Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Deep Survival Analysis With Clinical Variables for COVID-19.

In IEEE journal of translational engineering in health and medicine

OBJECTIVE : Millions of people have been affected by coronavirus disease 2019 (COVID-19), which has caused millions of deaths around the world. Artificial intelligence (AI) plays an increasing role in all areas of patient care, including prognostics. This paper proposes a novel predictive model based on one dimensional convolutional neural networks (1D CNN) to use clinical variables in predicting the survival outcome of COVID-19 patients.

METHODS AND PROCEDURES : We have considered two scenarios for survival analysis, 1) uni-variate analysis using the Log-rank test and Kaplan-Meier estimator and 2) combining all clinical variables ([Formula: see text]=44) for predicting the short-term from long-term survival. We considered the random forest (RF) model as a baseline model, comparing to our proposed 1D CNN in predicting survival groups.

RESULTS : Our experiments using the univariate analysis show that nine clinical variables are significantly associated with the survival outcome with corrected p < 0.05. Our approach of 1D CNN shows a significant improvement in performance metrics compared to the RF and the state-of-the-art techniques (i.e., 1D CNN) in predicting the survival group of patients with COVID-19.

CONCLUSION : Our model has been tested using clinical variables, where the performance is found promising. The 1D CNN model could be a useful tool for detecting the risk of mortality and developing treatment plans in a timely manner.

CLINICAL IMPACT : The findings indicate that using both Heparin and Exnox for treatment is typically the most useful factor in predicting a patient's chances of survival from COVID-19. Moreover, our predictive model shows that the combination of AI and clinical data can be applied to point-of-care services through fast-learning healthcare systems.

Chaddad Ahmad, Hassan Lama, Katib Yousef, Bouridane Ahmed

2023

CNN, COVID-19, clinical variables

General General

Deep learning-based fusion of widefield diffuse optical tomography and micro-CT structural priors for accurate 3D reconstructions.

In Biomedical optics express

Widefield illumination and detection strategies leveraging structured light have enabled fast and robust probing of tissue properties over large surface areas and volumes. However, when applied to diffuse optical tomography (DOT) applications, they still require a time-consuming and expert-centric solving of an ill-posed inverse problem. Deep learning (DL) models have been recently proposed to facilitate this challenging step. Herein, we expand on a previously reported deep neural network (DNN) -based architecture (modified AUTOMAP - ModAM) for accurate and fast reconstructions of the absorption coefficient in 3D DOT based on a structured light illumination and detection scheme. Furthermore, we evaluate the improved performances when incorporating a micro-CT structural prior in the DNN-based workflow, named Z-AUTOMAP. This Z-AUTOMAP significantly improves the widefield imaging process's spatial resolution, especially in the transverse direction. The reported DL-based strategies are validated both in silico and in experimental phantom studies using spectral micro-CT priors. Overall, this is the first successful demonstration of micro-CT and DOT fusion using deep learning, greatly enhancing the prospect of rapid data-integration strategies, often demanded in challenging pre-clinical scenarios.

Nizam Navid Ibtehaj, Ochoa Marien, Smith Jason T, Intes Xavier

2023-Mar-01

General General

Automated analysis framework for in vivo cardiac ablation therapy monitoring with optical coherence tomography.

In Biomedical optics express

Radiofrequency ablation (RFA) is a minimally invasive procedure that is commonly used for the treatment of atrial fibrillation. However, it is associated with a significant risk of arrhythmia recurrence and complications owing to the lack of direct visualization of cardiac substrates and real-time feedback on ablation lesion transmurality. Within this manuscript, we present an automated deep learning framework for in vivo intracardiac optical coherence tomography (OCT) analysis of swine left atria. Our model can accurately identify cardiac substrates, monitor catheter-tissue contact stability, and assess lesion transmurality on both OCT intensity and polarization-sensitive OCT data. To the best of our knowledge, we have developed the first automatic framework for in vivo cardiac OCT analysis, which holds promise for real-time monitoring and guidance of cardiac RFA therapy..

