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1 |
The AI-discovered aetiology of COVID-19 and rationale of the irinotecan+ etoposide combination therapy for critically ill COVID-19 patients
اتیولوژی هوش مصنوعی COVID-19 و منطق درمان ترکیبی irinotecan + etoposide برای بیماران COVID-19 که به شدت بیمار هستند کشف شده است-2020 We present the AI-discovered aetiology of COVID-19, based on a precise disease model of COVID-19 built under
five weeks that best matches the epidemiological characteristics, transmission dynamics, clinical features, and
biological properties of COVID-19 and consistently explains the rapidly expanding COVID-19 literature. We
present that SARS-CoV-2 implements a unique unbiased survival strategy of balancing viral replication with viral
spread by increasing its dependence on (i) ACE2-expressing cells for viral entry and spread, (ii) PI3K signaling in
ACE2-expressing cells for viral replication and egress, and (iii) viral- non-structural-and-accessory-protein-dependent
immunomodulation to balance viral spread and viral replication. We further propose the combination of
irinotecan (an in-market topoisomerase I inhibitor) and etoposide (an in-market topoisomerase II inhibitor)
could potentially be an exceptionally effective treatment to protect critically ill patients from death caused by
COVID-19-specific cytokine storms triggered by sepsis, ARDS, and other fatal comorbidities Keywords: Aetiology | Treatment | Cytokine storm | ICU | COVID-19 | ACE2 Irinotecan | Etoposide | SARS-CoV-2 |
مقاله انگلیسی |
2 |
A patient-similarity-based model for diagnostic prediction
یک مدل مبتنی بر شباهت بیمار برای پیش بینی تشخیصی-2020 Objective: To simulate the clinical reasoning of doctors, retrieve analogous patients of an index patient automatically
and predict diagnoses by the similar/dissimilar patients.
Methods: We proposed a novel patient-similarity-based framework for diagnostic prediction, which is inspired
by the structure-mapping theory about analogy reasoning in psychology. Patient similarity is defined as the
similarity between two patients’ diagnoses sets rather than a dichotomous (absence/presence of just one disease).
The multilabel classification problem is converted to a single-value regression problem by integrating the
pairwise patients’ clinical features into a vector and taking the vector as the input and the patient similarity as
the output. In contrast to the common k-NN method which only considering the nearest neighbors, we not only
utilize similar patients (positive analogy) to generate diagnostic hypotheses, but also utilize dissimilar patients
(negative analogy) are used to reject diagnostic hypotheses.
Results: The patient-similarity-based models perform better than the one-vs-all baseline and traditional k-NN
methods. The f-1 score of positive-analogy-based prediction is 0.698, significantly higher than the scores of
baselines ranging from 0.368 to 0.661. It increases to 0.703 when the negative analogy method is applied to
modify the prediction results of positive analogy. The performance of this method is highly promising for larger
datasets.
Conclusion: The patient-similarity-based model provides diagnostic decision support that is more accurate,
generalizable, and interpretable than those of previous methods and is based on heterogeneous and incomplete
data. The model also serves as a new application for the use of clinical big data through artificial intelligence
technology. Keywords: Patient similarity | Diagnostic prediction | Analogy reasoning | Machine learning |
مقاله انگلیسی |
3 |
Distinct Pathogenic Genes Causing Intellectual Disability and Autism Exhibit a Common Neuronal Network Hyperactivity Phenotype
ژنهای پاتوژن مشخص متمایز کننده ناتوانی ذهنی و اوتیسم از فنوتیپ بیش فعالی شبکه عصبی مشترک-2020 Pathogenic mutations in either one of the epigenetic
modifiers EHMT1, MBD5, MLL3, or SMARCB1 have
been identified to be causative for Kleefstra syndrome
spectrum (KSS), a neurodevelopmental disorder
with clinical features of both intellectual disability
(ID) and autism spectrum disorder (ASD). To understand
how these variants lead to the phenotypic
convergence in KSS, we employ a loss-of-function
approach to assess neuronal network development
at the molecular, single-cell, and network activity
level. KSS-gene-deficient neuronal networks all
develop into hyperactive networks with altered
network organization and excitatory-inhibitory balance.
