SignificanceHyperspectral microscopy grants the ability to characterize unique properties of tissues based on their spectral fingerprint. The ability to label and measure multiple molecular probes simultaneously provides pathologists and oncologists with a powerful tool to enhance accurate diagnostic and prognostic decisions. As the pathological workload grows, having an objective tool that provides companion diagnostics is of immense importance. Therefore, fast whole-slide spectral imaging systems are of immense importance for automated cancer prognostics that meet current and future needs.AimWe aim to develop a fast and accurate hyperspectral microscopy system that can be easily integrated with existing microscopes and provide flexibility for optimizing measurement time versus spectral resolution.ApproachThe method employs compressive sensing (CS) and a spectrally encoded illumination device integrated into the illumination path of a standard microscope. The spectral encoding is obtained using a compact liquid crystal cell that is operated in a fast mode. It provides time-efficient measurements of the spectral information, is modular and versatile, and can also be used for other applications that require rapid acquisition of hyperspectral images.ResultsWe demonstrated the acquisition of breast cancer biopsies hyperspectral data of the whole camera area within ∼1 s. This means that a typical 1 × 1 cm2 biopsy can be measured in ∼10 min. The hyperspectral images with 250 spectral bands are reconstructed from 47 spectrally encoded images in the spectral range of 450 to 700 nm.ConclusionsCS hyperspectral microscopy was successfully demonstrated on a common lab microscope for measuring biopsies stained with the most common stains, such as hematoxylin and eosin. The high spectral resolution demonstrated here in a rather short time indicates the ability to use it further for coping with the highly demanding needs of pathological diagnostics, both for cancer diagnostics and prognostics.

The sinoatrial node (SAN) is the primary heart pacemaker. The automaticity of SAN pacemaker cells is regulated by an integrated coupled-clock system. The beat interval (BI) of SAN, and its primary initiation location (inferior vs. superior) are determined by mutual entrainment among pacemaker cells and interaction with extrinsic effectors, including increased venous return which stretches the SAN. We aim to understand the mechanisms that link stretch to changes in BI and to heterogeneity of BI in the SAN.

Isolated SAN tissues of C57BL/6 mice were gradually stretched to different degrees [(low (5–10 % lengthening), medium (10–20 %), and high (20–40 %))] using motor controlled with a custom-made Arduino software. Recordings were acquired 30 s following each level of step. In 8/15 tissues, stretch led to a positive chronotropic response, while in 7/15 tissues, a negative chronotropic response was observed. In the positive chronotropic response group, BI was shortened in parallel to shortening of the local Ca²⁺ release (LCR) period, a readout of the degree of clock coupling. In the negative chronotropic response group, in parallel to a prolongation of BI and LCR period, an unsynchronized firing rate was observed among the cells upon application of stretch. Eliminating the mechano-electrical feedback by addition of blebbistatin disabled the stretch-induced chronotropic effect. Reduction of the sarcoplasmic reticulum Ca²⁺ levels, which mediates the mechano-electrical feedback, by addition of cyclopiazonic acid disabled the dual effect of stretch on SAN function and BI heterogeneity. Thus, the mechano-electric feedback mediates the dual effect of stretch in mouse SAN tissue.

12-lead electrocardiogram (ECG) recordings can be collected in any clinic and the interpretation is performed by a clinician. Modern machine learning tools may make them automatable. However, a large fraction of 12-lead ECG data is still available in printed paper or image only and comes in various formats. To digitize the data, smartphone cameras can be used. Nevertheless, this approach may introduce various artifacts and occlusions into the obtained images. Here we overcome the challenges of automating 12-lead ECG analysis using mobile-captured images and a deep neural network that is trained using a domain adversarial approach. The net achieved an average 0.91 receiver operating characteristic curve on tested images captured by a mobile device. Assessment on image from unseen 12-lead ECG formats that the network was not trained on achieved high accuracy. We further show that the network accuracy can be improved by including a small number of unlabeled samples from unknown formats in the training data. Finally, our models also achieve high accuracy using signals as input rather than images. Using a domain adaptation approach, we successfully classified cardiac conditions on images acquired by a mobile device and showed the generalizability of the classification using various unseen image formats.

