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General General

Trans-omic profiling between clinical phenoms and lipidomes among patients with different subtypes of lung cancer.

In Clinical and translational medicine

Lung cancer has high mortality, often accompanied with systemic metabolic disorders. The present study aimed at defining values of trans-nodules cross-clinical phenomic and lipidomic network layers in patients with adenocarcinoma (ADC), squamous cell carcinomas, or small cell lung cancer (SCLC). We measured plasma lipidomic profiles of lung cancer patients and found that altered lipid panels and concentrations varied among lung cancer subtypes, genders, ages, stages, metastatic status, nutritional status, and clinical phenome severity. It was shown that phosphatidylethanolamine elements (36:2, 18:0/18:2, and 18:1/18:1) were SCLC specific, whereas lysophosphatidylcholine (20:1 and 22:0 sn-position-1) and phosphatidylcholine (19:0/19:0 and 19:0/21:2) were ADC specific. There were statistically more lipids declined in male, <60 ages, late stage, metastasis, or body mass index < 22 . Clinical trans-omics analyses demonstrated that one phenome in lung cancer subtypes might be generated from multiple metabolic pathways and metabolites, whereas a metabolic pathway and metabolite could contribute to different phenomes among subtypes, although those needed to be furthermore confirmed by bigger studies including larger population of patients in multicenters. Thus, our data suggested that trans-omic profiles between clinical phenomes and lipidomes might have the value to uncover the heterogeneity of lipid metabolism among lung cancer subtypes and to screen out phenome-based lipid panels as subtype-specific biomarkers.

Zhu Zhenhua, Zhang Linlin, Lv Jiapei, Liu Xiaoxia, Wang Xiangdong


lipidomics, lung cancer, phenomes, subtypes, trans-omics

General General

Detection and segmentation of morphologically complex eukaryotic cells in fluorescence microscopy images via feature pyramid fusion.

In PLoS computational biology

Detection and segmentation of macrophage cells in fluorescence microscopy images is a challenging problem, mainly due to crowded cells, variation in shapes, and morphological complexity. We present a new deep learning approach for cell detection and segmentation that incorporates previously learned nucleus features. A novel fusion of feature pyramids for nucleus detection and segmentation with feature pyramids for cell detection and segmentation is used to improve performance on a microscopic image dataset created by us and provided for public use, containing both nucleus and cell signals. Our experimental results indicate that cell detection and segmentation performance significantly benefit from the fusion of previously learned nucleus features. The proposed feature pyramid fusion architecture clearly outperforms a state-of-the-art Mask R-CNN approach for cell detection and segmentation with relative mean average precision improvements of up to 23.88% and 23.17%, respectively.

Korfhage Nikolaus, Mühling Markus, Ringshandl Stephan, Becker Anke, Schmeck Bernd, Freisleben Bernd


General General

MultiPredGO: Deep Multi-Modal Protein Function Prediction by Amalgamating Protein Structure, Sequence, and Interaction.

In IEEE journal of biomedical and health informatics

Protein is an essential macro-nutrient for perceiving a wide range of biochemical activities in living cells. A deeper understanding of proteins and their respective functions is key to understand the biological regulations of cells. In this work, we have presented a novel multi-modal approach, named MultiPredGO, for predicting protein functions by utilizing two different kinds of information, namely protein sequence and the protein secondary structure. Here, our contributions are threefold; firstly, along with the protein sequence, we learn the feature representation from the protein structure. Secondly, we develop two different deep learning models after considering the characteristics of the underlying data patterns of the protein sequence and protein 3D structures. Finally, along with these two modalities, we have also utilized protein interaction information for expediting the efficiency of the proposed model in predicting the protein functions. For the underlying modalities, we have utilized various variations of the convolutional neural network for extracting features from them. As the protein function classes are dependent on each other, we have used a neuro-symbolic hierarchical classification model, which resembles the structure of Gene Ontology (GO), for effectively predicting the dependent protein functions. Finally, to validate the goodness of our proposed method (MultiPredGO), we have compared our results with various uni-modal along with two well-known multi-modal protein function prediction approaches, namely, INGA and DeepGO. Results show that the overall performance of the proposed approach in terms of accuracy, F-measure, precision and recall metrics are better than those by the state-of-the-art methods. MultiPredGO attains an average 13.05% and 30.87% improvements over the best existing comparing approach (DeepGO) for cellular component and molecular functions, respectively.

Giri Swagarika Jaharlal, Dutta Pratik, Halani Parth, Saha Sriparna


Public Health Public Health

Social Listening as a Rapid Approach to Collecting and Analyzing COVID-19 Symptoms and Disease Natural Histories Reported by Large Numbers of Individuals.

