Introduced the ITGB1-DT as a novel biomarker associated with five potential drugs using bioinformatics analysis of breast cancer proteomics data and RT-PCR

BACKGROUND

Breast cancer (BC) has been identified as a significant contributor to the rising number of female cancer deaths. As, it has become clear that breast cancer development depends on the interplay of several biological factors against a single molecule. This research aimed to use proteomics to gain a regulatory and metabolic understanding of BC pathophysiology.

METHOD

For the study, a breast cancer proteomics dataset was downloaded from ProteomeXchange and then analyzed by employing MaxQuant and Perseus. Functional enrichment analysis through Metascape and Cytoscape software showed DEPs related biomedical phenomena with potential abruption. The expression of selected lncRNA in terms of the highest connectivity parameters was then quantitatively assessed through RT-PCR in 30 tumor tissues of breast cancer patients, as compared to the adjacent healthy ones.

RESULT

The results indicated that among the 3048 identified proteins, 1149 were differentially expressed, which could be mainly enriched in several key terms. Furthermore, the obtained findings revealed that ITGB1-DT was significantly overexpressed in tumor tissues. Moreover, we found five potential compounds that could be attributed to ITGB1-DT targets (ATN-161, Firategrast, SB-683698, dabigatran-etexilate, and tranexamic-acid).

CONCLUSION

These analyses proposed that ITGB1-DT could be employed as a differentiated factor to identify breast tumor tissues in healthy samples. Besides this, Firategrast could be introduced as a potential remedial agent for breast cancer patients. Overall, from the analysis of a proteomics dataset, an integrative map was generated, and a novel biomarker that may have been implicated in the early detection of BC was introduced.

The nitrate uptake and growth of wheat were more inhibited under single-layer graphene oxide stress compared to multi-layer graphene oxide

Although the phytotoxicity of graphene-based materials has been investigated extensively, the effects of different graphene-based materials on nutrient uptake in plants remain unclear. Here, we analyzed the differences in phytotoxicity between single-layer graphene oxide (sGO) and multi-layer graphene oxide (mGO) by analyzing the growth status and nitrate (NO₃^-) accumulation in wheat plants at 0, 100, 200, 400, and 800 mg L^-1 graphene oxide supply. Both sGO and mGO displayed concentration-dependent inhibitory effects on biomass, root length, number of lateral roots, and nitrogen (N) nutrient status. Treatment with 400 mg L^-1 sGO caused 0.9-, 1.3-, and 1-fold higher reductions in NO₃^--N, assimilated N, and total N concentrations in roots, respectively, than mGO treatment. Analysis of root oxidative stress and in situ NO₃^- uptake revealed that sGO caused more significant damage to the root tip and a lower NO₃^- net influx rate than mGO. In addition, the expression of NO₃^- transporter (NRT) genes in roots, including NRT1.5, NRT2.1, NRT2.2, NRT2.3, and NRT2.4, under sGO treatment were lower than those under mGO treatment. Overall, sGO treatment induced a more severe inhibitory effect on root growth and NO₃^- uptake and accumulation than mGO treatment, accompanied by significant suppression of the expression of NRTs in sGO-treated roots. This study provides a physiological and molecular basis for studying the phytotoxic effects of various sizes of graphene oxide.

A multi-layer network model to assess school opening policies during a vaccination campaign: a case study on COVID-19 in France

We propose a multi-layer network model for the spread of an infectious disease that accounts for interactions within the family, between children in classes and schools, and casual contacts in the population. The proposed framework is designed to test several what-if scenarios on school openings during the vaccination campaigns, thereby assessing the safety of different policies, including testing practices in schools, diverse home-isolation policies, and targeted vaccination. We demonstrate the potentialities of our model by calibrating it on epidemiological and demographic data of the spring 2021 COVID-19 vaccination campaign in France. Specifically, we consider scenarios in which a fraction of the population is vaccinated, and we focus our analysis on the role of schools as drivers of the contagions and on the implementation of targeted intervention policies oriented to children and their families. We perform our analysis by means of a campaign of Monte Carlo simulations. Our findings suggest that transmission in schools may play a key role in the spreading of a disease. Interestingly, we show that children's testing might be an important tool to flatten the epidemic curve, in particular when combined with enacting temporary online education for classes in which infected students are detected. Finally, we test a vaccination strategy that prioritizes the members of large families and we demonstrate its good performance. We believe that our modeling framework and our findings could be of help for public health authorities for planning their current and future interventions, as well as to increase preparedness for future epidemic outbreaks.