Epilepsy is a neurological disease characterized by sudden, unprovoked seizures. The unexpected nature of epileptic seizures is a major component of the disease burden. Predicting seizure onset and alarming patients may allow timely intervention, which would improve clinical outcomes and patient quality of life. Currently, algorithms aiming to predict seizures suffer from a high false alarm rate, rendering them unsuitable for clinical use. We adopted here a risk-controlling prediction calibration method called Learn then Test to reduce false alarm rates of seizure prediction. This method calibrates the output of a "black-box" model to meet a specified false alarm rate requirement. The method was initially validated on synthetic data and subsequently tested on publicly available electroencephalogram (EEG) records from 15 patients with epilepsy by calibrating the outputs of a deep learning model. Validation showed that the calibration method rigorously controlled the false alarm rate at a user-desired level after our adaptation. Real data testing showed an average of 92% reduction in the false alarm rate, at the cost of missing 4 out of 9 seizures of 6 patients. Better-performing prediction models combined with the proposed method may facilitate the clinical use of real-time seizure prediction systems.

Metastases are the leading cause of cancer-associated deaths. A key process in metastasis is cell invasiveness, which is driven and controlled by cancer cell interactions with their microenvironment. We have previously shown that invasive cancer cells forcefully push into and indent physiological stiffness gels to cell-scale depths, where the percentage of indenting cells and their attained depths provide clinically relevant predictions of tumor invasiveness and the potential metastatic risk. The cell-attained, invasive indentation depths are directly affected by gel-microenvironment mechanics, which can concurrently modulate the cells' mechanics and force application capacity, in a complex, coordinated mechanobiological response. As it is impossible to experimentally isolate the different contributions of cell and gel mechanics to cancer cell invasiveness, we perform finite element modeling with literature-based parameters. Under average-scale, cell cytoplasm and nucleus mechanics and cell-applied force levels, increasing gel stiffness 1–50 kPa significantly reduced the attained indentation depth by >200%, while the gel's Poisson ratio reduced depths only by up to 20% and only when the ratio was >0.4; this reveals microenvironment mechanics that can promote invasiveness. Experiments with varying-invasiveness cancer cells exhibited qualitative variations in their responses to gel stiffness increase, for example large/small reduction in indentation depth or increase and then reduction. We quantitatively and qualitatively reproduced the different experimental responses via coordinated changes in cell mechanics and applied force levels. Thus, the different cancer cell capacities to adapt their mechanobiology in response to mechanically changing microenvironments likely determine the varying cancer invasiveness and metastatic risk levels in patients.

T1 mapping is a quantitative magnetic resonance imaging (qMRI) technique that has emerged as a valuable tool in the diagnosis of diffuse myocardial diseases. However, prevailing approaches have relied heavily on breath-hold sequences to eliminate respiratory motion artifacts. This limitation hinders accessibility and effectiveness for patients who cannot tolerate breath-holding. Image registration can be used to enable free-breathing T1 mapping. Yet, inherent intensity differences between the different time points make the registration task challenging. We introduce PCMC-T1, a physically-constrained deep-learning model for motion correction in free-breathing T1 mapping. We incorporate the signal decay model into the network architecture to encourage physically-plausible deformations along the longitudinal relaxation axis. We compared PCMC-T1 to baseline deep-learning-based image registration approaches using a 5-fold experimental setup on a publicly available dataset of 210 patients. PCMC-T1 demonstrated superior model fitting quality (R2: 0.955) and achieved the highest clinical impact (clinical score: 3.93) compared to baseline methods (0.941, 0.946 and 3.34, 3.62 respectively). Anatomical alignment results were comparable (Dice score: 0.9835 vs. 0.984, 0.988). Our code and trained models are available at https://github.com/eyalhana/PCMC-T1.

