A Semi-supervised Algorithm for Atrial Fibrillation Attack Prediction Using Convolution Auto-encoder of Time Series Signal

During the initial stages, atrial fibrillation (AF) typically presents as paroxysmal atrial fibrillation (PAF), which may further progress into persistent atrial fibrillation, leading to high-risk diseases such as ischemic stroke and heart failure. Given that the current machine learning algorithms used for predicting AF involve time-consuming and labor-intensive processes of feature extraction and labeling electrocardiogram data, this study proposes a novel two-stage semi-supervised AF attack prediction algorithm. The first stage is designed as unsupervised learning based on convolutional autoencoder (CAE) network when inputting RR interval time series signal, while the second stage is designed as supervised learning using a Long Short-Term Memory (LSTM) model. A training set consisting of 20 segments of PAF and 20 normal heart rates was used to evaluate the performance of the CAE-LSTM combination model. The results showed that the average accuracy and root mean square error of ten-fold cross-validation were 93.56% and 0.004, respectively, with an F1 parameter of 0.9345. In summary, the preliminary results suggest that the combination of unsupervised CAE model and supervised LSTM model can reduce the dimensionality of the input data while using a small amount of labeled data as input for subsequent classification. Furthermore, the proposed algorithm can be used for predicting atrial fibrillation when the sample size is limited.Clinical Relevance- Compared with common supervised methods, our proposed method only requires a small number of tagged ECG signals, which can reduce the workload of clinicians to complete the task of atrial fibrillation attack prediction.

The global prevalence of premature ovarian insufficiency: a systematic review and meta-analysis

OBJECTIVE

The objective of this review was to answer the global prevalence of premature ovarian insufficiency (POI), and explore the associated factors including etiopathology and regions with POI.

METHODS

The search was conducted on reports from a total of eight databases that comprised Chinese National Knowledge Infrastructure (CNKI), Wanfang, China BioMedical Literature Database (CBM), PubMed, the Cochrane Library, Embase, Web of Science and Ovid MEDLINE^® between 1946 and 2021. To analyze the source of heterogeneity, we performed subgroup analysis based on different etiologies and regions. Meta-analysis was carried out by Stata14.0 software.

RESULTS

The results showed that the global overall prevalence of POI among women was 3.5%. By subgroup analysis, the prevalence of POI among women with iatrogenic etiology was 11.2%, followed by autoimmunity (10.5%); the prevalence of POI by region was 11.3% at the highest in North America followed by South America (5.4%); and the prevalence of POI was 5.3% in a developing country, higher than 3.1% in a developed country. The trend of prevalence of POI over the past 20 years was on the rise (although p > 0.05).

CONCLUSION

We recommend that health and medical institutions strengthen public health awareness, achieve health-education goals related to POI and increase women's awareness of and attention to POI.

Super-Resolution Reconstruction of Terahertz Images Based on Residual Generative Adversarial Network with Enhanced Attention

Terahertz (THz) waves are widely used in the field of non-destructive testing (NDT). However, terahertz images have issues with limited spatial resolution and fuzzy features because of the constraints of the imaging equipment and imaging algorithms. To solve these problems, we propose a residual generative adversarial network based on enhanced attention (EA), which aims to pay more attention to the reconstruction of textures and details while not influencing the image outlines. Our method successfully recovers detailed texture information from low-resolution images, as demonstrated by experiments on the benchmark datasets Set5 and Set14. To use the network to improve the resolution of terahertz images, we create an image degradation algorithm and a database of terahertz degradation images. Finally, the real reconstruction of terahertz images confirms the effectiveness of our method.

Assessing the Readiness for Implementing the World Health Organization's ICOPE Approach in Hong Kong: Perspectives from Social Care and Policy Stakeholders

Although integrated care has been considered a key strategy in reforming health systems around the world, it seems hard to realise in practice, particularly in the part of medical social integration. Worse still, little is known about the capacity of social care professionals who implement it, or their perceived roles and responsibilities, as well as the barriers and facilitators that stakeholders from the health and social sectors identify as factors affecting the ICOPE implementation process. Therefore, the present study was performed to probe into these issues. Data were collected from an online survey based on the WHO ICOPE scorecard (N = 34), and focus groups with policy makers, managers, health and social care professionals (N = 47). Inductive analyses were performed in accordance with the service and system levels within the WHO ICOPE implementation framework. While the findings from the scorecard survey highlight the gap in actualizing the ICOPE approach within the existing social services and care structures, we found support for a model of integrated care underpinned by the WHO ICOPE approach. Factors that may hinder and facilitate ICOPE implementation include workforce capacity-building, coordinated networks and partnerships, and financial mechanisms. This finding can help inform subsequent actions that further support health and social care advancement and collaboration, and the implementation of the ICOPE approach.

A Dyadic Cooking-Based Intervention for Improving Subjective Health and Well-Being of Older Adults with Subjective Cognitive Decline and Their Caregivers: A Randomized Controlled Trial

OBJECTIVES

Evidence on the effectiveness of cooking activities as a well-being promotion intervention for older adults with subjective cognitive decline (SCD) and their caregivers is scarce. In view of this, a randomized controlled trial was conducted to examine whether a dyadic cooking-based intervention can improve the subjective health and well-being of older adults with SCD and their caregivers, as well as the cooking competence of the former group.

DESIGN

Randomized controlled trial.

SETTING

Community.

PARTICIPANTS

Sixty pairs of community-dwelling older adults aged 60 years or above with SCD (mean age = 78.4 years) and their caregivers (mean age = 65.3 years) were randomly assigned to the intervention group (N = 30 pairs) and the wait-list control group (N = 30 pairs).

INTERVENTION

The intervention was an innovative 5-week (two hours per week) dyadic cooking-based intervention employing procedural learning methods specifically adapted for older adults with SCD.

MEASUREMENTS

The outcome measures included 1) a well-being index composed by four indicators: life satisfaction, feeling of happiness, sense of purpose and meaning in life, and perceived health, and 2) cooking competence.

RESULTS

For both older adults with SCD and their caregivers, the increases in the well-being index were significantly greater in the intervention group than in the control group (β = 0.508, 95% CI [0.036, 0.980]). For older adults with SCD, the increases in the cooking competence score were significantly greater in the intervention group than in the control group (β = 1.629, 95% CI [0.165, 3.071]).

CONCLUSION

The dyadic cooking-based intervention resulted in improvements in the cooking competence and well-being of older adults with SCD, as well as the well-being of caregivers.

Trajectories of Intrinsic Capacity: Determinants and Associations with Disability

OBJECTIVES

Intrinsic capacity (IC) declines progressively with age, thereby increasing the risk of disability. However, it is less known whether IC trajectories are associated with disability. This study aims to identify the different patterns of IC trajectories in older people, and examine their determinants and associations with Instrumental Activities of Daily Living (IADL).

DESIGN

Cohort study.

