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Abstract The understanding of the molecular pathobiology of acute myeloid leukemia (AML) has spurred the identification of therapeutic targets and the development of corresponding novel targeted therapies. Since 2017, twelve agents have been approved for the treatment of AML subsets: the BCL2 inhibitor venetoclax; the CD33 antibody drug conjugate gemtuzumab ozogamicin; three FLT3 inhibitors (midostaurin, gilteritinib, quizartinib); three IDH inhibitors (ivosidenib and olutasidenib targeting IDH1 mutations; enasidenib targeting IDH2 mutations); two oral hypomethylating agents (oral poorly absorbable azacitidine; fully absorbable decitabine-cedazuridine [latter approved as an alternative to parenteral hypomethylating agents in myelodysplastic syndrome and chronic myelomonocytic leukemia but commonly used in AML]); and CPX-351 (encapsulated liposomal 5:1 molar ratio of cytarabine and daunorubicin), and glasdegib (hedgehog inhibitor). Other targeted therapies (menin inhibitors, CD123 antibody-drug conjugates) are showing promising results. To achieve optimal results in such a rare and heterogeneous entity as AML requires expertise, familiarity with this rare cancer, and the access to, and delivery of disparate therapies under rigorous supportive care conditions. In this review, we update the standard-of-care and investigational therapies and outline promising current and future research directions.

Today, as in every life-threatening disease, early diagnosis of brain tumors plays a life-saving role. The brain tumor is formed by the transformation of brain cells from their normal structures into abnormal cell structures. These formed abnormal cells begin to form in masses in the brain regions. Nowadays, many different techniques are employed to detect these tumor masses, and the most common of these techniques is Magnetic Resonance Imaging (MRI). In this study, it is aimed to automatically detect brain tumors with the help of ensemble deep learning architectures (ResNet50, VGG19, InceptionV3 and MobileNet) and Class Activation Maps (CAMs) indicators by employing MRI images. The proposed system was implemented in three stages. In the first stage, it was determined whether there was a tumor in the MR images (Binary Approach). In the second stage, different tumor types (Normal, Glioma Tumor, Meningioma Tumor, Pituitary Tumor) were detected from MR images (Multi-class Approach). In the last stage, CAMs of each tumor group were created as an alternative tool to facilitate the work of specialists in tumor detection. The results showed that the overall accuracy of the binary approach was calculated as 100% on the ResNet50, InceptionV3 and MobileNet architectures, and 99.71% on the VGG19 architecture. Moreover, the accuracy values of 96.45% with ResNet50, 93.40% with VGG19, 85.03% with InceptionV3 and 89.34% with MobileNet architectures were obtained in the multi-class approach.

The optimal design of a proportional-integral-derivative controller with two cascaded first-order low-pass filters (PID-FF) for non-ideal buck converters faces significant challenges, including effective disturbance rejection, robustness to parameter variations, and the mitigation of high-frequency signal noise, with existing approaches often struggling and leading to suboptimal performance in practical applications. This study addresses these challenges by introducing a constraint on the open-loop crossover frequency to mitigate high-frequency noise and ensuring the controller prioritizes maintaining constant output voltage and robust responsiveness to input voltage and load current variations. This study also introduces an innovative metaheuristic algorithm, the opposition-based snake optimizer with pattern search (OSOPS), designed to address these limitations. OSOPS enhances the Snake Optimizer (SO) by integrating opposition-based learning (OBL) and Pattern Search (PS), thereby improving its exploration and exploitation capabilities. The proposed algorithm design includes a crossover frequency constraint aimed at counteracting high-frequency noise and ensuring robust performance under diverse disturbances. The efficacy of the OSOPS algorithm is demonstrated through rigorous statistical box plot analysis and convergence response comparisons with the original SO algorithm. Additionally, we systematically compare the performance of the OSOPS-based PID–FF–controlled non-ideal buck converter system against systems utilizing the original SO algorithm and the classical pole placement (PP) method. This evaluation encompasses transient and frequency responses, disturbance rejection, and robustness analysis. The results reveal that the OSOPS-based system outperforms the SO- and PP-based systems with 14.21 % and 32.10 % faster rise times, along with 15.38 % and 84.95 % faster settling times, respectively. The OSOPS and SO systems also exhibit higher bandwidths, exceeding the PP-based system by 18.74 % and 17.03 %, respectively. By addressing the key challenges in PID-FF controller design for non-ideal buck converters, this study provides a substantial advancement in control strategy, promising enhanced performance in practical applications.

