A real-time QRS complex detection algorithm based on differential threshold method

Exact and fast automatic analyses of electrocardiogram (ECG) signal is critical for the computer based diagnosis of cardiac diseases, which has attracted many researches' attentions in recent years. In the present study, a novel QRS complex detection approach based on differential threshold method was proposed, which was designed as three analysis steps of an improved differential processor, a detector of the R wave, and a detector of the Q and S waves. The QRS complex detection performance of the proposed algorithm was further evaluated with clinical ECG records in patients coming from the widely recognized Massachusetts Institute of Technology Database (MIT-BIH DB). As a result, 67,167 beats were correctly determined with the proposed approach from a total of 67,197 QRS beats (30 ECG records in variant rhythms, 30 minutes for each), while the numbers of false positives and false negatives were 251 and 208, respectively. The summarized sensitivity and positive predictivity of the proposed method were obtained as 99.69% and 99.63%, respectively. Meanwhile, the computing time of the proposed algorithm for a ECG record of 30 min is only 1.40 seconds, which shows a distinct advantage on computation requirement compared with previous works. In conclusion, the proposed straightforward algorithm for QRS complex detection would potentially be implemented easily for portable monitoring applications with both a better performance and a lower computation requirement

Comparative Analysis of Bacteriophytochrome Agp2 and Its Engineered Photoactivatable NIR Fluorescent Proteins PAiRFP1 and PAiRFP2

Two photoactivatable near infrared fluorescent proteins (NIR FPs) named “PAiRFP1” and “PAiRFP2” are formed by directed molecular evolution from Agp2, a bathy bacteriophytochrome of Agrobacterium tumefaciens C58. There are 15 and 24 amino acid substitutions in the structure of PAiRFP1 and PAiRFP2, respectively. A comprehensive molecular exploration of these bacteriophytochrome photoreceptors (BphPs) are required to understand the structure dynamics. In this study, the NIR fluorescence emission spectra for PAiRFP1 were recorded upon repeated excitation and the fluorescence intensity of PAiRFP1 tends to increase as the irradiation time was prolonged. We also predicted that mutations Q168L, V244F, and A480V in Agp2 will enhance the molecular stability and flexibility. During molecular dynamics (MD) simulations, the average root mean square deviations of Agp2, PAiRFP1, and PAiRFP2 were found to be 0.40, 0.49, and 0.48 nm, respectively. The structure of PAiRFP1 and PAiRFP2 were more deviated than Agp2 from its native conformation and the hydrophobic regions that were buried in PAiRFP1 and PAiRFP2 core exposed to solvent molecules. The eigenvalues and the trace of covariance matrix were found to be high for PAiRFP1 (597.90 nm2) and PAiRFP2 (726.74 nm2) when compared with Agp2 (535.79 nm2). It was also found that PAiRFP1 has more sharp Gibbs free energy global minima than Agp2 and PAiRFP2. This comparative analysis will help to gain deeper understanding on the structural changes during the evolution of photoactivatable NIR FPs. Further work can be carried out by combining PCR-based directed mutagenesis and spectroscopic methods to provide strategies for the rational designing of these PAiRFPs

New records of Celaenorrhinus pyrrha de Nicéville, 1889 and C. munda (Moore, 1884) from China (Lepidoptera, Hesperiidae)

Celaenorrhinus pyrrha de Nicéville, 1889, a rare species of Hesperiidae previously known to be distributed from northeastern India to Indochina, is reported from southwestern Yunnan and southwestern Chongqing, China. A 658 bp COI gene sequence of this species is published for the first time. Although Chongqing is obviously isolated from the main distribution range, morphological characters of the specimens from this locality do not indicate a subspecies differentiation. Another rare taxon, C. munda munda (Moore, 1884), is also recorded from China for the first time based upon a male specimen from Cuona County in the Tibet Autonomous Region. This is the second specimen of C. munda from China, over 100 years after the holotype of C. munda joka Evans, 1949. The genitalia of both species are illustrated and described. Some taxonomic notes and a distribution map are provided as well

Identifying novel sphingosine kinase 1 inhibitors as therapeutics against breast cancer

Sphingosine kinase 1 (SphK1) is a promising therapeutic target against several diseases including mammary cancer. The aim of present work is to identify a potent lead compound against breast cancer using ligand-based virtual screening, molecular docking, MD simulations, and the MMPBSA calculations. The LBVS in molecular and virtual libraries yielded 20,800 hits, which were reduced to 621 by several parameters of drug-likeness, lead-likeness, and PAINS. Furthermore, 55 compounds were selected by ADMET descriptors carried forward for molecular interaction studies with SphK1. The binding energy (ΔG) of three screened compounds namely ZINC06823429 (–11.36 kcal/mol), ZINC95421501 (–11.29 kcal/mol), and ZINC95421070 (–11.26 kcal/mol) exhibited stronger than standard drug PF-543 (–9.9 kcal/mol). Finally, it was observed that the ZINC06823429 binds tightly to catalytic site of SphK1 and remain stable during MD simulations. This study provides a significant understanding of SphK1 inhibitors that can be used in the development of potential therapeutics against breast cancer

