Assessment of radiation-induced sterility, longevity, and reproductive characteristics of the Aedes aegypti (Diptera: Culicidae) strain of Swat: A step towards the implementation of SIT in Pakistan.

BACKGROUND OBJECTIVES: Biological approaches for vector mosquito control such as sterile insect technique (SIT) requires sterilization of male mosquitoes through specific radiation doses for sterility induction in males. Under the SIT program, the males used must be compatible with the wild males in vigor, flight and selection of mate. Much of this potential is determined by the diet provided in the larval stages and optimizing the irradiation doses that cause complete sterility but have very minimal effect on the lives of irradiated males. The current study is designed to evaluate gamma radiation doses for inducing sterility in male Aedes aegypti mosquitoes with a genomic background from Pakistan (Swat district) and to assess the effects of radiation exposure on the competency of irradiated males and other life traits of irradiated mosquitoes. METHODS: Sterilization of 17-hour-old male pupae (groups of 50 pupa/cup in three replicates) of the Ae. aegypti Swat strain was conducted using radiation doses of 30, 60, 70, 90, and 105 gray (Gy) from (Co60) at the Nuclear Institute for Food and Agriculture (NIFA) in Peshawar, Pakistan. Post-irradiated pupae were released in adult cages. Virgin females of the same cohort were released (1:1) to mate with the irradiated males. Mortality, longevity, mating competitiveness of males, and female fecundity were recorded. RESULTS: An average of 71 eggs per female was recorded in control with 86% hatch rate. Individual females mated with a 60 Gy treated males produced 60 ± 0.6 eggs per female with 17% hatch rate, and those mated with 70 Gy males produced 42 ± 0.01 eggs with a nil hatch rate, whereas females mated with males treated with ≥70 Gy doses did not reproduce to next generation. Females in groups of 50 mated with 60, 70 Gy treated males (equal pairs), produced 369±1.3 and 98±0.01 eggs with 15% and zero hatch rate. Significant dose dependent reduction in longevity was observed for >30 Gy doses. The matting competence of irradiated males was about half that of un-irradiated males. INTERPRETATION CONCLUSION: Aedes aegypti with a Pakistani genomic background treated with a ≥70 Gy dose of gamma radiation induced complete sterility in males and provided the first- step foundation for SIT application in Pakistan. Further extensive studies are required to optimize the SIT techniques so that fully sterile males with very minor quality changes can be produced on large scales for field trials.

Sustainable waste management and waste-to-energy in the context of a circular economy through various waste management technologies.

Inappropriate waste management is a considerable ecological risk, leading to detrimental effects on the soil, air, and water quality. It is imperative to address these concerns promptly to minimize the repercussions of solid waste on public health and the ecosystem. It is evident that the level of economic growth directly impacts waste generation. This study intends to use the life cycle assessment (LCA) technique to evaluate the environmental impacts of four alternative municipal solid waste (MSW) management scenarios in Peshawar City, Pakistan. The goal is to discover an option that is both sustainable and minimizes environmental damage. The study examined the system boundaries encompassing the collection and transportation of MSW, along with its processing and final disposal, employing composting, anaerobic digestion (AD), material recovery facilities (MRF), and landfill methods. Comprehensive field studies and an in-depth literature review provided the data regarding Peshawar's existing MSW management system and the proposed scenarios, all of which was inventoried in the OpenLCA 1.10.3 database. Following data collection, the CML-IA technique was employed to analyze the data, measuring the environmental footprint in terms of climate change potential, human toxicity, acidification potential, photochemical oxidation, and eutrophication. A sensitivity analysis was also performed to identify the influence of varying recycling rates on the environmental strain correlated with the proposed scenarios. The analysis results indicated that scenario S2, which combined composting, landfilling, and MRF, exhibited the least environmental impact compared to the other considered scenarios. Furthermore, the sensitivity analysis reflected an inverse correlation between alterations in the recycling rate and the total environmental impact. To counter the environmental problems arising from waste generation, it is essential to incorporate principles of the circular economy into the MSW management approach.

