BACKGROUND. Pemphigus, a rare autoimmune bullous disease mediated by antidesmoglein autoantibodies, can be controlled with systemic medication like rituximab and high-dose systemic corticosteroids combined with immunosuppressants. However, some patients continue to experience chronically recurrent blisters in a specific area and require long-term maintenance systemic therapy. METHODS. Skin with chronic blisters was obtained from patients with pemphigus. Immunologic properties of the skin were analyzed by immunofluorescence staining, bulk and single-cell RNA and TCR sequencing, and a highly multiplex imaging technique known as CO-Detection by indEXing (CODEX). Functional analyses were performed by flow cytometry and bulk RNA-Seq using peripheral blood from healthy donors. Intralesional corticosteroid was injected into patient skin, and changes in chronically recurrent blisters were observed. RESULTS. We demonstrated the presence of skin tertiary lymphoid structures (TLSs) with desmoglein-specific B cells in chronic blisters from patients with pemphigus. In the skin TLSs, CD4⁺ T cells predominantly produced CXCL13. These clonally expanded CXCL13⁺CD4⁺ T cells exhibited features of activated Th1-like cells and downregulated genes associated with T cell receptor–mediated signaling. Tregs are in direct contact with CXCL13⁺CD4⁺ memory T cells and increased CXCL13 production of CD4⁺ T cells through IL-2 consumption and TGF-β stimulation. Finally, intralesional corticosteroid injection improved chronic blisters and reduced skin TLSs in patients with pemphigus. CONCLUSION. Through this study we conclude that skin TLSs are associated with the persistence of chronically recurrent blisters in patients with pemphigus, and the microenvironmental network involving CXCL13⁺CD4⁺ T cells and Tregs within these structures plays an important role in CXCL13 production. TRIAL REGISTRATION. ClinicalTrials.gov NCT04509570. FUNDING. This work was supported by National Research Foundation of South Korea (NRF-2021R1C1C1007179) and Korea Drug Development Fund, which is funded by Ministry of Science and ICT; Ministry of Trade, Industry, and Energy; and Ministry of Health and Welfare (grant RS-2022-00165917).

The incidence of major hemorrhage and transfusion during liver transplantation has decreased significantly over the past decade, but major bleeding remains a common expectation. Massive intraoperative hemorrhage during liver transplantation can lead to mortality or reoperation. This study aimed to develop machine learning models for the prediction of massive hemorrhage and a scoring system which is applicable to new patients. Data were retrospectively collected from patients aged >18 years who had undergone liver transplantation. These data included emergency information, donor information, demographic data, preoperative laboratory data, the etiology of hepatic failure, the Model for End-stage Liver Disease (MELD) score, surgical history, antiplatelet therapy, continuous renal replacement therapy (CRRT), the preoperative dose of vasopressor, and the estimated blood loss (EBL) during surgery. The logistic regression model was one of the best-performing machine learning models. The most important factors for the prediction of massive hemorrhage were the disease etiology, activated partial thromboplastin time (aPTT), operation duration, body temperature, MELD score, mean arterial pressure, serum creatinine, and pulse pressure. The risk-scoring system was developed using the odds ratios of these factors from the logistic model. The risk-scoring system showed good prediction performance and calibration (AUROC: 0.775, AUPR: 0.753).

Park K, et al. Immune Netw. 2022 Aug;22(4):e29. https://doi.org/10.4110/in.2022.22.e29

Immunotherapies using new modalities, including antibody-based drugs, nanoparticle-delivered drugs, and adoptive cell therapy, have become major treatment options for immune-related diseases such as cancer, autoimmune diseases, and infections. Although data characterizing individual patients’ pharmacological responses, immune statuses, and clinical outcomes become increasingly available, predicting individual patients’ immunotherapeutic responses for developing and deploying optimal immunotherapies remains challenging. Here, we propose “multi-physiology modeling” of the immune system that integrates omics-based and dynamic systems modeling-based systems immunology and pharmacometrics modeling on top of basic and clinical immunology. The multi-physiology modeling approach aims to integrate different physiological systems to realistically simulate the multi-scale and complex interactions of the immune system under intervention by immunotherapeutic agents for predictive immunotherapies tailored to individual patients. This will accelerate not only our understanding of basic immunology related to immune-related diseases but also the efficiency and accuracy of clinical immunotherapeutics in the era of precision immunotherapy.

Chemotherapy often induces severe neutropenia due to the myelosuppressive effect. While predictive pharmacokinetic (PK)/pharmacodynamic (PD) models of absolute neutrophil count (ANC) after anticancer drug administrations have been developed, their deployments to routine clinics have been limited due to the unavailability of PK data and sparseness of PD (or ANC) data. Here, we sought to develop a model describing temporal changes of ANC in non-small cell lung cancer patients receiving (i) combined chemotherapy of paclitaxel and cisplatin and (ii) granulocyte colony stimulating factor (G-CSF) treatment when needed, under such limited circumstances. Maturation of myelocytes into blood neutrophils was described by transit compartments with negative feedback. The K-PD model was employed for drug effects with drug concentration unavailable and the constant model for G-CSF effects. The fitted model exhibited reasonable goodness of fit and parameter estimates. Covariate analyses revealed that ANC decreased in those without diabetes mellitus and female patients. Using the final model obtained, an R Shiny web-based application was developed, which can visualize predicted ANC profiles and associated risk of severe neutropenia for a new patient. Our model and application can be used as a supportive tool to identify patients at the risk of grade 4 neutropenia early and suggest dose reduction.