Abstract. Chemotherapy-induced neutropenia (CIN) is the major dose-limiting toxicity of systemic chemotherapy and it is associated with significant morbidity, mortality and treatment cost. The aim of the present study was to identify the risk factors that may predispose pediatric cancer patients who receive myelosuppressive chemotherapy to CIN and associated sequelae. A total of 113 neutropenia episodes were analyzed and the risk factors for CIN were classified as patient-specific, disease-specific and regimen-specific, while the consequences of CIN were divided into infectious and dose-modifying sequelae. The risks and consequences were analyzed to target high-risk patients with appropriate preventive strategies. Among our patients, 28% presented with a single neutropenia attack, while 72% experienced recurrent attacks during their treatment cycles. The mean absolute neutrophil count was 225.5±128.5 x10 9 ̸l (range, 10-497 x10 9 ̸l), starting 14.2±16.3 days (range, 2-100 days) after the onset of chemotherapy and resolving within 11.2±7.3 days, either with (45.1%) or without (54.9%) granulocyte colony-stimulating factor (G-CSF). No significant association was observed between any patient characteristics or disease stage and the risk for CIN. However, certain malignancies, such as acute lymphocytic leukemia (ALL), neuroblastoma and Burkitt's lymphoma, and certain regimens, such as induction block for ALL and acute myelocytic leukemia, exerted the most potent myelotoxic effect, with severe and prolonged episodes of neutropenia. G-CSF significantly shortened the duration of the episodes and enhanced bone marrow recovery. Febrile neutropenia was the leading complication among our cases (73.5%) and was associated with several documented infections, particularly mucositis (54.9%), respiratory (45.1%), gastrointestinal tract (38.9%) and skin (23.9%) infections. A total of 6% of our patients succumbed to infection-related complications. Neutropenia was responsible for treatment discontinuation (13.3%), dose delay (13.3%) and dose reduction (5.3%) in our patients.

In this paper, we define the fuzzy soft hermitian operator, which is a special type of fuzzy soft linear operators in fuzzy soft Hilbert spaces. Moreover, related theorems including fuzzy soft point spectrum theorem and more are introduced. Furthermore, an example in favor of the fuzzy soft hermitian operator and another one against it are investigated.

In this work, we define the fuzzy soft Hilbert spaceH based on the definition of the fuzzy soft inner product space (Ũ, < •, • >), introduced by Faried et al. [N. Faried, M. S. S. Ali, H. H. Sakr, Appl. Math. Inf. Sci., 14 (2020), 709-720], in terms of the fuzzy soft vectorṽ f G(e) . Moreover, we show that C n (A), R n (A) and 2 (A) are suitable examples of fuzzy soft Hilbert spaces. In addition, it is proved that the fuzzy soft orthogonal complement of any non-empty fuzzy soft subset ofH is a fuzzy soft closed fuzzy soft subspace ofH and we study some of the fuzzy soft Hilbert spaces properties and some of the fuzzy soft inner product spaces properties. Furthermore, we introduce the definition of the fuzzy soft orthogonal family and the fuzzy soft orthonormal family and introduce examples satisfying them. Moreover, we present the fuzzy soft Bessel's inequality and the fuzzy soft Parseval's formula in this generalized setting.

Due to the difficulty of representing problem parameters fuzziness using the soft set theory, the fuzzy soft set is regarded to be more general and flexible than using the soft set. In this paper, we define the fuzzy soft linear operator T~ in the fuzzy soft Hilbert space H~ based on the definition of the fuzzy soft inner product space U~,·,·~ in terms of the fuzzy soft vector v~fGe modified in our work. Moreover, it is shown that ℂnA, ℝnA and ℓ2A are suitable examples of fuzzy soft Hilbert spaces and also some related examples, properties and results of fuzzy soft linear operators are introduced with proofs. In addition, we present the definition of the fuzzy soft orthogonal family and the fuzzy soft orthonormal family and introduce examples satisfying them. Furthermore, the fuzzy soft resolvent set, the fuzzy soft spectral radius, the fuzzy soft spectrum with its different types of fuzzy soft linear operators and the relations between those types are introduced. Moreover, the fuzzy soft right shift operator and the fuzzy soft left shift operator are defined with an example of each type on ℓ2A. In addition, it is proved, on ℓ2A, that the fuzzy soft point spectrum of fuzzy soft right shift operator has no fuzzy soft eigenvalues, the fuzzy soft residual spectrum of fuzzy soft right shift operator is equal to the fuzzy soft comparison spectrum of it and the fuzzy soft point spectrum of fuzzy soft left shift operator is the fuzzy soft open disk λ~<~1~. Finally, it is shown that the fuzzy soft Hilbert space is fuzzy soft self-dual in this generalized setting.

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