DXA-based statistical models of shape and intensity outperform aBMD hip fracture prediction: A retrospective study.

Areal bone mineral density (aBMD) currently represents the clinical gold standard for hip fracture risk assessment. Nevertheless, it is characterised by a limited prediction accuracy, as about half of the people experiencing a fracture are not classified as at being at risk by aBMD. In the context of a progressively ageing population, the identification of accurate predictive tools would be pivotal to implement preventive actions. In this study, DXA-based statistical models of the proximal femur shape, intensity (i.e., density) and their combination were developed and employed to predict hip fracture on a retrospective cohort of post-menopausal women. Proximal femur shape and pixel-by-pixel aBMD values were extracted from DXA images and partial least square (PLS) algorithm adopted to extract corresponding modes and components. Subsequently, logistic regression models were built employing the first three shape, intensity and shape-intensity PLS components, and their ability to predict hip fracture tested according to a 10-fold cross-validation procedure. The area under the ROC curves (AUC) for the shape, intensity, and shape-intensity-based predictive models were 0.59 (95%CI 0.47-0.69), 0.80 (95%CI 0.70-0.90) and 0.83 (95%CI 0.73-0.90), with the first being significantly lower than the latter two. aBMD yielded an AUC of 0.72 (95%CI 0.59-0.82), found to be significantly lower than the shape-intensity-based predictive model. In conclusion, a methodology to assess hip fracture risk uniquely based on the clinically available imaging technique, DXA, is proposed. Our study results show that hip fracture risk prediction could be enhanced by taking advantage of the full set of information DXA contains.

Cost-Effectiveness Analysis of CT-Based Finite Element Modeling for Osteoporosis Screening in Secondary Fracture Prevention: An Early Health Technology Assessment in the Netherlands.

Objective. To conduct cost-utility analyses for Computed Tomography To Strength (CT2S), a novel osteoporosis screening service, compared with dual-energy X-ray absorptiometry (DXA), treat all without screening, and no screening methods for Dutch postmenopausal women referred to fracture liaison service (FLS). CT2S uses CT scans to generate femur models and simulate sideways fall scenarios for bone strength assessment. Methods. Early health technology assessment (HTA) was adopted to evaluate CT2S as a novel osteoporosis screening tool for secondary fracture prevention. We constructed a 2-dimensional simulation model considering 4 strategies (no screening, treat all without screening, DXA, CT2S) together with screening intervals (5 y, 2 y), treatments (oral alendronate, zoledronic acid), and discount rate scenarios among Dutch women in 3 age groups (60s, 70s, and 80s). Strategy comparisons were based on incremental cost-effectiveness ratios (ICERs), considering an ICER below €20,000 per QALY gained as cost-effective in the Netherlands. Results. Under the base-case scenario, CT2S versus DXA had estimated ICERs of €41,200 and €14,083 per QALY gained for the 60s and 70s age groups, respectively. For the 80s age group, CT2S was more effective and less costly than DXA. Changing treatment from weekly oral alendronate to annual zoledronic acid substantially decreased CT2S versus DXA ICERs across all age groups. Setting the screening interval to 2 y increased CT2S versus DXA ICERs to €100,333, €55,571, and €15,750 per QALY gained for the 60s, 70s, and 80s age groups, respectively. In all simulated populations and scenarios, CT2S was cost-effective (in some cases dominant) compared with the treat all strategy and cost-saving (more effective and less costly) compared with no screening. Conclusion. CT2S was estimated to be potentially cost-effective in the 70s and 80s age groups considering the willingness-to-pay threshold of the Netherlands. This early HTA suggests CT2S as a potential novel osteoporosis screening tool for secondary fracture prevention. Highlights: For postmenopausal Dutch women who have been referred to the FLS, direct access to CT2S may be cost-effective compared with DXA for age groups 70s and 80s, when considering the ICER threshold of the Netherlands. This study positions CT2S as a potential novel osteoporosis-screening tool for secondary fracture prevention in the clinical setting.A shorter screening interval of 2 y increases the effectiveness of both screening strategies, but the ICER of CT2S compared with DXA also increased substantially, which made CT2S no longer cost-effective for the 70s age group; however, it remains cost-effective for individuals in their 80s.Annual zoledronic acid treatment with better adherence may contribute to a lower cost-effectiveness ratio when comparing CT2S to DXA screening and the treat all strategies for all age groups.

