scholarly journals Intelli-NGS: Intelligent NGS, a deep neural network-based artificial intelligence to delineate good and bad variant calls from IonTorrent sequencer data

2019 ◽  
Author(s):  
Aditya Singh ◽  
Prateek Bhatia
Keyword(s):  
Artificial Intelligence ◽  
Hospital Setting ◽  
Machine Error ◽  
Validation Data ◽  
Good Recall ◽  
Sequencing Platform ◽  
Capital Costs ◽  
Online Databases ◽  
Second Generation Sequencing ◽  
Generation Sequencing

AbstractBackgroundIonTorrent is a second-generation sequencing platform with smaller capital costs than Illumina but is also prone to higher machine error than later. Given its lower costs, the platform is generally preferred in developing countries where next-generation sequencing is still a very exclusive technique. There are many software tools available for other platforms but IonTorrent. This makes the already tricky analysis part more error-prone.MotivationWe have been using the IonTorrent platform in our hospital setting for aiding diagnosis or treatment for the past couple of years. Given to our experience, analysis part of IonTorrent data takes the longest time and still, we used to get stuck with certain variants which seemed fine on looking at their metrics but were found to be negative in Sanger sequencing verification. This made us determined to develop a tool that could aid us in reducing false positive and negative rates while still retaining good recall. The artificial intelligence-based technique was our final choice after developing pipelines with less success.MethodologyThe artificial intelligence was developed from scratch in Python 3 using TensorFlow fully connected dense layers. The model takes VCF files as input and solves each variant based on the thirty-five parameters given by the IonTorrent platform, including the flow-space information which is missed by variant callers other than the default torrent variant caller.ResultsThe final trained model was able to achieve an accuracy of 93.08% and a ROC-AUC of 0.95 with GIAB validation data. The additional program that was written to run the model annotates each variant using online databases such as dbSNP, ClinVar and others. A probability score for each outcome for each variant is also provided to aid in decision making.AvailabilityThe model and running code are available for free only for non-commercial users at https://www.github.com/aditya-88/intelli-ngs.

scholarly journals How will artificial intelligence and bioinformatics change our understanding of IgA in the next decade?

2021 ◽  
Author(s):  
Roman David Bülow ◽  
Daniel Dimitrov ◽  
Peter Boor ◽  
Julio Saez-Rodriguez
Keyword(s):  
Artificial Intelligence ◽  
Multidisciplinary Approach ◽  
Immunoglobulin A ◽  
Improve Patient Care ◽  
Integrated Analysis ◽  
Clinical Expertise ◽  
End Stage ◽  
The Complement System ◽  
Improve Patient ◽  
Generation Sequencing

AbstractIgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney’s glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN’s pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex “big data,” requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

scholarly journals Detection and Diagnosis of Breast Cancer Using Artificial Intelligence Based Assessment of Maximum Intensity Projection Dynamic Contrast-Enhanced Magnetic Resonance Images

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 330
Author(s):  
Mio Adachi ◽  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Diagnostic Performance ◽  
Maximum Intensity Projection ◽  
Breast Mri ◽  
Magnetic Resonance Images ◽  
Maximum Intensity ◽  
Validation Data ◽  
Dynamic Contrast Enhanced ◽  
Contrast Enhanced

We aimed to evaluate an artificial intelligence (AI) system that can detect and diagnose lesions of maximum intensity projection (MIP) in dynamic contrast-enhanced (DCE) breast magnetic resonance imaging (MRI). We retrospectively gathered MIPs of DCE breast MRI for training and validation data from 30 and 7 normal individuals, 49 and 20 benign cases, and 135 and 45 malignant cases, respectively. Breast lesions were indicated with a bounding box and labeled as benign or malignant by a radiologist, while the AI system was trained to detect and calculate possibilities of malignancy using RetinaNet. The AI system was analyzed using test sets of 13 normal, 20 benign, and 52 malignant cases. Four human readers also scored these test data with and without the assistance of the AI system for the possibility of a malignancy in each breast. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were 0.926, 0.828, and 0.925 for the AI system; 0.847, 0.841, and 0.884 for human readers without AI; and 0.889, 0.823, and 0.899 for human readers with AI using a cutoff value of 2%, respectively. The AI system showed better diagnostic performance compared to the human readers (p = 0.002), and because of the increased performance of human readers with the assistance of the AI system, the AUC of human readers was significantly higher with than without the AI system (p = 0.039). Our AI system showed a high performance ability in detecting and diagnosing lesions in MIPs of DCE breast MRI and increased the diagnostic performance of human readers.