SETTING

Community centres in different regions in Hong Kong.

PARTICIPANTS AND MEASUREMENTS

Longitudinal data from community-dwelling older people aged 60 years or above (n = 1371) collected between 2016 and 2021 was analysed. Their mean age was 74.5 years, and 78.7% of them were female. Repeated measurements of a set of 14 self-reported items were used to generate IC scores at four time points using a bi-factor model. Latent class growth analysis was performed to identify classes with distinct IC trajectories. The association between class membership and IADL disability was then examined using logistic regression.

RESULTS

Three distinct IC trajectories were identified. The 1st class included those with the highest level of baseline IC and the least declining trajectory, whereas the 3rd class was composed by those with the lowest level of baseline IC and the most declining trajectory. Older age, female gender, lower perceived financial adequacy, living in public or subsidized housing, and chronic diseases were associated with the 3rd class. After adjusting for demographic factors, socioeconomic status, and the number of chronic diseases, the 1st class was more likely to preserve IADL when compared against the 2nd class, with OR being 3.179 (95% CI: 2.152-4.793), whereas for the 3rd class, the OR was 0.253 (95% CI: 0.178-0.359).

CONCLUSION

Monitoring IC trajectories is of relevance to clinical practice, as it helps shift the focus from treating acute episodes of illness to preserving the functional ability of older people.

Moving towards the ICOPE Approach: Evaluation of Community-Based Intervention Activities on Improving Intrinsic Capacity

OBJECTIVES

Different types of community-based intervention activities may have differential effects in improving the intrinsic capacity (IC) of older people. This study aims to (i) identify subgroups of older people based on their IC impairments, (ii) examine the differential associations between different types of activity participations and change in IC across subgroups, and (iii) assess whether the activity participation patterns of older people align with the way that would benefit them the most.

METHODS

Participants were community-dwelling older people aged 60 years or above. They were screened for IC impairments at baseline, and their participation records of different types (cognitive, physical, nutritional, mental, and social) of intervention activities were collected for one year. An aggregated IC score was created based on four IC domains including cognitive (self-rated memory), locomotor (self-rated difficulties in walking), vitality (self-rated weight loss), and psychological (subjective well-being). Cluster analysis was used to group homogenous participants. Mixed-effects regression was used to examine the associations between activity counts (i.e., number of sessions participated) and change in IC. Activity participation patterns were also compared across subgroups.

RESULTS

Data were obtained from 7,357 participants (mean age = 74.72 years). Four clusters were identified, including those who were relatively robust (cluster 1, N = 4,380, 59.5%), those who had cognitive decline (cluster 2, N = 2,134, 29.0%), those who had impaired mobility and vitality (cluster 3, N = 319, 4.3%), and those with poor psychological well-being (cluster 4, N = 524, 7.1%). Overall, activity count was associated with IC improvement (β = 0.073, 95% CI [0.037, 0.108]). However, as regards the cluster-specific results, different types of activities were associated with IC improvement for different specific clusters. For instance, cognitive activity count was associated with IC improvement only for cluster 2 (β = 0.491, 95% CI [0.258, 0.732]). Notably, none of the activity types were associated with IC improvement for cluster 1. Regarding the activity participation patterns, there were no significant differences across the four clusters (Wilk's Λ = 0.997, F = 1.400, p = .138).

CONCLUSIONS AND IMPLICATIONS

IC improvement depended on the activity types and IC status of older people. In view of this, a people-centred and targeted approach should be adopted to maximize the overall benefits of intervention activities.

An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal

The development of automatic monitoring and diagnosis systems for cardiac patients over the internet has been facilitated by recent advancements in wearable sensor devices from electrocardiographs (ECGs), which need the use of patient-specific approaches. Premature ventricular contraction (PVC) is a common chronic cardiovascular disease that can cause conditions that are potentially fatal. Therefore, for the diagnosis of likely heart failure, precise PVC detection from ECGs is crucial. In the clinical settings, cardiologists typically employ long-term ECGs as a tool to identify PVCs, where a cardiologist must put in a lot of time and effort to appropriately assess the long-term ECGs which is time consuming and cumbersome. By addressing these issues, we have investigated a deep learning method with a pre-trained deep residual network, ResNet-18, to identify PVCs automatically using transfer learning mechanism. Herein, features are extracted by the inner layers of the network automatically compared to hand-crafted feature extraction methods. Transfer learning mechanism handles the difficulties of required large volume of training data for a deep model. The pre-trained model is evaluated on the Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) Arrhythmia and Institute of Cardiological Technics (INCART) datasets. First, we used the Pan-Tompkins algorithm to segment 44,103 normal and 6423 PVC beats, as well as 106,239 normal and 9987 PVC beats from the MIT-BIH Arrhythmia and IN-CART datasets, respectively. The pre-trained model employed the segmented beats as input after being converted into 2D (two-dimensional) images. The method is optimized with the using of weighted random samples, on-the-fly augmentation, Adam optimizer, and call back feature. The results from the proposed method demonstrate the satisfactory findings without the using of any complex pre-processing and feature extraction technique as well as design complexity of model. Using LOSOCV (leave one subject out cross-validation), the received accuracies on MIT-BIH and INCART are 99.93% and 99.77%, respectively, suppressing the state-of-the-art methods for PVC recognition on unseen data. This demonstrates the efficacy and generalizability of the proposed method on the imbalanced datasets. Due to the absence of device-specific (patient-specific) information at the evaluating stage on the target datasets in this study, the method might be used as a general approach to handle the situations in which ECG signals are obtained from different patients utilizing a variety of smart sensor devices.

In silico prediction of natural compounds as potential multi-target inhibitors of structural proteins of SARS-CoV-2

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a colossal loss to human health and lives and has deeply impacted socio-economic growth. Remarkable efforts have been made by the scientific community in containing the virus by successful development of vaccines and diagnostic kits. Initiatives towards drug repurposing and discovery have also been undertaken. In this study, we compiled the known natural anti-viral compounds using text mining of the literature and examined them against four major structural proteins of SARS-CoV-2, namely, spike (S) protein, nucleocapsid (N) protein, membrane (M) protein and envelope (E) protein. Following computational approaches, we identified fangchinoline and versicolactone C as the compounds to exhibit strong binding to the target proteins and causing structural deformation of three structural proteins (N, S and M). We recommend the inhibitory effects of these compounds from our study should be experimentally validated against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

A Novel Smart Belt for Anxiety Detection, Classification, and Reduction Using IIoMT on Students' Cardiac Signal and MSY

The 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.

