Assessment of Total Antioxidant Capacity, 8-Hydroxy-2'-deoxy-guanosine, the Genetic Landscape, and Their Associations in BCR::ABL-1-Negative Chronic and Blast Phase Myeloproliferative Neoplasms.

Myeloproliferative neoplasms (MPNs), namely, polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis (PMF), are clonal stem cell disorders defined by an excessive production of functionally mature and terminally differentiated myeloid cells. MPNs can transform into secondary acute myeloid leukemia (sAML/blast phase MPN) and are linked to alterations in the redox balance, i.e., elevated concentrations of reactive oxygen species and markers of oxidative stress (OS), and changes in antioxidant systems. We evaluated OS in 117 chronic phase MPNs and 21 sAML cases versus controls by measuring total antioxidant capacity (TAC) and 8-hydroxy-2'-deoxy-guanosine (8-OHdG) concentrations. TAC was higher in MPNs than controls (p = 0.03), particularly in ET (p = 0.04) and PMF (p = 0.01). MPL W515L-positive MPNs had higher TAC than controls (p = 0.002) and triple-negative MPNs (p = 0.01). PMF patients who had treatment expressed lower TAC than therapy-free subjects (p = 0.03). 8-OHdG concentrations were similar between controls and MPNs, controls and sAML, and MPNs and sAML. We noted associations between TAC and MPNs (OR = 1.82; p = 0.05), i.e., ET (OR = 2.36; p = 0.03) and PMF (OR = 2.11; p = 0.03), but not sAML. 8-OHdG concentrations were not associated with MPNs (OR = 1.73; p = 0.62) or sAML (OR = 1.89; p = 0.49). In conclusion, we detected redox imbalances in MPNs based on disease subtype, driver mutations, and treatment history.

Invasive fusariosis in acute leukaemia patients-An outbreak in the haematology ward.

Fusarium, a common fungus, emerges as a pathogen in severely immunocompromised patients. We present a series of patients who developed invasive fusariosis (IF) during admission to an acute leukaemia ward: an outbreak of 12 cases in June and July 2018, followed by four sporadic cases until 2021. No case was reported earlier. All patients were clustered in the same location with indoor air and water installations found to be contaminated with Fusarium spp. thus a nosocomial outbreak was assumed. Following the water installation replacement, the number of Fusarium cases dramatically dropped to one or two isolated instances per year in the same location. All 16 patients had acute leukaemia and developed IF during severe neutropenia following induction therapy. IF diagnosis was based on positive blood cultures (14 patients) and/or on tissue biopsies (3 patients). The median time from admission to the IF onset was 20 days, and from the first day of severe neutropenia (≤500/mm3) was 11.5 days. All patients were febrile, eight had moderate-to-severe myalgias, eight had respiratory involvements: lung lesions and/or sinusitis and seven had characteristic skin lesions. Follow-up: 12 out of 16 (75%) were alive on Day 90; nine out of 15 (60%) were alive on Month 6. All with intractable neutropenia died. In severely neutropenic febrile patients, the triad of respiratory involvement/skin lesions/severe myalgia may suggest Fusarium aetiology. The ability to recover from neutropenia is critical to surmount IF. The indoor environment in immunocompromised dedicated settings must be constantly controlled.

Wolff-Parkinson-White syndrome in the era of catheter ablation: insights from a registry study of 2169 patients.

