Comparison between perfusion index, pleth variability index, and pulse pressure variability for prediction of hypotension during major abdominal surgery under general anaesthesia: A prospective observational study.

Background and Aims: Short-term hypotension after general anaesthesia can negatively impact surgical outcomes. This study compared the predictive potential of the pleth variability index (PVI), pulse pressure variability (PPV), and perfusion index (PI) for anaesthesia-induced hypotension. This study's primary objective was to evaluate the predictive potential of PI, PVI, and PPV for hypotension. Methods: This observational study included 140 adult patients undergoing major abdominal surgery under general anaesthesia. Mean arterial pressure, heart rate, PVI, PPV, and PI were collected at 1-min intervals up to 20 min post anaesthesia induction. Hypotension was assessed at 5-min and 15-min intervals. Receiver operating characteristic (ROC) curves were plotted to determine the diagnostic performance and best cut-off for continuous variables in predicting a dichotomous outcome. Statistical significance was kept at P < 0.05. Results: Hypotension prevalence within 5 and 15 min of anaesthesia induction was 36.4% and 45%, respectively. A PI cut-off of <3.5 had an area under the ROC curve (AUROC) of 0.647 (P = 0.004) for a 5-min hypotension prediction. The PVI's AUROC was 0.717 (P = 0.001) at cut-off >11.5, while PPV's AUROC was 0.742 (P = 0.001) at cut-off >12.5. At 15 min, PVI's AUROC was 0.615 (95% confidence interval 0.521-0.708, P = 0.020), with 54.9% positive predictive value and 65.2% negative predictive value. Conclusion: PVI, PPV, and PI predicted hypotension within 5 min after general anaesthesia induction. PVI had comparatively higher accuracy, sensitivity, specificity, and positive predictive value than PI and PPV when predicting hypotension at 15 min.

Novel Biomarker Prediction for Lung Cancer Using Random Forest Classifiers.

Lung cancer is considered the most common and the deadliest cancer type. Lung cancer could be mainly of 2 types: small cell lung cancer and non-small cell lung cancer. Non-small cell lung cancer is affected by about 85% while small cell lung cancer is only about 14%. Over the last decade, functional genomics has arisen as a revolutionary tool for studying genetics and uncovering changes in gene expression. RNA-Seq has been applied to investigate the rare and novel transcripts that aid in discovering genetic changes that occur in tumours due to different lung cancers. Although RNA-Seq helps to understand and characterise the gene expression involved in lung cancer diagnostics, discovering the biomarkers remains a challenge. Usage of classification models helps uncover and classify the biomarkers based on gene expression levels over the different lung cancers. The current research concentrates on computing transcript statistics from gene transcript files with a normalised fold change of genes and identifying quantifiable differences in gene expression levels between the reference genome and lung cancer samples. The collected data is analysed, and machine learning models were developed to classify genes as causing NSCLC, causing SCLC, causing both or neither. An exploratory data analysis was performed to identify the probability distribution and principal features. Due to the limited number of features available, all of them were used in predicting the class. To address the imbalance in the dataset, an under-sampling algorithm Near Miss was carried out on the dataset. For classification, the research primarily focused on 4 supervised machine learning algorithms: Logistic Regression, KNN classifier, SVM classifier and Random Forest classifier and additionally, 2 ensemble algorithms were considered: XGboost and AdaBoost. Out of these, based on the weighted metrics considered, the Random Forest classifier showing 87% accuracy was considered to be the best performing algorithm and thus was used to predict the biomarkers causing NSCLC and SCLC. The imbalance and limited features in the dataset restrict any further improvement in the model's accuracy or precision. In our present study using the gene expression values (LogFC, P Value) as the feature sets in the Random Forest Classifier BRAF, KRAS, NRAS, EGFR is predicted to be the possible biomarkers causing NSCLC and ATF6, ATF3, PGDFA, PGDFD, PGDFC and PIP5K1C is predicted to be the possible biomarkers causing SCLC from the transcriptome analysis. It gave a precision of 91.3% and 91% recall after fine tuning. Some of the common biomarkers predicted for NSCLC and SCLC were CDK4, CDK6, BAK1, CDKN1A, DDB2.

Understanding the Change in the Prevalence and Factors Influencing the Childhood Stunting Using District-Level Data from NFHS-4 and NFHS-5 in India.

To compare the district level prevalence of childhood stunting between NFHS-4 and NFHS-5 and to explore the correlates of it at the district level. Although malnutrition rates in India have decreased over a period, country is still a home for the highest number of stunted and wasted children in the world. Among the South Asian countries, India has the second highest number of stunted children. An ecological study conducted by using the data from fourth and fifth round of National Family Health Survey. Study concentrated on percentage of children who were stunted across 692 Indian districts during 2 survey periods and its correlates from NFHS-5. District level change in childhood stunting was calculated by differencing the NFHS-5 estimates from NFHS-4. Descriptive statistics were used to understand the nature of the variables and Moran's I statistic was calculated to check for the spatial autocorrelation in the childhood stunting. Spatial error regression model was used to identify the correlates of childhood stunting. Among the Indian districts considered, 243 districts showed the increase in childhood stunting between the time periods considered. Currently, about 33.56% of children in India are stunted and there is high spatial disparity in the prevalence of childhood stunting among the districts of it. Major hotspots of childhood stunting were found in the parts of UP, Bihar, Jharkhand, and West Bengal. Households access to improved sanitation facility, iodized salt, clean fuel, women 10 plus years of schooling, post-natal care of mother were found to be the significant protective factors. Closed spacing of births, teenage pregnancy, low BMI of women, childhood diarrhea, and anemia were found to be the significant risk factors of childhood stunting. Stunting depends on several other factors apart from poverty, working on these factors will help in reducing childhood stunting in India.

Acquired childhood aphasia as a consequence of COVID-19 and its differential diagnosis from speech-language pathologist perspective: A case study.

It is essential to be more vigilant in understanding impact of COVID-19 on children's speech and language skills. As studies in these lines are very sparse, it is imperative to profile these children and derive accurate diagnosis. Accurate diagnosis aids Speech-Language Pathologists (SLPs) to render speech and Language therapy systematically.

Ultrasound assisted extraction of pectin from waste Artocarpus heterophyllus fruit peel.

Four factors three level face centered central composite response surface design was employed in this study to investigate and optimize the effect of process variables (liquid-solid (LS) ratio (10:1-20:1ml/g), pH (1-2), sonication time (15-30min) and extraction temperature (50-70°C)) on the maximum extraction yield of pectin from waste Artocarpus heterophyllus (Jackfruit) peel by ultrasound assisted extraction method. Numerical optimization method was adapted in this study and the following optimal condition was obtained as follows: Liquid-solid ratio of 15:1ml/g, pH of 1.6, sonication time of 24min and temperature of 60°C. The optimal condition was validated through experiments and the observed value was interrelated with predicted value.