Role of Oxidative Stress and Inflammation in Insomnia Sleep Disorder and Cardiovascular Diseases: Herbal Antioxidants and Anti-inflammatory Coupled with Insomnia Detection using Machine Learning

Insomnia is well-known as trouble in sleeping and enormously influences human life due to the shortage of sleep. Reactive Oxygen Species (ROS) accrue in neurons during the waking state, and sleep has a defensive role against oxidative damage and dissipates ROS in the brain. In contrast, insomnia is the source of inequity between ROS generation and removal by an endogenous antioxidant defense system. The relationship between insomnia, depression, and anxiety disorders damages the cardiovascular systems' immune mechanisms and functions. Traditionally, polysomnography is used in the diagnosis of insomnia. This technique is complex, with a long time overhead. In this work, we have proposed a novel machine learning-based automatic detection system using the R-R intervals extracted from a single-lead electrocardiograph (ECG). Additionally, we aimed to explore the role of oxidative stress and inflammation in sleeping disorders and cardiovascular diseases, antioxidants' effects, and the psychopharmacological effect of herbal medicine. This work has been carried out in steps, which include collecting the ECG signal for normal and insomnia subjects, analyzing the signal, and finally, automatic classification. We used two approaches, including subjects (normal and insomnia), two sleep stages, i.e., wake and rapid eye movement, and three Machine Learning (ML)-based classifiers to complete the classification. A total number of 3000 ECG segments were collected from 18 subjects. Furthermore, using the theranostics approach, the role of mitochondrial dysfunction causing oxidative stress and inflammatory response in insomnia and cardiovascular diseases was explored. The data from various databases on the mechanism of action of different herbal medicines in insomnia and cardiovascular diseases with antioxidant and antidepressant activities were also retrieved. Random Forest (RF) classifier has shown the highest accuracy (subjects: 87.10% and sleep stage: 88.30%) compared to the Decision Tree (DT) and Support Vector Machine (SVM). The results revealed that the suggested method could perform well in classifying the subjects and sleep stages. Additionally, a random forest machine learning-based classifier could be helpful in the clinical discovery of sleep complications, including insomnia. The evidence retrieved from the databases showed that herbal medicine contains numerous phytochemical bioactives and has multimodal cellular mechanisms of action, viz., antioxidant, anti-inflammatory, vasorelaxant, detoxifier, antidepressant, anxiolytic, and cell-rejuvenator properties. Other herbal medicines have a GABA-A receptor agonist effect. Hence, we recommend that the theranostics approach has potential and can be adopted for future research to improve the quality of life of humans.

[Virtual screening of active ingredients of traditional Chinese medicine in treating COVID-19 based on molecular docking and molecular dynamic simulation]

We aim to screen out the active components that may have therapeutic effect on coronavirus disease 2019 (COVID-19) from the severe and critical cases' prescriptions in the "Coronavirus Disease 2019 Diagnosis and Treatment Plan (Trial Ninth Edition)" issued by the National Health Commission of the People's Republic of China and explain its mechanism through the interactions with proteins. The ETCM database and SwissADME database were used to screen the active components contained in 25 traditional Chinese medicines in 3 prescriptions, and the PDB database was used to obtain the crystal structures of 4 proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Molecular docking was performed using Autodock Vina and molecular dynamics simulations were performed using GROMACS. Binding energy results showed that 44 active ingredients including xambioona, gancaonin L, cynaroside, and baicalin showed good binding affinity with multiple targets of SARS-CoV-2, while molecular dynamics simulations analysis showed that xambioona bound more tightly to the nucleocapsid protein of SARS-CoV-2 and exerted a potent inhibitory effect. Modern technical methods are used to study the active components of traditional Chinese medicine and show that xambioona is an effective inhibitor of SARS-CoV-2 nucleocapsid protein, which provides a theoretical basis for the development of new anti-SARS-CoV-2 drugs and their treatment methods.

Withania somnifera (L.) Dunal (Ashwagandha) for the possible therapeutics and clinical management of SARS-CoV-2 infection: Plant-based drug discovery and targeted therapy

Coronavirus disease 2019 (COVID-19) pandemic has killed huge populations throughout the world and acts as a high-risk factor for elderly and young immune-suppressed patients. There is a critical need to build up secure, reliable, and efficient drugs against to the infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. Bioactive compounds of Ashwagandha [Withania somnifera (L.) Dunal] may implicate as herbal medicine for the management and treatment of patients infected by SARS-CoV-2 infection. The aim of the current work is to update the knowledge of SARS-CoV-2 infection and information about the implication of various compounds of medicinal plant Withania somnifera with minimum side effects on the patients' organs. The herbal medicine Withania somnifera has an excellent antiviral activity that could be implicated in the management and treatment of flu and flu-like diseases connected with SARS-CoV-2. The analysis was performed by systematically re-evaluating the published articles related to the infection of SARS-CoV-2 and the herbal medicine Withania somnifera. In the current review, we have provided the important information and data of various bioactive compounds of Withania somnifera such as Withanoside V, Withanone, Somniferine, and some other compounds, which can possibly help in the management and treatment of SARS-CoV-2 infection. Withania somnifera has proved its potential for maintaining immune homeostasis of the body, inflammation regulation, pro-inflammatory cytokines suppression, protection of multiple organs, anti-viral, anti-stress, and anti-hypertensive properties. Withanoside V has the potential to inhibit the main proteases (Mpro) of SARS-CoV-2. At present, synthetic adjuvant vaccines are used against COVID-19. Available information showed the antiviral activity in Withanoside V of Withania somnifera, which may explore as herbal medicine against to SARS-CoV-2 infection after standardization of parameters of drug development and formulation in near future.

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.

Computational analysis of PTP-1B site-directed mutations and their structural binding to potential inhibitors

Protein tyrosine phosphatase-1B (PTP-1B) is a well-known therapeutic target for diabetes and obesity as it suppresses insulin and leptin signaling. PTP-1B deletion or pharmacological suppression boosted glucose homeostasis and insulin signaling without altering hepatic fat storage. Inhibitors of PTP-1B may be useful in the treatment of type 2 diabetes, and shikonin, a naturally occurring naphthoquinone dye pigment, is reported to inhibit PTP-1B and possess antidiabetic properties. Since the cell contains a large number of phosphatases, PTP-1B inhibitors must be effective and selective. To explore more about the mechanism underlying the inhibitor's efficacy and selectivity, we investigated its top four pharmacophores and used site-directed mutagenesis to insert amino acid mutations into PTP-1B as an extension of our previous study where we identified 4 pharmacophores of shikonin. The study aimed to examine the site-directed mutations like R24Y, S215E, and S216C influence the binding of shikonin pharmacophores, which act as selective inhibitors of PTP-1B. To achieve this purpose, docking and molecular dynamics simulations of wild-type (WT) and mutant PTP-1B with antidiabetic compounds were undertaken. The simulation results revealed that site-directed mutations can change the hydrogen bond and hydrophobic interactions between shikonin pharmacophores and many residues in PTP-1B's active site, influencing the drug's binding affinity. These findings could aid researchers in better understanding PTP-1B inhibitors' selective binding mechanism and pave the path for the creation of effective PTP-1B inhibitors.

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'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.