The Study on the Clinical Phenotype and Function of HPRT1 Gene

2021 ◽  
Author(s):  
Miao Guo ◽  
Yucai Chen ◽  
Longlong Lin ◽  
Yilin Wang ◽  
Anqi Wang ◽  
...  
Keyword(s):  
Three Dimensional ◽  
Clinical Manifestations ◽  
Protein Translation ◽  
Dimensional Structure ◽  
Normal Individuals ◽  
Second Generation Sequencing ◽  
Self Harm ◽  
And Function ◽  
Neurogenetic Disease ◽  
Generation Sequencing

Abstract Background: Lesch-Nyhan disease (LND) is a rare x-linked purine metabolic neurogenetic disease caused by enzyme hypoxanthine-guanine phosphoriribosyltransferase(HGprt) deficiency, also known as self-destructive appearance syndrome. A series of manifestations are caused by abnormal purine metabolism. The typical clinical manifestations are hyperuricemia, growth retardation, mental retardation, short stature, dance-like athetosis, aggressive behavior, and compulsive self-harm.. Results: we identified a point mutation c.151C > T (p. Arg51*) in a pedigree. We analyzed the clinical characteristics of children in a family, and obtained the blood of their parents and siblings for second-generation sequencing. At the same time, we also analyzed and compared the expression of HPRT1 gene and predicted the three-dimensional structure of the protein. And we analyzed the clinical manifestations caused by the defect of the HPRT1 genethe mutation led to the termination of transcription at the 51st arginine, resulting in the production of truncated protein, and the relative expression of HPRT1 gene in patients was significantly lower than other family members and 10 normal individuals. Conclusion: this mutation leads to the early termination of protein translation and the formation of a truncated HPRT protein, which affects the function of the protein and generates corresponding clinical manifestations.

scholarly journals 195 The Application of Radiomics in Laryngeal Cancer: The Forgotten Cohort

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
A Rajgor ◽  
A McQueen ◽  
T Ali ◽  
E Aboagye ◽  
B Obara ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Head And Neck ◽  
Laryngeal Cancer ◽  
Prognostic Value ◽  
Head And Neck Cancers ◽  
High Dimensional ◽  
Naked Eye ◽  
Online Databases ◽  
Novel Method

Abstract Background Radiomics is a novel method of extracting data from medical images that is difficult to visualise through the naked eye. This technique transforms digital images that hold information on pathology into high-dimensional-data for analysis. Radiomics has the potential to enhance laryngeal cancer care and to date, has shown promise in various other specialties. Aim The aim of this review is to summarise the applications of this technique to laryngeal cancer and potential future benefits. Method A comprehensive systematic review-informed search of the MEDLINE and EMBASE online databases was undertaken. Keywords ‘laryngeal cancer’ OR ‘larynx’ OR ‘larynx cancer’ OR ‘head and neck cancer’ were combined with ‘radiomic’ OR ‘signature’ OR ‘machine learning’ OR ‘artificial intelligence’. Additional articles were obtained from bibliographies using the ‘snowball method’. Results Seventeen articles were identified that evaluated the role of radiomics in laryngeal cancer. Two studies affirmed the value of radiomics in improving the accuracy of staging, whilst fifteen studies highlighted the potential prognostic value of radiomics in laryngeal cancer. Twelve (of thirteen) studies incorporated an array of different head and neck cancers in the analysis and only one study assessed laryngeal cancer exclusively. Conclusions Literature to date has various limitations including, small and heterogeneous cohorts incorporating patients with head and neck cancers of distinct anatomical subsites and stages. The lack of uniform data on solely laryngeal cancer and radiomics means drawing conclusions is difficult, although these studies have affirmed its value. Further large prospective studies exclusively in laryngeal cancer are required to unlock its true potential.

scholarly journals Precision Medication of Tacrolimus Based on the Genotype by Using Second-generation Sequencing

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Yumeng Li
Keyword(s):  
Organ Transplantation ◽  
Second Generation ◽  
Low Cost ◽  
Single Nucleotide ◽  
Factors Affecting ◽  
Key Enzyme ◽  
Second Generation Sequencing ◽  
Degree Level ◽  
Generation Sequencing ◽  
Related Mortality

Organ transplantation has become a powerful strategy for the treatment of malignant diseases. Nevertheless, graft rejection is one of the main factors affecting graft survival after organ transplantation. Under this circumstance, the transplant-related mortality still keeps up. This invention includes the precise medication guidance of Tacrolimus (FK506) inapplicable population, against the side-effects of this drug. This invention, based on second-generation sequencing, has the advantages of relatively low cost and high sequencing throughput. During the design process, we collect the data of single nucleotide polymorphism (SNP) concerning the adverse drug reactions of Tacrolimus. Then we filter and summarize fifteen SNPs basing on importance degree (level >key enzyme>race). Thenceforth, after the process of analyzing the raw extract by operating BWA, Picard-tools, GATK, and Perl, we annotate SNPs by Annovar. Through this innovation, people can obtain further feedback on drugs that targets different genes in order to achieve the purpose of precision medication and minimizing the risks of misusing Tacrolimus.

