Dietary zinc requirement of juvenile stinging catfish Heteropneustes fossilis based on growth performance, haematology, and tissue mineral composition.

This investigation was done to determine how much zinc (Zn) the stinging catfish, Heteropneustes fossilis, needs in its diet. Five isonitrogenous (34.5% protein) and isolipidic (6.0% lipid) diets were prepared to contain graded levels of Zn (0, 10, 20, 30, and 40 mg kg-1), supplied as zinc sulfate (ZnSO4·7H2O), and referred to as Zn0, Zn10, Zn20, Zn30, and Zn40, respectively. A total of 600 fish (initial body weight: 1.41 ± 0.02 g) were stocked in 15 glass aquaria (40 fish/aquarium), each with 180 L water capacity. For ten weeks, each diet was hand fed to three groups of fish twice daily until they appeared satisfied. The highest weight gain and specific growth rate, and lowest feed conversion ratio were recorded in fish fed with a 30 mg Zn kg-1 diet. Zn contents in bone and muscle linearly increased up to 30 mg kg-1 Zn and then remained stable, while iron (Fe) and copper (Cu) contents in bone and muscle had an inverse pattern with the inclusion level of dietary Zn. Increasing dietary Zn levels up to 30 mg kg-1 was found to improve values of hematological parameters such as red blood cell (RBC), white blood cell (WBC), haemoglobin (Hb), and haematocrit (HCT). These values, however, decreased when the dietary Zn level was further increased. The serum alkaline phosphatase level was the highest in fish fed a diet containing 30 mg kg-1 of Zn. Regression analyses based on weight gain, specific growth rate, and bone and muscle Zn concentrations indicated that the optimum dietary Zn requirement for stinging catfish was in a range of 27.4-36.5 mg kg-1.

Effect of core material draft ratio and denier on core spun yarn and denim fabric properties pre and post washing.

Core-spun yarn (CSY) is utilized for better fabric characteristics like stretchability, durability, and comfortability. The study aims to investigate the influence of spandex drafts of core-spun yarn on denim fabric characteristics before and after washing treatment. Two types of denim fabrics were produced from two types of core-spun yarn, namely 16 + 40D, and 16 + 70D by applying 2.8, 3.0, 3.20 spandex drafts for 16 + 40D, and 3.40, 3.50, 3.60 spandex drafts for 16 + 70D. Prepared denim fabrics were desized, and acid-washed and the properties of denim fabric before and after washing were investigated as a function of spandex drafts and deniers. Accurate count, twist, and better elongation percentage were observed at 2.80 draft for 16 + 40D CSY and 3.4 draft for 16 + 70D CSY, but a higher imperfection index (IPI) value was obtained on those drafts. The strength of the denim fabric prepared with 16 + 40D CSY and 16 + 70D CSY were higher at 2.8 and 3.6 drafts, respectively. Higher shrinkage (%), ends per inch (EPI), and fabric weight of denim fabric was obtained after washing compared to before washing. The width of both fabrics decreased when the fabric was washed. Exploring various drafts of core material and their correlations with yarn and fabric properties provides valuable insights for textile manufacturers seeking to produce denim fabrics with optimum quality.

Assessment of brain tumor detection techniques and recommendation of neural network.

OBJECTIVES: Brain tumor classification is amongst the most complex and challenging jobs in the computer domain. The latest advances in brain tumor detection systems (BTDS) are presented as they can inspire new researchers to deliver new architectures for effective and efficient tumor detection. Here, the data of the multi-modal brain tumor segmentation task is employed, which has been registered, skull stripped, and histogram matching is conducted with the ferrous volume of high contrast. METHODS: This research further configures a capsule network (CapsNet) for brain tumor classification. Results of the latest deep neural network (NN) architectures for tumor detection are compared and presented. The VGG16 and CapsNet architectures yield the highest f1-score and precision values, followed by VGG19. Overall, ResNet152, MobileNet, and MobileNetV2 give us the lowest f1-score. RESULTS: The VGG16 and CapsNet have produced outstanding results. However, VGG16 and VGG19 are more profound architecture, resulting in slower computation speed. The research then recommends the latest suitable NN for effective brain tumor detection. CONCLUSIONS: Finally, the work concludes with future directions and potential new architectures for tumor detection.

