Inhibiting CDK4/6 in pancreatic ductal adenocarcinoma via microRNA-21.

Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive malignancies, with a 5-year survival rate of 5-10 %. The high mortality rate is due to the asymptomatic progression of clinical features in metastatic stages of the disease, which renders standard therapeutic options futile. PDAC is characterised by alterations in several genes that drive carcinogenesis and limit therapeutic response. The two most common genetic aberrations in PDAC are the mutational activation of KRAS and loss of the tumour suppressor CDK inhibitor 2A (CDKN2A), which culminate the activation of the cyclin-dependent kinase 4 and 6 (CDK4/6), that promote G1 cell cycle progression. Therapeutic strategies focusing on the CDK4/6 inhibitors such as palbociclib (PD-0332991) may potentially improve outcomes in this malignancy. MicroRNAs (miRs/miRNAs) are small endogenous non-coding RNA molecules associated with cellular proliferation, invasion, apoptosis, and cell cycle. Primarily, miR-21 promotes cell proliferation and a higher proportion of PDAC cells in the S phase, while knockdown of miR-21 has been linked to cell cycle arrest at the G2/M phase and inhibition of cell proliferation. In this study, using a CRISPR/Cas9 loss-of-function screen, we individually silenced the expression of miR-21 in two PDAC cell lines and in combination with PD-0332991 treatment, we examined the synergetic mechanisms of CDK4/6 inhibitors and miR-21 knockouts (KOs) on cell survival and death. This combination reduced cell proliferation, cell viability, increased apoptosis and G1 arrest in vitro. We further analysed the mitochondrial respiration and glycolysis of PDAC cells; then assessed the protein content of these cells and revealed numerous Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways associated with PD-0332991 treatment and miR-21 knocking out. Our results demonstrate that combined targeting of CDK4/6 and silencing of miR-21 represents a novel therapeutic strategy in PDAC.

Optoelectronic Pressure Sensor Based on the Bending Loss of Plastic Optical Fibers Embedded in Stretchable Polydimethylsiloxane.

We report the design and testing of a sensor pad based on optical and flexible materials for the development of pressure monitoring devices. This project aims to create a flexible and low-cost pressure sensor based on a two-dimensional grid of plastic optical fibers embedded in a pad of flexible and stretchable polydimethylsiloxane (PDMS). The opposite ends of each fiber are connected to an LED and a photodiode, respectively, to excite and measure light intensity changes due to the local bending of the pressure points on the PDMS pad. Tests were performed in order to study the sensitivity and repeatability of the designed flexible pressure sensor.

The Brillouin gain of vector modes in a few-mode fiber.

In this work, we demonstrate the measurement of the Brillouin gain spectra of vector modes in a few-mode fiber for the first time using a simple heterodyne detection technique. A tunable long period fiber grating is used to selectively excite the vector modes supported by the few-mode fiber. Further, we demonstrate the non-destructive measurement of the absolute effective refractive indices (n eff ) of vector modes with ~10-4 accuracy based on the acquired Brillouin frequency shifts of the modes. The proposed technique represents a new tool for probing and controlling vector modes as well as modes carrying orbital angular momentum in optical fibers with potential applications in advanced optical communications and multi-parameter fiber-optic sensing.

Distributed characterization of localized and stationary dynamic Brillouin gratings in polarization maintaining optical fibers.

We experimentally generate localized and stationary dynamic Brillouin gratings in a 5 m long polarization maintaining fiber by phase-modulation of the pumps with a pseudo-random bit sequence. The dynamic Brillouin gratings are characterized in terms of length, bandwidth, group delay and group delay ripple, optical signal-to-noise ratio and peak to sidelobe ratio by measuring the distribution of the complex reflected signal along the fiber through swept-wavelength interferometry. By numerical processing, the performance of an optimal modulation format enabling null off-peak reflections are estimated and compared to the pseudo-random bit sequence case.

SN algorithm: analysis of temporal clinical data for mining periodic patterns and impending augury.

BACKGROUND: EHR (Electronic Health Record) system has led to development of specialized form of clinical databases which enable storage of information in temporal prospective. It has been a big challenge for mining this form of clinical data considering varied temporal points. This study proposes a conjoined solution to analyze the clinical parameters akin to a disease. We have used "association rule mining algorithm" to discover association rules among clinical parameters that can be augmented with the disease. Furthermore, we have proposed a new algorithm, SN algorithm, to map clinical parameters along with a disease state at various temporal points. RESULT: SN algorithm is based on Jacobian approach, which augurs the state of a disease 'Sn' at a given temporal point 'Tn' by mapping the derivatives with the temporal point 'T0', whose state of disease 'S0' is known. The predictive ability of the proposed algorithm is evaluated in a temporal clinical data set of brain tumor patients. We have obtained a very high prediction accuracy of ~97% for a brain tumor state 'Sn' for any temporal point 'Tn'. CONCLUSION: The results indicate that the methodology followed may be of good value to the diagnostic procedure, especially for analyzing temporal form of clinical data.

