Kalman filter based short term prediction model for COVID-19 spread.

Corona Virus Disease 2019 (COVID19) has emerged as a global medical emergency in the contemporary time. The spread scenario of this pandemic has shown many variations. Keeping all this in mind, this article is written after various studies and analysis on the latest data on COVID19 spread, which also includes the demographic and environmental factors. After gathering data from various resources, all data is integrated and passed into different Machine Learning Models in order to check its appropriateness. Ensemble Learning Technique, Random Forest, gives a good evaluation score on the tested data. Through this technique, various important factors are recognized and their contribution to the spread is analyzed. Also, linear relationships between various features are plotted through the heat map of Pearson Correlation matrix. Finally, Kalman Filter is used to estimate future spread of SARS-Cov-2, which shows good results on the tested data. The inferences from the Random Forest feature importance and Pearson Correlation gives many similarities and few dissimilarities, and these techniques successfully identify the different contributing factors. The Kalman Filter gives a satisfying result for short term estimation, but not so good performance for long term forecasting. Overall, the analysis, plots, inferences and forecast are satisfying and can help a lot in fighting the spread of the virus.

Cardiac Output Assessment by Transthoracic Electrical Bio-impedance in Patients of Acute Myocardial Infarction: Comparative Analysis with Echocardiography.

Objectives: Transthoracic electrical bio-impedance (TEB) has been proposed as a non-invasive and continuous method of cardiac output (CO) measurement, but it still has not found wide usages in clinics. The present study measured CO, using a new instrument NICOMON, and compared it with Echocardiography (ECHO) in acute myocardial infarction (AMI) patients. Methods: In the present study 100 patients of AMI were assessed by both ECHO and NICOMON for cardiac output and ECHO is considered as a reference method for comparison. TEB CO was measured by passing an alternating current and measuring the bio-impedance across the thorax. End diastolic volume (EDV), End systolic volume (ESV) & Left ventricular outflow tract (LVOT) diameter, measured by ECHO were used to calculate CO. Various statistical methods like "t"-test & correlation coefficient (r) were used where found suitable. Results: Results: Mean TEB-CO (4.03±1.11 l/min) was significantly higher (p<0.001) than mean ECHO-CO (3.80±1.28 l/min) with a mean difference of 0.25±1.02 l/min. Conclusions: NICOMON measures CO non-invasively but, it needs more elaborative studies on a larger sample to establish it as an alternative method of ECHO for cardiac output measurement on regular basis.

Impedance cardiography for monitoring changes in cardiac output.

Impedance Cardiography (ICG) is a non invasive method useful for continuous monitoring of cardiac output but, it still has not found wide usage for measuring cardiac output in clinics and research. Most studies focused on comparing the cardiac output measured at rest with reference methods. In the present study we evaluated the validity of ICG against Doppler Echocardiography (DE) in measuring cardiac output changes that occur during static exercise. Cardiac output of 30 healthy males between 18-26 yrs of age was measured during supine rest, during and 5 min after completion of 3 minute static exercise by ICG and DE. The increase in cardiac output during exercise measured with ICG and DE does not differ significantly (1.04 +/- 0.72 L/min and 1.05 +/- 1.24 L/min respectively) and has significantly high correlation (r = 0.76, P < 0.001). The bias and limits of agreement are (-0.01 +/- 0.83) in acceptable limits. The pooled means of cardiac output measured by ICG and DE do not differ significantly and bears a significant correlation (r = 0.812, P < 0.001). The bias (d +/- s) calculated is 0.15 +/- 0.64 L/min. ICG could provide valid information regarding the relative changes in cardiac output.

Fractional Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread.

