45 research outputs found
Essays on Nonlinearities and Structural Breaks in the Relationships between Macroeconomic Variables
The dissertation is set out in three chapters, focusing on the structural changes that led to jobless recoveries in following the past three recessions, the asymmetric effects of fiscal stimulus over the business cycle, and the asymmetric effects of fiscal cuts and stimulus respectively. In the first chapter, I examine the three leading theoretical explanations for the recent jobless recoveries using a correlated unobserved components model of aggregate data for output, sales, employment, and hours. The main finding is that employment now respond to demand shocks in a way that is consistent with just-in-time utilization of labor resources. The second and the third chapter focus on asymmetric responses to fiscal policy. In the second chapter, I investigate the effects of government spending on U.S. economic activity using a threshold version of a structural vector autoregressive model. The empirical findings support state-dependent effects of fiscal policy. In particular, the effects of a government spending shock on output are significantly larger and more persistent when the economy has a high degree of underutilized resources than when the economy is close to capacity. The third chapter examines whether there are sign and size asymmetries in the responses of output, output components, and employment to fiscal policy. When the economy is not constrained, a large fiscal stimulus is more effective at increasing employment and output, and cuts have larger effects than increases
A Likelihood Ratio Test of Stationarity Based on a Correlated Unobserved Components Model
We propose a likelihood ratio (LR) test of stationarity based on a widely-used correlated unobserved components model. We verify the asymptotic distribution and consistency of the LR test, while a bootstrap version of the test is at least first-order accurate. Given empirically-relevant processes estimated from macroeconomic data, Monte Carlo analysis reveals that the bootstrap version of the LR test has better small-sample size control and higher power than commonly used bootstrap Lagrange multiplier (LM) tests, even when the correct parametric structure is specified for the LM test. A key feature of our proposed LR test is its allowance for correlation between permanent and transitory movements in the time series under consideration, which increases the power of the test given the apparent presence of non-zero correlations for many macroeconomic variables. Based on the bootstrap LR test, and in some cases contrary to the bootstrap LM tests, we can reject trend stationarity for U.S. real GDP, the unemployment rate, consumer prices, and payroll employment in favor of nonstationary processes with volatile stochastic trends.Stationarity Test, Likelihood Ratio, Unobserved Components, Parametric Bootstrap, Monte Carlo Simulation, Small-Sample Inference
Molecular Monitoring in Acute Myeloid Leukemia Patients Undergoing Matched Unrelated Donor: Hematopoietic Stem Cell Transplantation
Minimal residual disease (MRD) in acute myeloid leukemia (AML) is a complex, multi-modality assessment and much as its clinical implications at different points are extensively studied, it remains even now a challenging area. It is the disease biology that governs the modality of MRD assessment; in patients harboring specific molecular targets, high sensitivity techniques can be applied. In AML patients undergoing allogenic hematopoietic stem cell transplantation (alloHSCT), relapse in considered as leading cause for treatment failure. In post-transplant setting, regular MRD status assessment enables to identify patients at risk of impending relapse when early therapeutic intervention may be beneficent. We analyzed data of AML patients who underwent matched unrelated donor (MUD) HSCT since the introduction of this procedure in the Republic of North Macedonia. Chimeric fusion transcripts were identified in three patients; two of them positive for RUNX-RUNX1T1 transcript and one for CBFB-MYH11. One patient harbored mutation in the transcription factor CCAAT/enhancer binding protein Ξ± (CEBPA). Post-transplant MRD kinetics was measured by quantitative polymerase chain or multiplex fluorescent-PCR every three months after the transplantation during the first two years after the transplant. MRD negativity was achieved in three patients by the sixth month of HSCT, who were pre-transplant MRD positive. They sustained hematological and molecular remission for 19, 9 and 7Β months, respectively. The forth patient died due to transplant-related complication. Our experience suggests, when molecularly-defined AML patients undergo HSCT, regular MRD monitoring helps predict impending relapse and direct future treatment strategies
Use of chronic lymphocytic leukemia-international prognostic index in patient risk stratification-single center experience
Background: Several prognostic factors have been identified to predict the outcome of patients with chronic lymphocytic leukemia (CLL). To predict the time to first treatment (TFT) we integrated the data of clinical and biological markers in CLL-International prognostic index (CLL-IPI). Aim of the study was the determination of the impact of CLL-IPI in prediction of TFS in CLL patents.Methods: The study was set up retrospectively and included 90 patients with CLL diagnosed and treated at the university clinic of hematology for a period of time from January 2012 to January 2020. We incorporated the data of Binet staging system, most adverse cytogenetic marker and mutational status of immunoglobulin heavy chain in CLL-IPI.Results: The statistical data of the 90 patients showed that the median TFS for low CLL-IPI (N=24), intermediate CLL-IPI (N=40), high risk CLL-IPI (N=17) and very high risk group (N=9) according to the CLL-IPI scoring system was 20.1, 17.6, 7.1 and 5.8 months, respectively. Multivariate analysis indicated that del 17p (p<0.008) was independent prognostic factors of TFS.Conclusions: CLL-IPI is a powerful risk stratification tool for CLL patients and this system has also provided treatment recommendations for different patient risk subgroups.
