Examining driver injury severity in motor vehicle crashes: A copula-based approach considering temporal heterogeneity in a developing country context.

Using data from a developing country, the current study develops a copula-based joint modeling framework to study crash type and driver injury severity as two dimensions of the severity process. To be specific, a copula-based multinomial logit model (for crash type) and generalized ordered logit model (for driver severity) is estimated in the study. The data for our analysis is drawn from Bangladesh for the years of 2000 to 2015. Given the presence of multiple years of data, we develop a novel spline variable generation approach that facilitates easy testing of variation in parameters across time in crash type and severity components. A comprehensive set of independent variables including driver and vehicle characteristics, roadway attributes, environmental and weather information, and temporal factors are considered for the analysis. The model results identify several important variables (such as driving under the influence of drug and alcohol, speeding, vehicle type, maneuvering, vehicle fitness, location type, road class, road geometry, facility type, surface quality, time of the day, season, and light conditions) affecting crash type and severity while also highlighting the presence of temporal instability for a subset of parameters. The superior model performance was further highlighted by testing its performance using a holdout sample. Further, an elasticity exercise illustrates the influence of the exogenous variables on crash type and injury severity dimensions. The study findings can assist policy makers in adopting appropriate strategies to make roads safer in developing countries.

Implementation of a realistic artificial data generator for crash data generation.

In this paper, a framework is outlined to generate realistic artificial data (RAD) as a tool for comparing different models developed for safety analysis. The primary focus of transportation safety analysis is on identifying and quantifying the influence of factors contributing to traffic crash occurrence and its consequences. The current framework of comparing model structures using only observed data has limitations. With observed data, it is not possible to know how well the models mimic the true relationship between the dependent and independent variables. Further, real datasets do not allow researchers to evaluate the model performance for different levels of complexity of the dataset. RAD offers an innovative framework to address these limitations. Hence, we propose a RAD generation framework embedded with heterogeneous causal structures that generates crash data by considering crash occurrence as a trip level event impacted by trip level factors, demographics, roadway and vehicle attributes. Within our RAD generator we employ three specific modules: (a) disaggregate trip information generation, (b) crash data generation and (c) crash data aggregation. For disaggregate trip information generation, we employ a daily activity-travel realization for an urban region generated from an established activity-based model for the Chicago region. We use this data of more than 2 million daily trips to generate a subset of trips with crash data. For trips with crashes crash location, crash type, driver/vehicle characteristics, and crash severity. The daily RAD generation process is repeated for generating crash records at yearly or multi-year resolution. The crash databases generated can be employed to compare frequency models, severity models, crash type and various other dimensions by facility type - possibly establishing a universal benchmarking system for alternative model frameworks in safety literature.

An airport level framework for examining the impact of COVID-19 on airline demand.

In this study, we examine the influence of Coronavirus disease 2019 (COVID-19) on airline demand at the disaggregate resolution of airport. The primary focus of our proposed research effort is to develop a framework that provides a blueprint for airline demand recovery as COVID-19 cases evolve over time. Airline monthly demand data is sourced from Bureau of Transportation Statistics for 380 airports for 24 months from January 2019 through December 2020. The demand data is augmented with a host of independent variables including COVID-19 related factors, demographic characteristics and built environment characteristics at the county level, airport specific factors, spatial factors, temporal factors, and adjoining county attributes. The effect of COVID-19 related factors is identified by considering global and local COVID-19 transmission, temporal indicators of pandemic start and progress, and interactions of airline demand predictors with global and local COVID-19 indicators. Finally, we present a blueprint for airline demand recovery where we consider three hypothetical scenarios of COVID-19 transmission rates - expected, pessimistic and optimistic. The results at the airport level from these scenarios are aggregated at the state or regional level by adding the demand from all airports in the corresponding state or region. These trends are presented by State and Region to illustrate potential differences across various scenarios. The results highlight a potentially slow path to airline demand recovery until COVID-19 cases subside.