Interestingly, even though transcriptional
data reveal distinct regulatory mechanisms, KSS
target genes share similar functions in regulating
neuronal excitability and synaptic function, several
of which are associated with ID and ASD. Our results
show that KSS genes mainly converge at the level of
neuronal network communication, providing insights
into the pathophysiology of KSS and phenotypically
congruent disorders. |
مقاله انگلیسی |
4 |
A machine-learning-based prediction model of fistula formation after interstitial brachytherapy for locally advanced gynecological malignancies
یک مدل پیش بینی مبتنی بر یادگیری ماشینی از تشکیل فیستول پس از براکی تراپی بینابینی برای بدخیمی های ژنتیکی بومی محلی-2019 PURPOSE: External beam radiotherapy combined with interstitial brachytherapy is commonly
used to treat patients with bulky, advanced gynecologic cancer. However, the high radiation dose
needed to control the tumor may result in fistula development. There is a clinical need to identify
patients at high risk for fistula formation such that treatment may be managed to prevent this toxic
side effect. This work aims to develop a fistula prediction model framework using machine learning
based on patient, tumor, and treatment features.
METHODS AND MATERIALS: This retrospective study included 35 patients treated at our
institution using interstitial brachytherapy for various gynecological malignancies. Five patients
developed rectovaginal fistula and two developed both rectovaginal and vesicovaginal fistula. For
each patient, 31 clinical features of multiple data types were collected to develop a fistula prediction
framework. A nonlinear support vector machine was used to build the prediction model. Sequential
backward feature selection and sequential floating backward feature selection methods were used to
determine optimal feature sets. To overcome data imbalance issues, the synthetic minority oversampling
technique was used to generate synthetic fistula cases for model training.
RESULTS: Seven mixed data features were selected by both sequential backward selection and
sequential floating backward selection methods. Our prediction model using these features achieved
a high prediction accuracy, that is, 0.904 area under the curve, 97.1% sensitivity, and 88.5% specificity.
CONCLUSIONS: A machine-learningebased prediction model of fistula formation has been
developed for patients with advanced gynecological malignancies treated using interstitial brachytherapy.
This model may be clinically impactful pending refinement and validation in a larger series. Keywords: Machine learning | Support vector machine | Interstitial brachytherapy | Gynecologic cancer |
مقاله انگلیسی |
5 |
Machine Learning Models can Detect Aneurysm Rupture and Identify Clinical Features Associated with Rupture
مدلهای یادگیری ماشینی می توانند پارگی آنوریسم را تشخیص دهند و ویژگیهای بالینی مرتبط با پارگی را شناسایی کنند-2019 - BACKGROUND: Machine learning (ML) has been
increasingly used in medicine and neurosurgery. We
sought to determine whether ML models can distinguish
ruptured from unruptured aneurysms and identify features
associated with rupture.
- METHODS: We performed a retrospective review of
patients with intracranial aneurysms detected on vascular
imaging at our institution between 2002 and 2018. The
dataset was used to train 3 ML models (random forest,
linear support vector machine [SVM], and radial basis
function kernel SVM). Relative contributions of individual
predictors were derived from the linear SVM model.
- RESULTS: Complete data were available for 845 aneurysms
in 615 patients. Ruptured aneurysms (n [ 309, 37%)
were larger (mean 6.51 mm vs. 5.73 mm; P [ 0.02) and
more likely to be in the posterior circulation (20% vs.
11%; P < 0.001) than unruptured aneurysms. Area under
the receiver operating curve was 0.77 for the linear SVM,
0.78 for the radial basis function kernel SVM models, and
0.81 for the random forest model. Aneurysm location and
size were the 2 features that contributed most significantly
to the model. Posterior communicating artery,
anterior communicating artery, and posterior inferior
cerebellar artery locations were most highly associated
with rupture, whereas paraclinoid and middle cerebral
artery locations had the strongest association with
unruptured status.