Huang Ziyi, Zhao Xiaowei, Ziv Ohad, Laurita Kenneth R, Rollins Andrew M, Hendon Christine P

2023-Mar-01

General General

Sentinel lymph node mapping in patients with breast cancer using a photoacoustic/ultrasound dual-modality imaging system with carbon nanoparticles as the contrast agent: a pilot study.

In Biomedical optics express

Assessing the metastatic status of axillary lymph nodes is a common clinical practice in the staging of early breast cancers. Yet sentinel lymph nodes (SLNs) are the regional lymph nodes believed to be the first stop along the lymphatic drainage path of the metastasizing cancer cells. Compared to axillary lymph node dissection, sentinel lymph node biopsy (SLNB) helps reduce morbidity and side effects. Current SLNB methods, however, still have suboptimum properties, such as restrictions due to nuclide accessibility and a relatively low therapeutic efficacy when only a single contrast agent is used. To overcome these limitations, researchers have been motivated to develop a non-radioactive SLN mapping method to replace or supplement radionuclide mapping. We proposed and demonstrated a clinical procedure using a dual-modality photoacoustic (PA)/ultrasound (US) imaging system to locate the SLNs to offer surgical guidance. In our work, the high contrast of PA imaging and its specificity to SLNs were based on the accumulation of carbon nanoparticles (CNPs) in the SLNs. A machine-learning model was also trained and validated to distinguish stained SLNs based on single-wavelength PA images. In the pilot study, we imaged 11 patients in vivo, and the specimens from 13 patients were studied ex vivo. PA/US imaging identified stained SLNs in vivo without a single false positive (23 SLNs), yielding 100% specificity and 52.6% sensitivity based on the current PA imaging system. Our machine-learning model can automatically detect SLNs in real time. In the new procedure, single-wavelength PA/US imaging uses CNPs as the contrast agent. The new system can, with that contrast agent, noninvasively image SLNs with high specificity in real time based on the unique features of the SLNs in the PA images. Ultimately, we aim to use our systems and approach to substitute or supplement nuclide tracers for a non-radioactive, less invasive SLN mapping method in SLNB for the axillary staging of breast cancer.

Gu Liujie, Deng Handi, Bai Yizhou, Gao Jianpan, Wang Xuewei, Yue Tong, Luo Bin, Ma Cheng

2023-Mar-01

Pathology Pathology

Computational tools for exploring peptide-membrane interactions in gram-positive bacteria.

In Computational and structural biotechnology journal

The vital cellular functions in Gram-positive bacteria are controlled by signaling molecules known as quorum sensing peptides (QSPs), considered promising therapeutic interventions for bacterial infections. In the bacterial system QSPs bind to membrane-coupled receptors, which then auto-phosphorylate and activate intracellular response regulators. These response regulators induce target gene expression in bacteria. One of the most reliable trends in drug discovery research for virulence-associated molecular targets is the use of peptide drugs or new functionalities. In this perspective, computational methods act as auxiliary aids for biologists, where methodologies based on machine learning and in silico analysis are developed as suitable tools for target peptide identification. Therefore, the development of quick and reliable computational resources to identify or predict these QSPs along with their receptors and inhibitors is receiving considerable attention. The databases such as Quorumpeps and Quorum Sensing of Human Gut Microbes (QSHGM) provide a detailed overview of the structures and functions of QSPs. The tools and algorithms such as QSPpred, QSPred-FL, iQSP, EnsembleQS and PEPred-Suite have been used for the generic prediction of QSPs and feature representation. The availability of compiled key resources for utilizing peptide features based on amino acid composition, positional preferences, and motifs as well as structural and physicochemical properties, including biofilm inhibitory peptides, can aid in elucidating the QSP and membrane receptor interactions in infectious Gram-positive pathogens. Herein, we present a comprehensive survey of diverse computational approaches that are suitable for detecting QSPs and QS interference molecules. This review highlights the utility of these methods for developing potential biomarkers against infectious Gram-positive pathogens.