In this research, an optimization methodology was introduced for improving bipedal robot locomotion controlled by reinforcement learning (RL) algorithms. Specifically, the study focused on optimizing the Proximal Policy Optimization (PPO), Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), and Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithms. The optimization process utilized the Tree-structured Parzen Estimator (TPE), a Bayesian optimization technique. All RL algorithms were applied to the same environment, which was created within the OpenAI GYM framework and known as the bipedal walker. The optimization involved the fine-tuning of key hyperparameters, including learning rate, discount factor, generalized advantage estimation, entropy coefficient, and Polyak update parameters. The study comprehensively analyzed the impact of these hyperparameters on the performance of RL algorithms. The results of the optimization efforts were promising, as the fine-tuned RL algorithms demonstrated significant improvements in performance. The mean reward values for the 10 trials were as follows: PPO achieved an average reward of 181.3, A2C obtained an average reward of −122.2, SAC reached an average reward of 320.3, and TD3 had an average reward of 278.6. These outcomes underscore the effectiveness of the optimization approach in enhancing the locomotion capabilities of the bipedal robot using RL techniques.

Patients with relapsed acute myeloid leukemia (rAML) experience dismal outcomes. We performed a comprehensive analysis of patients with rAML to determine the genetic dynamics and survival predictive factors. We analyzed 875 patients with newly diagnosed AML who received intensive treatment (IT) or low-intensity treatment (LIT). Of these patients, 197 experienced subsequent rAML. Data was available for 164 patients, with a median time from CR/CRi to relapse of 6.5 months. Thirty-five of the 164 patients (21%) experienced relapse after allogeneic hematopoietic stem cell transplantation (alloSCT). At relapse mutations in genes involved in pathway signaling tended to disappear, whereas clonal hematopoiesis-related mutations or TP53 tended to persist. Patients with normal karyotypes tended to acquire cytogenetic abnormalities at relapse. Patients treated with IT had a higher emergence rate of TP53 mutations (16%), compared to patients treated with LIT (1%, P = 0.009). The overall response rates were 38% and 35% for patients treated with salvage IT or LIT, respectively. Seventeen patients (10%) underwent alloSCT after salvage therapy. The median overall survival (OS) duration after relapse was 5.3 months, with a 1-year OS rate of 17.6%. Complex karyotype (hazard ratio [HR] = 2.14, P < 0.001), a KMT2A rearrangement (HR = 3.52, P = 0.011), time in remission < 12 months (HR = 1.71, P = 0.011), and an elevated white blood cell count at relapse (HR = 2.38, P = 0.005) were independent risk factors for OS duration. More effective frontline and maintenance therapies are warranted to prevent rAML.

In this study, a speed sensorless control method is proposed for a photovoltaic water-pump system operating under variable atmospheric conditions. The developed system not only continuously determines the maximum power point of the photovoltaic system but also generates a reference speed appropriate for each power value produced. The MATLAB/Simulink model of the driver system incorporates Adaptive Power Control, Direct Torque Control, a 3-phase induction motor, and the power plant. The determination of the maximum power point of the photovoltaic system and the input reference speed information for the Direct Torque Control under varying atmospheric conditions is accomplished through the application of the Adaptive Power Control algorithm. Notably, the control process operates as a closed-loop system without the requirement of a speed sensor. Instead, the Extended Kalman–Bucy estimator is employed for speed estimation. Empirical evaluation of the proposed method was conducted under constant temperature conditions and varying irradiance values. The results clearly illustrate the consistent operation of the motor, with a constant torque production aligned with each identified maximum power point corresponding to distinct irradiance levels. The control system demonstrates robust performance and operational efficiency across the entire range of operating points. The efficacy of the Kalman estimator in monitoring the reference speed, the utilization of a single solar radiation sensor, and the successful implementation of sensorless operation are significant advantages of the developed adaptive approach. The demonstrated performance of the proposed system showcases its potential for enhancing the efficient and reliable control of photovoltaic water-pump systems in real-world applications.