COVID-19 disease has come up as a life-threatening outbreak at end of 2019. It has impacted almost all countries in the world. The major source of COVID-19 is a novel beta coronavirus. COVID-19 had a great impact on world throughout the year 2020. Now, the situation is becoming normal due to the invention of the vaccine. All major countries started large vaccination drives. Mathematical models are used to study the impact of different measures used to decrease pandemics. Mathematical models such as susceptible-infected-removed model and susceptible-exposed-infected-removed are used to predict the spread of diseases. But these models are not suitable to predict COVID-19 spread due to various preventive measures (social distancing and quarantine) applied to reduce spread. Hence, in the present manuscript, a novel fractional mathematical model with a social distancing parameter has been proposed to provide early COVID-19 spread estimation. Fractional calculus provides flexibility in choosing arbitrary order of derivative which controls data sensitivity. The model has been validated with real data set. It has been observed that the proposed model is highly accurate in spread estimation.

Comparison of frequency domain parameters of heart rate variability between women with polycystic ovarian disease and apparently healthy women.

Background: Polycystic ovary syndrome (PCOS) patients shows common features like increased insulin resistance and adiposity, which have been known to correlate with sympathetic hyperactivity. Objective: The aim of this study is to analyze the characteristics of heart rate variability in women with PCOS. To compare frequency domain parameters of heart rate variability (HRV) between women with PCOS and apparently healthy women. To study the impact of cardiometabolic parameters such as BMI and blood pressure on frequency-domain HRV parameters. Methods: A total of 30 women with PCOS aged 20 to 40 years (as per Rotterdam criteria) were enrolled as cases and 30 age-matched women having normal ovulatory cycles were enrolled as controls. HRV was recorded using an electrocardiography machine (ECG) machine. The following frequency-domain parameters were assessed: Total power, Very low frequency (VLF), VLF%, Low Frequency (LF), LF%, LF nu, High frequency (HF), HF%, HF nu, LF/HF ratio, short-term variability (SD1), and long-term variability (SD2), respectively. Results: Mean age of cases was 28.03 ± 5.33 years. Mean BMI of PCOS women was 25.39 ± 2.69 kg/m2. A total of 18 (60%) had BMI >25 kg/m2. Cases had significantly higher BMI, waist hip ratio, and blood pressure as compared with controls. None of the controls had BMI >25 kg/m2. Majority of cases (66.7%) had systolic blood pressure/diastolic blood pressure (SBP/DBP) >130/85 mmHg as compared with only 6 (20%) of controls (P < 0.001). For different frequency domain parameters, no statistically significant difference between two groups was observed for VLF and LF. Mean VLF%, LF%, LF (nu), and LF/HF were significantly higher in cases as compared with controls. For all, the other mean value was significantly lower in cases as compared with that of controls (P < 0.05). Conclusions: Autonomic nervous system is affected by PCOS status of women, and sympathetic hyperactivity is seen.

Deep Learning Capabilities for the Categorization of Microcalcification.

Breast cancer is the most common cancer in women worldwide. It is the most frequently diagnosed cancer among women in 140 countries out of 184 reporting countries. Lesions of breast cancer are abnormal areas in the breast tissues. Various types of breast cancer lesions include (1) microcalcifications, (2) masses, (3) architectural distortion, and (4) bilateral asymmetry. Microcalcification can be classified as benign, malignant, and benign without a callback. In the present manuscript, we propose an automatic pipeline for the detection of various categories of microcalcification. We performed deep learning using convolution neural networks (CNNs) for the automatic detection and classification of all three categories of microcalcification. CNN was applied using four different optimizers (ADAM, ADAGrad, ADADelta, and RMSProp). The input images of a size of 299 × 299 × 3, with fully connected RELU and SoftMax output activation functions, were utilized in this study. The feature map was obtained using the pretrained InceptionResNetV2 model. The performance evaluation of our classification scheme was tested on a curated breast imaging subset of the DDSM mammogram dataset (CBIS-DDSM), and the results were expressed in terms of sensitivity, specificity, accuracy, and area under the curve (AUC). Our proposed classification scheme outperforms the ability of previously used deep learning approaches and classical machine learning schemes.

Differentially expressed full-length, fusion and novel isoforms transcripts-based signature of well-differentiated keratinized oral squamous cell carcinoma.

