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Cancer cells have a remarkable ability to evade recognition and destruction by the immune system. At the same time, cancer has been associated with chronic inflammation, while certain autoimmune diseases predispose to the development of neoplasia. Although cancer immunotherapy has revolutionized antitumor treatment, immune-related toxicities and adverse events detract from the clinical utility of even the most advanced drugs, especially in patients with both, metastatic cancer and pre-existing autoimmune diseases. Here, the combination of multi-omics, data-driven computational approaches with the application of network concepts enables in-depth analyses of the dynamic links between cancer, autoimmune diseases, and drugs. In this review, we focus on molecular and epigenetic metastasis-related processes within cancer cells and the immune microenvironment. With melanoma as a model, we uncover vulnerabilities for drug development to control cancer progression and immune responses. Thereby, drug repurposing allows taking advantage of existing safety profiles and established pharmacokinetic properties of approved agents. These procedures promise faster access and optimal management for cancer treatment. Together, these approaches provide new disease-based and data-driven opportunities for the prediction and application of targeted and clinically used drugs at the interface of immune-mediated diseases and cancer towards next-generation immunotherapies.

During the last two decades several nanoscale materials were engineered for industrial and medical applications. Among them carbon nanotubes (CNTs) are the most exploited nanomaterials with global production of around 1000 tons/year. Besides several commercial benefits of CNTs, the fiber-like structures and their bio-persistency in lung tissues raise serious concerns about the possible adverse human health effects resembling those of asbestos fibers. In this review, we present a comparative analysis between CNTs and asbestos fibers using the following four parameters: (1) fibrous needle-like shape, (2) bio-persistent nature, (3) high surface to volume ratio and (4) capacity to adsorb toxicants/pollutants on the surface. We also compare mechanisms underlying the toxicity caused by certain diameters and lengths of CNTs and asbestos fibers using downstream pathways associated with altered gene expression data from both asbestos and CNT exposure. Our results suggest that indeed certain types of CNTs are emulating asbestos fiber as far as associated toxicity is concerned.

The electrical market scenario has changed drastically in the last decade. In the presence of increased competition and less tolerant players, more sophisticated methods are required to balance the diversity and differential pricing while promoting cooperation among the agents. In the monopolistic environment, the central utility incurred the total cost of the transmission expansion. But as the current scenario demands, there are several public and private market players. The growth will benefit all the players, so the total cost in transmission expansion can be divided among players as per the benefit received by each player. In this paper, a transmission system expansion planning problem in the cooperative environment using cooperative game theory (CGT) is framed for the power sector, in which various players can cooperate in a coordinated manner to maximize their benefit but ultimately strengthen the power grid. In this paper, we have modeled, analyzed and compared various cost allocation methods of cooperative game theory specifically for the cost allocation in a transmission expansion planning problem. The present work focuses on forming coalitions to calculate the costs using the forward search and frog leap optimization approach. We have compared the SCRB, BSV, ENSC, and ACA methods for transmission expansion planning while attempting to satisfy the axioms. We have also observed that bilateral Shapely value efficiently allocated the costs due to its decentralized approach and the sequencing of coalition formations to achieve the best possible cost allocations.

The field of ground-based γ-ray astronomy has made very significant advances over the last three decades with the extremely successful operations of several atmospheric Cherenkov telescopes worldwide. The advent of the imaging Cherenkov technique for indirect detection of cosmic γ rays has immensely contributed to this field with the discovery of more than 220 γ-ray sources in the Universe. This has greatly improved our understanding of the various astrophysical processes involved in the non-thermal emission at energies above 100 GeV. In this paper, we summarize the important results achieved by the Indian γ-ray astronomers from the GeV-TeV observations using imaging Cherenkov telescopes over the last two decades. We mainly emphasize the results obtained from the observations of active galactic nuclei with the TACTIC (TeV Atmospheric Cherenkov Telescope with Imaging Camera) telescope, which has been operational since 1997 at Mount Abu, India. We also discuss the future plans of the Indian γ-ray astronomy program with special focus on the scientific objectives of the recently installed 21 m diameter MACE (Major Atmospheric Cherenkov Experiment) telescope at Hanle, India.

