Biogenic synthesis of gold nanoparticles using dual extract of tulsi-Vinca for breast cancer tumor regression in mice.

Aim: This work aims to synthesize the gold nanoparticles (GNPs) using a dual extract of tulsi and Vinca (T+V-Gold) for breast cancer tumor regression. Methods: The GNPs were synthesized and characterized for their microscopic, spectroscopic and crystalline properties. Further, the GNPs were investigated for in vitro and in vivo studies for the treatment of the 4T1-induced triple-negative breast cancer murine model. Results: The GNPs for 4T1 tumor-challenged mice resulted in delayed tumor development and lower tumor burden, with T+V-Gold demonstrating the highest prevention of tumor spread. The antitumor effect of T+V-Gold is highly significant in the glutathione family antioxidants glutathione S-transferase and glutathione in tumor tissue samples. Conclusion: The bioefficacy and anticancer outcomes of T+V-Gold nanoformulation can be used as therapeutic agents and drug-delivery vehicles.

Household food waste and theory of planned behavior: A systematic review and meta-analysis.

The darker side of food behavior is that millions of tons of food have been shown the doors of garbage. Therefore, food waste behavior needs an eye to look upon. The purpose of this research is to inculcate the concept of systematic literature review along with meta-analysis in order to examine the Theory of Planned Behavior (TPB) with respect to food waste behavior. The methodology includes Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) that is conducted for the identification, screening, and inclusion of studies. In all, twenty-six independent studies with (N = 13373) met the inclusion criteria. For validating the related literature, random-effects meta-analysis has been applied for ascertaining the average correlation among the variables. More specifically, the present study also examines the sub-group analysis effect among TPB variables. The findings reveal that the strongest association was observed between Attitude and Intention followed by Subjective Norm (SN) and Intention (INT), Perceived Behavioral Control (PBC) and Intention, and Intention and Behavior. Furthermore, the subgroup analysis using multi-cultural groups explores the highest composite correlation in the case of other cultural groups that included countries like Canada. The outcomes of the present study seek to serve in the best interest of households, event management stakeholders, and food policy makers.

Chitosan reduced in-situ synthesis of gold nanoparticles on paper towards fabricating highly sensitive, stable uniform SERS substrates for sensing applications.

Surface-Enhanced Raman Spectroscopy (SERS) is a powerful surface-sensitive technique for molecular analysis. Its use is limited due to high cost, non-flexible rigid substrates such as silicon, alumina or glass and less reproducibility due to non-uniform surface. Recently, paper-based SERS substrates, a low-cost and highly flexible alternative, received significant attention. We report here a rapid, inexpensive method for chitosan-reduced, in-situ synthesis of gold nanoparticles (GNPs) on paper devices towards direct utilization as SERS substrates. GNPs have been prepared by reducing chloroauric acid with chitosan as a reducing and capping reagent on the cellulose-based paper surface at 100 °C, under the saturated humidity condition (100 % humidity). GNPs thus obtained were uniformly distributed on the surface and had fairly uniform particle size with a diameter of 10 ± 2 nm. Substrate coverage of resulting GNPs directly depended on the precursor's ratio, temperature and reaction time. Techniques such as TEM, SEM, and FE-SEM were utilized to determine the shape, size, and distribution of GNPs on paper substrate. SERS substrate produced by this simple, rapid, reproducible and robust method of chitosan-reduced, in situ synthesis of GNPs, showed exceptional performance and long-term stability, with a detection limit of up to 1 pM concentration of test analyte, R6G. Present paper-based SERS substrates are cost-effective, reproducible, flexible, and suitable for field applications.

State-of-the-art methods in healthcare text classification system: AI paradigm.

Machine learning has shown its importance in delivering healthcare solutions and revolutionizing the future of filtering huge amountd of textual content. The machine intelligence can adapt semantic relations among text to infer finer contextual information and language processing system can use this information for better decision support and quality of life care. Further, a learnt model can efficiently utilize written healthcare information in knowledgeable patterns. The word-document and document-document linkage can help in gaining better contextual information. We analyzed 124 research articles in text and healthcare domain related to the ML paradigm and showed the mechanism of intelligence to capture hidden insights from document representation where only a term or word is used to explain the phenomenon. Mostly in the research, document-word relations are identified while relations with other documents are ignored. This paper emphasizes text representations and its linage with ML, DL, and RL approaches, which is an important marker for intelligence segregation. Furthermore, we highlighted the advantages of ML and DL methods as powerful tools for automatic text classification tasks.

Effect of incremental feature enrichment on healthcare text classification system: A machine learning paradigm.

