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Higa Medical Systems to Market Cardiovascular System in Japan.

German Healthcare Company Sets Up Base in India.

Australian Food Company Enters Indian Market.

First Transgenic Farm to be Set Up in China.

Largest Medicine Chain Opens in Tianjin.

Monsanto Thailand Increases Efforts to Ease Concerns on GM Products.

An Overview — Molecular Diagnostics.

Interview with Mr Michael Tillmann, Managing Director, Roche Diagnostics, Asia Pacific.

Interview with Dr Paul Smit, Senior Vice President, Philips Medical Systems.

Developing a Complete Molecular Diagnostic Solution: Multiplex Analysis, High Performance, High Throughput and Automation.

Interview with QIAGEN World Leader in Molecular Diagnostics Solutions.

World Heart Day – At the Heart of Health

That Gut Feeling: How A Healthy Digestive System Has Everything To Do With It

Cellular Biomedicine Group (CBMG) and GE Healthcare Life Sciences China Announce Strategic Partnership to Establish Joint Technology Laboratory to Develop Control Processes for the Manufacture of CAR-T and Stem Cell Therapies.

Zuellig Pharma to Invest over $50 Million in Singapore-Based Innovation Centre.

Holmusk: Using Data to Improve Clinical Outcomes for Cardiovascular Disease in Singapore.

Singapore Eye Bank Sets Another Record in Local Cornea Donations in 2016.

Plasticell and King’s College London to Collaborate in Trials of Blood Platelet Substitute.

Merck Partners with University of California, San Diego (UCSD) to Fight Neglected Tropical Diseases.

Mundipharma Wins Approval for Antineoplastic Agent mundesine® as Treatment for Relapsed/Refractory Peripheral T-cell Lymphoma in Japan.

Asian Myeloma Network (AMN) Brings Clinical Trials to Cancer Patients in Asia and Provides Early Access to Effective Drugs.

APBN Interview with Professor Chng Wee Joo.

The interpretation of Electroencephalography (ECG) signals is difficult, because even subtle changes in the waveform can indicate a serious heart disease. Furthermore, these waveform changes might not be present all the time. As a consequence, it takes years of training for a medical practitioner to become an expert in ECG-based cardiovascular disease diagnosis. That training is a major investment in a specific skill. Even with expert ability, the signal interpretation takes time. In addition, human interpretation of ECG signals causes interoperator and intraoperator variability. ECG-based Computer-Aided Diagnosis (CAD) holds the promise of improving the diagnosis accuracy and reducing the cost. The same ECG signal will result in the same diagnosis support regardless of time and place. This paper introduces both the techniques used to realize the CAD functionality and the methods used to assess the established functionality. This survey aims to instill trust in CAD of cardiovascular diseases using ECG signals by introducing both a conceptional overview of the system and the necessary assessment methods.

Heart sound signal processing is a low-cost, and noninvasive method for the early diagnosis of various types of cardiovascular diseases. In this study, a parallel diagnosing method was proposed to detect various types of heart diseases and healthy heart samples. The proposed system can detect a person who might be simultaneously suffering from two or more heart diseases. Contributing to this line of investigation, effective features were obtained from the morphological and statistical features extracted from five frequency ranges of heart sounds. Applying such features in diagnosing any heart disease acts as a fingerprint specific to that disease. Therefore, the investigation of selected features, especially in each of the frequency ranges of heart sounds and murmurs, provided us with valuable information about the behavior of the diagnostic system in the detection of heart diseases. In addition to using features related to the nature of heart sounds, the proposed method of this study got rid of both the need to apply different filters needed to remove noise and dependence on a specific dataset. With the aid of the effective features in the parallel diagnosis of 15 different types of important and common heart diseases and a healthy class from each other, the diagnostic system of the present study was able to achieve the average accuracy of 97.06%, the average sensitivity of 97.99%, and the average specificity of 96.18% in the shortest possible time. The proposed approach is an important step in the screening and remote monitoring and tracking of disease progression.

Cardiovascular diseases are the leading cause of death worldwide. The development of models to support clinical decision is of great importance in the management of these diseases. This work aims to improve the performance exhibited by risk assessment scores that are applied in the clinical practice. This methodology has three main phases: (i) representation of scores as a decision tree; (ii) optimization of the decision tree thresholds using data from recent clinical datasets; (iii) transformation of the optimized decision tree into a new score.

This approach was validated in a cardiovascular disease secondary prevention context, supported by a dataset provided by the Portuguese Society of Cardiology ($N=13902$). The respective performance was assessed using statistical metrics and was compared with GRACE score, the reference in Portuguese clinical practice. The new model originated a better balance between the sensitivity and specificity when compared with the GRACE, originating an accuracy improvement of approximately 22%.