Fabrication of a multi-layered decellularized amniotic membranes as tissue engineering constructs

As a promising approach in tissue engineering, decellularization has become one of the mostly-studied research areas in tissue engineering thanks to its potential to bring about several advantages over synthetic materials since it can provide a 3-dimensional ECM structure with matching biomechanical properties of the target tissue. Amniotic membranes are the tissues that nurture the embryos during labor. Similarly, these materials have also been proposed for tissue regeneration in several applications. The main drawback in using amniotic membranes is the limited thickness of these materials since most tissues require a 3D matrix for an enhance regeneration. In order to prevent this limitation, here we report a facile fabrication methodology for multilayered amniotic membrane-based tissue constructs. The amniotic membranes of Wistar albino rats were first decellularized with the physical and chemical methods and utilized as scaffolds. Secondly, the prepared decellularized membranes were sutured to form a multilayered 3D structure. Within the study, 7 groups including control (PBS), were prepared based on physical and chemical decellularization methods. UV exposure and freezing techniques were used as a physical decellularization methods while hypertonic medium and SDS (sodium dodecyl sulfate) protocols were used as chemical decellularization methods. The combinations of both protocols were also used. In groups, A was the control and group B was applied just UV. In group C was applied UV and freezing. In addition to UV and freezing, in group D was applied hypertonic solution while group E was applied SDS (0.03 %). In group F was applied UV, freezing, hypertonic solution and SDS (0.03 %). In group G was applied UV, hypertonic solution, SDS (0.03 %) and freezing, respectively. Based on the histological and quantitative analyses, F and G groups were found as the most efficient decellularization protocols in rat amniotic membranes. Then, group F and G decellularized amniotic membranes were used to form scaffolds and thus-formed matrices were further characterized in vitro cell culture studies and mechanical tests. Cytotoxicity analyses performed using MTT showed a good cell viability in F and G groups scaffolds. The percentage viability rate was higher in G group (81.3 %) compared to F (75.33 %) and also cell viability in G group was found more meaningful according to p value which was obtained 0.007. Cellular adhesions after in vitro cell culture and morphology of scaffolds were evaluated by scanning electron microscopy (SEM). It was observed that the cells cultivated in equal amounts of tissue scaffolds were higher in the F compared to that observed in group G. The mechanical testing with 40 N force revealed 0.77 mm displacement in group F while it was 0.75 mm in group G. Moreover, according to force-controlled test, 2.9 mm displacement of F group and 1.2 mm displacement of G group was measured. As a result, this study shows that the multilayered decellularized amniotic membrane scaffolds support cell survival and adhesion and can form a flexible biomaterial with desired handling properties.

Effect of adhesive application method on repair bond strength of composite

Objectives

This study aimed to evaluate the effect of the application method of universal adhesives on the shear bond strength (SBS) of repaired composites, applied with different thicknesses.

Materials and Methods

The 84 specimens (Filtek Z350 XT) were prepared, stored in distilled water for a week and thermocycled (5,000 cycles, 5°C to 55°C). They were roughened using 400-grit sandpapers and etched with phosphoric acid. Then, specimens were equally divided into 2 groups; Single Bond Universal (SU) and Prime&Bond Universal (PB). Each group was subdivided into 3 subgroups according to application methods (n = 14); UC: 1 coat + uncuring, 1C: 1 coat + curing, 3C: 3 coats + curing. After storage of the repaired composite for 24 hours, specimens were subjected to the SBS test and the data were statistically analyzed by 2-way analysis of variance and independent t-tests. Specimens were examined with a stereomicroscope to analyze fracture mode and a scanning electron microscope to observe the interface.

Results

Adhesive material was a significant factor (p = 0.001). Bond strengths with SU were higher than PB. The highest strength was obtained from the 1C group with SU. Bonding in multiple layers increased adhesive thicknesses, but there was no significant difference in SBS values (p = 0.255). Failure mode was predominantly cohesive in old composites.

Conclusions

The application of an adequate bonding system plays an important role in repairing composite resin. SU showed higher SBS than PB and the additional layers increased the adhesive thickness without affecting SBS.

Multi-layer cell-free scaffolds for osteochondral defects of the knee: a systematic review and meta-analysis of clinical evidence

Purpose

The aim of this study was to analyze the clinical results provided by multi-layer cell-free scaffolds for the treatment of knee osteochondral defects.