Addition of Mercury Causes Quenching of NIR Fluorescence Emission Spectra of a Photoactivatable PAiRFP1 Protein

Background:

Biliverdin (BV) containing far-red light photoactivatable near-infrared fluorescent protein (NIR-FP) named PAiRFP1 has been developed by directed molecular evolution from one bathy bacteriophytochrome of Agrobacterium tumefaciens C58 called Agp2 or AtBphP2. Usually, the fluorescence intensity of the NIR emission spectra of PAiRFP1 tends to increase upon repeated excitation by far-red light.

Objective:

This study aimed at exploring the role of PAiRFP1 and its mutants, such as V386A, V480A, and Y498H, as NIR biosensors for the detection of Hg2+ ions in the buffer solutions.

Methods:

In this study, we used PCR-based site-directed reverse mutagenesis, fluorescence spectroscopy, and molecular modeling approaches on PAiRFP1 and its mutants.

Results:

It was found that PAiRFP1 and its mutants experienced strong quenching of NIR fluorescence emission spectra upon the addition of different concentrations (0-3μM) of mercuric chloride (HgCl2).

Conclusion:

We hypothesized that PAiRFP1 and its variants have some potential to be used as NIR biosensors for the in vitro detection of Hg2+ ions in biological media. Moreover, we also hypothesized that PAiRFP1 would be the best tool to use as a NIR biosensor to detect Hg2+ ions in living organisms because of its higher signal-to-noise (SNR) ratio than other infra-red fluorescent proteins.

[Automatic detection model of hypertrophic cardiomyopathy based on deep convolutional neural network]

The diagnosis of hypertrophic cardiomyopathy (HCM) is of great significance for the early risk classification of sudden cardiac death and the screening of family genetic diseases. This research proposed a HCM automatic detection method based on convolution neural network (CNN) model, using single-lead electrocardiogram (ECG) signal as the research object. Firstly, the R-wave peak locations of single-lead ECG signal were determined, followed by the ECG signal segmentation and resample in units of heart beats, then a CNN model was built to automatically extract the deep features in the ECG signal and perform automatic classification and HCM detection. The experimental data is derived from 108 ECG records extracted from three public databases provided by PhysioNet, the database established in this research consists of 14,459 heartbeats, and each heartbeat contains 128 sampling points. The results revealed that the optimized CNN model could effectively detect HCM, the accuracy, sensitivity and specificity were 95.98%, 98.03% and 95.79% respectively. In this research, the deep learning method was introduced for the analysis of single-lead ECG of HCM patients, which could not only overcome the technical limitations of conventional detection methods based on multi-lead ECG, but also has important application value for assisting doctor in fast and convenient large-scale HCM preliminary screening.

Effect of polyol osmolytes on the structure-function integrity and aggregation propensity of catalase: A comprehensive study based on spectroscopic and molecular dynamic simulation measurements

Owing to the ability of catalase to function under oxidative stress vis-à-vis its industrial importance, the structure-function integrity of the enzyme is of prime concern. In the present study, polyols (glycerol, sorbitol, sucrose, xylitol), were evaluated for their ability to modulate structure, activity and aggregation of catalase using in vitro and in silico approaches. All polyols increased catalase activity by decreasing K_m and increasing V_max resulting in enhanced catalytic efficiency (k_cat/K_m) of the enzyme, with glycerol being the most efficient with a k_cat/K_m increase from 4.38 × 10⁴ mM^-1 S^-1 (control) to 5.8 × 10⁵ mM^-1 S^-1. Correlatively with this, enhanced secondary structure with reduced hydrophobic exposure was observed in all polyols. Furthermore, increased stability, with an increase in melting temperature by 15.2 °C, and almost no aggregation was observed in glycerol. Overall, ability to regulate structure-function integrity and aggregation propensity was highest for glycerol and lowest for xylitol. Simulation studies were performed involving structural dynamics measurements, principal component analysis and free energy landscape analysis. Altogether, all polyols were stabilizing in nature and glycerol, in particular, has potential to efficiently prevent not only the antioxidant defense system but also might serve as a stability aid during industrial processing of catalase.

Investigating the binding mechanism of topiramate with bovine serum albumin using spectroscopic and computational methods

Various spectroscopic techniques involving fluorescence spectroscopy, circular dichroism (CD), and computational approaches were used to elucidate the molecular aspects of interaction between the antiepileptic drug topiramate and the multifunctional transport protein bovine serum albumin (BSA) under physiological conditions. Topiramate quenched BSA fluorescence in a static quenching mode, according to the Stern-Volmer quenching constant (K_sv ) data derived from fluorescence spectroscopy for the topiramate-BSA complex. The binding constant was also used to calculate the binding affinity for the topiramate-BSA interaction. Fluorescence and circular dichroism experiments demonstrate that the protein's tertiary structure is affected by the microenvironmental alterations generated by topiramate binding to BSA. To establish the exact binding site, interacting residues, and interaction forces involved in the binding of topiramate to BSA, molecular modeling and simulation approaches were used. According to the MMPBSA calculations, the average binding energy between topiramate and BSA is -421.05 kJ/mol. Topiramate was discovered to have substantial interactions with BSA, changing the structural dynamic and Gibbs free energy landscape patterns.

The Molecular Basis of the Effect of Temperature on the Structure and Function of SARS-CoV-2 Spike Protein

The remarkable rise of the current COVID-19 pandemic to every part of the globe has raised key concerns for the current public healthcare system. The spike (S) protein of SARS-CoV-2 shows an important part in the cell membrane fusion and receptor recognition. It is a key target for vaccine production. Several researchers studied the nature of this protein under various environmental conditions. In this work, we applied molecular modeling and extensive molecular dynamics simulation approaches at 0°C (273.15 K), 20°C (293.15 K), 40°C (313.15 K), and 60°C (333.15 K) to study the detailed conformational alterations in the SARS-CoV-2 S protein. Our aim is to understand the influence of temperatures on the structure, function, and dynamics of the S protein of SARS-CoV-2. The structural deviations, and atomic and residual fluctuations were least at low (0°C) and high (60°C) temperature. Even the internal residues of the SARS-CoV-2 S protein are not accessible to solvent at high temperature. Furthermore, there was no unfolding of SARS-CoV-2 spike S reported at higher temperature. The most stable conformations of the SARS-CoV-2 S protein were reported at 20°C, but the free energy minimum region of the SARS-CoV-2 S protein was sharper at 40°C than other temperatures. Our findings revealed that higher temperatures have little or no influence on the stability and folding of the SARS-CoV-2 S protein.

