Fractional Dynamics Foster Deep Learning of COPD Stage Prediction

Chronic obstructive pulmonary disease (COPD) is one of the leading causes of death worldwide. Current COPD diagnosis (i.e., spirometry) could be unreliable because the test depends on an adequate effort from the tester and testee. Moreover, the early diagnosis of COPD is challenging. The authors address COPD detection by constructing two novel physiological signals datasets (4432 records from 54 patients in the WestRo COPD dataset and 13824 medical records from 534 patients in the WestRo Porti COPD dataset). The authors demonstrate their complex coupled fractal dynamical characteristics and perform a fractional-order dynamics deep learning analysis to diagnose COPD. The authors found that the fractional-order dynamical modeling can extract distinguishing signatures from the physiological signals across patients with all COPD stages-from stage 0 (healthy) to stage 4 (very severe). They use the fractional signatures to develop and train a deep neural network that predicts COPD stages based on the input features (such as thorax breathing effort, respiratory rate, or oxygen saturation). The authors show that the fractional dynamic deep learning model (FDDLM) achieves a COPD prediction accuracy of 98.66% and can serve as a robust alternative to spirometry. The FDDLM also has high accuracy when validated on a dataset with different physiological signals.

The curse and blessing of abundance-the evolution of drug interaction databases and their impact on drug network analysis

BACKGROUND

Widespread bioinformatics applications such as drug repositioning or drug-drug interaction prediction rely on the recent advances in machine learning, complex network science, and comprehensive drug datasets comprising the latest research results in molecular biology, biochemistry, or pharmacology. The problem is that there is much uncertainty in these drug datasets-we know the drug-drug or drug-target interactions reported in the research papers, but we cannot know if the not reported interactions are absent or yet to be discovered. This uncertainty hampers the accuracy of such bioinformatics applications.

RESULTS

We use complex network statistics tools and simulations of randomly inserted previously unaccounted interactions in drug-drug and drug-target interaction networks-built with data from DrugBank versions released over the plast decade-to investigate whether the abundance of new research data (included in the latest dataset versions) mitigates the uncertainty issue. Our results show that the drug-drug interaction networks built with the latest dataset versions become very dense and, therefore, almost impossible to analyze with conventional complex network methods. On the other hand, for the latest drug database versions, drug-target networks still include much uncertainty; however, the robustness of complex network analysis methods slightly improves.

CONCLUSIONS

Our big data analysis results pinpoint future research directions to improve the quality and practicality of drug databases for bioinformatics applications: benchmarking for drug-target interaction prediction and drug-drug interaction severity standardization.

Graph coloring using the reduced quantum genetic algorithm

Genetic algorithms (GA) are computational methods for solving optimization problems inspired by natural selection. Because we can simulate the quantum circuits that implement GA in different highly configurable noise models and even run GA on actual quantum computers, we can analyze this class of heuristic methods in the quantum context for NP-hard problems. This paper proposes an instantiation of the Reduced Quantum Genetic Algorithm (RQGA) that solves the NP-hard graph coloring problem in O(N^1/2). The proposed implementation solves both vertex and edge coloring and can also determine the chromatic number (i.e., the minimum number of colors required to color the graph). We examine the results, analyze the algorithm convergence, and measure the algorithm's performance using the Qiskit simulation environment. Our Reduced Quantum Genetic Algorithm (RQGA) circuit implementation and the graph coloring results show that quantum heuristics can tackle complex computational problems more efficiently than their conventional counterparts.

Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug-Gene Interactions

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug-gene interaction data. We obtained drug-gene interaction data from an earlier version of DrugBank, built a drug-gene interaction network, and projected it as a drug-drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug-gene interaction data to generate a comprehensive drug repurposing hint list.

New Investigations with Lupane Type A-Ring Azepane Triterpenoids for Antimycobacterial Drug Candidate Design

Twenty lupane type A-ring azepano-triterpenoids were synthesized from betulin and its related derivatives and their antitubercular activity against Mycobacterium tuberculosis, mono-resistant MTB strains, and nontuberculous strains Mycobacterium abscessus and Mycobacterium avium were investigated in the framework of AToMIc (Anti-mycobacterial Target or Mechanism Identification Contract) realized by the Division of Microbiology and Infectious Diseases, NIAID, National Institute of Health. Of all the tested triterpenoids, 17 compounds showed antitubercular activity and 6 compounds were highly active on the H37Rv wild strain (with MIC 0.5 µM for compound 7), out of which 4 derivatives also emerged as highly active compounds on the three mono-resistant MTB strains. Molecular docking corroborated with a machine learning drug-drug similarity algorithm revealed that azepano-triterpenoids have a rifampicin-like antitubercular activity, with compound 7 scoring the highest as a potential M. tuberculosis RNAP potential inhibitor. FIC testing demonstrated an additive effect of compound 7 when combined with rifampin, isoniazid and ethambutol. Most compounds were highly active against M. avium with compound 14 recording the same MIC value as the control rifampicin (0.0625 µM). The antitubercular ex vivo effectiveness of the tested compounds on THP-1 infected macrophages is correlated with their increased cell permeability. The tested triterpenoids also exhibit low cytotoxicity and do not induce antibacterial resistance in MTB strains.