Methods

A systematic review was performed on PubMed, Web of Science, and Cochrane to identify studies evaluating the clinical efficacy of cell-free osteochondral scaffolds for knee lesions. A meta-analysis was performed on articles reporting results of the International Knee Documentation Committee (IKDC) and Tegner scores. The scores were analyzed as improvement from baseline to 1, 2, and ≥ 3 years of follow-up. The modified Coleman Methodology Score was used to assess the study methodology.

Results

A total of 34 studies (1022 patients) with a mean follow-up of 35 months was included. Only three osteochondral scaffolds have been investigated in clinical trials: while TruFit® has been withdrawn from the market for the questionable results, the analysis of MaioRegen and Agili-C™ provided clinical improvements at 1, 2, and ≥ 3 years of follow-up (all significantly higher than the baseline, p < 0.05), although with a limited recovery of the sport-activity level. A low rate of adverse events and an overall failure rate of 7.0% were observed, but the overall evidence level of the available studies is limited.

Conclusions

Multi-layer scaffolds may provide clinical benefits for the treatment of knee osteochondral lesions at short- and mid-term follow-up and with a low number of failures, although the sport-activity level obtained seems to be limited. Further research with high-level studies is needed to confirm the role of multi-layer scaffold for the treatment of knee osteochondral lesions.

Graphene-based THz absorber: adjustability via multiple gate biasing

A multi-layer absorber using multi-bias arrays of graphene is proposed. The design methodology using the equivalent circuit model and matching concept is described. A heavy optimization process is performed to optimize bias values for different functionality. Leveraging, increased control parameters due to multi-bias scheme and simple circuit model representation, two operational modes are achieved as wide-band and multi-band absorption. The proposed absorber can perfectly absorb THz incident waves between 5.2 ​THz−6.3 ​THz in wide-band mode while shows perfect absorption response in 5.3 ​THz,7.5 ​THz, ​8 ​THz, ​8.5 ​THz, ​9 ​THz,and 9.5 ​THzin multi-band operational mode. Besides, response dependency to layers thicknesses, electron relaxation time, chemical potentials, and incident angle are reported to express acceptable sensitivity of the device. Such a reconfigurable absorber is in demand for several applications, ranging from medical imaging to indoor communication.

Integrative statistical analyses of multiple liquid biopsy analytes in metastatic breast cancer

Background

Single liquid biopsy analytes (LBAs) have been utilized for therapy selection in metastatic breast cancer (MBC). We performed integrative statistical analyses to examine the clinical relevance of using multiple LBAs: matched circulating tumor cell (CTC) mRNA, CTC genomic DNA (gDNA), extracellular vesicle (EV) mRNA, and cell-free DNA (cfDNA).

Methods

Blood was drawn from 26 hormone receptor-positive, HER2-negative MBC patients. CTC mRNA and EV mRNA were analyzed using a multi-marker qPCR. Plasma from CTC-depleted blood was utilized for cfDNA isolation. gDNA from CTCs was isolated from mRNA-depleted CTC lysates. CTC gDNA and cfDNA were analyzed by targeted sequencing. Hierarchical clustering was performed within each analyte, and its results were combined into a score termed Evaluation of multiple Liquid biopsy analytes In Metastatic breast cancer patients All from one blood sample (ELIMA.score), which calculates the contribution of each analyte to the overall survival prediction. Singular value decomposition (SVD), mutual information calculation, k-means clustering, and graph-theoretic analysis were conducted to elucidate the dependence between individual analytes.

Results

A combination of two/three/four LBAs increased the prevalence of patients with actionable signals. Aggregating the results of hierarchical clustering of individual LBAs into the ELIMA.score resulted in a highly significant correlation with overall survival, thereby bolstering evidence for the additive value of using multiple LBAs. Computation of mutual information indicated that none of the LBAs is independent of the others, but the ability of a single LBA to describe the others is rather limited—only CTC gDNA could partially describe the other three LBAs. SVD revealed that the strongest singular vectors originate from all four LBAs, but a majority originated from CTC gDNA. After k-means clustering of patients based on parameters of all four LBAs, the graph-theoretic analysis revealed CTC ERBB2 variants only in patients belonging to one particular cluster.

Conclusions

The additional benefits of using all four LBAs were objectively demonstrated in this pilot study, which also indicated a relative dominance of CTC gDNA over the other LBAs. Consequently, a multi-parametric liquid biopsy approach deconvolutes the genomic and transcriptomic complexity and should be considered in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-021-00902-1.