Highly keratinized oral squamous cell carcinoma (OSCC) exhibits an improved response to treatment and prognosis compared with weakly keratinized OSCC. Therefore, we aimed to develop gene transcript signature and to identify novel full-length isoforms, fusion transcript and non-coding RNA to differentiate well-differentiated (WD) with Moderately Differentiated (MD)/Poorly Differentiated (PD)/WD-lymphadenopathy OSCC through, HTA, Isoform sequencing, and NanoString. Additionally, specific copy number gain and loss were also identify in WD keratinized OSCC through Oncoscan array and validated through Real-time PCR in histopathologically characterized FFPE-WD keratinized OSCC. Three-hundred-thirty-eight (338) differentially expressed full-length (FL) transcript isoforms (317 upregulated and 21 down-regulated in OSCC) were identified through Isoform Sequencing using the PacBio platform. Thirty-four (34) highly upregulated differentially expressed transcripts from IsoSeq data were also correlated with HTA2.0 and validated in 42 OSCC samples. We were able to identify 18 differentially expressed transcripts, 12 fusion transcripts, and two long noncoding RNAs. These transcripts were involved in increased cell proliferation, dysregulated metabolic reprogramming, oxidative stress, and immune system markers with enhanced immune rearrangements, suggesting a cancerous nature. However, an increase in proteasomal activity and hemidesmosome proteins suggested an improved prognosis and tumor cell stability in keratinized OSCC and helped to characterize WD with MD/PD/WD with lymphadenopathy OSCC. Additionally, novel isoforms of IL37, NAA10, UCHL3, SPAG7, and RAB24 were identified while in silico functionally validated SPAG7 represented the premalignant phenotype of keratinized (K4) OSCC. Most importantly we found copy number gain and overexpression of EGFR suggest that TKIs may also be used as therapeutics in WD-OSCCs.

Mesh-free based variational level set evolution for breast region segmentation and abnormality detection using mammograms.

Automatic segmentation of abnormal region is a crucial task in computer-aided detection system using mammograms. In this work, an automatic abnormality detection algorithm using mammographic images is proposed. In the preprocessing step, partial differential equation-based variational level set method is used for breast region extraction. The evolution of the level set method is done by applying mesh-free-based radial basis function (RBF). The limitation of mesh-based approach is removed by using mesh-free-based RBF method. The evolution of variational level set function is also done by mesh-based finite difference method for comparison purpose. Unsharp masking and median filtering is used for mammogram enhancement. Suspicious abnormal regions are segmented by applying fuzzy c-means clustering. Texture features are extracted from the segmented suspicious regions by computing local binary pattern and dominated rotated local binary pattern (DRLBP). Finally, suspicious regions are classified as normal or abnormal regions by means of support vector machine with linear, multilayer perceptron, radial basis, and polynomial kernel function. The algorithm is validated on 322 sample mammograms of mammographic image analysis society (MIAS) and 500 mammograms from digital database for screening mammography (DDSM) datasets. Proficiency of the algorithm is quantified by using sensitivity, specificity, and accuracy. The highest sensitivity, specificity, and accuracy of 93.96%, 95.01%, and 94.48%, respectively, are obtained on MIAS dataset using DRLBP feature with RBF kernel function. Whereas, the highest 92.31% sensitivity, 98.45% specificity, and 96.21% accuracy are achieved on DDSM dataset using DRLBP feature with RBF kernel function.

Noninvasive recording of electrocardiogram in conscious rat: A new device.

AIM: Electrocardiogram (ECG) is an important tool for the study of cardiac electrophysiology both in human beings and experimental animals. Existing methods of ECG recording in small animals like rat have several limitations and ECG recordings of the anesthetized rat lack validity for heart rate (HR) variability analysis. The aim of the present study was to validate the ECG data from new device with ECG of anesthetized rat. MATERIALS AND METHODS: The ECG was recorded on student's physiograph (BioDevice, Ambala) and suitable coupler and electrodes in six animals first by the newly developed device in conscious state and second in anesthetized state (stabilized technique). RESULTS: The data obtained were analyzed using unpaired t-test showed no significant difference (P < 0.05) in QTc, QRS, and HR recorded by new device and established device in rats. CONCLUSION: No previous study describes a similar ECG recording in conscious state of rats. Thus, the present method may be a most physiological and inexpensive alternative to other methods. In this study, the animals were not restrained; they were just secured and represent a potential strength of the study.

