Livestock Depredation by Large Carnivores and Human-Wildlife Conflict in Two Districts of Balochistan Province, Pakistan

Livestock herding is a vital practice in Balochistan, contributing to the economy and culture. The livestock sector is significant in Balochistan, providing 20% of the national stock. Large predators and their prey species, including livestock, have coexisted in these mountainous landscapes for centuries. The aim of the present research is to investigate the impacts of livestock depredation by large predators on livelihoods and predator conservation in two districts of Balochistan, Pakistan. A human-carnivore conflict survey was conducted from July to September 2019, collecting data from 311 residents in a selected study area. Large predators in the study area preyed on a total of 876 livestock during a one-year period, including 560 goats, 292 sheep, 19 cows, and 5 donkeys. The gray wolf is the leading predator, responsible for 66.3% of livestock depredation, followed by the caracal (24.3%), Asiatic jackal (8.9%), and striped hyena (0.6%). The total economic loss was USD 78,694. Overall, 80% of respondents had a negative perception of wolves compared to 24.4% for caracals. Only 20.6% of respondents knew about the importance of conserving carnivores. Livestock depredation by carnivores in the study area created a negative perception of these animals among people. There is a lack of awareness about the importance of conserving carnivore species and their role in the ecosystem. This lack of understanding has ultimately led to detrimental effects on predator populations. It is imperative to raise awareness among people about the ecological significance of carnivores through community meetings, seminars in educational institutions, and providing basic education to herders about effective livestock guarding practices.

Combinatorial effect of thymoquinone with chemo agents for tumor therapy

BACKGROUND

Most chemotherapeutics used in cancer therapies exhibit considerable side effects to the patients. Thus, developing new chemo agents to treat cancer patients with minimal toxic and side effects is urgently needed. Recently, the combination of different chemotherapeutics has become a promising strategy to treat malignancies. Thymoquinone (TQ) is a primary bioactive compound derived from the folk medicinal plant Nigella sativa, which has been found an antitumor, chemopreventive and chemopotentiating agent against human neoplastic diseases.

PURPOSE

We briefly summarize the current research of the biomolecular mechanisms of TQ and evaluate the existing literature on TQ adjuvant therapies against various cancers.

METHOD

The data in this review were gathered by several search engines including, Google Scholar, PubMed and ScienceDirect. We highlighted and classified the outcomes of both in vitro and in vivo experiments of TQ adjuvant therapies against human cancers and their chemopreventive activities on vital organs.

RESULTS

Several studies have shown that TQ synergistically potentiated the antitumor activity of numerous chemo agents against human neoplastic disease, including lung, breast, liver, colorectal, skin, prostate, stomach, bone and blood cancers. TQ also acted as a chemopreventive agent and reduced the toxicity of many chemo agents to vital organs, such as the heart, liver, kidneys and lungs.

CONCLUSION

In summary, we highly recommend an advanced evaluation of TQ adjuvant therapies at the level of preclinical and clinical trials, which could lead to a novel combinatorial therapy for cancer treatment with low or tolerable adverse effects on patients.

Short and long term modulation of tissue minerals concentrations following oral administration of black cumin (Nigella sativa L.) seed oil to laboratory rats

BACKGROUND

Nigella sativa, or commonly called black cumin is a small herb of family Ranunculaceae is a well-known medicinal plant but its effects on tissue mineral concentrations of animal bodies is unknown.

PURPOSE

To study the effect of oral administration of fixed oil of black cumin seeds on tissues mineral content using laboratory rats as experimental model.

STUDY DESIGN

Experimental animals were exposed to two oral doses of seed oil (60 and 120 ml kg^-1 body weight). Short- and long term experiments lasted 24 h and 60 days respectively, with three replicates each.

METHODS

Oil extracted from black cumin seeds was subjected to GC-MS to identify chemical components. Following the wet digestion in nitric acid, samples of whole blood and organs of rats were subjected to atomic absorption spectrophotometry for determination of elements concentrations. Data were compared statistically at p < .05.

RESULTS

Compared to control, Cr, Mn, Ni, Cu, Zn showed decrease, whereas Co, Na, Mg and K demonstrated increase, but Ca showed both increase and decrease in most of the tissues upon short term exposure to low and high doses of black cumin oil. During long term exposure, Cr, Fe, Mn, Cu exhibited decrease; Co, Na, Mg and Ca concentrations demonstrated an upregulation, whereas Ni and Zn showed increase and decrease in most of the tissues. Comparison of short term with long term experiments at low dose revealed increases in Fe, Zn, Cu, Mg, K and Ca, a decrease in Cr, Mn, Ni and Cu in most tissues, but both increase and decrease in Na. At high dose, an increase occurred in Fe, Ni, Zn, K, Ca, Mg, a decrease in Cr, while both increase and decrease in Cu, Co and Na concentrations.