A comprehensive county level model to identify factors affecting hospital capacity and predict future hospital demand.

The sustained COVID-19 case numbers and the associated hospitalizations have placed a substantial burden on health care ecosystem comprising of hospitals, clinics, doctors and nurses. However, as of today, only a small number of studies have examined detailed hospitalization data from a planning perspective. The current study develops a comprehensive framework for understanding the critical factors associated with county level hospitalization and ICU usage rates across the US employing a host of independent variables. Drawing from the recently released Department of Health and Human Services weekly hospitalization data, we study the overall hospitalization and ICU usage-not only COVID-19 hospitalizations. Developing a framework that examines overall hospitalizations and ICU usage can better reflect the plausible hospital system recovery path to pre-COVID level hospitalization trends. The models are subsequently employed to generate predictions for county level hospitalization and ICU usage rates in the future under several COVID-19 transmission scenarios considering the emergence of new COVID-19 variants and vaccination rates. The exercise allows us to identify vulnerable counties and regions under stress with high hospitalization and ICU rates that can be assisted with remedial measures. Further, the model will allow hospitals to understand evolving displaced non-COVID hospital demand.

Assessing the crash risks of evacuation: A matched case-control approach applied over data collected during Hurricane Irma.

Recent hurricane experiences have created concerns for transportation agencies and policymakers to find better evacuation strategies, especially after Hurricane Irma-which forced about 6.5 million Floridians to evacuate and caused a significant amount of delay due to heavy congestion. A major concern for issuing an evacuation order is that it may involve a high number of crashes in highways. In this study, we present a matched case-control based approach to understand the factors contributing to the increase in the number of crashes during evacuation. We use traffic data for a period of 5 to 10 min just before the crash occurred. For each crash observation, traffic data are collected from two upstream and two downstream detectors of the crash location. We estimate models for three different conditions: regular period, evacuation period, and combining both evacuation and regular period data. Model results show that, if there exist a high volume of traffic at an upstream station and a high variation of speed at a downstream station, the likelihood of crash occurrence increases. Using a panel mixed binary logit model, we also estimate the effect of evacuation itself on crash risk and find that, after controlling for traffic characteristics, during evacuation the chance of a crash is higher than in a regular period. Our findings have implications for evacuation declarations and highlight the need for better traffic management strategies during evacuation. Future studies may develop advanced real-time crash prediction models which would allow us to deploy proactive countermeasures to reduce crash occurrences during evacuation.

Enhancing non-motorist safety by simulating trip exposure using a transportation planning approach.

Traditionally, in developing non-motorized crash prediction models, safety researchers have employed land use and urban form variables as surrogate for exposure information (such as pedestrian, bicyclist volumes and vehicular traffic). The quality of these crash prediction models is affected by the lack of "true" non-motorized exposure data. High-resolution modeling frameworks such as activity-based or trip-based approach could be pursued for evaluating planning level non-motorist demand. However, running a travel demand model system to generate demand inputs for non-motorized safety is cumbersome and resource intensive. The current study is focused on addressing this drawback by developing an integrated non-motorized demand and crash prediction framework for mobility and safety analysis. Towards this end, we propose a three-step framework to evaluate non-motorists safety: (1) develop aggregate level models for non-motorist generation and attraction at a zonal level, (2) develop non-motorists trip exposure matrices for safety evaluation and (3) develop aggregate level non-motorists crash frequency and severity proportion models. The framework is developed for the Central Florida region using non-motorist demand data from National Household Travel Survey (2009) Florida Add-on and non-motorist crash frequency and severity data from Florida. The applicability of the framework is illustrated through extensive policy scenario analysis.

A comprehensive analysis of COVID-19 transmission and mortality rates at the county level in the United States considering socio-demographics, health indicators, mobility trends and health care infrastructure attributes.