BACKGROUND: The management of Wolff-Parkinson-White is based on the distinction between asymptomatic and symptomatic presentations, but evidence is limited in the asymptomatic population. METHODS AND RESULTS: The Wolff-Parkinson-White registry was an 8-year prospective study of either symptomatic or asymptomatic Wolff-Parkinson-White patients referred to our Arrhythmology Department for evaluation or ablation. Inclusion criteria were a baseline electrophysiological testing with or without radiofrequency catheter ablation (RFA). Primary end points were the percentage of patients who experienced ventricular fibrillation (VF) or potentially malignant arrhythmias and risk factors. Among 2169 enrolled patients, 1001 (550 asymptomatic) did not undergo RFA (no-RFA group) and 1168 (206 asymptomatic) underwent ablation (RFA group). There were no differences in clinical and electrophysiological characteristics between the 2 groups except for symptoms. In the no-RFA group, VF occurred in 1.5% of patients, virtually exclusively (13 of 15) in children (median age, 11 years), and was associated with a short accessory pathway antegrade refractory period (P<0.001) and atrioventricular reentrant tachycardia initiating atrial fibrillation (P<0.001) but not symptoms. In the RFA group, ablation was successful in 98.5%, and after RFA, no patients developed malignant arrhythmias or VF over the 8-year follow-up. Untreated patients were more likely to experience malignant arrhythmias and VF (log-rank P<0.001). Time-dependent receiver-operating characteristic curves for predicting VF identified an optimal anterograde effective refractory period of the accessory pathway cutoff of 240 milliseconds. CONCLUSIONS: The prognosis of the Wolff-Parkinson-White syndrome essentially depends on intrinsic electrophysiological properties of AP rather than on symptoms. RFA performed during the same procedure after electrophysiological testing is of benefit in improving the long-term outcomes.

Multipoint left ventricular pacing in a single coronary sinus branch improves mid-term echocardiographic and clinical response to cardiac resynchronization therapy.

INTRODUCTION: Cardiac resynchronization therapy (CRT) with multipoint left ventricular (LV) pacing in a single coronary sinus branch improves acute LV function. We hypothesized that multipoint pacing (MPP) can improve midterm echocardiographic and clinical response compared with conventional CRT. METHODS AND RESULTS: Consecutive patients receiving a CRT implant (Unify Quadra MP™ or Quadra Assura MP™ CRT-D and Quartet™ LV lead, St. Jude Medical, Sylmar, CA, USA) were randomized to receive biventricular (BiV) pacing with either conventional LV pacing (CONV group) or MPP (MPP group). For each patient, an optimal pacing configuration for the assigned pacing mode was programmed based on intraoperative pressure-volume (PV) loop measurements. A clinical evaluation and transthoracic echocardiogram were performed before implant (BASELINE) and at 3 months postimplant and analyzed by a blinded observer. A reduction in end-systolic volume (ESV) of ≥15% relative to BASELINE was prospectively defined as response to CRT. Forty-four patients (NYHA Class III, EF 29 ± 6%, QRS duration 152 ± 17 milliseconds) were enrolled and randomized. One patient in the MPP group was lost to follow-up and excluded from further analysis. After 3 months, 11 of 22 (50%) CONV patients and 16 of 21 (76%) MPP patients were classified as responders. ESV reduction, EF increase, and NYHA class reduction relative to BASELINE were significantly greater in the MPP group than in the CONV group (ESV: -21.0 ± 13.9 vs. -12.6 ± 11.1%, P = 0.03; EF: +9.8 ± 5.1 vs. +2.0 ± 7.8 percentage points, P < 0.001; ΔNYHA: -1.05 ± 0.22 vs. -0.72 ± 0.46 functional classes, P = 0.006). CONCLUSION: PV loop optimized BiV pacing with MPP resulted in an improved rate of response to CRT.

Acute Promyelocytic Leukemia: Review of Complications Related to All-Trans Retinoic Acid and Arsenic Trioxide Therapy.

The hallmark of acute promyelocytic leukemia (APL) is the presence of the characteristic fusion transcript of the promyelocytic leukemia gene with the retinoic acid receptor α gene (PML::RARA). The PML::RARA fusion is a molecular target for all-trans retinoic acid (ATRA) and arsenic trioxide (ATO). Therapies based on ATRA plus ATO have excellent outcomes in terms of complete remission rates, overall survival, and achievement of deep and durable molecular responses with a very low incidence of relapse. However, although the combination of ATRA and ATO has lower hematologic toxicity than standard chemotherapy, its use is associated with a spectrum of distinctive toxicities, such as differentiation syndrome, liver toxicity, QT interval prolongation, and neurotoxicity. Rigorous monitoring of patients' clinical evolution is indispensable for identifying and addressing each complication. The objective is to maintain an equilibrium between treatment-induced adverse events and therapeutic efficacy. This paper focused on non-hematologic complications associated with the combination of ATRA and ATO. Additionally, we discuss late-onset complications of this therapy. In summary, the majority of treatment-related adverse events are manageable, self-limiting, and reversible. More so, there seems to be a lower incidence rate of secondary neoplasms compared to standard chemotherapy. However, further research is required to assess how the ATRA plus ATO regimen affects the emergence of additional comorbidities.