Digital health interventions for osteoporosis and post-fragility fracture care.

The growing burden from osteoporosis and fragility fractures highlights a need to improve osteoporosis management across healthcare systems. Sub-optimal management of osteoporosis is an area suitable for digital health interventions. While fracture liaison services (FLSs) are proven to greatly improve care for people with osteoporosis, such services might benefit from technologies that enhance automation. The term 'Digital Health' covers a variety of different tools including clinical decision support systems, electronic medical record tools, patient decision aids, patient apps, education tools, and novel artificial intelligence (AI) algorithms. Within the scope of this review are AI solutions that use algorithms within health system registries to target interventions. Clinician-targeted, patient-targeted, or system-targeted digital health interventions could be used to improve management and prevent fragility fractures. This review was commissioned by The Royal Osteoporosis Society and Bone Research Academy during the production of the 2020 Research Roadmap (https://theros.org.uk), with the intention of identifying gaps where targeted research funding could lead to improved patient health. We explore potential uses of digital technology in the general management of osteoporosis. Evidence suggests that digital technologies can support multidisciplinary teams to provide the best possible patient care based on current evidence and to support patients in self-management. However, robust randomised controlled studies are still needed to assess the effectiveness and cost-effectiveness of these technologies.

Personalised 3D Assessment of Trochanteric Soft Tissues Improves HIP Fracture Classification Accuracy.

Passive soft tissues surrounding the trochanteric region attenuate fall impact forces and thereby control hip fracture risk. The degree of attenuation is related to Soft Tissue Thickness (STT). STT at the neutral hip impact orientation, estimated using a regression relation in body mass index (BMI), was previously shown to influence the current absolute risk of hip fracture (ARF0) and its fracture classification accuracy. The present study investigates whether fracture classification using ARF0 improves when STT is determined from the subject's Computed-Tomography (CT) scans (i.e. personalised) in an orientation-specific (i.e. 3D) manner. STT is calculated as the shortest distance along any impact orientation between a semi-automatically segmented femur surface and an automatically segmented soft tissue/air boundary. For any subject, STT along any of the 33 impact orientations analysed always exceeds the value estimated using BMI. Accuracy of fracture classification using ARF0 improves when using personalised 3D STT estimates (AUC = 0.87) instead of the BMI-based STT estimate (AUC = 0.85). The improvement is smaller (AUC = 0.86) when orientation-specificity of CT-based STT is suppressed and is nil when personalisation is suppressed instead. Thus, fracture classification using ARF0 improves when CT is used to personalise STT estimates and improves further when, in addition, the estimates are orientation specific.

Improving the Hip Fracture Risk Prediction with a Statistical Shape-and-Intensity Model of the Proximal Femur.

Severe predictions have been made regarding osteoporotic fracture incidence for the next years, with major economic and social impacts in a worldwide greying society. However, the performance of the currently adopted gold standard for fracture risk prediction, the areal Bone Mineral Density (aBMD), remains moderate. To overcome current limitations, the construction of statistical models of the proximal femur, based on three-dimensional shape and intensity (a hallmark of bone density), is here proposed for predicting hip fracture in a Caucasian postmenopausal cohort. Partial Least Square (PLS)-based statistical models of the shape, intensity and their combination were developed, and the corresponding modes and components were identified. Logistic regression models using the first two shape, intensity and shape-intensity PLS components were implemented and tested within a 10-fold cross-validation procedure as predictors of hip fracture. It emerged that (1) intensity components were superior to shape components in stratifying patients according to their fracture status, and that (2) a combination of intensity and shape improved patients risk stratification. The area under the ROC curve was 0.64, 0.85 and 0.92 for the models based on shape, intensity and shape-intensity combination respectively, against a 0.72 value for the aBMD standard approach. Based on these findings, the presented methodology turns out to be promising in tackling the need for an enhanced fracture risk assessment.