In vivo tumor detection with combined MR–Photoacoustic-Thermoacoustic imaging

Here, we report a new method using combined magnetic resonance (MR)–Photoacoustic (PA)–Thermoacoustic (TA) imaging techniques, and demonstrate its unique ability for in vivo cancer detection using tumor-bearing mice. Circular scanning TA and PA imaging systems were used to recover the dielectric and optical property distributions of three colon carcinoma bearing mice While a 7.0-T magnetic resonance imaging (MRI) unit with a mouse body volume coil was utilized for high resolution structural imaging of the same mice. Three plastic tubes filled with soybean sauce were used as fiducial markers for the co-registration of MR, PA and TA images. The resulting fused images provided both enhanced tumor margin and contrast relative to the surrounding normal tissues. In particular, some finger-like protrusions extending into the surrounding tissues were revealed in the MR/TA infused images. These results show that the tissue functional optical and dielectric properties provided by PA and TA images along with the anatomical structure by MRI in one picture make accurate tumor identification easier. This combined MR–PA–TA-imaging strategy has the potential to offer a clinically useful triple-modality tool for accurate cancer detection and for intraoperative surgical navigation

Recent Advances in Stretchable and Wearable Capacitive Electrophysiological Sensors for Long-Term Health Monitoring

Over the past several years, wearable electrophysiological sensors with stretchability have received significant research attention because of their capability to continuously monitor electrophysiological signals from the human body with minimal body motion artifacts, long-term tracking, and comfort for real-time health monitoring. Among the four different sensors, i.e., piezoresistive, piezoelectric, iontronic, and capacitive, capacitive sensors are the most advantageous owing to their reusability, high durability, device sterilization ability, and minimum leakage currents between the electrode and the body to reduce the health risk arising from any short circuit. This review focuses on the development of wearable, flexible capacitive sensors for monitoring electrophysiological conditions, including the electrode materials and configuration, the sensing mechanisms, and the fabrication strategies. In addition, several design strategies of flexible/stretchable electrodes, body-to-electrode signal transduction, and measurements have been critically evaluated. We have also highlighted the gaps and opportunities needed for enhancing the suitability and practical applicability of wearable capacitive sensors. Finally, the potential applications, research challenges, and future research directions on stretchable and wearable capacitive sensors are outlined in this review.</p

A novel smart belt for anxiety detection, classification, and reduction using IIoMT on students’ cardiac signal and MSY

Producción CientíficaThe prevalence of anxiety among university students is increasing, resulting in the negative impact on their academic and social (behavioral and emotional) development. In order for students to have competitive academic performance, the cognitive function should be strengthened by detecting and handling anxiety. Over a period of 6 weeks, this study examined how to detect anxiety and how Mano Shakti Yoga (MSY) helps reduce anxiety. Relying on cardiac signals, this study follows an integrated detection-estimation-reduction framework for anxiety using the Intelligent Internet of Medical Things (IIoMT) and MSY. IIoMT is the integration of Internet of Medical Things (wearable smart belt) and machine learning algorithms (Decision Tree (DT), Random Forest (RF), and AdaBoost (AB)). Sixty-six eligible students were selected as experiencing anxiety detected based on the results of self-rating anxiety scale (SAS) questionnaire and a smart belt. Then, the students were divided randomly into two groups: experimental and control. The experimental group followed an MSY intervention for one hour twice a week, while the control group followed their own daily routine. Machine learning algorithms are used to analyze the data obtained from the smart belt. MSY is an alternative improvement for the immune system that helps reduce anxiety. All the results illustrate that the experimental group reduced anxiety with a significant (p < 0.05) difference in group × time interaction compared to the control group. The intelligent techniques achieved maximum accuracy of 80% on using RF algorithm. Thus, students can practice MSY and concentrate on their objectives by improving their intelligence, attention, and memory.Sichuan Science y Programa de Tecnología - (2020YJ0225)China NSFC - (U2001207 y 61872248)Guangdong NSF - (2017A03031200385)Fundación de Ciencia y Tecnología de Shenzhen - (ZDSYS20190902092853047 y R2020A045)Project of DEGP - (2019KCXTD005)Guangdong “Pearl River Talent Recruitment Program” - (2019ZT08X603

Wearable Flexible Electronics Based Cardiac Electrode for Researcher Mental Stress Detection System Using Machine Learning Models on Single Lead Electrocardiogram Signal

In the modern world, wearable smart devices are continuously used to monitor people&rsquo;s health. This study aims to develop an automatic mental stress detection system for researchers based on Electrocardiogram (ECG) signals from smart T-shirts using machine learning classifiers. We used 20 subjects, including 10 from mental stress (after twelve hours of continuous work in the laboratory) and 10 from normal (after completing the sleep or without any work). We also applied three scoring techniques: Chalder Fatigue Scale (CFS), Specific Fatigue Scale (SFS), Depression, Anxiety, and Stress Scale (DASS), to confirm the mental stress. The total duration of ECG recording was 1800 min, including 1200 min during mental stress and 600 min during normal. We calculated two types of features, such as demographic and extracted by ECG signal. In addition, we used Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Logistic Regression (LR) to classify the intra-subject (mental stress and normal) and inter-subject classification. The DT leave-one-out model has better performance in terms of recall (93.30%), specificity (96.70%), precision (94.40%), accuracy (93.30%), and F1 (93.50%) in the intra-subject classification. Additionally, The classification accuracy of the system in classifying inter-subjects is 94.10% when using a DT classifier. However, our findings suggest that the wearable smart T-shirt based on the DT classifier may be used in big data applications and health monitoring. Mental stress can lead to mitochondrial dysfunction, oxidative stress, blood pressure, cardiovascular disease, and various health problems. Therefore, real-time ECG signals help assess cardiovascular and related risk factors in the initial stage based on machine learning techniques