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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Organ sparing management in rectal cancer. Are we there yet?

BACKGROUND: The "watch and wait" approach has recently been proposed as an alternative to surgery in locally-advanced rectal cancer patients that respond to neo-adjuvant chemoradiotherapy, in order to decrease its negative functional consequences upon the quality of life of these patients. Current methods show low accuracy for the identification of complete responders. MATERIALS AND METHODS: A review of the literature was conducted for articles published up to March 31th, 2019. Relevant studies were identified using bibliographic searches of Pubmed database. The keywords that were used in various combinations were: "neoadjuvant chemoradiotherapy", "non-operative management", "complete pathological response", "rectal cancer", "biomarkers", "staging". RESULTS: Magnetic resonance imaging can identify complete responders with a high accuracy using new protocols like diffusion weighted imaging. Positron emission tomography with 18-fluoro-deoxy-glucose shows a sensitivity of 90.9% and specificity of 80.3% for the prediction of complete pathologic response using the change in standardized uptake value. A panel of 15 metabolites was identified and shows potential to discriminate patient resistance and sensitivity to neo-adjuvant therapy (Area Under the Curve 0.80). Furthermore, pre-treatment peripheral blood neutrophil to lymphocyte ratio below 2 and platelet to lymphocyte ratio below 133.4 are significantly correlated with good tumor response (OR 2.49). Analysis of the pattern of carcinoembryonic antigen (CEA) clearance after neoadjuvant treatment conclude that an exponential decrease of the CEA levels is associated with significant tumor down staging and complete pathologic response. CONCLUSION: New methods of assessing the response to neo-adjuvant therapy in locally-advanced rectal cancer have emerged, showing promising results. Further studies need to assess the best combination between imaging and these biomarkers in order to increase the accuracy and standardize the criteria for non-operative management. KEY WORDS: Biomarkers, Complete pathologic response, Non-Operative management, Rectal cancer, Staging.

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.

Let's Talk About BiTEs and Other Drugs in the Real-Life Setting for B-Cell Acute Lymphoblastic Leukemia.

Background: Therapy for acute lymphoblastic leukemia (ALL) are currently initially efficient, but even if a high percentage of patients have an initial complete remission (CR), most of them relapse. Recent data shows that immunotherapy with either bispecific T-cell engagers (BiTEs) of chimeric antigen receptor (CAR) T cells can eliminate residual chemotherapy-resistant B-ALL cells. Objective: The objective of the manuscript is to present improvements in the clinical outcome for chemotherapy-resistant ALL in the real-life setting, by describing Romania's experience with bispecific antibodies for B-cell ALL. Methods: We present the role of novel therapies for relapsed B-cell ALL, including the drugs under investigation in phase I-III clinical trials, as a potential bridge to transplant. Blinatumomab is presented in a critical review, presenting both the advantages of this drug, as well as its limitations. Results: Bispecific antibodies are discussed, describing the clinical trials that resulted in its approval by the FDA and EMA. The real-life setting for relapsed B-cell ALL is described and we present the patients treated with blinatumomab in Romania. Conclusion: In the current manuscript, we present blinatumomab as a therapeutic alternative in the bridge-to-transplant setting for refractory or relapsed ALL, to gain a better understanding of the available therapies and evidence-based data for these patients in 2019.

Influence of Staphylococcus aureus attachment to the herpes simplex virus infected cells.

The purpose of this study was to investigate the response of HeLa cells to the interaction with inactivated Staphylococcus aureus cells and live challenge with herpes simplex virus (HSV).The results of this study are indicating that the interaction between the HeLa cells and S. aureus inactivated whole cells could modulate the host cell apoptosis and cytokine production, and therefore, influence the progression of HSV infection. The pre-treatment of HeLa cells with heat inactivated bacterial whole cells protects them from the occurrence of HSV mediated cytopathic effect, while the post viral infection treatment with bacterial cells prevents the high activation of bax/bcl-2 apoptotic pathway, a process that could change the fate of the infectious process triggered by the virus, and eventually reduce its multiplication rate. The pre-treatment of HeLa monolayer with inactivated bacterial cells 24 hours before the viral infection is increasing the expression level of TNF-a, IL-6 and IL-8 pro-inflammatory cytokines genes, also suggesting that bacterial antigens could contribute to the decrease of viral multiplication rate.

A Cloud Based Real-Time Collaborative Platform for eHealth.

For more than a decade, the eHealth initiative has been a government concern of many countries. In an Electronic Health Record (EHR) System, there is a need for sharing the data with a group of specialists simultaneously. Collaborative platforms alone are just a part of a solution, while a collaborative platform with parallel editing capabilities and with synchronized data streaming are stringently needed. In this paper, the design and implementation of a collaborative platform used in healthcare is introduced by describing the high level architecture and its implementation. A series of eHealth services are identified and usage examples in a healthcare environment are given.

Improving cardiac resynchronization therapy response with multipoint left ventricular pacing: Twelve-month follow-up study.

BACKGROUND: Cardiac resynchronization therapy (CRT) with multipoint left ventricular (LV) pacing (MultiPoint™ Pacing [MPP], St. Jude Medical) improves acute LV function and LV reverse remodeling at 3 months. OBJECTIVE: The purpose of this study was to test the hypothesis that MPP can also improve LV function at 12 months. METHODS: Consecutive patients receiving a CRT implant (Unify Quadra MP™ or Quadra Assura MP™ CRT-D and Quartet™ LV lead, St. Jude Medical) were randomized to receive pressure-volume (PV) loop optimized biventricular pacing with either conventional cardiac resynchronization therapy (CONV) or MPP. CRT response was defined by a reduction in end-systolic volume (ESV) ≥15% relative to BASELINE as determined by a blinded observer and alive status. RESULTS: Forty-four patients (New York Heart Association class III, ejection fraction [EF] 29% ± 6%, QRS 152 ± 17 ms) were enrolled and randomized to either CONV (N = 22) or MPP (N = 22). During the observation period, 2 patients died of noncardiac causes and 2 patients were lost to follow-up. After 12 months, 12 of 21 patients (57%) in the CONV group and 16 of 21 patients (76%) in the MPP group were classified as CRT responders (P = .33). ESV reduction and EF increase relative to BASELINE were significantly greater with MPP than with CONV (ESV: median -25%, interquartile range [IQR] [-39% to -20%] vs median -18%, IQR [-25% to -2%], P = .03; EF: median +15%, IQR [8% to 20%] vs median +5%, IQR [-1% to 8%], P <.001). CONCLUSION: Sustaining the trend observed 3 months postimplant, PV loop-guided multipoint LV pacing resulted in greater LV reverse remodeling and increased LV function at 12 months compared to PV loop-guided conventional CRT.