Use of manganese oxide nanoparticle (MnO2 NPs) and Pseudomonas putida for the management of wilt disease complex of carrot.

Wilt disease complex of carrot is caused by Ralstonia solancearum and Meloidogyne incognita and is responsible for considerable yield loss. Manganese oxide nanoparticle (MnO2 NPs) and Pseudomonas putida were used alone and in combination for the management of wilt disease complex. In vitro, MnO2 NPs 0.10 g.L-l caused 49.36% reduction in hatching and 14.23% mortality of second stage juveniles (J2) of M. incognita while paper disc dipped in MnO2 NPs suspension caused 0.51 mm inhibition zone around R. solanacearum in nutrient agar medium. Inoculation of P. putida to plants with pathogens caused a similar increase in plant growth, chlorophyll and carotenoid contents as caused by foliar spray with 0.10 g.L-1 MnO2 NPs. Use of P. putida with NPs foliar spray to plants with pathogens caused a greater increase in plant growth, chlorophyll and carotenoid contents than with P. putida or NPs foliar spray. Inoculation of M. incognita/R. solanacearum/M. incognita plus R. solanacearum/P. putida/MnO2 NPs and MnO2 NPs plus P. putida caused increase in proline contents. Root colonization by P. putida was reduced in plants with test pathogens. Foliar application of MnO2 NPs and P. putida reduce wilt disease indices. Galling and populations of M. incognita was also reduced in plants co-inoculated with R. solanacearum. The greatest reduction in nematode populations and galling was observed in plants with NPs spray together with P. putida. Principal component analysis demonstrated a clear influence of NPS and P. putida and their combination on various studied parameters in diseased plants.

A Vehicle-Edge-Cloud Framework for Computational Analysis of a Fine-Tuned Deep Learning Model.

The cooperative, connected, and automated mobility (CCAM) infrastructure plays a key role in understanding and enhancing the environmental perception of autonomous vehicles (AVs) driving in complex urban settings. However, the deployment of CCAM infrastructure necessitates the efficient selection of the computational processing layer and deployment of machine learning (ML) and deep learning (DL) models to achieve greater performance of AVs in complex urban environments. In this paper, we propose a computational framework and analyze the effectiveness of a custom-trained DL model (YOLOv8) when deployed in diverse devices and settings at the vehicle-edge-cloud-layered architecture. Our main focus is to understand the interplay and relationship between the DL model's accuracy and execution time during deployment at the layered framework. Therefore, we investigate the trade-offs between accuracy and time by the deployment process of the YOLOv8 model over each layer of the computational framework. We consider the CCAM infrastructures, i.e., sensory devices, computation, and communication at each layer. The findings reveal that the performance metrics results (e.g., 0.842 mAP@0.5) of deployed DL models remain consistent regardless of the device type across any layer of the framework. However, we observe that inference times for object detection tasks tend to decrease when the DL model is subjected to different environmental conditions. For instance, the Jetson AGX (non-GPU) outperforms the Raspberry Pi (non-GPU) by reducing inference time by 72%, whereas the Jetson AGX Xavier (GPU) outperforms the Jetson AGX ARMv8 (non-GPU) by reducing inference time by 90%. A complete average time comparison analysis for the transfer time, preprocess time, and total time of devices Apple M2 Max, Intel Xeon, Tesla T4, NVIDIA A100, Tesla V100, etc., is provided in the paper. Our findings direct the researchers and practitioners to select the most appropriate device type and environment for the deployment of DL models required for production.

Collaborative Perception-The Missing Piece in Realizing Fully Autonomous Driving.