Abstract In TP53 wild-type acute myeloid leukemia (AML), inhibition of MDM2 can enhance p53 protein expression and potentiate leukemic cell apoptosis. MDM2 inhibitor (MDM2i) monotherapy in AML has shown modest responses in clinical trials but combining options of MDM2i with other potent AML-directed agents like cytarabine and venetoclax could improve its efficacy. We conducted a phase I clinical trial (NCT03634228) to study the safety and efficacy of milademetan (an MDM2i) with low-dose cytarabine (LDAC)±venetoclax in adult patients with relapsed refractory (R/R) or newly diagnosed (ND; unfit) TP53 wild-type AML and performed comprehensive CyTOF analyses to interrogate multiple signaling pathways, the p53-MDM2 axis and the interplay between pro/anti-apoptotic molecules to identify factors that determine response and resistance to therapy. Sixteen patients (14 R/R, 2 N/D treated secondary AML) at a median age of 70 years (range, 23–80 years) were treated in this trial. Two patients (13%) achieved an overall response (complete remission with incomplete hematological recovery). Median cycles on trial were 1 (range 1–7) and at a median follow-up of 11 months, no patients remained on active therapy. Gastrointestinal toxicity was significant and dose-limiting (50% of patients ≥ grade 3). Single-cell proteomic analysis of the leukemia compartment revealed therapy-induced proteomic alterations and potential mechanisms of adaptive response to the MDM2i combination. The response was associated with immune cell abundance and induced the proteomic profiles of leukemia cells to disrupt survival pathways and significantly reduced MCL1 and YTHDF2 to potentiate leukemic cell death. The combination of milademetan, LDAC±venetoclax led to only modest responses with recognizable gastrointestinal toxicity. Treatment-induced reduction of MCL1 and YTHDF2 in an immune-rich milieu correlate with treatment response.

Abstract Background Historically, adults with relapsed-refractory acute lymphoblastic leukemia (ALL) experienced poor outcomes with intensive chemotherapy. This mature analysis explores the benefit of the addition of sequential blinatumomab to low-intensity mini-Hyper-CVD chemotherapy with inotuzumab ozogamicin in this setting. Methods Mini-Hyper-CVD (cyclophosphamide and dexamethasone at 50% dose reduction, no anthracycline, methotrexate at 75% dose reduction, cytarabine at 83% dose reduction) was combined with inotuzumab during the first 4 courses. From Patient #68 and onwards, inotuzumab was given in reduced and fractionated doses, and blinatumomab was added sequentially for 4 courses. Maintenance therapy with prednisone, vincristine, 6-mercaptopurine and methotrexate was given for 12 courses, and blinatumomab for 4 additional courses. Results Among 110 patients (median age, 37 years) treated, 91 (83%) responded (complete response, 69 patients, 63%). Measurable residual disease negativity was documented in 75 patients (82% of responders). Fifty-three patients (48%) received allogeneic stem cell transplantation (SCT). Hepatic sinusoidal obstruction syndrome occurred in 9/67 patients (13%) on the original inotuzumab schedule and in 1/43 (2%) on the modified schedule. With a median follow-up of 48 months, the median overall survival (OS) was 17 months, and the 3 year OS was 40%. The 3 year OS was 34% with mini-Hyper-CVD plus inotuzumab and 52% with additional blinatumomab (P = 0.16). By landmark analysis at 4 months, the 3 year OS was 54%, similar between patients who did or did not receive allogeneic SCT. Conclusion Low-intensity mini-Hyper-CVD plus inotuzumab with or without blinatumomab showed efficacy in patients with relapsed-refractory ALL, with better survival after the addition of blinatumomab. Trial registration The trial was registered on clinicaltrials.gov with the identifier NCT01371630.

Automatic recognition of illegal substations is of great importance, since most of the leakage electricity in Turkey is due to the use of these substations in agricultural fields. One of the most effective ways to detect illegal substations is to employ remote sensing images and machine learning techniques together. Because, thanks to remote sensing images, it is possible to analyze illegal substations on huge agricultural lands in a short time. In this study, illegal substations on the agricultural fields in the southeast Anatolian region, which is one of the regions where leakage electricity are most common, have been detected with the aid of Landsat-8 OLI images and machine learning algorithm. The proposed study has been carried out in several stages, respectively. In the first stage, the locations of 42 substations and 21 non-substation objects on the pilot area have been recorded with the help of GPS and these coordinates have been later transferred to the Landsat-8 OLI image dated on 14 June 2019. In the second stage, an image analysis has been performed by calculating the spectral band parameters from the Landsat-8 OLI images. In the next stage, relationships among illegal substations and non-substation objects have been set by utilizing the statistical metrics of obtained spectral band parameters. In the last stage, by utilizing LSTM (Long Short-Term Memory) method, which is a recurrent neural network model that has gained popularity in both remote sensing and various scientific disciplines in recent years and the Logit-Boost method, which is one of the popular boosting machine learning algorithms, automatic recognition of substations has been performed with an average accuracy of 88.89% for Logit-Boost method and 84.21% for LSTM method. It is notable from this study that the Logit-Boost Algorithm yields more proficient results than the LSTM model.

