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Non-invasive photoplethysmography (PPG) technology was developed to track heart rate during motion. Automated analysis of PPG has made it useful in both clinical and non-clinical applications. However, PPG-based heart rate tracking is a challenging problem due to motion artifacts (MAs) which are main contributors towards signal degradation as they mask the location of heart rate peak in the spectra. A practical analysis system must have good performance in MA removal as well as in tracking. In this article, we have presented state-of-art techniques in both areas of the automated analysis, i.e., MA removal and heart rate tracking, and have concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracking. Hence, future systems will be composed of machine learning-based trackers fed with either empirically decomposed signal or from output of adaptive filter.

Phonocardiogram (PCG) signal represents recording of sounds and murmurs resulting from heart auscultation. Analysis of these PCG signals is critical in diagnosis of different heart diseases. Over the years, a variety of methods have been proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains. This paper presents a comprehensive survey of different methods proposed for automatic analysis of PCG signals with the objective to evaluate the current state-of-the-art and to determine the potential domains of effective analysis. An important aspect of our contribution is that the review is carried out as a function of domains of analysis rather than simply discussing different methods. Our method further splits analysis into pre-processing, localization, and classification, and details are presented in terms of techniques and classifiers used during these phases. Finally, results are summarized for normal heart beat, noisy heart beat, and different pathologies using metrices like accuracy and detection rate. In addition to time and frequency domain, time-frequency based methods including wavelet, empirical mode decomposition (EMD) and time-frequency representation (TFR) are selected for detailed analysis. The review concludes that the time-frequency representations like EMD and wavelets represent areas of exploration in future along with perspective of using different time-frequency techniques together.

To discuss VEXAS (vacuoles, E1 enzyme, X-linked, autoinflammatory, somatic) syndrome, including the clinical and pathologic features, diagnostic challenges, and treatment options. A case-based approach and pertinent literature review were used to highlight the features of VEXAS syndrome, describe how to make the diagnosis, and discuss available therapies. VEXAS syndrome is an adult-onset, progressive systemic inflammatory disorder with overlapping rheumatologic and hematologic manifestations, including an increased risk of myelodysplastic neoplasms and plasma cell neoplasms. The disorder is associated with a somatic mutation of the X-linked UBA1 gene involved in ubiquitylation, typically involving p.Met41; however, rare variations have been identified outside this region. Patients often present with complex histories and see physicians from multiple specialties before receiving the diagnosis, which is often delayed. Symptoms are related to inflammation as well as cytopenias, particularly macrocytic anemia. Characteristic cytoplasmic vacuoles are present in myeloid (granulocytic, monocytic) and erythroid precursors in the vast majority of cases. Either clinicians or pathologists may suspect a diagnosis of VEXAS syndrome depending on the clinical presentation and bone marrow findings. More studies are needed to determine the best therapeutic options, which are currently limited.

A microfluidic flow cytometer-based point-of-care (POC) analyzer was validated against an in-laboratory hematology analyzer (Sysmex XN Automated Hematology System). Concordance on a full complete blood cell count (CBC) with 5-part differential, as performed by operators with no prior clinical laboratory experience, was evaluated. We prospectively collected 376 venous blood specimens (376) from individuals with self-reported medical conditions and from apparently healthy individuals. Forty-six additional remnant specimens were acquired to ensure coverage of analytic measuring ranges. Parallel testing was performed, with up to 7 hours between testing on the POC and Sysmex XN analyzers. Regression analysis resulted in r values of 0.998 to 0.932 for all parameters of a 5-part differential CBC other than basophils (0.709). The mean percentage bias from the reference method, inclusive of the upper and lower reporting limits, was less than 2% for parameters other than lymphocytes (-6.4%), monocytes (25.9%), eosinophils (12.2%), and basophils (-15%). Overall agreement on abnormal flagging was 93.3%. The Cito CBC microflow cytometer (CytoChip Inc) provides a CBC with a 5-part differential with accuracy, precision, and abnormal flagging equivalent to a moderate-complexity hematology analyzer. It has the key features required of a POC device that can be operated in a waived setting: minimum space requirements, rapid results, single-action measurement (no sample processing or dilution), ease of use, and minimal blood volume.