Chronic limb-threatening ischemia (CLTI) results in devastating complications such as lower-limb amputations. In this study, a genome-wide plasma microRNAs (miRNA) sequencing was performed to identify miRNA(s) associated with CLTI. Blood samples were collected from early stage CLTI patients (ABI < 0.9) and non-PAD controls (ABI ≥ 0.9) for 3 experiments: discovery phase (n = 23), confirmatory phase (n = 52) and validation phase (n = 20). In the discovery phase, next generation sequencing (NGS) was used to identify miRNA circulating in the plasma CLTI (n = 13) patients, compared to non-PAD controls (n = 10). Two down-regulated miRNAs (miRNA-6843-3p and miRNA-6766-5p) and three upregulated miRNAs (miRNA-1827, miRNA-320 and miRNA-98-3p) were identified (≥2-fold change). In the confirmatory phase, these 5 deregulated miRNAs were further investigated in non-PAD (n = 21) and CTLI (n = 31) patients using qRT-PCR. Only miRNA-1827 was found to be significantly upregulated (≥3-fold, p-value < 0. 001) in the CLTI group. Lastly, to minimize the influence of confounding factors, miRNA-1827 plasma levels were validated in a third cohort of CLTI patients (n = 10) matched to non-PAD controls (n = 10). Our analysis demonstrated that miRNA-1827 expression was increased in the CLTI cohort (≥2-folds, p-value < 0.001). In summary, circulating miRNA-1827 is significantly elevated in patients with CLTI.

Medicinal plants are the main natural pools for the primary health care system, ethno-medicine, as well as traditional Indian system of several medicines. Uttarakhand also known as ‘Herbal State’, is a rich source of medicinal plants and traditional medicinal knowledge. A great deal of information about medicinal plants of Uttarakhand is scattered in different forms. Although many medicinal plant databases are available, currently there is no cohesive manually curated database of medicinal plants widely distributed in Uttarakhand state. A comprehensive database has been developed, known as the Uttarakhand Medicinal Plants Database (UMPDB). UMPDB provides extensive information on botanical name, common name, taxonomy, genomic taxonomy id, habit, habitat, location in Uttarakhand, part use, medicinal use, genomic information (including number of nucleotides, proteins, ESTs), chemical information, and scientific literature. Annotated medicinal plants integrated in the current version of the database were collected from the existing books, databases, and available literature. The current version of UMPDB contains the 1127 records of medicinal plants which belong to 153 plant families distributed across 13 districts of Uttarakhand. The primary goal of developing this database is to provide traditional, genomic, and chemical descriptions of the medicinal plants exclusively found in various regions of Uttarakhand. We anticipate that embedded information in the database would help users to readily obtain desired information.

Germline mutations in Breast cancer susceptibility genes 1 and 2 (BRCA1 and BRCA2) cause breast, ovarian, and other cancers, and the chemotherapeutic drug doxorubicin (Dox) is widely used to treat these cancers. However, Dox use is limited by the latent induction of severe cardiotoxicity known as Dox-induced cardiomyopathy, for which there are no specific treatments currently available. Dox is administered into the systemic circulation, where it readily translocates into sub-cellular compartments and disrupts the integrity of DNA. Accumulating evidence indicates that oxidative stress, DNA damage, inflammation, and apoptosis all play a central role in Dox-induced cardiomyopathy. The BRCA1 and BRCA2 proteins are distinct as they perform crucial yet separate roles in the homologous recombination repair of DNA double-strand breaks, thereby maintaining genomic integrity. Additionally, both BRCA1 and BRCA2 mitigate oxidative stress and apoptosis in both cardiomyocytes and endothelial cells. Accordingly, BRCA1 and BRCA2 are essential regulators of pathways that are central to the development of cardiomyopathy induced by Doxorubicin. Despite extensive investigations, there exists a gap in knowledge about the role of BRCA1 and BRCA2 in Doxorubicin-induced cardiomyopathy. Here, we review the previous findings and associations about the expected role and associated mechanisms of BRCA1 and 2 in Dox-induced cardiomyopathy and future perspectives.