Rapid hydrothermal synthesis of mesoporous NiS2 and NiSe2 nanostructures for wastewater treatment.

In the present study, single phase nickel based disulphide (NiS2) and diselenide (NiSe2) nanostructures were prepared hydrothermally in a short time span (4 h) under subcritical temperature (160 °C). The nanostructures grow in cubic crystal phases. Average crystallite sizes and intrinsic microstrains were determined using Williamson-Hall (W-H) plot analysis. Hollow NiS2 nanospheres and bipyramidal NiSe2 nanostructures are reported that are suitable for surface related applications. Thermal gravimetric analysis (TGA) indicated high stability of the nanostructures at elevated temperatures. Optical studies indicated visible light activeness of the nanostructures exhibiting sharp band edges. The nanostructures are mesoporous in nature with NiS2 and NiSe2 having respectively a large specific surface area of 310 m2/g and 177 m2/g. A primarily work done to determine the electrochemical nature of the nanostructures showed the materials are pseudo-capacitive in nature with specific capacitances of 1022 F/g and 480 F/g respectively for NiS2 and NiSe2. The photo-catalytic activity of the nanostructures was explored against a colourless pollutant; phenol. The nanostructures degraded most of the phenol (>90 %) under visible light illumination and the reusability experiments performed determined industrial value of the photocatalysts.

Re-Evaluation of the Cross-Reactions of the Antibody against the Causative Agent for Paracoccidioidomycosis Ceti; Paracoccidioides ceti and the Related Fungal Species.

Paracoccidioidomycosis ceti (PCM-C) is a chronic granulomatous keloidal dermatitis in cetaceans that has been reported worldwide and is caused by Paracoccidioides ceti. Serological cross-reactions among highly pathogenic fungal infections and related diseases have been reported. However, the true cross-reaction of antibodies against P. ceti has remained unknown due to the use of positive control sera from infected dolphins. This study aimed to re-evaluate antibodies from mechanically dislodged fungal cells in the infected tissue of a PCM-C case and demonstrate the actual cross-reaction. The results revealed a limited cross-reaction between PCM-C and paracoccidioidomycosis, while the antibodies did not react with other pathogens such as Coccidioides posadasii, Histoplasama capsulatum, and Arthrographis kalrae. Thus, the method for evaluation of the antibody against PCM-C is reliable, and there is potential for epidemiological study.

Investigating the Precise Identification of Citrullination Sites with High-Performance Score Metrics using a Powerful Computation Predicting Tool.

BACKGROUND: To elucidate the detailed mechanisms of citrullination at the molecular level and design drugs applicable to major human diseases, predicting protein citrullination sites (PCSs) is essential. Using experimental approaches to predict PCSs is time-consuming and costly. However, there is a limited scope of the current PCS predictors. In particular, most predictors are commonly used for PCS prediction and have limited performance scores. OBJECTIVE: This work aims to provide an improved sophisticated predictor of citrullination sites using a benchmark dataset in a machine learning platform. METHODS: This study presents a reliable citrullination site predictor based on a benchmark dataset containing a 1:1 ratio of positive and negative samples. We classified citrullination sites using the Composition of the K-Spaced Amino Acid Pairs (CKSAAP) and Support Vector Machine (SVM). RESULTS: We developed PCS predictors using integrated machine-learning methods that produced the highest average scores. Using 10-fold cross-validation on test datasets, the True Positive Rate (TPR) was 98.34%, the True Negative Rate (TNR) was 99.44%, the accuracy was 98.89%, the Mathew Correlation Coefficient (MCC) was 98.21%, the Area Under the ROC Curve (AUC) was 0.999, and the partial Area Under the ROC Curve (pAUC) was 0.1968. CONCLUSION: According to overall performance, our developed predictor has a significantly higher implementation in comparison with the current tools on the same benchmark dataset. Moreover, it showed better performance metrics on both test and training datasets. Our developed predictor is promising and can be implemented as a complementary technique for identifying fast and precise citrullination sites.