Interplay between lymphocyte subpopulation, inflammatory cytokines and their correlation with oxidative stress parameters in COVID-19
Our objective was to investigate the inflammatory and oxidative stress markers in patients with moderate and severe form of COVID-19. In addition, we show the correlation between changes in lymphocyte subsets and markers of oxidative stress as a tool for patient classification. IL-6 and VEGF were analysed by utilizing a High Sensitivity Evidence Investigatorβ’ Biochip Array technology. The total antioxidant capacity (PAT) and the free radical concentrations (d-ROM) were measured in serum utilizing analytical photometric system FRAS5. Peripheral blood was used to determine CD45 + mononuclear, B, T, and NK cells using a multi-parameter flow cytometric immunophenotypic test.
Statistically significant differences in IL-6 and VEGF levels were observed between the two patient groups. Decreased values of the absolute number of lymphocytes and their CD4 + and CD8 + positive T cells, NK cells, and CD8 were obtained. In the moderate group, good correlations were found between IL-6 and VEGF and NK cells (r = 0.6973, p <0.05; for IL6 and r = 0.6498, p <0, for VEGF. 05). Cytokines were correlated with CD45+ (r = 0.5610, p <0.05; for IL-6 and r = 0.5462, p <0.05 for VEGF). The oxidative stress index can be used as a cheaper alternative and as a triage tool between severe and moderate illnesses, after showing good correlation with more expensive patient classification analysis
Clinically significant drug interactions of Eltrombopag: a retrospective study from the clinical pharmacist perspective
Introduction: Thrombopoietin is the main cytokine regulating megakaryopoiesis and platelet production. Eltrombopag interacts with the transmembrane domain of thrombopoietin receptors and initiates signaling cascades inducing proliferation and differentiation from bone marrow progenitor cells. The aim of the study was to determine drug interaction at patients that are receiving Eltrombopag along with other medications.
Materials and methods: A retrospective, longitudinal study was conducted at the Hematology Clinic in Skopje, N. Macedonia. A clinical pharmacist, focusing on Eltrombopag and concomitant medications interactions, reviewed a total number of 16 patientβs histories for the period of 6 months (January-June 2023). Anamnestic data on additional drugs, herbal supplements, vitamins, minerals were also taken. Potential drug interactions were identified using Stockley's interactions checker, categorized by severity and subclassified into co-administered drugs altering pharmacokinetics.
Results: A total number of 73 interactions were identified, of which 23 (31.51%) were with moderate clinical relevance, 14 (19.18%) were with no clinical importance and required counseling about possible adverse effects and additional monitoring. The rest of 36 (49,32%) interactions were without clinical significance. Additionally, we determine that 7 (9.59%) of total interactions directly related to patients receiving Eltrombopag (ciclosporin, atorvastatin, rosuvastatin, dexamethasone, prednisolone, valsartan, and magnesium) and categorized as moderate and needs close monitoring.
