Lovely Professional University

Cancer is an abnormal state of cells where they undergo uncontrolled proliferation and produce aggressive malignancies that causes millions of deaths every year. With the new understanding of the molecular mechanism(s) of disease progression, our knowledge about the disease is snowballing, leading to the evolution of many new therapeutic regimes and their successive trials. In the past few decades, various combinations of therapies have been proposed and are presently employed in the treatment of diverse cancers. Targeted drug therapy, immunotherapy, and personalized medicines are now largely being employed, which were not common a few years back. The field of cancer discoveries and therapeutics are evolving fast as cancer type-specific biomarkers are progressively being identified and several types of cancers are nowadays undergoing systematic therapies, extending patients' disease-free survival thereafter. Although growing evidence shows that a systematic and targeted approach could be the future of cancer medicine, chemotherapy remains a largely opted therapeutic option despite its known side effects on the patient's physical and psychological health. Chemotherapeutic agents/pharmaceuticals served a great purpose over the past few decades and have remained the frontline choice for advanced-stage malignancies where surgery and/or radiation therapy cannot be prescribed due to specific reasons. The present report succinctly reviews the existing and contemporary advancements in chemotherapy and assesses the status of the enrolled drugs/pharmaceuticals; it also comprehensively discusses the emerging role of specific/targeted therapeutic strategies that are presently being employed to achieve better clinical success/survival rate in cancer patients.

In today's digital age, FinTech’s have become universal, transforming the way individuals conduct financial transactions. However, with the convenience of Fintech also come challenges that users encounter. This book chapter investigates into the changing landscape of Fintech challenges, drawing insights from a comprehensive survey conducted among various respondents The survey finds three primary obstacles faced by users during Fintech: network issues, time consumption, and privacy concerns. Notably, a significant proportion of respondents reported encountering network-related problems, highlighting the critical role of stable connectivity in facilitating seamless transactions. Moreover, the chapter explores into the foiling experienced by users due to the time-and privacy nature of the payment process, shedding light on the need for streamlined and efficient payment mechanisms. Building upon the survey findings, the chapter offers valuable insights into potential strategies and solutions to address these challenges. By elucidating practical approaches and technological innovations aimed at enhancing Fintech systems' efficiency and security, the chapter equips readers with actionable knowledge to navigate the digital payment landscape effectively. This study's findings add to academic discussion and have practical implications for policymakers, industry practitioners, and educators.

Emerged in the middle of 20th century, composite materials are now one of the hotspot research topics in the modern technology. Their promising characteristics make them suitable for enormous applications in industrial field such as aerospace, automotive, construction, sports, bio-medical and many others. These materials reveal remarkable structural and mechanical properties such as high strength to weight ratio, resistance to chemicals, fire, corrosion and wear; being economical to manufacture. Herein, an overview of composite materials, their characterization, classification and main advantages linked to physical and mechanical properties based on the recent studies are presented. There, were presented the conventional manufacturing techniques of composite and their applications. It was highlighted the tremendous need to discovery new generation of composites that should incorporate the synthetic or natural materials by implementing new efficient manufacturing processes. In the combination of matrix and reinforcement materials, the use of natural materials as constituent are compulsory in order to obtain a complete material degradable as environmentally friendly.

The textile sector is 14% of total industrial production in India and contributes to about 4% of the gross domestic product and earns about 27% of India's total foreign exchange. Worldwide, up to 10,000 dyes are available and their annual production is above 7 × 105 metric tons, which are being used not only in textile sector but also applied in paper, food and pharmaceutical industries. Textile industries in India have been consuming more than 100 L of water to process 1 kg of textiles, and have contributed heavily in polluting surface and ground water resources in many regions of the country. The toxic and carcinogenic effect of untreated textile effluent is well understood. The decolorization and detoxification of industrial dye effluents is most important aspect and is major concern to meet environmental regulations. This paper presents a review of literature on the significance of bioremediation technologies over other physico-chemical methods for efficient removal of textile dyes from industrial waste effluents to improve the fragile ecosystems in different regions of the world.The present review paper (a) symbolizes the applications of existing conventional physical and chemical approaches for the decolorization/degradation of textile dyes, (b) describes their merits and demerits, (c) emphasizes on the existing literature on microbial decolorization of textile dyes precisely by using bacteria (aerobic and anaerobic conditions), fungi and algae, and (d) involvement of various enzymes from different biological sources for the decolorization of various textile dyes and their mechanism of action.Over the years, researchers have developed several bioremediation technologies to treat textile effluents, but little effort has been made to put the entire literature review of these technologies in one refereed paper, our review paper is an attempt to compile the existing information on various treatment technologies of textile effluent, so that these technologies can be shared widely for site specific situations.

