Medications like dextromethorphan, used to treat coughs caused by cold and flu, could potentially be repurposed to help people quit smoking cigarettes, according to a study by Penn State College of Medicine and University of Minnesota researchers. They developed a novel machine learning method, where computer programs analyze data sets for patterns and trends, to identify the drugs and said that some of them are already being tested in clinical trials. Cigarette smoking is risk factor for cardiovascular disease, cancer and respiratory diseases and accounts for nearly half a million deaths in the United States each year. While smoking behaviors can be learned and unlearned, genetics also plays a role in a person's risk for engaging in those behaviors. The researchers found in a prior study that people with certain genes are more likely to become addicted to tobacco.

Consultant cardiologist Professor Faisal Sharif at GUH welcomed the addition of the CorPath Robotic Angioplasy as "a game changer". "We recently successfully completed the first case and, going forward, we will be performing these procedures regularly," he said. He said robotic innovations have come a long way in the last 10 years. "We in Galway are delighted to have performed the first robotic-guided coronary intervention in Ireland and the UK," he said. "The main advantage of robotics is that it is safe and very precise in stent placement. It allows the accurate placement for up to 1mm at a time."

SmartCardia 7L Patch is a breakthrough 7/14 day patch that offers real-time 7-Lead ECG and vitals with AI SaaS* *SmartCardia solution approved as SCaAI patch and cloud platform in Europe (CE Class IIa) - ECG, respiration, SpO2, activity and cloud based arrhythmia detection.

A question has increasingly plagued me since I began studying our relationship with technology about two decades ago: Will we ever pay attention again? The concern arose from measuring the shrinking attention spans of hundreds of knowledge workers in a variety of work roles. Whether we're talking about a Gen Z or a baby boomer, a CEO or an administrative assistant, attention spans on our computers and phones are short and declining. To study people's attention on their devices, with my research team at the University of California, Irvine, and with colleagues at Microsoft Research, I observed people in their natural environments and created living laboratories. We used sophisticated computer logging techniques to measure attention spans and heart rate monitors and wearable devices to measure stress.

Volta Medical, a France-based health technology company developing AI solutions to assist electrophysiologist physicians and surgeons, has secured €36 million in Series B funding. With this, the total funding raised by the company accounts for €70 million. The investment round was led by the US-based Vensana Capital alongside participation from Lightstone Ventures (which backed Dunzo and Nimbus Therapeutics) and existing investor Gilde Healthcare. The funding will help accelerate new product development, support additional clinical trials, prepare for full-scale US commercialisation, and pursue further regulatory approvals. The company's lead product, VOLTA VX1, is the first commercially available AI decision-support software to help guide physicians with identification and real-time annotation of unique abnormalities on 3D anatomical and electrical maps of the heart.

Today, almost half of the world's population does not have access to proper healthcare, with many people driven into poverty because of high health expenses. It is estimated that over $140 billion is required annually to meet the health-related sustainable development goal objectives. Further, significant health technology, digital technology, and artificial intelligence (AI) investments are needed to bridge the health service gap in emerging markets. Many health-related startups and tech innovators have started integrating AI with their products and solutions, showing promise of improved diagnoses, reduced costs, and proper access to remote health services. COVID-19 has also accelerated the pace of transition to digital health applications, including those that integrate AI. Health startups and tech companies aiming to integrate AI technologies account for a large proportion of AI-specific investments, accounting for up to $2 billion in 2018 (Figure 1).

Combining machine learning with multimodal electrochemical sensing can significantly improve the analytical performance of biosensors, according to new findings from a Penn State research team. These improvements may benefit noninvasive health monitoring, such as testing that involves saliva or sweat. The findings were published this month in Analytica Chimica Acta. The researchers developed a novel analytical platform that enabled them to selectively measure multiple biomolecules using a single sensor, saving space and reducing complexity as compared to the usual route of using multi-sensor systems. In particular, they showed that their sensor can simultaneously detect small quantities of uric acid and tyrosine--two important biomarkers associated with kidney and cardiovascular diseases, diabetes, metabolic disorders, and neuropsychiatric and eating disorders--in sweat and saliva, making the developed method suitable for personalized health monitoring and intervention.