Opportunistic diagnosis of osteoporosis, fragile bone strength and vertebral fractures from routine CT scans; a review of approved technology systems and pathways to implementation.

Osteoporosis causes bones to become weak, porous and fracture more easily. While a vertebral fracture is the archetypal fracture of osteoporosis, it is also the most difficult to diagnose clinically. Patients often suffer further spine or other fractures, deformity, height loss and pain before diagnosis. There were an estimated 520,000 fragility fractures in the United Kingdom (UK) in 2017 (costing £4.5 billion), a figure set to increase 30% by 2030. One way to improve both vertebral fracture identification and the diagnosis of osteoporosis is to assess a patient's spine or hips during routine computed tomography (CT) scans. Patients attend routine CT for diagnosis and monitoring of various medical conditions, but the skeleton can be overlooked as radiologists concentrate on the primary reason for scanning. More than half a million CT scans done each year in the National Health Service (NHS) could potentially be screened for osteoporosis (increasing 5% annually). If CT-based screening became embedded in practice, then the technique could have a positive clinical impact in the identification of fragility fracture and/or low bone density. Several companies have developed software methods to diagnose osteoporosis/fragile bone strength and/or identify vertebral fractures in CT datasets, using various methods that include image processing, computational modelling, artificial intelligence and biomechanical engineering concepts. Technology to evaluate Hounsfield units is used to calculate bone density, but not necessarily bone strength. In this rapid evidence review, we summarise the current literature underpinning approved technologies for opportunistic screening of routine CT images to identify fractures, bone density or strength information. We highlight how other new software technologies have become embedded in NHS clinical practice (having overcome barriers to implementation) and highlight how the novel osteoporosis technologies could follow suit. We define the key unanswered questions where further research is needed to enable the adoption of these technologies for maximal patient benefit.

A systematic approach to the scale separation problem in the development of multiscale models.

Throughout engineering there are problems where it is required to predict a quantity based on the measurement of another, but where the two quantities possess characteristic variations over vastly different ranges of time and space. Among the many challenges posed by such 'multiscale' problems, that of defining a 'scale' remains poorly addressed. This fundamental problem has led to much confusion in the field of biomedical engineering in particular. The present study proposes a definition of scale based on measurement limitations of existing instruments, available computational power, and on the ranges of time and space over which quantities of interest vary characteristically. The definition is used to construct a multiscale modelling methodology from start to finish, beginning with a description of the system (portion of reality of interest) and ending with an algorithmic orchestration of mathematical models at different scales within the system. The methodology is illustrated for a specific but well-researched problem. The concept of scale and the multiscale modelling approach introduced are shown to be easily adaptable to other closely related problems. Although out of the scope of this paper, we believe that the proposed methodology can be applied widely throughout engineering.

Finite element analysis informed variable selection for femoral fracture risk prediction.

