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The excessive use of chemical fertilizers and pesticides has been a threat to agricultural production, soil and health. Present research was conducted mainly to focus the adverse impacts of pesticide use on human health and to describe the current status of pesticide use in Jaspur village of Cumilla district. Out of 65 households data were collected from 45, by using a direct interview method with a pretested questionnaire .The questionnaire included both structured and non-structured questions. It had been observed that a maximum of 52.5% farmers said that they were affected through mouth and breathing, 22.5% farmers were affected through eye, skin, mouth and breathing, 5% through eye, skin and mouth and 5% through mouth. The signs and symptoms of illness due to exposure of pesticides were burning/stinging/itching eyes, skin redness/white patches, cough, excessive salivation, nausea/vomiting, shortness of breath etc. About 37.50% respondents waited one week to collect vegetables after pesticide use. 77.5% respondents did not get any training about pesticide use as a result they were highly dependent on dealers. Lack of awareness was a great problem in this village. So in order to increase awareness farmers training was essential. Although this was a social study but it was observed that scientific study was required in this village.

A novel analytical protocol was developed for general quality screening of chicken meat based on IEF and protein extraction. To demonstrate the developed protocol, 24 chickens were divided into three groups; each had eight chickens. The chickens in Group 1 were slaughtered by exsanguination, Group 2 asphyxiated in water, and that in Group 3 were infected by new castle disease virus. Proteins were extracted from the meat samples by using pure water as an extractant, separated by IEF, verified by western blot, and quantified via imaging analysis. The relevant experiments demonstrated that two myoglobin (Mb) bands were detected at pI 6.8 and 7.04 for all samples of Groups 1, 2, and 3, but there were additional hemoglobin (Hb) bands at pI 7.09 and 7.13 (P < 0.05) for the samples of Groups 2 and 3. The results implied that Hb bands might be a potential biomarker for the screening of chicken meat quality. The RSD values of two Mb bands (pI 6.8 and 7.04) in Group 1 were respectively 4.08 and 3.63%, the ones of two Hb bands (pI 7.09 and 7.13) in Group 2 were 3.66 and 2.10%, and those in Group 3 were 2.17% and 2.77%, respectively. All the RSD values indicated high stability and reliability of the developed protocol. Additionally, the protocol had a direct readout of protein bands in IEF without staining. However, it was time-consuming and had high cost. Even so, the relevant general method and finding have potential for screening of chicken meat quality.

Assays of uric acid (UA) in human urine and blood are of great significance to clinic diagnoses of many diseases, such as gout, renal failure, and hyperuricemia. Portable and visual quantification method, however, has been rarely developed for POCT detection of UA in urine and blood samples. In this work, we developed the electrophoresis titration (ET) model, the relevant sensing method and the portable chip device for portable and visual detection of UA in both urine and blood based on moving reaction boundary (MRB). In the model, uricase catalyzes UA in the anode well to allantoin, producing hydrogen peroxide, which oxidizes leucocrystal violet dye without charge to crystal violet (CV+) with one positive charge. Under an electric field, a MRB was created between the violet CV+ moving from the anode well into the channel and the alkaline sodium acetate in channel, resulting in an ET sensing. The model indicated that the distance of MRB under given conditions was as a function of UA content, implying an extraordinary simple sensing for UA. Based on the MRB-ET model, a series of experiments were conducted. The results evidently validated the model and method of ET-MRB. The experiments not only demonstrated the high facility and portability of MRB-ET model, but also showed the visuality, selectivity and rapidity. In addition, the experiments revealed the fair sensitivity (

