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Surgery Surgery

SARS-CoV-2 in Solid Organ Transplant Recipients: A Structured Review of 2020.

In Transplantation proceedings ; h5-index 25.0

BACKGROUND : The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is challenging health systems all over the world. Particularly high-risk groups show considerable mortality rates after infection. In 2020, a huge number of case reports, case series, and consecutively various systematic reviews have been published reporting on morbidity and mortality risk connected with SARS-CoV-2 in solid organ transplant (SOT) recipients. However, this vast array of publications resulted in an increasing complexity of the field, overwhelming even for the expert reader.

METHODS : We performed a structured literature review comprising electronic databases, transplant journals, and literature from previous systematic reviews covering the entire year 2020. From 164 included articles, we identified 3451 cases of SARS-CoV-2-infected SOT recipients.

RESULTS : Infections resulted in a hospitalization rate of 84% and 24% intensive care unit admissions in the included patients. Whereas 53.6% of patients were reported to have recovered, cross-sectional overall mortality reported after coronavirus disease 2019 (COVID-19) was at 21.1%. Synoptic data concerning immunosuppressive medication attested to the reduction or withdrawal of antimetabolites (81.9%) and calcineurin inhibitors (48.9%) as a frequent adjustment. In contrast, steroids were reported to be increased in 46.8% of SOT recipients.

CONCLUSIONS : COVID-19 in SOT recipients is associated with high morbidity and mortality worldwide. Conforming with current guidelines, modifications of immunosuppressive therapies mostly comprised a reduction or withdrawal of antimetabolites and calcineurin inhibitors, while frequently maintaining or even increasing steroids. Here, we provide an accessible overview to the topic and synoptic estimates of expectable outcomes regarding in-hospital mortality of SOT recipients with COVID-19.

Quante Markus, Brake Linda, Tolios Alexander, Della Penna Andrea, Steidle Christoph, Gruendl Magdalena, Grishina Anna, Haeberle Helene, Guthoff Martina, Tullius Stefan G, Königsrainer Alfred, Nadalin Silvio, Löffler Markus W


Internal Medicine Internal Medicine

Delirium occurrence and association with outcomes in hospitalized COVID-19 patients.

In International psychogeriatrics

Delirium is reported to be one of the manifestations of coronavirus infectious disease 2019 (COVID-19) infection. COVID-19 hospitalized patients are at a higher risk of delirium. Pathophysiology behind the association of delirium and COVID-19 is uncertain. We analyzed the association of delirium occurrence with outcomes in hospitalized COVID-19 patients, across all age groups, at Mayo Clinic hospitals.A retrospective study of all hospitalized COVID-19 patients at Mayo Clinic between March 1, 2020 and December 31, 2020 was performed. Occurrence of delirium and outcomes of mortality, length of stay, readmission, and 30-day mortality after hospital discharge were measured. Chi-square test, student t-test, survival analysis, and logistic regression analysis were performed to measure and compare outcomes of delirium group adjusted for age, sex, Charlson comorbidity score, and COVID-19 severity with no-delirium group.A total of 4351 COVID-19 patients were included in the study. Delirium occurrence in the overall study population was noted to be 22.4%. The highest occurrence of delirium was also noted in patients with critical COVID-19 illness severity. A statistically significant OR 4.35 (3.27-5.83) for in-hospital mortality and an OR 4.54 (3.25-6.38) for 30-day mortality after discharge in the delirium group were noted. Increased hospital length of stay, 30-day readmission, and need for skilled nursing facility on discharge were noted in the delirium group. Delirium in hospitalized COVID-19 patients is a marker for increased mortality and morbidity. In this group, outcomes appear to be much worse when patients are older and have a critical severity of COVID-19 illness.

Pagali Sandeep, Fu Sunyang, Lindroth Heidi, Sohn Sunghwan, Burton M Caroline, Lapid Maria


COVID-19 severity, delirium, hospitalized COVID-19, outcomes

General General

Machine learning-based scoring models to predict hematopoietic stem cell mobilization in allogeneic donors.

In Blood advances

Mobilized peripheral blood has become the primary source of hematopoietic stem cells for both autologous and allogeneic stem cell transplantation. Granulocyte Colony-Stimulating Factor (G-CSF) is currently the standard agent used in the allogeneic setting. Despite the high mobilization efficacy in most donors, G-CSF requires 4-5 days of daily administration, and a small percentage of the donors fail to mobilize an optimal number of stem cells necessary for a safe allogeneic stem cell transplant. In this study, we retrospectively reviewed 1361 related allogeneic donors who underwent stem cell mobilization at Washington University. We compared the standard mobilization agent G-CSF with five alternative mobilization regimens, including GM-CSF, G-CSF+GM-CSF, GM-CSF + Plerixafor, Plerixafor and BL-8040. Cytokine-based mobilization strategies (G-CSF or in combination with GM-CSF) induce higher CD34 cell yield after 4-5 consecutive days of treatment, while CXCR4 antagonists (plerixafor and BL-8040) induce significantly less but rapid mobilization on the same day. Next, using a large dataset containing the demographic and baseline laboratory data from G-CSF-mobilized donors, we established machine learning (ML)-based scoring models that can be used to predict patients who may have less than optimal stem cell yields after a single leukapheresis session. To our knowledge, this is the first prediction model at the early donor screening stage, which may help identify allogeneic stem cell donors who may benefit from alternative approaches to enhance stem cell yields thus insuring safe and effective stem cell transplantation.