Cancer, a multifactorial disease characterized by uncontrolled cellular proliferation, remains a global health challenge with significant morbidity and mortality. Genomic and molecular aberrations, coupled with environmental factors, contribute to its heterogeneity and complexity. Chemotherapeutic agents like doxorubicin (Dox) have shown efficacy against various cancers but are hindered by dose-dependent cytotoxicity, particularly on vital organs like the heart and brain. Autophagy, a cellular process involved in self-degradation and recycling, emerges as a promising therapeutic target in cancer therapy and neurodegenerative diseases. Dysregulation of autophagy contributes to cancer progression and drug resistance, while its modulation holds the potential to enhance treatment outcomes and mitigate adverse effects. Additionally, emerging evidence suggests a potential link between autophagy, DNA damage, and caretaker breast cancer genes BRCA1/2, highlighting the interplay between DNA repair mechanisms and cellular homeostasis. This review explores the intricate relationship between cancer, Dox-induced cytotoxicity, autophagy modulation, and the potential implications of autophagy in DNA damage repair pathways, particularly in the context of BRCA1/2 mutations.

The growing demand for agricultural output and limited resources encourage precision applications to generate higher-order output by utilizing minimal inputs of seed, fertilizer, land, and water. An electronically operated planter was developed, considering problems like ground-wheel skidding, field vibration, and the lack of ease in field adjustments of ground-wheel-driven seed-metering plates. The seed-metering plate of each unit of the developed planter is individually driven by a brushless direct current (BLDC) motor, and a BLDC motor-based aspirator is attached for pneumatic suction of seeds. The revolutions per minute (RPM) of the seed-metering plate are controlled by a microcontroller as per the received data relating to RPM from the ground wheel and the current RPM of the seed-metering plate. A feedback loop with proportional integral derivative (PID) control is responsible for reducing the error. Additionally, each row unit is attached to a parallelogram-based depth control system that can provide depth between 0 and 100 mm. The suction pressure in each unit is regulated as per seed type using the RPM control knob of an individual BLDC motor-based aspirator. The row-to-row spacing can be changed from 350 mm to any desired spacing. The cotton variety selected for the study was RCH 659, and the crucial parameters like orifice size, vacuum pressure, and forward speed were optimized in the laboratory with the adoption of a central composite rotatable design. An orifice diameter of 2.947 mm with vacuum pressure of 3.961 kPa and forward speed of 4.261 km/h was found optimal. A quality feed index of 93% with a precision index of 8.01% was observed from laboratory tests under optimized conditions. Quality feed index and precision index values of 88.8 and 12.75%, respectively, were obtained from field tests under optimized conditions.

We report on the properties of central engines in the γ-ray blazars located at high redshifts beyond z > 0.4, where the extra-galactic background light (EBL) starts affecting their γ-ray spectra. The physical engine that provides power to the blazars of very high bolometric luminosity is assumed to be a highly collimated jet of matter moving relativistically away from the supermassive black hole (SMBH), located in the central region of the host galaxy, in a direction aligned toward the Earth. Due to their peculiar geometry and special physical conditions, blazars at redshifts beyond z > 0.4 are bright enough to be detected in the γ-ray energy band. In this work, we investigate the physical properties of high-z γ-ray blazars detected by the Large Area Telescope (LAT) on board the Fermi satellite. We also study the properties of their emission regions and the central engines and discuss cosmological and astrophysical implications.