Logistic regression classification (LRC) is widely used to develop models to predict the risk of femoral fracture. LRC models based on areal bone mineral density (aBMD) alone are poor, with area under the receiver operator curve (AUROC) scores reported to be as low as 0.63. This has led to researchers investigating methods to extract further information from the image to increase performance. Recently, the use of active shape (ASM) and appearance models (AAM) have resulted in moderate improvements, but there is a risk that inclusion of too many modes will lead to overfitting. In addition, there are concerns that the effort required to extract the additional information does not justify the modest improvement in fracture risk prediction. This raises the question, are we reaching the limits of the information that can be extracted from an image? Finite element analysis was used in combination with active shape and appearance modelling to select variables to develop LRC models of fracture risk. Active shape and active appearance models were constructed based on a previously reported cohort of 94 post-menopausal Caucasian women (47 with and 47 without a fracture). T-tests were used to identify differences between the two groups for each mode of variation. Femur strength was predicted for two load cases, stance and a fall. Stepwise multi-variate linear regression was used to identify shape and appearance modes that were predictors of strength for the femurs in the training set. Femurs were also synthetically generated to explore the influence of the first 10 modes of the shape and appearance models. Identified modes of variation were then used to generate LRC models to predict fracture risk. Only 6 modes, 4 active appearance and 2 active shape modes, were identified that had a significant influence on predicted fracture strength. Of these, only two active appearance modes were needed to substantially improve the predictive mode performance (ΔAUROC = 0.080). The addition of 3 more modes (1 AAM and two ASM) further improved the performance of the classifier (ΔAUROC = 0.123). Further addition of modes did not result in any further substantial improvements. Based on these findings, it is suggested that we are reaching the limits of the information that can be extracted from an image to predict fracture risk.

Heterogeneity in The Mechanical Properties of Integrins Determines Mechanotransduction Dynamics in Bone Osteoblasts.

Bone cells are exposed to dynamic mechanical stimulation that is transduced into cellular responses by mechanotransduction mechanisms. The extracellular matrix (ECM) provides a physical link between loading and bone cells, where mechanoreceptors, such as integrins, initiate mechanosensation. Though this relationship is well studied, the dynamic interplay between mechanosensation, mechanotransduction and cellular responses is unclear. A hybrid-multiscale model combining molecular, cellular and tissue interactions was developed to examine links between integrins' mechanosensation and effects on mechanotransduction, ECM modulation and cell-ECM interaction. The model shows that altering integrin mechanosensitivity threshold (MT) increases mechanotransduction durations from hours to beyond 4 days, where bone formation starts. This is relevant to bone, where it is known that a brief stimulating period provides persistent influences for over 24 hours. Furthermore, the model forecasts that integrin heterogeneity, with respect to MT, would be able to induce sustained increase in pERK baseline > 15% beyond 4 days. This is analogous to the emergence of molecular mechanical memory signalling dynamics. Therefore, the model can provide a greater understanding of mechanical adaptation to differential mechanical responses at different times. Given reduction of bone sensitivity to mechanical stimulation with age, these findings may lead towards useful therapeutic targets for upregulation of bone mass.

Correction to: Are CT-Based Finite Element Model Predictions of Femoral Bone Strength Clinically Useful?

The original version "Are CT-Based Finite Element Model Predictions of Femoral Bone Strengthening Clinically Useful?"

A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population.

Osteoporotic hip fractures are a major healthcare problem. Fall severity and bone strength are important risk factors of hip fracture. This study aims to obtain a mechanistic explanation for fracture risk in dependence of these risk factors. A novel modelling approach is developed that combines models at different scales to overcome the challenge of a large space-time domain of interest and considers the variability of impact forces between potential falls in a subject. The multiscale model and its component models are verified with respect to numerical approximations made therein, the propagation of measurement uncertainties of model inputs is quantified, and model predictions are validated against experimental and clinical data. The main results are model predicted absolute risk of current fracture (ARF0) that ranged from 1.93 to 81.6% (median 36.1%) for subjects in a retrospective cohort of 98 postmenopausal British women (49 fracture cases and 49 controls); ARF0 was computed up to a precision of 1.92 percentage points (pp) due to numerical approximations made in the model; ARF0 possessed an uncertainty of 4.00 pp due to uncertainties in measuring model inputs; ARF0 classified observed fracture status in the above cohort with AUC = 0.852 (95% CI 0.753-0.918), 77.6% specificity (95% CI 63.4-86.5%) and 81.6% sensitivity (95% CI 68.3-91.1%). These results demonstrate that ARF0 can be computed using the model with sufficient precision to distinguish between subjects and that the novel mechanism of fracture risk determination based on fall dynamics, hip impact and bone strength can be considered validated.

Are CT-Based Finite Element Model Predictions of Femoral Bone Strength Clinically Useful?

