Anticancer efficacy of 6-thioguanine loaded chitosan nanoparticles with or without curcumin.

6-Thioguanine encapsulated chitosan nanoparticles (6-TG-CNPs) has formulated by the ionic-gelation method. Morphologically, the 6-TG-CNPs were spherical and showed mean size, PDI, zeta potential, and entrapment efficiency of 261.63 ± 6.01 nm, 0.34 ± 0.10, +15.97 ± 0.46 mV and 44.27%, respectively. The IR spectra confirmed the 6-TG complex with chitosan. The in vitro drug release profile of 6-TG-CNPs revealed an increase in sustained-release (91.40 ± 1.08% at 48 h) at pH 4.8 compared to less sustained-release (73.96 ± 1.12% at 48 h) at pH 7.4. The MTT assay was conducted on MCF-7 and PA-1 cell lines at 48 h incubation to determine % cell viability. The IC50 values of 6-TG, 6-TG-CNPs, and curcumin for MCF-7 were 23.09, 17.82, and 15.73 µM, respectively. Likewise, IC50 values of 6-TG, 6-TG-CNPs, and curcumin for PA-1 were 5.81, 3.92, and 12.89 µM, respectively. A combination of 6-TG-CNPs (IC25) with curcumin (IC25) on PA-1 and MCF-7 showed % cell viability of 43.67 ± 0.02 and 49.77 ± 0.05, respectively. The in vitro cytotoxicity potential in terms of % cell viability, early apoptosis, G2/M phase arrest, and DNA demethylating activity of 6-TG-CNPs alone and combination with curcumin proved to be more effective than that of 6-TG on PA-1 cells.

A hybrid LIBS-Raman system combined with chemometrics: an efficient tool for plastic identification and sorting.

Classification of plastics is of great importance in the recycling industry as the littering of plastic wastes increases day by day as a result of its extensive use. In this paper, we demonstrate the efficacy of a combined laser-induced breakdown spectroscopy (LIBS)-Raman system for the rapid identification and classification of post-consumer plastics. The atomic information and molecular information of polyethylene terephthalate, polyethylene, polypropylene, and polystyrene were studied using plasma emission spectra and scattered signal obtained in the LIBS and Raman technique, respectively. The collected spectral features of the samples were analyzed using statistical tools (principal component analysis, Mahalanobis distance) to categorize the plastics. The analyses of the data clearly show that elemental information and molecular information obtained from these techniques are efficient for classification of plastics. In addition, the molecular information collected via Raman spectroscopy exhibits clearly distinct features for the transparent plastics (100% discrimination), whereas the LIBS technique shows better spectral feature differences for the colored samples. The study shows that the information obtained from these complementary techniques allows the complete classification of the plastic samples, irrespective of the color or additives. This work further throws some light on the fact that the potential limitations of any of these techniques for sample identification can be overcome by the complementarity of these two techniques. Graphical Abstract ᅟ.

Primary splenic lymphoma: a case report.

Primary splenic lymphomas (PSL) constitute a rare variety of splenic neoplasms. As a secondary lymphoid organ, the spleen is usually involved by lymphomas as part of the systemic illness. However, rarely it can be the exclusive site of disease burden. An elderly lady presented with symptoms and signs of splenomegaly. After evaluation she was found to have a splenic tumor. Splenectomy was done which revealed primary splenic lymphoma. This case report highlights the evaluation and management of this illness.

Classification of protein profiles using fuzzy clustering techniques: an application in early diagnosis of oral, cervical and ovarian cancer.

Present study has brought out a comparison of PCA and fuzzy clustering techniques in classifying protein profiles (chromatogram) of homogenates of different tissue origins: Ovarian, Cervix, Oral cancers, which were acquired using HPLC-LIF (High Performance Liquid Chromatography-Laser Induced Fluorescence) method developed in our laboratory. Study includes 11 chromatogram spectra each from oral, cervical, ovarian cancers as well as healthy volunteers. Generally multivariate analysis like PCA demands clear data that is devoid of day-to-day variation, artifacts due to experimental strategies, inherent uncertainty in pumping procedure which is very common activities during HPLC-LIF experiment. Under these circumstances we demonstrate how fuzzy clustering algorithm like Gath Geva followed by Sammon mapping outperform PCA mapping in classifying various cancers from healthy spectra with classification rate up to 95 % from 60%. Methods are validated using various clustering indexes and shows promising improvement in developing optical pathology like HPLC-LIF for early detection of various cancers in all uncertain conditions with high sensitivity and specificity.

Protein profile analysis of cellular samples from the cervix for the objective diagnosis of cervical cancer using HPLC-LIF.