Type II Supernova 1987A (SN 1987A), observed in 1987, released an energy of Q≈3×1053 erg. This huge energy is essentially the magnitude of gravitational potential or self-gravitational energy (PE) of a new born cold neutron star having a gravitational compactness or redshift zb≈0.15. One may wonder what could be the upper limit on the amount of energy that might be released with the formation of a cold Ultra Compact Object (UCO) with an arbitrary high zb. Accordingly, here, for the first time, we obtain an analytical expression for the PE of a homogeneous general relativistic UCO assuming it to be cold and static. It is found that the PE of a homogeneous UCO of mass M may exceed Mc2 and be as large as 1.34 Mc2. This result, though surprising, follows from an exact and correct analytical calculation based on the standard General Theory of Relativity (GTR). Further, UCOs supported by tangential stresses may be inhomogeneous and much more massive than neutron stars with PE ∼ 2.1 Mc2 Thus, in principle, formation of an UCO of a few solar masses (M⊙) might release an energy Q∼1055 erg.

Rapidly rotating neutron stars with very strong surface magnetic fields are observed to emit pulsed emission in the whole range of electromagnetic spectrum from radio to high-energy gamma rays. These so-called pulsars are known for their exceptional rotational stability. The radio emission from pulsars is generally believed to be powered by the rotational energy of neutron stars. More than 3000 pulsars have been currently known from radio observations; however, only about 10% are observed in the high-energy gamma ray band. The Fermi-LAT observations in the energy range above 100 MeV have discovered more than 300 pulsars. However, the origin of high-energy non-thermal radiation from pulsars is not completely understood and remains an active area of research. In this contribution, we report a summary of observational features of the gamma ray pulsars and briefly discuss observability for the MACE gamma ray telescope, which has just started its regular science operation at Hanle in India. Six gamma ray pulsars, other than the well-known Crab and Geminga, are identified as probable candidates for MACE observations.

In this paper, an adaptive depth and heading control of an autonomous underwater vehicle using the concept of an adaptive neuro-fuzzy inference system (ANFIS) is designed. The autonomous underwater vehicle dynamics have six degrees of freedom, which are highly nonlinear and time-varying. It is affected by environmental effects such as ocean currents and tidal waves. Due to nonlinear dynamics designing, a stable controller in an autonomous underwater vehicle is a difficult end to achieve. Fuzzy logic and neural network control blocks make up the proposed control design to control the depth and heading angle of autonomous underwater vehicle. The neural network is trained using the back-propagation algorithm. In the presence of noise and parameter variation, the proposed adaptive controller’s performance is compared with that of the self-tuning fuzzy-PID and fuzzy logic controller. Simulations are conducted to obtain the performance of both controller models in terms of overshoot, and the rise time and the result of the proposed adaptive controller exhibit superior control performance and can eliminate the effect of uncertainty.

In this study, the authors have designed biocompatible nano-vesicles using graphene oxide (GO) for the release of chlorambucil (CHL) drugs targeting cancerous cells. The GO sheets were first sulfonated and conjugated with folic acid (FA) molecules for controlled release and high loading efficiency of CHL. The chlorambucil (CHL) drug loading onto the functionalized GO surface was performed through π-π stacking and hydrophobic interactions with the aromatic planes of GO. The drug loading and “in vitro” release from the nano-vesicles at different pH were studied. The average particle size, absorption, and loading efficiency (%) of FA-conjugated GO sheets (CHL-GO) were observed to be 300 nm, 58%, and 77%, respectively. The drug release study at different pH (i.e., 7.4 and 5.5) showed a slight deceleration at pH 7.4 over pH 5.5. The amount of drug released was very small at pH 7.4 in the first hour which progressively increased to 24% after 8 h. The rate of drug release was faster at pH 5.5; initially, 16% to 27% in the first 3 h, and finally it reached 73% after 9 h. These observations indicate that the drug is released more rapidly at acidic pH with a larger amount of drug-loading ability. The rate of drug release from the CHL-loaded GO was 25% and 75% after 24 h. The biotoxicity study in terms of % cell viability of CHL-free and CHL-loaded GO against human cervical adenocarcinoma cell line was found to have lower cytotoxicity of CHL-loaded nano-vesicles (IC50 = 18 μM) as compared to CHL-free (IC50 = 8 μM). It is concluded that a high drug-loading efficiency and controlled release with excellent biotoxicity of CHL-GO offers an excellent application in the biomedical field.

Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.

Diabetic retinopathy (DR) is an ophthalmological disease that causes damage in the blood vessels of the eye. DR causes clotting, lesions or haemorrhage in the light-sensitive region of the retina. Person suffering from DR face loss of vision due to the formation of exudates or lesions in the retina. The detection of DR is critical to the successful treatment of patients suffering from DR. The retinal fundus images may be used for the detection of abnormalities leading to DR. In this paper, an automated ensemble deep learning model is proposed for the detection and classification of DR. The ensembling of a deep learning model enables better predictions and achieves better performance than any single contributing model. Two deep learning models, namely modified DenseNet101 and ResNeXt, are ensembled for the detection of diabetic retinopathy. The ResNeXt model is an improvement over the existing ResNet models. The model includes a shortcut from the previous block to next block, stacking layers and adapting split–transform–merge strategy. The model has a cardinality parameter that specifies the number of transformations. The DenseNet model gives better feature use efficiency as the dense blocks perform concatenation. The ensembling of these two models is performed using normalization over the classes followed by maximum a posteriori over the class outputs to compute the final class label. The experiments are conducted on two datasets APTOS19 and DIARETDB1. The classifications are carried out for both two classes and five classes. The images are pre-processed using CLAHE method for histogram equalization. The dataset has a high-class imbalance and the images of the non-proliferative type are very low, therefore, GAN-based augmentation technique is used for data augmentation. The results obtained from the proposed method are compared with other existing methods. The comparison shows that the proposed method has higher accuracy, precision and recall for both two classes and five classes. The proposed method has an accuracy of 86.08 for five classes and 96.98% for two classes. The precision and recall for two classes are 0.97. For five classes also, the precision and recall are high, i.e., 0.76 and 0.82, respectively.

Blazars are observed to emit non-thermal radiation across the entire electromagnetic spectrum from the radio to the very-high-energy γ-ray region. The broadband radiation measured from a blazar is dominated by emission from a relativistic plasma jet which is assumed to be powered by a spinning supermassive black hole situated in the central region of the host galaxy. The formation of jets, their mode of energy transport, actual power budget, and connection with the central black hole are among the most fundamental open problems in blazar research. However, the observed broadband spectral energy distribution from blazars is generally explained by a simple one-zone leptonic emission model. The model parameters place constraints on the contributions from the magnetic field, radiation field, and kinetic power of particles to the emission region in the jet. This in turn constrains the minimum power transported by the jet from the central engine. In this work, we explore the potential of machine learning frameworks including linear regression, support vector machine, adaptive boosting, bagging, gradient boosting, and random forests for the estimation of the mass of the supermassive black hole at the center of the host galaxy of blazars using the best-fit emission model parameters derived from the broadband spectral energy distribution modeling in the literature. Our study suggests that the support vector machine, adaptive boosting, bagging, and random forest algorithms can predict black hole masses with reasonably good accuracy.