Translation, Cultural Adaptation, and Validation into Romanian of the Myeloproliferative Neoplasm Symptom Assessment Form-Total Symptom Score (MPN-SAF TSS or MPN-10) Questionnaire.

Background: Patients with myeloproliferative neoplasms (MPNs) experience a high disease-related symptom burden. A specific instrument to evaluate quality of life (QoL), i.e., the MPN Symptom Assessment Form Total Symptom Score (MPN-SAF TSS; MPN-10), was developed. We conducted the translation, cultural adaptation, and validation into Romanian of the MPN-10. Methods: We translated the MPN-10 and tested its psychometric properties. Results: We recruited 180 MPN patients: 66 polycythemia vera (36.67%), 61 essential thrombocythemia (33.89%), 51 primary and secondary myelofibrosis (SMF) (28.33%), and 2 MPN-unclassifiable (1.11%). The mean TSS was 19.51 ± 16.51 points. Fatigue, inactivity, and concentration problems were the most cumbersome symptoms. We detected scoring differences between MPN subtypes regarding weight loss (p < 0.001), fatigue (p = 0.006), early satiety (p = 0.007), night sweats (p = 0.047), pruritus (p = 0.05), and TSS (p = 0.021). There were strong positive associations between TSS and inactivity, fatigue, and concentration problems, and moderate negative correlations between QoL scores and all MPN-10 items. Cronbach's α internal consistency coefficient was 0.855. The Kaiser-Meyer-Olkin construct validity test result was 0.870 and the Bartlett Sphericity Test was significant (p < 0.001). Symptom scores were loaded into one single factor according to the exploratory factor analysis. Conclusions: The Romanian MPN-10 version displayed excellent psychometric properties and is a reliable instrument for assessing symptom burden and QoL in Romanian MPN patients.

Case report: High-risk acute promyelocytic leukemia and COVID-19-related myocarditis one patient, two cytokine storms.

Acute promyelocytic leukemia (APL) is a unique, highly curable subtype of acute myeloid leukemia, owing to the therapeutic advances of the last decades which led to high complete remission rates and excellent long-term survival. Nevertheless, it remains associated with high early mortality rates. Early death is the major cause of treatment failure in APL and is mainly attributed to coagulopathy, differentiation syndrome, and less commonly, infectious events. Timely recognition of each complication plays a crucial role in the management of patients diagnosed with APL. Coronavirus Infectious Disease 2019 (COVID-19) has shown great heterogeneity in patient presentation. Clinical manifestations range from asymptomatic disease to severe forms, mainly characterized by a hyperinflammatory syndrome leading to acute respiratory distress and multiorgan failure. Patients with acute leukemia and concomitant COVID-19-related hyperinflammatory syndrome have particularly poor outcomes. We hereby report the case of a 28-year-old male patient who was diagnosed with high-risk APL, with severe associated coagulopathy at presentation. He was treated with chemotherapy according to the AIDA regimen. The first week of induction therapy was complicated by a differentiation syndrome manifesting as fever not attributable to infection and respiratory distress with pulmonary infiltrates, resolved after ATRA discontinuation and corticotherapy. On the fourth week of treatment, he tested positive for acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with minor pulmonary involvement. Clinical manifestations over the following days included tachycardia and hypotension, associated with elevated inflammatory markers and cardiac biomarkers (troponin I x58 upper NV). Cardiovascular magnetic resonance imaging was consistent with myocarditis. COVID-19-associated myocarditis was successfully treated with methylprednisolone, intravenous immunoglobulins and Anakinra. Differentiation syndrome and COVID-19-associated myocarditis are two life-threatening complications that adversely impact survival. However, early recognition and prompt treatment initiation can improve clinical outcomes, as was the case of our patient.

