Goldsteinjonasson0070

5-HT receptors expressed throughout the human body are targets for established therapeutics and various drugs in development. Their diversity of structure and function reflects the important role 5-HT receptors play in physiologic and pathophysiological processes. The present review offers a framework for the official receptor nomenclature and a detailed understanding of each of the 14 5-HT receptor subtypes, their roles in the systems of the body, and, where appropriate, the (potential) utility of therapeutics targeting these receptors. SIGNIFICANCE STATEMENT This review provides a comprehensive account of the classification and function of 5-hydroxytryptamine receptors, including how they are targeted for therapeutic benefit.With advances in digital stethoscopes, internet of things, signal processing and machine learning, chest sounds can be easily collected and transmitted to the cloud for remote monitoring and diagnosis. However, low quality of recordings complicates remote monitoring and diagnosis, particularly for neonatal care. This paper proposes a new method to objectively and automatically assess the signal quality to improve the accuracy and reliability of heart rate (HR) and breathing rate (BR) estimation from noisy neonatal chest sounds. A total of 88 10-second long chest sounds were taken from 76 preterm and full-term babies. Six annotators independently assessed the signal quality, number of detectable beats, and breathing periods from these recordings. For quality classification, 187 and 182 features were extracted from heart and lung sounds, respectively. After feature selection, class balancing, and hyperparameter optimization, a dynamic binary classification model was trained. Then HR and BR were automatically estimated from the chest sound and several approaches were compared. The results of subject-wise leave-one-out cross-validation, showed that the model distinguished high and low quality recordings in the test set with 96% specificity, 81% sensitivity and 93% accuracy for heart sounds, and 86% specificity, 69% sensitivity and 82% accuracy for lung sounds. The HR and BR estimated from high quality sounds resulted in significantly less median absolute error (4bpm and 12bpm difference, respectively) compared to those from low quality sounds. The methods presented in this work, facilitates automated neonatal chest sound auscultation for future telehealth applications.We combine tracking information from a tangible object instrumented with capacitive sensors and an optical tracking system, to improve contact rendering when interacting with tangibles in VR. A human-subject study shows that combining capacitive sensing with optical tracking significantly improves the visuohaptic synchronization and immersion of the VR experience.The ultrasound (US) imaging technique has been applied to scoliosis assessment, and the proxy Cobb angle can be acquired on the US coronal images. The spinous process angle (SPA) is a valuable parameter to indicate 3-D deformity of spine. However, the SPA cannot be measured on US images since the spinous process (SP) is merged in the soft tissue layer and impossible to be identified on the coronal view directly. A new method based on the gradient vector flow (GVF) snake model was proposed to automatically locate SP position on the US transverse images, and the density-based spatial clustering of application with noise (DBSCAN) was used to remove the outliers out of the detected location results. With marking the SP points on the US coronal image, the SP curve was interpolated and the SPA was measured. The algorithm was evaluated on 50 subjects with various severity of scoliosis, and two raters measured the SPA on both US images and radiographs manually. learn more The mean absolute differences (MADs) of SPAs obtained from the two modalities were 3.4° ± 2.4° and 3.6° ± 2.8° for the two raters, respectively, which were less than the clinical acceptance error (5°), and the results reported a good linear correlation ( ) between the US method and radiography. It indicates that the proposed method can be a promising approach for SPA measurement using the US imaging technique.Despite that deep learning has achieved state-of-the-art performance for medical image segmentation, its success relies on a large set of manually annotated images for training that are expensive to acquire. In this paper, we propose an annotation-efficient learning framework for segmentation tasks that avoids annotations of training images, where we use an improved Cycle-Consistent Generative Adversarial Network (GAN) to learn from a set of unpaired medical images and auxiliary masks obtained either from a shape model or public datasets. We first use the GAN to generate pseudo labels for our training images under the implicit high-level shape constraint represented by a Variational Auto-encoder (VAE)-based discriminator with the help of the auxiliary masks, and build a Discriminator-guided Generator Channel Calibration (DGCC) module which employs our discriminator's feedback to calibrate the generator for better pseudo labels. To learn from the pseudo labels that are noisy, we further introduce a noise-robust iterative learning method using noise-weighted Dice loss. We validated our framework with two situations objects with a simple shape model like optic disc in fundus images and fetal head in ultrasound images, and complex structures like lung in X-Ray images and liver in CT images. Experimental results demonstrated that 1) Our VAE-based discriminator and DGCC module help to obtain high-quality pseudo labels. 2) Our proposed noise-robust learning method can effectively overcome the effect of noisy pseudo labels. 3) The segmentation performance of our method without using annotations of training images is close or even comparable to that of learning from human annotations.Large-scale datasets with high-quality labels are desired for training accurate deep learning models. However, due to the annotation cost, datasets in medical imaging are often either partially-labeled or small. For example, DeepLesion is such a large-scale CT image dataset with lesions of various types, but it also has many unlabeled lesions (missing annotations). When training a lesion detector on a partially-labeled dataset, the missing annotations will generate incorrect negative signals and degrade the performance. Besides DeepLesion, there are several small single-type datasets, such as LUNA for lung nodules and LiTS for liver tumors. These datasets have heterogeneous label scopes, i.e., different lesion types are labeled in different datasets with other types ignored. In this work, we aim to develop a universal lesion detection algorithm to detect a variety of lesions. The problem of heterogeneous and partial labels is tackled. First, we build a simple yet effective lesion detection framework named Lesion ENSemble (LENS). LENS can efficiently learn from multiple heterogeneous lesion datasets in a multi-task fashion and leverage their synergy by proposal fusion. Next, we propose strategies to mine missing annotations from partially-labeled datasets by exploiting clinical prior knowledge and cross-dataset knowledge transfer. Finally, we train our framework on four public lesion datasets and evaluate it on 800 manually-labeled sub-volumes in DeepLesion. Our method brings a relative improvement of 49% compared to the current state-of-the-art approach in the metric of average sensitivity. We have publicly released our manual 3D annotations of DeepLesion in.Despite decades of research, we lack a mechanistic framework capable of predicting how movement-related signals are transformed into the diversity of muscle spindle afferent firing patterns observed experimentally, particularly in naturalistic behaviors. Here, a biophysical model demonstrates that well-known firing characteristics of mammalian muscle spindle Ia afferents - including movement history dependence, and nonlinear scaling with muscle stretch velocity - emerge from first principles of muscle contractile mechanics. Further, mechanical interactions of the muscle spindle with muscle-tendon dynamics reveal how motor commands to the muscle (alpha drive) versus muscle spindle (gamma drive) can cause highly variable and complex activity during active muscle contraction and muscle stretch that defy simple explanation. Depending on the neuromechanical conditions, the muscle spindle model output appears to 'encode' aspects of muscle force, yank, length, stiffness, velocity, and/or acceleration, providing an extendable, multiscale, biophysical framework for understanding and predicting proprioceptive sensory signals in health and disease.Fibroblasts play an essential role in organogenesis and the integrity of tissue architecture and function. Growth in most solid tumors is dependent upon remodeling 'stroma', composed of cancer-associated fibroblasts (CAFs) and extracellular matrix (ECM), which plays a critical role in tumor initiation, progression, metastasis, and therapeutic resistance. Recent studies have clearly established that the potent immunosuppressive activity of stroma is a major mechanism by which stroma can promote tumor progression and confer resistance to immune-based therapies. Herein, we review recent advances in identifying the stroma-dependent mechanisms that regulate cancer-associated inflammation and antitumor immunity, in particular, the interactions between fibroblasts and immune cells. We also review the potential mechanisms by which stroma can confer resistance to immune-based therapies for solid tumors and current advancements in stroma-targeted therapies.

