An investigation into the design of a device to treat haemorrhagic stroke.

In this study, we present the design considerations of a device to assist in the potential treatment of hemorrhagic stroke with the aim of stopping blood from flowing out into brain tissue. We present and model three designs for the clinical scenarios when saccular aneurysms rupture in the middle cerebral artery in the brain. We evaluate and model these three designs using computer aided design software, SolidWorks, which allows the devices to be tested using finite element analysis and also enables us to justify that the materials chosen were suitable for potential use. Computational fluid dynamics modelling were used to demonstrate and analyse the flow of blood through the artery under conditions of normal and ruptured states. We conclude that our device could potentially be useful in the treatment of hemorrhagic stroke, and the modelling process is useful in assisting in determining the performance of our devices.

Tissue Morphology and Gene Expression Characterisation of Transplantable Adenocarcinoma Bearing Mice Exposed to Fluorodeoxyglucose-Conjugated Magnetic Nanoparticles.

Fluorodeoxyglucose-conjugated magnetic nanoparticles, designed to target cancer cells with high specificity when heated by an alternating magnetic field, could provide a low-cost, non-toxic treatment for cancer. However, it is essential that the in vivo impacts of such technologies on both tumour and healthy tissues are characterised fully. Profiling tissue gene expression by semi-quantitative reverse transcriptase real-time PCR can provide a sensitive measurement of tissue response to treatment. However, the accuracy of such analyses is dependent on the selection of stable reference genes. In this study, we determined the impact of fluorodeoxyglucose-conjugated magnetic nanoparticles on tumour and non-tumour tissue gene expression and morphology in MAC16 adenocarcinoma established male NMRI mice. Mice received an injection of 8 mg/kg body weight fluorodeoxyglucose-conjugated magnetic nanoparticles either intravenously in to the tail vein, directly into the tumour or subcutaneously directly overlying the tumour. Tissues from mice were sampled between 70 minutes and 12 hours post injection. Using the bioinformatic geNorm tool, we established the stability of six candidate reference genes (Hprt, Pgk1, Ppib, Sdha, Tbp and Tuba); we observed Pgk1 and Ppib to be the most stable. We then characterised the expression profiles of several apoptosis genes of interest in our adenocarcinoma samples, observing differential expression in response to mode of administration and exposure duration. Using histological assessment and fluorescent TUNNEL staining, we observed no detrimental impact on either tumour or non-tumour tissue morphology or levels of apoptosis. These observations define the underlying efficacy of fluorodeoxyglucose-conjugated magnetic nanoparticles on tumour and non-tumour tissue morphology and gene expression, setting the basis for future studies.

An in-vivo pilot study into the effects of FDG-mNP in cancer in mice.

PURPOSE: Previously, fluorodeoxy glucose conjugated magnetite nanoparticles (FDG-mNPs) injected into cancer cells in conjunction with the application of magnetic hyperthermia have shown promise in new FDG-mNPs applications. The aim of this study was to determine potential toxic or unwanted effects involving both tumour cells and normal tissue in other organs when FDG-mNPs are administered intravenously or intratumourally in mice. MATERIALS AND METHODS: FDG-mNPs were synthesized. A group of six prostate-tumour bearing mice were injected with 23.42 mg/ml FDG-mNPs (intravenous injection, n = 3; intratumoural injection into the prostate tumour, n = 3). Mice were euthanized and histological sampling of tissue was conducted for the prostate tumour, as well as for lungs, lymph nodes, liver, kidneys, spleen, and brain, at 1 hour (n = 2) and 7 days (n = 4) post-injection. A second group of two normal (non-cancerous) mice received the same injection intravenously into the tail vein and were euthanised at 3 and 6 months post-injection, respectively, to investigate if FDG-mNPs remained in organs at those time points. RESULTS: In prostate-tumour bearing mice, FDG-mNPs concentrated in the prostate tumour, while relatively small amounts were found in the organs of other tissues, particularly the spleen and the liver; FDG-mNP concentrations decreased over time in all tissues. In normal mice, no detrimental effects were found in either mouse at 3 or 6 months. CONCLUSION: Intravenous or intratumoural FDG-mNPs can be safely administered for effective cancer cell destruction. Further research on the clinical utility of FDG-mNPs will be conducted by applying hyperthermia in conjunction with FDG-mNPs in mice.