Titanium mesh-reinforced calcium sulfate for structural bone grafts

Calcium sulfate (CS) possesses many of the requirements for an ideal bone graft material: it is biodegradable, biocompatible, and osteoconductive. However, its relatively low strength and brittleness are major obstacles to its use as a structural bone implant. Although the strength of CS can be improved by reducing porosity, its brittleness remains a major obstacle towards its use as bone graft. Here we combine two powerful toughening strategies which are found in advanced ceramics and in natural bone: Multi-layered architectures and ductile reinforcements. We first used stress analysis and micromechanics to generate design guidelines that ensure the proper failure sequence and maximize properties. We then fabricated and tested fully dense CS by hydrostatic compression layered with layers of titanium woven mesh. Flexural experiments in hydrated conditions confirmed that the ductility and strength of titanium and the adhesion at the titanium-CS interfaces (controlled by the size of the Ti mesh) are critical factors in the mechanical performance of the composite. Our best design exhibited a toughness 180 times larger than that of plain CS, together with a 46% increase in strength.

Effective analysis of degree of polymerization of polysialic acids in mass spectrometry by combining novel sample preparation and dynamic instrument optimization methods

This work demonstrates a mass spectrometry technique to improve data reliability when analyzing degree of polymerization (DP) of high-mass polysialic acids (PSAs). Matrix-assisted laser-desorption/ionization (MALDI) time-of-flight mass spectrometry is the technique of choice for analyzing large molecules due to its wide mass working range; however, the observed DP of PSAs using such an instrument is unreliable owing to sensitivity bias towards low-mass ions. A multi-layer MALDI sample preparation protocol is demonstrated in the current study to improve PSA sensitivity, and a dynamic instrument optimization method (DIOM) is employed to minimize detector saturation over a wide mass range. The DP information obtained in the DIOM combines a series of mass spectral data obtained with individually optimized instrument parameters to minimize the problem of sensitivity bias in respective mass ranges. The resultant mass spectra facilitate unambiguous determination of DP in the high-mass range due to significantly improved spectral quality. The main instrument parameters involved in the optimization process include extraction delay in MALDI ion source as well as the cutoff mass of the ion detector. In comparison to conventional methods, the DIOM doubles the maximum DP that can be unambiguously identified by mass spectrometry.

Prolonging food shelf-life by dual actives release from multi-layered polymer particles

Biodegradable polymer based 'controlled release packaging' technology has ability to release packaging actives in controlled manner to prolong the food shelf-life. Currently available systems are not sufficiently capable of releasing multiple actives in sustainable fashion. Hence, the purpose of this study was to develop dual actives (antioxidant and antibacterial) loaded multilayered microparticles in one step and to release them at rates suitable for long-term inhibition of bacterial growth as well as lipid oxidation in food. In order to achieve this goal, 2 kinds of multilayered polymer particles made up of PLLA (Poly(l-lactic acid)) and PLGA (Poly(dl-lactic-co-glycolic acid) with varying viscosity were developed using emulsion solvent evaporation method. Surprisingly, low viscous PLGA resulted tri-layered particles (PLGA/PLLA/PLGA: shell/middle/core) instead of bi-layered (PLGA/PLLA: shell/core) particles as observed for high viscous PLGA. The mechanism of formation of tri-layered particles was investigated in detail. The outermost layer consisted of relatively more hydrophilic polymer PLGA along with benzoic acid (antibacterial) and the inner core comprised of hydrophobic polymer PLLA and tocopherol (antioxidant). Release study demonstrated that release rate of dual actives were significantly accelerated from tri-layered particles in comparison to bi-layered one and their release profiles can be well explained with the help of Ridger-Peppas model. Both sets of particles exhibited long-term antibacterial (against both Escherichia coli and Staphylococcus aureus) as well as antioxidant effect over a period of 60 days. The results show for the first time the feasibility of using multilayered microparticles to prolong the food shelf-life by simultaneous release of multiple actives.

Identifying communities from multiplex biological networks by randomized optimization of modularity

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering. On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challenge benchmark, the results strongly depend on the selected GWAS dataset and enrichment p -value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected. The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM.

