Narrow Results

Blood component non-invasive measurement based on near-infrared (NIR) spectroscopy has become a favorite topic in the field of biomedicine. However, the various noises from instrument measurement and the varying background from absorption of other components (except target analyte) in blood are the main causes, which influenced the prediction accuracy of multivariable calibration. Thinking of backgrounds and noises are always found in high-scale approximation and low-scale detail coefficients. It is possible to identify them by wavelet transform (WT), which has multi-resolution trait and can break spectral signals into different frequency components retaining the same resolution as the original signal. Meanwhile, associating with a criterion of uninformative variable elimination (UVE), it is better to eliminate backgrounds and noises simultaneously and visually. Basic principle and application technology of this pretreatment method, wavelet transform with UVE criterion, were presented in this paper. Three experimental near-infrared spectra data sets, including aqueous solution with four components data sets, plasma data sets, body oral glucose tolerance test (OGTT) data sets, which, including glucose (the target analyte in this study), have all been used in this paper as examples to explain this pretreatment method. The effect of selected wavelength bands in the pretreatment process were discussed, and then the adaptability of different pretreatment method for the uncertainty complex NIR spectra model in blood component non-invasive measurements were also analyzed. This research indicates that the pretreatment methods of wavelet transform with UVE criterion can be used to eliminate varying backgrounds and noises for experimental NIR spectra data directly. Under the spectra area of 1100 to 1700 nm, utilizing this pretreatment method is helpful for us to get a more simple and higher precision multivariable calibration for blood glucose non-invasive measurement. Furthermore, by comparing with some other pretreatment methods, the results imply that the method applied in this study has more adaptability for the complex NIR spectra model. This study gives us another path for improving the blood component non-invasive measurement technique based on NIR spectroscopy.

To address the emerging security challenges in Multi-Access Edge Computing (MEC), it is imperative that solutions go beyond the current infrastructure-centric measures. These methods, including authentication and access control, are insufficient to combat malware that conceals itself within ME applications. The acknowledged flaws in the ME application layer necessitate an immediate call for creative solutions. In this work, we propose a non-invasive security architecture for MEC, meticulously designed to strike a balance between performance burden and security protection capabilities. The objective of the design contains three major aspects, i.e. user experience, service density and serviceability. We conduct a thorough evaluation that enables us to quantify the significance of high bandwidth, low user experience latency and MEC serviceability. The experimental results and ablation studies indicate that our proposed method effectively balances user experience and security capabilities. This not only provides a practical and cost-effective solution but also establishes a strong precedent for the community to develop a secure MEC with superior performance in real-world production environments.

Ai Scientific Awarded R&D Start Grant.

CSIRO Drug Effective against Bird Flu.

AustCancer Commences Anti-cancer Vaccine Phase II Trial.

New Approach against Cancer.

Non-invasive Cancer Test.

China’s Chemical Pharmaceutical Sector Q1 2003 Performance.

International Generic Companies Target India’s Manufacture Infrastructure.

Cardinal Health Sets up Regional Office in Singapore.

BRV Enters Agreement with Genedata.

Powering Up Paralyzed Muscles – Functional Electrical Stimulation.

Latest Technology Innovations Provide a Boost to Stroke Rehabilitation.

An Impulse Radio Ultra Wideband System for Contactless Non-invasive Respiratory Monitoring.

Diagnosis of Alzheimer's Disease Using Electric Signals of the Brain – A Grand Challenge.

AUSTRALASIA – Mighty River Honey: Saving the Bee Through Social Enterprise

SINGAPORE – A*STAR’S IME AND INEX Innovations Exchange to Develop Non-invasive Prenatal Diagnostic Technology

SINGAPORE – CellResearch Corporation receives new US patent approval for exclusive stem cell technology amidst new partnerships

SINGAPORE – A Gel That Can Make Drugs Last Longer

JAPAN – Japanese Biotech JCR Pharmaceuticals Selects Medidata’s Cloud-Based Platform to Power Rare Disease Clinical Research in Japan

PARIS – MedDay Reports Additional Positive Data of its Pivotal Phase III Study With MD1003 in Patients with Progressive Multiple Sclerosis

GERMANY – Merck Serono Introduces New Eeva Test Version Aiming for Optimized Assisted Reproductive Outcomes

AFRICA – Ebola Genome Insights Indicate that Containment Worked

UNITED STATES – TissueGene, Inc. Announces FDA Acceptance Of Invossa™ As Proprietary Name For TissueGene-C

UNITED STATES – FDA Approves Technology Upgrade for Recipients of First Commercially Available Cochlear Implant

SINGAPORE – Letting Go.