A Calibration-Free Laser-Induced Breakdown Spectroscopy (CF-LIBS) Quantitative Analysis Method Based on the Auto-Selection of an Internal Reference Line and Optimized Estimation of Plasma Temperature.

The quantitative analysis accuracy of calibration-free laser-induced breakdown spectroscopy (CF-LIBS) is severely affected by the self-absorption effect and estimation of plasma temperature. Herein, a CF-LIBS quantitative analysis method based on the auto-selection of internal reference line and the optimized estimation of plasma temperature is proposed. The internal reference line of each species is automatically selected from analytical lines by a programmable procedure through easily accessible parameters. Furthermore, the self-absorption effect of the internal reference line is considered during the correction procedure. To improve the analysis accuracy of CF-LIBS, the particle swarm optimization (PSO) algorithm is introduced to estimate the plasma temperature based on the calculation results from the Boltzmann plot. Thereafter, the species concentrations of a sample can be calculated according to the classical CF-LIBS method. A total of 15 certified alloy steel standard samples of known compositions and elemental weight percentages were used in the experiment. Using the proposed method, the average relative errors of Cr, Ni, and Fe calculated concentrations were 4.40%, 6.81%, and 2.29%, respectively. The quantitative results demonstrated an improvement compared with the classical CF-LIBS method and the promising potential of in situ and real-time application.

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan.

Feature selection through validation and un-censoring of endovascular repair survival data for predicting the risk of re-intervention.

BACKGROUND: Feature selection (FS) process is essential in the medical area as it reduces the effort and time needed for physicians to measure unnecessary features. Choosing useful variables is a difficult task with the presence of censoring which is the unique characteristic in survival analysis. Most survival FS methods depend on Cox's proportional hazard model; however, machine learning techniques (MLT) are preferred but not commonly used due to censoring. Techniques that have been proposed to adopt MLT to perform FS with survival data cannot be used with the high level of censoring. The researcher's previous publications proposed a technique to deal with the high level of censoring. It also used existing FS techniques to reduce dataset dimension. However, in this paper a new FS technique was proposed and combined with feature transformation and the proposed uncensoring approaches to select a reduced set of features and produce a stable predictive model. METHODS: In this paper, a FS technique based on artificial neural network (ANN) MLT is proposed to deal with highly censored Endovascular Aortic Repair (EVAR). Survival data EVAR datasets were collected during 2004 to 2010 from two vascular centers in order to produce a final stable model. They contain almost 91% of censored patients. The proposed approach used a wrapper FS method with ANN to select a reduced subset of features that predict the risk of EVAR re-intervention after 5 years to patients from two different centers located in the United Kingdom, to allow it to be potentially applied to cross-centers predictions. The proposed model is compared with the two popular FS techniques; Akaike and Bayesian information criteria (AIC, BIC) that are used with Cox's model. RESULTS: The final model outperforms other methods in distinguishing the high and low risk groups; as they both have concordance index and estimated AUC better than the Cox's model based on AIC, BIC, Lasso, and SCAD approaches. These models have p-values lower than 0.05, meaning that patients with different risk groups can be separated significantly and those who would need re-intervention can be correctly predicted. CONCLUSION: The proposed approach will save time and effort made by physicians to collect unnecessary variables. The final reduced model was able to predict the long-term risk of aortic complications after EVAR. This predictive model can help clinicians decide patients' future observation plan.

Dynamic analysis of submerged microscale plates: the effects of acoustic radiation and viscous dissipation.