Environment perception plays a crucial role in enabling collaborative driving automation, which is considered to be the ground-breaking solution to tackling the safety, mobility, and sustainability challenges of contemporary transportation systems. Despite the fact that computer vision for object perception is undergoing an extraordinary evolution, single-vehicle systems' constrained receptive fields and inherent physical occlusion make it difficult for state-of-the-art perception techniques to cope with complex real-world traffic settings. Collaborative perception (CP) based on various geographically separated perception nodes was developed to break the perception bottleneck for driving automation. CP leverages vehicle-to-vehicle and vehicle-to-infrastructure communication to enable vehicles and infrastructure to combine and share information to comprehend the surrounding environment beyond the line of sight and field of view to enhance perception accuracy, lower latency, and remove perception blind spots. In this article, we highlight the need for an evolved version of the collaborative perception that should address the challenges hindering the realization of level 5 AD use cases by comprehensively studying the transition from classical perception to collaborative perception. In particular, we discuss and review perception creation at two different levels: vehicle and infrastructure. Furthermore, we also study the communication technologies and three different collaborative perception message-sharing models, their comparison analyzing the trade-off between the accuracy of the transmitted data and the communication bandwidth used for data transmission, and the challenges therein. Finally, we discuss a range of crucial challenges and future directions of collaborative perception that need to be addressed before a higher level of autonomy hits the roads.

Analyzing Factors Influencing Situation Awareness in Autonomous Vehicles-A Survey.

Autonomous driving of higher automation levels asks for optimal execution of critical maneuvers in all environments. A crucial prerequisite for such optimal decision-making instances is accurate situation awareness of automated and connected vehicles. For this, vehicles rely on the sensory data captured from onboard sensors and information collected through V2X communication. The classical onboard sensors exhibit different capabilities and hence a heterogeneous set of sensors is required to create better situation awareness. Fusion of the sensory data from such a set of heterogeneous sensors poses critical challenges when it comes to creating an accurate environment context for effective decision-making in AVs. Hence this exclusive survey analyses the influence of mandatory factors like data pre-processing preferably data fusion along with situation awareness toward effective decision-making in the AVs. A wide range of recent and related articles are analyzed from various perceptive, to pick the major hiccups, which can be further addressed to focus on the goals of higher automation levels. A section of the solution sketch is provided that directs the readers to the potential research directions for achieving accurate contextual awareness. To the best of our knowledge, this survey is uniquely positioned for its scope, taxonomy, and future directions.

Unlocking Mutual Gains-An Experimental Study on Collaborative Autonomous Driving in Urban Environment.

Convoy driving, a specialized form of collaborative autonomous driving, offers a promising solution to the multifaceted challenges that transportation systems face, including traffic congestion, pollutant emissions, and the coexistence of connected autonomous vehicles (CAVs) and human-driven vehicles on the road, resulting in mixed traffic flow. While extensive research has focused on the collective societal benefits of convoy driving, such as safety and comfort, one critical aspect that has been overlooked is the willingness of individual vehicles to participate in convoy formations. While the collective benefits are evident, individual vehicles may not readily embrace this paradigm shift without explicit tangible benefits and incentives to motivate them. Moreover, the objective of convoy driving is not solely to deliver societal benefits but also to provide incentives and reduce costs at the individual level. Therefore, this research bridges this gap by designing and modeling the societal benefits, including traffic flow optimization and pollutant emissions, and individual-level incentives necessary to promote convoy driving. We model a fundamental diagram of mixed traffic flow, considering various factors such as CAV penetration rates, coalition intensity, and coalition sizes to investigate their relationships and their impact on traffic flow. Furthermore, we model the collaborative convoy driving problem using the coalitional game framework and propose a novel utility function encompassing incentives like car insurance discounts, traffic fine reductions, and toll discounts to encourage vehicle participation in convoys. Our experimental findings emphasize the need to strike a balance between CAV penetration rate, coalition intensity, size, and speed to realize the benefits of convoy driving at both collective and individual levels. This research aims to align the interests of road authorities seeking sustainable transportation systems and individual vehicle owners desiring tangible benefits, envisioning a future where convoy driving becomes a mutually beneficial solution.

Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3GPP Standards.

