Objective: The aim of this study was the validation of the European Organization for Research and Treatment of Cancer's Multiple Myeloma Module (QLQ-MY20) in Portuguese myeloma patients.
Methods: A total of 213 Portuguese patients diagnosed with multiple myeloma participated in this study and were assessed with the European Organization for Research and Treatment of Cancer's (EORTC) Questionnaire C30 (QLQ-C30), the EORTC Multiple Myeloma Module (QLQ-MY20), the Hospital Anxiety and Depression Scale (HADS) and the Satisfaction with Social Support Scale (SSSS).
Results: The validated version includes 17 items presenting good global adjustment and good internal consistency. Overall, the Portuguese validation maintains the original model with the exception of three items that were excluded. The instrument also showed good reliability and good convergent and divergent validity.
Conclusion: The Portuguese version of the EORTC Multiple Myeloma Module questionnaire seems to be a valid instrument for myeloma patients to help monitor interventions in this population focused on the promotion of quality of life.
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http://dx.doi.org/10.1111/ecc.13128 | DOI Listing |
Int Immunopharmacol
January 2025
Department of Biotherapy, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu 610041 Sichuan Province, People's Republic of China. Electronic address:
Chimeric antigen receptor T (CAR-T) cell therapy targeting B cell mature antigen (BCMA) has shown remarkable clinical benefits in treating multiple myeloma (MM). Bortezomib, a proteasome inhibitor approved as a first-line agent for MM for two decades, has demonstrated potent antitumor activity. In this study, we found that bortezomib treatment stabilizes the expression of BCMA and conceived the hypothesis that BCMA CAR-T therapy combined with bortezomib would enhance the anti-MM efficacy.
View Article and Find Full Text PDFPharmaceutics
January 2025
Laboratory Medical Immunology, Department of Laboratory Medicine, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands.
Multiple Myeloma (MM) is a hematologic malignancy caused by clonally expanded plasma cells that produce a monoclonal immunoglobulin (M-protein), a personalized biomarker. Recently, we developed an ultra-sensitive mass spectrometry method to quantify minimal residual disease (MS-MRD) by targeting unique M-protein peptides. Therapeutic antibodies (t-Abs), key in MM treatment, often lead to deep and long-lasting responses.
View Article and Find Full Text PDFPharmaceuticals (Basel)
January 2025
Department of Organic Chemistry and Technology, Faculty of Chemical Technology and Biotechnology, Budapest University of Technology and Economics, Műegyetem rkp. 3, 1111 Budapest, Hungary.
Methylenebisphosphonic derivatives including hydroxy-methylenebisphosphonic species may be of potential biological activity, and a part of them is used in the treatment of bone diseases. Methylenebisphosphonates may be obtained by the Michaelis-Arbuzov reaction of suitably α-substituted methylphosphonates and trialkyl phosphites or phosphinous esters, while the hydroxy-methylene variations are prepared by the Pudovik reaction of α-oxophosphonates and different >P(O)H reagents, such as diethyl phosphite and diarylphosphine oxides. After converting α-hydroxy-benzylphosphonates and -phosphine oxides to the α-halogeno- and α-sulfonyloxy derivatives, they were utilized in the Michaelis-Arbuzov reaction with trialkyl phosphites and ethyl diphenylphosphinite to afford the corresponding bisphosphonate, bis(phosphine oxide) and phosphonate-phosphine oxide derivatives.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Hematology, Theagenion Cancer Hospital, 54639 Thessaloniki, Greece.
Multiple Myeloma (MM) is a complex hematological malignancy characterized by the clonal proliferation of malignant plasma cells within bone marrow (BM) [...
View Article and Find Full Text PDFCancers (Basel)
January 2025
Bioinformatics Department, The Cyprus Institute of Neurology & Genetics, 6 Iroon Avenue, Ayios Dometios, 2371 Nicosia, Cyprus.
Background: The accurate staging of multiple myeloma (MM) is essential for optimizing treatment strategies, while predicting the progression of asymptomatic patients, also referred to as monoclonal gammopathy of undetermined significance (MGUS), to symptomatic MM remains a significant challenge due to limited data. This study aimed to develop machine learning models to enhance MM staging accuracy and stratify asymptomatic patients by their risk of progression.
Methods: We utilized gene expression microarray datasets to develop machine learning models, combined with various data transformations.
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