In Silico Studies on Psilocybin Drug Derivatives Against SARS-CoV-2 and Cytokine Storm of Human Interleukin-6 Receptor

Various metabolites identified with therapeutic mushrooms have been found from different sources and are known to have antibacterial, antiviral, and anticancer properties. Over thousands soil growth-based mushroom metabolites have been discovered, and utilized worldwide to combat malignancy. In this study, psilocybin-mushroom that contains the psychedelic compounds such as psilacetin, psilocin, and psilocybine were screened and found to be inhibitors of SARS-CoV-2 Mprotease. It has been found that psilacetin, psilocin, and psilocybine bind to Mprotease with −6.0, −5.4, and −5.8 kcal/mol, respectively. Additionally, the psilacetin was found to inhibit human interleukin-6 receptors to reduce cytokine storm. The binding of psilacetin to Mprotease of SARS-CoV-2 and human interleukin-6 receptors changes the structural dynamics and Gibbs free energy patterns of proteins. These results suggested that psilocybin-mushroom could be utilized as viable potential chemotherapeutic agents for SARS-CoV-2.

[Automatic epileptic seizure detection algorithm based on dual density dual tree complex wavelet transform]

It is very important for epilepsy treatment to distinguish epileptic seizure and non-seizure. In this study, an automatic seizure detection algorithm based on dual density dual tree complex wavelet transform (DD-DT CWT) for intracranial electroencephalogram (iEEG) was proposed. The experimental data were collected from 15 719 competition data set up by the National Institutes of Health (NINDS) in Kaggle. The processed database consisted of 55 023 seizure epochs and 501 990 non-seizure epochs. Each epoch was 1 second long and contained 174 sampling points. Firstly, the signal was resampled. Then, DD-DT CWT was used for EEG signal processing. Four kinds of features include wavelet entropy, variance, energy and mean value were extracted from the signal. Finally, these features were sent to least squares-support vector machine (LS-SVM) for learning and classification. The appropriate decomposition level was selected by comparing the experimental results under different wavelet decomposition levels. The experimental results showed that the features selected in this paper were different between seizure and non-seizure. Among the eight patients, the average accuracy of three-level decomposition classification was 91.98%, the sensitivity was 90.15%, and the specificity was 93.81%. The work of this paper shows that our algorithm has excellent performance in the two classification of EEG signals of epileptic patients, and can detect the seizure period automatically and efficiently.

High-resolution MD simulation studies to get mechanistic insights into the urea-induced denaturation of human Sphingosine kinase 1

BACKGROUND

Sphingosine kinase 1 (SPhK1) is a crucial signaling enzyme involved in cell proliferation, cellular survival, stimulation of angiogenesis, and apoptosis prevention. Recently, we have reported the unfolding kinetics of SPhK1 using molecular dynamics (MD) simulation, circular dichroism and fluorescence spectroscopy. We found that SPhK1 showed a biphasic unfolding with an intermediate state (~ 4.0 M urea).

OBJECTIVE

We aim to understand the impact of MD simulation duration on the structure, function and dynamics of proteins. In order to get deeper insights into the folding mechanism an extended MD simulation is required.

METHOD

Here, we extended the MD simulations time scale from 100 to 300 ns on SPhK1 at increasing urea concentration to explore structural changes in the SPhK1.

RESULTS

The results suggested a constant form of the unfolding of SPhK1 upon extending the simulation time scale at different urea concentrations. Furthermore, we showed step by step unfolding and percentage of secondary structure contents in SPhK1 under the influence of urea at each concentration.

CONCLUSION

The results from the current work revealed a uniform pattern of the SPhK1 unfolding at different urea concentrations. This study provides deeper mechanistic insights into the urea-induced denaturation of SPhK1.

Remdesivir Strongly Binds to RNA-Dependent RNA Polymerase, Membrane Protein, and Main Protease of SARS-CoV-2: Indication From Molecular Modeling and Simulations

Development of new drugs is a time-taking and expensive process. Comprehensive efforts are being made globally toward the search of therapeutics against SARS-CoV-2. Several drugs such as remdesivir, favipiravir, ritonavir, and lopinavir have been included in the treatment regimen and shown effective results in several cases. Among the existing broad-spectrum antiviral drugs, remdesivir is found to be more effective against SARS-CoV-2. Remdesivir has broad-spectrum antiviral action against many single-stranded RNA viruses including pathogenic SARS-CoV and Middle East respiratory syndrome coronavirus (MERS-CoV). In this study, we proposed that remdesivir strongly binds to membrane protein (Mprotein), RNA-dependent RNA polymerase (RDRP), and main protease (Mprotease) of SARS-CoV-2. It might show antiviral activity by inhibiting more than one target. It has been found that remdesivir binds to Mprotease, Mprotein, and RDRP with -7.8, -7.4, and -7.1 kcal/mol, respectively. The structure dynamics study suggested that binding of remdesivir leads to unfolding of RDRP. It has been found that strong binding of remdesivir to Mprotein leads to decrease in structural deviations and gyrations. Additionally, the average solvent-accessible surface area of Mprotein decreases from 127.17 to 112.12 nm², respectively. Furthermore, the eigenvalues and the trace of the covariance matrix were found to be low in case of Mprotease-remdesivir, Mprotein-remdesivir, and RDRP-remdesivir. Binding of remdesivir to Mprotease, Mprotein, and RDRP reduces the average motions in protein due to its strong binding. The MMPBSA calculations also suggested that remdesivir has strong binding affinity with Mprotein, Mprotease, and RDRP. The detailed analysis suggested that remdesivir has more than one target of SARS-CoV-2.

DOP76 No durable impact of COVID-19 on disease activity and microbiome composition in patients with IBD

Background

Although patients with inflammatory bowel disease (IBD) reported an increased frequency of gastrointestinal (GI) symptoms following infection, the durable impact of COVID-19 on underlying IBD is not well defined.

Methods

In 118 IBD patients with COVID-19, clinical and endoscopic IBD activity, laboratory markers (ESR, CRP, hemoglobin (Hb), fecal calprotectin(FCP)), and medication utilization was assessed up to 6 months post-infection and compared to during infection or up to 6 months prior to infection. Active disease was defined by a Harvey Bradshaw Index > 4, Mayo Score ≥2, SES-CD ≥2, Mayo endoscopic score ≥1. 16S rRNA analysis was used to evaluate microbiome composition in a subset of 12 patients before and after COVID-19.

Results

Although upper respiratory (86.6%) and new GI symptoms (39.1%) were common in patients with IBD, there was no significant change in IBD clinical disease activity (Pre vs. Post-COVID-19 HBI: 4.7 vs. 4.9; partial Mayo: 3.0 vs. 2.1), endoscopic evaluation (Pre vs. Post-COVID-19 SES-CD: 7.2 vs. 8.9, Mayo endoscopic score: 1.5 vs. 1.7), or laboratory markers (Pre vs. Post-COVID-19 CRP: 1.2 vs. 1.3; ESR: 25 vs. 26; Hb 12.8 vs. 13.2; FCP: 388 vs. 250) up to 7 months post-COVID-19 compared to the 6 months prior to infection (Table 1). Overall active disease was present in 60% of the cohort prior to COVID-19 and 55% and 59% during and post-COVID-19, respectively. More subjects (8.5%) reported a delay in medical therapy during COVID-19, but there were no differences in the need for corticosteroids, a change in medical therapy, or IBD-related surgery or hospitalization during or post-COVID-19 compared to the prior 6 months. Microbiome composition stratified by underlying IBD disease activity, but did not show significant change post-COVID-19 (Figure 1).