TMS-EEG perturbation biomarkers for Alzheimer's disease patients classification.

The combination of TMS and EEG has the potential to capture relevant features of Alzheimer's disease (AD) pathophysiology. We used a machine learning framework to explore time-domain features characterizing AD patients compared to age-matched healthy controls (HC). More than 150 time-domain features including some related to local and distributed evoked activity were extracted from TMS-EEG data and fed into a Random Forest (RF) classifier using a leave-one-subject out validation approach. The best classification accuracy, sensitivity, specificity and F1 score were of 92.95%, 96.15%, 87.94% and 92.03% respectively when using a balanced dataset of features computed globally across the brain. The feature importance and statistical analysis revealed that the maximum amplitude of the post-TMS signal, its Hjorth complexity and the amplitude of the TEP calculated in the window 45-80 ms after the TMS-pulse were the most relevant features differentiating AD patients from HC. TMS-EEG metrics can be used as a non-invasive tool to further understand the AD pathophysiology and possibly contribute to patients' classification as well as longitudinal disease tracking.

Automatic sleep scoring with LSTM networks: impact of time granularity and input signals.

Supervised automatic sleep scoring algorithms are usually trained using sleep stage labels manually annotated on 30 s epochs of PSG data. In this study, we investigate the impact of using shorter epochs with various PSG input signals for training and testing a Long Short Term Memory (LSTM) neural network. An LSTM model is evaluated on the provided 30 s epoch sleep stage labels from a publicly available dataset, as well as on 10 s subdivisions. Additionally, three independent scorers re-labeled a subset of the dataset on shorter time windows. The automatic sleep scoring experiments were repeated on the re-annotated subset.The highest performance is achieved on features extracted from 30 s epochs of a single channel frontal EEG. The resulting accuracy, precision and recall were of 92.22%, 67.58% and 66.00% respectively. When using a shorter epoch as input, the performance decreased by approximately 20%. Re-annotating a subset of the dataset on shorter time epochs did not improve the results and further altered the sleep stage detection performance. Our results show that our feature-based LSTM classification algorithm performs better on 30 s PSG epochs when compared to 10 s epochs used as input. Future work could be oriented to determining whether varying the epoch size improves classification outcomes for different types of classification algorithms.

Characterizing TMS-EEG perturbation indexes using signal energy: initial study on Alzheimer's Disease classification.

Transcranial Magnetic Stimulation (TMS) combined with EEG recordings (TMS-EEG) has shown great potential in the study of the brain and in particular of Alzheimer's Disease (AD). In this study, we propose an automatic method of determining the duration of TMS-induced perturbation of the EEG signal as a potential metric reflecting the brain's functional alterations. A preliminary study is conducted in patients with Alzheimer's disease (AD). Three metrics for characterizing the strength and duration of TMS-evoked EEG (TEP) activity are proposed and their potential in identifying AD patients from healthy controls was investigated. A dataset of TMS-EEG recordings from 17 AD and 17 healthy controls (HC) was used in our analysis. A Random Forest classification algorithm was trained on the extracted TEP metrics and its performance is evaluated in a leave-one-subject-out cross-validation. The created model showed promising results in identifying AD patients from HC with an accuracy, sensitivity and specificity of 69.32%, 72.23% and 66.41%, respectively. Clinical relevance- Three preliminary metrics were proposed to quantify the strength and duration of the response to TMS on EEG data. The proposed metrics were successfully used to identify Alzheimer's disease patients from healthy controls. These results proved the potential of this approach which will provide additional diagnostic value.

Preliminary study on the impact of EEG density on TMS-EEG classification in Alzheimer's disease.