The aim of this paper is to study the dynamic characteristics of micromechanical rectangular plates used as sensing elements in a viscous compressible fluid. A novel modelling procedure for the plate-fluid interaction problem is developed on the basis of linearized Navier-Stokes equations and no-slip conditions. Analytical expression for the fluid-loading impedance is obtained using a double Fourier transform approach. This modelling work provides us an analytical means to study the effects of inertial loading, acoustic radiation and viscous dissipation of the fluid acting on the vibration of microplates. The numerical simulation is conducted on microplates with different boundary conditions and fluids with different viscosities. The simulation results reveal that the acoustic radiation dominates the damping mechanism of the submerged microplates. It is also proved that microplates offer better sensitivities (Q-factors) than the conventional beam type microcantilevers being mass sensing platforms in a viscous fluid environment. The frequency response features of microplates under highly viscous fluid loading are studied using the present model. The dynamics of the microplates with all edges clamped are less influenced by the highly viscous dissipation of the fluid than the microplates with other types of boundary conditions.

Fragmented QRS complexes have predictive value of imperfect ST-segment resolution in patients with STEMI after primary percutaneous coronary intervention.

OBJECTIVES: To investigate the clinical characteristics of patients with the fragmented QRS complexes (fQRS) and the predictive value of fQRS in patients undergoing primary percutaneous coronary intervention (p-PCI). METHODS: The study enrolled 227 consecutive patients with ST-elevation myocardial infarction who underwent p-PCI. Baseline clinical characteristics, the percentage of ST-segment resolution, and parameters of electrocardiography and coronary angiography were investigated. The relationship between fQRS on pre-PCI and post-PCI electrocardiogram and the percentage of ST-segment resolution after PCI were studied. RESULTS: Patients with fQRS have higher troponin I, creatine kinase-MB levels, prolonged QRS duration, higher Gensini score, lower percentage of total ST-segment resolution, and left ventricular ejection fraction compared with patients without fQRS. Gensini score (odds ratio [OR], 1.013; 95% confidence interval [CI], 1.002-1.024; P < .006) and percentage of total ST-segment resolution (OR, 0.384; 95% CI, 0.186-0.795; P = .01) were independently associated with the presence of fQRS. A multivariate logistic regression analysis selected presence of fQRS pre-PCI (OR, 2.908; 95% CI, 1.095-7.723; P = .032) and the number of leads with fQRS before PCI (OR, 1.582; 95% CI, 1.250-2.002; P < .001) as independent predictors of imperfect ST-segment resolution. CONCLUSIONS: The presence of fQRS is a predictor in ST-elevation myocardial infarction patients undergoing p-PCI. The occurrence of fQRS is beneficial to identify the patients with severe coronary lesion, left ventricular contraction dysfunction, and larger areas of ischemic injury.

An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study.

BACKGROUND: Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. METHODS: Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. RESULTS: 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p<0.001). CONCLUSION: This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.

Bayesian neural network approach for determining the risk of re-intervention after endovascular aortic aneurysm repair.

This article proposes a Bayesian neural network approach to determine the risk of re-intervention after endovascular aortic aneurysm repair surgery. The target of proposed technique is to determine which patients have high chance to re-intervention (high-risk patients) and which are not (low-risk patients) after 5 years of the surgery. Two censored datasets relating to the clinical conditions of aortic aneurysms have been collected from two different vascular centers in the United Kingdom. A Bayesian network was first employed to solve the censoring issue in the datasets. Then, a back propagation neural network model was built using the uncensored data of the first center to predict re-intervention on the second center and classify the patients into high-risk and low-risk groups. Kaplan-Meier curves were plotted for each group of patients separately to show whether there is a significant difference between the two risk groups. Finally, the logrank test was applied to determine whether the neural network model was capable of predicting and distinguishing between the two risk groups. The results show that the Bayesian network used for uncensoring the data has improved the performance of the neural networks that were built for the two centers separately. More importantly, the neural network that was trained with uncensored data of the first center was able to predict and discriminate between groups of low risk and high risk of re-intervention after 5 years of endovascular aortic aneurysm surgery at center 2 (p = 0.0037 in the logrank test).

Evidence-based nanoscopic and molecular framework for excipient functionality in compressed orally disintegrating tablets.