BACKGROUND: Several research efforts have evaluated the impact of various factors including a) socio-demographics, (b) health indicators, (c) mobility trends, and (d) health care infrastructure attributes on COVID-19 transmission and mortality rate. However, earlier research focused only on a subset of variable groups (predominantly one or two) that can contribute to the COVID-19 transmission/mortality rate. The current study effort is designed to remedy this by analyzing COVID-19 transmission/mortality rates considering a comprehensive set of factors in a unified framework. METHODS AND FINDINGS: We study two per capita dependent variables: (1) daily COVID-19 transmission rates and (2) total COVID-19 mortality rates. The first variable is modeled using a linear mixed model while the later dimension is analyzed using a linear regression approach. The model results are augmented with a sensitivity analysis to predict the impact of mobility restrictions at a county level. Several county level factors including proportion of African-Americans, income inequality, health indicators associated with Asthma, Cancer, HIV and heart disease, percentage of stay at home individuals, testing infrastructure and Intensive Care Unit capacity impact transmission and/or mortality rates. From the policy analysis, we find that enforcing a stay at home order that can ensure a 50% stay at home rate can result in a potential reduction of about 33% in daily cases. CONCLUSIONS: The model framework developed can be employed by government agencies to evaluate the influence of reduced mobility on transmission rates at a county level while accommodating for various county specific factors. Based on our policy analysis, the study findings support a county level stay at home order for regions currently experiencing a surge in transmission. The model framework can also be employed to identify vulnerable counties that need to be prioritized based on health indicators for current support and/or preferential vaccination plans (when available).

Highway safety assessment and improvement through crash prediction by injury severity and vehicle damage using Multivariate Poisson-Lognormal model and Joint Negative Binomial-Generalized Ordered Probit Fractional Split model.

INTRODUCTION: Predicting crash counts by severity plays a dominant role in identifying roadway sites that experience overrepresented crashes, or an increase in the potential for crashes with higher severity levels. Valid and reliable methodologies for predicting highway accidents by severity are necessary in assessing contributing factors to severe highway crashes, and assisting the practitioners in allocating safety improvement resources. METHODS: This paper uses urban and suburban intersection data in Connecticut, along with two sophisticated modeling approaches, i.e. a Multivariate Poisson-Lognormal (MVPLN) model and a Joint Negative Binomial-Generalized Ordered Probit Fractional Split (NB-GOPFS) model to assess the methodological rationality and accuracy by accommodating for the unobserved factors in predicting crash counts by severity level. Furthermore, crash prediction models based on vehicle damage level are estimated using the same two methodologies to supplement the injury severity in estimating crashes by severity when the sample mean of severe injury crashes (e.g., fatal crashes) is very low. RESULTS: The model estimation results highlight the presence of correlations of crash counts among severity levels, as well as the crash counts in total and crash proportions by different severity levels. A comparison of results indicates that injury severity and vehicle damage are highly consistent. CONCLUSIONS: Crash severity counts are significantly correlated and should be accommodated in crash prediction models. Practical application: The findings of this research could help select sound and reliable methodologies for predicting highway accidents by injury severity. When crash data samples have challenges associated with the low observed sampling rates for severe injury crashes, this research also confirmed that vehicle damage can be appropriate as an alternative to injury severity in crash prediction by severity.

The Potential Impacts of Urban and Transit Planning Scenarios for 2031 on Car Use and Active Transportation in a Metropolitan Area.

