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Cervical cancer (CC) is one of the leading causes of cancer‐related death in females worldwide. Genome‐wide association studies (GWASs) have identified CC‐related susceptibility loci in HLA regions. To investigate the associations between HLA genes and cervical intraepithelial neoplasia (CIN) or cervical cancer (CC), six loci of HLA class I (HLA‐A, ‐B, and ‐C) and II (HLA‐DRB1, ‐DPB1, and ‐DQB1) were selected for genotyping, and the associations between these alleles or their haplotypes with CIN or CC risk or protection from disease were evaluated. In total, 2193 participants, including 909 healthy individuals in the control group, 769 patients with CC, and 515 patients with CIN2+ (CIN II and III), were enrolled in the current study. HLA genes were genotyped using the NGSgo Illumina MiSeq workflow, and the associations between these loci and CIN2+ or CC at the allele and haplotype levels were analyzed. The allele frequencies of HLA‐A*33:03, B*58:01, C*03:02, DPB1*05:01, and DRB1*12:01 were lower in both the CC and CIN2+ groups than in the control group, whereas those of B*55:02, C*04:03, and DPB1*03:01 were higher in the CC group than in the control group. In the histologic CC type analysis, the differences in the frequencies of these alleles in squamous cell carcinoma (SCC) of the cervix and stage I CC showed a consistent trend. In the haplotype analysis, the frequency of A*33:03‐C*03:02‐B*58:01 was lower in the CC and CIN2+ groups than in the control group, and that of A*24:02‐C*04:03‐B*15:25 was higher in the CC group than in both the control and CIN2+ groups. These three different haplotype frequencies were also identified in the FIGO CC stage analysis. In addition, in human papilloma virus (HPV) genotype analyses, the frequencies of HLA‐C*03:02 and DPB1*05:01 were significantly lower in the CC and CIN2+ groups than in the control group, and in SCC subgroup, the frequencies of HLA‐DQB1*04:01 and DRB1*04:05 were higher in the HPV other genotype infection group than in the HPV16 infection group. In both HPV16 single infection and coinfection with other HPVs, the frequency of haplotype A*33:03‐C*03:02‐B*58:01 was lower in both CC and CIN2+ than in the control group, while the frequencies of A*11:01‐C*14:02‐B*51:01 and A*24:02‐C*03:04‐B*13:01 were higher in the CIN2+ than in CC and the control group. In the HPV16 and other HPV infection comparisons, the frequencies of DRB1*04:05‐DQB1*04:01‐DPB1*02:01 and DRB1*11:01‐DQB1*03:01‐DPB1*05:01 were lower in the HPV16 infection group than in the other HPV infection group. Our results suggest that the HLA class I and II genes may affect the risk of CIN and CC as well as the histologic CC types and FIGO stages of CC in the Han Chinese population. In addition, HLA genes were associated with HPV16 infection at both the allelic and haplotype levels. [ABSTRACT FROM AUTHOR]

Few studies have reported the associations of granulocyte colony-stimulating factor (G-CSF) with cytokine release syndrome (CRS), neurotoxic events (NEs) and efficacy after chimeric antigen receptor (CAR) T-cell therapy for relapsed or refractory (R/R) B-cell acute lymphoblastic leukemia (B-ALL). We present a retrospective study of 67 patients with R/R B-ALL who received anti-CD19 CAR T-cell therapy, 41 (61.2%) patients received G-CSF (G-CSF group), while 26 (38.8%) did not (non-G-CSF group). Patients had similar duration of grade 3–4 neutropenia between the two groups. The incidences of CRS and NEs were higher in G-CSF group, while no differences in severity were found. Further stratified analysis showed that the incidence and severity of CRS were not associated with G-CSF administration in patients with low bone marrow (BM) tumor burden. None of the patients with low BM tumor burden developed NEs. However, there was a significant increase in the incidence of CRS after G-CSF administration in patients with high BM tumor burden. The duration of CRS in patients who used G-CSF was longer. There were no significant differences in response rates at 1 and 3 months after CAR T-cell infusion, as well as overall survival (OS) between the two groups. In conclusion, our results showed that G-CSF administration was not associated with the incidence or severity of CRS in patients with low BM tumor burden, but the incidence of CRS was higher after G-CSF administration in patients with high BM tumor burden. The duration of CRS was prolonged in G-CSF group. G-CSF administration was not associated with the efficacy of CAR T-cell therapy. [ABSTRACT FROM AUTHOR]

Bone marrow-derived CD34-positive (CD34+) endothelial progenitor cells (EPCs) has unique functions in the mechanism of compensatory lung growth (CLG). The content of this study is mainly to describe the effect of microRNA (miR)-155 in the mechanisms of EPCs and CLG. Our study found that transfection of miR-155 mimic could promote EPC proliferation, migration and tube formation, while transfection of miR-155 inhibitor had the opposite effect. It was also found that transfection of pc-JARID2 inhibited EPC proliferation, migration and tube formation, while transfection of si-JARID2 had the opposite effect. miR-155 can target and negatively regulate JARID2 expression. Overexpression of JARID2 weakened the promoting effects of miR-155 mimic on EPC proliferation, migration, and tubular formation, while silencing JARID2 weakened the inhibitory effects of miR-155 inhibitors on EPC proliferation, migration, and tubular formation. Transplantation of EPCs transfected with miR-155 mimic into the left lung model effectively increased lung volume, total alveolar number, diaphragm surface area, and lung endothelial cell number, while transplantation of EPCs co-transfected with miR-155 mimic and pc-JARID2 reversed this phenomenon. Overall, we found that miR-155 activates CD34+ EPC by targeting negative regulation of JARID2 and promotes CLG. [ABSTRACT FROM AUTHOR]

• A shuffle strategy regroups cell instances to create new relationship for modeling. • An approach enhances Transformers to be instance aware to eliminate perturbations. • The distribution, similarity and relationship are highlighted with training bags. • With natural and regrouped bags, the models can learn diverse relationships. The rapid on-site evaluation (ROSE) technique improves pancreatic cancer diagnosis by enabling immediate analysis of fast-stained cytopathological images. Automating ROSE classification could not only reduce the burden on pathologists but also broaden the application of this increasingly popular technique. However, this approach faces substantial challenges due to complex perturbations in color distribution, brightness, and contrast, which are influenced by various staining environments and devices. Additionally, the pronounced variability in cancerous patterns across samples further complicates classification, underscoring the difficulty in precisely identifying local cells and establishing their global relationships. To address these challenges, we propose an instance-aware approach that enhances the Vision Transformer with a novel shuffle instance strategy (SI-ViT). Our approach presents a shuffle step to generate bags of shuffled instances and corresponding bag-level soft-labels, allowing the model to understand relationships and distributions beyond the limited original distributions. Simultaneously, combined with an un-shuffle step, the traditional ViT can model the relationships corresponding to the sample labels. This dual-step approach helps the model to focus on inner-sample and cross-sample instance relationships, making it potent in extracting diverse image patterns and reducing complicated perturbations. Compared to state-of-the-art methods, significant improvements in ROSE classification have been achieved. Aiming for interpretability, equipped with instance shuffling, SI-ViT yields precise attention regions that identifying cancer and normal cells in various scenarios. Additionally, the approach shows excellent potential in pathological image analysis through generalization validation on other datasets. By proposing instance relationship modeling through shuffling, we introduce a new insight in pathological image analysis. The significant improvements in ROSE classification leads to protential AI-on-site applications in pancreatic cancer diagnosis. The code and results are publicly available at https://github.com/sagizty/MIL-SI. [ABSTRACT FROM AUTHOR]