The work investigates the adhesive/cohesive molecular and physical interactions together with nanoscopic features of commonly used orally disintegrating tablet (ODT) excipients microcrystalline cellulose (MCC) and D-mannitol. This helps to elucidate the underlying physico-chemical and mechanical mechanisms responsible for powder densification and optimum product functionality. Atomic force microscopy (AFM) contact mode analysis was performed to measure nano-adhesion forces and surface energies between excipient-drug particles (6-10 different particles per each pair). Moreover, surface topography images (100 nm2-10 µm2) and roughness data were acquired from AFM tapping mode. AFM data were related to ODT macro/microscopic properties obtained from SEM, FTIR, XRD, thermal analysis using DSC and TGA, disintegration testing, Heckel and tabletability profiles. The study results showed a good association between the adhesive molecular and physical forces of paired particles and the resultant densification mechanisms responsible for mechanical strength of tablets. MCC micro roughness was 3 times that of D-mannitol which explains the high hardness of MCC ODTs due to mechanical interlocking. Hydrogen bonding between MCC particles could not be established from both AFM and FTIR solid state investigation. On the contrary, D-mannitol produced fragile ODTs due to fragmentation of surface crystallites during compression attained from its weak crystal structure. Furthermore, AFM analysis has shown the presence of extensive micro fibril structures inhabiting nano pores which further supports the use of MCC as a disintegrant. Overall, excipients (and model drugs) showed mechanistic behaviour on the nano/micro scale that could be related to the functionality of materials on the macro scale.

[Association between fragmented QRS complexes and imperfect ST-segment resolution in patients with ST-elevation myocardial infarction after primary percutaneous coronary intervention].

OBJECTIVE: To explore the relationship between fragmented QRS complexes (fQRS) and imperfect ST-segment resolution in ST elevation myocardial infarction (STEMI) patients undergoing primary percutaneous coronary intervention (p-PCI). METHODS: This study included 227 consecutive patients with STEMI who underwent p-PCI. They were divided into two groups: ECG with fQRS (n = 142) and without fQRS (n = 85). Baseline clinical characteristics,Gensini score, coronary angiography features and the rate of ST-segment resolution were compared between the two groups. RESULTS: (1) Patients with fQRS of ECG had higher cTnI, CK, CK-MB levels and Gensini score, prolonged QRS interval, lower rate of ST-segment resolution and left ventricular ejection fraction (LVEF) than in patients without fQRS (all P < 0.01 or P < 0.05). (2) Pearson correlation analysis showed that the rate of ST-segment resolution (r = -0.207, P = 0.002),Gensini score (r = 0.191, P = 0.004), LVEF(r = -0.188, P = 0.006), cTnI(r = 0.172, P = 0.010), and the TIMI grade post p-PCI (r = -0.148, P = 0.028) were significantly related with the presence of fQRS. (3) Multivariate logistic regression analysis demonstrated that presence of fQRS at pre-PCI (OR = 2.908, 95%CI:1.095-7.723, P = 0.032) , the number of leads with fQRS before PCI (OR = 1.582, 95%CI:1.250-2.002, P < 0.001), and increased QRS interval (OR = 0.955, 95%CI: 0.924-0.988, P = 0.008) were independent predictors of imperfect ST-segment resolution. CONCLUSIONS: fQRS is related to imperfect ST-segment resolution in STEMI patients undergoing p-PCI.fQRS may be a useful parameter to identify the patients with severe coronary lesion, larger areas of ischemic injury and myocardial infarction as well as severe left ventricular contracted dysfunction.

Probucol for the prevention of cystatin C-based contrast-induced acute kidney injury following primary or urgent angioplasty: a randomized, controlled trial.