Life cycle assessment of iron-biomass supported catalyst for Fischer Tropsch synthesis.

The iron-based biomass-supported catalyst has been used for Fischer-Tropsch synthesis (FTS). However, there is no study regarding the life cycle assessment (LCA) of biomass-supported iron catalysts published in the literature. This study discusses a biomass-supported iron catalyst's LCA for the conversion of syngas into a liquid fuel product. The waste biomass is one of the source of activated carbon (AC), and it has been used as a support for the catalyst. The FTS reactions are carried out in the fixed-bed reactor at low or high temperatures. The use of promoters in the preparation of catalysts usually enhances C5+ production. In this study, the collection of precise data from on-site laboratory conditions is of utmost importance to ensure the credibility and validity of the study's outcomes. The environmental impact assessment modeling was carried out using the OpenLCA 1.10.3 software. The LCA results reveals that the synthesis process of iron-based biomass supported catalyst yields a total impact score in terms of global warming potential (GWP) of 1.235E + 01 kg CO2 equivalent. Within this process, the AC stage contributes 52% to the overall GWP, while the preparation stage for the catalyst precursor contributes 48%. The comprehensive evaluation of the iron-based biomass supported catalyst's impact score in terms of human toxicity reveals a total score of 1.98E-02 kg 1,4-dichlorobenzene (1,4-DB) equivalent.

Geographic and Racial Disparities in Chimeric Antigen Receptor-T Cells and Bispecific Antibodies Trials Access for Diffuse Large B-Cell Lymphoma.

BACKGROUND: We investigate the geographical and racial disparities in accessing CAR-T and bispecific antibodies trials for DLBCL. MATERIALS AND METHODS: ClinicalTrials.gov was searched, and 75 trials with at least 1 open site in the US were included. 2020 US Census Bureau data was used to obtain data on race and ethnicity. SPSS version 26 was used for analysis. RESULTS: There were 62 CAR-T and 13 bispecific antibodies trials with 6221 enrolled or expected to enroll patients. Eighty-five percent of the clinical trials were only open in the US, and the majority 64% were pharmaceutical-funded. There were 126 unique study sites distributed over 31 states with 11 (0-51) mean number of trials per state and 4.5 (1-26) and 4.4 (1-24) mean number of CAR-T and bispecific antibodies trials per site, respectively. Southern states had the most number of trials 31%, followed by Midwestern 25%, Northeastern 24%, and Western 20%. The highest number of study locations were in California 13, New York 9, and Pennsylvania 9, while the highest number of open studies were in California 51, Texas 32, and New York 23. Twenty states had no open CAR-T or bispecific antibodies trials. Only 33% of African Americans (AA) lived in a county with a trial, and 7 out of 10 states with the highest proportion of AA residents (18.6%-41.4%) have no or less than 4 trial sites. Of the 62 counties analyzed, 92% were White predominant, while only 8% were AA predominant (P = .009). CONCLUSIONS: Strategies should be framed to address the observed disparities and to improve access.

Overview of environmental and economic viability of activated carbons derived from waste biomass for adsorptive water treatment applications.

In adsorptive water treatment applications, the exploration of waste-derived activated carbon (AC) has gained substantial attention in scientific research. The use of waste materials as precursors for AC has gained attention due to its economic viability and potential to reduce the consumption of non-renewable resources. However, there is a lack of comprehensive literature regarding the costs and environmental impacts associated with the waste-based AC production and application. As sustainability practices gain importance, there has been an increase in research dedicated to estimating costs and conducting life cycle assessment (LCA) of AC production from waste sources. However, there is a need for thorough literature reviews that cover various methodologies and conclusions. The primary objective of this study is to provide a comprehensive overview and analysis of the economic and environmental factors related to the use of waste-derived AC in water treatment. LCA studies indicate that utilizing waste materials for AC production can lead to significant resource and energy savings compared to conventional methods relying on fossil resources. The cost of AC is influenced by factors such as precursor material cost, energy requirements during production (optimizable on an industrial scale), and properties of the resulting material. Additionally, the review emphasizes the significance of waste-based AC regeneration for sustainable viability. Evaluating the environmental and economic costs is crucial to support sustainability claims and avoid unsupported assertions. Overall, this study contributes to understanding the potential of waste-derived AC in water treatment and highlights the need for further research in this area.