This paper addresses the design of an optimally executed proportional-integral-derivative (PID) controller, tailored for the speed regulation of a direct current (DC) motor. To achieve this objective, we present a novel hybrid algorithm, combining the gazelle optimization algorithm (GOA) with the effective simplex search method known as the Nelder-Mead (NM) technique. The fusion of these algorithms yields an innovative hybridized version, striking the balance between exploration and exploitation. The proposed approach, named the gazelle simplex optimizer (GSO), showcases great promise when applied to the task of controlling the speed regulation of a DC motor using the PID controller. To identify the optimal values for PID gains, we harness the power of a novel objective function as well, which guides the GSO in determining the most favorable controller settings. Rigorous comparative simulations are then undertaken, where we pit the GSO against several other algorithms, namely the reptile search algorithm, prairie dog optimization algorithm, weighted mean of vectors optimization, and the original GOA algorithm. These simulations allow us to assess the system’s behavior through various lenses, such as statistical tests, time and frequency domain responses, robustness analysis, and changes in the objective function. The evaluations from these comprehensive tests demonstrate the superiority of the GSO-based PID controlled DC motor speed regulation system. The GSO exhibits better performance than the alternative algorithms, providing solid evidence of its effectiveness. Furthermore, the proposed GSO approach outperforms other reported PID tuning methods, affirming its prowess in achieving superior speed regulation for DC motors.

This study presents a comprehensive investigation of ferroresonance, a dangerous electrical phenomenon that poses significant financial risks. Using real electrical transmission line parameters, we simulated synthetic ferroresonance scenarios on a model and analyzed the resulting data using high-order spectral analysis methods, including the Wigner-Ville method, Welch method, frequency-power analysis, and spectral methods. Our analysis revealed changes in frequency and power before and after ferroresonance, with third and fourth-order cumulants being calculated. We confirmed the accuracy of the power transmission line’s base frequency and power before the ferroresonance event and determined the frequency and power values before and after ferroresonance with frequency-power analysis. Our cumulative analysis results showed symmetrical results that are consistent with the properties of ferroresonance. Additionally, we found that the Wigner Ville method’s high-resolution results were significantly more effective than conventional methods. Our study’s findings provide valuable insights into ferroresonance’s behavior and may inform the development of more effective prevention and mitigation strategies.

Photovoltaic (PV) panels exhibit a non-linear current-voltage characteristic with a Maximum Power Point (MPP) that varies due to environmental factors such as solar radiation and ambient temperature. In this study, an Artificial Neural Network (ANN)-based MPPT method, called the ANN-based Adaptive Reference Voltage (ARV) method, is proposed to determine the optimal operating point of the PV panel. The ANN-based ARV method is a voltage-controlled approach that can adapt to changing atmospheric conditions. The performance of the proposed method is evaluated using both a normal Proportional-Integral (PI) controller and an anti-windup PI controller. Comparative analysis is conducted with the widely used Perturb and Observe (P&O) and Incremental Conductance (INC) methods in the MATLAB/Simulink environment, considering three different atmospheric scenarios with varying radiation levels according to EN50530 standards. The proposed method demonstrates superior efficiency with overall results of 99.4%, 95.9%, and 96% in scenario 1, scenario 2, and scenario 3, respectively. Particularly, the proposed method exhibits notable superiority in rapidly changing atmospheric conditions.