PURPOSE OF REVIEW: This study reviews the available literature to compare the accuracy of areal bone mineral density derived from dual X-ray absorptiometry (DXA-aBMD) and of subject-specific finite element models derived from quantitative computed tomography (QCT-SSFE) in predicting bone strength measured experimentally on cadaver bones, as well as their clinical accuracy both in terms of discrimination and prediction. Based on this information, some basic cost-effectiveness calculations are performed to explore the use of QCT-SSFE instead of DXA-aBMD in (a) clinical studies with femoral strength as endpoint, (b) predictor of the risk of hip fracture in low bone mass patients. RECENT FINDINGS: Recent improvements involving the use of smooth-boundary meshes, better anatomical referencing for proximal-only scans, multiple side-fall directions, and refined boundary conditions increase the predictive accuracy of QCT-SSFE. If these improvements are adopted, QCT-SSFE is always preferable over DXA-aBMD in clinical studies with femoral strength as the endpoint, while it is not yet cost-effective as a hip fracture risk predictor, although pathways that combine both QCT-SSFE and DXA-aBMD are promising.

Optimization of extraction process of inulin from Indian millets (jowar, bajra and ragi)-characterization and cost analysis.

Prebiotic biomolecule, namely, inulin was extracted from Indian millets, namely, jowar (Sorghum vulgare), bajra (Pennisetum glaucum) and ragi (Eleusine coracana). Through qualitative assessment using Fourier Transform Infrared spectroscopy, the presence of functional groups of inulin in the above mentioned Indian millets were verified. The values of degree of polymerization of inulin derived from jowar, bajra and ragi were determined to be 27, 39 and 23 respectively. A comparative analysis of growth of Lactobacillus casei was carried out in presence of both lactose and inulin extracted from three millets and the commercial one. It was observed that the bajra inulin and lactose combination exhibited the best bacterial growth. The prebiotic effectiveness of different varieties of inulin was calculated to be in the following order: bajra > jowar > ragi > commercial inulin. Therefore the results on bajra inulin were highlighted in this article. Inulin yield from bajra was optimized as a function of temperature, HCl concentration and heating period. The maximum inulin yield (0.4727 g/g bajra) was obtained at temperature 70 °C, HCl concentration of 0.8 M and heating period of 60 min. The prebiotic activity score of bajra inulin (= 3.2) was measured to be much higher than commercial inulin (= 1.0). Growth dynamics of Lactobacillus casei on lactose, bajra inulin and mixture of lactose and bajra inulin were found to be of Monod type, Haldane type and Multi-substrate-summative type respectively. The techno-economic analysis based on the production cost of inulin from raw bajra seeds suggested that it was much cheaper than commercial inulin.

Bioremediation of methylene blue dye using Bacillus subtilis MTCC 441.

Methylene blue (MB) commonly found in the textile industry effluent has been chosen as a model dye to investigate bioremediation using Bacillus subtilis MTCC 441. Both free cells and calcium alginate immobilized cells have been used to remove MB from the effluent. The operating variables of initial concentration of dye (20-60 mg/L), inoculum size (4-8%) and temperature (25-35 °C) have been varied judiciously during the kinetic study in a batch contactor. A maximum removal of 91.68% is obtained when 20 mg/L MB solution was inoculated with 8% inoculum and cultured for 6 h at 30 °C. Continuous removal of MB has been studied in a fixed bed contactor using immobilized cells as packing materials. Influent concentration (10-30 mg/L) was varied and breakthrough parameters have been determined. With increase in influent concentration from 10 mg/L to 30 mg/L, percentage removal of dye decreases from 72.44% to 49.62%.

Multiscale modeling methods in biomechanics.