Gender Phenotyping of Patients with Obstructive Sleep Apnea Syndrome Using a Network Science Approach

We defined gender-specific phenotypes for men and women diagnosed with obstructive sleep apnea syndrome (OSAS) based on easy-to-measure anthropometric parameters, using a network science approach. We collected data from 2796 consecutive patients since 2005, from 4 sleep laboratories in Western Romania, recording sleep, breathing, and anthropometric measurements. For both genders, we created specific apnea patient networks defined by patient compatibility relationships in terms of age, body mass index (BMI), neck circumference (NC), blood pressure (BP), and Epworth sleepiness score (ESS). We classified the patients with clustering algorithms, then statistically analyzed the groups/clusters. Our study uncovered eight phenotypes for each gender. We found that all males with OSAS have a large NC, followed by daytime sleepiness and high BP or obesity. Furthermore, all unique female phenotypes have high BP, followed by obesity and sleepiness. We uncovered gender-related differences in terms of associated OSAS parameters. In males, we defined the pattern large NC–sleepiness–high BP as an OSAS predictor, while in women, we found the pattern of high BP–obesity–sleepiness. These insights are useful for increasing awareness, improving diagnosis, and treatment response.

MicroRNAs mediate liver transcriptome changes upon soy diet intervention in mice

Soy‐based diets have triggered the interest of the research community due to their beneficial effects on a wide variety of pathologies like breast and prostate cancer, diabetes and atherosclerosis. However, the molecular details underlying these effects are far from being completely understood; several recent attempts have been made to elucidate the soy‐induced liver transcriptome changes in different animal models. Here we used Next Generation Sequencing to identify a set of microRNAs specifically modulated by short‐term soy‐enriched diet in young male mice and estimated their impact on the liver transcriptome assessed by microarray. Clustering and topological community detection (CTCD) network analysis of STRING generated interactions of transcriptome data led to the identification of four topological communities of genes characteristically altered and putatively targeted by microRNAs upon soy diet intervention.

SAS score: Targeting high-specificity for efficient population-wide monitoring of obstructive sleep apnea

Proposal

This paper investigates a novel screening tool for Obstructive Sleep Apnea Syndrome (OSAS), which aims at efficient population-wide monitoring. To this end, we introduce SAS_score which provides better OSAS prediction specificity while maintaining a high sensitivity.

Methods

We process a cohort of 2595 patients from 4 sleep laboratories in Western Romania, by recording over 100 sleep, breathing, and anthropometric measurements per patient; using this data, we compare our SAS_score with state of the art scores STOP-Bang and NoSAS through area under curve (AUC), sensitivity, specificity, negative predictive value (NPV), and positive predictive value (PPV). We also evaluate the performance of SAS_score by considering different Apnea–Hypopnea Index (AHI) diagnosis cut-off points and show that custom refinements are possible by changing the score’s threshold.

Results

SAS_score takes decimal values within the interval (2, 7) and varies linearly with AHI; it is based on standardized measures for BMI, neck circumference, systolic blood pressure and Epworth score. By applying the STOP-Bang and NoSAS questionnaires, as well as the SAS_score on the patient cohort, we respectively obtain the AUC values of 0.69 (95% CI 0.66-0.73, p < 0.001), 0.66 (95% CI 0.63-0.68, p < 0.001), and 0.73 (95% CI 0.71-0.75, p < 0.001), with sensitivities values of 0.968, 0.901, 0.829, and specificity values of 0.149, 0.294, 0.359, respectively. Additionally, we cross-validate our score with a second independent cohort of 231 patients confirming the high specificity and good sensitivity of our score. When raising SAS_score’s diagnosis cut-off point from 3 to 3.7, both sensitivity and specificity become roughly 0.6.

Conclusions

In comparison with the existing scores, SAS_score is a more appropriate screening tool for monitoring large populations, due to its improved specificity. Our score can be tailored to increase either sensitivity or specificity, while balancing the AUC value.

Weighted Betweenness Preferential Attachment: A New Mechanism Explaining Social Network Formation and Evolution

The dynamics of social networks is a complex process, as there are many factors which contribute to the formation and evolution of social links. While certain real-world properties are captured by the degree-driven preferential attachment model, it still cannot fully explain social network dynamics. Indeed, important properties such as dynamic community formation, link weight evolution, or degree saturation cannot be completely and simultaneously described by state of the art models. In this paper, we explore the distribution of social network parameters and centralities and argue that node degree is not the main attractor of new social links. Consequently, as node betweenness proves to be paramount to attracting new links - as well as strengthening existing links -, we propose the new Weighted Betweenness Preferential Attachment (WBPA) model, which renders quantitatively robust results on realistic network metrics. Moreover, we support our WBPA model with a socio-psychological interpretation, that offers a deeper understanding of the mechanics behind social network dynamics.