Cellular vehicle-to-everything (C-V2X) is one of the enabling vehicular communication technologies gaining momentum from the standardization bodies, industry, and researchers aiming to realize fully autonomous driving and intelligent transportation systems. The 3rd Generation Partnership Project (3GPP) standardization body has actively been developing the standards evolving from 4G-V2X to 5G-V2X providing ultra-reliable low-latency communications and higher throughput to deliver the solutions for advanced C-V2X services. In this survey, we analyze the 3GPP standard documents relevant to V2X communication to present the complete vision of 3GPP-enabled C-V2X. To better equip the readers with knowledge of the topic, we describe the underlying concepts and an overview of the evolution of 3GPP C-V2X standardization. Furthermore, we provide the details of the enabling concepts for V2X support by 3GPP. In this connection, we carry out an exhaustive study of the 3GPP standard documents and provide a logical taxonomy of C-V2X related 3GPP standard documents divided into three categories: 4G, 4G & 5G, and 5G based V2X services. We provide a detailed analysis of these categories discussing the system architecture, network support, key issues, and potential solution approaches supported by the 3GPP. We also highlight the gap and the need for intelligence in the execution of different operations to enable the use-case scenarios of Level-5 autonomous driving. We believe, the paper will equip readers to comprehend the technological standards for the delivery of different ITS services of the higher level of autonomous driving.

Green banking practices, bank reputation, and environmental awareness: evidence from Islamic banks in a developing economy.

Fulfilling the international considerations of environment, societal, and governance challenges, the financial industry, especially banks, has initiated "Go Green" practices to help sustain the environment and enhance "banking" across the globe. Amidst the green and climate-friendly drives, there is scarce literature highlighting the banks' green practices, environmental awareness, and their effects on bank reputation, especially the reputation of Islamic banks. This study aims to investigate the green banking practices of Islamic banks in a developing Islamic country. Focusing on the greening ambitions of banks, this study argues that the reputation of Islamic banks can be better enhanced through adopting green banking initiatives that will beget better climatic outcomes in Muslim societies. Therefore, the study illumes green banking practices and their impact on the reputation of Islamic banks in Pakistan. Moreover, this study checks the moderation effect of employees' environmental awareness on banks' reputation. The study used deductive rationale and quantified the employees' data to unravel their go-green perceptions and bank green activities. In this regard, the 390 response data, collected through a survey from the employees of Islamic banks, were analyzed through Smart-PLS, using structural equation modeling technique. The study finds that banks' employees-related practices (ERPs), daily operations-related practices (DORPs), customers-related practices (CRPs), and banks' policy-related practices (PRPs) have a significant positive influence on bank reputation. The authors also find that there is a significant moderating impact of environmental awareness between the relationships of ERPs, DORPs, CRPs, PRPs, and bank reputation. The study might increase understating and enlighten regulators and bank management to sustainably transform their operations to green banking practices, particularly adding to the environmental sustainability in Pakistan.

How Do Autonomous Vehicles Decide?

The advancement in sensor technologies, mobile network technologies, and artificial intelligence has pushed the boundaries of different verticals, e.g., eHealth and autonomous driving. Statistics show that more than one million people are killed in traffic accidents yearly, where the vast majority of the accidents are caused by human negligence. Higher-level autonomous driving has great potential to enhance road safety and traffic efficiency. One of the most crucial links to building an autonomous system is the task of decision-making. The ability of a vehicle to make robust decisions on its own by anticipating and evaluating future outcomes is what makes it intelligent. Planning and decision-making technology in autonomous driving becomes even more challenging, due to the diversity of the dynamic environments the vehicle operates in, the uncertainty in the sensor information, and the complex interaction with other road participants. A significant amount of research has been carried out toward deploying autonomous vehicles to solve plenty of issues, however, how to deal with the high-level decision-making in a complex, uncertain, and urban environment is a comparatively less explored area. This paper provides an analysis of decision-making solutions approaches for autonomous driving. Various categories of approaches are analyzed with a comparison to classical decision-making approaches. Following, a crucial range of research gaps and open challenges have been highlighted that need to be addressed before higher-level autonomous vehicles hit the roads. We believe this survey will contribute to the research of decision-making methods for autonomous vehicles in the future by equipping the researchers with an overview of decision-making technology, its potential solution approaches, and challenges.