Myc is one of the most commonly deregulated oncogenes in human cancer, yet therapies directly targeting Myc hyperactivation are not presently available in the clinic. The evolutionarily conserved function of Myc in modulating protein synthesis control is critical to the Myc oncogenic program. Indeed, enhancing the protein synthesis capacity of cancer cells directly contributes to their survival, proliferation, and genome instability. Therefore, inhibiting enhanced protein synthesis may represent a highly relevant strategy for the treatment of Myc-dependent human cancers. However, components of the translation machinery that can be exploited as therapeutic targets for Myc-driven cancers remain poorly defined. Here, we uncover a surprising and important functional link between Myc and mammalian target of rapamycin (mTOR)-dependent phosphorylation of eukaryotic translation initiation factor 4E binding protein-1 (4EBP1), a master regulator of protein synthesis control. Using a pharmacogenetic approach, we find that mTOR-dependent phosphorylation of 4EBP1 is required for cancer cell survival in Myc-dependent tumor initiation and maintenance. We further show that a clinical mTOR active site inhibitor, which is capable of blocking mTOR-dependent 4EBP1 phosphorylation, has remarkable therapeutic efficacy in Myc-driven hematological cancers. Additionally, we demonstrate the clinical implications of these results by delineating a significant link between Myc and mTOR-dependent phosphorylation of 4EBP1 and therapeutic response in human lymphomas. Together, these findings reveal that an important mTOR substrate is found hyperactivated downstream of Myc oncogenic activity to promote tumor survival and confers synthetic lethality, thereby revealing a unique therapeutic approach to render Myc druggable in the clinic.

The coronavirus disease 2019 (COVID-19) pandemic has triggered governments worldwide to implement severe restrictions on physical therapy protocols in order to better control the spread of the virus. One of the mechanisms of providing physical therapy patient care during this era is via telemedicine. Telerehabilitation or telerehab is a technological visual-audio system that serves patients, including those with a spine injury, ailment, or postoperatively, with neurological deficits. In this scoping review, we discuss the development of telerehab, the technological advances in the field, and the usage of telerehab specifically pertaining to spine patients, and comment on the advancement of telerehab in the time of COVID-19. There is preliminary evidence that suggests that the adoption of telerehab in lieu of face-to-face interventions is beneficial for reducing pain and improving physical function in patients afflicted with chronic nonmalignant musculoskeletal pain from low back pain, lumbar stenosis, neck pain, and osteoarthritis. Availability is important, as the necessary technology should be accessible to all participants. Safety and security should be addressed, as the passage of patient data over the Internet requires secure confidentiality. Ease-of-use is crucial to promote practicality, user-friendly operation, and adherence to therapy. The combination of evidence-based methodologies with cost-effective services will serve as a basis for the further expansion of vital telerehab services and increases reimbursement by health insurance providers.

Heart rate tracking using Photoplethysmography (PPG) suffers from motion artifacts, which can change signal structure in a way that the spectral peak due to motion artifacts (MAs) can mask the actual peak related to the heart rate. To handle the problem just mentioned, a novel mathematical model for heart rate (HR) tracking is introduced. Our technique is based on a mathematical model for a multichannel PPG. The model uses a fixed-resolution spectrum of Fast Fourier Transform (FFT), Chirplet Z Transform (CZT) spectra at various resolutions, confinement of spectral space, previous heart rate, range of the signal, and a golden seed (GS) algorithm to generate the next heart rate. GS algorithm is a novel technique which is introduced to handle the masking of spectral peaks related to HRs. The GS algorithm utilizes intensity profiling, the Singular Spectrum Analysis (SSA) algorithm, spectral multiplication and subtraction, and proximity clustering to enhance the masked peak. The average time taken by our technique is 21.21ms and a mean average error of 2.12 on the IEEE signal processing Cup 2015 makes it fit for the real-time applications.

