Use of defibrotide in COVID-19 pneumonia: comparison of a phase II study and a matched real-world cohort control.

The coronavirus disease 2019 (COVID-19) pandemic led to an unprecedented burden on healthcare systems around the world and a severe global socioeconomic crisis, with more than 750 million confirmed cases and at least 7 million deaths reported by December 31, 2023. The DEFI-VID19 study (clinicaltrials gov. Identifier: NCT04335201), a phase II, single-arm, multicenter, open-label trial was designed in mid-2020 to assess the safety and efficacy of defibrotide in treating patients with COVID-19 pneumonia. Defibrotide was administered at a dose of 25 mg/kg intravenously, divided into four daily doses over a planned 14-day period for patients with COVID-19 pneumonia receiving non-invasive ventilation. The primary endpoint was respiratory failure-free survival (RFFS). Overall survival (OS), the number of post-recovery days, and adverse events were the secondary endpoints. For comparison, a contemporaneous control cohort receiving standard of care only was retrospectively selected by applying the eligibility criteria of the DEFI-VID19 trial. To adjust for the imbalance between the two cohorts in terms of baseline variable distributions, an outcome regression analysis was conducted. In adjusted analysis, patients receiving defibrotide reported a trend towards higher RFFS (hazard ratio [HR]=0.71; 95% confidence interval [CI]: 0.34-1.29; P=0.138) and OS (HR=0.78; 95% CI: 0.33-1.53; P=0.248]) and showed a significantly increased number of post-recovery days (difference in means =3.61; 95% CI: 0.97-6.26; P=0.0037). Despite concomitant thromboprophylaxis with low molecular weight heparin, the safety profile of defibrotide proved to be favorable. Taken together, our findings suggest that defibrotide may represent a valuable addition to the COVID-19 therapeutic options.

Diagnostic capabilities, clinical features, and longitudinal UBA1 clonal dynamics of a nationwide VEXAS cohort.

VEXAS is a prototypic hemato-inflammatory disease combining rheumatologic and hematologic disorders in a molecularly defined nosological entity. In this nationwide study, we aimed at screenshotting the current diagnostic capabilities and clinical-genomic features of VEXAS, and tracked UBA1 longitudinal clonal dynamics upon different therapeutics, including allogeneic hematopoietic cell transplant. We leveraged a collaboration between the Italian Society of Experimental Hematology and of Rheumatology and disseminated a national survey to collect clinical and molecular patient information. Overall, 13/29 centers performed UBA1 genomic testing locally, including Sanger sequencing (46%), next-generation sequencing (23%), droplet digital polymerase chain reaction (8%), or combination (23%). A total of 41 male patients were identified, majority (51%) with threonine substitutions at Met41 hotspot, followed by valine and leucine (27% and 8%). Median age at VEXAS diagnosis was 67 years. All patients displayed anemia (median hemoglobin 9.1 g/dL), with macrocytosis. Bone marrow vacuoles were observed in most cases (89%). The most common rheumatologic association was polychondritis (49%). A concomitant myelodysplastic neoplasm/syndrome (MDS) was diagnosed in 71% of patients (n = 28), chiefly exhibiting lower Revised International Prognostic Scoring System risk profiles. Karyotype was normal in all patients, except three MDS cases showing -Y, t(12;16)(q13;q24), and +8. The most frequently mutated gene was DNMT3A (n = 10), followed by TET2 (n = 3). At last follow-up, five patients died and two patients progressed to acute leukemia. Longitudinal UBA1 clonal dynamics demonstrated mutational clearance following transplant. We collected a nationwide interdisciplinary VEXAS patient cohort, characterized by heterogeneous rheumatologic manifestations and treatments used. MDS was diagnosed in 71% of cases. Patients exhibited various longitudinal UBA1 clonal dynamics.

Autoimmune manifestations in VEXAS: Opportunities for integration and pitfalls to interpretation.