Prediction of patient's neurological recovery from cervical spinal cord injury through XGBoost learning approach.

Due to the diversity of patient characteristics, therapeutic approaches, and radiological findings, it can be challenging to predict outcomes based on neurological consequences accurately within cervical spinal cord injury (SCI) entities and based on machine learning (ML) technique. Accurate neurological outcomes prediction in the patients suffering with cervical spinal cord injury is challenging due to heterogeneity existing in patient characteristics and treatment strategies. Machine learning algorithms are proven technology for achieving greater prediction outcomes. Thus, the research employed machine learning model through extreme gradient boosting (XGBoost) for attaining superior accuracy and reliability followed with other MI algorithms for predicting the neurological outcomes. Besides, it generated a model of a data-driven approach with extreme gradient boosting to enhance fault detection techniques (XGBoost) efficiency rate. To forecast improvements within functionalities of neurological systems, the status has been monitored through motor position (ASIA [American Spinal Injury Association] Impairment Scale [AIS] D and E) followed by the method of prediction employing XGBoost, combined with decision tree for regression logistics. Thus, with the proposed XGBoost approach, the enhanced accuracy in reaching the outcome is 81.1%, and from other models such as decision tree (80%) and logistic regression (82%), in predicting outcomes of neurological improvements within cervical SCI patients. Considering the AUC, the XGBoost and decision tree valued with 0.867 and 0.787, whereas logistic regression showed 0.877. Therefore, the application of XGBoost for accurate prediction and decision-making in the categorization of pre-treatment in patients with cervical SCI has reached better development with this study.

Viscous dissipation effect on unsteady magneto-convective heat-mass transport passing in a vertical porous plate with thermal radiation.

The effects of radiative and viscous dissipation on the transfer of unsteady magnetic-conductive heat-mass across a vertically porous sheet is studied in this article. The non-dimensional ODEs are solved by applying the Finite Difference Method (FDM) through the MATLAB software numerically. The fluid temperature and velocity enhance for uplifting values of the Eckert number. Enhancing values of the transpiration parameter the velocity, concentration, and temperature distributions reduce. The local skin friction enhances about 9%, and 18% due to increase the Eckert number (0.5-3.0) and Dufour number (0.5-4.0), respectively and reduces 17%, 38%, and 31% due to increase Prandtl number (0.71-7.0), magnetic force parameter (0.5-3.0), and suction parameter (0.5-3.0), respectively. Enhancing values of the Eckert number (0.5-3.0) reduces the heat transfer rate by 40%. The increasing value of the Prandtl number (0.71-7.0) and the suction parameter (0.5-3.0) increases the heat transfer rate by 27% and 92%, respectively. With an increase in the values of the Schmidt number (0.22-0.67), the mass transfer rate increased by approximately 94%. At last, the numerical results of this paper has compared with the previously published paper. We noticed that the comparison has an excellent acceptance.

An Optimized Deep Learning Approach for the Prediction of Social Distance Among Individuals in Public Places During Pandemic.