Patients with colorectal cancer often present with anaemia and require red blood cell transfusions (RBCT) during their peri-operative course. Evidence suggests a significant association between RBCT and poor long-term outcomes in surgical patients, but the findings in colorectal cancer are contradictory.

The aim of this retrospective, single-centre, cohort study was to investigate the prognostic role of peri-operative RBCT in a large cohort of patients with stage I-III colorectal cancer submitted to curative surgery between 2005 and 2017. The propensity score matching technique was applied to adjust for potential confounding factors.

Among 1,414 patients operated within the study period, 895 fulfilled the inclusion criteria 29.6% (n=265) received peri-operative RBCT. The group that received peri-operative RBCT was significantly older (p<0.001), had more comorbidities (p<0.001), more advanced tumours (p<0.001) and more colon tumours (p=0.002) and stayed in hospital longer (p<0.001). Post-operative mortality was 7-fold higher (2.3 vs 0.3%, p=0.01) in this group. Survival outcomes were significantly worse in the group receiving RBCT than in the group not receiving RBCT for both overall (64.5 vs 80.1%, p<0.001) and cancer-specific survival (74.3 vs 85.1%, p<0.001). On multivariable analysis, peri-operative RBCT was significantly associated with poorer overall survival (hazard ratio 1.51, p=0.009). When transfused and non-transfused cases were paired through the propensity score matching technique considering main clinico-pathological features, no differences in overall and cancer-specific survival were found.

Our data suggest that, after adjustment for potential confounding factors, no significant association exists between RBCT and prognosis in colorectal cancer.

Our data suggest that, after adjustment for potential confounding factors, no significant association exists between RBCT and prognosis in colorectal cancer.