Chitkara University

Doxorubicin (DOX) constitutes the major constituent of anti-cancer treatment regimens currently in clinical use. However, the precise mechanisms of DOX's action are not fully understood. Emerging evidence points to the pleiotropic anticancer activity of DOX, including its contribution to DNA damage, reactive oxygen species (ROS) production, apoptosis, senescence, autophagy, ferroptosis, and pyroptosis induction, as well as its immunomodulatory role. This review aims to collect information on the anticancer mechanisms of DOX as well as its influence on anti-tumor immune response, providing a rationale behind the importance of DOX in modern cancer therapy.

Abstract magnified image J. Heterocyclic Chem., (2010).

The Generative Pre-trained Transformer (GPT) represents a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, its enabling technologies, their impact on various applications, emerging challenges, and potential solutions.

Tomato is one of the most essential and consumable crops in the world. Tomatoes differ in quantity depending on how they are fertilized. Leaf disease is the primary factor impacting the amount and quality of crop yield. As a result, it is critical to diagnose and classify these disorders appropriately. Different kinds of diseases influence the production of tomatoes. Earlier identification of these diseases would reduce the disease's effect on tomato plants and enhance good crop yield. Different innovative ways of identifying and classifying certain diseases have been used extensively. The motive of work is to support farmers in identifying early-stage diseases accurately and informing them about these diseases. The Convolutional Neural Network (CNN) is used to effectively define and classify tomato diseases. Google Colab is used to conduct the complete experiment with a dataset containing 3000 images of tomato leaves affected by nine different diseases and a healthy leaf. The complete process is described: Firstly, the input images are preprocessed, and the targeted area of images are segmented from the original images. Secondly, the images are further processed with varying hyper-parameters of the CNN model. Finally, CNN extracts other characteristics from pictures like colors, texture, and edges, etc. The findings demonstrate that the proposed model predictions are 98.49% accurate.

Here a combined DFT, SCAPS-1D, and wxAMPS frameworks are used to investigate the optimized designs of Cs 2 BiAgI 6 lead-free double perovskite-based solar cells from ninety-six device structures using various electron and hole charge transport layers.

Machine Learning (ML) is used in healthcare sectors worldwide. ML methods help in the protection of heart diseases, locomotor disorders in the medical data set. The discovery of such essential data helps researchers gain valuable insight into how to utilize their diagnosis and treatment for a particular patient. Researchers use various Machine Learning methods to examine massive amounts of complex healthcare data, which aids healthcare professionals in predicting diseases. In this research, we are using an online UCI dataset with 303 rows and 76 properties. Approximately 14 of these 76 properties are selected for testing, which is necessary to validate the performances of different methods. The isolation forest approach uses the data set’s most essential qualities and metrics to standardize the information for better precision. This analysis is based on supervised learning methods, i.e., Naive Bayes, SVM, Logistic regression, Decision Tree Classifier, Random Forest, and K- Nearest Neighbor. The experimental results demonstrate the strength of KNN with eight neighbours order to test the effectiveness, sensitivity, precision, and accuracy, F1-score; as compared to other methods, i.e., Naive Bayes, SVM (Linear Kernel), Decision Tree Classifier with 4 or 18 features, and Random Forest classifiers.

Recently, fast dissolving films are gaining interest as an alternative of fast dissolving tablets. The films are designed to dissolve upon contact with a wet surface, such as the tongue, within a few seconds, meaning the consumer can take the product without need for additional liquid. This convenience provides both a marketing advantage and increased patient compliance. As the drug is directly absorbed into systemic circulation, degradation in gastrointestinal tract and first pass effect can be avoided. These points make this formulation most popular and acceptable among pediatric and geriatric patients and patients with fear of choking. Over-the-counter films for pain management and motion sickness are commercialized in the US markets. Many companies are utilizing transdermal drug delivery technology to develop thin film formats. In the present review, recent advancements regarding fast dissolving buccal film formulation and their evaluation parameters are compiled.