Hepatocellular carcinoma (HCC) is a common neoplastic transformation of the hepatocytes, which has high morbidity and mortality worldwide, particularly in Eastern Asia. HCC is also developed as a consequence of chronic liver cirrhosis, and both diseases are difficult to diagnosis and differentiate. Accurate noninvasive biomarkers for HCC and cirrhosis are urgently needed. In the search for novel candidates, small extracellular vesicles (sEVs) were isolated from the serum of liver cancer patients, liver cirrhosis patients, healthy control subjects, as well as the culture media of hepatocellular carcinoma cells (HepG2) and normal hepatocyte cells (Lo2). Isolated sEVs were confirmed by size distribution analysis, morphological analysis, and surface biomarker tests. Mass spectrometry based label-free quantification revealed 61 and 63 differentially expressed proteins in the serum sEVs of liver cirrhosis patients and liver cancer patients (p 0.05), respectively. The proteomics data of cell-derived sEVs were combined for the selection of valuable candidates. Promising proteins were further verified by immunoassay, including thrombospondin-1 (THBS1), fibulin-1(FBLN1), and fibrinogen gamma chain (FGG), which could differentiate healthy control from liver cancer or liver cirrhosis. Our findings verified the hypothesis that cancer-related proteomics signatures are present in the sEVs of patient's serum and might be monitored for the evaluation of liver cancer and liver cirrhosis.

This research conducts a short review, comparative study and performs sentence level subjectivity analysis using three different machine learning algorithms for movie reviews. Support Vector Machine (SVM) and Naive Bayes Model have been used for subjective/objective classification using the same data-set (rotten tomato movie reviews) to analyze the result as well as to find the best outcome. Multilayer perceptron (MLP) has been implemented later to the feasibility of it’s in differentiating subjective and objective notions. In sentence level classification, using stop words produce a better result than not using stops words. A comparative analysis has also been included to support this claim.

Extracellular vesicles (EVs) are cell-derived microparticles present in most body fluids, mainly including microvesicles and exosomes. EV-harbored proteins have emerged as novel biomarkers for the diagnosis and prediction of different cancers. We successfully isolated microvesicles and exosomes from human saliva, which were further characterized comprehensively. Salivary EV protein profiling in normal subjects and lung cancer patients was systematically compared through utilizing LC-MS/MS-based label-free quantification. 785 and 910 proteins were identified from salivary exosomes and microvesicles, respectively. According to statistical analysis, 150 and 243 proteins were revealed as dysregulated candidates in exosomes and microvesicles for lung cancer. Among them, 25 and 40 proteins originally from distal organ cells were found in the salivary exosomes and microvesicles of lung cancer patients. In particular, 5 out of 25 and 9 out of 40 are lung-related proteins. Six potential candidates were selected for verification by Western blot, and four of them, namely, BPIFA1, CRNN, MUC5B, and IQGAP, were confirmed either in salivary microvesicles or in exosomes. Our data collectively demonstrate that salivary EVs harbor informative proteins that might be used for the detection of lung cancer through a noninvasive way.

The extraction of membrane proteins remain a challenge due to innate hydrophobicity, dynamic discrepancy, and restrain effect of membrane lipids. Nanomaterials with high surface area have competency of hydrophobic-hydrophobic lipid interactions. It is shown here that both graphene and graphene oxide dissolved in solubilization buffer are viable sorbents for efficient extraction of membrane proteins. LC-MS/MS analysis further revealed that graphene (50-200 nm) and graphene oxide (50-200 nm) can enrich more kinds of membrane proteins than a commercially available kit. Graphene was further applied to the enrichment of membrane proteins of normal cells as well as cancer cells, and 1079 and 872 proteins were identified, respectively, among which 56.5% and 60.5% were membrane proteins. In particular, 241 proteins were significantly regulated in cancer cells. Gene expression of 15 proteins was verified by qRT-PCR, and 4 of them were further quantified by immunoassay. These data collectively demonstrate that graphene has great potential to improve membrane protein extractions and thus can serve downstream cancer proteomics. Graphical abstract Two dimensional carbon nanomaterials, including graphene and graphene oxide, were employed as solid matrix to avoid lipid bilayer interference and enhance the extraction efficiency of membrane and membrane associated proteins. The strategy will benefit downstream membrane proteomics analysis.

This paper investigates a new approach of finding sentence level sentiment analysis using different machine learning algorithms. Three different machine learning algorithms — SVM (Support Vector Machine), Naive Bayes and MLP (Multilayer Layer Perceptron) have been used both for sentiment analysis. Moreover two different classifiers of Naive Bayes and two different types of SVM kernels have been used in this work to identify and analyze the difference in accuracy as well as to find the best outcome among all the experiments. For sentiment analysis aclimdb movie review dataset has been used. Lastly, the impact of stop words and number of attributes in accuracy for sentiment analysis has also been illustrated.