Association rule mining based study for identification of clinical parameters akin to occurrence of brain tumor.

Healthcare sector is generating a large amount of information corresponding to diagnosis, disease identification and treatment of an individual. Mining knowledge and providing scientific decision-making for the diagnosis & treatment of disease from the clinical dataset is therefore increasingly becoming necessary. Aim of this study was to assess the applicability of knowledge discovery in brain tumor data warehouse, applying data mining techniques for investigation of clinical parameters that can be associated with occurrence of brain tumor. In this study, a brain tumor warehouse was developed comprising of clinical data for 550 patients. Apriori association rule algorithm was applied to discover associative rules among the clinical parameters. The rules discovered in the study suggests - high values of Creatinine, Blood Urea Nitrogen (BUN), SGOT & SGPT to be directly associated with tumor occurrence for patients in the primary stage with atleast 85% confidence and more than 50% support. A normalized regression model is proposed based on these parameters along with Haemoglobin content, Alkaline Phosphatase and Serum Bilirubin for prediction of occurrence of STATE (brain tumor) as 0 (absent) or 1 (present). The results indicate that the methodology followed will be of good value for the diagnostic procedure of brain tumor, especially when large data volumes are involved and screening based on discovered parameters would allow clinicians to detect tumors at an early stage of development.

Docking-MM-GB/SA and ADME screening of HIV-1 NNRTI inhibitor: nevirapine and its analogues.

Nevirapine and its synthetic analogues, a class of non-nucleoside inhibitors (NNRTIs) of HIV-1 reverse transcriptase (RT), have been the objective of numerous studies focused to prepare better and safer anti-HIV drugs. We developed a library of nevirapine analogues (47) using combinatorial design and with structural modification at X, Y and R substituents in the parent structure of nevirapine. Their molecular interactions and binding affinities with reverse transcriptase (3HVT and 1VRT) have been studied using the docking-molecular mechanics based generalized Born/surface area (MM-GB/SA) solvation model. Final screening of these analogues is based on absorption, distribution, metabolism and excretion (ADME) properties. The proposed NNRTI analogues dock in a similar position and orientation in the active site of RT as co-crystallized nevirapine. In addition a linear correlation was observed between the calculated free energy of binding (FEB) and pIC50 for the inhibitors with correlation coefficient R2 of 0.9948, suggesting that the docked structure orientation and the interaction energies are reasonable. The electrostatic energy terms estimated by GB/SA showed important role on prediction of binding affinity (R2 = 17.2 %). Since we used two different HIV-1 RT crystal structures (3HVT and 1VRT), which are at different resolution (2.9 and 2.2 A), we propose that structures with resolutions better than 3 A can be used to produce reasonable docking results. Few analogues showed high binding affinity and activity with RT in compare to co-crystallized nevirapine. These analogues also well qualify ADME properties and showed good druggable characters. The work addressed to modify the X, Y and R substituents in the nevirapine scaffold to prepare synthetic analogues for second generation drug development against RT.

Docking mode of delvardine and its analogues into the p66 domain of HIV-1 reverse transcriptase: screening using molecular mechanics-generalized born/surface area and absorption, distribution, metabolism and excretion properties.

Delvardine and its structural derivatives are important non-nucleoside HIV-1 reverse transcriptase inhibitors (NNRTIs). In this work,15 delvardine analogues were studied. A free energy-of-binding (FEB)expression was developed in the form of an optimized linear combination of van der Waal (vdW), electrostatic, solvation and solvent-accessible surface area (SASA) energy terms. The solvation energy terms estimated by generalized born/surface area (GB/SA) play an important role in predicting the binding affinity of delvardine analogues. Out of 15 derivatives, substitution of CH3 with H at the Y and R positions, as well as substitution of SO2CH3 with only CH2 at the Z position in S2, S8 and S12 analogues, were found to be the most potent (glide score = -7.60, -8.06 and -7.44; pIC50 =7.28, 7.37 and 7.64) in comparison with the template delvardine (which is used currently as the drug candidate). All the three analogues also passed the absorption, distribution,metabolism and excretion (ADME) screening and Lipinski's rule of 5, and have the potential to be used for second-generation drug development. The work demonstrates that dock molecular mechanics-generalized born/surface area (MM-GB/SA-ADME) is a promising approach to predict the binding activity of ligands to the receptor and further screen for a successful candidate drug in a computer-aided rational drug design.