Identifying communities from multiplex biological networks by randomized optimization of modularity

The identification of communities, or modules, is a common operation in the analysis of large biological networks. The Disease Module Identification DREAM challenge established a framework to evaluate clustering approaches in a biomedical context, by testing the association of communities with GWAS-derived common trait and disease genes. We implemented here several extensions of the MolTi software that detects communities by optimizing multiplex (and monoplex) network modularity. In particular, MolTi now runs a randomized version of the Louvain algorithm, can consider edge and layer weights, and performs recursive clustering.

On simulated networks, the randomization procedure clearly improves the detection of communities. On the DREAM challenge benchmark, the results strongly depend on the selected GWAS dataset and enrichment p-value threshold. However, the randomization procedure, as well as the consideration of weighted edges and layers generally increases the number of trait and disease community detected.

The new version of MolTi and the scripts used for the DMI DREAM challenge are available at: https://github.com/gilles-didier/MolTi-DREAM.

Multi-layer cube sampling for liver boundary detection in PET-CT images

Liver metabolic information is considered as a crucial diagnostic marker for the diagnosis of fever of unknown origin, and liver recognition is the basis of automatic diagnosis of metabolic information extraction. However, the poor quality of PET and CT images is a challenge for information extraction and target recognition in PET-CT images. The existing detection method cannot meet the requirement of liver recognition in PET-CT images, which is the key problem in the big data analysis of PET-CT images. A novel texture feature descriptor called multi-layer cube sampling (MLCS) is developed for liver boundary detection in low-dose CT and PET images. The cube sampling feature is proposed for extracting more texture information, which uses a bi-centric voxel strategy. Neighbour voxels are divided into three regions by the centre voxel and the reference voxel in the histogram, and the voxel distribution information is statistically classified as texture feature. Multi-layer texture features are also used to improve the ability and adaptability of target recognition in volume data. The proposed feature is tested on the PET and CT images for liver boundary detection. For the liver in the volume data, mean detection rate (DR) and mean error rate (ER) reached 95.15 and 7.81% in low-quality PET images, and 83.10 and 21.08% in low-contrast CT images. The experimental results demonstrated that the proposed method is effective and robust for liver boundary detection.

How does the canine paw pad attenuate ground impacts? A multi-layer cushion system

Macroscopic mechanical properties of digitigrade paw pads, such as non-linear elastic and variable stiffness, have been investigated in previous studies; however, little is known about the micro-scale structural characteristics of digitigrade paw pads, or the relationship between these characteristics and the exceptional cushioning of the pads. The digitigrade paw pad consists of a multi-layered structure, which is mainly comprised of a stratified epithelium layer, a dermis layer and a subcutaneous layer. The stratified epithelium layer and dermal papillae constitute the epidermis layer. Finite element analyses were carried out and showed that the epidermis layer effectively attenuated the ground impact across impact velocities of 0.05–0.4 m/s, and that the von Mises stresses were uniformly distributed in this layer. The dermis layer encompassing the subcutaneous layer can be viewed as a hydrostatic system, which can store, release and dissipate impact energy. All three layers in the paw pad work as a whole to meet the biomechanical requirements of animal locomotion. These findings provide insights into the biomechanical functioning of digitigrade paw pads and could be used to facilitate bio-inspired, ground-contacting component development for robots and machines, as well as contribute to footwear design.

Summary: This study examines the micro-scale structural characteristics of the digitigrade paw pad and analyses how these structures work together to further clarify the paw pad cushioning mechanism.

Multi-scale brain networks

The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution.

IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework

Accuracy plays a vital role in the medical field as it concerns with the life of an individual. Extensive research has been conducted on disease classification and prediction using machine learning techniques. However, there is no agreement on which classifier produces the best results. A specific classifier may be better than others for a specific dataset, but another classifier could perform better for some other dataset. Ensemble of classifiers has been proved to be an effective way to improve classification accuracy. In this research we present an ensemble framework with multi-layer classification using enhanced bagging and optimized weighting. The proposed model called "HM-BagMoov" overcomes the limitations of conventional performance bottlenecks by utilizing an ensemble of seven heterogeneous classifiers. The framework is evaluated on five different heart disease datasets, four breast cancer datasets, two diabetes datasets, two liver disease datasets and one hepatitis dataset obtained from public repositories. The analysis of the results show that ensemble framework achieved the highest accuracy, sensitivity and F-Measure when compared with individual classifiers for all the diseases. In addition to this, the ensemble framework also achieved the highest accuracy when compared with the state of the art techniques. An application named "IntelliHealth" is also developed based on proposed model that may be used by hospitals/doctors for diagnostic advice.