Pneumonia is among the top diseases which cause most of the deaths all over the world. Virus, bacteria and fungi can all cause pneumonia. However, it is difficult to judge the pneumonia just by looking at chest X-rays. The aim of this study is to simplify the pneumonia detection process for experts as well as for novices. We suggest a novel deep learning framework for the detection of pneumonia using the concept of transfer learning. In this approach, features from images are extracted using different neural network models pretrained on ImageNet, which then are fed into a classifier for prediction. We prepared five different models and analyzed their performance. Thereafter, we proposed an ensemble model that combines outputs from all pretrained models, which outperformed individual models, reaching the state-of-the-art performance in pneumonia recognition. Our ensemble model reached an accuracy of 96.4% with a recall of 99.62% on unseen data from the Guangzhou Women and Children’s Medical Center dataset.

The era of artificial neural network (ANN) began with a simplified application in many fields and remarkable success in pattern recognition (PR) even in manufacturing industries. Although significant progress achieved and surveyed in addressing ANN application to PR challenges, nevertheless, some problems are yet to be resolved like whimsical orientation (the unknown path that cannot be accurately calculated due to its directional position). Other problem includes; object classification, location, scaling, neurons behavior analysis in hidden layers, rule, and template matching. Also, the lack of extant literature on the issues associated with ANN application to PR seems to slow down research focus and progress in the field. Hence, there is a need for state-of-the-art in neural networks application to PR to urgently address the above-highlights problems for more successes. The study furnishes readers with a clearer understanding of the current, and new trend in ANN models that effectively addresses PR challenges to enable research focus and topics. Similarly, the comprehensive review reveals the diverse areas of the success of ANN models and their application to PR. In evaluating the performance of ANN models, some statistical indicators for measuring the performance of the ANN model in many studies were adopted. Such as the use of mean absolute percentage error (MAPE), mean absolute error (MAE), root mean squared error (RMSE), and variance of absolute percentage error (VAPE). The result shows that the current ANN models such as GAN, SAE, DBN, RBM, RNN, RBFN, PNN, CNN, SLP, MLP, MLNN, Reservoir computing, and Transformer models are performing excellently in their application to PR tasks. Therefore, the study recommends the research focus on current models and the development of new models concurrently for more successes in the field.

Recent scientific studies have established a relationship between the consumption of phytochemicals such as carotenoids, polyphenols, isoprenoids, phytosterols, saponins, dietary fibers, polysaccharides, etc., with health benefits such as prevention of diabetes, obesity, cancer, cardiovascular diseases, etc. This has led to the popularization of phytochemicals. Nowadays, foods containing phytochemicals as a constituent (functional foods) and the concentrated form of phytochemicals (nutraceuticals) are used as a preventive measure or cure for many diseases. The health benefits of these phytochemicals depend on their purity and structural stability. The yield, purity, and structural stability of extracted phytochemicals depend on the matrix in which the phytochemical is present, the method of extraction, the solvent used, the temperature, and the time of extraction.

Plants face a variety of abiotic stresses, which generate reactive oxygen species (ROS), and ultimately obstruct normal growth and development of plants. To prevent cellular damage caused by oxidative stress, plants accumulate certain compatible solutes known as osmolytes to safeguard the cellular machinery. The most common osmolytes that play crucial role in osmoregulation are proline, glycine-betaine, polyamines, and sugars. These compounds stabilize the osmotic differences between surroundings of cell and the cytosol. Besides, they also protect the plant cells from oxidative stress by inhibiting the production of harmful ROS like hydroxyl ions, superoxide ions, hydrogen peroxide, and other free radicals. The accumulation of osmolytes is further modulated by phytohormones like abscisic acid, brassinosteroids, cytokinins, ethylene, jasmonates, and salicylic acid. It is thus important to understand the mechanisms regulating the phytohormone-mediated accumulation of osmolytes in plants during abiotic stresses. In this review, we have discussed the underlying mechanisms of phytohormone-regulated osmolyte accumulation along with their various functions in plants under stress conditions.