NIR Calibration-Model Services Spectroscopy and Chemometrics News Weekly 33, 2022 NIRS NIR Spectroscopy MachineLearning Spectrometer Spectrometric Analytical Chemistry Chemical Analysis Lab Labs Laboratories Laboratory Software IoT Sensors QA QC Testing Quality LINK Spektroskopie und Chemometrie Neuigkeiten Wöchentlich 33, 2022 NIRS NIR Spektroskopie MachineLearning Spektrometer IoT Sensor Nahinfrarot Chemie Analytik Analysengeräte Analysentechnik Analysemethode Nahinfrarotspektroskopie Laboranalyse LINK Spettroscopia e Chemiometria Weekly News 33, 2022 NIRS NIR Spettroscopia MachineLearning analisi chimica Spettrale Spettrometro Chem IoT Sensore Attrezzatura analitica Laboratorio analisi prova qualità Analysesystem QualityControl LINK Near-Infrared Spectroscopy (NIRS) "Comparative Performance of NIR-Hyperspectral Imaging Systems" LINK "Near infrared spectroscopy calibration strategies to predict multiple nutritional parameters of pasture species from different functional groups" LINK "Near-infrared spectroscopy as a tool to assist Sargassum fusiforme quality grading: Harvest time discrimination and polyphenol prediction" LINK "Sensors : Using Vis-NIR Spectroscopy for Predicting Quality Compounds in Foods" LINK "Development of an amino acid sequence-dependent analytical method for peptides using near-infrared spectroscopy" LINK "NDT model study of crown pear based on near infrared spectroscopy" LINK "Analyzing the Water Confined in Hydrogel Using Near-Infrared Spectroscopy" LINK "Foods : Finite Element Analysis and Near-Infrared Hyperspectral Reflectance Imaging for the Determination of Blueberry Bruise Grading" LINK "Application of near infrared spectroscopy in sub-surface monitoring of petroleum contaminants in laboratory-prepared soils" LINK "Identification of multiple raisins by feature fusion combined with NIR spectroscopy" LINK " … of quality markers for quality control of Zanthoxylum nitidum using ultra-performance liquid chromatography coupled with near infrared spectroscopy" LINK "Karakterisasi Fitokimia Enkapsulasi Nira Tebu Powder dengan Menggunakan Varietas BL, PSDK-923, dan PSBM-901" LINK "Inside the Egg--Demonstrating Provenance Without the Cracking Using Near Infrared Spectroscopy" LINK "Organic resources from Madagascar: Dataset of chemical and near-infrared spectroscopy measurements" LINK "An alternative method for identification of industrial tomato hybrids using NIRS" LINK "Uniformity evaluation of stem distribution in cut tobacco and single cigarette by near infrared spectroscopy" LINK "A General and Scalable Vision Framework for Functional Near-Infrared Spectroscopy Classification" LINK "Near infrared spectroscopy for the pre-cure freezing discrimination of Montanera Iberian dry-cured lomito" LINK "Determination of Moisture and Protein Content in Living Mealworm Larvae (Tenebrio molitor L.) Using Near-Infrared Reflectance Spectroscopy (NIRS)" LINK "Towards Inline Prediction of Color Development for Wood Stained with Chemical Stains Using Near-Infrared Spectroscopy" LINK "Comparison Between Pure Component Modeling Approaches for Monitoring Pharmaceutical Powder Blends with Near-Infrared Spectroscopy in Continuous Manufacturing Schemes" LINK "Potential of NIRS technology for the determination of cannabinoid content in industrial hemp (Cannabis sativa L.)" 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So interested people will connect.

We conducted experiments to verify the robustness of our calibration procedure based on polynomial fitting. We replicated the process by taking 250 randomly picked times from the learning set. Finally, we explore the frequencies of mean and SD error as shown in Figure 15. Overall, the highest frequencies of both SBP and DBP mean error falls between 4 and 5 mmHg, which satisfies AAMI standards. Similarly, the highest frequency of SD errors is less than 8 mmHg, which also qualifies the AAMI protocol. In addition, 9 out of 35 candidates proceed 10 times of data collection to calculate the intraclass correlation coefficient (ICC). Figure 16 shows the ICC result of each candidate. The average ICC of SBP and DBP are 0.8 and 0.76, respectively.

Just a few years ago, there were no legions of deep learning scientists developing intelligent products and services at major companies and startups. When the youngest among us (the authors) entered the field, machine learning did not command headlines in daily newspapers. Our parents had no idea what machine learning was, let alone why we might prefer it to a career in medicine or law. Machine learning was a forward-looking academic discipline with a narrow set of real-world applications. And those applications, e.g., speech recognition and computer vision, required so much domain knowledge that they were often regarded as separate areas entirely for which machine learning was one small component. Neural networks then, the antecedents of the deep learning models that we focus on in this book, were regarded as outmoded tools. In just the past five years, deep learning has taken the world by surprise, driving rapid progress in fields as diverse as computer vision, natural language processing, automatic speech recognition, reinforcement learning, and statistical modeling. With these advances in hand, we can now build cars that drive themselves with more autonomy than ever before (and less autonomy than some companies might have you believe), smart reply systems that automatically draft the most mundane emails, helping people dig out from oppressively large inboxes, and software agents that dominate the worldʼs best humans at board games like Go, a feat once thought to be decades away. Already, these tools exert ever-wider impacts on industry and society, changing the way movies are made, diseases are diagnosed, and playing a growing role in basic sciences--from astrophysics to biology.