Association of Gender, Diagnosis, and Obesity With Retention Rate of Secukinumab in Spondyloarthropathies: Results Form a Multicenter Real-World Study

Background: Secukinumab has been shown effective for psoriatic arthritis (PsA) and axial spondylarthritis (AxSpA) in randomized trials. The aim of this study was to analyze baseline patient and disease characteristics associated with a better retention rate of secukinumab under real-world conditions.Patients and Methods: Real-life, prospective multicenter observational study involving 138 patients, 61 PsA and 77 AxSpA, who were analyzed at baseline, 6, 12 months and subsequently every year after starting secukinumab regardless of the line of treatment. Demographics and disease characteristics, measures of activity, secukinumab use, and adverse events were collected. Drug survival was analyzed using Kaplan-Meier curves and factors associated with discontinuation were evaluated using Cox regression. The machine-learning J48 decision tree classifier was also applied.Results: During the 1st year of treatment, 75% of patients persisted with secukinumab, but accrued 71% (n = 32) in total losses (n = 45). The backward stepwise (Wald) method selected diagnosis, obesity, and gender as relevant variables, the latter when analyzing the interactions. At 1 year of follow-up, the Cox model showed the best retention rate in the groups of AxSpa women (95%, 95% CI 93–97%) and PsA men (89%, 95% CI 84–93%), with the worst retention in PsA women (66%, 95% CI 54–79%). The J48 predicted secukinumab retention with an accuracy of 77.2%. No unexpected safety issues were observed.Conclusions: Secukinumab shows the best retention rate at 1 year of treatment in AxSpA women and in PsA men, independently of factors such as the time of disease evolution, the line of treatment or the initial dose of the drug.

Risk factors associated with hospitalization and evolution in kidney transplant patients with COVID-19: A single-center retrospective cohort study

Background: The SARS -CoV -2 infection has had a major impact on kidney transplant patients. Our single -center experience aims to analyze the risk factors for affected patient hospitalization and predictors of worse clinical outcome on admission. Material and methods: A retrospective cohort study with kidney transplant patients with positive PCR for SARS -CoV -19 between March 16th 2020 and February 11th 2021 was conducted. Demographic characteristics and clinical and laboratory information on admission was collected and analyzed to assess risk factors related to patient hospitalization and disease evolution. Results: Seventy -six kidney transplant recipients diagnosed with COVID -19 were included and divided into hospitalized (n=48) and non- -hospitalized (n=28) patients. Two hospitalized patients were not taken into account for the analysis due to a lack of data, and the remaining patients were divided into mild -moderate (n=25) and severe pneumonia (n=21). Lasso and multivariate logistic regression demonstrated that age (OR 1.041, p=0.039) and hypertension (OR 4.177, p=0.040) were risk factors for hospitalization, while time after transplant (OR 0.993, p=0.029) decreases the probability of being hospitalized. Analyses also revealed that SpO2 ≤92% on admission (OR 8.954, p= 0.026) and overweight/obesity (OR 13.453, p= 0.001) were related to a worse evolution and severe pneumonia among hospitalized recipients. Seven patients died due to COVID -19 complications. Conclusion: Age and hypertension are risk factors for hospitalization among positive COVID -19 patients, while time after transplant decreases the probability of being hospitalized. Overweight/obesity and levels of SpO2 ≤92% on admission were the main risk factors that could help to predict the severity of COVID -19 disease in our series.

Natural Barcodes for Longitudinal Single Cell Tracking of Leukemic and Immune Cell Dynamics

Blood malignancies provide unique opportunities for longitudinal tracking of disease evolution following therapeutic bottlenecks and for the monitoring of changes in anti-tumor immunity. The expanding development of multi-modal single-cell sequencing technologies affords newer platforms to elucidate the mechanisms underlying these processes at unprecedented resolution. Furthermore, the identification of molecular events that can serve as in-vivo barcodes now facilitate the tracking of the trajectories of malignant and of immune cell populations over time within primary human samples, as these permit unambiguous identification of the clonal lineage of cell populations within heterogeneous phenotypes. Here, we provide an overview of the potential for chromosomal copy number changes, somatic nuclear and mitochondrial DNA mutations, single nucleotide polymorphisms, and T and B cell receptor sequences to serve as personal natural barcodes and review technical implementations in single-cell analysis workflows. Applications of these methodologies include the study of acquired therapeutic resistance and the dissection of donor- and host cellular interactions in the context of allogeneic hematopoietic stem cell transplantation.