VEXAS (Vacuoles, E1 enzyme, X-linked, Autoinflammatory, Somatic) is a novel entity manifesting with a multiplicity of clinical features. Somatic mutations of the UBA1 gene in hematopoietic stem cells constitute the genetic basis of VEXAS. As an X-linked disorder, most cases occur in men, classically developing symptoms during the fifth to sixth decade of life. Considering its multidisciplinary nature involving numerous branches of internal medicine, VEXAS has elicited a wide medical interest and several medical conditions have been associated with this disease. Even so, its recognition in everyday clinical practice is not necessarily straightforward. Close collaboration between different medical specialists is mandatory. Patients with VEXAS may manifest a range of features from manageable cytopenias to disabling and life-threatening autoimmune phenomena with limited responses to therapy, with the potential for progression to hematological malignancies. Diagnostic and treatment guidelines are exploratory and include a range of rheumatological and supportive care treatments. Allogeneic hematopoietic stem cell transplantation is potentially curative, but its risks are significant and its position in the treatment algorithm is yet to be defined. Herein, we present the variegated manifestations of VEXAS, provide practice criteria for diagnostic testing of UBA1, and discuss potential treatment options, including allogeneic hematopoietic stem cell transplantation, current evidence, and future directions.

Impact of inotuzumab ozogamicin on outcome in relapsed or refractory acute B-cell lymphoblastic leukemia patients prior to allogeneic hematopoietic stem cell transplantation and risk of sinusoidal obstruction syndrome/venous occlusive disease.

We evaluated 58 patients with relapsed or refractory (r/r) acute B-lymphoblastic leukemia (B-ALL; median age, 42.5 years; range, 16-69 years), treated with inotuzumab ozogamicin (INO) between 2016-2022 and who received an allogeneic hematopoietic stem cell transplantation (allo-HCT) consecutively. Forty-seven (81%) of the 58 patients were heavily pretreated receiving intensive chemotherapy +/- TKI, blinatumomab in 24 (41%) and allo-HCT at first-line in 11 (19%) patients. Complete remission (CR) rate prior to allo-HCT was 84%. Median follow-up was 30.5 months and median overall survival (OS) measured from start of INO was 11.2 months. Oneand 2-years OS rates were 50% (95%-CI, 38.4-56.1%) and 36.7% (95%-CI, 25.5-52.9%), respectively. Sinusoidal obstruction syndrome/venous occlusive disease (SOS/VOD) after allo-HCT occurred in 17 (29%) patients. Of those, 9 (53%) patients died due to SOS/VOD and multi-organ failure. Two had received >2 INO cycles (3 cycles, 5 cycles, n=1, each), all others ≤2 INO cycles prior to allo-HCT. Logistic regression analysis revealed conditioning with double alkylators (P=0.038) and allo-HCT during first-line therapy (P=0.050) as significant risk factors for SOS/VOD and in trend allo-HCT ≤ 60 days from last INO application (P=0.07), whereas number of INO cycles before allo-HCT and time between last INO application and allo-HCT were not significant. Relapse/progressive disease occurred in 20 (34%) patients. Of those, five (25%) patients are still alive, whereas 15 succumbed of their disease. Treatment with INO seems to be an effective approach with successful bridge-to-transplant. However, risk of SOS/VOD is high, necessitating continuous monitoring and recognition of SOS/VOD risk factors.

Meso-scale topological cues influence extracellular matrix production in a large deformation, elastomeric scaffold model.