Adaptive EWMA control chart using Bayesian approach under ranked set sampling schemes with application to Hard Bake process.

The memory-type control charts, such as cumulative sum (CUSUM) and exponentially weighted moving average control chart, are more desirable for detecting a small or moderate shift in the production process of a location parameter. In this article, a novel Bayesian adaptive EWMA (AEWMA) control chat utilizing ranked set sampling (RSS) designs is proposed under two different loss functions, i.e., square error loss function (SELF) and linex loss function (LLF), and with informative prior distribution to monitor the mean shift of the normally distributed process. The extensive Monte Carlo simulation method is used to check the performance of the suggested Bayesian-AEWMA control chart using RSS schemes. The effectiveness of the proposed AEWMA control chart is evaluated through the average run length (ARL) and standard deviation of run length (SDRL). The results indicate that the proposed Bayesian control chart applying RSS schemes is more sensitive in detecting mean shifts than the existing Bayesian AEWAM control chart based on simple random sampling (SRS). Finally, to demonstrate the effectiveness of the proposed Bayesian-AEWMA control chart under different RSS schemes, we present a numerical example involving the hard-bake process in semiconductor fabrication. Our results show that the Bayesian-AEWMA control chart using RSS schemes outperforms the EWMA and AEWMA control charts utilizing the Bayesian approach under simple random sampling in detecting out-of-control signals.

Assessment of heavy metals and associated oxidative stress in occupationally exposed workers from Bannu and Karak Districts in Pakistan.

Heavy metals (HMs) are extensively found in occupationally exposed miners and industrial workers, which may cause serious health-related problems to the large workforce. In order to evaluate the impact of these toxic pollutants, we have investigated the effect of cadmium (Cd), chromium (Cr), copper (Cu), and lead (Pb) concentration on exposed workers of mining, and woolen textile mill and compared the findings with unexposed individuals. From each category like exposed workers (mining, and woolen mill textile site) and unexposed individuals, 50 blood samples were taken. The occurrence of HMs in a sample was investigated through atomic absorption spectrometry while the oxidative stress marker malondialdehyde (MDA) and antioxidant enzyme statuses such as superoxide dismutase (SOD) and catalase (CAT) were analyzed in exposed and control samples. The results showed significant (p < 0.05) variation in Cd, Cr, Cu, and Pb levels in exposed and control samples. The concentration of Cd in the blood of WMWs, KMWs, and control group was 5.75, 3.89, and 0.42 µg/dL, respectively. On the other hand, the concentration of Pb in the blood of WMWs, MWs, and control was 32.34, 24.39, and 0.39 µg/dL while the concentrations of Cr and Cu in the blood of WMWs, MWs, and control group were 11.61 and 104.14 µg/dL, 4.21 and 113.21 µg/dL, 0.32 and 65.53 µg/dL, respectively. An increase in MDA was recorded in the exposed workers' group as compared to control subjects, whereas SOD and CAT activities decreased. Meanwhile, MDA was significantly and positively (p < 0.01) correlated with HMs, while negative significant correlations were found among HMs with SOD and CAT.

Moving towards sustainability: how do low-carbon energy, current account balance, and reserves induce environmental deterioration in the Big 3?