-CONCLUSIONS: ML models are capable of accurately
distinguishing ruptured from unruptured aneurysms and
identifying features associated with rupture. Consistent
with prior studies, location and size show the strongest
association with aneurysm rupture. Key words : Aneurysm | Aneurysm rupture | Artificial intelligence | Machine learning | Subarachnoid hemorrhage |
مقاله انگلیسی |
6 |
Automatic staging model of heart failure based on deep learning
مدل مرحله بندی خودکار نارسایی قلبی مبتنی بر یادگیری عمیق-2019 Heart failure (HF) is a disease that is harmful to human health. Recent advances in machine learningyielded new techniques to train deep neural networks, which resulted in highly successful applica-tions in many pattern recognition tasks such as object detection and speech recognition. To improve thediagnostic accuracy of HF staging, this study evaluates the performance of deep learning-based modelson combined features for its categorization. We proposed a novel deep convolutional neural network-Recurrent neural network (CNN-RNN) model for automatic staging of heart failure diseases in real-timeand dynamically. We employed the data segmentation and data augmentation pre-processing datasetto make the classification performance of the proposed architecture better. Specifically, this paper useconvolutional neural network (CNN) as a feature extractor instead of training the entire network toextract the characteristics of the electrocardiogram (ECG) signals and form a feature set. We combine theabove feature set with other clinical features, feed the combined features to RNN for classification, andfinally obtain 5 classification results. Experiments shows that the CNN-RNN model proposed in this paperachieved an accuracy of 97.6%, the sensitivity of 96.3%, specificity of 97.4% and proportion of 97.1% fortwo seconds of ECG segments. We obtained an accuracy, sensitivity, specificity and proportion of 96.2%,96.9%, 95.7%, and 94.3% respectively for five seconds of ECG duration. The model can be used as an aid tohelp clinicians confirm their diagnosis. Keywords:Heart failure | Staging model | Deep learning | Deep CNN-RNN model |
مقاله انگلیسی |
7 |
Mining featured biomarkers associated with vascular invasion in HCC by bioinformatics analysis with TCGA RNA sequencing data
استخراج نشانگرهای زیستی مرتبط با تهاجم عروق در HCC با تجزیه و تحلیل بیوانفورماتیک با داده های توالی TCNA RNA-2019 This study aims to identify the feature genes associated with vascular invasion in hepatocellular carcinoma
(HCC). Here, the RNA sequencing data related to vascular invasion in The Cancer Genome Atlas (TCGA) database,
including 292 HCC patients with complete clinical data were included in our study as the training dataset
for construction and E-TABM-36, including 41 HCC patients with complete clinical data was used as the validation
dataset. Following data normalization, differentially expressed mRNA and copy number (CN) were selected
between with and without vascular invasion samples. A support vector machine (SVM) classifier was
constructed and validated in GSE9828 and GSE20017 datasets. Total 59 feature genes were found by the SVM
classifier. Using Cox regression analysis, three clinical features, including Patholigic T, Stage and vascular invasion
and 6 optimal prognostic genes, including ANO1, EPHX2, GFRA1, OLFM2, SERPINA10 and TKT were
significantly correlated with prognosis. A risk score formula was developed to assess the prognostic value of 6
optimal prognostic genes, which were identified to possess the most remarkable correlation with overall survival
in HCC patients. By performing in vitro experiments, we observed TKT was significantly increased, but OLFM2
was decreased in high metastatic potential HCC cell lines (SK-HEP-1 and MHCC-97 H) compared with low
metastatic potential cell line Huh7 and normal human liver cell line LO2 using western blotting analysis.
Knockdown of TKT in MHCC-97H or overexpression of OLFM2 in SK-HEP-1 significantly suppressed cell migration
and invasion using transwell assays. Our results demonstrated that TKT and OLFM2 might be novel
independent biomarkers for predicting survival based on the presence of vascular invasion in patients with HCC. Keywords: Hepatocellular carcinoma | Vascular invasion | Support vector machine | Prognosis |
مقاله انگلیسی |
8 |
Using Machine Learning to Classify Individuals With Alcohol Use Disorder Based on Treatment Seeking Status
استفاده از یادگیری ماشین برای طبقه بندی افراد مبتلا به اختلال در مصرف الکل بر اساس وضعیت به دنبال یافتن درمان-2019 Objective: The authors used a decision tree classifier to reduce neuropsychological, behavioral and laboratory
measures to a subset of measures that best predicted whether an individual with alcohol use disorder (AUD)
seeks treatment.