Physical cues are decisive factors in extracellular matrix (ECM) formation and elaboration. Their transduction across scale lengths is an inherently symbiotic phenomenon that while influencing ECM fate is also mediated by the ECM structure itself. This study investigates the possibility of enhancing ECM elaboration by topological cues that, while not modifying the substrate macro scale mechanics, can affect the meso-scale strain range acting on cells incorporated within the scaffold. Vascular smooth muscle cell micro-integrated, electrospun scaffolds were fabricated with comparable macroscopic biaxial mechanical response, but different meso-scale topology. Seeded scaffolds were conditioned on a stretch bioreactor and exposed to large strain deformations. Samples were processed to evaluate ECM quantity and quality via: biochemical assay, qualitative and quantitative histological assessment and multi-photon analysis. Experimental evaluation was coupled to a numerical model that elucidated the relationship between the scaffold micro-architecture and the strain acting on the cells. Results showed an higher amount of ECM formation for the scaffold type characterized by lowest fiber intersection density. The numerical model simulations associated this result with the differences found for the change in cell nuclear aspect ratio and showed that given comparable macro scale mechanics, a difference in material topology created significant differences in cell-scaffold meso-scale deformations. These findings reaffirmed the role of cell shape in ECM formation and introduced a novel notion for the engineering of cardiac tissue where biomaterial structure can be designed to both mimick the organ level mechanics of a specific tissue of interest and elicit a desirable cellular response.

Self-lubricating, low-friction, wear-resistant Al-based quasicrystalline coatings.

After gas atomization, a quasicrystalline powder based on aluminium was used to prepare a thick coating by high-velocity oxygen-fuel flame torch spraying. This layer was deposited on top of a bond-coat layer on a steel plate. A post-spraying annealing treatment turned the two layers to their stable state, a γ-brass crystal and an icosahedral quasicrystal, respectively. The projection parameters were selected in such a way that the coating behaved like a self-lubricating material, which offered very good wear resistance (duration of pin-on-disk tests superior to 5 km with negligible material loss) and low friction (µ ≤ 6% against sintered tungsten carbide), in contrast to the state of the art. This property was achieved thanks to, on the one hand, excellent bonding to the substrate via the bound coat, and on the other hand, presence at the boundaries between quasicrystalline flakes of a mixture of both threefold and fourfold coordinated carbon originating from spray processing. Application to hard materials used in mechanical devices is appealing, especially because soft, lubricating additives may not be needed, thus considerably increasing the lifetime of the devices and reducing waste of materials.

In vitro effect of calcium nanophosphate and high-concentrated fluoride agents on enamel erosion: an AFM study.

BACKGROUND: Calcium nanophosphate paste can provide ions to remineralize enamel. There are, however, no data available about the remineralizing effect of this paste on the prevention of enamel erosion, when compared with highly concentrated fluoride agents. AIM: To analyze the effect of calcium nanophosphate paste, fluoride gel, and varnish to protect against enamel erosion using surface Knoop hardness (KNH) and atomic force microscopy (AFM). DESIGN: Forty enamel blocks (4 × 4 mm) of third molars were used for 4 groups (n = 10): 1.23% fluoride gel (Fluorgel-DFL(®) ); calcium nanophosphate paste (Desensibilize NanoP-FGM(®) ); fluoride varnish (Duraphat-Colgate(®) ) and control (without agent). The specimens were immersed in cola drink for 5 min and 2 h in artificial saliva, 4× per day for 5 days. The agents were applied before the first erosive cycle. KNH values were obtained before and after the erosive challenge. The surface morphology was evaluated by AFM. anova, Tukey's, and T-Student tests were applied. RESULTS: After erosion, no significant difference was found for KNH among gel, nanophosphate, and varnish groups; however, they showed higher KNH than control group. Gel and nanophosphate paste showed a protective layer formation on enamel surface by AFM. CONCLUSIONS: The calcium nanophosphate paste showed similar protection against enamel erosion compared with high-concentrated fluoride agents, even containing lower fluoride concentration.

Empowering PET: harnessing deep learning for improved clinical insight.