Promoting financial sustainability is the focus of current state policies while addressing the concerns of environmental pollution. The alarming impacts of climate change on economies motivate us to revisit an intensive empirical study to explore the dynamic relationships of low-carbon energy, current account balance, and reserves with carbon dioxide (CO2) emissions in the most polluted countries across the globe for the years 1981-2020. We applied the dynamic autoregressive distributive lag (D-ARDL) simulation model to investigate the short and long-run connection. The empirical outcomes of the study uncover that in the short run, a 1% increase in renewable energy reduces CO2-based emissions by 0.417%, 0.169%, and 0.619% in China, the USA, and India, respectively. We further explored that China's and the USA's economic growth causes environmental deterioration. In contrast, a 1% increase in current account balances improves the environmental quality of China and India by 0.3% and 0.6%, respectively. This research concludes that model variables significantly impact the environment. Therefore, it is necessary to draw policy implications to increase the consumption of low-carbon energy to sustain economic growth by limiting the adverse impacts of economic activities.

Assessment of variability in nutritional quality of wild edible fruit of Monotheca buxifolia (Falc.) A. DC. Along the altitudinal gradient in Pakistan.

The fruit of Monotheca buxifolia is among the underutilized wild edible fruits that grows in Hindukush and Suleiman range mountains of Pakistan. Mountain communities consume this wild fruit as a food, medicine and it provides an important source of income. In this study, we aimed to investigate the total yield and the effect that altitude plays on its proximate composition and mineral contents as determined through phytochemical screening of this economically important wild fruit. Our results indicate a significant increase in the total fruit yield with increasing trunk diameter (R2 = 0.98), height (R2 = 0.95) and cover (R2 = 0.92). The proximate composition shows that the crude fat and carbohydrate contents of Monotheca fruit significantly varies (P < 0.05) along the altitudinal gradient. Similarly, ANOVA followed by post hoc Tukey HSD, further confirms the variation (P < 0.01) in moisture contents of the fruit. Dry matter ranged from 95.28 ± 4.64 to 108 ± 3.70 g kg- 1 of the fruit's edible portion, while protein contents varied between 17.16 % and 20.44 %. The fruits were found to be rich in minerals containing sizeable amounts of potassium, iron, phosphorus, sodium, nitrogen, magnesium, and copper. Significant difference was observed in the nitrogen (P < 0.01), potassium (P < 0.05) and sodium (P < 0.05) contents of the fruit along the altitudinal gradient. Similarly, tannins, flavonoids, saponins, reducing sugars, terpenoids, anthraquinones, and cardiac glycosides, were the most prominent chemicals found in Monotheca fruit. It was concluded that Monotheca fruit is a good source of carbohydrates, proteins, macro and micro-nutrients which fluctuated along the altitudinal gradient.

Cholinesterase activity as a potential biomarker for neurotoxicity induced by pesticides in vivo exposed Oreochromis niloticus (Nile tilapia): assessment tool for organophosphates and synthetic pyrethroids.

Organophosphates (OPs) and synthetic pyrethroids (SPs) are the most popular broad spectrum pesticides, used in agriculture as they have a strong pesticidal activity while also being biodegradable in the environment. The present study aimed to demonstrate the effects of these pesticides on the Acetylcholinesterase (AChE) activity in brain, gills and body muscles of Oreochromis niloticus - an important enzyme for the assessment and biomonitoring pollution caused by neurotoxins in the environment. The fish were exposed for 24 and 48 h to the LC0 concentrations of the malathion (1.425 mg/L), the chlorpyrifos (0.125 mg/L) and the λ-cyhalothrin (0.0039 mg/L), respectively. The activity of the AChE was significantly increased (p < 0.05) at 24 h and decreased at 48 h (except for the chlorpyrifos-treated brain and gills while tissues had shown no activity at 48 h's exposure) in all pesticides-treated tissues. The maximum increase in the activity and inhibition in the AChE activity were recorded as +92% and -52% in the chlorpyrifos and the lambda-cyhalothrin exposed brain tissues, respectively. Thus, the alterations in the AChE activities indicated that the applied pesticides are highly neurotoxic to fish and the enzyme (AChE) could be used as a useful biomarker for estimation of water pollution.

Biochar Produced from Saudi Agriculture Waste as a Cement Additive for Improved Mechanical and Durability Properties-SWOT Analysis and Techno-Economic Assessment.