In recent years, terrorism has increasingly threatened human security, causing violence, fear, and damage to both the general public and specific targets. These attacks create unrest among individuals and within society. Leveraging the recent advancements in deep machine learning, several intelligent systems have been developed to predict terrorist attacks. However, existing state-of-the-art models are limited, lack support for big data, suffer from accuracy issues, and require extensive modifications. Therefore, to fill this gap, herein, we propose an integrated Big Data deep learning-based predictive model to predict the probability of a terrorist attack. We treat the series of terrorist activities as a sequence modeling problem and propose a big data long short-term memory network. It is a layered model capable of processing large-scale data. Our proposed model can learn from past events and forecast future attacks. The proposed model predicts the likely location of future attacks at the city, country, and regional levels. The experimental study of the proposed model was carried out on the samples in the global terrorism dataset, and promising results are reported on a number of standard evaluation metrics, accuracy, precision, Recall, and F1 score. The obtained results suggest that the proposed model contributes substantially to predicting the probability of an attack at a particular location. The identification of potential locations of an attack allows law enforcement agencies to take suitable preventative measures to combat terrorism effectively.

PI3K pathway activation is a common and early event in prostate cancer, from loss of function mutations in PTEN, or activating mutations in PIK3Ca or AKT leading to constitutive activation, induction of growth factor-receptors kinase EphB4 and its ligand ephrin-B2. We hypothesized that induction of EphB4 is an early event required for tumor initiation. Secondly, we hypothesized that EphB4 remains relevant when prostate cancer becomes androgen independent. Genetic mouse model of conditional PTEN deletion in prostate epithelium induces tumor in all mice. We tested this model against EPHB4 wild type and deleted in prostate epithelium. This allowed us to test its role in tumor initiation. We also tested an orthogonal approach by using decoy soluble EphB4 to block bidirectional signaling resulting from EphB4-ephrin-B2 interaction. Role of EphB4-ephrin-B2 in androgen deprived mice was tested for role in refractory cancer model. PTEN deletion induces EphB4 and ephrin-B2 in prostate cancer which was substantially reduced when EPHB4 is deleted in the same prostate epithelial cells. sEphB4-alb fusion protein with improved pharmacokinetics similarly inhibited tumor formation, thus establishing the role in tumor initiation. sEphB4-alb retained the efficacy in castration resistant androgen independent prostate cancer. We have thus observed that induction of EphB4 is required for the initiation of prostate cancer in PTEN null mouse and that signaling downstream from EphB4 is required in androgen deprivation and thus castration resistant prostate cancer. Pharmacological inhibition of EphB4 pathway reproduced the results. Targeting EphB4 should be tested in prostate cancer especially those resistant to androgen deprivation therapy. EphB4 and ephrin-B2 receptor ligand pair is induced in PTEN null prostate cancer, which significantly contributes to the tumor initiation. Secondly, EphB4-ephrin-B2 pathway continue to promote tumor progression even in androgen deprivation and thus hormone refractory tumor. EphB4-ephrin-B2 may be candidates for precision medicine with biomarker-based patient selection with and without concurrent standard of care.

Textual content appearing in videos represents an interesting index for semantic retrieval of videos (from archives), generation of alerts (live streams), as well as high level applications like opinion mining and content summarization. The key components of such systems require detection and recognition of textual content which also make the subject of our study. This paper presents a comprehensive framework for detection and recognition of textual content in video frames. More specifically, we target cursive scripts taking Urdu text as a case study. Detection of textual regions in video frames is carried out by fine-tuning deep neural networks based object detectors for the specific case of text detection. Script of the detected textual content is identified using convoluational neural networks (CNNs), while for recognition, we propose a UrduNet, a combination of CNNs and long short- term memory (LSTM) networks. A benchmark dataset containing cursive text with more than 13,000 video frame is also developed. A comprehensive series of experiments is carried out reporting an F-measure of 88.3% for detection while a recognition rate of 87%.