This review aims to take a journey into the transformative impact of artificial intelligence (AI) on positron emission tomography (PET) imaging. To this scope, a broad overview of AI applications in the field of nuclear medicine and a thorough exploration of deep learning (DL) implementations in cancer diagnosis and therapy through PET imaging will be presented. We firstly describe the behind-the-scenes use of AI for image generation, including acquisition (event positioning, noise reduction though time-of-flight estimation and scatter correction), reconstruction (data-driven and model-driven approaches), restoration (supervised and unsupervised methods), and motion correction. Thereafter, we outline the integration of AI into clinical practice through the applications to segmentation, detection and classification, quantification, treatment planning, dosimetry, and radiomics/radiogenomics combined to tumour biological characteristics. Thus, this review seeks to showcase the overarching transformation of the field, ultimately leading to tangible improvements in patient treatment and response assessment. Finally, limitations and ethical considerations of the AI application to PET imaging and future directions of multimodal data mining in this discipline will be briefly discussed, including pressing challenges to the adoption of AI in molecular imaging such as the access to and interoperability of huge amount of data as well as the "black-box" problem, contributing to the ongoing dialogue on the transformative potential of AI in nuclear medicine.Relevance statementAI is rapidly revolutionising the world of medicine, including the fields of radiology and nuclear medicine. In the near future, AI will be used to support healthcare professionals. These advances will lead to improvements in diagnosis, in the assessment of response to treatment, in clinical decision making and in patient management.Key points• Applying AI has the potential to enhance the entire PET imaging pipeline.• AI may support several clinical tasks in both PET diagnosis and prognosis.• Interpreting the relationships between imaging and multiomics data will heavily rely on AI.

Sorafenib maintenance in FLT3-ITD mutated AML after allogeneic HCT: a real-world, single-center experience.

Despite allogeneic hematopoietic stem cell transplant (allo-HCT) and the development of novel FLT3 inhibitors in both induction (midostaurin) and in the relapsed/refractory setting (gilteritinib), FLT3-ITD mutated leukemia (FLT3-ITD+ AML) still represents a challenge for modern hematology. Sorafenib is, to this date, the only inhibitor that demonstrated efficacy in improving both progression-free and overall survival as post-HCT maintenance therapy, even if its use in this setting has not been approved so far by regulatory agencies. The aim of our study was to evaluate the feasibility, safety, and efficacy of sorafenib maintenance in preventing early relapse in FLT3-ITD+ AML after HCT in a single-center experience. We analyzed 26 consecutive patients who received post-HCT 2-year maintenance with sorafenib at our center between 2017 and 2023. The median time from HCT to sorafenib start was 130 days, and the median dosage was 200 mg per day. Two (8%) and three (12%) patients discontinued maintenance due to toxicity and disease relapse, respectively. Eight (31%) patients terminated the 2-year maintenance and stopped sorafenib, while 13 patients are still under treatment. Overall, 21/26 patients (81%) are alive and in stable complete remission as outlined by a 2-year disease-free survival of 83.61%. No major long-term toxicity was reported at the last follow-up. Our real-world experience supports the use of sorafenib as a feasible and effective therapeutic option in post-HCT maintenance for FLT3-ITD+ AML.

Coadministration of isavuconazole and sirolimus in allogeneic hematopoietic stem cell transplant recipients.

Background: Invasive fungal infections (IFIs) represent a major cause of morbidity among allogeneic hematopoietic stem cell transplantation (allo-HSCT). Isavuconazole (ISA) is a broad-spectrum triazole with favorable safety profile. Objectives and design: Herein, we evaluate the real life coadministration of ISA and sirolimus in allo-HSCT recipients in a single-center retrospective analysis, describing clinical efficacy, safety, and therapeutic drug monitoring (TDM) of both drugs. Methods: All consecutive allo-HSCT recipients who received the coadministration of ISA and sirolimus for at least 2 weeks between July 2017 and December 2022 were included in this retrospective analysis. TDM was longitudinally performed during treatment. IFIs were classified according to the revised European Organization for Research and Treatment of Cancer/Mycoses Study Group consensus criteria. Results: A total of 51 recipients were included in the analysis. A total of 17 patients received ISA as continuous antifungal treatment for IFI diagnosed before transplant: one patient experienced a probable invasive pulmonary aspergillosis, and one patient switched from ISA to liposomal amphotericin B for a possible IFI. A total of 34 patients started ISA as antifungal therapy for IFI diagnosed after transplant. Sixteen of 34 were treated for a proven/probable breakthrough IFI during mold-active prophylaxis: 6/16 patients died for IFI after a median of 51 days of ISA. Eighteen of 34 started ISA as empirical therapy for a possible IFI: 15/18 patients were alive with resolution of infection after 6 weeks, 1 died for disease progression, and 2 had empirically changed antifungal therapy due to pneumonia progression. Clinical and radiological response rate was 68% after 90 days from IFI diagnosis. No toxicities related to drug-drug interaction have been registered in patients reaching concomitant therapeutic levels of ISA and sirolimus. Conclusion: The coadministration of ISA and sirolimus was safe and feasible in this cohort, confirming favorable clinical efficacy in patients with multiple-drug coadministration.