Social distancing is considered as the most effective prevention techniques for combatting pandemic like Covid-19. It is observed in several places where these norms and conditions have been violated by most of the public though the same has been notified by the local government. Hence, till date, there has been no proper structure for monitoring the loyalty of the social-distancing norms by individuals. This research has proposed an optimized deep learning-based model for predicting social distancing at public places. The proposed research has implemented a customized model using detectron2 and intersection over union (IOU) on the input video objects and predicted the proper social-distancing norms continued by individuals. The extensive trials were conducted with popular state-of-the-art object detection model: regions with convolutional neural networks (RCNN) with detectron2 and fast RCNN, RCNN with TWILIO communication platform, YOLOv3 with TL, fast RCNN with YOLO v4, and fast RCNN with YOLO v2. Among all, the proposed (RCNN with detectron2 and fast RCNN) delivers the efficient performance with precision, mean average precision (mAP), total loss (TL) and training time (TT). The outcomes of the proposed model focused on faster R-CNN for social-distancing norms and detectron2 for identifying the human 'person class' towards estimating and evaluating the violation-threat criteria where the threshold (i.e., 0.75) is calculated. The model attained precision at 98% approximately (97.9%) with 87% recall score where intersection over union (IOU) was at 0.5.

Seroprevalences Against Paracoccidioides cetii: A Causative Agent for Paracoccidiomycosis Ceti (PCM-C) and Coccidioides posadasii; for Coccidioidomycosis (CCM) in Dall's Porpoise (Phocoenoides dalli) and Harbor Porpoise (Phocoena phocoena) Stranded at Hokkaido, Japan.

Paracoccidiodomycosis ceti (PCM-C) is a zoonotic mycosis characterized by chronic granulomatous cutaneous lesions in cetaceans. It is distributed worldwide and is caused by an unculturable fungus; Paracoccidioides cetii. On the other hand, coccidioidomycosis (CCM), caused by Coccidioides spp., is also a zoonotic and highly pathogenic fungal infection endemic in both American continents. Even though the Far East is not an endemic area of CCM, an autochthonous case has been reported in China. Although the seroprevalence against P. cetii in captive dolphins was 61.0%, there is no information on wild dolphins living in cold waters. The present study aimed to investigate the seroprevalence against P. cetii and C. posadasii in 15 Dall's porpoises (Phocoenoides dalli) and 11 harbor porpoises (Phocoena phocoena) stranded in Hokkaido, Japan. The seroprevalence against P. cetii in the above dolphins was 26.9% (7/26), which was recorded only in Dall's porpoises (7/15), and that against C. posadasii was 15.4% (4/26), three in Dall's porpoises and one in harbor porpoise. The present study demonstrated positive seroprevalence against P. cetii and C. posadasii in wild cetaceans living in the subarctic areas of the Far East as the first records, and would issue the warning those who live in the area were exposed to the causative agent of CCM from seawater.

Natural Garlic Organosulfur Compounds Prevent Metabolic Disorder of Lipid and Glucose by Increasing Gut Commensal Bacteroides acidifaciens.

A number of reports of the effects of garlic on gut microbiota revealed that the active garlic organosulfur compounds (OSCs) are destabilized by the action of alliinase during garlic preparation. In this study, garlic alliinase was deactivated to obtain stable garlic OSCs. Experiments with C57BL/6J mice fed with lipid and glucose metabolic disorder-inducing Western diet (WD) revealed that stable garlic OSCs prevented the disorder by increasing the relative abundance of gut Bacteroides acidifaciens. Molecular analysis indicated that garlic OSCs inhibited dyslipidemia and fatty liver by increasing taurine and subsequently promoting hepatic fatty acid ß-oxidation. In parallel, garlic OSCs could meliorate glucose homeostasis by inhibiting dipeptidyl peptidase-4 (DPP-4) and hepatic gluconeogenesis. In vitro bacterial culture experiments revealed that garlic OSCs directly increased the growth of gut Bacteroides acidifaciens. The results of this study demonstrate that the molecular mechanism of the preventive effect of garlic OSCs on the WD-induced metabolic disorder is attributed to the enhanced growth of Bacteroides acidifaciens and the consequent increase in taurine.

Combinational Variation Temperature and Soil Water Response of Stomata and Biomass Production in Maize, Millet, Sorghum and Rice.