Infinite impulse response (IIR) systems, with their ability to model intricate system behaviors, have proven to be a powerful class of digital filters. However, accurately identifying the optimal filter parameters for system emulation remains a challenge. Metaheuristic algorithms have emerged as effective tools for parameter optimization in IIR filter design, allowing for the exploration of parameter spaces and the discovery of suitable filter sets. This paper introduces a novel adaptive algorithm, named simulated annealing aided artificial hummingbird optimizer (AHA-SA), which combines the strengths of the artificial hummingbird algorithm (AHA) and simulated annealing (SA). The synergistic integration of AHA and SA in the AHA-SA optimizer enables efficient search space exploration, rapid convergence, and the attainment of precise solutions. Extensive experiments demonstrate the superiority of the AHA-SA optimizer over competitive algorithms, both in terms of solution quality and convergence speed. The proposed optimizer presents a promising solution for optimization problems in various domains, with its simplicity, intuitive workflow, and potential for widespread adoption.

Abstract In older/unfit newly diagnosed patients with FLT3 mutated acute myeloid leukemia (AML), lower intensity chemotherapy (LIC) in combination with either a FLT3 inhibitor or with venetoclax results in poor overall survival (median 8 to 12.5 months). We performed a retrospective analysis of 87 newly diagnosed FLT3 mutated AML patients treated on triplet (LIC + FLT3 inhibitor + Venetoclax, [N = 27]) and doublet (LIC + FLT3 inhibitor, [N = 60]) regimens at our institution. Data were collected from prospective clinical trials in 75% (N = 65) and 25% (N = 22) who received the same treatment regimens outside of a clinical trial. Triplet therapy was associated with significantly higher rates of complete remission (CR) (67% versus 32%, P = 0.002), CR/CRi (93% versus 70%, P = 0.02), FLT3-PCR negativity (96% versus 54%, P 0.5 (40 versus 21 days, P = 0.15) and platelet > 50 K (29 versus 25 days, P = 0.6) among responders was numerically longer with triplets, but 60-day mortality was similar (7% v 10%). With a median follow-up of 24 months (median 12 months for triplet arm, and 63 months for doublet arm), patients receiving a triplet regimen had a longer median overall survival (not reached versus 9.5 months, P

Abstract Background Treated secondary acute myeloid leukemia (ts-AML)—i.e., AML arising from a previously treated antecedent hematologic disorder—is associated with very poor outcomes. The optimal frontline treatment regimen for these patients is uncertain. Methods We retrospectively analyzed 562 patients who developed AML from preceding myelodysplastic syndrome or chronic myelomonocytic leukemia for which they had received a hypomethylating agent (HMA). Patients with ts-AML were stratified by frontline AML treatment with intensive chemotherapy (IC, n = 271), low-intensity therapy (LIT) without venetoclax (n = 237), or HMA plus venetoclax (n = 54). Results Compared with IC or LIT without venetoclax, HMA plus venetoclax resulted in higher CR/CRi rates (39% and 25%, respectively; P = 0.02) and superior OS (1-year OS 34% and 17%, respectively; P = 0.05). The benefit of HMA plus venetoclax was restricted to patients with non-adverse risk karyotype, where HMA plus venetoclax resulted in a median OS of 13.7 months and 1-year OS rate of 54%; in contrast, for patients with adverse risk karyotype, OS was similarly dismal regardless of treatment approach (median OS 3–5 months). A propensity score analysis accounting for relevant clinical variables confirmed the significant OS benefit of HMA plus venetoclax, as compared with other frontline treatment approaches. In a landmark analysis, patients with ts-AML who underwent subsequent hematopoietic stem cell transplantation (HSCT) had superior 3-year OS compared to non-transplanted patients (33% vs. 8%, respectively; P = 0.003). Conclusions The outcomes of ts-AML are poor but may be improved with use of an HMA plus venetoclax-based regimen, followed by HSCT, particularly in those with a non-adverse risk karyotype.