Rebirth of Distributed AI-A Review of eHealth Research.

The envisioned smart city domains are expected to rely heavily on artificial intelligence and machine learning (ML) approaches for their operations, where the basic ingredient is data. Privacy of the data and training time have been major roadblocks to achieving the specific goals of each application domain. Policy makers, the research community, and the industrial sector have been putting their efforts into addressing these issues. Federated learning, with its distributed and local training approach, stands out as a potential solution to these challenges. In this article, we discuss the potential interplay of different technologies and AI for achieving the required features of future smart city services. Having discussed a few use-cases for future eHealth, we list design goals and technical requirements of the enabling technologies. The paper confines its focus on federated learning. After providing the tutorial on federated learning, we analyze the Federated Learning research literature. We also highlight the challenges. A solution sketch and high-level research directions may be instrumental in addressing the challenges.

Collaborative Autonomous Driving-A Survey of Solution Approaches and Future Challenges.

Sooner than expected, roads will be populated with a plethora of connected and autonomous vehicles serving diverse mobility needs. Rather than being stand-alone, vehicles will be required to cooperate and coordinate with each other, referred to as cooperative driving executing the mobility tasks properly. Cooperative driving leverages Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication technologies aiming to carry out cooperative functionalities: (i) cooperative sensing and (ii) cooperative maneuvering. To better equip the readers with background knowledge on the topic, we firstly provide the detailed taxonomy section describing the underlying concepts and various aspects of cooperation in cooperative driving. In this survey, we review the current solution approaches in cooperation for autonomous vehicles, based on various cooperative driving applications, i.e., smart car parking, lane change and merge, intersection management, and platooning. The role and functionality of such cooperation become more crucial in platooning use-cases, which is why we also focus on providing more details of platooning use-cases and focus on one of the challenges, electing a leader in high-level platooning. Following, we highlight a crucial range of research gaps and open challenges that need to be addressed before cooperative autonomous vehicles hit the roads. We believe that this survey will assist the researchers in better understanding vehicular cooperation, its various scenarios, solution approaches, and challenges.

Regression to the mean for the bivariate binomial distribution.

Regression to the mean (RTM) occurs when subjects having relatively high or low measurements are remeasured and found closer to the population mean. This phenomenon can potentially lead to an inaccurate conclusion in a pre-post study design. Expressions are available for quantifying RTM when the distribution of pre and post observations are bivariate normal and bivariate Poisson. However, situations exist where the response variables are the number of successes in a fixed number of trials and follow the bivariate binomial distribution. In this article, expressions for quantifying RTM effects are derived when the underlying distribution is the bivariate binomial. Unlike the normal and Poisson distributions, the correlation between pre and post observations can be either negative or positive under the bivariate binomial distribution and the severity of RTM is greater in the former case. The percentage relative difference is used to highlight the differences in quantifying RTM under the bivariate binomial distribution and normal and Poisson approximations to the bivariate binomial distribution. Expressions for estimating RTM using the method of maximum likelihood along with its asymptotic distribution are derived. A simulation study is conducted to empirically assess the statistical properties of the RTM estimator and its asymptotic distribution. Data examples using the number of obese individuals and the number of nonconforming cardboard cans are discussed.

Quantifying the regression to the mean effect in Poisson processes.

Regression to the mean (RTM) can occur whenever an extreme observation is selected from a population and a later observation is closer to the population mean. A consequence of this phenomenon is that natural variability can be mistaken as real change. Simple expressions are available to quantify RTM when the underlying distribution is bivariate normal. However, there are many real-world situations, which are better approximated as a Poisson process. Examples include the number of hard disk failures during a year, the number of cargo ships damaged by waves, daily homicide counts in California, and the number of deaths per quarter attributable to acquired immune deficiency syndrome in Australia. In this paper, we derive expressions for quantifying RTM effects for the bivariate Poisson distribution for both the homogeneous and inhomogeneous cases. Statistical properties of our derivations have been evaluated through a simulation study. The asymptotic distributions of RTM estimators have been derived. The RTM effect for the number of people killed in road accidents in different regions of New South Wales (Australia) is estimated using maximum likelihood.