The Kingdom of Saudi Arabia generates an enormous amount of date palm waste, causing severe environmental concerns. Green and strong concrete is increasingly demanded due to low carbon footprints and better performance. In this research work, biochar derived from locally available agriculture waste (date palm fronds) was used as an additive to produce high-strength and durable concrete. Mechanical properties such as compressive and flexural strength were evaluated at 7, 14, and 28 days for control and all other mixes containing biochar. In addition, the durability properties of the concrete samples for the mixes were investigated by performing electric resistivity and ultra-sonic pulse velocity testing. Finally, a SWOT (strengths, weaknesses, opportunities, and threats) analysis was carried out to make strategic decisions about biochar's use in concrete. The results demonstrated that the compressive strength of concrete increased to 28−29% with the addition of 0.75−1.5 wt% of biochar. Biochar-concrete containing 0.75 wt% of biochar showed 16% higher flexural strength than the control specimen. The high ultrasonic pulse velocity (UPV) values (>7.79 km/s) and low electrical resistivity (<22.4 kΩ-cm) of biochar-based concrete confirm that the addition of biochar resulted in high-quality concrete free from internal flaws, cracks, and better structural integrity. SWOT analysis indicated that biochar-based concrete possessed improved performance than ordinary concrete, is suitable for extreme environments, and has opportunities for circular economy and applications in various construction designs. However, cost and technical shortcomings in biochar production and biochar-concrete mix design are still challenging.

Assessment of Artificial Intelligence Strategies to Estimate the Strength of Geopolymer Composites and Influence of Input Parameters.

Geopolymers might be the superlative alternative to conventional cement because it is produced from aluminosilicate-rich waste sources to eliminate the issues associated with its manufacture and use. Geopolymer composites (GPCs) are gaining popularity, and their research is expanding. However, casting, curing, and testing specimens requires significant effort, price, and time. For research to be efficient, it is essential to apply novel approaches to the said objective. In this study, compressive strength (CS) of GPCs was anticipated using machine learning (ML) approaches, i.e., one single method (support vector machine (SVM)) and two ensembled algorithms (gradient boosting (GB) and extreme gradient boosting (XGB)). All models' validity and comparability were tested using the coefficient of determination (R2), statistical tests, and k-fold analysis. In addition, a model-independent post hoc approach known as SHapley Additive exPlanations (SHAP) was employed to investigate the impact of input factors on the CS of GPCs. In predicting the CS of GPCs, it was observed that ensembled ML strategies performed better than the single ML technique. The R2 for the SVM, GB, and XGB models were 0.98, 0.97, and 0.93, respectively. The lowered error values of the models, including mean absolute and root mean square errors, further verified the enhanced precision of the ensembled ML approaches. The SHAP analysis revealed a stronger positive correlation between GGBS and GPC's CS. The effects of NaOH molarity, NaOH, and Na2SiO3 were also observed as more positive. Fly ash and gravel size: 10/20 mm have both beneficial and negative impacts on the GPC's CS. Raising the concentration of these ingredients enhances the CS, whereas increasing the concentration of GPC reduces it. Gravel size: 4/10 mm has less favorable and more negative effects. ML techniques will benefit the construction sector by offering rapid and cost-efficient solutions for assessing material characteristics.

Re-examining the nexuses of communicable diseases, environmental performance, and dynamics of sustainable Development in OECD countries.

We investigate the determinants of communicable diseases (CDs) and nexus of financial development, economic development, and renewable energy consumption to address the issues of ecological footprint level, the impacts of communicable diseases (CDs), and economic growth of the OECD countries throughout 2000-2019. The results from FMOLS and DOLS reveal that the levels of financial development, energy consumption, and trade volume significantly contribute to overcoming the death toll occurring due to CDs. As regards the growth function, the level of trade in the economy is significantly associated with economic growth. The findings reveal that the improvements and developments in the financial sectors and trading activities cause a reduction in the infection cases represented by COVID-19. In contrast, economic growth does have a negative but insignificant impact upon COVID-19. We conclude that sound financial development combined with economic and environmental regulations could be strategically helpful to cope with CDs.