SINGAPORE & JAPAN – ASLAN Pharmaceuticals Announces First Patient Enrolled in Phase 1 Study of Varlitinib in Japan.

JAPAN & UNITED STATES – Santen and twoXAR Announce Strategic Research Collaboration to Discover New Glaucoma Treatments.

UNITED STATES – New Study Finds Extensive Use of Fluorinated Chemicals in Fast Food Wrappers.

UNITED STATES – What Doesn’t Kill You Makes You Stronger.

UNITED STATES – Non-Invasive Test Offers Quick Skin Cancer Diagnosis.

UNITED STATES – Life Under Pressure.

AUSTRALIA & THAILAND – Commencement of BIT225 Phase 2 HIV-1 Clinical Trial.

CANADA – Understanding the Genetics of Human Height.

TAIWAN – TLC Announces Collaboration with CMUH in Exploring Solutions for Unmet Medical Needs in Oncology and Osteoarthritis.

The Asian cancer.

Eliminating viral hepatitis in Indonesia by 2030.

Liver cancer treatments - A decade later.

Like a sniper.

Hepatocellular carcinoma detection using artificially engineered materials.

A new type of non-invasive deep-brain stimulation is conceived and demonstrated by computer simulations. The process is based on spatio-temporal Fourier synthesis using multiple electrode pairs with sinusoidal current drive to limit skin sensations and concentrate the stimulus power to a small spatial volume and into large rare spikes in the time domain, while the signal power at the skin is steady and small. Exotic time signals are also shown, such as the cases of high-frequency prime harmonics, quasi-random and chirping stimulations. The first one is able to generate sharp spikes with low frequency while its carriers are high-frequency harmonics that easily conduct via the skin and brain tissue. Open questions are, among others, the best shapes and timing of spikes. The answers require experimental tests and explorations in animal models and human subjects.

Aortic pressure measurement is of significant clinical importance. However, the techniques require invasive approach, such as cardiac catheterization. In this study, we are providing the analysis for the aortic pressure to be determined non-invasively, as well as indicating how it can be employed to determine cardiac contractility, compliance and peripheral resistance. We record systolic and diastolic pressure during the cardiac cycle using cuff method, assumed that the systolic phase of the supra-systolic cuff signal and the diastolic phase of the sub-diastolic cuff signal most closely approximate systolic and diastolic aortic pressure, respectively. The pressure curves for the systolic phase are derived from the aortic volume-time curve. In both Ayuredic-medicine and traditional Chinese-medicine, the pressure-pulse shape is felt to provide diagnosis information concerning diseases and disorders. In this regard, a precise evaluation of the aortic pressure-time profile and correlation of its shape parameters with diseases (using traditional Chinese and Ayuredic medical knowledge-base system) would constitute a significant contribution to medicine.

Thermography is a non-invasive and non-contact imaging technique widely used in the medical arena. This paper investigates the analysis of thermograms with the use of biostatistical methods and Artificial Neural Networks (ANN). It is desired that through these approaches, highly accurate diagnosis using thermography techniques can be established.

The proposed advanced technique is a multipronged approach comprising of Linear Regression (LR), Radial Basis Function Network (RBFN) and Receiver Operating Characteristics (ROC). It is a novel and integrative technique that can be used to analyze complicated and large numerical data. In this study, the advanced technique will be used to analyze breast cancer thermogram for diagnosis purposes.

The use of LR shows the correlation between the variables and the actual health status (healthy or cancerous) of the subject, which is decided by using mammography. This is important when selecting the variables to be used as inputs, in particular, for building the neural network.

For ANN, RBFN is applied. Based on the various inputs fed into the network, RBFN will be trained to produce the desired outcome, which is either positive for cancerous or negative for healthy cases. When this is done, the RBFN algorithm will possess the ability to predict the outcome when there are new input variables. The advantages of using RBFN include fast training, superior classification and decision making abilities as compared to other networks such as back-propagation.

Next, ROC is used to evaluate the accuracy, sensitivity and specificity of the outcome of RBFN Test files. The best results obtained are an accuracy (score) rate of 80.95%, with 81.2% sensitivity and 88.2% specificity. For breast cancer diagnosis, clinical examination by experienced doctors has an accuracy rate of approximately 60–70%. Hence, the proposed method has a higher accuracy rate than the existing practice.

Through the use of Bio-statistical methods and ANN, improvements are made in thermography application with regard to achieving a higher level of accuracy rate in diagnosis as compared to clinical examination. It has now become possible to use thermography as a powerful adjunct tool for breast cancer detection, together with mammography for diagnosis purposes.