Abstract Preclinically, enasidenib and azacitidine (ENA + AZA) synergistically enhance cell differentiation, and venetoclax (VEN), a small molecule Bcl2 inhibitor (i) is particularly effective in IDH2 mutated acute myeloid leukemia (IDH2 mut AML). This open label phase II trial enrolled patients (pts) with documented IDH2 mut AML. All patients received AZA 75 mg/m2/d x 7 d/cycle and ENA 100 mg QD continuously. Concomitant Bcl2i and FLT3i were allowed (NCT03683433).Twenty-six pts received ENA + AZA (median 68 years, range, 24–88); 7 newly diagnosed (ND) and 19 relapsed/refractory (R/R). In R/R AML patients, three had received prior ENA and none had received prior VEN. The composite complete remission rate (CRc) [complete remission (CR) or complete remission with incomplete hematologic recovery (CRi)] was 100% in ND AML, and 58% in R/R AML. Median OS was not reached in ND AML with median follow-up of 13.1 months (mo); Pts treated in first relapse had improved OS than those with ≥2 relapse (median OS not reached vs 5.2 mo; HR 0.24, 95% CI 0.07–0.79, p = 0.04). Two patients received ENA + AZA with a concomitant FLT3i, one responding ND AML patient and one nonresponding R/R AML patient. Seven R/R AML pts received ENA + AZA + VEN triplet, and with median follow up of 11.2 mo, median OS was not reached and 6-mo OS was 70%. The most frequent treatment-emergent adverse events include febrile neutropenia (23%). Adverse events of special interest included all-grade IDH differentiation syndrome (8%) and indirect hyperbilirubinemia (35%). ENA + AZA was a well-tolerated, and effective therapy for elderly pts with IDH2 mut ND AML as well as pts with R/R AML. The addition of VEN to ENA + AZA appears to improve outcomes in R/R IDH2 mut AML. Clinical trial registration information: https://clinicaltrials.gov/.NCT03683433

In this study, for the diagnosis and classification of breast cancer, we used and applied five classical pre-trained deep convolutional neural network models (DCNN) which have proven successful many times in different fields (ResNet-18, AlexNet, GoogleNet and SuffleNet). To make pre-trained DCNN models suitable for the purpose of our study, we updated some layers according to the new situation by using the transfer learning technique. We did not change the weights of all layers used in these five pre-trained DCNN models. Instead, we just gave new weights to the new layers so that new layers adapt faster to emerging new DCNN models. With these five pre-trained DCNN models, we have realized a quadruple classification as "cancer", "normal", "actionable" and "benign", and a binary classification as "actionable + cancer" and "normal + benign". With these two separate classification and diagnosis studies, we have carried out comparative experimental examination and analysis of pre-trained DCNN models for breast cancer diagnosis. In the study, it was concluded that successful results can be achieved with pre-trained DCNN models without extra time-consuming procedures such as feature extraction, and DCNN can perform quite successfully in cancer diagnosis and image comment.

Abstract Background The natural course of untreated chronic myeloid leukemia (CML) is progression to an aggressive blast phase. Even in the current era of BCR-ABL1 tyrosine kinase inhibitors (TKIs), the outcomes of blast phase CML remain poor with no consensus frontline treatment approach. Methods We retrospectively analyzed the response rates and survival outcomes of 104 consecutive patients with myeloid blast phase CML (CML-MBP) treated from 2000 to 2019 based on 4 different frontline treatment approaches: intensive chemotherapy (IC) + TKI (n = 20), hypomethylating agent (HMA) + TKI (n = 20), TKI alone (n = 56), or IC alone (n = 8). We also evaluated the impact of TKI selection and subsequent allogeneic stem cell transplant (ASCT) on patient outcomes. Results Response rates were similar between patients treated with IC + TKI and HMA + TKI. Compared to treatment with TKI alone, treatment with IC/HMA + TKI resulted in a higher rate of complete remission (CR) or CR with incomplete count recovery (CRi) (57.5% vs 33.9%, p

Abstract In 1957, Hillestad et al. defined acute promyelocytic leukemia (APL) for the first time in the literature as a distinct type of acute myeloid leukemia (AML) with a “rapid downhill course” characterized with a severe bleeding tendency. APL, accounting for 10–15% of the newly diagnosed AML cases, results from a balanced translocation, t(15;17) (q22;q12-21), which leads to the fusion of the promyelocytic leukemia (PML) gene with the retinoic acid receptor alpha (RARA) gene. The PML–RARA fusion oncoprotein induces leukemia by blocking normal myeloid differentiation. Before using anthracyclines in APL therapy in 1973, no effective treatment was available. In the mid-1980s, all-trans retinoic acid (ATRA) monotherapy was used with high response rates, but response durations were short. Later, the development of ATRA, chemotherapy, and arsenic trioxide combinations turned APL into a highly curable malignancy. In this review, we summarize the evolution of APL therapy, focusing on key milestones that led to the standard-of-care APL therapy available today and discuss treatment algorithms and management tips to minimize induction mortality.