We highlight here the fact that the distantly observed luminosity of a spherically symmetric compact star radiating thermal radiation isotropically is higher by a factor of (1+zb)2 compared to the corresponding flat space-time case, where zb is the surface gravitational redshift of the compact star. In particular, we emphasize that if the thermal radiation is indeed emitted isotropically along the respective normal directions at each point, this factor of increment (1+zb)2 remains unchanged even if the compact object would lie within its photon sphere. Since a canonical neutron star has zb≈0.1, the actual X-ray luminosity from the neutron star surface could be ∼20% higher than what would be interpreted by ignoring the general relativistic effects described here. For a static compact object, supported by only isotropic pressure, compactness is limited by the Buchdahl limit zb<2.0. However, for compact objects supported by anisotropic pressure, zb could be even higher (zb<5.211). In addition, in principle, there could be ultra-compact objects having zb≫1. Accordingly, the general relativistic effects described here might be quite important for studies of thermal radiation from some ultra-compact objects.

Fatty acid binding proteins (FABPs) are proteins found in the cytosol that contribute to disorders related to the cardiovascular system, including atherosclerosis and metabolic syndrome. Functionally, FABPs serve as intracellular lipid chaperones, interacting with hydrophobic ligands and mediating their transportation to sites of lipid metabolism. To date, nine unique members of the FABP family (FABP 1–9) have been identified and classified according to the tissue in which they are most highly expressed. In the literature, FABP3 has been shown to be a promising clinical biomarker for coronary and peripheral artery disease. Given the rising incidence of cardiovascular disease and its associated morbidity/mortality, identifying biomarkers for early diagnosis and treatment is critical. In this review, we highlight key discoveries and recent studies on the role of FABP3 in cardiovascular disorders, with a particular focus on its clinical relevance as a biomarker for peripheral artery disease.

Data normalization is a data preprocessing task and one of the first to be performed during intellectual analysis, particularly in the case of tabular data. The importance of its implementation is determined by the need to reduce the sensitivity of the artificial intelligence model to the values of the features in the dataset to increase the studied model’s adequacy. This paper focuses on the problem of effectively preprocessing data to improve the accuracy of intellectual analysis in the case of performing medical diagnostic tasks. We developed a new two-step method for data normalization of numerical medical datasets. It is based on the possibility of considering both the interdependencies between the features of each observation from the dataset and their absolute values to improve the accuracy when performing medical data mining tasks. We describe and substantiate each step of the algorithmic implementation of the method. We also visualize the results of the proposed method. The proposed method was modeled using six different machine learning methods based on decision trees when performing binary and multiclass classification tasks. We used six real-world, freely available medical datasets with different numbers of vectors, attributes, and classes to conduct experiments. A comparison between the effectiveness of the developed method and that of five existing data normalization methods was carried out. It was experimentally established that the developed method increases the accuracy of the Decision Tree and Extra Trees Classifier by 1–5% in the case of performing the binary classification task and the accuracy of the Bagging, Decision Tree, and Extra Trees Classifier by 1–6% in the case of performing the multiclass classification task. Increasing the accuracy of these classifiers only by using the new data normalization method satisfies all the prerequisites for its application in practice when performing various medical data mining tasks.

Angiogenesis, the formation of new blood vessels from pre-existing ones is a biological process that ensures an adequate blood flow is maintained to provide the cells with a sufficient supply of nutrients and oxygen within the body. Numerous soluble growth factors and inhibitors, cytokines, proteases as well as extracellular matrix proteins and adhesion molecules stringently regulate the multi-factorial process of angiogenesis. The properties and interactions of key angiogenic molecules such as vascular endothelial growth factors (VEGFs), fibroblast growth factors (FGFs) and angiopoietins have been investigated in great detail with respect to their molecular impact on angiogenesis. Since the discovery of angiogenic growth factors, much research has been focused on their biological actions and their potential use as therapeutic targets for angiogenic or anti-angiogenic strategies in a context-dependent manner depending on the pathologies. It is generally accepted that these factors play an indispensable role in angiogenesis. However, it is becoming increasingly evident that this is not their only role and it is likely that the angiogenic factors have important functions in a wider range of biological and pathological processes. The additional roles played by these molecules in numerous pathologies and biological processes beyond angiogenesis are discussed in this review.