Transcranial magnetic stimulation co-registered with electroencephalographic (TMS-EEG) has previously proven a helpful tool in the study of Alzheimer's disease (AD). In this work, we investigate the use of TMS-evoked EEG responses to classify AD patients from healthy controls (HC). By using a dataset containing 17AD and 17HC, we extract various time domain features from individual TMS responses and average them over a low, medium and high density EEG electrode set. Within a leave-one-subject-out validation scenario, the best classification performance for AD vs. HC was obtained using a high-density electrode with a Random Forest classifier. The accuracy, sensitivity and specificity were of 92.7%, 96.58% and 88.82% respectively. Clinical relevance- TMS-EEG responses were successfully used to identify Alzheimer's disease patients from healthy controls.

Hepatitis B, C and D coinfection in HIV-infected patients: prevalence and progress.

The HAART therapy has improved life expectancy enabling long latency conditions caused by the hepatitis viruses that became the leading cause of death in HIV infected patients. In this study a group of 300 patients aged from 18 to 63 years were selected in order to assess the prevalence and consequences of HIV and the hepatitis B (HBV), C (HCV) and D (HDV) viruses coinfections. Study groups were designed for each coinfection. These groups were in turn divided in case groups formed of coinfected participants and control groups consisting of mono-infected participants. This classification was obtained by testing the participants for the presence of specific infection markers using the ELISA technique. As a result, in regard to the HIV/HBV coinfection the study group consisted of 16 coinfected participants and 114 HBV-infected participants resulting in a prevalence of the coinfection of 14%. In the case of the HIV/HDV coinfection the study group consisted of 5 coinfected participants and 45 HDV-infected participants. The prevalence of the HIV/HCV coinfection was 25% out of the 170 HCV-infected participants. The effect of the coinfections on the expression and levels of the infection markers was analyzed in constrast to those encountered in the case of the mono-infection. The observed changes in the expression of the specific hepatitis markers indicate the impact of the coinfection with HIV on the progression of the hepatitis infections. In addition, the inadequate immune response towards the hepatitis viruses in the case of the coinfected participants leads to the development of cirrhosis and end stage liver disease.

Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques.

Neurodegenerative diseases have shown an increasing incidence in the older population in recent years. A significant amount of research has been conducted to characterize these diseases. Computational methods, and particularly machine learning techniques, are now very useful tools in helping and improving the diagnosis as well as the disease monitoring process. In this paper, we provide an in-depth review on existing computational approaches used in the whole neurodegenerative spectrum, namely for Alzheimer's, Parkinson's, and Huntington's Diseases, Amyotrophic Lateral Sclerosis, and Multiple System Atrophy. We propose a taxonomy of the specific clinical features, and of the existing computational methods. We provide a detailed analysis of the various modalities and decision systems employed for each disease. We identify and present the sleep disorders which are present in various diseases and which represent an important asset for onset detection. We overview the existing data set resources and evaluation metrics. Finally, we identify current remaining open challenges and discuss future perspectives.

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis.

Methods developed for automatic sleep stage detection make use of large amounts of data in the form of polysomnographic (PSG) recordings to build predictive models. In this study, we investigate the effect of several dimensionality reduction techniques, i.e., principal component analysis (PCA), factor analysis (FA), and autoencoders (AE) on common classifiers, e.g., random forests (RF), multilayer perceptron (MLP), long-short term memory (LSTM) networks, for automated sleep stage detection. Experimental testing is carried out on the MGH Dataset provided in the "You Snooze, You Win: The PhysioNet/Computing in Cardiology Challenge 2018". The signals used as input are the six available (EEG) electoencephalographic channels and combinations with the other PSG signals provided: ECG - electrocardiogram, EMG - electromyogram, respiration based signals - respiratory efforts and airflow. We observe that a similar or improved accuracy is obtained in most cases when using all dimensionality reduction techniques, which is a promising result as it allows to reduce the computational load while maintaining performance and in some cases also improves the accuracy of automated sleep stage detection. In our study, using autoencoders for dimensionality reduction maintains the performance of the model, while using PCA and FA the accuracy of the models is in most cases improved.

An annotated video dataset for computing video memorability.