Pneumonia is a viral infection which affects a significant proportion of individuals, especially in developing and penurious countries where contamination, overcrowded, and unsanitary living conditions are widespread, along with the lack of healthcare infrastructures. Pneumonia produces pericardial effusion, a disease wherein fluids fill the chest and create inhaling problems. It is a difficult step to recognize the presence of pneumonia quickly in order to receive treatment services and improve survival chances. Deep learning, is a field of artificial intelligence which is used in the successful development of prediction models. There are various ways of detecting pneumonia such as CT-scan, pulse oximetry, and many more among which the most common way is X-ray tomography. On the other hand, examining chest X-rays (CXR) is a tough process susceptible to subjective variability. In this work, a deep learning(DL) model using VGG16 is utilized for detecting and classifying pneumonia using two CXR image datasets. The VGG16 with Neural Networks (NN) provides an accuracy value of 92.15%, recall as 0.9308, precision as 0.9428, and F1-Score0.937 for the first dataset. Furthermore, the experiment using NN with VGG16 has been performed on another CXR dataset containing 6,436 images of pneumonia, normal and covid-19. The results for the second dataset provide accuracy, recall, precision, and F1-score as 95.4%, 0.954, 0.954, and 0.954, respectively. The research outcome exhibits that VGG16 with NN provides better performance than VGG16 with Support Vector Machine (SVM), VGG16 with K-Nearest Neighbor (KNN), VGG16 with Random Forest (RF), and VGG16 with Naïve Bayes (NB) for both datasets. Further, the proposed work results exhibit improved performance results for both datasets 1 and 2 in comparison to existing models.

Abstract Academic and corporate interest in ethical leadership, corporate social responsibility (CSR), and firm performance has attracted a lot of attention in recent years. In fact, many research papers and journal special issues have been focused on these three domains. In this context, this paper conducts a systematic review on the concepts of ethical leadership and CSR and their impact on firm performance. 114 papers published over a period of 58 years (1958–2016) were selected and analyzed according to descriptive and content perspectives to propose a conceptual framework and define a future research agenda. In fact, the main results allow us to derive six main propositions representing possible areas of investigation to direct research on the topic. More in details, the body of literature highlights that financial factors are the main barriers affecting the adoption of CSR practices. On the contrary, internal and external environment was found to represent a critical success factor in the adoption of CSR practices. Finally, the results highlight that personal values have impact on ethical leadership that in turn has direct positive impact on CSR and direct and indirect impact on firm performance.

Nutraceuticals are the nourishing components (hybrid of nutrition and pharmaceuticals) that are biologically active and possess capability for maintaining optimal health and benefits. These products play a significant role in human health care and its endurance, most importantly for the future therapeutic development. Nutraceuticals have received recognition due to their nutritional benefits along with therapeutic effects and safety profile. Nutraceuticals are globally growing in the field of services such as health care promotion, disease reduction, etc. Various drug nutraceutical interactions have also been elaborated with various examples in this review. Several patents on nutraceuticals in agricultural applications and in various diseases have been stated in the last section of review, which confirms the exponential growth of nutraceuticals' market value. Nutraceuticals have been used not only for nutrition but also as a support therapy for the prevention and treatment of various diseases, such as to reduce side effects of cancer chemotherapy and radiotherapy. Diverse novel nanoformulation approaches tend to overcome challenges involved in formulation development of nutraceuticals. Prior information on various interactions with drugs may help in preventing any deleterious effects of nutraceuticals products. Nanotechnology also leads to the generation of micronized dietary products and other nutraceutical supplements with improved health benefits. In this review article, the latest key findings (clinical studies) on nutraceuticals that show the therapeutic action of nutraceutical's bioactive molecules on various diseases have also been discussed.