Conclusion

COVID-19 showed no durable impact on clinical IBD disease activity or microbiome composition supporting guidelines for continued maintenance care.

Impact of amino acid substitutions on the behavior of a photoactivatable near infrared fluorescent protein PAiRFP1

A photoactivatable near-infrared fluorescent protein (NIR-FP) PAiRFP1 has been developed by 15 amino acid substitutions in its nonfluorescent template Agp2. In our previous communication, we investigated the role of three amino acids in PHY domain distal from BV molecule. The impact of the twelve amino acids in GAF domain, especially five residues near BV-binding pocket is unclear. In this paper, PCR based reverse mutagenesis, spectroscopic methods, molecular modelling and simulations have been employed to explore the roles of these substitutions during the molecular evolution of PAiRFP1. It was found that the residue L163 is important for protein folding in PAiRFP1. The residues F244 and C280 exerted remarkable effects on molar extinction coefficient, NIR fluorescence quantum yield, molecular brightness, fluorescence fold, and dark recovery rate. The residues F244 and V276 modulate the maximum absorption and emission peak position. The reverse mutant L168M exhibited a higher fluorescence fold than PAiRFP1. Additionally, the reverse mutants V203A, V294E, S218G and D127G possessed better spectral properties than PAiRFP1. This study is important for the rational design of a better BphP-based photoactivatable NIR-FPs.

Progress in Detection of Insomnia Sleep Disorder: A Comprehensive Review

Lack of adequate sleep is a major source of many harmful diseases related to heart, brain, psychological changes, high blood pressure, diabetes, weight gain, etc. 40 to 50% of the world's population is suffering from poor or inadequate sleep. Insomnia is a sleep disorder in which an individual complaint of difficulties in starting/continuing sleep at least four weeks regularly. It is estimated that 70% of heart diseases are generated during insomnia sleep disorder. The main objective of this study is to determine all work conducted on insomnia detection and to make a database. We used two procedures including network visualization techniques on two databases including PubMed and Web of Science to complete this study. We found 169 and 36 previous publications of insomnia detection in the PubMed and the Web of Science databases, respectively. We analyzed 10 datasets, 2 databases, 21 genes, and 23 publications with 30105 subjects of insomnia detection. This work has revealed the future way and gap so far directed on insomnia detection and has also tried to provide objectives for the future work to be proficient in a scientific and significant manner.

Detection, Treatment Planning, and Genetic Predisposition of Bruxism: A Systematic Mapping Process and Network Visualization Technique

Lack of sleep generates many disorders; bruxism is one of them. It has affected almost 31% of the world population. The purpose of this paper is to determine the volume of the research conducted on bruxism and to create a database. We highlight critical issues for further research commitments and communications. This paper designs a comprehensive and very perception-based picture of the bruxism disorder. The research based work uses three methods such as systematic mapping process, network visualization, and literature review. Software such as VOSviewer, MATLAB, and MEGA-X have been utilized to analyze the work. We have researched deep insights of information to retrieve the present understanding of bruxism disorder from dental to psychological concepts, from engineering detection to clinical treatment, and from temporomandibular disorder to biological genes. We found 10 keywords and 77 items of bruxism in PubMed, Scopus, Google Scholar and Web of Science databases based on the previous publication. These keywords and items are helpful to all type of researchers, which includes engineering, science and medical background personals. 11 genes and 75 research articles with approximately 115077 subjects for the analysis of detection, treatment, child and adolescent bruxism have been reviewed in the research work. In conclusion, it has been found that bruxism altogether has sleep, neurological, dental and genetic disorder components and is a complex phenomenon. This study has also mentioned the future direction and research gap so far conducted on bruxism and has also tried to provide goals for the upcoming research to be accomplished in a more significant and scientific manner.

A Novel Reconstruction Method with Time Reversal Algorithm and Evaluations for Photoacoustic Tomography

Photoacoustic tomography (PAT) is a new modality with a high-resolution and a high contrast, the reconstruction process of which can result in improvements of the imaging quality. The aim of this study is to present an attempt to develop a novel reconstruction method with a time reversal algorithm (TR) for the PAT in an inhomogeneous media. By incorporating the finite difference time domain method (FDTD), the imaging reconstruction with the TR algorithm was formulated firstly. Then, two numerical simulations and an ex vivo experiment were both carried out to evaluate the capability of the proposed method in this work. The obtained results showed qualitatively the PAT images could be reconstructed well both in an inhomogeneous numerical model from different scanning ways and in a phantom model embedded with an ex vivo tumor of patients. Although the TR based algorithms may cost a bit more time than another in reconstruction process, it would be more practical approach for photoacoustic tomography with arbitrary scanning ways and better capability of imaging in inhomogeneous media, could also encourage us to further discuss its applicability of tumor detection in patients.

Single Lead ECG-based Ventricular Repolarization Classification for Early Identification of Unexpected Ventricular Fibrillation

Malignant ventricular arrhythmia (especially ventricular fibrillation (VF)) is the main reason which causes sudden cardiac death (SCD). This paper presents an automatic SCD-patient classifier we developed to identify patients with unexpected VF using 60-minutes continuous single-lead electrocardiograms (ECG) signals before that. Patients are classified as having SCD if the majority of their recorded ventricular repolarization (VR) is recognized as characteristic of unexpected VF. Thus, the classifier's underlying task is to recognize individual VR delineated from single-lead ECG signals as SCD VR, where VR from non-SCD patients are used as controls. With the reported clinical practices of SCD, we extracted five morphological and temporal features (both commonly used and newly developed ones) from ECG signals for VR classification. To evaluate classification performance, we trained and tested k nearest neighbor classifier, a decision tree classifier, and a Naïve Bayes classifier using five-fold cross validation on 36 one-hour ECG signals (18 from patients at risk of SCD and 18 from control people). We compared the performance of these three classifiers, and the patient-classification sensitivity is approximately 98.02-99.51%. Moreover, the k nearest neighbor with a higher accuracy (98.89%) and specificity (98.27%) performed better than the other two. Importantly, the results show obvious superiorities of performance over that in the same duration and of usefulness over several minutes given by related works.Clinical Relevance- This could be integrated into a real-time, long-term out-of-hospital SCD predictor to improve the warning veracity and bring forward the warning time, especially for patients with implantable cardiac defibrillators or pacemakers, etc..

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.