Accounting for regression to the mean under the bivariate t-distribution.

Regression to the mean occurs when an unusual observation is followed by a more typical outcome closer to the population mean. In pre- and post-intervention studies, treatment is administered to subjects with initial measurements located in the tail of a distribution, and a paired sample t-test can be utilized to assess the effectiveness of the intervention. The observed change in the pre-post means is the sum of regression to the mean and treatment effects, and ignoring regression to the mean could lead to erroneous conclusions about the effectiveness of the treatment effect. In this study, formulae for regression to the mean are derived, and maximum likelihood estimation is employed to numerically estimate the regression to the mean effect when the test statistic follows the bivariate t-distribution based on a baseline criterion or a cut-off point. The pre-post degrees of freedom could be equal but also unequal such as when there is missing data. Additionally, we illustrate how regression to the mean is influenced by cut-off points, mixing angles which are related to correlation, and degrees of freedom. A simulation study is conducted to assess the statistical properties of unbiasedness, consistency, and asymptotic normality of the regression to the mean estimator. Moreover, the proposed methods are compared with an existing one assuming bivariate normality. The p-values are compared when regression to the mean is either ignored or accounted for to gauge the statistical significance of the paired t-test. The proposed method is applied to real data concerning schizophrenia patients, and the observed conditional mean difference called the total effect is decomposed into the regression to the mean and treatment effects.

Designing and synthesis of perovskite nanocrystals: a promising wide-spectrum solar light-responsive photocatalyst and lead ion sensor.

Perovskites are an emerging material with a variety of applications, ranging from their solar light conversion capability to their sensing efficiency. In current study, perovskite nanocrystals (PNCs) were designed using theoretical density functional theory (DFT) analysis. Moreover, the theoretically designed PNCs were fabricated and confirmed by various characterization techniques. The calculated optical bandgap from UV-Vis and fluorescence spectra were 2.15 and 2.05 eV, respectively. The average crystallite size of PNCs calculated from Scherrer equation was 15.18 nm, and point of zero charge (PZC) was obtained at pH 8. The maximum eosin B (EB) removal efficiency by PNCs was 99.56% at optimized conditions following first-order kinetics with 0.98 R2 value. The goodness of the response surface methodology (RSM) model was confirmed from analysis of variance (ANOVA), with the experimental F value (named after Ronald Fisher) of 194.66 being greater than the critical F value F0.05, 14, 14 = 2.48 and a lack of fit value of 0.0587. The Stern-Volmer equation with a larger Ksv value of 1.303710 × 10 6 for Pb2+ suggests its greater sensitivity for Pb2+ among the different metals tested.

Some improved ratio, product, and regression estimators of finite population mean when using minimum and maximum values.

Efficient estimation of finite population mean is carried out by using the auxiliary information meaningfully. In this paper we have suggested some modified ratio, product, and regression type estimators when using minimum and maximum values. Expressions for biases and mean squared errors of the suggested estimators have been derived up to the first order of approximation. The performances of the suggested estimators, relative to their usual counterparts, have been studied, and improved performance has been established. The improvement in efficiency by making use of maximum and minimum values has been verified numerically.

Potential of metal and metal oxide nanoparticles in plant disease diagnostics and management: Recent advances and challenges.

Plant diseases caused by phytopathogens are a severe threat to global food production. Management of plant diseases mostly rely on the application of pesticides which have several adverse effects on the ecosystem. Innovative and high-performance diagnostic tools are useful for the early detection of phytopathogens. Emerging role of metal and metal oxides nanoparticles (NPs) in plant disease diagnostics to combat crop diseases has been described. These NPs constitute new weapons against plant pathogens and facilitate the early diagnosis/management of crop diseases specifically in resource-poor conditions. The interactions between NPs, phytopathogens and plants showed great diversity and multiplicity which reduces chances of the development of resistant pathogen strains. The present article discusses the available literature as well as challenges and research gaps that are essential in the successful utilization of metal and metal oxide NPs for precise and timely detection and management of plant diseases.