CsSnI3 is considered to be a viable alternative to lead (Pb)-based perovskite solar cells (PSCs) due to its suitable optoelectronic properties. The photovoltaic (PV) potential of CsSnI3 has not yet been fully explored due to its inherent difficulties in realizing defect-free device construction owing to the nonoptimized alignment of the electron transport layer (ETL), hole transport layer (HTL), efficient device architecture, and stability issues. In this work, initially, the structural, optical, and electronic properties of the CsSnI3 perovskite absorber layer were evaluated using the CASTEP program within the framework of the density functional theory (DFT) approach. The band structure analysis revealed that CsSnI3 is a direct band gap semiconductor with a band gap of 0.95 eV, whose band edges are dominated by Sn 5s/5p electrons After performing the DFT analysis, we investigated the PV performance of a variety of CsSnI3-based solar cell configurations utilizing a one-dimensional solar cell capacitance simulator (SCAPS-1D) with different competent ETLs such as IGZO, WS2, CeO2, TiO2, ZnO, PCBM, and C60. Simulation results revealed that the device architecture comprising ITO/ETL/CsSnI3/CuI/Au exhibited better photoconversion efficiency among more than 70 different configurations. The effect of the variation in the absorber, ETL, and HTL thickness on PV performance was analyzed for the above-mentioned configuration thoroughly. Additionally, the impact of series and shunt resistance, operating temperature, capacitance, Mott–Schottky, generation, and recombination rate on the six superior configurations were evaluated. The J–V characteristics and the quantum efficiency plots for these devices are systematically investigated for in-depth analysis. Consequently, this extensive simulation with validation results established the true potential of CsSnI3 absorber with suitable ETLs including ZnO, IGZO, WS2, PCBM, CeO2, and C60 ETLs and CuI as HTL, paving a constructive research path for the photovoltaic industry to fabricate cost-effective, high-efficiency, and nontoxic CsSnI3 PSCs.

Citrus fruit diseases have an egregious impact on both the quality and quantity of the citrus fruit production and market. Automatic detection of severity is essential for the high-quality production of fruit. In the current work, a citrus fruit dataset is preprocessed by rescaling and establishing bounding boxes with labeled image software. Then, a selective search, which combines the capabilities of both an extensive search and graph-based segmentation, is applied. The proposed deep neural network (DNN) model is trained to detect targeted areas of the disease with its severity level using citrus fruits that have been labeled with the help of a domain expert with four severity levels (high, medium, low and healthy) as ground truth. Transfer learning using VGGNet is applied to implement a multi-classification framework for each class of severity. The model predicts the low severity level with 99% accuracy, and the high severity level with 98% accuracy. The model demonstrates 96% accuracy in detecting healthy conditions and 97% accuracy in detecting medium severity levels. The result of the work shows that the proposed approach is valid, and it is efficient for detecting citrus fruit disease at four levels of severity.

Despite not being utilized as considerably as other antidepressants in the therapy of depression, the monoamine oxidase inhibitors (MAOIs) proceed to hold a place in neurodegeneration and to have a somewhat broad spectrum in respect of the treatment of neurological and psychiatric conditions. Preclinical and clinical studies on MAOIs have been developing in recent times, especially on account of rousing discoveries manifesting that these drugs possess neuroprotective activities. The altered brain levels of monoamine neurotransmitters due to monoamine oxidase (MAO) are directly associated with various neuropsychiatric conditions like Alzheimer's disease (AD). Activated MAO induces the amyloid-beta (Aβ) deposition via abnormal cleavage of the amyloid precursor protein (APP). Additionally, activated MAO contributes to the generation of neurofibrillary tangles and cognitive impairment due to neuronal loss. No matter the attention of researchers on the participation of MAOIs in neuroprotection has been on monoamine oxidase-B (MAO-B) inhibitors, there is a developing frame of proof indicating that monoamine oxidase-A (MAO-A) inhibitors may also play a role in neuroprotection. The therapeutic potential of MAOIs alongside the complete understanding of the enzyme's physiology may lead to the future advancement of these drugs.

The principle objective of formulation of lipid-based drugs is to enhance their bioavailability. The use of lipids in drug delivery is no more a new trend now but is still the promising concept. Lipid-based drug delivery systems (LBDDS) are one of the emerging technologies designed to address challenges like the solubility and bioavailability of poorly water-soluble drugs. Lipid-based formulations can be tailored to meet a wide range of product requirements dictated by disease indication, route of administration, cost consideration, product stability, toxicity, and efficacy. These formulations are also a commercially viable strategy to formulate pharmaceuticals, for topical, oral, pulmonary, or parenteral delivery. In addition, lipid-based formulations have been shown to reduce the toxicity of various drugs by changing the biodistribution of the drug away from sensitive organs. However, the number of applications for lipid-based formulations has expanded as the nature and type of active drugs under investigation have become more varied. This paper mainly focuses on novel lipid-based formulations, namely, emulsions, vesicular systems, and lipid particulate systems and their subcategories as well as on their prominent applications in pharmaceutical drug delivery.