Additive Manufacturing (AM) has revolutionized the manufacturing industry in several directions. Laser powder bed fusion (LPBF), a powder bed fusion AM process, has been dramatically accepted in various industries due to its versatility with several materials, including alloys. This comprehensive review article primarily explains the basic principle of the LPBF process, scientific and technological progress of several inter-related parameters, feedstock materials, produced properties/defects, and insights of numerical modelling to virtually understand the process behavior. Specific attention has been given to selective laser-meted (LPBFed) properties, driven through the microstructure formations and, thereby, concerning defects. The scope of the post-processing techniques to refine microstructure has also been discussed in this review paper. It has been identified that the defects are vital in LPBF process and are primarily governed by the process parameters. Therefore, a wisely chosen, optimized set of parameters can play a crucial role in minimizing defects considerably. Finally, the numerical modeling discussed in this review paper will help the researchers understand the LPBF process.

Solid lipid nanoparticles (SLNs) are nanocarriers developed as substitute colloidal drug delivery systems parallel to liposomes, lipid emulsions, polymeric nanoparticles, and so forth. Owing to their unique size dependent properties and ability to incorporate drugs, SLNs present an opportunity to build up new therapeutic prototypes for drug delivery and targeting. SLNs hold great potential for attaining the goal of targeted and controlled drug delivery, which currently draws the interest of researchers worldwide. The present review sheds light on different aspects of SLNs including fabrication and characterization techniques, formulation variables, routes of administration, surface modifications, toxicity, and biomedical applications.

The plant-Trichoderma-pathogen triangle is a complicated web of numerous processes. Trichoderma spp. are avirulent opportunistic plant symbionts. In addition to being successful plant symbiotic organisms, Trichoderma spp. also behave as a low cost, effective and ecofriendly biocontrol agent. They can set themselves up in various patho-systems, have minimal impact on the soil equilibrium and do not impair useful organisms that contribute to the control of pathogens. This symbiotic association in plants leads to the acquisition of plant resistance to pathogens, improves developmental processes and yields and promotes absorption of nutrient and fertilizer use efficiency. Among other biocontrol mechanisms, antibiosis, competition and mycoparasitism are among the main features through which microorganisms, including Thrichoderma, react to the presence of other competitive pathogenic organisms, thereby preventing or obstructing their development. Stimulation of every process involves the biosynthesis of targeted metabolites like plant growth regulators, enzymes, siderophores, antibiotics, etc. This review summarizes the biological control activity exerted by Trichoderma spp. and sheds light on the recent progress in pinpointing the ecological significance of Trichoderma at the biochemical and molecular level in the rhizosphere as well as the benefits of symbiosis to the plant host in terms of physiological and biochemical mechanisms. From an applicative point of view, the evidence provided herein strongly supports the possibility to use Trichoderma as a safe, ecofriendly and effective biocontrol agent for different crop species.

The most popular additive manufacturing (AM) technologies to produce titanium alloy parts are electron beam melting (EBM), selective laser melting (SLM) and directed energy deposition (DED). This investigation explores mainly these three techniques and compares these three methods comprehensively in terms of microstructure, tensile properties, porosity, surface roughness and residual stress based on the information available in the literature. It was found that the microstructure is affected by the highest temperature generated and the cooling rate which can be tailored by the input variables of the AM processes. The parts produced from EBM have strength comparable to that of conventionally fabricated counterparts. SLM and DED yield superior strength, which can be up to 25% higher than traditionally manufactured products. Due to the presence of larger tensile residual stress, surface roughness and porosity, AM fabricated parts have lower fatigue life compared to those of from traditional methods. EBM parts have slightly lower fracture toughness (i.e., lower fatigue life) than conventionally produced parts while SLM and DED have significantly lower fracture toughness. Annealing, hot isostatic pressing, stress relief and additional machining processes improve the characteristics of parts produced from AM. Ti–6Al–4V alloy parts fabricated via AM may have limited applications despite the high demands in aerospace or biomedical engineering. Since rapid product development using 3D printers leads to significant cost reductions more recently, it is expected that more opportunities may soon be available for the AM of titanium alloys with newer AM processes such as cold spray additive manufacturing (CSAM) and additive friction stir deposition (AFSD).