Environmental responses of stomatal conductance (gs) as basic information for a photosynthesis-transpiration-coupled model have been increasing under global warming. This study identified the impact of gs behavior under different soil water statuses and temperatures in rice, maize, millet, and sorghum. The experiments consisted of various soil moisture statuses from flooding to drying and combination of soil moisture status and temperature. There was a reduction in shoot biomass of maize and sorghum caused by decreasing of gs, photosynthesis (A), and transpiration (E) in early imposed waterlogging without dependent temperature, whereas millet and rice were dependent on temperature variation. The effect of gradual soil drying, gs, A, and E of maize, millet, and sorghum were caused by low temperature, except rice. The impact of the combination of various soil water statuses and temperatures on gs is important for the trade-off between A and E, and consequently shoot biomass. However, we discovered that an ability to sustain gs is essential for photo assimilation and maintaining leaf temperature through evapotranspiration for biomass production, a mechanism of crop avoidance in variable soil water status and temperature.

Effect of different wavelengths of LED light on the growth, chlorophyll, ß-carotene content and proximate composition of Chlorella ellipsoidea.

Chlorella ellipsoidea is a freshwater green microalga that has great prospect for the sustainable development of aquaculture industry. Microalgae require optimal lighting conditions for efficient photosynthesis. The key to cost-effective algal biomass production is to optimize algae growth conditions. This study aimed to investigate the effects of various wavelengths viz. white (380-750 nm), green (510 nm), blue (475 nm), and red (650 nm) light-emitting diodes (LEDs) on the growth, pigment content (chlorophyll-a, chlorophyll-b, and ß-carotene), and proximate composition of C. ellipsoidea with a photoperiod of 12 h:12 h light: dark cycle under indoor environmental conditions. C. ellipsoidea was cultured in Bold's Basal Medium for 18 days. The cell density (125.36×105 cells ml-1), cell dry weight (58.9 ± 4.57 mg L-1), optical density (1.66 ± 0.08 g L-1), chlorophyll-a (7.31 ± 0.04 µg ml-1), chlorophyll-b (2.73 ± 0.13 µg ml-1), and ß-carotene (0.39 ± 0.04 µg ml-1) content of C. ellipsoidea were significantly (P < 0.05) higher at 15th-day when cultured under blue LED light exposure. Significantly lower growth and nutritional values were obtained under red LED light exposure compared to the control and other LEDs spectra. In Pearson correlation analysis, the cell density and cell dry weight values showed a strong positive correlation with the values of pigment contents of C. ellipsoidea in all the treatments. The LEDs light spectra showed significant effects on proximate composition of C. ellipsoidea. Protein and lipid contents of C. ellipsoidea were significantly higher in blue LED growth conditions compared to white, green, and red LEDs. C. ellipsoidea cells were 3-7.04 µm in size and the maximum area of the cell was 38.94 µm2 in blue LED treatment. Results of this study demonstrated that blue LED light spectra was the most suitable condition to induce nutritionally rich biomass production of C. ellipsoidea, which can be used as a potential source of fish feed towards sustainable aquaculture.

Recognition of human emotions using EEG signals: A review.

Assessment of the cognitive functions and state of clinical subjects is an important aspect of e-health care delivery, and in the development of novel human-machine interfaces. A subject can display a range of emotions that significantly influence cognition, and emotion classification through the analysis of physiological signals is a key means of detecting emotion. Electroencephalography (EEG) signals have become a common focus of such development compared to other physiological signals because EEG employs simple and subject-acceptable methods for obtaining data that can be used for emotion analysis. We have therefore reviewed published studies that have used EEG signal data to identify possible interconnections between emotion and brain activity. We then describe theoretical conceptualization of basic emotions, and interpret the prevailing techniques that have been adopted for feature extraction, selection, and classification. Finally, we have compared the outcomes of these recent studies and discussed the likely future directions and main challenges for researchers developing EEG-based emotion analysis methods.