Lead-free Cs2BiAgI6 has garnered a lot of research interest recently due to its suitability as a potential absorber layer in the solar cell (SC) architecture owing to its low cost, good stability, and high efficiency. The main highlight of this research work includes the photovoltaic (PV) performance enhancement of Cs2BiAgI6 double perovskite solar cells (PSCs) by optimizing the optoelectronic parameters of the absorber, electron transport layer (ETL), hole transport layer (HTL), and various interface layers. Solar Cell Capacitance Simulator One dimension (SCAPS-1D) numerical simulation was used to optimize the performance of Cs2BiAgI6 absorber-based SCs consisting of copper barium thiostannate (CBTS) as the HTL and TiO2, PCBM, ZnO, IGZO, SnO2, and WS2 as ETLs. The role of the non-lead cesium-based halide perovskite absorber layer in the improvement of the SC performance was systematically investigated through a variation in the thickness, doping density, and defect density of the absorber layer, ETL, and HTL. The performance of the investigated device architectures is largely dependent on the thickness of the absorber layer, acceptor density, defect density, and the combination of different ETLs and HTLs. We found that TiO2, PCBM, ZnO, IGZO, SnO2, and WS2 ETL-based optimized devices recorded a power conversion efficiency (PCE) of 23.14, 23.71, 23.69, 22.97, 23.61, and 21.72%, respectively. Furthermore, the effect of series and shunt resistances, temperature, capacitance, and Mott–Schottky for the six optimized devices was estimated along with the computation of the corresponding generation and recombination rates, current density–voltage (J–V), and quantum efficiency (QE) characteristics. The PV parameters obtained from this comprehensive analysis are finally compared with the earlier published theoretical and experimental reports on Cs2BiAgI6 absorber-based SCs.

A transcriptional regulatory nuclear factor kappa B (NF-κB) protein is a modulator of cellular biological activity via binding to a promoter region in the nucleus and transcribing various protein genes. The recent research implicated the intensive role of nuclear factor kappa B (NF-κB) in diseases like autoimmune disorder, inflammatory, cardiovascular and neurodegenerative diseases. Therefore, targeting the nuclear factor kappa B (NF-κB) protein offers a new opportunity as a therapeutic approach. Activation of IκB kinase/NF-κB signaling pathway leads to the development of various pathological conditions in human beings, such as neurodegenerative, inflammatory disorders, autoimmune diseases, and cancer. Therefore, the transcriptional activity of IκB kinase/NF- κB is strongly regulated at various cascade pathways. The nuclear factor NF-kB pathway plays a major role in the expression of pro-inflammatory genes, including cytokines, chemokines, and adhesion molecules. In response to the diverse stimuli, the cytosolic sequestered NF-κB in an inactivated form by binding with an inhibitor molecule protein (IkB) gets phosphorylated and translocated into the nucleus further transcribing various genes necessary for modifying various cellular functions. The various researches confirmed the role of different family member proteins of NF-κB implicated in expressing various genes products and mediating various cellular cascades. MicroRNAs, as regulators of NF- κB microRNAs play important roles in the regulation of the inflammatory process. Therefore, the inhibitor of NF-κB and its family members plays a novel therapeutic target in preventing various diseases. Regulation of NF- κB signaling pathway may be a safe and effective treatment strategy for various disorders.

