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Thrombocytopenia is a potential dialysis-related treatment complication. Developments in bio-compatible dialyser membranes have decreased the occurrence of thrombocytopenia. We investigated whether thrombopoiesis is impaired in haemodialysis patients by measuring the thrombopoietin level and absolute immature platelet number ( AIPN) in the blood of patients undergoing haemodialysis. Samples were collected from the dialysis tubing pre- and post- haemodialysis in a cohort of 45 well-characterized haemodialysis patients. Thrombopoietin levels and AIPN increased following haemodialysis, despite no change in platelet count. Observed increase in release of immature platelets from the bone marrow following haemodialysis indicates possible complement activation secondary to interaction between blood constituents and the dialysis membrane. [ABSTRACT FROM AUTHOR]

The article explores a specific type of paroxysmal nocturnal haemoglobinuria (PNH) known as ahemolytic PNH or white cell PNH. Unlike typical PNH, this subtype does not exhibit signs of hemolysis but is characterized by bone marrow failure and thrombophilia. The document suggests that this subtype may be caused by a PIGA mutation in a committed progenitor cell rather than a stem cell. The authors recommend considering screening for ahemolytic PNH in cases of thromboembolic events and bone marrow failure without hemolysis. [Extracted from the article]

Background: TP53 mutations (TP53MT) occur in diverse genomic configurations. Particularly, biallelic inactivation is associated with poor overall survival in cancer. Lesions affecting only one allele might not be directly leukemogenic, questioning the presence of cryptic biallelic subclones in cases with dismal prognosis. Methods: We have collected clinical and molecular data of 7400 patients with myeloid neoplasms and applied a novel model by identifying an optimal VAF cutoff using a statistically robust strategy of sampling-based regression on survival data to accurately classify the TP53 allelic configuration and assess prognosis more precisely. Results: Overall, TP53MT were found in 1010 patients. Following the traditional criteria, 36% of the cases were classified as single hits, while 64% exhibited double hits genomic configuration. Using a newly developed molecular algorithm, we found that 579 (57%) patients had unequivocally biallelic, 239 (24%) likely contained biallelic, and 192 (19%) had most likely monoallelic TP53MT. Interestingly, our method was able to upstage 192 out of 352 (54.5%) traditionally single hit lesions into a probable biallelic category. Such classification was further substantiated by a survival-based model built after re-categorization. Among cases traditionally considered monoallelic, the overall survival of those with probable monoallelic mutations was similar to the one of wild-type patients and was better than that of patients with a biallelic configuration. As a result, patients with certain biallelic hits, regardless of the disease subtype (AML or MDS), had a similar prognosis. Similar results were observed when the model was applied to an external cohort. In addition, single-cell DNA studies unveiled the biallelic nature of previously considered monoallelic cases. Conclusion: Our novel approach more accurately resolves TP53 genomic configuration and uncovers genetic mosaicism for the use in the clinical setting to improve prognostic evaluation. Key Points: Patients with single TP53 mutations and variant allele frequency more than 23% act biologically like biallelic TP53 cases. Traditionally defined single hits might contain subclones with biallelic inactivation, which negatively influences prognosis. [ABSTRACT FROM AUTHOR]

Objective. Patients diagnosed with hematologic malignancies are at increased risk for severe SARS-CoV-2 infection. We evaluated the serological IgG response following two doses of the SARS-CoV-2 vaccine in patients with hematologic malignancies. Methods. Patients treated at UT Southwestern Medical Center with a diagnosis of a myeloid or lymphoid neoplasm were included. SARS-CoV-2 vaccination response was defined as a positive quantifiable spike IgG antibody titer. Results. Sixty patients were included in the study and 60% were diagnosed with a myeloid neoplasm. The majority (85%) of the patients with a myeloid malignancy and 50% of the patients with a lymphoid malignancy mounted a serological response after receiving two doses of the vaccine. Conclusion. Vaccination should be offered irrespective of ongoing treatment or active disease. Findings require validation in a larger cohort of patients. [ABSTRACT FROM AUTHOR]