Abstract Background Second-generation FLT3-inhibitors (FLT3i) demonstrated single-agent composite CR rates (CRc) of 45–55% in patients with relapsed/refractory (R/R) FLT3-mutated AML in phase II/III trials. However, > 85% of patients treated were prior FLT3i naïve. The response rates to sequential FLT3i exposure remain poorly defined. Methods We retrospectively reviewed patients with FLT3-mutated AML between November 2006 and December 2019. Results In frontline patients treated with a FLT3i (cohort 1), the CRc rates and median overall survival (OS) with the first (n = 56), second (n = 32), and third FLT3i-based (n = 8) therapy were 77%, 31%, and 25%, and 16.7 months, 6.0 months, and 1.4 months, respectively. In patients receiving a FLT3i-based therapy for the first time in a R/R AML setting (cohort 2), the CRc rates and median OS were 45%, 21%, and 10%, and 7.9 months, 4.0 months, and 4.1 months with the first (n = 183), second (n = 89), and third/fourth (n = 29) FLT3i-based therapy, respectively. In cohort 1, CRc rates with single-agent FLT3i (n = 21) versus FLT3i-based combinations (n = 19) in second/third sequential FLT3i exposures were 19% versus 42%, respectively. In cohort 2, the CRc rates with single-agent FLT3i (n = 82) versus FLT3i-based combinations (n = 101) in first FLT3i exposure were 34% versus 53%, respectively, and those with single-agent FLT3i (n = 63) versus FLT3i-based combinations (n = 55) in second/third/fourth sequential FLT3i exposures were 13% versus 25%, respectively. Conclusion CRc rates drop progressively with sequential exposure to FLT3i’s in FLT3-mutated AML. In all settings, CRc rates were higher with FLT3i-based combinations compared with single-agent FLT3i therapy in similar FLT3i exposure settings.

Despite the promising result with FLT3 inhibitors in AML, the emergence of resistance poses a significant challenge, leading to a shorter response duration and inferior survival. This is frequently driven by on-target or parallel prosurvival mutations. The emergence of BCR–ABL1 as a mechanism of possible clonal evolution in relapsed AML has rarely been reported. Here we report our experience with three patients who had emergent BCR–ABL1 fusion at relapse after FLT3 inhibitors–based therapies. The first patient was refractory to multiple lines of therapies, including FLT3 inhibitors–based therapy. Patients 2 and 3 showed some response to combined FLT3-inhibitor and BCR–ABL targeted therapy (gilteritinib and ponatinib). The availability of effective targeted therapies for BCR–ABL1 makes this an important aberration to proactively identify and possibly target at relapse post–FLT3-inhibitor therapies.

An amendment to this paper has been published and can be accessed via the original article.

AIM: To investigate the serum levels of vascular endothelial growth factor receptor-2 (VEGFR-2) and adropin in age-related macular degeneration (AMD) patients. METHODS: Ninety-eight AMD patients were included in the study. Seventy-eight age- and sex-matched healthy volunteers were recruited as the control group. Fundus florescein angiography and optical coherence tomography were performed to assess the posterior segment details. Serum VEGFR-2 and adropin levels were measured using enzyme-linked immunosorbent assays and compared between the study groups. RESULTS: AMD group had significantly increased foveal retinal thickness, serum LDL and HDL levels and significantly decreased subfoveal choroidal thickness (P =0.01, 0.047, 0.025 and

This study examines the levels of acylated and desacylated ghrelin, preptin, leptin, and nesfatin-1 peptide changes related to the body mass index (BMI). The subjects were allocated to 5 groups depending on their BMIs as follows: Group I (BMI 40 kg/m2). Serum acylated and desacylated ghrelin, preptin, and leptin levels were measured by the enzyme-linked immunosorbent assay (ELISA) and nesfatin-1 was measured by the enzyme immunoassay (EIA). Desacylated ghrelin levels showed a gradual and statistically significant drop from Group I to Group V, while preptin and leptin levels exhibited a gradual and significant increase from Group I to Group IV. Serum nesfatin-1 levels gradually, but not significantly, increased from Group I to Group III and showed a significant decrease in Groups IV and V. In conclusion, leptin, preptin, and acylated ghrelin (AG) levels increased with higher BMI, whereas desacylated ghrelin (DAG) decreased and nesfatin-1 showed no clear relationship to BMI.