The segmentation, detection, and extraction of infected tumor area from magnetic resonance (MR) images are a primary concern but a tedious and time taking task performed by radiologists or clinical experts, and their accuracy depends on their experience only. So, the use of computer aided technology becomes very necessary to overcome these limitations. In this study, to improve the performance and reduce the complexity involves in the medical image segmentation process, we have investigated Berkeley wavelet transformation (BWT) based brain tumor segmentation. Furthermore, to improve the accuracy and quality rate of the support vector machine (SVM) based classifier, relevant features are extracted from each segmented tissue. The experimental results of proposed technique have been evaluated and validated for performance and quality analysis on magnetic resonance brain images, based on accuracy, sensitivity, specificity, and dice similarity index coefficient. The experimental results achieved 96.51% accuracy, 94.2% specificity, and 97.72% sensitivity, demonstrating the effectiveness of the proposed technique for identifying normal and abnormal tissues from brain MR images. The experimental results also obtained an average of 0.82 dice similarity index coefficient, which indicates better overlap between the automated (machines) extracted tumor region with manually extracted tumor region by radiologists. The simulation results prove the significance in terms of quality parameters and accuracy in comparison to state-of-the-art techniques.

BACKGROUND: For more than three decades, the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) has provided a framework to quantify health loss due to diseases, injuries, and associated risk factors. This paper presents GBD 2023 findings on disease and injury burden and risk-attributable health loss, offering a global audit of the state of world health to inform public health priorities. This work captures the evolving landscape of health metrics across age groups, sexes, and locations, while reflecting on the remaining post-COVID-19 challenges to achieving our collective global health ambitions. METHODS: The GBD 2023 combined analysis estimated years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 375 diseases and injuries, and risk-attributable burden associated with 88 modifiable risk factors. Of the more than 310 000 total data sources used for all GBD 2023 (about 30% of which were new to this estimation round), more than 120 000 sources were used for estimation of disease and injury burden and 59 000 for risk factor estimation, and included vital registration systems, surveys, disease registries, and published scientific literature. Data were analysed using previously established modelling approaches, such as disease modelling meta-regression version 2.1 (DisMod-MR 2.1) and comparative risk assessment methods. Diseases and injuries were categorised into four levels on the basis of the established GBD cause hierarchy, as were risk factors using the GBD risk hierarchy. Estimates stratified by age, sex, location, and year from 1990 to 2023 were focused on disease-specific time trends over the 2010-23 period and presented as counts (to three significant figures) and age-standardised rates per 100 000 person-years (to one decimal place). For each measure, 95% uncertainty intervals [UIs] were calculated with the 2·5th and 97·5th percentile ordered values from a 250-draw distribution. FINDINGS: Total numbers of global DALYs grew 6·1% (95% UI 4·0-8·1), from 2·64 billion (2·46-2·86) in 2010 to 2·80 billion (2·57-3·08) in 2023, but age-standardised DALY rates, which account for population growth and ageing, decreased by 12·6% (11·0-14·1), revealing large long-term health improvements. Non-communicable diseases (NCDs) contributed 1·45 billion (1·31-1·61) global DALYs in 2010, increasing to 1·80 billion (1·63-2·03) in 2023, alongside a concurrent 4·1% (1·9-6·3) reduction in age-standardised rates. Based on DALY counts, the leading level 3 NCDs in 2023 were ischaemic heart disease (193 million [176-209] DALYs), stroke (157 million [141-172]), and diabetes (90·2 million [75·2-107]), with the largest increases in age-standardised rates since 2010 occurring for anxiety disorders (62·8% [34·0-107·5]), depressive disorders (26·3% [11·6-42·9]), and diabetes (14·9% [7·5-25·6]). Remarkable health gains were made for communicable, maternal, neonatal, and nutritional (CMNN) diseases, with DALYs falling from 874 million (837-917) in 2010 to 681 million (642-736) in 2023, and a 25·8% (22·6-28·7) reduction in age-standardised DALY rates. During the COVID-19 pandemic, DALYs due to CMNN diseases rose but returned to pre-pandemic levels by 2023. From 2010 to 2023, decreases in age-standardised rates for CMNN diseases were led by rate decreases of 49·1% (32·7-61·0) for diarrhoeal diseases, 42·9% (38·0-48·0) for HIV/AIDS, and 42·2% (23·6-56·6) for tuberculosis. Neonatal disorders and lower respiratory infections remained the leading level 3 CMNN causes globally in 2023, although both showed notable rate decreases from 2010, declining