Importance: Early prognostication of patients hospitalized with COVID-19 who may require mechanical ventilation and have worse outcomes within 30 days of admission is useful for delivering appropriate clinical care and optimizing resource allocation. Objective: To develop machine learning models to predict COVID-19 severity at the time of the hospital admission based on a single institution data. Design, setting, and participants: We established a retrospective cohort of patients with COVID-19 from University of Texas Southwestern Medical Center from May 2020 to March 2022. Easily accessible objective markers including basic laboratory variables and initial respiratory status were assessed using Random Forest's feature importance score to create a predictive risk score. Twenty-five significant variables were identified to be used in classification models. The best predictive models were selected with repeated tenfold cross-validation methods. Main outcomes and measures: Among patients with COVID-19 admitted to the hospital, severity was defined by 30-day mortality (30DM) rates and need for mechanical ventilation. Results: This was a large, single institution COVID-19 cohort including total of 1795 patients. The average age was 59.7 years old with diverse heterogeneity. 236 (13%) required mechanical ventilation and 156 patients (8.6%) died within 30 days of hospitalization. Predictive accuracy of each predictive model was validated with the 10-CV method. Random Forest classifier for 30DM model had 192 sub-trees, and obtained 0.72 sensitivity and 0.78 specificity, and 0.82 AUC. The model used to predict MV has 64 sub-trees and returned obtained 0.75 sensitivity and 0.75 specificity, and 0.81 AUC. Our scoring tool can be accessed at https://faculty.tamuc.edu/mmete/covid-risk.html. Conclusions and relevance: In this study, we developed a risk score based on objective variables of COVID-19 patients within six hours of admission to the hospital, therefore helping predict a patient's risk of developing critical illness secondary to COVID-19. [ABSTRACT FROM AUTHOR]

• Hematopoietic stem cell transplant and adoptive cellular therapies put patients at risk of clotting and bleeding. • In a retrospective cohort of patients receiving enoxaparin, anti-Xa based dosing resulted in decreased doses of enoxaparin. • Large randomized studies are needed to demonstrate the utility of anti-Xa monitoring in this unique patient population. [ABSTRACT FROM AUTHOR]

Summary: COVID‐19, caused by SARS‐CoV‐2, is a contagious life‐threatening viral disease that has killed more than three million people worldwide to date. Attempts have been made to identify biomarker(s) to stratify disease severity and improve treatment and resource allocation. Patients with SARS‐COV‐2 infection manifest with a higher inflammatory response and platelet hyperreactivity; this raises the question of the role of thrombopoiesis in COVID‐19 infection. Immature platelet fraction (IPF, %) and immature platelet counts (IPC, ×109/l) can be used to assess thrombopoiesis. This study investigates whether the level of thrombopoiesis correlates with COVID‐19 severity. A large cohort of 678 well‐characterized COVID‐19 patients was analyzed, including 658 (97%) hospitalized and 139 (21%) admitted to the intensive care unit (ICU). Elevated percentage IPF at presentation was predictive of length of hospitalization (P < 0·01) and ICU admission (P < 0·05). Additionally, percentage IPF at the peak was significantly higher among ICU patients than non‐ICU patients (6·9 ± 5·1 vs 5·3 ± 8·4, P < 0·01) and among deceased patients than recovered patients (7·9 ± 6·3 vs 5·4 ± 7·8, P < 0·01). Furthermore, IPC at the peak was significantly higher among ICU patients than non‐ICU patients (18·5 ± 16·2 vs. 13·2 ± 8·3, P < 0·05) and among patients on a ventilator than those not (22·1 ± 20·1 vs.13·4 ± 8·4, P < 0·05). Our study demonstrated that elevated initial and peak values of percentage IPF and IPC might serve as prognostic biomarkers for COVID‐19 progression to severe conditions. [ABSTRACT FROM AUTHOR]