Object Counting via Group and Graph Attention Network.

Object counting, defined as the task of accurately predicting the number of objects in static images or videos, has recently attracted considerable interest. However, the unavoidable presence of background noise prevents counting performance from advancing further. To address this issue, we created a group and graph attention network (GGANet) for dense object counting. GGANet is an encoder-decoder architecture incorporating a group channel attention (GCA) module and a learnable graph attention (LGA) module. The GCA module groups the feature map into several subfeatures, each of which is assigned an attention factor through the identical channel attention. The LGA module views the feature map as a graph structure in which the different channels represent diverse feature vertices, and the responses between channels represent edges. The GCA and LGA modules jointly avoid the interference of irrelevant pixels and suppress the background noise. Experiments are conducted on four crowd-counting datasets, two vehicle-counting datasets, one remote-sensing counting dataset, and one few-shot object-counting dataset. Comparative results prove that the proposed abbr achieves superior counting performance.

Schrödinger cat states of a 16-microgram mechanical oscillator.

According to quantum mechanics, a physical system can be in any linear superposition of its possible states. Although the validity of this principle is routinely validated for microscopic systems, it is still unclear why we do not observe macroscopic objects to be in superpositions of states that can be distinguished by some classical property. Here we demonstrate the preparation of a mechanical resonator in Schrödinger cat states of motion, where the ∼1017 constituent atoms are in a superposition of two opposite-phase oscillations. We control the size and phase of the superpositions and investigate their decoherence dynamics. Our results offer the possibility of exploring the boundary between the quantum and classical worlds and may find applications in continuous-variable quantum information processing and metrology with mechanical resonators.

Enhancing Fingerprint Liveness Detection Accuracy Using Deep Learning: A Comprehensive Study and Novel Approach.

Liveness detection for fingerprint impressions plays a role in the meaningful prevention of any unauthorized activity or phishing attempt. The accessibility of unique individual identification has increased the popularity of biometrics. Deep learning with computer vision has proven remarkable results in image classification, detection, and many others. The proposed methodology relies on an attention model and ResNet convolutions. Spatial attention (SA) and channel attention (CA) models were used sequentially to enhance feature learning. A three-fold sequential attention model is used along with five convolution learning layers. The method's performances have been tested across different pooling strategies, such as Max, Average, and Stochastic, over the LivDet-2021 dataset. Comparisons against different state-of-the-art variants of Convolutional Neural Networks, such as DenseNet121, VGG19, InceptionV3, and conventional ResNet50, have been carried out. In particular, tests have been aimed at assessing ResNet34 and ResNet50 models on feature extraction by further enhancing the sequential attention model. A Multilayer Perceptron (MLP) classifier used alongside a fully connected layer returns the ultimate prediction of the entire stack. Finally, the proposed method is also evaluated on feature extraction with and without attention models for ResNet and considering different pooling strategies.

The Development of an Intelligent Agent to Detect and Non-Invasively Characterize Lung Lesions on CT Scans: Ready for the "Real World"?