Land use and transportation scenarios can help evaluate the potential impacts of urban compact or transit-oriented development (TOD). Future scenarios have been based on hypothetical developments or strategic planning but both have rarely been compared. We developed scenarios for an entire metropolitan area (Montreal, Canada) based on current strategic planning documents and contrasted their potential impacts on car use and active transportation with those of hypothetical scenarios. We collected and analyzed available urban planning documents and obtained key stakeholders' appreciation of transportation projects on their likelihood of implementation. We allocated 2006-2031 population growth according to recent trends (Business As Usual, BAU) or alternative scenarios (current planning; all in TOD areas; all in central zone). A large-scale and representative Origin-Destination Household Travel Survey was used to measure travel behavior. To estimate distances travelled by mode, in 2031, we used a mode choice model and a simpler method based on the 2008 modal share across population strata. Compared to the BAU, the scenario that allocated all the new population in already dense areas and that also included numerous public transit projects (unlikely to be implemented in 2031), was associated with greatest impacts. Nonetheless such major changes had relatively minor impacts, inducing at most a 15% reduction in distances travel by car and a 28% increase in distances walked, compared to a BAU. Strategies that directly target the reduction of car use, not considered in the scenarios assessed, may be necessary to induce substantial changes in a metropolitan area.

Environmental and health impacts of transportation and land use scenarios in 2061.

We compared numbers of trips and distances by transport mode, air pollution and health impacts of a Business As Usual (BAU) and an Ideal scenario with urban densification and reductions in car share (76%-62% in suburbs; 55%-34% in urban areas) for the Greater Montreal (Canada) for 2061. We estimated the population in 87 municipalities using a demographic model and population projections. Year 2031 (Y2031) trips (from mode choice modeling) and distances were used to estimate those of Y2061. Emissions of nitrogen dioxide (NO2) and carbon dioxide (CO2) were estimated and NO2 used with dispersion modeling to estimate concentrations. Walking and Public Transit (PT) use and corresponding distances walked in Y2061 were >70% higher for the Ideal scenario vs the BAU, while car share and distances were <40% lower. NO2 levels were slightly lower in the Ideal scenario vs the BAU, but always higher in the urban core. Health impacts, summarized with disability adjusted life years (DALY), differed between urban and suburb areas but globally, the Ideal scenario reduced the impacts of the Y2061 BAU by 33% DALY. Percentages of car and PT trips were similar for the Y2031 and Y2061 BAU but kms travelled by car, CO2 and NO2 increased, due to increased populations. Drastic measures to decrease car share appear necessary to substantially reduce impacts of transportation.

Multivariate copula temporal modeling of intersection crash consequence metrics: A joint estimation of injury severity, crash type, vehicle damage and driver error.

This study employs a copula-based multivariate temporal ordered probit model to simultaneously estimate the four common intersection crash consequence metrics - driver error, crash type, vehicle damage and injury severity - by accounting for potential correlations due to common observed and unobserved factors, while also accommodating the temporal instability of model estimates over time. To this end, a comprehensive literature review of relevant studies was conducted; four different copula model specifications including Frank, Clayton, Joe and Gumbel were estimated to identify the dominant factors contributing to each crash consequence indicator; the temporal effects on model estimates were investigated; the elasticity effects of the independent variables with regard to all four crash consequence indicators were measured to express the magnitude of the effects of an independent variable on the probability change for each level of four indicators; and specific countermeasures were recommended for each of the contributing factors to improve the intersection safety. The model goodness-of-fit illustrates that the Joe copula model with the parameterized copula parameters outperforms the other models, which verifies that the injury severity, crash type, vehicle damage and driver error are significantly correlated due to common observed and unobserved factors and, accounting for their correlations, can lead to more accurate model estimation results. The parameterization of the copula function indicates that their correlation varies among different crashes, including crashes that occurred at stop-controlled intersections, four-leg intersections and crashes which involved drivers younger than 25. The model coefficient estimates indicate that the driver's age, driving under the influence of drugs and alcohol, intersection geometry and control types, and adverse weather and light conditions are the most critical factors contributing to severe crash consequences. The coefficient estimates of four-leg intersections, yield and stop-controlled intersections and adverse weather conditions varied over time, which indicates that the model estimation of crash data may not be stable over time and should be accommodated in crash prediction analysis. In the end, relevant countermeasures corresponding to law enforcement and intersection infrastructure design are recommended to all of the contributing factors identified by the model. It is anticipated that this study can shed light on selecting valid statistical models for crash data analysis, identifying intersection safety issues, and helping develop effective countermeasures to improve intersection safety.