On the Extended Generalized Inverted Kumaraswamy Distribution.

In this work, we provide a new generated class of models, namely, the extended generalized inverted Kumaraswamy generated (EGIKw-G) family of distributions. Several structural properties (survival function (sf), hazard rate function (hrf), reverse hazard rate function (rhrf), quantile function (qf) and median, s th raw moment, generating function, mean deviation (md), etc.) are provided. The estimates for parameters of new G class are derived via maximum likelihood estimation (MLE) method. The special models of the proposed class are discussed, and particular attention is given to one special model, the extended generalized inverted Kumaraswamy Burr XII (EGIKw-Burr XII) model. Estimators are evaluated via a Monte Carlo simulation (MCS). The superiority of EGIKw-Burr XII model is proved using a lifetime data applications.

Influence Diagnostic Methods in the Poisson Regression Model with the Liu Estimator.

There is a long history of interest in modeling Poisson regression in different fields of study. The focus of this work is on handling the issues that occur after modeling the count data. For the prediction and analysis of count data, it is valuable to study the factors that influence the performance of the model and the decision based on the analysis of that model. In regression analysis, multicollinearity and influential observations separately and jointly affect the model estimation and inferences. In this article, we focused on multicollinearity and influential observations simultaneously. To evaluate the reliability and quality of regression estimates and to overcome the problems in model fitting, we proposed new diagnostic methods based on Sherman-Morrison Woodbury (SMW) theorem to detect the influential observations using approximate deletion formulas for the Poisson regression model with the Liu estimator. A Monte Carlo method is done for the assessment of the proposed diagnostic methods. Real data are also considered for the evaluation of the proposed methods. Results show the superiority of the proposed diagnostic methods in detecting unusual observations in the presence of multicollinearity compared to the traditional maximum likelihood estimation method.

On computing critical factors based healthy behavior index for behavior assessment.

OBJECTIVE: Ubiquitous computing has supported personalized health through a vast variety of wellness and healthcare self-quantification applications over the last decade. These applications provide insights for daily life activities but unable to portray the comprehensive impact of personal habits on human health. Therefore, in order to facilitate the individuals, we have correlated the lifestyle habits in an appropriate proportion to determine the overall impact of influenced behavior on the well-being of humans. MATERIALS AND METHODS: To study the combined impact of personal behaviors, we have proposed a methodology to derive the comprehensive Healthy Behavior Index (HBI) consisting of two major processes: (1) Behaviors' Weight-age Identification (BWI), and (2) Healthy Behavior Quantification and Index (HBQI) modeling. The BWI process identifies the high ranked contributing behaviors through life-expectancy based weight-age, whereas HBQI derives a mathematical model based on quantification and indexing of behavior using wellness guidelines. RESULTS: The contributing behaviors are identified through text mining technique and verified by seven experts with a Kappa agreement level of 0.379. A real-world user-centric statistical evaluation is applied through User Experience Questionnaire (UEQ) method to evaluate the impact of HBI service. This HBI service is developed for the Mining Minds, a wellness management application. This study involves 103 registered participants (curious about the chronic disease) for a Korean wellness management organization. They used the HBI service over 12 weeks, the results for which were evaluated through UEQ and user feedback. The service reliability for the Cronbach's alpha coefficient greater than 0.7 was achieved using HBI service whereas the stimulation coefficient of the value 0.86 revealed significant effect. We observed an overall novelty of the value 0.88 showing the potential interest of participants. CONCLUSIONS: The comprehensive HBI has demonstrated positive user experience concerning the stimulation for adapting the healthy behaviors. The HBI service is designed independently to work as a service, so any other wellness management service-enabled platform can consume it to evaluate the healthy behavior index of the person for recommendation generation, behavior indication, and behavior adaptation.

Age, neutrophil lymphocyte ratio, and radiographic assessment of the quantity of lung edema (RALE) score to predict in-hospital mortality in COVID-19 patients: a retrospective study.