Sleep apnea (SA) syndrome is a respiratory disorder that occurs during the sleep. Polysomnography (PSG) has been widely applied by clinicians as a gold standard in the clinical diagnosis of SA syndrome. However, the use of PSG is inconvenient, intrusive, and significantly affects the sleep quality of patient. In this paper, we provide a nonintrusive solution for SA detection. Specifically, a force sensor was employed for the noninvasive vital sign acquisition during the patient’s sleep, where the respiratory signal was extracted adaptively by using the morphological filter. It was observed that the morphological variations before and during the occurrence of the SA events were significant for the SA discrimination. By taking advantage of the differential features with respect to the respiratory signal, the recognition of the SA event was performed using classifiers. For validation, the all-night PSG recordings of 12 volunteers with 8 SA syndrome patients were obtained from the National Clinical Research Center for Respiratory Disease. Numerical results showed that the proposed scheme achieved an averaged accuracy, sensitivity and specificity of 83.67%, 58.57% and 85.13%, respectively, for the SA recognition.

This work demonstrated the preparation and characterization of a novel 4,4-difluoro-4-bora-3a,4a-diaza-s-indacene (BODIPY) derivative containing boronic acid group and investigation of its non-invasive/non-enzymatic fluorescence sensor behavior for determination of glucose. The novel BODIPY derivative bearing boronic acid pinacol ester (BODIPY-1) was synthesized by Sonogashira coupling reaction between Iodo-BODIPY and 4-ethynylphenylboronic acid pinacol ester. The target novel BODIPY-2 compound which was used as a fluorescence probe for the determination of glucose was synthesized from BODIPY-1by changing the pinacol ester group to the boric acid moieties. The fluorescence intensity of the BODIPY-2 fluorophore decreased when it interacted with the glucose. Sensing performance towards to glucose of this probe was evaluated in detail concerning the suitable solvent, linear concentration range, convenient pH, limit of detection (LOD), limit of quantification (LOQ) and selectivity. The LOD value of BODIPY-2 was found 0.19 mM toward glucose. Also, the complex stoichiometry between the BODIPY-2 and glucose molecules was determined by Job’s plot technique.

Early embryonic imaging of cardiovascular development in mammalian models requires a method that can penetrate through and distinguish the many tissue layers with high spatial and temporal resolution. In this paper we evaluate the capability of Optical Coherence Tomography (OCT) technique for structural 3D embryonic imaging in mouse embryos at different stages of the developmental process ranging from 7.5 dpc up to 10.5 dpc. Obtained results suggest that the collected data is suitable for quantitative and qualitative measurements to assess cardiovascular function in mouse models, which is likely to expand our knowledge of the complexity of the embryonic heart, and its development into an adult heart.

This paper presents a new non-invasive approach to predict the status of high total cholesterol (TC) level in blood using bioimpedance and the artificial neural network (ANN) techniques. The input parameters for the ANN model are acquired from a non-invasive bioelectrical impedance analysis (BIA) measurement technique. The measurement data were obtained from 260 volunteered participants. A total of 190 subject's data were used for the ANN training purpose and the remaining 70 subject's data were used for model testing. Six parameters from the BIA parameters were found to be significant predictors for TC level in blood using logistic regression analysis. The six input predictors for the ANN modeling are age, body mass index (BMI), body capacitance, basal metabolic rate, extracellular mass and lean body mass. Four ANN techniques such as the gradient descent with momentum, the resilient, the scaled conjugate gradient and the Levenberg–Marquardt were used and compared for predicting the high TC level in the blood. The finding showed that the resilient method was the best model with prediction accuracy, sensitivity, specificity and area under the curve value obtained from the test data were 82.9%, 85.4%, 79.3% and 0.83%, respectively.

Majority of global neonatal deaths is due to sepsis. A vast portion of these deaths occurs in developing countries due to inaccessibility of hospitals or lack of resources. Blood culture is the test to confirm sepsis, but it requires the presence of laboratory and is time-consuming. Therefore, we require simple, easy to use methods to predict sepsis in homes. Majority of the available prediction models need invasive parameters and hence become useless in the rural areas of developing countries where laboratory facilities do not exist. Non-invasive prediction models overcome these challenges to predict neonatal sepsis in places where there is a scarcity of laboratories. The aim and objective of this study are as follows: (i) to develop a practical, non-invasive prediction-model for neonatal sepsis which can be used in the rural areas of developing countries and to validate its performance. (ii) To compare the prognostic performance of the non-invasive prediction model with invasive prediction model and (iii) to create a prototype of the hardware which calculates the probability of the sepsis in neonates and sends the real-time data to the cloud. For this retrospective analysis, we extracted the data of 1446 neonates from Medical Information Mart for Intensive care III (MIMIC) database. Using stepwise logistic regression analysis, we developed and validated two prediction models. These two models were named as model NI and model O. Model O contains invasive as well as non-invasive parameters whereas model NI contains only non-invasive parameters. Model NI performed equally well in comparison to Model O despite using different predictors. The area under ROC curves for model NI and model O were 0.879 (95% CI: 0.857 to 0.899) and 0.861 (95% CI: 0.838 to 0.881) respectively. Both models were statistically significant with $p$-value$<0.001$.