Comfort and compressional characteristics of padding bandages

BACKGROUND

Padding bandage is an essential component of the multi-layer compression system used for chronic venous management. Padding plays a critical role in managing pressure over bony prominences and ensuring uniform pressure distribution around the limb circumference. Moreover, it helps in the management of heat, moisture and body fluids or exudates during the course of treatment to provide comfort to the patients.

OBJECTIVE

To study the effect of structural and constructional parameters on the compressional (pressure absorption or distribution) and comfort (air, moisture and heat transmission) characteristics of the padding.

METHODS

This research focuses on the examination of polypropylene based nonwoven padding samples. Critical factors, i.e., fiber linear density, needling density and mass per unit area, have been chosen for this study to find their significance on the performance of padding. Simple laboratory based methods have been proposed to examine pressure reduction and comfort characteristics of the padding.

RESULTS

Pressure absorption by the padding decreases with increase in mass per unit area and needling density of the padding. A padding composed of thicker fiber absorbs more pressure compared to padding made from thinner fiber. On examining comfort, it was found that the air and moisture vapor transmission increase with decrease in mass per unit area and needling density but have opposite effects with fiber linear density (p<0.01). The heat transmission decreases with increase in both mass per unit area and fiber linear density but has opposite effect for needling density.

CONCLUSION

Padding composed of thick fiber with low mass per unit area and needling density could be more effective in pressure management and ensuring comfort. These results could be very useful for health practitioners, fabric engineers and manufactures to understand the significance of fibrous materials and their role in compression management, and could be further used as design consideration to optimized padding performance.

Signaling network of lipids as a comprehensive scaffold for omics data integration in sputum of COPD patients

Chronic obstructive pulmonary disease (COPD) is a heterogeneous and progressive inflammatory condition that has been linked to the dysregulation of many metabolic pathways including lipid biosynthesis. How lipid metabolism could affect disease progression in smokers with COPD remains unclear. We cross-examined the transcriptomics, proteomics, metabolomics, and phenomics data available on the public domain to elucidate the mechanisms by which lipid metabolism is perturbed in COPD. We reconstructed a sputum lipid COPD (SpLiCO) signaling network utilizing active/inactive, and functional/dysfunctional lipid-mediated signaling pathways to explore how lipid-metabolism could promote COPD pathogenesis in smokers. SpLiCO was further utilized to investigate signal amplifiers, distributers, propagators, feed-forward and/or -back loops that link COPD disease severity and hypoxia to disruption in the metabolism of sphingolipids, fatty acids and energy. Also, hypergraph analysis and calculations for dependency of molecules identified several important nodes in the network with modular regulatory and signal distribution activities. Our systems-based analyses indicate that arachidonic acid is a critical and early signal distributer that is upregulated by the sphingolipid signaling pathway in COPD, while hypoxia plays a critical role in the elevated dependency to glucose as a major energy source. Integration of SpLiCo and clinical data shows a strong association between hypoxia and the upregulation of sphingolipids in smokers with emphysema, vascular disease, hypertension and those with increased risk of lung cancer.

Numerical modelling and experimental investigation of drug release from layered silicone matrix systems

Medical devices and polymeric matrix systems that release drugs or other bioactive compounds are of interest for a variety of applications. The release of the drug can be dependent on a number of factors such as the solubility, diffusivity, dissolution rate and distribution of the solid drug in the matrix. Achieving the goal of an optimal release profile can be challenging when relying solely on traditional experimental work. Accurate modelling complementing experimentation is therefore desirable. Numerical modelling is increasingly becoming an integral part of research and development due to the significant advances in computer simulation technology. This work focuses on numerical modelling and investigation of multi-layered silicone matrix systems. A numerical model that can be used to model multi-layered systems was constructed and validated by comparison with experimental data. The model could account for the limited dissolution rate and effect of the drug distribution on the release profiles. Parametric study showed how different factors affect the characteristics of drug release. Multi-layered medical silicone matrices were prepared in special moulds, where the quantity of drug in each layer could be varied, and release was investigated with Franz-diffusion cell setup. Data for long-term release was fitted to the model and the full depletion of the system predicted. The numerical model constructed for this study, whose input parameters are the diffusion, effective dissolution rate and dimensional solubility coefficients, does not require any type of steady-state approximation. These results indicate that numerical model can be used as a design tool for development of controlled release systems such as drug-loaded medical devices.