Genomic mutation patterns and prognostic value in de novo and secondary acute myeloid leukemia: A multicenter study from China.

Acute myeloid leukemia (AML) can manifest as de novo AML (dn-AML) or secondary AML (s-AML), with s-AML being associated with inferior survival and distinct genomic characteristics. The underlying reasons for this disparity remain to be elucidated. In this multicenter study, next-generation sequencing (NGS) was employed to investigate the mutational landscape of AML in 721 patients from June 2020 to May 2023.Genetic mutations were observed in 93.34% of the individuals, with complex variations (more than three gene mutations) present in 63.10% of them. TET2, ASXL1, DNMT3A, TP53 and SRSF2 mutations showed a higher prevalence among older individuals, whereas WT1 and KIT mutations were more commonly observed in younger patients. BCOR, BCORL1, ZRSR2, ASXL1 and SRSF2 exhibited higher mutation frequencies in males. Additionally, ASXL1, NRAS, PPMID, SRSF2, TP53 and U2AF1 mutations were more common in patients with s-AML, which PPM1D was more frequently associated with therapy-related AML (t-AML). Advanced age and hyperleukocytosis independently served as adverse prognostic factors for both types of AML; however, s-AML patients demonstrated a greater number of monogenic adverse prognostic factors compared to dn-AML cases (ASXL1, PPM1D, TP53 and U2AF1 in s-AML vs. FLT3, TP53 and U2AF1 in dn-AML). Age and sex-related gene mutations suggest epigenetic changes may be key in AML pathogenesis. The worse prognosis of s-AML compared to dn-AML could be due to the older age of s-AML patients and more poor-prognosis gene mutations. These findings could improve AML diagnosis and treatment by identifying potential therapeutic targets and risk stratification biomarkers.

The dynamics of B-cell reconstitution post allogeneic hematopoietic stem cell transplantation: A real-world study.

BACKGROUND: The immune reconstitution after allogeneic hematopoietic stem cell transplantation (allo-HSCT) is crucial for preventing infections and relapse and enhancing graft-versus-tumor effects. B cells play an important role in humoral immunity and immune regulation, but their reconstitution after allo-HSCT has not been well studied. METHODS: In this study, we analyzed the dynamics of B cells in 252 patients who underwent allo-HSCT for 2 years and assessed the impact of factors on B-cell reconstitution and their correlations with survival outcomes, as well as the development stages of B cells in the bone marrow and the subsets in the peripheral blood. RESULTS: We found that the B-cell reconstitution in the bone marrow was consistent with the peripheral blood (p = 0.232). B-cell reconstitution was delayed by the male gender, age >50, older donor age, the occurrence of chronic and acute graft-versus-host disease, and the infections of fungi and cytomegalovirus. The survival analysis revealed that patients with lower B cells had higher risks of death and relapse. More importantly, we used propensity score matching to obtain the conclusion that post-1-year B-cell reconstitution is better in females. Meanwhile, using mediation analysis, we proposed the age-B cells-survival axis and found that B-cell reconstitution at month 12 posttransplant mediated the effect of age on patient survival (p = 0.013). We also found that younger patients showed more immature B cells in the bone marrow after transplantation (p = 0.037). CONCLUSION: Our findings provide valuable insights for optimizing the management of B-cell reconstitution and improving the efficacy and safety of allo-HSCT.

Spin characteristics in conjugated stable diradicals.

Spin properties are intrinsic characters of electrons. Radical molecules contain unpaired electron(s), and their unique chemical and physical properties make them an ideal platform for investigating spin properties in molecular systems. Among them, the burgeoning interest in stable conjugated diradicals is attributed to their distinctive characteristics, notably the dynamic resonance structures between open-shell and closed-shell forms, the malleability of their spin states, and the profound influence of intermolecular spin-spin interactions. A deep understanding of the spin characteristics of unpaired electrons in stable conjugated diradicals provides guidance for the design, synthesis, and characterization of radical-based materials. In this review, we discuss the unique spin delocalization, spin states, and spin-spin coupling characteristics of conjugated diradicals and emphasize how to precisely control these spin characteristics to understand their role in the molecules and as functional radical materials.