Neurodegenerative diseases (NDDs) are the primary cause of disabilities in the elderly people. Growing evidence indicates that oxidative stress, mitochondrial dysfunction, neuroinflammation and apoptosis are associated with aging and the basis of most neurodegenerative disorders. Quercetin is a flavonoid with significant pharmacological effects and promising therapeutic potential. It is widely distributed among plants and typically found in daily diets mainly in fruits and vegetables. It shows a number of biological properties connected to its antioxidant activity. Neuroprotection by quercetin has been reported in many in vitro as well as in in vivo studies. However, the exact mechanism of action is still mystery and similarly there are a number of hypothesis exploring the mechanism of neuroprotection. Quercetin enhances neuronal longevity and neurogenesis by modulating and inhibiting wide number of pathways. This review assesses the food sources of quercetin, its pharmacokinetic profile, structure activity relationship and its pathophysiological role in various NDDs and it also provides a synopsis of the literature exploring the relationship between quercetin and various downstream signalling pathways modulated by quercetin for neuroprotection for eg. nuclear factor erythroid 2-related factor 2 (Nrf2), Paraoxonase-2 (PON2), c-Jun N-terminal kinase (JNK), Tumour Necrosis Factor alpha (TNF-α), Peroxisome Proliferator-Activated Receptor Gamma Coactivator 1-alpha (PGC-1α), Sirtuins, Mitogen-activated protein kinases (MAPKs) signalling cascades, CREB (Cyclic AMP response element binding protein) and Phosphoinositide 3- kinase(PI3K/Akt). Therefore, the aim of the present review was to elaborate on the cellular and molecular mechanisms of the quercetin involved in the protection against NDDs.

The power conversion efficiency (PCE) of cesium lead halide (CsPbX 3, X = l, Br, and Cl)-based all-inorganic perovskite solar cells (PSCs) is still struggling to compete with conventional organic–inorganic halide perovskites. A combined material and device-related analysis is much needed to understand the working principle to explore the efficiency potential of CsPbX 3 -based PSCs. Therefore, here, density functional theory (DFT) and SCAPS-1D-based studies were reported to evaluate the photovoltaic (PV) performance of CsPbBr 3 -based PSCs. DFT is first applied to assess and extract structural and optoelectronic properties (band structure, density of states, Fermi surface, and absorption coefficient) of the considered absorber layer. The calculated electronic band gap ( E g ) of the CsPbBr 3 absorber was 1.793 eV, which matched well with the earlier computed theoretical value. Additionally, the Pb 6p orbital contributed largely to the calculated density of states (DOS), and the electronic charge density map showed that the Pb atom acquired the majority of charges. In order to examine the optical response of CsPbBr 3, optical characteristics were computed and correlated with electronic properties for its probable photovoltaic applications. Fermi surface computation showed multiband characters. Furthermore, to look for a suitable combination of the charge transport layer, a total of nine HTLs (Cu 2 O, CuSCN, P3HT, PEDOT:PSS, Spiro-MeOTAD, CuI, V 2 O 5, CBTS, and CFTS) and six ETLs (TiO 2, PCBM, ZnO, C 60, IGZO, and WS 2 ) are used considering the experimental E g (2.3 eV). The best power conversion efficiency (PCE) of 13.86% is reported for TiO 2 and CFTS in combination with the CsPbBr 3 absorber. The effects of operating temperature, series and shunt resistances, Mott–Schottky, capacitance, generation and recombination rates, quantum efficiency, and current–voltage density were also examined. The resulting PV properties were also compared with previously published data. Results reported in this study will pave the way for the development of high-efficiency all-inorganic CsPbBr 3 -based solar cells in the future.

Blockchain and artificial intelligence technologies are novel innovations in healthcare sector. Data on healthcare indices are collected from data published on Web of Sciences and other Google survey from various governing bodies. In this review, we focused on various aspects of blockchain and artificial intelligence and also discussed about integrating both technologies for making a significant difference in healthcare by promoting the implementation of a generalizable analytical technology that can be integrated into a more comprehensive risk management approach. This article has shown the various possibilities of creating reliable artificial intelligence models in e-Health using blockchain, which is an open network for the sharing and authorization of information. Healthcare professionals will have access to the blockchain to display the medical records of the patient, and AI uses a variety of proposed algorithms and decision-making capability, as well as large quantities of data. Thus, by integrating the latest advances of these technologies, the medical system will have improved service efficiency, reduced costs, and democratized healthcare. Blockchain enables the storage of cryptographic records, which AI needs.

Herein, we used TiO 2 as the ETL and CBTS as the HTL in a CsPbI 3 -based PSC and optimized it using SCAPS-1D software, where the final optimization of the device gave a maximum PCE of 19.06%.