Immunologic and gene expression profiles of spontaneous canine oligodendrogliomas.

Malignant glioma (MG), the most common primary brain tumor in adults, is extremely aggressive and uniformly fatal. Several treatment strategies have shown significant preclinical promise in murine models of glioma; however, none have produced meaningful clinical responses in human patients. We hypothesize that introduction of an additional preclinical animal model better approximating the complexity of human MG, particularly in interactions with host immune responses, will bridge the existing gap between these two stages of testing. Here, we characterize the immunologic landscape and gene expression profiles of spontaneous canine glioma and evaluate its potential for serving as such a translational model. RNA in situ hybridization, flowcytometry, and RNA sequencing were used to evaluate immune cell presence and gene expression in healthy and glioma-bearing canines. Similar to human MGs, canine gliomas demonstrated increased intratumoral immune cell infiltration (CD4+, CD8+ and CD4+Foxp3+ T cells). The peripheral blood of glioma-bearing dogs also contained a relatively greater proportion of CD4+Foxp3+ regulatory T cells and plasmacytoid dendritic cells. Tumors were strongly positive for PD-L1 expression and glioma-bearing animals also possessed a greater proportion of immune cells expressing the immune checkpoint receptors CTLA-4 and PD-1. Analysis of differentially expressed genes in our canine populations revealed several genetic changes paralleling those known to occur in human disease. Naturally occurring canine glioma has many characteristics closely resembling human disease, particularly with respect to genetic dysregulation and host immune responses to tumors, supporting its use as a translational model in the preclinical testing of prospective anti-glioma therapies proven successful in murine studies.

Wearable PPG sensor based alertness scoring system.

Quantifying mental alertness in today's world is important as it enables the person to adopt lifestyle changes for better work efficiency. Miniaturized sensors in wearable devices have facilitated detection/monitoring of mental alertness. Photoplethysmography (PPG) sensors through Heart Rate Variability (HRV) offer one such opportunity by providing information about one's daily alertness levels without requiring any manual interference from the user. In this paper, a smartwatch based alertness estimation system is proposed. Data collected from PPG sensor of smartwatch is processed and fed to machine learning based model to get a continuous alertness score. Utility functions are designed based on statistical analysis to give a quality score on different stages of alertness such as awake, long sleep and short duration power nap. An intelligent data collection approach is proposed in collaboration with the motion sensor in the smartwatch to reduce battery drainage. Overall, our proposed wearable based system provides a detailed analysis of alertness over a period in a systematic and optimized manner. We were able to achieve an accuracy of 80.1% for sleep/awake classification along with alertness score. This opens up the possibility for quantifying alertness levels using a single PPG sensor for better management of health related activities including sleep.

A novel method for accurate estimation of HRV from smartwatch PPG signals.

Photoplethysmography(PPG) as a non-invasive tool for monitoring various cardiovascular parameters, has become popular due to the ease of wearable integration and pervasive nature. Due to unobtrusive nature of sensor placement at wrist, smartwatches and wrist based fitness bands have gained popularity. However, any movement of the wrist along with frequent loose contacts significantly corrupts the PPG signal. Reliable peak detection from the corrupted PPG signal is essential for any further processing, as many physiological quantities such as heart rate variability (HRV) depends on the peak-to-peak distances in the PPG signal, known as the RR Series. This paper attempts to provide a robust algorithm for peak detection in noise & motion artefact corrupted PPG signals. The algorithm consists of steps to remove the baseline drift in the PPG signal using wavelet filtering and trend removal and subsequent peak detection using autocorrelation for each pseudo-periodic segment of the signal. The validation of the method is done by comparing the PPG peaks detected by the algorithm with RR series extracted from simultaneously captured ECG signal.