Network science meets respiratory medicine for OSAS phenotyping and severity prediction

Obstructive sleep apnea syndrome (OSAS) is a common clinical condition. The way that OSAS risk factors associate and converge is not a random process. As such, defining OSAS phenotypes fosters personalized patient management and population screening. In this paper, we present a network-based observational, retrospective study on a cohort of 1,371 consecutive OSAS patients and 611 non-OSAS control patients in order to explore the risk factor associations and their correlation with OSAS comorbidities. To this end, we construct the Apnea Patients Network (APN) using patient compatibility relationships according to six objective parameters: age, gender, body mass index (BMI), blood pressure (BP), neck circumference (NC) and the Epworth sleepiness score (ESS). By running targeted network clustering algorithms, we identify eight patient phenotypes and corroborate them with the co-morbidity types. Also, by employing machine learning on the uncovered phenotypes, we derive a classification tree and introduce a computational framework which render the Sleep Apnea Syndrome Score (SAS_Score); our OSAS score is implemented as an easy-to-use, web-based computer program which requires less than one minute for processing one individual. Our evaluation, performed on a distinct validation database with 231 consecutive patients, reveals that OSAS prediction with SAS_Score has a significant specificity improvement (an increase of 234%) for only 8.2% sensitivity decrease in comparison with the state-of-the-art score STOP-BANG. The fact that SAS_Score has bigger specificity makes it appropriate for OSAS screening and risk prediction in big, general populations.

Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection algorithms, we link the network clusters to 9 relevant pharmacological properties. Out of the 1141 drugs from the DrugBank 4.1 database, our extensive literature survey and cross-checking with other databases such as Drugs.com, RxList, and DrugBank 4.3 confirm the predicted properties for 85% of the drugs. As such, we argue that network analysis offers a high-level grasp on a wide area of pharmacological aspects, indicating possible unaccounted interactions and missing pharmacological properties that can lead to drug repositioning for the 15% drugs which seem to be inconsistent with the predicted property. Also, by using network centralities, we can rank drugs according to their interaction potential for both simple and complex multi-pathology therapies. Moreover, our clustering approach can be extended for applications such as analyzing drug-target interactions or phenotyping patients in personalized medicine applications.

Obesity in association with Sleep Apnea Syndrome as predictor for coronary-vascular comorbidities

BACKGROUND AND AIMS

Sleep apnea syndrome (SAS) is a common disorder with growing awareness. We sought to evaluate if the presence of obesity in patients with SAS is associated with a high risk for development of coronary-vascular comorbidities.

METHODS

We performed a retrospective study that included 1370 patients (30.3% female and 69.7% male) diagnosed with SAS from May 2005 to May 2012. The collected data included body mass index (BMI), waist/ hip ratio, abdominal, neck, hip circumference and Epworth Sleepiness Scale. The positive diagnostic of SAS was based on apnea-hypopnea index (AHI) provided by polysomnography, and patient comorbidities were obtained from the sleep laboratory records.

RESULTS

From the total of 1370 patients, 989 (72%) had grade I to III obesity, 305 (22%) were overweight and only 76 (6%) had a normal weight. Cardiovascular comorbidities were presented in 60.6% of patients, with coronary disease ranking first (34.2%) followed by heart failure (22.6%) and stroke (3.8%). The predictors for cardiovascular comorbidities were coronary disease (OR 2.1, 95% Cl 1.20-3.39, p = 0.0063), heart failure (OR 3.44, 95% Cl 1.60-7.74, p < 0.001) but not stroke (OR 2.3 95% Cl 0.57-13.84, p = 0.357). Analyzing the polysomnography parameters we found a strong correlation for AHI (p < 0.0001), oxygen desaturation index (p < 0.0001) and mean average oxyhaemoglobin saturation (p < 0.0001).

CONCLUSIONS

Overweight and obese patients with SAS have a poor outcome, being at high risk of developing other comorbidities like coronary disease and heart failure.

A high-availability architecture for continuous monitoring of sleep disorders

We present a complete technical solution for continuously monitoring vital signs required for observing sleep apnoea events, one of the major sleep respiratory disorders. Based on industry accepted medical devices, we developed a GSM-based remote data acquisition and transfer module that is integrated via a set of web services into the server side of the application. The back-end is responsible with aggregating all the data, and, based on machine learning techniques, it provides a first level of filtering in order to warn about possible abnormalities. The proposed solution is currently under the test phase at the "Victor Babes" Hospital in Timisoara, Romania.