Method: Clinical measures (N = 178) from 778 individuals with AUD were used to construct an alternating decision tree (ADT) with 10 measures that best classified individuals as treatment or not treatment-seeking for AUD. ADTs were validated by two methods: using cross-validation and an independent dataset (N = 236). For comparison, two other machine learning techniques were used as well as two linear models. Results: The 10 measures in the ADT classifier were drinking behavior, depression and drinking-related psychological problems, as well as substance dependence. With cross-validation, the ADT classified 86% of individuals correctly. The ADT classified 78% of the independent dataset correctly. Only the simple logistic model was similar in accuracy; however, this model needed more than twice as many measures as ADT to classify at comparable accuracy. Interpretation: While there has been emphasis on understanding differences between those with AUD and controls, it is also important to understand, within those with AUD, the features associated with clinically important outcomes. Since the majority of individuals with AUD do not receive treatment, it is important to understand the clinical features associated with treatment utilization; the ADT reported here correctly classified the majority of individuals with AUD with 10 clinically relevant measures, misclassifying b7% of treatment seekers, while misclassifying 38% of non-treatment seekers. These individual clinically relevant measures can serve, potentially, as separate targets for treatment. Funding: Funding for this work was provided by the Intramural Research Programs of the National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Drug Abuse (NIDA) and the Center for Information Technology (CIT). Research in Context: Evidence Before This Study: Less than 10% of persons who meet lifetime criteria for Alcohol Use Disorder (AUD) receive treatment. As the etiology of AUD represents a complex interaction between neurobiological, social, environmental and psychological factors, low treatment utilization likely stems from barriers on multiple levels. Given this issue, it is important from both a research and clinical standpoint to determine what characteristics are associated with treatment utilization in addition to merely asking individuals if they wish to enter treatment. At the level of clinical research, if there are phenotypic differences between treatment and nontreatment-seekers that directly influence outcomes of early-phase studies, these phenotypic differences are a potential confound in assessing the utility of an experimental treatment for AUD. ... Keywords: Machine learning | Treatment utilization | Alcohol Use Disorder |
مقاله انگلیسی |
9 |
Determining relevant biomarkers for prediction of breast cancer using anthropometric and clinical features: A comparative investigation in machine learning paradigm
تعیین نشانگرهای زیستی مربوط به پیش بینی سرطان پستان با استفاده از خصوصیات آنتروپومتریک و کلینیکی: بررسی مقایسه ای در پارادایم یادگیری ماشین-2019 Early detection of breast cancer plays crucial role in planning and result of associated
treatment. The purpose of this article is threefold: (i) to investigate whether or not clinical
features obtained using routine blood analysis combined with anthropometric measure-
ments can be utilized for envisaging breast cancer using predictive machine learning
techniques; (ii) to explore the role of various machine learning components such as feature
selection, data division protocols and classification to determine suitable biomarkers for
breast cancer prediction; and (iii) to evaluate a recent database of clinical and anthropometric
measurements acquired from normal individuals and individuals suffering from breast
cancer. A database consisting of anthropometric and clinical attributes is used in the
experiments. Various feature selection and statistical significance analysis methods are
used to determine the relevance of various features. Furthermore, popular classifiers such as
kernel based support vector machine (SVM), Naïve Bayesian, linear discriminant, quadratic
discriminant, logistic regression, K-nearest neighbor (K-NN) and random forest were implemented
and evaluated for breast cancer risk prediction using these features. Results of
feature selection techniques indicate that among the nine features considered in this study,
glucose, age and resistin are found to be most relevant and effective biomarkers for breast
cancer prediction. Further, when these three features are used for classification, the medium
K-NN classifier achieves the highest classification accuracy of 92.105% followed by medium
Gaussian SVM which achieves classification accuracy of 83.684% under hold out data
division protocol. Keywords: Breast cancer biomarkers | Machine learning | Expert systems | Clinical features | Feature selection |
مقاله انگلیسی |
10 |
Systemic sclerosis: clinical features and management
اسکلروز سیستمیک: ویژگی های بالینی و مدیریت-2018 Systemic sclerosis (SSc) differs from other multisystem connective tis
sue/autoimmune diseases in that its clinical features result mainly from
a combination of fibrosis and vascular abnormality (rather than from
inflammation). This has major implications for management. SSc is
associated with high morbidity and mortality, and is often very painful
and disabling. There are two major subtypes, defined on the basis of
the extent of skin involvement: limited (often previously referred to
as CREST) and diffuse cutaneous. The two subtypes have very
different natural histories, autoantibody associations and prognoses,
and require different approaches to management, at least in their
early stages. The two most characteristic features of SSc are Ray
naud’s phenomenon (which can be very severe) and skin thickening
(ʻsclerodermaʼ). Although both cause troublesome, often disabling
symptoms, it is the internal organ involvement of the disease that
can be life-threatening. This article discusses recent advances in
early diagnosis, clinical features and the approach to investigation
and management. It is an exciting time for clinicians with an interest
in SSc, because following on from the development of new treatments
for several organ-based complications (e.g. pulmonary arterial hyper
tension, digital ulceration), several promising ʻdisease-modifyingʼ ther
apies (including antifibrotics) are currently being studied in clinical
trials.
Keywords: MRCP; pulmonary fibrosis; pulmonary hypertension; Raynaud’s phenomenon; scleroderma; scleroderma renal crisis; sys temic sclerosis |
مقاله انگلیسی |