scholarly journals Veno-venous extracorporeal membrane oxygenation successfully treated a case of severe pulmonary hemorrhage caused by leptospirosis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
H. J. Wang ◽  
G. Z. Chen ◽  
C. J. Zhou ◽  
Y. FU ◽  
L. N. YAO
Keyword(s):  
Extracorporeal Membrane Oxygenation ◽  
Mainland China ◽  
Pulmonary Hemorrhage ◽  
Lavage Fluid ◽  
Case Presentation ◽  
Membrane Oxygenation ◽  
Second Generation Sequencing ◽  
Very High ◽  
Generation Sequencing ◽  
Secondary Condition

Abstract Background Pulmonary hemorrhage is an important complication of leptospirosis. Once acute respiratory distress syndrome (ARDS) occurs as a secondary condition, treatment is extremely difficult and the mortality rate is very high. Case presentation The patient was a 49-year-old. He was admitted to the hospital because he had experienced a fever and cough for 4 days. Hemorrhage, respiratory failure, ARDS and other symptoms appeared soon after admission. Due to severe pulmonary hemorrhage secondary to ARDS, mechanical ventilation was performed through tracheal intubation. During intubation, the patient suffered cardiac arrest, and the patient’s condition worsened. He was confirmed to have leptospirosis through second-generation sequencing of the alveolar lavage fluid. Finally, we successfully treated the patient with penicillin as an anti-infective medication and venous-venous extracorporeal membrane oxygenation (v-vECMO). To the best of our knowledge, this report is the first to describe the successful application of ECMO in mainland China. Conclusions Leptospirosis can induce serious but transient ARDS with a better prognosis than other causes of ARDS. Our patient was successfully treated with V-vECMO.

scholarly journals Artificial intelligence method to classify ophthalmic emergency severity based on symptoms: a validation study

BMJ Open ◽  
2020 ◽  
Vol 10 (7) ◽  
pp. e037161
Author(s):  
Hyunmin Ahn
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Model Performance ◽  
Armed Forces ◽  
Validation Data ◽  
Data Set ◽  
Orange Yellow ◽  
Hospital Visits ◽  
Input Variables ◽  
Fully Connected

ObjectivesWe investigated the usefulness of machine learning artificial intelligence (AI) in classifying the severity of ophthalmic emergency for timely hospital visits.Study designThis retrospective study analysed the patients who first visited the Armed Forces Daegu Hospital between May and December 2019. General patient information, events and symptoms were input variables. Events, symptoms, diagnoses and treatments were output variables. The output variables were classified into four classes (red, orange, yellow and green, indicating immediate to no emergency cases). About 200 cases of the class-balanced validation data set were randomly selected before all training procedures. An ensemble AI model using combinations of fully connected neural networks with the synthetic minority oversampling technique algorithm was adopted.ParticipantsA total of 1681 patients were included.Major outcomesModel performance was evaluated using accuracy, precision, recall and F1 scores.ResultsThe accuracy of the model was 99.05%. The precision of each class (red, orange, yellow and green) was 100%, 98.10%, 92.73% and 100%. The recalls of each class were 100%, 100%, 98.08% and 95.33%. The F1 scores of each class were 100%, 99.04%, 95.33% and 96.00%.ConclusionsWe provided support for an AI method to classify ophthalmic emergency severity based on symptoms.

scholarly journals A case of varicella zoster encephalitis with glossopharyngeal and vagus nerve injury as primary manifestation combined with medulla lesion

2019 ◽  
Vol 47 (5) ◽  
pp. 2256-2261 ◽  
Author(s):  
Duan-Hua Cao ◽  
Ya-Nan Xie ◽  
Ye Ji ◽  
Jing-Zhe Han ◽  
Jian-Guo Zhu
Keyword(s):  
Facial Nerve ◽  
Nerve Injury ◽  
Final Diagnosis ◽  
Vagus Nerves ◽  
Human Herpes ◽  
Varicella Zoster ◽  
Human Herpes Virus ◽  
Lateral Medullary Syndrome ◽  
Second Generation Sequencing ◽  
Generation Sequencing

Varicella zoster virus (VZV) can invade the brainstem or brain via the glossopharyngeal, vagus , or facial nerve, resulting in brainstem inflammation or encephalitis. We report the case of a 66-year-old male patient with a primary manifestation of medulla injury of the glossopharyngeal and vagus nerves, combined with a medulla lesion, who was misdiagnosed with lateral medullary syndrome. Facial nerve injury and earache subsequently occurred and human herpes virus 3 (VZV) was detected by second-generation sequencing of the cerebrospinal fluid. The final diagnosis was varicella zoster encephalitis, which improved after antiviral therapy.

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