Understanding CLL biology through mouse models of human genetics

Rapid advances in large-scale next-generation sequencing studies of human samples have progressively defined the highly heterogeneous genetic landscape of chronic lymphocytic leukemia (CLL). At the same time, the numerous challenges posed by the difficulties in rapid manipulation of primary B cells and the paucity of CLL cell lines have limited the ability to interrogate the function of the discovered putative disease “drivers,” defined in human sequencing studies through statistical inference. Mouse models represent a powerful tool to study mechanisms of normal and malignant B-cell biology and for preclinical testing of novel therapeutics. Advances in genetic engineering technologies, including the introduction of conditional knockin/knockout strategies, have opened new opportunities to model genetic lesions in a B-cell–restricted context. These new studies build on the experience of generating the MDR mice, the first example of a genetically faithful CLL model, which recapitulates the most common genomic aberration of human CLL: del(13q). In this review, we describe the application of mouse models to the studies of CLL pathogenesis and disease transformation from an indolent to a high-grade malignancy (ie, Richter syndrome [RS]) and treatment, with a focus on newly developed genetically inspired mouse lines modeling recurrent CLL genetic events. We discuss how these novel mouse models, analyzed using new genomic technologies, allow the dissection of mechanisms of disease evolution and response to therapy with greater depth than previously possible and provide important insight into human CLL and RS pathogenesis and therapeutic vulnerabilities. These models thereby provide valuable platforms for functional genomic analyses and treatment studies.

Chronic inflammatory response modulation against Leishmania (L.) amazonensis by homeopathic thymulin and antimonium crudum inBalb/c mice

In previous studies it was found that thymulin 5cH (thymic hormone) can modulate immune processes in several diseases. Additionally, the Antimonium crudum has used in dogs bearing leishmaniosis, according to the similia principle. We studied the inflammatory and immune modulation by thymulin 5CH and Antimonium crudum 30CH treatment in mice experimentally inoculated with Leishmania (L.) amazonensis. Male adult Balb/c mice were inoculated with Leishmania (2x105 promastigotes) into the footpad to induce inflammatory response and peritoneum and spleen cells were evaluated by flow cytometry after 60 days. Animals were divided in 3 groups (n=10): thymulin 5cH, Antimonium crudum 30cH and vehicle /control. Treatment was made in blind, daily, in water/alcohol 30% diluted 1:2500 in drinking water, during all experimental period. CD11b (activated phagocytes and B1 cells), CD19 (B1 cells and B2), CD4 and CD8 (effective T lymphocytes) markers were used to identify immune cells subsets in peritoneal washing fluid and spleen cell suspension. Mice treated with thymulin 5cH presented increase in peritoneal and spleen B1 stem cells (X2=0.0001) and higher CD8+/CD4+ ratio in spleen, regarding to the control. Also, Antimonium crudum 30CH induced a mild increase in B1 cells in peritoneum and spleen ( both X2, p=0.0001). Further histological analysis of the primary lesion will be done in the next step, to elucidate the impact of these findings in the disease evolution. The results show that both treatments stimulate B1 stem cell proliferation and suggest the cooperation of T spleen lymphocytes in the process.

Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata

Background Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7%-3% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. Objective This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. Methods In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. Results In total, 32 of 40 (80%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81%) images of those predicted as responders showed a favorable response to the treatment. Conclusions The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features. Conflicts of Interest None declared.

CXCL8/CXCR2 signaling mediates bone marrow fibrosis and represents a therapeutic target in myelofibrosis