BACKGROUND AND OBJECTIVE: Healthcare tweets are particularly challenging due to its sparse layout and its limited character size. Compared to previous method based on "bag of words" (BOW) model, this study uniquely identifies the enrichment protocol and learns how semantically different aspects of feature selection such as BOW (feature F0), term frequency inverse document frequency (TF-IDF, feature F1), and latent semantic indexing (LSI, feature F2) when applied sequentially with classifier improves the overall performance. METHODS: To study this enrichment concept, our ML model is tested on two kinds of diverse data sets: (i) D1: Disease data with conjunctivitis, diarrhea, stomach ache, cough and nausea related tweets, and (ii) D2: WebKB4 dataset, while adapting three kind of classifiers (a) C1: support vector machine with radial basis function (SVMR), (b) C2: Multi-layer perceptron (MLP) and (c) C3: Random Forest (RF). Partition protocol (K10) was adapted with different performance metrics to evaluate machine learning (ML)-system. RESULTS: Using the combination of F1, C1, D1, K10, ML accuracy was: 94%, while with F2, C1, D1, K10, ML accuracy was 97%. Using the incremental feature enrichment from F0 to F2, K10 protocol gave F1 improvement over F0 by 4.98% on Disease dataset, while F2 improvement over F0 was by 11.78% on WebKB4 dataset. We demonstrated the generalization over memorization process in our ML-design. The system was tested for stability and reliability. CONCLUSIONS: We conclude that semantically different aspects of feature selection, when adapted sequentially, leads to improvement in ML-accuracy for healthcare data sets. We validated the system by taking non-healthcare data sets.

Fabrication of cost-effective and efficient paper-based device for viscosity measurement.

Use of paper-based devices for affordable diagnostics is gaining interest due to unique advantages such as affordability, portability, easy disposability and inherent capillarity. As capillary transportation is an integral component of paper-based devices, low sample volume with faster measurement becomes an additional advantage. We have developed a simple, paper-based microfluidic device suitable for measuring the viscosity of Newtonian fluids as well as a few non-Newtonian fluids with sample volume as little as 12-20 µL. The results could be obtained much faster than the conventional methods. A comparative analysis of the results obtained with our paper-based viscometer and with that of the conventional Ostwald viscometer shows a correlation coefficient greater than 0.99. Apart from viscosity measurement, the paper-based devices were tested for protein denaturation and polymer molecular weight determination. Our results show that the paper-based viscometer could be a potential alternative for the conventional viscometers in the viscosity range from 0.9 cP up till ∼40 cP, with added benefits in terms of time, cost and low sample volume requirement.

Healthcare Text Classification System and its Performance Evaluation: A Source of Better Intelligence by Characterizing Healthcare Text.

A machine learning (ML)-based text classification system has several classifiers. The performance evaluation (PE) of the ML system is typically driven by the training data size and the partition protocols used. Such systems lead to low accuracy because the text classification systems lack the ability to model the input text data in terms of noise characteristics. This research study proposes a concept of misrepresentation ratio (MRR) on input healthcare text data and models the PE criteria for validating the hypothesis. Further, such a novel system provides a platform to amalgamate several attributes of the ML system such as: data size, classifier type, partitioning protocol and percentage MRR. Our comprehensive data analysis consisted of five types of text data sets (TwitterA, WebKB4, Disease, Reuters (R8), and SMS); five kinds of classifiers (support vector machine with linear kernel (SVM-L), MLP-based neural network, AdaBoost, stochastic gradient descent and decision tree); and five types of training protocols (K2, K4, K5, K10 and JK). Using the decreasing order of MRR, our ML system demonstrates the mean classification accuracies as: 70.13 ± 0.15%, 87.34 ± 0.06%, 93.73 ± 0.03%, 94.45 ± 0.03% and 97.83 ± 0.01%, respectively, using all the classifiers and protocols. The corresponding AUC is 0.98 for SMS data using Multi-Layer Perceptron (MLP) based neural network. All the classifiers, the best accuracy of 91.84 ± 0.04% is shown to be of MLP-based neural network and this is 6% better over previously published. Further we observed that as MRR decreases, the system robustness increases and validated by standard deviations. The overall text system accuracy using all data types, classifiers, protocols is 89%, thereby showing the entire ML system to be novel, robust and unique. The system is also tested for stability and reliability.

A study of clinical presentation and delays in management of acute myocardial infarction in community.

To assess the medico social demographics of acute myocardial infarction (AMI) in our community we studied 609 patients presenting between January 2008 to December 2008 with a detailed questionnaire in four centres of UP. Medical attention was sought late (> 6 hours) in 316 (51.6%), thrombolysis was obtained in 45.2% (275) and presentation was atypical in 16.3% (99). 36.2% (221) had pre-monitory symptoms of which 68% (150) ignored the same while of 32% (71) who did seek medical attention 47.9% (37) were brushed away as non-cardiac in origin. 20.3% (46/226) of hypertension, 23.2% (43/185) of diabetes and 83.4% (91/109) of hyperlipidaemia was diagnosed post event. We conclude that at least half of patients with AMI do not get definitive therapy, at least one in 10 patients do not have the classical symptoms, reasonable proportion are unaware of their risk factors, and a good majority have pre-monitory symptoms which get overlooked.

Right isomerism with complex cardiac anomalies presenting with dysphagia--a case report.

Isomerism or Heterotaxy syndromes are rare multifaceted congenital anomalies with multi-system involvement. Grouped under the broad category of Situs Ambiguous defects, these often pose diagnostic difficulties due to their varied and confusing anatomy. Since patients rarely survive into adulthood due to cardiovascular complications, the etiology and natural history of such conditions are not fully understood. Imaging provides the most accurate non invasive method for diagnosis and thereby, prognosis in such cases. We present a case of right sided Isomerism with complex cardiac anomalies in a 17 year old adolescent, who presented with dysphagia as one of the main complaints. Multi modality imaging demonstrated the intricate abnormalities in vital systems.