by 16·5% (10·6-22·0) and 24·8% (7·4-36·7), respectively. Injury-related age-standardised DALY rates decreased by 15·6% (10·7-19·8) over the same period. Differences in burden due to NCDs, CMNN diseases, and injuries persisted across age, sex, time, and location. Based on our risk analysis, nearly 50% (1·27 billion [1·18-1·38]) of the roughly 2·80 billion total global DALYs in 2023 were attributable to the 88 risk factors analysed in GBD. Globally, the five level 3 risk factors contributing the highest proportion of risk-attributable DALYs were high systolic blood pressure (SBP), particulate matter pollution, high fasting plasma glucose (FPG), smoking, and low birthweight and short gestation-with high SBP accounting for 8·4% (6·9-10·0) of total DALYs. Of the three overarching level 1 GBD risk factor categories-behavioural, metabolic, and environmental and occupational-risk-attributable DALYs rose between 2010 and 2023 only for metabolic risks, increasing by 30·7% (24·8-37·3); however, age-standardised DALY rates attributable to metabolic risks decreased by 6·7% (2·0-11·0) over the same period. For all but three of the 25 leading level 3 risk factors, age-standardised rates dropped between 2010 and 2023-eg, declining by 54·4% (38·7-65·3) for unsafe sanitation, 50·5% (33·3-63·1) for unsafe water source, and 45·2% (25·6-72·0) for no access to handwashing facility, and by 44·9% (37·3-53·5) for child growth failure. The three leading level 3 risk factors for which age-standardised attributable DALY rates rose were high BMI (10·5% [0·1 to 20·9]), drug use (8·4% [2·6 to 15·3]), and high FPG (6·2% [-2·7 to 15·6]; non-significant). INTERPRETATION: Our findings underscore the complex and dynamic nature of global health challenges. Since 2010, there have been large decreases in burden due to CMNN diseases and many environmental and behavioural risk factors, juxtaposed with sizeable increases in DALYs attributable to metabolic risk factors and NCDs in growing and ageing populations. This long-observed consequence of the global epidemiological transition was only temporarily interrupted by the COVID-19 pandemic. The substantially decreasing CMNN disease burden, despite the 2008 global financial crisis and pandemic-related disruptions, is one of the greatest collective public health successes known. However, these achievements are at risk of being reversed due to major cuts to development assistance for health globally, the effects of which will hit low-income countries with high burden the hardest. Without sustained investment in evidence-based interventions and policies, progress could stall or reverse, leading to widespread human costs and geopolitical instability. Moreover, the rising NCD burden necessitates intensified efforts to mitigate exposure to leading risk factors-eg, air pollution, smoking, and metabolic risks, such as high SBP, BMI, and FPG-including policies that promote food security, healthier diets, physical activity, and equitable and expanded access to potential treatments, such as GLP-1 receptor agonists. Decisive, coordinated action is needed to address long-standing yet growing health challenges, including depressive and anxiety disorders. Yet this can be only part of the solution. Our response to the NCD syndemic-the complex interaction of multiple health risks, social determinants, and systemic challenges-will define the future landscape of global health. To ensure human wellbeing, economic stability, and social equity, global action to sustain and advance health gains must prioritise reducing disparities by addressing socioeconomic and demographic determinants, ensuring equitable health-care access, tackling malnutrition, strengthening health systems, and improving vaccination coverage. We live in times of great opportunity. FUNDING: Gates Foundation and Bloomberg Philanthropies.

In the last few decades, the vast potential of nanomaterials for biomedical and healthcare applications has been extensively investigated. Several case studies demonstrated that nanomaterials can offer solutions to the current challenges of raw materials in the biomedical and healthcare fields. This review describes the different nanoparticles and nanostructured material synthesis approaches and presents some emerging biomedical, healthcare, and agro-food applications. This review focuses on various nanomaterial types (e.g., spherical, nanorods, nanotubes, nanosheets, nanofibers, core-shell, and mesoporous) that can be synthesized from different raw materials and their emerging applications in bioimaging, biosensing, drug delivery, tissue engineering, antimicrobial, and agro-foods. Depending on their morphology (e.g., size, aspect ratio, geometry, porosity), nanomaterials can be used as formulation modifiers, moisturizers, nanofillers, additives, membranes, and films. As toxicological assessment depends on sizes and morphologies, stringent regulation is needed from the testing of efficient nanomaterials dosages. The challenges and perspectives for an industrial breakthrough of nanomaterials are related to the optimization of production and processing conditions.