Using a collection of publicly available links to short form video clips of an average of 6 seconds duration each, 1275 users manually annotated each video multiple times to indicate both long-term and short-term memorability of the videos. The annotations were gathered as part of an online memory game and measured a participant's ability to recall having seen the video previously when shown a collection of videos. The recognition tasks were performed on videos seen within the previous few minutes for short-term memorability and within the previous 24 to 72 hours for long-term memorability. Data includes the reaction times for each recognition of each video. Associated with each video are text descriptions (captions) as well as a collection of image-level features applied to 3 frames extracted from each video (start, middle and end). Video-level features are also provided. The dataset was used in the Video Memorability task as part of the MediaEval benchmark in 2020.

Automatic Sleep Stage Detection: A Study on the Influence of Various PSG Input Signals.

Automatic sleep stage detection can be performed using a variety of input signals from a polysomnographic (PSG) recording. In this study, we investigate the effect of different input signals on the performance of feature-based automatic sleep stage classification algorithms with both a Random Forest (RF) and Multilayer Perceptron (MLP) classifier. Combinations of the EEG (electroencephalographic) signal and ECG (electrocardiographic), EMG (electromyographic) and respiratory signals as input are investigated as input with respect to using single channel and multi-channel EEG as input. The Physionet "You Snooze, You Win" dataset is used for the study. The RF classifier consistently outperforms our MLP implementation in all cases and is positively affected by specific signal combinations. The overall classification performance using a single channel EEG is high (an accuracy, precision and recall of 86.91 %, 89.52%, 86.91% respectively) using RF. The results are comparable to the performance obtained using six EEG channels as input. Adding respiratory signals to the inputs processed by RF increases the N2 stage detection performance with 20%, while adding the EMG signal improves the accuracy of the REM stage detection with 5%. Our analysis shows that adding specific signals as input to RF improves the accuracy of specific sleep stages and increases the overall performance. Using a combination of EEG and respiratory signals we achieved an accuracy of 93% for the RF classifier.

Detection of Epileptic Seizures using Unsupervised Learning Techniques for Feature Extraction.

Automatic epileptic seizure prediction from EEG (electroencephalogram) data is a challenging problem. This is due to the complex nature of the signal itself and of the generated abnormalities. In this paper, we investigate several deep network architectures i.e. stacked autoencoders and convolutional networks, for unsupervised EEG feature extraction. The proposed EEG features are used to solve the prediction of epileptic seizures via Support Vector Machines. This approach has many benefits: (i) it allows to achieve a high accuracy using small size sample data, e.g. 1 second EEG data; (ii) features are determined in an unsupervised manner, without the need for manual selection. Experimental validation is carried out on real-world data, i.e. the CHB-MIT dataset. We achieve an overall accuracy, sensitivity and specificity of up to 92%, 95% and 90% respectively.

Breast myeloid sarcoma after allogeneic stem cell transplantation for acute myelomonocytic leukemia - case report.

Defined as a rare extramedullary tumor, myeloid sarcoma (MS) is in the attention of specialists, although the information in the literature is represented especially through case reports. MS can precede acute myeloid leukemia (AML), appear simultaneous and can be the only manifestation of leukemia relapse after allogeneic stem cell transplantation (allo-SCT). We present the case of a 30-year-old female diagnosed with acute myelomonocytic leukemia (AML M4), with complete remission (CR) after chemotherapy, followed by allo-SCT for consolidation. After five months, the patient presented right breast tumors. Ultrasound-guided biopsy of the breast lesion displayed diffuse infiltration of undifferentiated tumor cells, with blastic granulocytic features, strongly immunopositive for cluster of differentiation (CD) 45, CD99, CD34 and myeloperoxidase (MPO) and negative for all epithelial markers [MNF116, cytokeratin 7 (CK7), estrogen receptor (ER), progesterone receptor (PR), E-cadherin]. The final diagnosis was AML relapse with breast MS. After multiple leukemia relapses with breast MS, the patient died with cerebral bleeding secondary to severe thrombocytopenia.