In Population health management

Given the severe and rapid impact of COVID-19, the pace of information sharing has been accelerated. However, traditional methods of disseminating and digesting medical information can be time-consuming and cumbersome. In a pilot study, the authors used social listening to quickly extract information from social media channels to explore what people with COVID-19 are talking about regarding symptoms and disease progression. The goal was to determine whether, by amplifying patient voices, new information could be identified that might have been missed through other sources. Two data sets from social media groups of people with or presumed to have COVID-19 were analyzed: a Facebook group poll, and conversation data from a Reddit group including detailed disease natural history-like posts. Content analysis and a customized analytics engine that incorporates machine learning and natural language processing were used to quickly identify symptoms mentioned. Key findings include more than 20 symptoms in the data sets that were not listed in online lists of symptoms from 4 respected medical information sources. The disease natural history-like posts revealed that people can experience symptoms for many weeks and that some symptoms change over time. This study demonstrates that social media can offer novel insights into patient experiences as a source of real-world data. This inductive research approach can quickly generate descriptive information that can be used to develop hypotheses and new research questions. Also, the method allows rapid assessments of large numbers of social media conversations that could be applied to monitor public health for emerging and rapidly spreading diseases such as COVID-19.

Picone Maria, Inoue Sarah, DeFelice Christopher, Naujokas Marisa F, Sinrod Jay, Cruz Vanessa A, Stapleton Jessica, Sinrod Emily, Diebel Sarah E, Wassman Edward Robert


COVID-19, content analysis, data mining, disease natural histories, social listening, social media

General General

iDPPIV-SCM: A sequence-based predictor for identifying and analyzing dipeptidyl peptidase IV (DPP-IV) inhibitory peptides using a scoring card method.

In Journal of proteome research

The inhibition of dipeptidyl peptidase IV (DPP-IV, E.C. is well recognized as a new avenue for the treatment of Type 2 diabetes (T2D). Until now, peptide-like DDP-IV inhibitors have been shown to normalize the blood glucose concentration in T2D subjects. To the best of our knowledge, there is yet no computational model for predicting and analyzing DPP-IV inhibitory peptides using sequence information. In this study, we present for the first time a simple and easily interpretable sequence-based predictor using the scoring card method (SCM) for modeling the bioactivity of DPP-IV inhibitory peptides (iDPPIV-SCM). Particularly, the iDPPIV-SCM was developed by employing the SCM method together with the propensity scores of amino acids. Rigorous independent test results demonstrated that the proposed iDPPIV-SCM was found to be superior to those of well-known machine learning (ML) classifiers (e.g. k-nearest neighbor, logistic regression and decision tree) with demonstrated improvements of 2-11%, 4-22% and 7-10% for accuracy, MCC and AUC, respectively, while also achieving comparable results to that of support vector machine. Furthermore, the analysis of estimated propensity scores of amino acids as derived from the iDPPIV-SCM was performed so as to provide a more in-depth understanding on the molecular basis for enhancing the DPP-IV inhibitory potency. Taken together, these results revealed that iDPPIV-SCM was superior to those of other well-known ML classifiers owing to its simplicity, interpretability and validity. For the convenience of biologists, the predictive model is deployed as a publicly accessible web server at It is anticipated that iDPPIV-SCM can serve as an important tool for the rapid screening of promising DPP-IV inhibitory peptides prior to their synthesis.

Charoenkwan Phasit, Kanthawong Sakawrat, Nantasenamat Chanin, Hasan Md Mehedi, Shoombuatong Watshara


General General

3D MOF Assisted Self-Polarized Ferroelectret: An Effective Auto-Powered Remote Healthcare Monitoring Approach.

In Langmuir : the ACS journal of surfaces and colloids

In recent years, flexible and sensitive pressure sensors are of extensive interest in healthcare monitoring, artificial intelligence and national security. In this context, we report the synthetic procedure of a 3D metal-organic framework (MOF) comprising with cadmium (Cd) metals as nodes and isoniazid (INH) moieties as organic linkers (CdI2-INH=CMe2) for designing self-polarized ferroelectret based highly mechano-sensitive skin sensor. The as synthesized MOF preferentially nucleate stable piezoelectric β-phase in poly(vinylidene fluoride) (PVDF) and also give rise to a porous ferroelectret composite film. Benefiting from the porous structure of 3D MOF, composite ferroelectret film based ultra-sensitive pressure sensor (mechano-sensitivity of 8.52 V/kPa within 1kPa of pressure range) as well as high throughput (e.g., power density of 32 W/cm2) mechanical energy harvester (MEH) has been designed. Simulation based finite element method (FEM) analysis indicating that geometrical stress confinement effect within the inter-pore region of the ferroelectret structure synergistically influences the mechano-electrical property of the MEH. In addition, 143 pC/N (~ 4.5 times higher than commercial piezoelectric PVDF film) of piezoelectric charge coefficient (d33) magnitude and superior response time (tr~8 ms) of this composite ferroelectret film enable to detect different physiological signals such as coughing, pronunciation, gulping behaviour makes it a promising candidate for early intervention of healthcare which may play a significant role in accurate alert of influenza and chronic obstructive pulmonary disease (COPD) related symptoms. In addition, MEH enables to track the subtle pressure change in wrist pulse that indicates its usefulness of effective mechano-sensitivity. Since the cardiovascular signal is one of the vital parameter that can determine the on-going physiological conditions, thus the wireless transmission of the detected wrist pulse signal has been demonstrated. All these features coupled with wireless data transmission indicate the promising application of MOF assisted composite ferroelectret film in non-invasive real time remote heath care monitoring.

Roy Krittish, Jana Srikanta, Ghosh Sujoy Kumar, Mahanty Biswajit, Mallick Zinnia, Sarkar Subrata, Sinha Chittaranjan, Mandal Dipankar