CONCLUSION

Our study demonstrates that oral administration of black cumin seeds oil to laboratory rats significantly alters tissue trace elements and electrolytes concentrations. The study appears beneficial but indicates modulatory role of black cumin oil as regards mineral metabolism with far reaching implications in health and disease.

Survival curves of Listeria monocytogenes in chorizos modeled with artificial neural networks

Using artificial neural networks (ANNs), a highly accurate model was developed to simulate survival curves of Listeria monocytogenes in chorizos as affected by the initial water activity (a(w0)) of the sausage formulation, temperature (T), and air inflow velocity (F) where the sausages are stored. The ANN-based survival model (R(2)=0.970) outperformed the regression-based cubic model (R(2)=0.851), and as such was used to derive other models (using regression) that allow prediction of the times needed to drop count by 1, 2, 3, and 4 logs (i.e., nD-values, n=1, 2, 3, 4). The nD-value regression models almost perfectly predicted the various times derived from a number of simulated survival curves exhibiting a wide variety of the operating conditions (R(2)=0.990-0.995). The nD-values were found to decrease with decreasing a(w0), and increasing T and F. The influence of a(w0) on nD-values seems to become more significant at some critical value of a(w0), below which the variation is negligible (0.93 for 1D-value, 0.90 for 2D-value, and <0.85 for 3D- and 4D-values). There is greater influence of storage T and F on 3D- and 4D-values than on 1D- and 2D-values.

Modeling the survival of Salmonella spp. in chorizos

The survival of Salmonella spp. in chorizos has been studied under the effect of storage conditions; namely temperature (T=6, 25, 30 degrees C), air inflow velocity (F=0, 28.4 m/min), and initial water activity (a(w0)=0.85, 0.90, 0.93, 0.95, 0.97). The pH was held at 5.0. A total of 20 survival curves were experimentally obtained at various combinations of operating conditions. The chorizos were stored under four conditions: in the refrigerator (Ref: T=6 degrees C, F=0 m/min), at room temperature (RT: T=25 degrees C, F=0 m/min), in the hood (Hd: T=25 degrees C, F=28.4 m/min), and in the incubator (Inc: T=30 degrees C, F=0 m/min). Semi-logarithmic plots of counts vs. time revealed nonlinear trends for all the survival curves, indicating that the first-order kinetics model (exponential distribution function) was not suitable. The Weibull cumulative distribution function, for which the exponential function is only a special case, was selected and used to model the survival curves. The Weibull model was fitted to the 20 curves and the model parameters (alpha and beta) were determined. The fitted survival curves agreed with the experimental data with R(2)=0.951, 0.969, 0.908, and 0.871 for the Ref, RT, Hd, and Inc curves, respectively. Regression models relating alpha and beta to T, F, and a(w0) resulted in R(2) values of 0.975 for alpha and 0.988 for beta. The alpha and beta models can be used to generate a survival curve for Salmonella in chorizos for a given set of operating conditions. Additionally, alpha and beta can be used to determine the times needed to reduce the count by 1 or 2 logs t(1D) and t(2D). It is concluded that the Weibull cumulative distribution function offers a powerful model for describing microbial survival data. A comparison with the pathogen modeling program (PMP) revealed that the survival kinetics of Salmonella spp. in chorizos could not be adequately predicted using PMP which underestimated the t(1D) and t(2D). The mean of the Weibull probability density function correlated strongly with t(1D) and t(2D), and can serve as an alternative to the D-values normally used with first-order kinetic models. Parametric studies were conducted and sensitivity of survival to operating conditions was evaluated and discussed in the paper. The models derived herein provide a means for the development of a reliable risk assessment system for controlling Salmonella spp. in chorizos.

A probabilistic neural network approach for modeling and classification of bacterial growth/no-growth data

In this paper, we propose to use probabilistic neural networks (PNNs) for classification of bacterial growth/no-growth data and modeling the probability of growth. The PNN approach combines both Bayes theorem of conditional probability and Parzen's method for estimating the probability density functions of the random variables. Unlike other neural network training paradigms, PNNs are characterized by high training speed and their ability to produce confidence levels for their classification decision. As a practical application of the proposed approach, PNNs were investigated for their ability in classification of growth/no-growth state of a pathogenic Escherichia coli R31 in response to temperature and water activity. A comparison with the most frequently used traditional statistical method based on logistic regression and multilayer feedforward artificial neural network (MFANN) trained by error backpropagation was also carried out. The PNN-based models were found to outperform linear and nonlinear logistic regression and MFANN in both the classification accuracy and ease by which PNN-based models are developed.