Assessment of Geographical Distribution of Emerging Zoonotic Toxoplasma gondii Infection in Women Patients Using Geographical Information System (GIS) in Various Regions of Khyber Pakhtunkhwa (KP) Province, Pakistan.

Toxoplasmosis is a zoonotic parasitic disease caused by T. gondii, an obligate intracellular apcomplexan zoonotic parasite that is geographically worldwide in distribution. The parasite infects humans and all warm-blooded animals and is highly prevalent in various geographical regions of the world, including Pakistan. The current study addressee prevalence of Toxoplasma infection in women in various geographical regions, mapping of endemic division and t district of Khyber Pakhtunkhwa province through geographical information system (GIS) in order to locate endemic regions, monitor seasonal and annual increase in prevalence of infection in women patients. Setting: Tertiary hospitals and basic health care centers located in 7 divisions and 24 districts of Khyber Pakhtunkhwa (KP) province of Pakistan. During the current study, 3586 women patients from 7 divisions and 24 districts were clinically examined and screened for prevalence of T. gondii infection. Participants were screened for Toxoplasma infection using ICT and latex agglutination test (LAT) as initial screening assay, while iELISA (IgM, IgG) was used as confirmatory assay. Mapping of the studied region was developed by using ArcGIS 10.5. Spatial analyst tools were applied by using Kriging/Co-kriging techniques, followed by IDW (Inverse Distance Weight) techniques. Overall prevalence of T. gondii infection was found in 881 (24.56%) patients. A significant (<0.05) variation was found in prevalence of infection in different divisions and districts of the province. Prevalence of infection was significantly (<0.05) high 129 (30.07%) in Kohat Division, followed by 177 (29.06%), 80 (27.87%), 287 (26.72%), 81 (21.21%), 47 (21.07%), and 80 (13.71%) cases in Hazara Division, D.I Khan Division, Malakand Division, Mardan Division, Bannu Division, and Peshawar Division. Among various districts, a significant variation (<0.05) was found in prevalence of infection. Prevalence of infection was significantly (<0.05) high 49 (44.95%) in district Karak, while low (16 (10.81%) in district Nowshera. No significant (>0.05) seasonal and annual variation was found in prevalence of Toxoplasma infection. LAT, ICT and ELISA assays were evaluated for prevalence of infection, which significantly (<0.05) detected T. gondii antibodies. LAT, ICT and ELISA assays significantly (<0.05) detected infection, while no significant (>0.05) difference was found between positivity of LAT and ICT assays. A significant difference (<0.05) was found in positivity of Toxoplasma-specific (IgM), (IgG) and (IgM, IgG) immunoglobulin by ICT and ELISA assay. The current study provides comprehensive information about geographical distribution, seasonal and annual variation of Toxoplasmosis infection in various regions of Khyber Pakhtunkhwa province of Pakistan. Infection of T. gondii in women shows an alarming situation of disease transmission from infected animals in the studied region, which is not only a serious and potential threat for adverse pregnancy outcomes, but also cause socioeconomic burden and challenges for various public and animal health organizations in Pakistan and across the country.

Regulation of ERK2 phosphorylation by histamine in splenocytes.

