Human babesiosis is an important tick-borne infectious disease. We investigated human babesiosis in the Gansu province and found that it is prevalent in this area with a prevalence of 1.3%. Results of gene sequencings indicate that 1.3% of patients were positive for Babesia divergens. This initial report of human B. divergens infections in Gansu Province should raise awareness of human babesiosis.

Background: Human babesiosis, caused by parasites of the genus Babesia, is an emerging and re-emerging tick-borne disease that is mainly transmitted by tick bites and infected blood transfusion. Babesia duncani has caused majority of human babesiosis in Canada; however, limited data are available to correlate its genomic information and biological features. Results: We generated a B. duncani reference genome using Oxford Nanopore Technology (ONT) and Illumina sequencing technology and uncovered its biological features and phylogenetic relationship with other Apicomplexa parasites. Phylogenetic analyses revealed that B. duncani form a clade distinct from B. microti, Babesia spp. infective to bovine and ovine species, and Theileria spp. infective to bovines. We identified the largest species-specific gene family that could be applied as diagnostic markers for this pathogen. In addition, two gene families show signals of significant expansion and several genes that present signatures of positive selection in B. duncani, suggesting their possible roles in the capability of this parasite to infect humans or tick vectors. Conclusions: Using ONT sequencing and Illumina sequencing technologies, we provide the first B. duncani reference genome and confirm that B. duncani forms a phylogenetically distinct clade from other Piroplasm parasites. Comparative genomic analyses show that two gene families are significantly expanded in B. duncani and may play important roles in host cell invasion and virulence of B. duncani. Our study provides basic information for further exploring B. duncani features, such as host-parasite and tick-parasite interactions.

Gastric cancer (GC) is a leading cause of cancer-related deaths worldwide, with early diagnosis remaining a significant challenge. Available serum biomarkers lack specificity, making it difficult to accurately identify early non-metastatic GC cases. Reliable diagnostic biomarkers that can detect early GC are critical to improve prognosis. We employed serum proteomics combined with bioinformatics to identify genes differentially expressed in the serum of non-metastatic GC patients. Single-cell RNA sequencing (ScRNA-seq) and immune infiltration analysis were performed to evaluate the relationship between gene expression and immune cell function. Then we evaluated 107 machine learning models for biomarker-based early GC diagnosis and develops a nomogram validated for accuracy and clinical utility, subsequently comparing the performance of potential biomarkers with traditional tumor markers in diagnosing early gastric cancer. Quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) and immunohistochemical staining using the Human Protein Atlas (HPA) database were used to validate the differential expression of candidate genes in GC tissues and adjacent non-cancerous tissues. The proteomic analysis identified several genes upregulated in the serum of GC patients compared to healthy controls. Single-cell RNA sequencing analysis further revealed that these upregulated genes were associated with altered immune cell infiltration in the tumor microenvironment. The glmBoost + XGBoost model incorporating B2M, CFL1, CTSD, and HSP90AB1 demonstrated strong diagnostic performance (mean AUC = 0.792), with 101 algorithm combinations achieving an average AUC > 0.7. A nomogram integrating gene expression and clinical data was developed, validated through calibration and decision curve analyses, highlighting its potential for early GC diagnosis. Additionally, four genes—TAGLN2, HSP90AB1, SH3BGRL3, and CFL1—were found to be highly expressed in non-metastatic GC tissues and were significantly correlated with immune infiltration, including CD8 + T cells, monocytes, and myeloid-derived suppressor cells. These findings were validated by qRT-PCR and immunohistochemical analyses, confirming their elevated expression in GC tissues. TAGLN2, HSP90AB1, SH3BGRL3 and CFL1 are potential diagnostic biomarkers for early-stage GC, with strong associations with immune cell infiltration. Machine learning model shows excellent diagnostic performance. These results provide a foundation for future studies to improve early diagnosis and individualized treatment strategies for GC. Serum proteomics identified genes upregulated in non-metastatic gastric cancer. Single-cell RNA sequencing analysis revealed gene expression correlates with immune cell infiltration in gastric cancer. Machine learning model and nomograph show that potential markers have good diagnostic performance. Gastric cancer tissues validated TAGLN2, HSP90AB1, SH3BGRL3, and CFL1 as upregulated. Identified biomarkers are associated with immune infiltration, indicating a role in gastric cancer progression. Potential biomarkers could improve early diagnosis and personalized management of gastric cancer.

Background: Six species of apicomplexan parasites of the genus Babesia, namely B. microti, B. divergens, B. duncani, B. motasi, B. crassa–like and B. venatorum, are considered to be the primary causal agents of human babesiosis in endemic areas. These six species possess variable degrees of virulence for their primary hosts. Therefore, the accurate identification of these species is critical for the adoption of appropriate therapeutic strategies. Methods: We developed a real-time PCR–high-resolution melting (qPCR-HRM) approach targeting 18S ribosomal RNA gene of five Babesia spp. based on melting temperature (T_m) and genotype confidence percentage values. This approach was then evaluated using 429 blood samples collected from patients with a history of tick bites, 120 DNA samples mixed with plasmids and 80 laboratory-infected animal samples. Results: The sensitivity and specificity of the proposed qPCR-HRM method were 95% and 100%, respectively, and the detection limit was 1–100 copies of the plasmid with the cloned target gene. The detection level depended on the species of Babesia analyzed. The primers designed in this study ensured not only the high interspecific specificity of our proposed method but also a high versatility for different isolates from the same species worldwide. Additionally, the Tm obtained from the prepared plasmid standard is theoretically suitable for identifying isolates of all known sequences of the five Babesia species. Conclusions: The developed detection method provides a useful tool for the epidemiological investigation of human babesiosis and pre-transfusion screening. Graphical