Bacillus spp. Contamination: A Novel Risk Originated From Animal Feed to Human Food Chains in South-Eastern Bangladesh.

The current study provides information on Bacillus spp. contamination along with present status in commercially available poultry and animal feeds as well as animal-derived products in Bangladesh. The research has been conducted to determine if animal feed and its components are a source of Bacillus spp. contamination in feed and food chain. Out of 180 different feeds, milk, egg, and human stool samples, 218 Bacillus spp. were isolated and identified by cultural morphology, microscopic, biochemical, and molecular characteristics where B. cereus, B. subtilis, B. amyloliquefaciens, B. licheniformis, B. thuringiensis, B. megaterium, and B. coagulans accounted for 51, 22, 9.1, 5.9, 5, 3.6, and 2.2%, respectively. Regarding the enumeration of total viable count and total Bacillus count, correspondingly 67 and 39% samples were found to be contaminated with above 10,000 CFU/g, while highest contamination was 85 and 75% in broiler feed, respectively. The total number of bacteria above the regulatory limits in commercially available feeds indicates a poor compliance with regulation and abuse administration in the Bangladeshi market. Moreover, a hospital-based survey showed that food-borne Bacillus spp. contributed to 4.5% human diarrhea cases and 25% food contamination associated with vegetables, rice, RTE food, milk, and egg, accounting for 46, 34, 14, 4, and 2%, respectively. B. cereus was the dominant isolate correspondingly accounting for 56 and 51% egg and milk contamination followed by B. amyloliquefaciens (32%) and B. thuringiensis (12%) in egg and B. subtilis (25%), B. amyloliquefaciens (12%), B. thuringiensis (6.4%), and B. coagulans (3.2%) in milk, respectively. Toxin gene profiling of Bacillus spp. revealed that B. cereus constituted a principal part of virulence, while B. thuringiensis, B. licheniformis, B. megaterium, B. coagulans, and B. subtilis showed genetic diversity and B. amyloliquefaciens had not carried any toxin gene. Detection rate of enterotoxin genes (nheA, nheB, nheC, cytK, hblA, hblC, hblD, and entFM) showed that 55% isolates carried nheABC genes, 80% entFM, and 71% cytK, whereas only 33% of the isolates contained hblACD gene clusters. These virulence genes were posing a threat to human health due to spread across the food and feed chain. Finally, our findings support the hypothesis that B. cereus might contribute to clinical diarrhea, gizzard erosion, and lung infection in duck and poultry, and that it contaminates animal-derived foods resulting in toxicity and antibacterial resistance to humans. Therefore, maximal tolerance limits of Bacillus spp. and their potential risks to the animal industry are urgently needed to clarify. Moreover, Bacillus spp.-induced toxin residual must be altered for human health via food chain transmission.

Growth and reproduction performances of earthworm (Perionyx excavatus) fed with different organic waste materials.

OBJECTIVE: The purpose of the experiment was to examine the effect of different food sources on the growth and reproduction performances of an epigeic earthworm Perionyx excavatus. MATERIALS AND METHODS: The experiment was carried out in 18 cylindrical plastic containers for 10 weeks. The study was designed with six treatments, each having three replicates. In control treatment (T1), cow dung was used as the only food source for the earthworm. In another five treatments, water hyacinth (T2), chopped banana plant trunk (T3), vegetable scrap (T4), paddy straw (T5), and sugarcane bagasse (T6) were used as food sources with cow dung as bedding material. RESULTS: The maximum weight gain of earthworm P. excavatus was 3,294.7 ± 4.5 mg for the food staff of vegetable scrap (T4). Earthworm P. excavatus fed with chopped banana plant trunk (T3) showed a very similar weight gain of 3,243.7 ± 3.8 mg. On the contrary, the minimum weight gain was 1,799.7 ± 3.5 mg for the food staff of paddy straw (T5). The maximum cocoon number of 137.33 ± 6.46 mg was observed in T3, whereas a minimum number of 36.67 ± 4.16 mg in T1. The highest number of hatchlings (12.33 ± 0.88 mg) was recorded in treatment T3, whereas the least number of hatchling (5.00 ± 0.58 mg) was observed in T1. CONCLUSION: It was concluded that, among the six different food sources, chopped banana plant trunk was preferable food source to the earthworm for growth and reproduction.