A prosthetic heart valve including a valve housing configured to be positioned adjacent to a heart valve annulus, one or more flaps moveably coupled to the valve housing, a passage defined by an inner surface of at least one of the valve housing and the one or more flaps, wherein the passage is configured to facilitate blood flow therethrough along a longitudinal axis thereof, from an inflow side to an outflow side of the valve, one or more openings through the inner surface located such that at an open position of the heart valve, a portion of the blood flow flowing along the longitudinal axis of the passage is redirected from the passage through the one or more openings, wherein the direction of the redirected blood flow has a component that is normal to the longitudinal axis of the passage, thereby mitigating stagnation of the blood flow and thus mitigating a risk of blood clots formation.

Obstructive sleep apnea (OSA) is a serious medical condition with a high prevalence, although diagnosis remains a challenge. Existing home sleep tests may provide acceptable diagnosis performance but have shown several limitations. In this retrospective study, we used 12,923 polysomnography recordings from six independent databases to develop and evaluate a deep learning model, called OxiNet, for the estimation of the apnea-hypopnea index from the oximetry signal. We evaluated OxiNet performance across ethnicity, age, sex, and comorbidity. OxiNet missed 0.2% of all test set moderate-to-severe OSA patients against 21% for the best benchmark.

Real-time monitoring of immune state and response to therapy can provide a means to stratify patients and increase the number of responders who will benefit from approved and emerging immunotherapies. The accessibility of immune cells in the skin provides an opportunity for local immune modulation as well as for noninvasive sampling of disease biomarkers in the skin interstitial fluid (ISF). Here, a monitoring strategy for melanoma immunotherapy is investigated by longitudinal sampling of biomarkers in the skin ISF using Hyaluronic acid (HA)-based microneedles (MNs). Focused ultrasound ablation and delivery of nanoparticulate stimulator of interferon genes agonist are used as model immunotherapies. It is shown that this combination therapy induces potent inflammatory responses in a melanoma mouse model, promoting tumor elimination and immune memory formation. Indeed, quantifying soluble, protein-based biomarkers following therapy using conventional immunoassay reveals a pronounced proinflammatory program in the tumor. However, conventional assays fail to detect the low concentration of biomarkers in plasma and in MN-sampled ISF. It is shown that ultrasensitive single molecule arrays (Simoa) effectively detected proinflammatory biomarkers that are upregulated in response to the therapy in MN-sampled ISF, in plasma, and in tumors, supporting the feasibility of monitoring melanoma immunotherapy using MNs.

Background: Biomechanics is crucial in enhancing sports performance and preventing injury. Traditionally, discrete point analysis is used to analyze important kinetic and kinematic data points, reducing continuous data to a single point. One-dimensional Statistical Parametric Mapping (spm1d) offers a more comprehensive approach by assessing entire movement curves instead of isolated peak values. Nevertheless, spm1d is still underutilized in various sports and sports-related injuries. Purpose: To summarize the existing literature on the application of spm1d in sports biomechanics, including the kinetics and kinematics of the hip, knee, and ankle joints, as well as to identify gaps in the literature that may require further research. Methods: A scoping review was conducted, searching PubMed, Embase, Web of Science, and ProQuest databases. English peer-reviewed studies using SPM to assess lower limb kinetics or kinematics in different sports or sports-related injuries were included. In contrast, reviews, meta-analyses, conference abstracts, grey literature, and studies focusing on non-kinetic or kinematic outcomes were excluded. Results: The review yielded 139 papers, with an increased number of studies published in the last three years. Of these studies, 91 examined healthy individuals (64%), and 51 focused on injured populations (36%). Running (n=28), cutting (n=21), and jumping/landing (n=15) were the most common activities. The most prevalent sport-related injuries examined were anterior cruciate ligament rupture (n=21), chronic ankle instability (n=16), and hip-related pain (n=9). Research gaps include the underrepresentation of common sports and movements, small sample size, lack of studies in non-laboratory settings and varied active age groups, and absence of evaluations on the effects of protective sports gear other than shoes. Conclusion: The application of spm1d in sports biomechanics demonstrates diverse uses in sports performance, injury reduction, and rehabilitation. While spm1d shows promise in improving our understanding of sports biomechanics, there are still significant gaps in the literature that present future research opportunities.