Down-regulation of cyclin-dependent kinase-4 and MAPK through estrogen receptor mediated cell cycle arrest in human breast cancer induced by gold nanoparticle tagged toxin protein NKCT1.

AIM: The aim of this study was to determine whether gold nanoparticles conjugated cytotoxic protein NKCT1 (GNP-NKCT1) acted through the estrogen receptor mediated pathway in MCF-7 cells and to establish the MAPK and PI3k/Akt signal transduction pathway. METHODS: Apoptosis was done by flow cytometry. BrdU incorporation and nuclear proliferating antigen was measured by flow cytometry. Wound healing assay along with matrigel chamber invasion and migration was done. Expression of MMP9 was checked by flow cytometry and also by gelatin zymography. To analyze the regulation of signaling protein, western blot was done. MTT assay was done to evaluate the ligand receptor pathway using the estrogen receptor negative cell line (MDA-MB-231) for inhibitor effects. RESULTS: Treatment of GNP-NKCT1 (3.9 µg/ml) exhibited 38.04% early apoptosis and 4.29% late apoptotic cell. GNP-NKCT1 significantly inhibited both cell migration and invasion with suppressed expression of MMP9. In addition, treatment of cultured human breast cancer MCF7 cells with GNP-NKCT1 reversely suppressed the incorporation of BrdU, with reduced expression of Ki-67. The western blot analysis showed that GNP-NKCT1 arrested cell cycle progression through upregulation of the kinase inhibitor protein p21 and inactivation of G1-cylin dependent kinase (CDK4). GNP-NKCT1 suppressed nuclear translocation of nuclear factor kappa B (NF-κB) and also abrogated the phosphorylation of p38 mitogen activated protein kinase (MAPK), phosphatidylinositide-3-kinase (PI3k), Akt and extracellular regulated kinase (ERK1/2). MTT assay indicated that GNP-NKCT1 reduced proliferation in the estrogen receptor induced ER negative breast cancer cell line (MDA-MB-231). Addition of, ER inhibitor (tamoxifen) and PI3K inhibitor (wortmannin) to cells resulted in reduced expression of Ki-67 and MMP-9. CONCLUSION: The data suggested that GNP-NKCT1 induced MCF7 cell inhibition may occur through estrogen receptor pathway via inactivation of CDK4 and inactivation of PI3K/Akt, ERK1/2 and p38 MAPK signaling pathway with inhibitory effects on NF-κB, reducing the activity of MMP9. This result provides a new mechanism to explain the role of gold nanoparticles conjugated NKCT1 as a potent anti-metastatic agent in MCF7 cells.

Evaluation of cytotoxicity of a purified venom protein from Naja kaouthia (NKCT1) using gold nanoparticles for targeted delivery to cancer cell.

In our earlier report, gold nanoparticle (GNP) and snake venom protein toxin NKCT1 were conjugated and primary characteristics were done. In this communication, further characteristics of GNP-NKCT1 were done with TGA, BET, Zeta potential, ICP-MS, FTIR, XPS, and in vitro release kinetics for its physicochemical, molecular nature and bonding. TGA and ICP-MS showed that the number of conjugation was 40 ± 5 to 90 ± 8 NKCT1 per gold nanoparticles. FTIR and XPS corresponding to (CO), (NH), (SS) reformulated the conjugation of GNP with NKCT1. The efficacy of GNP-NKCT1 on cancer cells were analyzed by MTT assay which demonstrated superior cytotoxic effects as compared to native NKCT1. IC50 dose of GNP-NKCT1 was less than 4 µg/ml in cancer cell lines, whereas in case of NKCT1 it was average 8 µg/ml. Twice dose of IC50 of GNP-NKCT1 even showed less toxicity compared to unconjugated NKCT1, towards normal epithelial or fibroblast cell and also in peripheral blood mononuclear lymphocytes. Flow cytometry analysis revealed that percentage of apoptotic C6 cells was much higher in GNP-NKCT1 treatment (54.58%) than that of NKCT1 treatment (26.79%). Flow cytometric analysis of cell cycle using GNP-NKCT1 on C6 cancer cells revealed that it arrested the cell cycle at Go/G1 phases. In diethylnitrosamine (DEN) induced in vivo hepatocarcinoma mice, the activities of hepatic enzymes- aspartate transaminase (AST) and alanine transaminase (ALT), alkaline phosphatase (ALP), lactate dehydrogenase (LDH) and activities of antioxidant enzymes- superoxide dismutase (SOD), catalase (CAT), glutathione (GSH) and glutathione peroxidase (GPx) were restored by GNP-NKCT1. This study indicated the capability of gold nanoparticles in enhancing the cancer cell uptake of NKCT1 and also suggested that GNP-NKCT1 might be a good source of anti-carcinoma or anti-sarcoma targeted agent.