Biodegradable polymers have played an important role in the delivery of drugs in a controlled and targeted manner. Polylactic-co-glycolic acid (PLGA) is one of the extensively researched synthetic biodegradable polymers due to its favorable properties. It is also known as a 'Smart Polymer' due to its stimuli sensitive behavior. A wide range of PLGA-based drug delivery systems have been reported for the treatment or diagnosis of various diseases and disorders. The present review provides an overview of the chemistry, physicochemical properties, biodegradation behavior, evaluation parameters and applications of PLGA in drug delivery. Different drug-polymer combinations developed into drug delivery or carrier systems are enumerated and discussed.

Plants are often exposed to unfavorable environmental conditions, for instance abiotic stresses, which dramatically alter distribution of plant species among ecological niches and limit the yields of crop species. Among these, drought stress is one of the most impacting factors which alter seriously the plant physiology, finally leading to the decline of the crop productivity. Drought stress causes in plants a set of morpho-anatomical, physiological and biochemical changes, mainly addressed to limit the loss of water by transpiration with the attempt to increase the plant water use efficiency. The stomata closure, one of the first consistent reactions observed under drought, results in a series of consequent physiological/biochemical adjustments aimed at balancing the photosynthetic process as well as at enhancing the plant defense barriers against drought-promoted stress (e.g., stimulation of antioxidant systems, accumulation of osmolytes and stimulation of aquaporin synthesis), all representing an attempt by the plant to overcome the unfavorable period of limited water availability. In view of the severe changes in water availability imposed by climate change factors and considering the increasing human population, it is therefore of outmost importance to highlight: (i) how plants react to drought; (ii) the mechanisms of tolerance exhibited by some species/cultivars; and (iii) the techniques aimed at increasing the tolerance of crop species against limited water availability. All these aspects are necessary to respond to the continuously increasing demand for food, which unfortunately parallels the loss of arable land due to changes in rainfall dynamics and prolonged period of drought provoked by climate change factors. This review summarizes the most updated findings on the impact of drought stress on plant morphological, biochemical and physiological features and highlights plant mechanisms of tolerance which could be exploited to increase the plant capability to survive under limited water availability. In addition, possible applicative strategies to help the plant in counteracting unfavorable drought periods are also discussed.

The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper different machine learning algorithms and deep learning are applied to compare the results and analysis of the UCI Machine Learning Heart Disease dataset. The dataset consists of 14 main attributes used for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled using Isolation Forest, and data are also normalized for getting better results. And how this study can be combined with some multimedia technology like mobile devices is also discussed. Using deep learning approach, 94.2% accuracy was obtained.

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.

Breast cancer is the second leading cause of death for women worldwide. The heterogeneity of this disease presents a big challenge in its therapeutic management. However, recent advances in molecular biology and immunology enable to develop highly targeted therapies for many forms of breast cancer. The primary objective of targeted therapy is to inhibit a specific target/molecule that supports tumor progression. Ak strain transforming, cyclin-dependent kinases, poly (ADP-ribose) polymerase, and different growth factors have emerged as potential therapeutic targets for specific breast cancer subtypes. Many targeted drugs are currently undergoing clinical trials, and some have already received the FDA approval as monotherapy or in combination with other drugs for the treatment of different forms of breast cancer. However, the targeted drugs have yet to achieve therapeutic promise against triple-negative breast cancer (TNBC). In this aspect, immune therapy has come up as a promising therapeutic approach specifically for TNBC patients. Different immunotherapeutic modalities including immune-checkpoint blockade, vaccination, and adoptive cell transfer have been extensively studied in the clinical setting of breast cancer, especially in TNBC patients. The FDA has already approved some immune-checkpoint blockers in combination with chemotherapeutic drugs to treat TNBC and several trials are ongoing. This review provides an overview of clinical developments and recent advancements in targeted therapies and immunotherapies for breast cancer treatment. The successes, challenges, and prospects were critically discussed to portray their profound prospects.