Oxford BioTherapeutics (OBT) has entered a multi-year partnership with Roche to discover antibody-based therapeutics for cancer treatment.
The partnership will leverage OBT’s OGAP-Verify discovery platform, which claims to offer greater sensitivity and enables target selection with enhanced drug development attributes.
OBT has entered a multi-year partnership with Roche to discover antibody-based therapeutics for cancer treatment.
In addition, traditional techniques do not allow researchers to optimally handle diverse sample types, including DNA samples from common sources such as saliva, blood, or formalin-fixed paraffin-embedded (FFPE) tissues. Such relative inflexibility limits the ability of scientists to efficiently compare sequencing results between different samples from the same individual as well as between different individuals, further complicating downstream data collection and reliability.
Library preparation kits that are designed to improve the efficiency and reproducibility of WGS workflows are a welcome addition to the sequencing repertoires of laboratory scientists engaged in cutting-edge and scalable translational research. Importantly, the versatility, adaptability, and turnaround time of novel library preparation solutions have the power to standardize protocols, eliminate workflow bottlenecks, preserve resources, and uncover new opportunities. Overall, preparation kits that have built-in adaptability to the inherent variability of WGS protocols enable more straightforward optimization and better results than traditional approaches.
Covaris’s truCOVER WGS PCR-free Library Prep Kit is a versatile, cutting-edge solution that addresses the inherent complexity of WGS workflows using a rapid, reliable, reproducible, efficient, and cost-effective approach. The kit enables adaptable PCR-free library preparation from different types of samples, including saliva, blood, and FFPE for a wide range of downstream sequencing workflow applications. The truCOVER kit streamlines library preparation processes by eradicating rate-limiting obstacles such as fragmentation bias and eliminating the need for PCR-based quality control steps.
A team of researchers from the Universitat Politècnica de València (UPV) and the French National Center for Scientific Research (CNRS) has developed the world’s most advanced software to study the human cerebellum using high-resolution NMR images.
Called DeepCeres, this software will help in the research and diagnosis of diseases such as ALS, schizophrenia, autism and Alzheimer’s, among others. The work of the Spanish and French researchers has been published in the journal NeuroImage.
Despite its small size compared to the rest of the brain, the cerebellum contains approximately 50% of all brain neurons and plays a fundamental role in cognitive, emotional and motor functions.
Magnetized algae micro swimmers retain speed and maneuverability, showing promise for targeted drug delivery in confined biological environments. A team of researchers at the Max Planck Institute for Intelligent Systems (MPI-IS) in Stuttgart has developed a biohybrid microswimmer coated with magn
One stormy Monday in March, 1827, the German composer Ludwig van Beethoven passed away after a protracted illness.
Long non-coding RNAs (long ncRNAs,) are a type of RNA, generally defined as transcripts more than 200 nucleotides that are not translated into protein.
Long non-coding transcripts are found in many species.
LncRNAs are extensively reported to be involved in transcriptional regulation, and epigenetic regulation.
Long non coding RNA has been proven to be associated with multiple diseases, such as cardiovascular diseases, rheumatic diseases, cancer etc.
More detailed information ons are provided in the link below.
The healthcare industry faces a significant shift towards digital health technology, with a growing demand for real-time and continuous health monitoring and disease diagnostics [1, 2, 3]. The rising prevalence of chronic diseases, such as diabetes, heart disease, and cancer, coupled with an aging population, has increased the need for remote and continuous health monitoring [4, 5, 6, 7]. This has led to the emergence of artificial intelligence (AI)-based wearable sensors that can collect, analyze, and transmit real-time health data to healthcare providers so that they can make efficient decisions based on patient data. Therefore, wearable sensors have become increasingly popular due to their ability to provide a non-invasive and convenient means of monitoring patient health. These wearable sensors can track various health parameters, such as heart rate, blood pressure, oxygen saturation, skin temperature, physical activity levels, sleep patterns, and biochemical markers, such as glucose, cortisol, lactates, electrolytes, and pH and environmental parameters [1, 8, 9, 10]. Wearable health technology includes first-generation wearable technologies, such as fitness trackers, smartwatches, and current wearable sensors, and is a powerful tool in addressing healthcare challenges [2].
The data collected by wearable sensors can be analyzed using machine learning (ML) and AI algorithms to provide insights into an individual’s health status, enabling early detection of health issues and the provision of personalized healthcare [6,11]. One of the most significant advantages of AI-based wearable health technology is to promote preventive healthcare. This enables individuals and healthcare providers to proactively address symptomatic conditions before they become more severe [12,13,14,15]. Wearable devices can also encourage healthy behavior by providing incentives, reminders, and feedback to individuals, such as staying active, hydrating, eating healthily, and maintaining a healthy lifestyle by measuring hydration biomarkers and nutrients.