Pro-inflammatory signaling is a hallmark feature of human cancer, including in myeloproliferative neoplasms (MPNs), most notably myelofibrosis (MF). Dysregulated inflammatory signaling contributes to fibrotic progression in MF; however, the individual cytokine mediators elicited by malignant MPN cells to promote collagen-producing fibrosis and disease evolution remain yet to be fully elucidated. Previously we identified a critical role for combined constitutive JAK/STAT and aberrant NF-kB pro-inflammatory signaling in myelofibrosis development. Using single-cell transcriptional and cytokine-secretion studies of primary MF patient cells and two separate murine models of myelofibrosis, we extend this previous work and delineate the role of CXCL8/CXCR2 signaling in MF pathogenesis and bone marrow fibrosis progression. MF patient hematopoietic stem/progenitor cells are enriched in a CXCL8/CXCR2 gene signature and display dose- dependent proliferation and fitness in response to exogenous CXCL8 ligand in vitro. Genetic deletion of Cxcr2 in the hMPLW515L adoptive transfer model abrogates fibrosis and extends overall survival, and pharmacologic inhibition of the CXCR1/2 pathway improves hematologic parameters, attenuates bone marrow fibrosis, and synergizes with JAK inhibitor therapy. Our mechanistic insights provide a rationale for therapeutic targeting of the CXCL8/CXCR2 pathway in MF patients at risk for continued fibrotic progression.

Use of Artificial Intelligence as a Predictor of the Response to Treatment in Alopecia Areata (Preprint)

BACKGROUND Artificial intelligence (AI) has emerged in dermatology with some studies focusing on skin disorders such as skin cancer, atopic dermatitis, psoriasis, and onychomycosis. Alopecia areata (AA) is a dermatological disease whose prevalence is 0.7%-3% in the United States, and is characterized by oval areas of nonscarring hair loss of the scalp or body without evident clinical variables to predict its response to the treatment. Nonetheless, some studies suggest a predictive value of trichoscopic features in the evaluation of treatment responses. Assuming that black dots, broken hairs, exclamation marks, and tapered hairs are markers of negative predictive value of the treatment response, while yellow dots are markers of no response to treatment according to recent studies, the absence of these trichoscopic features could indicate favorable disease evolution without treatment or even predict its response. Nonetheless, no studies have reportedly evaluated the role of AI in AA on the basis of trichoscopic features. OBJECTIVE This study aimed to develop an AI algorithm to predict, using trichoscopic images, those patients diagnosed with AA with a better disease evolution. METHODS In total, 80 trichoscopic images were included and classified in those with or without features of negative prognosis. Using a data augmentation technique, they were multiplied to 179 images to train an AI algorithm, as previously carried out with dermoscopic images of skin tumors with a favorable response. Subsequently, 82 new images of AA were presented to the algorithm, and the algorithm classified these patients as responders and non-responders; this process was reviewed by an expert trichologist observer and presented a concordance higher than 90% with the algorithm identifying structures described previously. Evolution of the cases was followed up to truly determine their response to treatment and, therefore, to assess the predictive value of the algorithm. RESULTS In total, 32 of 40 (80%) images of patients predicted as nonresponders scarcely showed response to the treatment, while 34 of 42 (81%) images of those predicted as responders showed a favorable response to the treatment. CONCLUSIONS The development of an AI algorithm or tool could be useful to predict AA evolution and its response to treatment. However, further research is needed, including larger sample images or trained algorithms, by using images previously classified in accordance with the disease evolution and not with trichoscopic features.

Evolution of Hemodynamic Parameters Simulated by Means of Diffusion Models

Diffusion is a well-known physical phenomenon governing such processes as movement of particles or transportation of heat. In this paper, we prove that a close analogy to those processes exists in medical data behavior, and that changes in the values of medical parameters measured while treating patients may be described using diffusion models as well. The medical condition of a patient is usually described by a set of discrete values. The evolution of that condition and, consequently, of the disease has the form of a transition of that set of discrete values, which correspond to specific parameters. This is a typical medical diagnosis scheme. However, disease evolution is a phenomenon that is characterized by continuously varying, temporal characteristics. A mathematical disease evolution model is, in fact, a continuous diffusion process from one discrete slot of the diagnosed parameter value to another inside the mentioned set. The ability to predict such diffusion-related properties offer precious support in diagnostic decision-making. We have examined several hundred patients while conducting a medical research project. All patients were under treatment to stabilize their hemodynamic parameters. A diffusion model relied upon simulating the results of treatment is proposed here. Time evolution of thoraric fluid content (TFC) has been used as the illustrative example. The objective is to prove that diffusion models are a proper and convenient solution for predicting disease evolution processes. We applied the Fokker-Planck equation (FPE), considering it to be most adequate for examining the treatment results by means of diffusion. We confirmed that the phenomenon of diffusion explains the evolution of the heart disease parameters observed. The evolution of TFC has been chosen as an example of a hemodynamic parameter.