NKCT1 (purified Naja kaouthia protein toxin) conjugated gold nanoparticles induced Akt/mTOR inactivation mediated autophagic and caspase 3 activated apoptotic cell death in leukemic cell.

BACKGROUND: Gold nanoparticle (GNP) and snake venom protein toxin NKCT1 was conjugated as stated earlier (Bhowmik et al., 2013). The aim of this study was to explore the caspase dependent apoptotic pathway and autophagy inducing ability of gold nanoparticles tagged snake venom protein toxin NKCT1 (GNP-NKCT1) in human leukemic U937 and K562 cell line. METHODS: GNP-NKCT1 induced apoptosis in U937 and K562 cell line were assessed through mitochondrial membrane potential assay, ROS generation assay, caspase 3 pathways and western blotting. GNP-NKCT1 induced autophagic pathway was detected through Akt, mTOR and PI3K expression by western blotting. Autophagic cell death also checked after addition of caspase 3 inhibitor and which also reconfirmed by western blotting of autophagic marker protein, lysosomal staining. RESULTS: Loss of mitochondrial membrane potential was occurred in both the leukemic cell line after induction by GNP-NKCT1 and treatment of which also exhibited high ROS generation. Caspase 3 expression of cell was also increased. With caspase 3 inhibitor, GNP-NKCT1 downregulated PI3K/Akt and mTOR expression and thus undergoing autophagic cell death. Lysosomal staining confirmed lysosomal enzyme involvement in the autophagic response. Up regulation of Atg 3, Atg12, Beclin 1, LC3-II protein and BIF-1 and down regulation of Atg4B were also showed by blotting. CONCLUSION: The results demonstrated that conjugation of Gold nanoparticles with NKCT1 could induce an alternate cell death pathway other than apoptosis in the form of autophagy in leukemic cell. GENERAL SIGNIFICANCE: This study might provide the understanding area of chemotherapeutic drug development from natural resources like snake venoms.

Dimensionality Reduction Based Optimization Algorithm for Sparse 3-D Image Reconstruction in Diffuse Optical Tomography.

Diffuse optical tomography (DOT) is a relatively low cost and portable imaging modality for reconstruction of optical properties in a highly scattering medium, such as human tissue. The inverse problem in DOT is highly ill-posed, making reconstruction of high-quality image a critical challenge. Because of the nature of sparsity in DOT, sparsity regularization has been utilized to achieve high-quality DOT reconstruction. However, conventional approaches using sparse optimization are computationally expensive and have no selection criteria to optimize the regularization parameter. In this paper, a novel algorithm, Dimensionality Reduction based Optimization for DOT (DRO-DOT), is proposed. It reduces the dimensionality of the inverse DOT problem by reducing the number of unknowns in two steps and thereby makes the overall process fast. First, it constructs a low resolution voxel basis based on the sensing-matrix properties to find an image support. Second, it reconstructs the sparse image inside this support. To compensate for the reduced sensitivity with increasing depth, depth compensation is incorporated in DRO-DOT. An efficient method to optimally select the regularization parameter is proposed for obtaining a high-quality DOT image. DRO-DOT is also able to reconstruct high-resolution images even with a limited number of optodes in a spatially limited imaging set-up.

