vivo analgesic activity of ethanolic extracts of twomedicinal plants—Scoparia dulcis L. And Ficus racemosa Linn

Abstract Fruits and whole herb of two medicinal plants, Ficus racemosa Linn. (Moraceae) and Scoparia dulcis L. (Scrophulariacae) were extracted in 95% ethanol to evaluate for centrally acting analgesic potential using hot plate and peripheral pharmacological actions using acetic acid induced writhing test in mice. The crude extracts of both the plants were found to have significant (p<0.001) analgesic activity at the oral dose of 100 & 200 mg/kg b. wt., in the tested models. In hot plate test S. dulcis showed increased latency period than F. racemosa whereas in acetic acid induced writhing test F. racemosa showed reduced number of writhes than S. dulcis at two dose levels which are significant (p<0.001) compared to control. The results obtained support the use of fruits of F. racemosa and whole herb of S. dulcis in painful conditions acting both centrally and peripherally

Machine Learning Approaches to Identify Patient Comorbidities and Symptoms That Increased Risk of Mortality in COVID-19

Providing appropriate care for people suffering from COVID-19, the disease caused by the pandemic SARS-CoV-2 virus, is a significant global challenge. Many individuals who become infected may have pre-existing conditions that may interact with COVID-19 to increase symptom severity and mortality risk. COVID-19 patient comorbidities are likely to be informative regarding the individual risk of severe illness and mortality. Determining the degree to which comorbidities are associated with severe symptoms and mortality would thus greatly assist in COVID-19 care planning and provision. To assess this we performed a meta-analysis of published global literature, and machine learning predictive analysis using an aggregated COVID-19 global dataset. Our meta-analysis suggested that chronic obstructive pulmonary disease (COPD), cerebrovascular disease (CEVD), cardiovascular disease (CVD), type 2 diabetes, malignancy, and hypertension as most significantly associated with COVID-19 severity in the current published literature. Machine learning classification using novel aggregated cohort data similarly found COPD, CVD, CKD, type 2 diabetes, malignancy, and hypertension, as well as asthma, as the most significant features for classifying those deceased versus those who survived COVID-19. While age and gender were the most significant predictors of mortality, in terms of symptom–comorbidity combinations, it was observed that Pneumonia–Hypertension, Pneumonia–Diabetes, and Acute Respiratory Distress Syndrome (ARDS)–Hypertension showed the most significant associations with COVID-19 mortality. These results highlight the patient cohorts most likely to be at risk of COVID-19-related severe morbidity and mortality, which have implications for prioritization of hospital resource

The putative tumour suppressor protein Latexin is secreted by prostate luminal cells and is downregulated in malignancy

Loss of latexin (LXN) expression negatively correlates with the prognosis of several human cancers. Despite association with numerous processes including haematopoietic stem cell (HSC) fate, inflammation and tumour suppression, a clearly defined biological role for LXN is still lacking. Therefore, we sought to understand LXN expression and function in the normal and malignant prostate to assess its potential as a therapeutic target. Our data demonstrate that LXN is highly expressed in normal prostate luminal cells but downregulated in high Gleason grade cancers. LXN protein is both cytosolic and secreted by prostate cells and expression is directly and potently upregulated by all-trans retinoic acid (atRA). Whilst overexpression of LXN in prostate epithelial basal cells did not affect cell fate, LXN overexpression in the luminal cancer line LNCaP reduced plating efficiency. Transcriptome analysis revealed that LXN overexpression had no direct effects on gene expression but had significant indirect effects on important genes involved in both retinoid metabolism and IFN-associated inflammatory responses. These data highlight a potential role for LXN in retinoid signaling and inflammatory pathways. Investigating the effects of LXN on immune cell function in the tumour microenvironment (TME) may reveal how observed intratumoural loss of LXN affects the prognosis of many adenocarcinomas

Evaluation of seakeeping analysis of a fishing vessel using a wave buoy and onboard motions monitoring device

Seakeeping analysis is important in assessing the performance of floating structure in waves. To perform Seakeeping calculations, the wave characteristics as well as the response of the vessel are needed. The analysis normally requires reliable computer programs to calculate response amplitude operator (RAO) and accurate seaway representation. For seaway representation theoretical spectra can be used but it is always preferable to use the measured spectra which can be obtained through full scale measurement. On the other hand theoretical spectra can be used for comparison. This paper presents the results of a full-scale measurement of wave and vessel motions taken from a Malaysian fishing vessel. The vessel operates off the East Coast of Peninsular Malaysia. Wave buoy was used to measure wave data and Vessel Motion Monitoring System (VMMS) was used to measure the vessel motions. They are basically composed of a set of accelerometers gyroscopes and wind sensors. The data processing and analyzing is done using LabVIEW software. Finally, the main analysis of the results obtained is in the form of spectral analysis of wave and vessel motions. From them the RAO can be obtained, which is the key to all Seakeeping analysis

Seakeeping Analysis of a Fishing Vessel Operating in Malaysian Water

Seakeeping analysis is important in assessing the performance of floating structure in waves. To perform Seakeeping calculations, the wave characteristics as well as the response of the vessel are needed. The analysis normally requires reliable computer programs to calculate response amplitude operator (RAO) and accurate seaway representation. For seaway representation, theoretical spectra can be used but it is more preferable to use the measured spectra which can be obtained through full scale measurement. On the other hand theoretical spectra can be used for comparison. This paper presents the results of a full-scale measurement of wave and vessel motions taken from a Malaysian fishing vessel. The vessel operates off the East Coast of Peninsular Malaysia. Wave buoy was used to measure wave data and Vessel Motion Monitoring System (VMMS) was used to measure the vessel motions. They are basically composed of a set of accelerometers gyroscopes and wind sensors. The data processing and analyzing is done using LabVIEW software. Finally, the main analysis of the results obtained is in the form of spectral analysis of wave and vessel motions. From them the RAO can be obtained, which is the key to all Seakeeping analysis. Keywords: Seakeeping, response amplitude operator (RAO), full-scale measurement, wave spectr

Feature selection with biased sample distributions

Feature selection concerns the problem of selecting a number of important features (w.r.t. the class labels) in order to build accurate prediction models. Traditional feature selection methods, however, fail to take the sample distributions into the consideration which may lead to poor predictions for minority class examples. Due to the sophistication and the cost involved in the data collection process, many applications, such as Biomedical research, commonly face biased data collections with one class of examples (e.g., diseased samples) significantly less than other classes (e.g., normal samples). For these applications, the minority class examples, such as disease samples, credit card frauds, and network intrusions, are only a small portion of the data collections but deserve full attentions for accurate prediction. In this paper, we propose three filtering techniques, Higher Weight (HW), Differential Minority Repeat (DMR) and Balanced Minority Repeat (BMR), to identify important features from biased data collections. Experimental comparisons with the ReliefF method on five datasets demonstrate the effectiveness of the proposed methods in selecting informative features from data with biased sample distributions