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INDIA – A novel form of gene regulation in bacteria.

INDIA – Algal biofuels are no energy panacea.

JAPAN – Medical Data Vision enhances the quality of medical care with Actian Vectorwise.

SINGAPORE – Singapore heart surgeon to receive honour from The Royal College of Surgeons of Edinburgh.

SINGAPORE – ELGA^® to deliver innovative water purification at new Singapore General Hospital expansion.

AUSTRALIA – Specialised Therapeutics Australia: New drug to fight hospital superbug infection.

AUSTRALIA – Group of genes hold the clue in migraine cases.

AUSTRALIA – CT scans can triple risk of brain cancer, leukemia.

BRAZIL – Science can do more for sustainable development.

MIDDLE EAST – Particles and persecution: why we should care about Iranian physicists.

EUROPE – Medicyte coordinates EU-funded collaboration on Biomimetic Bioartificial Liver.

EUROPE – Selvita and Orion Pharma achieve a research milestone in Alzheimer's Disease Program.

EUROPE – Zinforo (ceftaroline fosamil) receives positive CHMP opinion in the European Union for the treatment of patients with serious skin infections or community acquired pneumonia.

USA – Vein grown from girl's own stem cells transplanted.

USA – Hidden vitamin in milk yields remarkable health benefits - Weill Cornell researchers show tiny vitamin in milk, in high doses, makes mice leaner, faster and stronger.

USA – New report finds biotechnology companies are participating in 39% of all projects in development for new medicines and technologies for neglected diseases.

USA – TriReme Medical receives FDA clearance for expanded matrix of sizes of Chocolate PTA balloon catheter.

USA – New data show investigational compound dapagliflozin demonstrated significant reductions in blood sugar levels when added to sitagliptin in adults with type 2 diabetes at 24 weeks, with results maintained over 48 weeks.

USA – Zalicus successfully completes Phase 1 single ascending dose study with Z944, a novel, oral T-Type Calcium Channel Blocker.

USA – Study provides clues to clinical trial cost savings.

Finite element (FE) simulations can be utilized to predict contact pressures at the bone/implant interface as well as to identify the position and shape of the contact region. However, the accuracy and reliability of FE models of the bone/implant interface reconstructed from tomographic images may be affected by a number of factors such as the presence of image artifacts, the magnitude of geometric errors made in the reconstruction process, the type of boundary and loading conditions hypothesized in the model, the nonlinear solver utilized for computing the contact pressure distribution, and the element type. This paper attempts to estimate the global effect of the aforementioned factors. For this purpose, a cylindrical contact problem — pin/muff — portraying a simplified model of the bone/implant interface is considered. The accuracy of numerical predictions is estimated by comparing contact pressures predicted by an FE model reconstructed from computed tomography (CT) scan images and by an "ideal", experimentally validated FE model. Two different couplings, i.e. chromium-cobalt alloy and titanium implants, are considered. In the former case, image artifacts complicate the reconstruction process of model geometry and lead to less accurate predictions on contact pressure distribution; conversely, the limited streaking effects occurring in the titanium pin case allow us to precisely reconstruct coupling geometry. Finally, a rather clear correlation between errors on contact pressure and geometric errors made in the reconstruction process is found only for the titanium pin.

Cancers typically are both highly dangerous and common. Among these, lung cancer has one of the lowest survival rates compared to other cancers. CT scans can reveal dense masses of different shapes and sizes; in the lungs, these are called lung nodules. This study applied a computer-aided diagnosis (CAD) system to detect candidate nodules — and diagnose it either solitary or juxtapleural — with equivalent diameters, ranging from 7.78mm to 22.48mm in a 2D CT slice. Pre-processing and segmentation is a very important step to segment and enhance the CT image. A segmentation and enhancement algorithm is achieved using bilateral filtering, Thresholding the gray-level transformation function, Bounding box and maximum intensity projection. Border artifacts are removed by clearing the lung border, erosion, dilation and superimposing. Feature extraction is done by extracting 20 gray-level co-occurrence matrix features from four directions: $0^{\circ }$, $45^{\circ }$, $90^{\circ }$ and $135^{\circ }$ and one distance of separation ($d=1$ pixel). In the classification step, two classifiers are proposed to classify two types of nodules based on their locations: as juxtapleural or solitary nodules. The two classifiers are a deep learning convolutional neural network (CNN) and the K-nearest neighbor (KNN) algorithm. Random oversampling and 10-fold cross-validation are used to improve the results. In our CAD system, the highest accuracy and sensitivity rates achieved by the CNN were 96% and 95%, respectively, for solitary nodule detection. The highest accuracy and sensitivity rates achieved by the KNN model were 93.8% and 96.7%, respectively, and K was set to 1 to detect juxtapleural nodules.

Image de-noising is an essential tool for removing unwanted signals from an image. In Computed Tomography (CT) images, the image quality is degraded by the absorption of X-rays and quantum noise, which is generated due to the excitement of X-ray photons. Removal of noise and preservation of information in the CT images becomes a challenge for an imaging algorithm design. During the algorithm design selection of dataset is an important aspect for deducing results. The dataset used in this research comprises of 60 CT scan images of liver cancer archived from the arterial contrast enhanced phase. In this phase the cancer cells appear more intense as compared to the healthy liver tissue due to the absorption of contrast enhancing reagent. The experimentation for appropriate noise removal filter selection is done by testing the images using Mean, Median and Weiner Filters. The filter selected should give an image output which has minimal randomness, sharper boundaries and no blur. The de-noised image will provide a better visibility of the disease to the radiologist and physician. The performance parameters used for the assessment of various filters used in the study include visual assessment, entropy and signal to noise ratio (SNR) of the images. Median filter gives an accuracy of 96%, mean filter is 76.2% accurate with respect to original information and Weiner filters has an accuracy of 79.7%.