Histamine is implicated in allergic disease and asthma and ERK1/2 is involved in allergic inflammation including Th2 differentiation and proliferation. This study was designed to study the effects of histamine on ERK1/2 phosphorylation in splenocytes. C57/BL6 splenocytes were treated with different concentrations of histamine (10(-4) to 10(-11) M). Histamine (10(-4) M) increased ERK2 phosphorylation. There was, however, no significant effect seen at other concentrations (10(-11) to 10(-6) M). Surprisingly, H1 receptor agonist ß-histine (10(-5) M), H2 agonist amthamine (10(-5) M), H3 agonist methimepip (10(-6) M), and H4 agonist 4-methyl histamine (10(-6) M), all increased ERK2 phosphorylation. H1R antagonist pyrilamine (10(-6) M), H2R antagonist ranitidine (10(-5) M), H3/H4R antagonist thioperamide (10(-6) M), and H3R antagonist clobenpropit (10(-5) M) inhibited histamine-mediated ERK2 phosphorylation suggesting that all four histamine receptor subtypes played some role in this phosphorylation. Because tumor necrosis factor-α (TNF-α) causes phosphorylation of ERK1/2, we investigated whether histamine acted via secretion of TNF-α to affect ERK1/2 phosphorylation. As a consequence, TNF-α knockout mice were used and we found that there was inhibition of ERK1 and ERK2 phosphorylation by H2, H3, and H4 agonists. This was in contrast to the wild-type splenocytes where histamine augmented the phosphorylation of ERK2 via H2, H3, and H4 receptors. In TNF-α knockout mice histamine did not affect the phosphorylation of ERK2 via H1 receptors. The results suggested that histamine indirectly caused the ERK2 phosphorylation via its effects on the secretion of TNF-α and these effects were mediated via H1, H2, H3, and H4 receptors.

Outcomes of Diffuse Large B-Cell Non-Hodgkin's Lymphoma After Gemcitabine-Based Second Salvage Chemotherapy: A Single-Center Study.

Background Diffuse large B-cell lymphoma (DLBCL) is the most common subtype of non-Hodgkin's lymphoma with a five-year survival of 60%-70% with chemoimmunotherapy consisting of the R-CHOP combination (rituximab, cyclophosphamide, vincristine, doxorubicin, and prednisone), with a relapse/refractory rate of 20-50%. Salvage therapy with HDT-ASCT is the treatment of choice for patients with relapsed/refractory disease with a success rate of 50%-60%. Patients who do not respond to the first salvage regimen or who relapsed after the first salvage regimen, with or without high-dose chemotherapy (HDT)-autologous stem cell transplantation (ASCT), have poor overall responses and survival and should be offered novel therapies. The objective of our study was to evaluate responses to second salvage, gemcitabine-based therapy with or without HDT-ASCT in a resource-limited setting. Materials and methods This was a retrospective study, including 55 patients aged >18 years, diagnosed with DLBCL and having received gemcitabine-based second salvage chemotherapy. Results The median age was 34 years, only one patient achieved progression-free survival (PFS) of >12 months with ORR of 27% to two cycles of gemcitabine-based combination, two years PFS and OS of 9.6% and 34%, respectively, and a median PFS and OS of four months and 13 months, respectively. Conclusion DLBCL patients, refractory to first-line and first salvage chemotherapy, should be considered for novel therapies or opt for palliative care rather than second salvage chemotherapy and HDT-ASCT, which results in poor overall response and significant toxicities.

Comparative Study of Protective Effect of Cimetidine and Verapamil on Paracetamol-Induced Hepatotoxicity in Mice.

Paracetamol, chemically known as acetaminophen, if taken in higher doses has hepatotoxic potential. Cimetidine by inhibiting the cytochromal enzymes and reducing the production of the toxic metabolite can reduce the hepatotoxic potential while Verapamil can act as a hepatoprotective by maintaining calcium homeostasis. The present study was conducted to study the hepatoprotective activity of Cimetidine and Verapamil against the toxicity induced by paracetamol. In addition to the group receiving only distilled water or 300 mg/kg paracetamol additional groups were added treated with 150 mg/kg Cimetidine and Verapamil alone or both. The Liver function tests and histopathology revealed hepatotoxicity in the group receiving paracetamol (PCM) while normal parameters were observed in the groups receiving Cimetidine and Verapamil. Our results strongly suggested that Cimetidine and Verapamil possess hepatoprotective potential against paracetamol induced hepatotoxicity.