BACKGROUND: Probucol, a lipid-lowering drug with potent antioxidant properties, may reduce the risk of cystatin C (CyC)-based contrast-induced acute kidney injury (CIAKI). The aim of this study was to observe the incidence of CyC-based CIAKI and assess the efficacy of probucol on prevention of CIAKI following primary or urgent coronary angioplasty. METHODS: A total of 204 patients with acute coronary syndrome (ACS) were prospectively randomized to a control group (108 patients, 74 male, 65.4 ± 12.5 years) or probucol group (96 patients, 67 male, 65.1 ± 10.5 years) 1000 mg orally before primary or urgent angioplasty and 500 mg twice daily for 3 days following intervention. Serum CyC and serum creatinine (Scr) concentrations were measured before, and on day 1, day 2 and day 3 after coronary intervention. RESULTS: The clinical characteristics of the patients from the two groups were similar. Scr-based CIAKI was developed in 23 patients of the control group (21.3%) and in 4 patients of the probucol group (4.2%) (P<0.001). Furthermore, CyC-based CIAKI occurred in 56 patients of the control group (51.9%) and 28 patients of the probucol group (29.2%) (P<0.001). The CyC increase ≥ 10% after exposure to contrast medium was the best increment cutoff value for the early identification of patients at risk of CIAKI. CONCLUSIONS: Our study suggests that CyC is a reliable marker for early identification and ruling out the patients at the risk of CIAKI. Among the patients with ACS who are undergoing primary or urgent angioplasty, prophylactic treatment with probucol reduces the risk of both Scr and CyC-based CIAKI.

A novel silicon membrane-based biosensing platform using distributive sensing strategy and artificial neural networks for feature analysis.

A novel biosensing system based on a micromachined rectangular silicon membrane is proposed and investigated in this paper. A distributive sensing scheme is designed to monitor the dynamics of the sensing structure. An artificial neural network is used to process the measured data and to identify cell presence and density. Without specifying any particular bio-application, the investigation is mainly concentrated on the performance testing of this kind of biosensor as a general biosensing platform. The biosensing experiments on the microfabricated membranes involve seeding different cell densities onto the sensing surface of membrane, and measuring the corresponding dynamics information of each tested silicon membrane in the form of a series of frequency response functions (FRFs). All of those experiments are carried out in cell culture medium to simulate a practical working environment. The EA.hy 926 endothelial cell lines are chosen in this paper for the bio-experiments. The EA.hy 926 endothelial cell lines represent a particular class of biological particles that have irregular shapes, non-uniform density and uncertain growth behaviour, which are difficult to monitor using the traditional biosensors. The final predicted results reveal that the methodology of a neural-network based algorithm to perform the feature identification of cells from distributive sensory measurement has great potential in biosensing applications.

The effect of supporting a surgeon's wrist on their hand tremor.

BACKGROUND: Operative tremor can greatly influence the outcome of certain, precise, microsurgical operations. Reducing a surgeons tremor may not only improve the operative results but decrease the operative time. Previous studies have only measured uni or bi directional tremor and therefore have been unable to calculate both the overall tremor amplitude and the tremor reduction by resting the wrists. MATERIALS AND METHODS: We measured the tremor of 21 neurologically normal volunteers while performing a micromanipulation task, with and without wrist support. Measurements were acquired in three dimensions using three accelerometers attached to the hand, allowing an overall tremor amplitude to be calculated. RESULTS: Resting the wrist on a gelled surface decreases an individuals tremor by a factor of 2.67 (P = 0). CONCLUSIONS: Supporting the wrists significantly decreases the amplitude of the tremor. Surgeons should consider using wrist supports when performing parts of operations which necessitate a high degree of accuracy.

Robotic technology applied to meet the requirements of tools to support microsurgery and cellular surgery.

Microsurgery and processes involving cell manipulation or cell surgery are clinical practices where the operator works at or beyond the threshold of human dexterity. Current tools available are conventional in their design, and this limits consistency and the level of reliability and achievement. Surgical robotic devices have been explored to improve precision in minimal access surgical procedures to augment control of tool points in tissues, and have enabled feedback of sensory data from which the operator is able to deduce information on the tool at the working site. In this paper, relevant technologies are described that can be harnessed to improve perception of tool point interaction with tissues at the working site and to improve tool control at the small scale required in clinical practice for microsurgery and for cell surgery.