Background: Available data suggest that case fatality rate of COVID-19 patients in Surabaya is higher than global cases. Thus, it is important to identify risk factors to prevent the mortality. This study aimed to assess the factors associated with hospital mortality of COVID-19 patients, and develop a prediction score based on these findings. Methods: We analyzed 111 patients, who were diagnosed with COVID-19 based on reverse-transcriptase polymerase chain reaction. The following patient characteristics were obtained from records: age, gender, type of symptoms, onset of symptoms, neutrophil lymphocyte ratio (NLR), absolute lymphocyte count, chest x-ray abnormalities, lung involvement, type of lesion, radiographic assessment of the quantity of lung edema (RALE) score, and mortality. Data were analyzed using SPSS 25.0. Results Multivariate analysis showed that age >50 years ( p=0.043), NLR score >5.8 ( p=0.016) and RALE score >2 ( p=0.002) can predict the mortality of COVID-19 patients in the hospital. ROC curve analysis of the score ability to predict mortality showed an area under the curve of 0.794. The cut-off point is 4.5, with a sensitivity of 96.7% and specificity of 49.4% to predict the mortality of COVID-19 patient in the hospital. Conclusions Age, NLR score and RALE score were associated with mortality of COVID-19 patients in the hospital and might be used as a predictor for mortality of COVID-19 patients in health care centre where radiologists are available. The prediction score may be useful for frontline physicians to effectively manage patients with a higher score to prevent mortality.

Selective anti-ErbB3 aptamer modified sorafenib microparticles: In vitro and in vivo toxicity assessment.

The delivery of aptamer modified therapeutic moieties to specific tissue sites has become one of the major therapeutic choices to reduce the toxicity of inhibitory drugs. Bearing this in mind, the current study was designed using sorafenib (SFB) encapsulated microparticles (MP) prepared with biodegradable poly (D, L-lactic-co-glycolic acid) (PLGA) copolymer. The surfaces of these microparticles were modified with RNA aptamer having a binding affinity towards ErbB3 receptors. SFB-loaded MP (MPS) were prepared by o/w solvent evaporation method and the surface was coupled with the amino group of aptamer by EDC/NHS chemistry. Physiochemical investigations were done by dynamic light scattering, scanning electron microscopy and FTIR. In vitro apoptosis assay, cell viability assay and metastatic progression showed a significant decrease (p < 0.001) in vitro cell viability for MPS and MPS-Apt as compared to MP. The synergistic combination of SFB and aptamer also decreased the metastatic progression of cells for an extended period. Microparticles were also evaluated for in vivo toxicity in female BALB/c mice. It was evident that the presence of aptamer decreased the generalized toxicity of MPS-Apt, as measured by mean body weight loss and blood profiles, keeping all the blood formed elements level within acceptable limits. The histopathological investigations showed some necrotic and pyknotic bodies. In a similar fashion, liver function test and renal function tests showed pronounced effects of formulations on vital organs.

Robust Hidden Markov Model based intelligent blood vessel detection of fundus images.

In this paper, we consider the challenging problem of detecting retinal vessel networks. Precise detection of retinal vessel networks is vital for accurate eye disease diagnosis. Most of the blood vessel tracking techniques may not properly track vessels in presence of vessels' occlusion. Owing to problem in sensor resolution or acquisition of fundus images, it is possible that some part of vessel may occlude. In this scenario, it becomes a challenging task to accurately trace these vital vessels. For this purpose, we have proposed a new robust and intelligent retinal vessel detection technique on Hidden Markov Model. The proposed model is able to successfully track vessels in the presence of occlusion. The effectiveness of the proposed technique is evaluated on publically available standard DRIVE dataset of the fundus images. The experiments show that the proposed technique not only outperforms the other state of the art methodologies of retinal blood vessels segmentation, but it is also capable of accurate occlusion handling in retinal vessel networks. The proposed technique offers better average classification accuracy, sensitivity, specificity, and area under the curve (AUC) of 95.7%, 81.0%, 97.0%, and 90.0% respectively, which shows the usefulness of the proposed technique.