Xiang Jingyu, Shi Min, Fiala Mark A, Gao Feng, Rettig Michael P, Uy Geoffrey L, Schroeder Mark A, Weilbaecher Katherine N, Stockerl-Goldstein Keith, Mollah Shamim, DiPersio John F


General General

DTi2Vec: Drug-target interaction prediction using network embedding and ensemble learning.

In Journal of cheminformatics

Drug-target interaction (DTI) prediction is a crucial step in drug discovery and repositioning as it reduces experimental validation costs if done right. Thus, developing in-silico methods to predict potential DTI has become a competitive research niche, with one of its main focuses being improving the prediction accuracy. Using machine learning (ML) models for this task, specifically network-based approaches, is effective and has shown great advantages over the other computational methods. However, ML model development involves upstream hand-crafted feature extraction and other processes that impact prediction accuracy. Thus, network-based representation learning techniques that provide automated feature extraction combined with traditional ML classifiers dealing with downstream link prediction tasks may be better-suited paradigms. Here, we present such a method, DTi2Vec, which identifies DTIs using network representation learning and ensemble learning techniques. DTi2Vec constructs the heterogeneous network, and then it automatically generates features for each drug and target using the nodes embedding technique. DTi2Vec demonstrated its ability in drug-target link prediction compared to several state-of-the-art network-based methods, using four benchmark datasets and large-scale data compiled from DrugBank. DTi2Vec showed a statistically significant increase in the prediction performances in terms of AUPR. We verified the "novel" predicted DTIs using several databases and scientific literature. DTi2Vec is a simple yet effective method that provides high DTI prediction performance while being scalable and efficient in computation, translating into a powerful drug repositioning tool.

Thafar Maha A, Olayan Rawan S, Albaradei Somayah, Bajic Vladimir B, Gojobori Takashi, Essack Magbubah, Gao Xin


Cheminformatics, Drug repositioning, Drug–target interaction, Ensemble learning, Heterogeneous network, Link prediction, Network embedding, Random walk, Representation learning

General General

Automatic segmentation of blood cells from microscopic slides: A comparative analysis.

In Tissue & cell

With the recent developments in deep learning, automatic cell segmentation from images of microscopic examination slides seems to be a solved problem as recent methods have achieved comparable results on existing benchmark datasets. However, most of the existing cell segmentation benchmark datasets either contain a single cell type, few instances of the cells, not publicly available. Therefore, it is unclear whether the performance improvements can generalize on more diverse datasets. In this paper, we present a large and diverse cell segmentation dataset BBBC041Seg1, which consists both of uninfected cells (i.e., red blood cells/RBCs, leukocytes) and infected cells (i.e., gametocytes, rings, trophozoites, and schizonts). Additionally, all cell types do not have equal instances, which encourages researchers to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. Furthermore, we conduct a comparative study using both classical rule-based and recent deep learning state-of-the-art (SOTA) methods for automatic cell segmentation and provide them as strong baselines. We believe the introduction of BBBC041Seg will promote future research towards clinically applicable cell segmentation methods from microscopic examinations, which can be later used for downstream tasks such as detecting hematological diseases (i.e., malaria).

Depto Deponker Sarker, Rahman Shazidur, Hosen Md Mekayel, Akter Mst Shapna, Reme Tamanna Rahman, Rahman Aimon, Zunair Hasib, Rahman M Sohel, Mahdy M R C


Benchmark, Blood-cell segmentation, Deep learning, Microscopy data

General General

Detection of ataxia in low disability MS patients by hybrid convolutional neural networks based on images of plantar pressure distribution.

In Multiple sclerosis and related disorders

BACKGROUND : This study aimed to detect ataxia in patients with multiple sclerosis (PwMS) with a deep learning-based approach based on images showing plantar pressure distribution of the patients. The secondary aim of the study was to investigate an alternative and objective method in the early diagnosis of ataxia in these patients.

METHODS : A total of 105 images showing plantar pressure distribution of 43 ataxic PwMS and 62 healthy individuals were analyzed. The images were resized for the models including VGG16, VGG19, ResNet, DenseNet, MobileNet, NasNetMobile, and NasNetLarge. Feature vectors were extracted from the resized images and then classified using Support Vector Machines (SVM), K-Nearest Neighbors (K-NN), and Artificial Neural Network (ANN). A 10-fold cross-validation was applied to increase the validity of the classifiers.

RESULTS : The VGG19-SVM hybrid model showed the highest accuracy, sensitivity, and specificity values (89.23%, 89.65%, and 88.88%, respectively).

CONCLUSION : The proposed method provided an automatic decision support system for detecting ataxia based on images showing plantar pressure distribution in patients with PwMS. The performance of the proposed method indicated that this method can be applied in clinical practice to establish a rapid diagnosis of ataxia that is asymptomatic or difficult to detect clinically and that it can be recommended as a useful aid for the physician in clinical practice.

Balgetir Ferhat, Bilek Furkan, Kakakus Serkan, Arslan-Tuncer Seda, Demir Caner Feyzi


Ataxia, Convolutional neural networks, Multiple sclerosis, Plantar pressure distribution