sicca and N. mucosa strains in the GenBank and MALDI-TOF MS databases were supposed to be corrected. Updated genomic classification strategy for originally identified N. mucosa and N. sicca strains was recommended to be adopted in GenBank.

Reemergent local outbreaks of coronavirus disease 2019 (COVID-19) have occurred in China, yet few Chinese response strategies and its evaluation have been reported. This study presents a preliminary assessment of Chinese strategy in controlling reemergent local outbreaks of COVID-19. Time course of accumulative and daily new cases and time-varying reproductive numbers (Rt) of outbreak areas were presented. The asymptomatic rate, days required to control the outbreaks, seeding time (ST), and doubling time (DT) of areas with over 96 reemergent cases were calculated. National and local year-on-year growth rates of gross domestic product (GDP) were presented. Accumulative numbers of 30, 8, 11, 430, 15, 139, 1,067, 382, 42, and 94 confirmed reemergent COVID-19 cases were diagnosed in Hulun Buir, Shanghai, Tianjin, Kashgar, Qingdao, Dalian, Urumchi, Beijing, Jilin, and Harbin, respectively. Among them, maximum rate of asymptomatic infections was 81.9%. Time required to control the local outbreaks in the areas given above varied from 29 to 51 days. After activation of outbreak responses, the late-stage DTs of Kashgar, Urumchi, Beijing, and Dalian were apparently lengthened compared to the early-stage DTs. Although the year-on-year GDP growth rate of Urumchi was slightly affected, the GDP growth rate of Dalian, Beijing, Jilin, and Harbin kept rising during the reemergence. Moreover, the year-on-year GDP growth rate of Mainland China turned positive regardless of the reemergent local outbreaks. In general, the Chinese strategy to maintain the status of no or minimal transmission was effective in balancing the control of COVID-19 reemergent local outbreak and the recovery of economy.

Chronic disseminated candidiasis (CDC) is a severe complication with high morbidity and mortality in patients with hematological malignancies who have undergone chemotherapy. Blood or sterile liver biopsy cultures are negative due to recurrent empirical antifungal therapy. With the escalating resistance to azole-based antifungal drugs in infection by Candida species, pathogen identification is becoming increasingly important for determining definitive diagnosis and treatment strategy. In this case report, we present, for the first time, diagnostic confirmation of a culture-negative CDC case with Candida tropicalis infection using a combination of metagenomics next-generation sequencing and calcofluor white staining.

Patients afflicted with Wilson’s disease (WD) may encounter hepatic and extraneous manifestations due to the progressive accumulation of copper in the liver and other subsequent organs. Copper-chelating agents, such as D-penicillamine (DPA), are commonly utilized in the medical treatment of copper overload in WD. Manipulating the composition of gut microbiota appropriately can enhance drug efficacy and safety. This study aims to investigate how targeted intervention on gut microbiota influences the effectiveness of copper removal in a WD model during DPA treatment. First, following a 4-week treatment of DPA, the liver copper concentration and gut microbial composition were assessed in the WD mice model to identify potential candidates for targeted regulation of gut microbiota. Second, after 8 weeks of manipulating the gut microbiota during DPA treatment, various parameters including blood liver function indicators, tissue copper load, hepatic histopathological features, and gut microbiota were investigated in WD mice. The findings demonstrated that the presence of Akkermansia significantly enhances the efficacy of DPA, leading to a more efficient elimination of copper from tissues and a greater improvement in liver injury, liver dysfunction, and gut dysbiosis. In contrast, Butyricimonas has an antagonistic effect. The results of gene function prediction analysis indicated that the altered gut microbial function by DPA and Akk is primarily linked to energy generation/utilization, amino acid, fatty acid, lipid, and nucleic acid metabolisms. To summarize, this study provides experimental evidence for the potential application of targeted regulation of gut microbiota in the adjunctive therapy of copper dysregulation disease. IMPORTANCE: Copper is an essential element in virtually all living organisms. Wilson’s disease (WD) is a representative disorder caused by the disruption of copper homeostasis. Oral-chelating agents are the first-line treatment for copper-overloaded diseases, with D-penicillamine (DPA) being the prototypical drug. However, the efficacy and adverse effects of DPA remain challenging in its use for WD treatment. In our study, the supplementation of Akkermansia muciniphila (Akk), a key gut microbe, along with DPA was demonstrated to enhance copper removal, ameliorate liver injury and dysfunction, and restore gut dysbiosis in a mouse model of WD. These findings highlight the significant potential applications of targeted modulation of gut microbiota as “pharmacomicrobiomics” in adjunctive therapy for disorders involving copper dysregulation.