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Surgery Surgery

Machine learning identification of specific changes in myeloid cell phenotype during bloodstream infections.

In Scientific reports ; h5-index 158.0

The early identification of bacteremia is critical for ensuring appropriate treatment of nosocomial infections in intensive care unit (ICU) patients. The aim of this study was to use flow cytometric data of myeloid cells as a biomarker of bloodstream infection (BSI). An eight-color antibody panel was used to identify seven monocyte and two dendritic cell subsets. In the learning cohort, immunophenotyping was applied to (1) control subjects, (2) postoperative heart surgery patients, as a model of noninfectious inflammatory responses, and (3) blood culture-positive patients. Of the complex changes in the myeloid cell phenotype, a decrease in myeloid and plasmacytoid dendritic cell numbers, increase in CD14+CD16+ inflammatory monocyte numbers, and upregulation of neutrophils CD64 and CD123 expression were prominent in BSI patients. An extreme gradient boosting (XGBoost) algorithm called the "infection detection and ranging score" (iDAR), ranging from 0 to 100, was developed to identify infection-specific changes in 101 phenotypic variables related to neutrophils, monocytes and dendritic cells. The tenfold cross-validation achieved an area under the receiver operating characteristic (AUROC) of 0.988 (95% CI 0.985-1) for the detection of bacteremic patients. In an out-of-sample, in-house validation, iDAR achieved an AUROC of 0.85 (95% CI 0.71-0.98) in differentiating localized from bloodstream infection and 0.95 (95% CI 0.89-1) in discriminating infected from noninfected ICU patients. In conclusion, a machine learning approach was used to translate the changes in myeloid cell phenotype in response to infection into a score that could identify bacteremia with high specificity in ICU patients.

Gosset Christian, Foguenne Jacques, Simul Mickaël, Tomsin Olivier, Ammar Hayet, Layios Nathalie, Massion Paul B, Damas Pierre, Gothot André


General General

Cross-modal semantic autoencoder with embedding consensus.

In Scientific reports ; h5-index 158.0

Cross-modal retrieval has become a topic of popularity, since multi-data is heterogeneous and the similarities between different forms of information are worthy of attention. Traditional single-modal methods reconstruct the original information and lack of considering the semantic similarity between different data. In this work, a cross-modal semantic autoencoder with embedding consensus (CSAEC) is proposed, mapping the original data to a low-dimensional shared space to retain semantic information. Considering the similarity between the modalities, an automatic encoder is utilized to associate the feature projection to the semantic code vector. In addition, regularization and sparse constraints are applied to low-dimensional matrices to balance reconstruction errors. The high dimensional data is transformed into semantic code vector. Different models are constrained by parameters to achieve denoising. The experiments on four multi-modal data sets show that the query results are improved and effective cross-modal retrieval is achieved. Further, CSAEC can also be applied to fields related to computer and network such as deep and subspace learning. The model breaks through the obstacles in traditional methods, using deep learning methods innovatively to convert multi-modal data into abstract expression, which can get better accuracy and achieve better results in recognition.

Sun Shengzi, Guo Binghui, Mi Zhilong, Zheng Zhiming


Ophthalmology Ophthalmology

Genome-wide detection of cytosine methylations in plant from Nanopore data using deep learning.

In Nature communications ; h5-index 260.0

In plants, cytosine DNA methylations (5mCs) can happen in three sequence contexts as CpG, CHG, and CHH (where H = A, C, or T), which play different roles in the regulation of biological processes. Although long Nanopore reads are advantageous in the detection of 5mCs comparing to short-read bisulfite sequencing, existing methods can only detect 5mCs in the CpG context, which limits their application in plants. Here, we develop DeepSignal-plant, a deep learning tool to detect genome-wide 5mCs of all three contexts in plants from Nanopore reads. We sequence Arabidopsis thaliana and Oryza sativa using both Nanopore and bisulfite sequencing. We develop a denoising process for training models, which enables DeepSignal-plant to achieve high correlations with bisulfite sequencing for 5mC detection in all three contexts. Furthermore, DeepSignal-plant can profile more 5mC sites, which will help to provide a more complete understanding of epigenetic mechanisms of different biological processes.

Ni Peng, Huang Neng, Nie Fan, Zhang Jun, Zhang Zhi, Wu Bo, Bai Lu, Liu Wende, Xiao Chuan-Le, Luo Feng, Wang Jianxin


General General

Decision tree outcome prediction of acute acetaminophen exposure in the United States: a study of 30,000 cases from the National Poison Data System.