Automated pancreatic segmentation and fat fraction evaluation based on a self-supervised transfer learning network.

Accurate segmentation of the pancreas from abdominal computed tomography (CT) images is challenging but essential for the diagnosis and treatment of pancreatic disorders such as tumours and diabetes. In this study, a dataset with 229 sets of high-resolution CT images was generated and annotated. We proposed a novel 3D segmentation model named nnTransfer (nonisomorphic transfer learning) net, which employs generative model structure for self-supervision to facilitate the network's learning of image attributes from unlabelled data. The effectiveness for pancreas segmentation of nnTransfer was assessed using the Hausdorff distance (HD) and Dice similarity coefficient (DSC) on the dataset. Additionally, a histogram analysis with local thresholding was used to achieve automated whole-volume measurement of pancreatic fat (fat volume fraction, FVF). The proposed technique performed admirably on the dataset, with DSC: 0.937 ± 0.019 and HD: 2.655 ± 1.479. The mean pancreas volume and FVF of the pancreas were 91.95 ± 23.90 cm3 and 12.67 % ± 9.84 %, respectively. The nnTransfer functioned flawlessly and autonomously, facilitating the use of the FVF to evaluate pancreatic disease, particularly in patients with diabetes.

Mast cells express IL17A, IL17F and RORC, are activated and persist with IL-17 production in resolved skin of patients with chronic plaque-type psoriasis.

Little is known about IL-17 expression in psoriasis and the actual cellular source of IL-17 remains incompletely defined. We show that high numbers of IL-17 + mast cells persisted in resolved lesions after treatment (anti-IL-17A, anti-IL-23, UVB or topical dithranol) and correlated inversely with the time span in remission. IL-17 + mast cells were found in T cell-rich areas and often close to resident memory T cells (Trm) in active psoriasis and resolved lesional skin. Digital cytometry by deconvolution of RNA-seq data showed that activated mast cells were increased in psoriatic skin, while resting mast cells were almost absent and both returned to normal levels after treatment. When primary human skin mast cells were stimulated with T cell cytokines (TNFα, IL-22 and IFNγ), they responded by releasing more IL-17A, as measured by ELISA. In situ mRNA detection using padlock probes specific for transcript variants of IL17A, IL17F, and RORC (encoding the Th17 transcription factor RORγt) revealed positive mRNA signals for IL17A, IL17F, and RORCin tryptase + cells, demonstrating that mast cells have the transcriptional machinery to actively produce IL-17. Mast cells thus belong to the center of the IL-23/IL-17 axis and high numbers of IL-17 + mast cells predict an earlier disease recurrence.

Driver mutation subtypes involve with differentiated immunophenotypes influencing pancreatic cancer outcomes.

Despite many studies focusing on the prognostic biomarkers in pancreatic adenocarcinomas (PAADs), there is ill-informed about the relationships between their genomic features and immune characteristics. Herein, we deeply investigated the involvement of major driver mutation subtypes with immunophenotypes impacting PAAD outcomes. Based on public data analyses of RNA expression-based immune subtypes in PAAD, in contrast to KRAS G12D & TP53 co-mutant patients with poor outcomes, the best immune subtype C3 (inflammatory) characterized by high Th1/Th2 ratio was relatively enriched in KRASnon-G12DTP53wt patients with better survival, whereas the inferior subtype C2 (IFN-γ dominant) with low Th1/Th2 ratio was more common in the former than in the latter. Moreover, contrary to the highly immunosuppressive microenvironment (high Treg, high ratio of Treg to tumor-specific CD4+ T cell) in KRASG12DTP53mut patients, KRASG12VTP53wt individuals exhibited an inflamed context profiled by multiplex immunohistochemistry. It could be responsible for their outstanding survival advantage over others in postsurgical PAAD patients receiving adjuvant chemotherapy as shown by our cohort. Together, KRASG12VTP53wt may be a promising biomarker for prognostic evaluation and screening certain candidates with PAAD to get desirable survival benefit from adjuvant chemotherapy.