Conclusion: This study demonstrates toxicity potential of Eltrombopag at patients associated with concomitant medicines. Close collaboration of physicians and clinical pharmacists is necessary in all cases where patients are receiving Eltrombopag along with other medications in order all significant interactions to be identified, prevented and managed
ΠΠ΅Π·Π±Π΅Π΄Π½ΠΎΡΡ Π½Π° ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈΡΠ΅ Π²ΠΎ ΠΊΠ»ΠΈΠ½ΠΈΡΠΊΠ° ΠΏΡΠ°ΠΊΡΠ° - ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ° Π·Π° ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΠ°Π½ ΠΏΡΠΈΡΡΠ°ΠΏ Π΄ΠΎ Π»Π΅ΠΊΠΎΡ Π»Π΅Π½Π°Π»ΠΈΠ΄ΠΎΠΌΠΈΠ΄
ΠΠ΅Π½Π°Π»ΠΈΠ΄ΠΎΠΌΠΈΠ΄ Π΅ Π»Π΅ΠΊ ΠΊΠΎΡ ΠΏΠΎΡΠ΅Π΄ΡΠ²Π° Π°Π½ΡΠΈΠ½Π΅ΠΎΠΏΠ»Π°ΡΡΠΈΡΠ½ΠΎ, Π°Π½ΡΠΈΠ°Π½Π³ΠΈΠΎΠ³Π΅Π½ΠΎ, ΠΏΡΠΎΠ΅ΡΠΈΡΡΠΎΠΏΠΎΠ΅ΡΡΠΊΠΎ ΠΈ
ΠΈΠΌΡΠ½ΠΎΠΌΠΎΠ΄ΡΠ»Π°ΡΠΎΡΠ½ΠΎ Π΄Π΅ΡΡΡΠ²ΠΎ, Π½ΠΎ ΠΏΡΠΈΡΠΎΠ° ΠΏΠΎΡΠ΅Π΄ΡΠ²Π° ΠΈ ΠΈΠ·ΡΠ°Π·Π΅Π½ΠΎ ΡΠ΅ΡΠ°ΡΠΎΠ³Π΅Π½ΠΎ Π΄Π΅ΡΡΡΠ²ΠΎ. ΠΠ»ΠΈΠ½ΠΈΡΠΊΠΈΠΎΡ
ΡΠ°ΡΠΌΠ°ΡΠ΅Π²Ρ ΠΏΡΠΈ ΠΠΠ£ Π£Π½ΠΈΠ²Π΅ΡΠ·ΠΈΡΠ΅ΡΡΠΊΠ° ΠΊΠ»ΠΈΠ½ΠΈΠΊΠ° Π·Π° Ρ
Π΅ΠΌΠ°ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ° - Π‘ΠΊΠΎΠΏΡΠ΅ Π΅ ΠΎΠ΄Π³ΠΎΠ²ΠΎΡΠ΅Π½ Π·Π°
ΡΠΏΡΠΎΠ²Π΅Π΄ΡΠ²Π°ΡΠ΅ Π½Π° ΠΏΡΠΎΡΠ΅ΡΠΎΡ Π½Π° ΡΠ°ΡΠΌΠ°ΠΊΠΎΠ²ΠΈΠ³ΠΈΠ»Π°Π½ΡΠ°. ΠΠ°ΡΠΎΠ°, ΡΠΎ ΡΠ΅Π» ΠΏΠΎΠ΄ΠΎΠ±ΡΡΠ²Π°ΡΠ΅ Π½Π° Π±Π΅Π·Π±Π΅Π΄Π½ΠΎΡΡΠ°