Green Synthesis, Experimental and Theoretical Studies to Discover Novel Binders of Exosomal Tetraspanin CD81 Protein

A new class of benzothiazole-appended quinoline derivatives (6-8) was synthesized via one-pot TPGS micellar-mediated acid-catalyzed nucleophilic addition, followed by aerobic oxidative cyclization of 3-formylquinoline-2-one (2), 3-formylquinoline-2-thione (3), and 2-azidoquinoline-3-carbaldehyde (4) individually with 2-amino thiophenol (5). The structures of the prepared compounds were confirmed using suitable spectroscopic methods complemented with single-crystal X-ray diffraction analysis. Time-dependent density functional theory-based optimization of molecular structures, bond lengths, bond angles, HOMO-LUMO energy gaps, and molecular electrostatic potential maps was theoretically computed at the B3LYP/6-311++g(d) level. The molecular docking studies recommended that 6-8 bound to the active site cavity of CD81 effectively with the binding energies of -6.9, -6.3, and -6.5 kcal mol^-1, respectively. Further, MD simulation studies of compound 6 suggested that the binding resulted in the stabilization of the CD81 molecule. Thus, all theoretical predictions associated with the experimental verifications motivated to discover novel approaches for cancer therapy.

An Adaptive Respiratory Motion Compensation Algorithm with Singular Value Decomposition for Intracardiac Catheter Tracking. Annu Int Conf IEEE Eng Med Biol Soc 2020;2020:5065-5068. [PMID: 33019125 DOI: 10.1109/embc44109.2020.9176152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

During radiofrequency catheterization for atrial fibrillation, how to accurately obtain non-X-ray intracardiac catheter position is crucial to successful endocardial mapping and ablation treatment. The major limitation of the cost-effective intracardiac catheter tracking with transthoracic electrical-fields is that the distribution of electrical conductivity within the volume torso remains dynamics and nonlinear and changes with the patient's respiratory motion. Studies have shown respiratory motion-induced catheter localization error over 20 mm. In this study, we present a novel adaptive respiratory motion compensation algorithm with singular value decomposition for reducing the interference of respiration to ensure the accuracy of intracardiac catheter localization. Animal experiments in swine were carried out for assessing the performance of the propose method through a comparison with a traditional filtering method. The obtained results demonstrate that the proposed adaptive filter based on the SVD performed well to track the original information of catheter position by accurately and timely removing the respiratory interference in case of either a fast- or slow- moving catheter operation. Future applications of this algorithm would be potentially useful for intracardiac catheter localization and real-time tracking.

Convolutional Neural Network Based Detection of Atrial Fibrillation Combing R-R intervals and F-wave Frequency Spectrum

Atrial Fibrillation (AF) is one of the arrhythmias that is common and serious in clinic. In this study, a novel method of AF classification with a convolutional neural network (CNN) was proposed, and particularly two cardiac rhythm features of R-R intervals and F-wave frequency spectrum were combined into the CNN for a good applicability of mobile application. Over 23 patients' ten-hours of Electrocardiogram (ECG) records were collected from the MIT-BIH database, and each of which was segmented into 10s-data fragments to train the designed CNN and evaluate the performance of the proposed method. Specifically, a total of 83,461 fragments were collected, 49,952 fragments of which are the normal fragments (type-N) and the others are the AF fragments. As results, the obtained average accuracy of the proposed method combining the two proposed features is 97.3%, which is shown a relative higher accuracy comparing with either that of the detection with the feature of R-R intervals (95.7%) or that with the feature of F-wave frequency spectrum (93.9%). Additionally, the sensitivity and the specificity of the present method are both of a high level of 97.4% and 97.2%, respectively. In conclusion, the CNN based approach by combining the R-R interval series and the F-wave frequency spectrum would be effectively to improve the performance of AF detection. Moreover, the proposed classification of AF with 10s-data fragments also could be potentially useful for a wearable real-time monitoring application for a pre-hospital screening of AF.

A Numerical Evaluation of Multi-Lead Subcutaneous Implantable Cardioverter Defibrillator for Low Energy and Less Damage in Swine

In the rescue of patients with sudden cardiac death, cardiac electric defibrillation is usually implemented, but the myocardial damage caused by exceeding defibrillation shock is irreversible. The aim of this study is to provide a numerically implanted optimization of a subcutaneous implantable cardioverter defibrillator (S-ICD) for low shock energy and less myocardial damage. In this paper, three anatomically realistic finite element models of swine were constructed for the evaluation study of six Can-Lead configurations with various number of leads at different placements. For each Can-Lead configuration, corresponding numerical modeling and simulation with the finite element method (FEM) were performed to pre-surgically assess quantitatively the efficiency of a certain defibrillation shock, besed on our previously reported multi-criteria evaluation of cardiac defibrillation. The results show that the outcome of an S-ICD shock depends on numbers of the implanted leads as well as the location of Can-Lead, and suggest that an S-ICD implantation with a left pectoral Can and two leads (located at the midline and at the left last ribs oblique upward) would possess the best efficiency of defibrillation, which could offer another option in clinical practice. In conclusion, on basis of the pre-surgical simulation of an S-ICD configuration, an individual optimization of cardiac defibrillation would be potentially useful for a lower energy and a less myocardial damage compared with current implantations with only the knowledge of clinical practices.

Mechanistic insights into the urea-induced denaturation of a non-seleno thiol specific antioxidant human peroxiredoxin 6

Peroxiredoxin 6 (Prdx6) is a unique enzyme among mammalian peroxiredoxins as it lacks resolving cysteine. It is found to be involved in number of different diseases including tumours and its expression level is highest in lungs as compared to other organs. It has been found that Prdx6 plays a significant role different metabolic diseases, ocular damage, neurodegeneration and male infertility. It is a bifunctional protein having phospholipase A₂ and peroxidase (also has the ability to reduce phospholipid hydroperoxides) activities. In order to complete the peroxidise reaction cycle it requires glutathione catalyzed by glutathione S-transferase. Equilibrium unfolding and conformational stability of Prdx6 was studied by using urea as a chemical denaturant to understand the changes it goes under cellular stress conditions. Three different spectroscopic methods were employed to monitor urea-induced denaturation. From the results obtained, it was found that the urea denaturation of Prdx6 follows a variable two state process due to non-coincidence of the normalized transition curves obtained from different optical probes. The different denaturation curves were normalized and thermodynamic parameters, ΔG_D^o, Gibbs free energy change related to the urea-induced denaturation, midpoint of denaturation (C_m), and m = (δΔG_D / [urea]) were obtained. The structural information of Prdx6 were further analysed by several parameters obtained by 100 ns MD simulation. The results of MD simulation clearly favour the outcome of spectroscopic studies.