(1) Background: Once lung lesions are identified on CT scans, they must be characterized by assessing the risk of malignancy. Despite the promising performance of computer-aided systems, some limitations related to the study design and technical issues undermine these tools' efficiency; an "intelligent agent" to detect and non-invasively characterize lung lesions on CT scans is proposed. (2) Methods: Two main modules tackled the detection of lung nodules on CT scans and the diagnosis of each nodule into benign and malignant categories. Computer-aided detection (CADe) and computer aided-diagnosis (CADx) modules relied on deep learning techniques such as Retina U-Net and the convolutional neural network; (3) Results: Tests were conducted on one publicly available dataset and two local datasets featuring CT scans acquired with different devices to reveal deep learning performances in "real-world" clinical scenarios. The CADe module reached an accuracy rate of 78%, while the CADx's accuracy, specificity, and sensitivity stand at 80%, 73%, and 85.7%, respectively; (4) Conclusions: Two different deep learning techniques have been adapted for CADe and CADx purposes in both publicly available and private CT scan datasets. Experiments have shown adequate performance in both detection and diagnosis tasks. Nevertheless, some drawbacks still characterize the supervised learning paradigm employed in networks such as CNN and Retina U-Net in real-world clinical scenarios, with CT scans from different devices with different sensors' fingerprints and spatial resolution. Continuous reassessment of CADe and CADx's performance is needed during their implementation in clinical practice.

Dynamics of polyclonal immuno-reconstitution after allogeneic transplant with post-transplant cyclophosphamide and letermovir.

Cytomegalovirus (CMV) reactivations are strong stimulators of immune-reconstitution (IR) in hematopoietic stem cell transplantation (HSCT) recipients. Herein, we analyzed 317 CMV-seropositive consecutive patients (n = 109 letermovir, LTV; n = 208 no-LTV), undergoing HSCT with post-transplant cyclophosphamide (PTCy) and calcineurin inhibitor- (CNI) free graft-versus-host-disease (GvHD) prophylaxis. At day+90, median CD19+/mm3 was higher in LTV-cohort: 5.5 [0;439] versus 2 [0;294], p = 0.008; median CD3+/mm3 counts were lower in LTV-cohort, with no differences in CD4+, CD8+ and NK-cells. At day+180 median CD3+, CD4+ and CD8+/mm3 values were comparable between groups. Higher CD19+/mm3 counts were observed in LTV-cohort: 62 [0; 2983] versus 42 [0; 863]. Significantly higher median NK/mm3 values were seen in LTV-cohort: 225.5 [0;763] versus 163.5 [0;1181], p = 0.0003. The impact of LTV on B-cell IR at 3 months and NK-cell levels at 6 months was retained in multivariate analysis (p < 0.01), whereas the effect on T-cells was not confirmed. Moreover, we confirmed a significant reduction of clinically-relevant CMV, and moderate-to- severe chronic GvHD in LTV-cohort. Overall, in our study the use of LTV was associated with a slight improvement of B-cell and NK-cells reconstitution, with only minor impact on T-cell subsets, giving new insights on polyclonal IR for HSCT recipients in the LTV era.

Splenic irradiation prior to allogeneic transplant conditioning in myelofibrosis: A pilot experience.

INTRODUCTION: In the era of JAK inhibitors, allogeneic stem cell transplantation (HSCT) remains the only curative treatment for patients with Myelofibrosis (MF). Splenic irradiation (SI) may be used to reduce spleen size and related symptoms. METHODS: We conducted a retrospective analysis on 14 patients with MF who underwent HSCT with SI from any donor source at our center between June 2016 and March 2021. All patients received a conditioning backbone based on treosulfan and fludarabine, with post-transplant cyclophosphamide (PTCy) and sirolimus as graft-versus-host disease (GvHD) prophylaxis. Patients received SI with 10 Gy involved-field radiotherapy in five 2-Gy fractions over the course of a week prior to the beginning of conditioning. RESULTS: At transplant all patients were transfusion-dependent and had splenomegaly (median bipolar diameter by ultrasound: 20.75 cm). Overall, 12 patients had received ruxolitinib prior to transplant. Re-evaluation of spleen dimensions was available for 13 patients: median splenic bipolar diameter after at least 3 months from transplant decreased by a median of 25%. With a median post-transplant follow-up of 25 months, 6 patients remain in CR with full-donor chimerism, 3 patients died due to NRM. Overall, 4 patients relapsed. At last follow-up, nine patients are currently alive and achieved transfusion-independence. CONCLUSIONS: In a small cohort of mostly ruxolitinib pre-treated patients, SI and treosulfan-based conditioning appeared a safe and effective tool to reduce spleen dimensions and ameliorate symptoms. Future prospective studies with adequate sample size are warranted to further investigate the usefulness and safety of this approach in MF.