Π½Π° ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈΡΠ΅, Π½Π° ΠΊΠ»ΠΈΠ½ΠΈΠΊΠ°ΡΠ° ΡΠ΅ ΡΠΏΡΠΎΠ²Π΅Π΄Π΅ ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΠ°Π½Π° Π΄ΠΈΡΡΡΠΈΠ±ΡΡΠΈΡΠ° Π½Π° Π»Π΅ΠΊΠΎΡ ΡΠ°Π±Π»Π΅ΡΠΈ
Π»Π΅Π½Π°Π»ΠΈΠ΄ΠΎΠΌΠΈΠ΄ ΠΎΠ΄ 5 mg. KΠ°ΠΊΠΎ ΠΌΠ΅ΡΠΊΠ° Π·Π° ΠΌΠΈΠ½ΠΈΠΌΠ°Π»ΠΈΠ·Π°ΡΠΈΡΠ° Π½Π° ΡΠΈΠ·ΠΈΠΊΠΎΡ (ΠΠΠ ) Π·Π° ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈΡΠ΅ ΠΊΠΎΠΈ Π³ΠΎ
ΠΏΡΠΈΠΌΠ°Π°Ρ ΠΎΠ²ΠΎΡ Π»Π΅ΠΊ ΡΠ΅ ΡΠΏΡΠΎΠ²Π΅Π΄Π΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ°ΡΠ° Π·Π° ΠΊΠΎΠ½ΡΡΠΎΠ»ΠΈΡΠ°Π½ ΠΏΡΠΈΡΡΠ°ΠΏ (CAP), ΡΠΏΠΎΡΠ΅Π΄ ΠΊΠΎΡΠ°
ΠΏΡΠΎΠΏΠΈΡΡΠ²Π°ΡΠ΅ΡΠΎ Π½Π° Π»Π΅ΠΊΠΎΡ Π³ΠΎ Π²ΡΡΠ°Ρ ΠΈΡΠΊΠ»ΡΡΠΈΠ²ΠΎ Π»Π΅ΠΊΠ°ΡΠΈ ΠΊΠΎΠΈ ΡΠ΅ Π΅Π²ΠΈΠ΄Π΅Π½ΡΠΈΡΠ°Π½ΠΈ Π²ΠΎ Π Π΅Π³ΠΈΡΡΠ°ΡΠΎΡ Π½Π°
Π΅Π΄ΡΡΠΈΡΠ°Π½ΠΈ Π»Π΅ΠΊΠ°ΡΠΈ Π·Π° ΡΠΏΡΠΎΠ²Π΅Π΄ΡΠ²Π°ΡΠ΅ Π½Π° ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ°ΡΠ° Π·Π° ΠΏΡΠ΅Π²Π΅Π½ΡΠΈΡΠ° Π½Π° Π±ΡΠ΅ΠΌΠ΅Π½ΠΎΡΡ (PPP). Π‘ΠΎ ΡΠΎΠ° ΡΠ΅
Π²ΠΎΠ΄Π΅ΡΠ΅ Π³ΡΠΈΠΆΠ° ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΡ Π΄Π° Π³ΠΎ ΠΏΡΠΈΠΌΠΈ Π»Π΅ΠΊΠΎΡ ΡΠ°ΠΌΠΎ Π΄ΠΎΠΊΠΎΠ»ΠΊΡ ΡΠ΅ ΠΈΡΠΏΠΎΠ»Π½Π΅ΡΠΈ Π±Π°ΡΠ°ΡΠ°ΡΠ° ΠΎΠ΄ PPP.
ΠΠΈΠ΄Π΅ΡΡΠΈ Π»Π΅ΠΊΠΎΡ ΡΠ΅ ΠΈΠ·Π»Π°ΡΡΠ²Π° ΠΈ Π²ΠΎ ΡΠΏΠ΅ΡΠΌΠ°ΡΠ°, Π²ΠΎ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠ°ΡΠ° ΠΏΠΎΠΊΡΠ°Ρ ΠΆΠ΅Π½ΠΈ ΠΌΠΎΡΠ°ΡΠ΅ Π΄Π° Π±ΠΈΠ΄Π°Ρ Π²ΠΊΠ»ΡΡΠ΅Π½ΠΈ