Unveiling Pharmacological Responses and Potential Targets Insights of Identified Bioactive Constituents of Cuscuta reflexa Roxb. Leaves through In Vivo and In Silico Approaches.

Cuscuta reflexa Roxb. is traditionally used by the indigenous communities of Bangladesh to treat different diseases, such as pain, edema, tumor, jaundice, and skin infections. This study tested neuro-pharmacological, anti-nociceptive, and antidiarrheal activities by in vivo and in silico experiments for the metabolites extracted (methanol) from the leaves of Cuscuta reflexa (MECR). During the anxiolytic evaluation analyzed by elevated plus maze and hole board tests, MECR (200 and 400 mg/kg) exhibited a significant dose-dependent reduction of anxiety-like behavior in mice. Similarly, mice treated with MECR demonstrated a dose-dependent decrease in the time of immobility in both forced swimming and tail suspension tests. In addition, anti-nociceptive activity was assessed by the chemical-induced (acetic acid and formalin) pain models. In both cases, 400 mg/kg was found to be most effective and significantly (p < 0.001) inhibited acetic acid stimulated writhing and formalin-induced licking (pain response) in mice. Furthermore, antidiarrheal efficacy determined by the castor-oil induced diarrheal model manifested an evident inhibition of diarrheal stool frequency. In parallel, previously isolated bioactive compounds were documented based on the biological activities and subjected to in silico studies to correlate with the current pharmacological outcomes. The selected isolated compounds (15) displayed favorable binding affinities to potassium channels, human serotonin receptor, COX-1, COX-2, M3 muscarinic acetylcholine receptor, and 5-HT3 receptor proteins. Additionally, the ADME/T and toxicological properties were justified to unveil their drug-like properties and toxicity level. Overall, Cuscuta reflexa is bioactive and could be a potential source for the development of alternative medicine.

Endothelium-independent and calcium channel-dependent relaxation of the porcine cerebral artery by different species and strains of turmeric.

OBJECTIVE: To clarify the underlying mechanism of turmeric, which is traditionally used as a medicinal plant for the treatment of cardiovascular disorders, such as hypertension, and palpitations. METHODS: Methanol extracts of different turmeric were used. A tissue-organ-bath system was used to investigate the vasoactive effects of methanol extracts from 5 kinds of turmeric on isolated porcine basilar arteries. The arterial rings were suspended in physiological solution that was maintained at 37 °C temperature with a continuous supply of 95% O2 and 5% CO2. RESULTS: All turmeric extracts (20-800 µg/mL) induced concentration-dependent relaxation of the isolated porcine basilar artery pre-contracted with U46619 (1-5 × 10-9 M) in arterial rings with or without endothelium. There were no significant differences in the relaxation induced by different turmeric or between the endothelium-intact and denuded arteries. In depolarized, Ca2+-free medium, the turmeric extracts inhibited CaCl2-induced contractions and caused a concentration-dependent rightward shift of the response curves. In addition, propranolol (a non-specific ß-adrenoceptor antagonist) slightly inhibited the relaxation induced by turmeric. In contrast, Nω-nitro-l-arginine, indomethacin, tetraethylammonium, glibenclamide and 4-aminopyridine did not affect turmeric-induced relaxation. CONCLUSION: These results demonstrated that turmeric induced endothelium-independent relaxation of the porcine basilar artery, which may be due to the inhibition of extracellular and intracellular Ca2+ receptors and the partial inhibition of ß-adrenergic receptors in vascular smooth muscle cells.