More and more frequently, computational biomechanics deals with problems where the portion of physical reality to be modeled spans over such a large range of spatial and temporal dimensions, that it is impossible to represent it as a single space-time continuum. We are forced to consider multiple space-time continua, each representing the phenomenon of interest at a characteristic space-time scale. Multiscale models describe a complex process across multiple scales, and account for how quantities transform as we move from one scale to another. This review offers a set of definitions for this emerging field, and provides a brief summary of the most recent developments on multiscale modeling in biomechanics. Of all possible perspectives, we chose that of the modeling intent, which vastly affect the nature and the structure of each research activity. To the purpose we organized all papers reviewed in three categories: 'causal confirmation,' where multiscale models are used as materializations of the causation theories; 'predictive accuracy,' where multiscale modeling is aimed to improve the predictive accuracy; and 'determination of effect,' where multiscale modeling is used to model how a change at one scale manifests in an effect at another radically different space-time scale. Consistent with how the volume of computational biomechanics research is distributed across application targets, we extensively reviewed papers targeting the musculoskeletal and the cardiovascular systems, and covered only a few exemplary papers targeting other organ systems. The review shows a research subdomain still in its infancy, where causal confirmation papers remain the most common. WIREs Syst Biol Med 2017, 9:e1375. doi: 10.1002/wsbm.1375 For further resources related to this article, please visit the WIREs website.

Fibrin structural and diffusional analysis suggests that fibers are permeable to solute transport.

Fibrin hydrogels are promising carrier materials in tissue engineering. They are biocompatible and easy to prepare, they can bind growth factors and they can be prepared from a patient's own blood. While fibrin structure and mechanics have been extensively studied, not much is known about the relation between structure and diffusivity of solutes within the network. This is particularly relevant for solutes with a size similar to that of growth factors. A novel methodological approach has been used in this study to retrieve quantitative structural characteristics of fibrin hydrogels, by combining two complementary techniques, namely confocal fluorescence microscopy with a fiber extraction algorithm and turbidity measurements. Bulk rheological measurements were conducted to determine the impact of fibrin hydrogel structure on mechanical properties. From these measurements it can be concluded that variations in the fibrin hydrogel structure have a large impact on the rheological response of the hydrogels (up to two orders of magnitude difference in storage modulus) but only a moderate influence on the diffusivity of dextran solutes (up to 25% difference). By analyzing the diffusivity measurements by means of the Ogston diffusion model we further provide evidence that individual fibrin fibers can be semi-permeable to solute transport, depending on the average distance between individual protofibrils. This can be important for reducing mass transport limitations, for modulating fibrinolysis and for growth factor binding, which are all relevant for tissue engineering. STATEMENT OF SIGNIFICANCE: Fibrin is a natural biopolymer that has drawn much interest as a biomimetic carrier in tissue engineering applications. We hereby use a novel combined approach for the structural characterization of fibrin networks based on optical microscopy and light scattering methods that can also be applied to other fibrillar hydrogels, like collagen. Furthermore, our findings on the relation between solute transport and fibrin structural properties can lead to the optimized design of fibrin hydrogel constructs for controlled release applications. Finally, we provide new evidence for the fact that fibrin fibers may be permeable for solutes with a molecular weight comparable to that of growth factors. This finding may open new avenues for tailoring mass transport properties of fibrin carriers.

Mixed consortia in bioprocesses: role of microbial interactions.

The utilization of mixed consortia or mixed culture has become a current research trend of applied microbiology, bioprocess engineering and biotechnology. The constituent microorganisms of such mixed cultures can jointly perform complex processes efficiently, yielding the desired product at an augmented rate, in comparison to monocultures. It is understandable that the interactions between the microbial partners in these mixed cultures are expected to have a significant impact on the combined performance of the microorganisms and the bioprocess as a whole. Prevalence of positive interactions (commensalism or mutualism) among microbial members of a mixed culture or consortia can significantly enhance the product outcome of the bioprocess, ensuring their industrial application and long-term stability. On the contrary, negative interaction (parasitism, predation or ammensalism) leads to elimination of microbial members from the consortia causing the destruction of community structure as well as disruption of cumulative performance. Therefore, a priori knowledge on the type of interaction between the microorganisms is also essential for the optimization of the performance of the designed consortia. This could only be achieved through the study of inter-microbial interaction prevailing in a mixed culture. In the present article, different bioprocess applications of mixed cultures, currently in practice along with types of positive microbial interactions involved, have been reviewed. Complexity of mixed cultures from the perspective of multiple types of intra-culture relationships has been explained in detail. Overall, the necessity for more in-depth research studies on "microbial interaction" in mixed culture bioprocesses has been stressed in the article.