Protection against osteoarthritis in experimental animals by nanogold conjugated snake venom protein toxin gold nanoparticle-Naja kaouthia cytotoxin 1.

BACKGROUND & OBJECTIVES: Increased severity of osteoarthritis (OA) and adverse side effects of its treatment led to the search for alternative therapies. It was previously reported that snake venom protein toxin Naja kaouthia cytotoxin 1 (NKCT1) and gold nanoparticle (GNP) individually have potential against excremental arthritis. In this study, we analyzed the protective activity of GNP conjugated protein toxin NKCT1 (GNP-NKCT1) against experimental OA. METHODS: Gold nanoparticle conjugation with NKCT1 (GNP-NKCT1) was done and its physiochemical properties were studied. OA was induced in male albino rats by intra-articular injection of bacterial collagenase and treatment was done with NKCT1/GNP-NKCT1/standard drug (indomethacin). Physical parameter (ankle diameter), urinary markers (hydroxyproline, glucosamine, pyridinoline, deoxypyridinoline), serum and synovial membrane pro-inflammatory markers [tumour necrosis factor-alpha (TNF-α), interleukin-1ß (IL-1ß), IL-17, vascular endothelial growth factor (VEGF)] and matrix metalloproteinase 1 (MMP1) were measured. Joint histopathology and scanning electron microscopy imaging of articular cartilage surface were also done. RESULTS: Physical parameters, urinary markers, serum and synovial membrane pro-inflammatory makers and MMP1 were increased in arthritic rats and significantly restored after GNP-NKCT1/NKCT1 treatment. Joint histopathology and scanning electron microscopy imaging of articular cartilage surface also indicated the protective effect of GNP-NKCT1 against inflammatory response and cartilage degradation in osteoarthritic rats. INTERPRETATION & CONCLUSIONS: In this study restoration of the arthritic markers and bone degradation by GNP-NKCT1 treatment indicated the anti-osteoarthritic property of GNP-NKCT1. Further studies need to be done to confirm these findings.

Nano gold conjugation, anti-arthritic potential and toxicity studies of snake Naja kaouthia (Lesson, 1831) venom protein toxin NKCT1 in male albino rats and mice.

Nanoscience and Nanotechnology have found their way in the fields of pharmacology and medicine. The conjugation of drug to nanoparticles combines the properties of both. In this study, gold nanoparticle (GNP) was conjugated with NKCT1, a cytotoxic protein toxin from Indian cobra venom for evaluation of anti-arthritic activity and toxicity in experimental animal models. GNP conjugated NKCT1 (GNP-NKCT1) synthesized by NaBH4 reduction method was stable at room temperature (25 +/- 2 degrees C), pH 7.2. Hydrodynamic size of GNP-NKCT1 was 68-122 nm. Arthritis was developed by Freund's complete adjuvant induction in male albino rats and treatment was done with NKCT1/GNP-NKCT1/standard drug. The paw/ankle swelling, urinary markers, serum markers and cytokines were changed significantly in arthritic control rats which were restored after GNP-NKCT1 treatment. Acute toxicity study revealed that GNP conjugation increased the minimum lethal dose value of NKCT1 and partially reduced the NKCT1 induced increase of the serum biochemical tissue injury markers. Histopathological study showed partial restoration of toxic effect in kidney tissue after GNP conjugation. Normal lymphocyte count in culture was in the order of GNP-NKCT1 > NKCT1 > Indomethacine treatment. The present study confirmed that GNP conjugation increased the antiarthritic activity and decreased toxicity profile of NKCT1.