Globally supported radial basis function based collocation method for evolution of level set in mass segmentation using mammograms.

Computer-aided detection systems play an important role for the detection of breast abnormalities using mammograms. Global segmentation of mass in mammograms is a complex process due to low contrast mammogram images, irregular shape of mass, speculated margins, and the presence of intensity variations of pixels. This work presents a new approach for mass detection in mammograms, which is based on the variational level set function. Mesh-free based radial basis function (RBF) collocation approach is employed for the evolution of level set function for segmentation of breast as well as suspicious mass region. The mesh-based finite difference method (FDM) is used in literature for evolution of level set function. This work also showcases a comparative study of mesh-free and mesh-based approaches. An anisotropic diffusion filter is employed for enhancement of mammograms. The performance of mass segmentation is analyzed by computing statistical measures. Binarized statistical image features (BSIF) and variants of local binary pattern (LBP) are computed from the segmented suspicious mass regions. These features are given as input to the supervised support vector machine (SVM) classifier to classify suspicious mass region as mass (abnormal) or non-mass (normal) region. Validation of the proposed algorithm is done on sample mammograms taken from publicly available Mini-mammographic image analysis society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Combined BSIF features perform better as compared to LBP variants with the performance reported as 97.12% sensitivity, 92.43% specificity, and 98% AUC with 5.12 FP/I on DDSM dataset; and 95.12% sensitivity, 92.41% specificity, and 95% AUC with 4.01FP/I on MIAS dataset.

Effect of Metaboreflex on Cardiovascular System in Subjects of Metabolic Syndrome.

INTRODUCTION: Metaboreflex is a reflex in which muscle receptors send signals regarding metabolic (metabolites accumulation like lactic acid, potassium, adenosine) conditions of the muscles to nucleus tractus solitarius via afferent III and IV fibres to cause haemodynamic adjustments in order to regulate blood flow on the basis of the status of contracting muscle. Dysregulation in its mechanism in metabolic syndrome is demonstrated. AIM: To study the effect of metaboreflex by both isometric and rhythmic handgrip exercise on CVS parameters {Blood Pressure (BP), Cardiac Output (CO) and Systemic Vascular Resistance (SVR)} in subjects of metabolic syndrome. MATERIALS AND METHODS: In this study, 27 subjects aged 25 to 45 years were enrolled after ethical clearance and proper consent. They were divided into: a) subjects without metabolic syndrome; and b) subjects with metabolic syndrome. Impedance cardiovasography was done to assess cardiac parameters (systolic and diastolic blood pressure, cardiac output, systemic vascular resistance). Pre-exercise parameters were assessed followed by isometric exercise and post-isometric exercise parameter measurement. Again after rest, rhythmic exercise was followed. Finally post exercise parameters were assessed. Student paired t-test for comparison between pre and post exercise parameters were done. RESULTS: Changes in diastolic BP following exercise were statistically significant in subjects without metabolic syndrome (p-value 0.01 and 0.001 following isometric and rhythmic exercise respectively). In subjects with metabolic syndrome also these changes were significant, but to a lesser extent (p-value 0.1 and 0.01 respectively for isometric and rhythmic exercise). Changes in systolic BP following exercise were statistically significant in subjects without metabolic syndrome (p-value 0.001 and 0.001 following isometric and rhythmic exercise respectively). In subjects with metabolic syndrome also these changes were significant (p-value 0.01 and 0.001 respectively for isometric and rhythmic exercise). CONCLUSION: Diminished pressor response is found after exercise in subjects with metabolic syndrome.