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Simple Summary: Colorectal cancer (CRC) is the second leading cause of cancer deaths worldwide. Genetic alterations promote cancer development and its spread to distant sites. Medications that counteract the effects of these alterations/mutations, called targeted therapies, are used to treat CRC. Immunotherapy, another class of medications that promotes immune system mediated recognition and destruction of cancer cells, is used for treatment of a small subset of CRC patients. Here we review the current state of knowledge and ongoing research into biomarkers, targeted therapy, and immunotherapy for CRC treatment. Colorectal cancer (CRC) is the second leading cause of cancer deaths worldwide. Although the overall incidence of CRC is decreasing, the incidence of young-onset CRC, characterized by a diagnosis of CRC before age 50, is increasing. Outcomes for CRC patients are improving, partly due to comprehensive molecular characterization of tumors and novel therapeutic strategies. Advances in genomic and transcriptomic analyses using blood- and tumor-tissue-based sequencing have facilitated identification of distinct tumor subtypes harboring unique biological characteristics and therapeutic vulnerabilities. These insights have led to the development and incorporation of targeted therapies and immunotherapy in CRC treatment. In this review, we discuss the molecular landscape and key oncogenes/tumor suppressors contributing to CRC tumorigenesis, metastasis, and therapeutic resistance. We also discuss personalized therapeutic strategies for subsets of CRC patients and provide an overview of evolving novel treatments being evaluated in clinical trials. [ABSTRACT FROM AUTHOR]

Featured Application: Physiological parameters of drivers, including the electrocardiogram (ECG), heart rate and heart rate variability, respiration activity and rate, can be measured in a contactless way using capacitively coupled and radar sensors. This potentially enables applications to monitor the driver's physical and emotional status and well-being and contribute to preventing accidents and provide health-related services. Unobtrusive monitoring of drivers' physiological parameters is a topic gaining interest, potentially allowing to improve the performance of safety systems to prevent accidents, as well as to improve the driver's experience or provide health-related services. In this article, two unobtrusive sensing techniques are evaluated: capacitively coupled sensing of the electrocardiogram and respiration, and radar-based sensing of heartbeat and respiration. A challenge for use of these techniques in vehicles are the vibrations and other disturbances that occur in vehicles to which they are inherently more sensitive than contact-based sensors. In this work, optimized sensor architectures and signal processing techniques are proposed that significantly improve the robustness to artefacts. Experimental results, conducted under real driving conditions on public roads, demonstrate the feasibility of the proposed approach. R peak sensitivities and positive predictivities higher than 98% both in highway and city traffic, heart rate mean absolute error of 1.02 bpm resp. 2.06 bpm in highway and city traffic and individual beat R-R interval 95% percentile error within ±27.3 ms are demonstrated. The radar experimental results show that respiration can be measured while driving and heartbeat can be recovered from vibration noise using an accelerometer-based motion reduction algorithm. [ABSTRACT FROM AUTHOR]

Obstructive sleep apnea (OSA) patients would strongly benefit from comfortable home diagnosis, during which detection of wakefulness is essential. Therefore, capacitively-coupled electrocardiogram (ccECG) and bioimpedance (ccBioZ) sensors were used to record the sleep of suspected OSA patients, in parallel with polysomnography (PSG). The three objectives were quality assessment of the unobtrusive signals during sleep, prediction of sleep–wake using ccECG and ccBioZ, and detection of high-risk OSA patients. First, signal quality indicators (SQIs) determined the data coverage of ccECG and ccBioZ. Then, a multimodal convolutional neural network (CNN) for sleep–wake prediction was tested on these preprocessed ccECG and ccBioZ data. Finally, two indices derived from this prediction detected patients at risk. The data included 187 PSG recordings of suspected OSA patients, 36 (dataset "Test") of which were recorded simultaneously with PSG, ccECG, and ccBioZ. As a result, two improvements were made compared to prior studies. First, the ccBioZ signal coverage increased significantly due to adaptation of the acquisition system. Secondly, the utility of the sleep–wake classifier increased as it became a unimodal network only requiring respiratory input. This was achieved by using data augmentation during training. Sleep–wake prediction on "Test" using PSG respiration resulted in a Cohen's kappa (κ) of 0.39 and using ccBioZ in κ = 0.23. The OSA risk model identified severe OSA patients with a κ of 0.61 for PSG respiration and κ of 0.39 using ccBioZ (accuracy of 80.6% and 69.4%, respectively). This study is one of the first to perform sleep–wake staging on capacitively-coupled respiratory signals in suspected OSA patients and to detect high risk OSA patients based on ccBioZ. The technology and the proposed framework could be applied in multi-night follow-up of OSA patients. [ABSTRACT FROM AUTHOR]