ΠΈ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈ ΠΎΠ΄ ΠΌΠ°ΡΠΊΠ° ΠΏΠΎΠΏΡΠ»Π°ΡΠΈΡΠ°. ΠΠ° Π»Π΅ΠΊΠΎΡ ΡΠ°Π±Π»Π΅ΡΠΈ Π»Π΅Π½Π°Π»ΠΈΠ΄ΠΎΠΌΠΈΠ΄ 5 mg, PPP Π΅ ΡΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΠΊΠ°Ρ 36
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈ (23 ΠΌΠ°ΠΆΠΈ ΠΈ 13 ΠΆΠ΅Π½ΠΈ), Π²ΠΎ ΡΠ΅ΠΊ Π½Π° 18 ΠΌΠ΅ΡΠ΅ΡΠΈ, Π·Π°ΠΏΠΎΡΠ½ΡΠ²Π°ΡΡΠΈ ΠΎΠ΄ ΠΎΠΊΡΠΎΠΌΠ²ΡΠΈ 2021 Π³ΠΎΠ΄ΠΈΠ½Π°. Πo
ΠΎΠ²ΠΎΡ ΠΏΠ΅ΡΠΈΠΎΠ΄ Π»Π΅ΠΊΠΎΡ Π΅ ΠΈΠ·Π΄Π°Π΄Π΅Π½ Π½Π° 14 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈ ΡΠΎ ΠΌΡΠ»ΡΠΈΠΏΠ΅Π½ ΠΌΠΈΠ΅Π»ΠΎΠΌ ΠΊΠΎΠΈ ΠΏΡΠ΅ΡΡ
ΠΎΠ΄Π½ΠΎ ΠΏΡΠΈΠΌΠΈΠ»Π΅ Π±Π°ΡΠ΅ΠΌ
Π΅Π΄Π½Π° Π»ΠΈΠ½ΠΈΡΠ° Π½Π° ΡΠ΅ΡΠ°ΠΏΠΈΡΠ° (Π²ΠΎ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΡΠ° ΡΠΎ Π΄Π΅ΠΊΡΠ°ΠΌΠ΅ΡΠ°Π·ΠΎΠ½), 22 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈ ΡΠΎ ΡΠ΅ΡΠ°ΠΏΠΈΡΠ° Π½Π°
ΠΎΠ΄ΡΠΆΡΠ²Π°ΡΠ΅ Π½Π° Π½ΠΎΠ²ΠΎΠ΄ΠΈΡΠ°Π³Π½ΠΎΡΡΠΈΡΠΈΡΠ°Π½ ΠΌΡΠ»ΡΠΈΠΏΠ΅Π½ ΠΌΠΈΠ΅Π»ΠΎΠΌ ΠΊΠ°Ρ ΠΊΠΎΠΈ Π΅ ΡΠΏΡΠΎΠ²Π΅Π΄Π΅Π½Π° ΡΡΠ°Π½ΡΠΏΠ»Π°Π½ΡΠ°ΡΠΈΡΠ° Π½Π°
Π°Π²ΡΠΎΠ»ΠΎΠ³Π½ΠΈ ΠΌΠ°ΡΠΈΡΠ½ΠΈ ΠΊΠ»Π΅ΡΠΊΠΈ ΠΈ 1 ΠΏΠ°ΡΠΈΠ΅Π½Ρ ΡΠΎ Π΄ΠΈΡΠ°Π³Π½ΠΎΠ·Π° ΠΠ΅-Π₯ΠΎΡΠΊΠΈΠ½ΠΎΠ² ΡΠΎΠ»ΠΈΠΊΡΠ»Π°ΡΠ΅Π½ Π»ΠΈΠΌΡΠΎΠΌ. Π‘ΠΎ
Π΅Π΄ΡΡΠΈΡΠ°ΡΠ΅ Π½Π° ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΈΡΠ΅ ΠΈ ΠΏΡΠ΅Π·Π΅ΠΌΠ°ΡΠ΅ Π½Π° ΡΠΈΡΠ΅ ΠΏΡΠΎΠΏΠΈΡΠ°Π½ΠΈ ΠΌΠ΅ΡΠΊΠΈ ΠΎΠ΄ PPP, ΠΏΠΎΡΠ΅Π½ΡΠΈΡΠ°Π»Π½ΠΈΠΎΡ ΡΠΈΠ·ΠΈΠΊ
ΠΎΠ΄ ΡΠ΅ΡΠ°ΡΠΎΠ³Π΅Π½ΠΎΡΠΎ Π΄Π΅ΡΡΡΠ²ΠΎ Π½Π° Π»Π΅ΠΊΠΎΡ Π±Π΅ΡΠ΅ ΡΠ²Π΅Π΄Π΅Π½ Π½Π° ΠΌΠΈΠ½ΠΈΠΌΡΠΌ