Human T-lymphotropic virus type 1 (HTLV-1)-associated myelopathy/tropical spastic paraparesis (HAM/TSP) is a chronic neuroinflammatory disease. Given the established role of the gut-brain axis in other neurological diseases such as multiple sclerosis, the role of the gut microbiome in the pathogenesis of HAM/TSP remains a critical unexplored area. The aim of this study was to characterize alterations in the gut microbiome associated with HTLV-1 infection and its clinical stages. We performed a cross-sectional analysis of the gut microbiome from 112 Brazilian individuals, including 24 healthy controls and 88 HTLV-1-infected individuals at different disease stages: 38 HAM patients, 17 patients with intermediate syndromes, and 33 asymptomatic carriers. Fecal samples were collected and analyzed using Illumina MiSeq sequencing to assess bacterial composition and diversity. Functional analysis was performed to identify differentially enriched gene categories and Kyoto Encyclopedia of Genes and Genomes (KEGG) modules. Significant dysbiosis was observed in HTLV-1-infected individuals, characterized by reduced bacterial diversity, an inverted Firmicutes/Bacteroidetes ratio, and specific changes in bacterial genera. Notably, HAM patients exhibited decreased Faecalibacterium and increased Ruminococcus_g2 abundance. These associations should be interpreted with caution, as patient cohorts were significantly older and differed in sex distribution from healthy controls. Functional analysis revealed 13 differentially enriched gene categories and five KEGG modules that were more abundant in HAM patients, indicating alterations in metabolic processes. These findings provide the first comprehensive insight into gut microbiome changes associated with HTLV-1 infection and disease progression. This study provides the first comprehensive insight into gut microbiome changes associated with HTLV-1 infection and disease progression. The identified microbial signatures and functional alterations highlight potential diagnostic and therapeutic targets for HTLV-1-associated diseases, particularly HAM. These findings open new avenues for further research and clinical applications.

Accurate assessment of sperm concentration and motility is critical for the diagnosis and management of male infertility. However, current methods, manual hemocytometer counting and commercial computer-aided sperm analysis (CASA) systems, are limited by labor intensity, human error, and variable performance under diverse sample conditions. Here, we present an artificial intelligence (AI)-driven computer vision tool for high-resolution, quantitative analysis of sperm motility and concentration. In a prospective study of 26 semen samples (22 patients, 4 donors), we benchmarked the AI model against manual tracking (using Fiji software) and a commercial CASA system (Hamilton Thorne IVOS II). Our method computed concentration and motility parameters, including straight-line velocity (VSL), curvilinear velocity (VCL), average path velocity (VAP), linearity (LIN), amplitude of lateral head displacement (ALH_max), and beat cross frequency (BCF). Calibration using donor samples enabled accurate mapping of tracked sperm counts to concentrations. The AI tool presented a strong linear correlation with manual tracking (R² = 0.93-0.98; Root Mean Square Error (RMSE) = 3.3-7.3 μm/s for VSL, VCL, VAP), and outperformed CASA in both accuracy and consistency across all motility parameters. Post-calibration, ALH_max and BCF estimates improved substantially, with a 30%-50% reduction in RMSE. Grading of sperm motility by the AI model aligned closely with manual classification, avoiding the systematic misclassification typically observed with CASA. Furthermore, the AI system exhibited higher repeatability and robustness across duplicate samples and variable imaging conditions, with deviations below ± 2%. These findings demonstrate that our AI-based tool offers a quantitative and reliable alternative to current semen analysis platforms, supporting improved fertility diagnostics and potentially a more informative treatment process.

Skeletal muscle is essential for voluntary movement and exhibits a remarkable capacity for regeneration following injury. NFIX, a member of the Nuclear Factor I (NFI) family of transcription factors, plays a critical role in both skeletal muscle development and regeneration. Despite its emerging importance, the molecular basis of NFIX-mediated DNA recognition and transcriptional regulation in skeletal muscle remains poorly defined. Here, we demonstrate that NFIX promotes key cellular processes in skeletal muscle cells, as siRNA-mediated knockdown of NFIX significantly reduces cell proliferation, increases apoptosis, and impairs differentiation. Transcriptomic analysis revealed that NFIX regulates a network of genes involved in muscle metabolism, stress responses, and immune inflammatory responses. Biophysical characterization showed that NFIX exists as a monomer in solution and binds palindromic DNA with a 1:1 stoichiometry. A high-resolution crystal structure of the NFIX_DBD bound to palindromic DNA reveals a monomeric binding mode driven by base-specific recognition of the TGGCA motif. Mutations that disrupt key DNA-contacting residues abolished both DNA binding and transcriptional activation in luciferase reporter assays. Together, these findings define the molecular mechanism of NFIX-dependent gene regulation in skeletal muscle and establish a structural framework for its function, providing new insights into the potential therapeutic targeting of NFIX in muscle diseases.

X. Geng, N. Zhang, Z. Li, M. Zhao, H. Zhang, J. Li, Smart Med. 2024, 3(2), e20240004.

In Figure 4B, the cornea photo of the saline group on Day 5 was erroneously placed in the panel labeled Day 7, and the original photo intended for Day 7 was omitted. We have now corrected the figure by replacing the mislabeled photo with the corresponding Day 7 cornea photo. This error did not affect the statistical conclusions or overall interpretation of the study.

We sincerely apologize for this error.