Measuring oxygen saturation of blood (SpO2) clinically plays a vital role in patient’s health monitoring. In fact, monitoring oxygen level is necessary for people having respiratory problems (pulmonary hypertension) and in other critical conditions. The primary motivation of this work is to develop a low cost computer-based oxygen saturation monitoring system using an embedded system along with lab windows CVI platform. The process of calculating the level of oxygen saturation in the blood using non-invasive method is also called as pulse oximetry, which consists of LED and photo detectors, using MSP430FG4618 microcontroller. The MSP430 employed in designing the microcontroller firmware program for digitization and transmission of the data from sensor to the computer. NI-based Lab windows/CVI Platform was developed as a part of this project to receive, plot, save data and determine the accuracy of SpO2 value. In this proposed system, we have achieved the maximum accuracy of 99.49% which is better than the previously developed methods. The proposed system is also designed with the low cost and low power consuming modules.

Diabetes mellitus (DM) indicates elevated glucose concentration in blood. In type 1 diabetes, the pancreas produces inadequate insulin whereas in type 2 diabetes, the body is incapable to utilize the insulin present. Insulin is required to transport glucose into the cells. The insulin resistance by the cells causes the glucose level in the blood to increase. At present, the clinical methods available to diagnose DM are invasive. The diagnosis of DM is done by either pricking the fingertip or drawing blood from the vein followed by the quantification of blood glucose in terms of $mg/dL$. Continuous monitoring is limited as skin is punctured or venous blood is extracted. Spectroscopic analysis of hair, nail, saliva and urine possess the potential to differentiate the hyperglycaemic from the healthy subjects facilitating non-intrusive diagnosis of diabetes. The variation in the incident wavelength following the interaction with the sample is measured by a spectrometer. Based on the energy of the excitation source, the molecular structures present in the sample will either vibrate or absorb and emit photons that produce a spectrum. The samples were collected from both the groups of subjects and pre-processed prior to further examination. The samples were then characterized using the Fourier-transform infrared (FTIR) spectroscopy. The spectral output was pre-processed, filtered and analyzed so as to discriminate between the diabetic and healthy subjects. Although the spectral band of nail and hair samples appears to be identical, a difference in the amplitude was observed between both diabetic and normal subjects at 1450, 1520, 1632, 2925 cm${}^{-1}$. The area under curve (AUC) in the range of 3600 to 3100 cm-1 is a prominent marker in the discrimination. The peak wavelength and AUC were utilized as a biomarker to discriminate the diabetic and normal individuals.

In regenerative medicine it is important to be able to understand how cells are behaving in response to stimuli. The stimuli can be biological signals or materials. Raman spectroscopy allows the non-invasive real time monitoring of live cells in vitro by interpretation of spectra. Materials are being developed to use as templates (scaffolds) for tissue regeneration. The morphology of the pore structure is critical if tissue is to populate the scaffold. X-ray microcomputed tomography is the only method that can obtain 3D images of pore networks. Novel image analysis has been developed that can quantify pore networks. There is potential for this technique to be used to image tissue growth into scaffolds ex vivo. The next challenge is to adapt these two promising techniques to monitor the response of cells to porous scaffolds, including that of cells within the porous network.

Traditional Chinese Medicine (TCM) is increasing in popularity all over the world. However, due to the high demand for TCM products, quality control becomes a challenge as many formulations have a complicated composition. Moreover, in addition to quantitative control of ingredients, the geographic origin and the species must be identified. Analytical techniques employed so far, including mass spectroscopy, NMR spectroscopy and separation techniques, are all destructive, expensive and time-consuming, and therefore they are not really suitable for high-throughput quality control. For this purpose, near-infrared (NIR) offers novel highly and effective possibilities for a very fast, non-invasive and simultaneous investigation of a manifold of different questions. The present chapter aims to point out the efficiency of modern NIR methodology in addressing the above-mentioned problems through several well-selected examples. Thus, this contribution should help to convince the implementation of such an analytical method in quality control at different stages of the production process.