In Basic & clinical pharmacology & toxicology

Acetaminophen is one of the most commonly used analgesic drugs in the United States. However, the outcomes of acute acetaminophen overdose might be very serious in some cases. Therefore, prediction of the outcomes of acute acetaminophen exposure is crucial. This study is a six-year retrospective cohort study using National Poison Data System data (NPDS). A decision tree algorithm was used to determine the risk predictors of acetaminophen exposure. The decision tree model had an accuracy of 0.839, an accuracy of 0.836, a recall of 0.72, a specificity of 0.86, and an F1 _ score of 0.76 for the test group and an accuracy of 0.848, a recall of 0.85, a recall of 0.74, a specificity of 0.87 and an F1 _ score of 0.78 for the training group. Our results showed that elevated serum levels of liver enzymes, other liver function test abnormality, anorexia, acidosis, electrolyte abnormality, increased bilirubin, coagulopathy, abdominal pain, coma, increased anion gap, tachycardia, and hypotension were the most important factors in determining the outcome of acute acetaminophen exposure. Therefore, the decision tree model is a reliable approach in determining the prognosis of acetaminophen exposure cases and can be used in an emergency room or during hospitalization.

Mehrpour Omid, Saeedi Farhad, Hoyte Christopher


Acetaminophen, Decision tree, Machine learning, Poisoning

General General

Automatic cough classification for tuberculosis screening in a real-world environment.

In Physiological measurement ; h5-index 36.0

\textit{Objective:} The automatic discrimination between the coughing sounds produced by patients with tuberculosis (TB) and those produced by patients with other lung ailments. \textit{Approach:} We present experiments based on a dataset of 1358 forced cough recordings obtained in a developing-world clinic from 16 patients with confirmed active pulmonary TB and 35 patients suffering from respiratory conditions suggestive of TB but confirmed to be TB negative. Using nested cross-validation, we have trained and evaluated five machine learning classifiers: logistic regression (LR), support vector machines (SVM), k-nearest neighbour (KNN), multilayer perceptrons (MLP) and convolutional neural networks (CNN). \textit{Main Results:} Although classification is possible in all cases, the best performance is achieved using LR. In combination with feature selection by sequential forward selection (SFS), our best LR system achieves an area under the ROC curve (AUC) of 0.94 using 23 features selected from a set of 78 high-resolution mel-frequency cepstral coefficients (MFCCs). This system achieves a sensitivity of 93\% at a specificity of 95\% and thus exceeds the 90\% sensitivity at 70\% specificity specification considered by the World Health Organisation (WHO) as a minimal requirement for a community-based TB triage test. \textit{Significance:} The automatic classification of cough audio sounds, when applied to symptomatic patients requiring investigation for TB, can meet the WHO triage specifications for the identification of patients who should undergo expensive molecular downstream testing. This makes it a promising and viable means of low cost, easily deployable frontline screening for TB, which can benefit especially developing countries with a heavy TB burden.

Pahar Madhurananda, Klopper Marisa, Reeve Byron, Warren Rob, Theron Grant, Niesler Thomas R


TB, cough classification, machine learning, triage test, tuberculosis

oncology Oncology

Bisdemethoxycurcumin alleviates vandetanib-induced cutaneous toxicity in vivo and in vitro through autophagy activation.

In Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie

High incidence of cutaneous toxicity ranging from 29.2% to 71.2% has been reported during clinical use of vandetanib, which is a multi-target kinase inhibitor indicated for the treatment of unresectable medullary thyroid carcinoma. The cutaneous toxicity of vandetanib has limited its clinical benefits, but the underlying mechanisms and protective strategies are not well studied. Hence, we firstly established an in vivo model by continuously administrating vandetanib at 55 mg/kg/day to C57BL/6 for 21 days and verified that vandetanib could induce skin rash in vivo, which was consistent with the clinical study. We further cultured HaCaT and NHEK cells, the immortalized or primary human keratinocyte line, and investigated vandetanib (0-10 μM, 0-24 h)-caused alteration in cellular survival and death processes. The western blot showed that the expression level of apoptotic-related protein, c-PARP, c-Caspase 3 and Bax were increased, while the anti-apoptotic protein Bcl2 and MCL1 level were decreased. Meanwhile, vandetanib downregulated mitochondrial membrane potential which in turn caused the release of Cytochrome C, excessive production of reactive oxygen species and DNA damage. Furthermore, we found that 5 μM bisdemethoxycurcumin partially rescued vandetanib-induced mitochondria pathway-dependent keratinocyte apoptosis via activation of autophagy in vivo and in vitro, thereby ameliorated cutaneous toxicity. Conclusively, our study revealed the mechanisms of vandetanib-induced apoptosis in keratinocytes during the occurrence of cutaneous toxicity, and suggested bisdemethoxycurcumin as a potential protective drug. This work provided a potentially promising therapeutic strategy for the treatment of vandetanib-induced cutaneous toxicity.

Jin Ying, Chen Xueqin, Gao Zizheng, Shen Xiaofei, Fu Huangxi, Pan Zezheng, Yan Hao, Yang Bo, He Qiaojun, Xu Zhifei, Luo Peihua


Apoptosis, Autophagy, Bisdemethoxycurcumin, Cutaneous toxicity, Vandetanib