Case Report: Favorable outcome of allogeneic hematopoietic stem cell transplantation in SARSCoV2 positive recipient, risk-benefit balance between infection and leukemia.

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) in SARS-CoV-2 positive candidates is usually delayed until the clinical resolution of the infection's symptoms and a negative nasopharyngeal molecular test. However, prolonged SARS-CoV-2 positivity has been frequently observed in haematological malignancies, thus representing a challenge for the timing of transplant procedures. Here, we report on the case of a 34-year-old patient with recent pauci-symptomatic COVID-19 undergoing transplant for high-risk acute B-lymphoblastic leukemia before achieving viral clearance. Shortly before their scheduled allogeneic HSCT from a matched unrelated donor, the patient developed mild Omicron BA.5 infection receiving nirmatrelvir/ritonavir with fever resolution within 72 hours. Twenty-three days after COVID-19 diagnosis, because of increasing minimal residual disease values in the context of high-risk refractory leukemia and clinical resolution of SARS-2-CoV infection with reduction of viral load at surveillance nasopharyngeal swabs, it was decided not to delay further allo-HSCT. During myelo-ablative conditioning, the nasopharyngeal SARS-CoV-2 viral load increased while the patient remained asymptomatic. Consequently, two days before the transplant, intra-muscular tixagevimab/cilgavimab 300/300 mg and a 3-day course of intravenous remdesivir were administered. During the pre-engraftment phase, veno-occlusive disease (VOD) occurred at day +13, requiring defibrotide treatment to obtain a slow but complete recovery. The post-engraftment phase was characterized by mild COVID-19 at day +23 (cough, rhino-conjunctivitis, fever) that spontaneously resolved, achieving viral clearance at day +28. At day +32, she experienced grade I acute graft-versus host disease (a-GVHD, skin grade II) treated with steroids and photo-apheresis, without further complications during follow-up until day +180. Addressing the issue of allo-HSCT timing in patients recovering from SARS-CoV-2 infection with high-risk malignant diseases is challenging because of 1] the high risk of COVID-19 clinical progression, 2] the impact of transplant delay on leukemia prognosis and 3] the occurrence of endothelial complications such as VOD, a-GVHD, and transplant associated thrombotic micro-angiopathy. Our report describes the favourable outcome of allo-HSCT in a recipient with active SARS-CoV2 infection and high-risk leukemia thanks to timely anti-SARS-CoV-2 preventive therapies and prompt management of transplant-related complications.

Sign and Human Action Detection Using Deep Learning.

Human beings usually rely on communication to express their feeling and ideas and to solve disputes among themselves. A major component required for effective communication is language. Language can occur in different forms, including written symbols, gestures, and vocalizations. It is usually essential for all of the communicating parties to be fully conversant with a common language. However, to date this has not been the case between speech-impaired people who use sign language and people who use spoken languages. A number of different studies have pointed out a significant gaps between these two groups which can limit the ease of communication. Therefore, this study aims to develop an efficient deep learning model that can be used to predict British sign language in an attempt to narrow this communication gap between speech-impaired and non-speech-impaired people in the community. Two models were developed in this research, CNN and LSTM, and their performance was evaluated using a multi-class confusion matrix. The CNN model emerged with the highest performance, attaining training and testing accuracies of 98.8% and 97.4%, respectively. In addition, the model achieved average weighted precession and recall of 97% and 96%, respectively. On the other hand, the LSTM model's performance was quite poor, with the maximum training and testing performance accuracies achieved being 49.4% and 48.7%, respectively. Our research concluded that the CNN model was the best for recognizing and determining British sign language.

Image Augmentation Techniques for Mammogram Analysis.

Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods' performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.