Kumar Shreya, Balaya Rex Devasahayam Arokia, Kanekar Saptami, Raju Rajesh, Prasad Thottethodi Subrahmanya Keshava, Kandasamy Richard K

2023

3-HBA, 3–Hydroxybenzoic Acid, AAC, Amino Acid Composition, ABC, ATP-binding cassette, ACD, Available Chemicals Database, AIP, Autoinducing Peptide, AMP, Anti-Microbial Peptide, ATP, Adenosine Triphosphate, Agr, Accessory gene regulator, BFE, Binding Free Energy, BIP Inhibitors, BIP, Biofilm Inhibitory Peptides, BLAST, Basic Local Alignment Search Tool, BNB, Bernoulli Naïve-Bayes, CADD, Computer-Aided Drug Design, CSP, Competence Stimulating Peptide, CTD, Composition-Transition-Distribution, D, Aspartate, DCH, 3,3′-(3,4-dichlorobenzylidene)-bis-(4-hydroxycoumarin), DT, Decision Tree, FDA, Food and Drug Administration, GBM, Gradient Boosting Machine, GDC, g-gap Dipeptide, GNB, Gaussian NB, Gram-positive bacteria, H, Histidine, H-Kinase, Histidine Kinase, H-phosphotransferase, Histidine Phosphotransferase, HAM, Hamamelitannin, HGM, Human Gut Microbiota, HNP, Human Neutrophil Peptide, IT, Information Theory Features, In silico approaches, KNN, K-Nearest Neighbors, MCC, Mathew Co-relation Coefficient, MD, Molecular Dynamics, MDR, Multiple Drug Resistance, ML, Machine Learning, MRSA, Methicillin Resistant S. aureus, MSL, Multiple Sequence Alignment, OMR, Omargliptin, OVP, Overlapping Property Features, PCP, Physicochemical Properties, PDB, Protein Data Bank, PPIs, Protein-Protein Interactions, PSM, Phenol-Soluble Modulin, PTM, Post Translational Modification, QS, Quorum Sensing, QSCN, QS communication network, QSHGM, Quorum Sensing of Human Gut Microbes, QSI, QS Inhibitors, QSIM, QS Interference Molecules, QSP inhibitors, QSP predictors, QSP, QS Peptides, QSPR, Quantitative Structure Property Relationship, Quorum sensing peptides, RAP, RNAIII-activating protein, RF, Random Forest, RIP, RNAIII-inhibiting peptide, ROC, Receiver Operating Characteristic, SAR, Structure-Activity Relationship, SFS, Sequential Forward Search, SIT, Sitagliptin, SVM, Support Vector Machine, TCS, Two-Component Sensory, TRAP, Target of RAP, TRG, Trelagliptin, WHO, World Health Organization, mRMR, minimum Redundancy and Maximum Relevance

General General

BrainGENIE: The Brain Gene Expression and Network Imputation Engine.

In Translational psychiatry ; h5-index 60.0

In vivo experimental analysis of human brain tissue poses substantial challenges and ethical concerns. To address this problem, we developed a computational method called the Brain Gene Expression and Network-Imputation Engine (BrainGENIE) that leverages peripheral-blood transcriptomes to predict brain tissue-specific gene-expression levels. Paired blood-brain transcriptomic data collected by the Genotype-Tissue Expression (GTEx) Project was used to train BrainGENIE models to predict gene-expression levels in ten distinct brain regions using whole-blood gene-expression profiles. The performance of BrainGENIE was compared to PrediXcan, a popular method for imputing gene expression levels from genotypes. BrainGENIE significantly predicted brain tissue-specific expression levels for 2947-11,816 genes (false-discovery rate-adjusted p < 0.05), including many transcripts that cannot be predicted significantly by a transcriptome-imputation method such as PrediXcan. BrainGENIE recapitulated measured diagnosis-related gene-expression changes in the brain for autism, bipolar disorder, and schizophrenia better than direct correlations from blood and predictions from PrediXcan. We developed a convenient software toolset for deploying BrainGENIE, and provide recommendations for how best to implement models. BrainGENIE complements and, in some ways, outperforms existing transcriptome-imputation tools, providing biologically meaningful predictions and opening new research avenues.

Hess Jonathan L, Quinn Thomas P, Zhang Chunling, Hearn Gentry C, Chen Samuel, Kong Sek Won, Cairns Murray, Tsuang Ming T, Faraone Stephen V, Glatt Stephen J

2023-Mar-22