Hyperspectral dark-field microscopy of human breast lumpectomy samples for tumor margin detection in breast-conserving surgery.

Significance: Hyperspectral dark-field microscopy (HSDFM) and data cube analysis algorithms demonstrate successful detection and classification of various tissue types, including carcinoma regions in human post-lumpectomy breast tissues excised during breast-conserving surgeries. Aim: We expand the application of HSDFM to the classification of tissue types and tumor subtypes in pre-histopathology human breast lumpectomy samples. Approach: Breast tissues excised during breast-conserving surgeries were imaged by the HSDFM and analyzed. The performance of the HSDFM is evaluated by comparing the backscattering intensity spectra of polystyrene microbead solutions with the Monte Carlo simulation of the experimental data. For classification algorithms, two analysis approaches, a supervised technique based on the spectral angle mapper (SAM) algorithm and an unsupervised technique based on the K-means algorithm are applied to classify various tissue types including carcinoma subtypes. In the supervised technique, the SAM algorithm with manually extracted endmembers guided by H&E annotations is used as reference spectra, allowing for segmentation maps with classified tissue types including carcinoma subtypes. Results: The manually extracted endmembers of known tissue types and their corresponding threshold spectral correlation angles for classification make a good reference library that validates endmembers computed by the unsupervised K-means algorithm. The unsupervised K-means algorithm, with no a priori information, produces abundance maps with dominant endmembers of various tissue types, including carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma. The two carcinomas' unique endmembers produced by the two methods agree with each other within <2% residual error margin. Conclusions: Our report demonstrates a robust procedure for the validation of an unsupervised algorithm with the essential set of parameters based on the ground truth, histopathological information. We have demonstrated that a trained library of the histopathology-guided endmembers and associated threshold spectral correlation angles computed against well-defined reference data cubes serve such parameters. Two classification algorithms, supervised and unsupervised algorithms, are employed to identify regions with carcinoma subtypes of invasive ductal carcinoma and invasive mucinous carcinoma present in the tissues. The two carcinomas' unique endmembers used by the two methods agree to <2% residual error margin. This library of high quality and collected under an environment with no ambient background may be instrumental to develop or validate more advanced unsupervised data cube analysis algorithms, such as effective neural networks for efficient subtype classification.

Prospective observational study on biomarkers of response in pancreatic ductal adenocarcinoma.

Adjuvant chemotherapy benefits patients with resected pancreatic ductal adenocarcinoma (PDAC), but the compromised physical state of post-operative patients can hinder compliance. Biomarkers that identify candidates for prompt adjuvant therapy are needed. In this prospective observational study, 1,171 patients with PDAC who underwent pancreatectomy were enrolled and extensively followed-up. Proteomic profiling of 191 patient samples unveiled clinically relevant functional protein modules. A proteomics-level prognostic risk model was established for PDAC, with its utility further validated using a publicly available external cohort. More importantly, through an interaction effect regression analysis leveraging both clinical and proteomic datasets, we discovered two biomarkers (NDUFB8 and CEMIP2), indicative of the overall sensitivity of patients with PDAC to adjuvant chemotherapy. The biomarkers were validated through immunohistochemistry on an internal cohort of 386 patients. Rigorous validation extended to two external multicentic cohorts-a French multicentric cohort (230 patients) and a cohort from two grade-A tertiary hospitals in China (466 patients)-enhancing the robustness and generalizability of our findings. Moreover, experimental validation through functional assays was conducted on PDAC cell lines and patient-derived organoids. In summary, our cohort-scale integration of clinical and proteomic data demonstrates the potential of proteomics-guided prognosis and biomarker-aided adjuvant chemotherapy for PDAC.