Seeded droplet microfluidic system for small molecule crystallization

A microfluidic approach to seeded crystallization has been demonstrated using abacavir hemisulfate, a nucleoside analog reverse transcriptase inhibitor, in droplet reactors to control polymorphism and produce particles with a low particle size distribution. Two techniques are introduced: (1) the first technique involves an emulsion system consisting of a dispersed phase solvent and a continuous phase, which holds slight solubility of the dispersed phase solvent. The dispersed phase contains both a dissolved active pharmaceutical ingredient (API) and seeds of the desired polymorph. While the continuous phase enables solvent extraction, the negligible solubility of the API allows for growth of seeds inside droplets via extraction and subsequent API saturation. This technique demonstrates the ability to crystallize the API in spherical agglomerates via slow extraction of droplets. (2) The second technique utilizes a combined dispersed phase by joining in-flow a seed suspension stream with a supersaturated active pharmaceutical ingredient (API) stream. The combined dispersed phase is emulsified in a continuous phase for which the dispersed phase solvent and the API are both insoluble - droplets are incubated at temperatures below their saturation limit to induce crystal growth. Decreasing the concentration of seeds in its input stream resulted in a decreased number of crystals per droplet, increase in crystal size, and decrease in PSD. Temperature cycling was utilized as a proof of concept to demonstrate the ability to reduce the number of seeds per droplet where the optimal goal is to obtain a single seed per droplet for all droplets. Utilizing this approach in conjunction with the ability to produce monodispersed droplet reactors allows for enhanced control of particle size distribution (PSD) by precisely controlling the available mass for each individual seed crystal. The development of this technique as a proof-of-concept for crystallization can be expanded to manufacturing scales in a continuous manner using parallelized droplet generators and flow reactors to precisely control the temperature and crystal growth kinetics of individual droplets.

Mechanism of pH-induced conformational changes in MurE ligase obtained from Salmonella enterica serovar Typhi

Communicated by Ramaswamy H. Sarma.

Effects of reverse genetic mutations on the spectral and photochemical behavior of a photoactivatable fluorescent protein PAiRFP1

Bacteriophytochrome photoreceptors (BphPs) containing biliverdin (BV) have great potential for the development of genetically engineered near-infrared fluorescent proteins (NIR FPs). We investigated a photoactivatable fluorescent protein PAiRFP1, was engineered through directed molecular evolution. The coexistence of both red light absorbing (Pr) and far-red light absorbing (Pfr) states in dark is essential for the photoactivation of PAiRFP1. The PCR based site-directed reverse mutagenesis, spectroscopic measurements and molecular dynamics (MD) simulations were performed on three targeted sites V386A, V480A and Y498H in PHY domain to explore their potential effects during molecular evolution of PAiRFP1. We found that these substitutions did not affect the coexistence of Pr and Pfr states but led to slight changes in the photophysical parameters. The covalent docking of biliverdin (cis and trans form) with PAiRFP1 was followed by several 100 ns MD simulations to provide some theoretical explanations for the coexistence of Pr and pfr states. The results suggested that experimentally observed coexistence of Pr and Pfr states in both PAiRFP1 and mutants were resulted from the improved stability of Pr state. The use of experimental and computational work provided useful understanding of Pr and Pfr states and the effects of these mutations on the photophysical properties of PAiRFP1.

Automatic detection of High Frequency Oscillations (80-500Hz) based on Convolutional Neural Network in Human Intracerebral Electroencephalogram

Recently, high-frequency oscillations (HFOs) of range 80-500 Hz in electroencephalogram (EEG) recordings of epilepsy patients are considered as a reliable marker of epileptic seizure. In the present work, an automatic detection of HFOs represents an isolated peak (an `island') in a time-frequency plot based on convolutional neural network (CNN) was proposed. Initially, three patients with medically intractable epilepsy were recruited. They underwent a presurgical monitoring individually with around 54-90 channels of intracranial electroencephalograph (iEEG). Then, a specific CNN with five layers was developed with a total of 18,400 time-frequency island pictures marked with a label of either a real HFO or a false HFO. They are in the range of 80-500 Hz in the recorded iEEGs of 312 hours. Besides, over 7940 pictures including 3970 real HFO events and 3970 false HFO events except the training set were used to evaluate the performance of the current proposed method. As a result, the obtained precision of HFO events, the value of the recall, and the F1 score of the proposed CNN were found to be 94.19%, 89.37%, and 91.71%, respectively. Additionally, the automatic detection time of each HFO event is limited within 1-3 seconds. In summary, the proposed HFOs detector with deep learning would be more efficient and useful in the diagnosis of epilepsy as compared with the current manual determination of each HFOs from a long-term multichannel iEEGs recordings.

Functional implications of pH-induced conformational changes in the Sphingosine kinase 1

Sphingosine kinase 1 (SphK1) catalyzes the conversion of sphingosine to sphingosine-1-phosphate that acts as a bioactive signalling molecule, and regulates various cellular processes including lymphocyte trafficking, angiogenesis and response to apoptotic stimuli. Abnormal expression of SphK1 has been observed in a wide range of cancers highlighting their role in tumour growth and metastasis. This enzyme also plays a critical role in metabolic and inflammatory diseases, including pulmonary fibrosis, diabetic neuropathy and Alzheimer's disease. In the present study, we have investigated the structural and conformational changes in SphK1 at varying pH using various spectroscopic techniques. Consistent results were observed with the function of SphK1 at corresponding pH values. SphK1 maintains its secondary and tertiary structure in the pH range of 7.5-10.0. However, protein aggregation was observed in the acidic pH range (4.0-6.5). At pH 2.0, the SphK1 exists in the molten-globule state. Kinase assay also shows that SphK1 activity was optimal in the pH range of 7.5-8.5. To complement in vitro results, we have performed 100 ns molecular dynamics simulation to examine the effect of pH on the structural stability of SphK1 at molecular level. SphK1 maintains its native conformation in the alkaline pH range with some residual fluctuations detected at acidic pH. A considerable correlation was noticed between spectroscopic, enzymatic activity and MD simulation studies. pH dependent structural changes can be further implicated to understand its association with disease condition, and cellular homeostasis with respect to protein function under variable pH conditions.

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.

Investigation of guanidinium chloride-induced unfolding pathway of sphingosine kinase 1

Sphingosine kinase 1 (SphK1) is a lipid kinase which plays vital role in the regulation of varieties of biological processes including, cell growth, apoptosis and mitogenesis. In the present study, we investigated the guanidinium chloride (GdmCl)-induced denaturation of SphK1 at pH 8.0 and 25 °C using two different spectroscopic probes, i.e., mean residue ellipticity at 222 nm ([θ]₂₂₂) and fluorescence emission maxima (λ_max). A significant overlap between the transition curves obtained from both the spectral properties indicate that GdmCl-induced unfolding of SphK1 follows two-state process i.e., Native (N) ⇌ Denatured (D) state. Interestingly, a visible protein aggregation was observed at low concentrations of GdmCl ([GdmCl] ≤ 1.5 M). The analysis of transition curves was done to estimate the thermodynamic parameters associated with the stability of SphK1. To complement our experimental findings, 100 ns molecular dynamics (MD) simulations were performed. Spectroscopic studies together with MD simulations provided mechanistic insights of unfolding pathway of SphK1 along with its stability parameters.