Experimental studies and two-dimensional modelling of a packed bed bioreactor used for production of galacto-oligosaccharides (GOS) from milk whey.

In the present study, extensive experimental investigations and detailed theoretical analysis of a two-dimensional packed bed bioreactor, employed for the production of galacto-oligosaccharides (GOS) from milk whey were performed. Model equations, in one- and two-dimensions, capable of predicting the substrate concentration distribution in the bioreactor were developed by coupling mass balance equation with appropriate velocity distribution equation and solved numerically. Validation of the proposed model equations was done by a set of experimental data obtained from the bioreactor. The effects of reactor to catalyst particle diameter ratio (d t/d p), feed flowrate (10(-6)-10(-9) m(3) s(-1)), and initial lactose concentration (50-200 kg m(-3)) on substrate concentration distribution were investigated in detail. While, the distribution of substrate concentration in axial direction was independent of d t/d p, it was observed that for d t/d p <40, significant radial concentration distribution existed. It was further observed that the substrate conversion and product yield obtained experimentally showed an excellent agreement (97 ± 2 %) with the results predicted by the two-dimensional model equation, whereas, the results predicted by the one-dimensional model equation did not lie within the desired confidence level (<90 %). The results were confirmed by both curve fitting and statistical analysis. The prediction of substrate concentration distribution in axial and radial directions using the developed two-dimensional model equation is necessary for computing the bioreactor volume to achieve the desired GOS yield.

Role of gradients in vocal fold elastic modulus on phonation.

New studies show that the elastic properties of the vocal folds (VFs) vary locally. In particular strong gradients exist in the distribution of elastic modulus along the length of the VF ligament, which is an important load-bearing constituent of the VF tissue. There is further evidence that changes in VF health are associated with alterations in modulus gradients. The role of VF modulus gradation on VF vibration and phonation remains unexplored. In this study the magnitude of the gradient in VF elastic modulus is varied, and sophisticated computational simulations are performed of the self-oscillation of three-dimensional VFs with realistic modeling of airflow physical properties. Results highlight that phonation frequency, characteristic modes of deformation and phase differences, glottal airflow rate, spectral-width of vocal output, and glottal jet dynamics are dependent on the magnitude of VF elastic modulus gradation. The results advance the understanding of how VF functional gradation can lead to perceptible changes in speech quality.

Analysis of cell growth dynamics of Pediococcus acidilactici in the presence of inulin in an optimized microenvironment.

The present investigation deals with the optimization of cell growth rate of the candidate probiotic Pediococcus acidilactici in the presence of the specific prebiotic inulin. Three independent variables viz. concentration of inulin, concentration of glucose and pH have been selected for optimization study using response surface methodology. Theoretical analysis indicates that the maximum cell growth rate occurs at pH 7, 20 g/dm(3) concentration of inulin and 20 g/dm(3) concentration of glucose. Validation of these values has been done through a set of programmed experiments. Studies on cell dynamics in the presence of different concentrations of inulin have also been carried out to identify any limitation on the initial inulin concentration. Results clearly indicate that cell growth is enhanced with the increase in inulin concentration. However, there is a critical value of the prebiotic concentration (20 g/dm(3) inulin) beyond which the cell growth is inhibited. A summative type growth model has been proposed to explain the growth behaviour of P. acidilactici in the presence of the dual substrate, i.e. glucose and inulin. While growth on glucose follows Monod model, Haldane-type substrate-inhibited growth model holds good for growth on inulin. Intrinsic kinetic parameters for all the model equations have been determined experimentally.