Protein profiles of cytologic samples from the cervix were studied using High Performance Liquid Chromatographic (HPLC) separation combined with ultra-sensitive laser induced fluorescence (LIF) detection. HPLC-LIF protein profiles of samples from clinically normal subjects, individuals suffering from cervical cancer (different stages), and subjects who had other gynecological problems related to cervix, like erosion of cervix and Nabothian cyst, but no malignancy, were subjected to Principal Component Analysis (PCA). The application of HPLC-LIF protein profiling combined with PCA was found to be a highly efficient method for discrimination of different classes of samples with high sensitivity and specificity. Diagnostic accuracy and optimal threshold - decision criterion - for objective discrimination were estimated using sensitivity-specificity pairs and Youden's index (J) plots.

Probing oxidative stress in single erythrocytes with Raman Tweezers.

Raman Tweezers have been successfully applied to characterize chemically-induced oxidative stress on optically-trapped live, single erythrocytes. There is significant enhancement in Raman peak intensities corresponding to SS and C-S stretching modes that are induced by oxidative stress. This is consistent with the formation of mixed disulphides between protein SH groups and low-molecular-mass thiols such as glutathione during oxidative damage to cells. Enhancement in glutathione level as a protective response against oxidative stress has been observed. Principal component analysis of the data yields good discrimination between spectra of normal and stress-induced red blood cells.

Optical screening of oral cancer: technology for emerging markets.

Oral cancer is the sixth most common cancer in the world. It is one of the most prevalent cancers in the developing countries of South Asia accounting for one third of the world burden. Sixty percent of the cancers are advanced by the time they are detected. Two methods of optical spectroscopy for detection of oral cancer have been discussed here. These methods are simple, easy to handle and non-invasive. The evaluation of the data is done automatically using pattern recognition techniques, making the screening subjective.

Studies on the luteolytic, oestrogenic and follicle-stimulating hormone like activity of plant Rhaphidophora pertusa (Roxb.).

The phytochemical, pharmacological and toxicological feature of plant Rhaphidophora pertusa (Roxb.) was done. Phytosteroids, flavonoids, tannins and glucosides were detected in the plant extracts. In cross-bred (Zebu X Holstein-Friesian or Jersey) dairy cows, subsequent to prostaglandin (PG) induced oestrus, to each group (n=4), cloprostenol (PG control) 100 microg i.m. on day 10, the rice gruel (vehicle) was fed on day 10 or the fresh stem (1 kg/animal/day) in rice gruel on day 9, or days 9 and 10, or days 9-11 of the oestrous cycle. Each group received subcutaneously either 5% gum acacia suspension or the plant ethyl acetate or methanol extract (1g in gum acacia) on days 8 (to bannur ewes) or 10 (to dairy cows) of the oestrous cycle. In PG control cows or ewes, there was induction of oestrus in 48 h and a fall in serum progesterone concentration. The feeding of fresh stem in the rice gruel or the s.c. administration of the plant extract did not induce oestrus or significantly (P>0.05) alter the serum progesterone, bilirubin, calcium, creatinine, phosphorus, magnesium and glucose concentrations or the total erythrocyte and leucocyte count, differential leucocyte count and haemoglobin concentration. The plant did not cause any toxicity in the cow or ewe. In immature rats, the aqueous or methanol (hot or cold) extract did not cause any follicle-stimulating hormone (FSH)-like activity. The methanol extract increased the uterine weight in ovariectomised rats. This suggested the presence of oestrogenic activity in the plant. In conclusion, the present study revealed the presence of oestrogenic activity in the plant and the absence of luteolytic or FSH-like or toxic activity.

Principal component analysis and artificial neural network analysis of oral tissue fluorescence spectra: classification of normal premalignant and malignant pathological conditions.

Pulsed laser-induced autofluorescence spectroscopic studies of pathologically certified normal, premalignant, and malignant oral tissues were carried out at 325 nm excitation. The spectral analysis and classification for discrimination among normal, premalignant, and malignant conditions were performed using principal component analysis (PCA) and artificial neural network (ANN) separately on the same set of spectral data. In case of PCA, spectral residuals, Mahalanobis distance, and scores of factors were used for discrimination among normal, premalignant, and malignant cases. In ANN, parameters like mean, spectral residual, standard deviation, and total energy were used to train the network. The ANN used in this study is a classical multiplayer feed-forward type with a back-propagation algorithm for the training of the network. The specificity and sensitivity were determined in both classification schemes. In the case of PCA, they are 100 and 92.9%, respectively, whereas for ANN they are 100 and 96.5% for the data set considered.