Purification and characterization of Taxol and 10-Deacetyl baccatin III from the bark, needles, and endophytes of Taxus baccata by preparative high-performance liquid chromatography, ultra-high-performance liquid chromatography-mass spectrometry, and nuclear magnetic resonance.

Taxol and 10-Deacetyl baccatin III are major taxanes in the bark, needles, and endophytes of Taxus baccata. The current study aimed to develop a process for their separation from different matrices. Crude taxoid was prepared by extraction of samples with methanol, followed by partitioning with dichloromethane and precipitation with hexane. Analytical high-performance liquid chromatography involved isocratic elution on C18 column (4.6 × 250 mm, 5 µm) with methanol-water (70:30 v/v) at a flow rate of 1 ml/min. Injection volume was 20 µl and detection was carried out at 227 nm. The content of Taxol and 10-Deacetyl baccatin III in bark, needles and endophytic culture broth was 11.19 and 1.75 µg/mg; 11.19 and 1.75 µg/mg; and 2.80 and 0.22 µg/L, respectively. Preparative high-performance liquid chromatography was done on C18 column (10 × 250 mm, 5 µm) at a flow rate of 10 ml/min. About 20 g crude taxoid was processed in < 3 h with a recovery of about 90% for both the analytes. The purity of recovered Taxol and 10-Deacetyl baccatin III determined by ultra-high-performance liquid chromatography-mass spectrometry was found to be 95.78 ± 3.63% and 99.72 ± 0.18%, respectively. The structure of recovered Taxol was confirmed by nuclear magnetic resonance. The method can find use in biotransformation studies.

An angiotensin I-converting enzyme insertion/deletion polymorphism is associated with Pakistani asthmatic cases and controls.

Asthma is a chronic disease due to inflammation of the airways of lungs that is clinically characterized by variable symptoms including wheezing, coughing and shortness of breath. Angiotensin I-converting enzyme (ACE) plays a major role in fibrous tissue formation and is highly expressed in lungs. The main aim of this research work was to study the role of ACE insertion/deletion (I/D) polymorphism, rs4646994, in asthma in Pakistani patients. A total of 854 subjects, including 333 asthma patients and 521 ethnically matched controls, were studied. The ACE (I/D) polymorphism was genotyped using polymerase chain reaction (PCR). Chi-square, Fisher's exact and Hardy-Weinberg equilibrium tests were used to compare groups. Homozygous insertion genotype II (p less than 0.0001, OR=3.38) and insertion allele (I) was significantly more frequent in Pakistani asthmatics than in healthy controls (p=0.0007, OR=1.40). The ID genotype (p less than 0.0001, OR=0.43) and the deletion allele (D) were associated with protection of disease in Pakistani patients (p=0.0007, OR=0.71). These data suggest the involvement of ACE I/D polymorphism in asthma risk in the Pakistani population. This marker may be an important indication in the molecular mechanism of asthma and can become a useful tool in risk assessment and help in designing strategy to combat disease.

Cost-sensitive Bayesian control policy in human active sensing.

An important but poorly understood aspect of sensory processing is the role of active sensing, the use of self-motion such as eye or head movements to focus sensing resources on the most rewarding or informative aspects of the sensory environment. Here, we present behavioral data from a visual search experiment, as well as a Bayesian model of within-trial dynamics of sensory processing and eye movements. Within this Bayes-optimal inference and control framework, which we call C-DAC (Context-Dependent Active Controller), various types of behavioral costs, such as temporal delay, response error, and sensor repositioning cost, are explicitly minimized. This contrasts with previously proposed algorithms that optimize abstract statistical objectives such as anticipated information gain (Infomax) (Butko and Movellan, 2010) and expected posterior maximum (greedy MAP) (Najemnik and Geisler, 2005). We find that C-DAC captures human visual search dynamics better than previous models, in particular a certain form of "confirmation bias" apparent in the way human subjects utilize prior knowledge about the spatial distribution of the search target to improve search speed and accuracy. We also examine several computationally efficient approximations to C-DAC that may present biologically more plausible accounts of the neural computations underlying active sensing, as well as practical tools for solving active sensing problems in engineering applications. To summarize, this paper makes the following key contributions: human visual search behavioral data, a context-sensitive Bayesian active sensing model, a comparative study between different models of human active sensing, and a family of efficient approximations to the optimal model.