University of Kerala

Background Breast cancer is subdivided into four distinct subtypes based on the status of hormone receptors (HR) and human epidermal growth factor receptor 2 (HER2) as HER2⁻/HR⁺, HER2⁺/HR⁺, HER2⁺/HR⁻ and HER2⁻/HR⁻. Among this, ERα positive breast cancer, even though they respond to endocrine treatment, half of the patients acquire resistance and progress with metastasis despite ERα status. Spatio-temporal changes in ERα and their loss under treatment pressure have been reported in a subset of patients, which is a serious problem. Results We have demonstrated that in vitro-generated resistance is correlated with the down regulation of ERα. To study the ERα status transition in live cells, triple-negative breast cancer cells were engineered to express EGFP-ERα, which further supported the existence of complex intracellular signaling that regulates ERα plasticity even in unperturbed conditions. Single-cell clones generate heterogeneity and loss of expression depending on proliferative cues. However, the initial response of cells to 4 μM of 4-hydroxytamoxifen and 1 μM of endoxifen involves up-regulation of ERα, likely due to its early effect on the proteasome or autophagy pathway. Supporting this, inhibition of autophagy and the proteasome further enhanced the expression of ERα. Systematic analysis of RNA sequencing of ERα stable cells further confirmed that ERα regulates diverse intracellular signaling networks such as ubiquitin, proteasome pathways, cell proliferation and Unfolded Protein Responses (UPR), implicating its direct role in post-translational protein modifications. Cell cycle indicator probe expressing receptor-positive breast cancer cells confirmed the ERα expression heterogeneity both in 2D and 3D culture in a cell cycle phase-independent manner. Conclusions Overall, the study confirms the cell’s intrinsic post-transcriptional mechanisms of ERα plasticity that could play a role in receptor heterogeneity and tumor progression under endocrine treatment, which warrants further investigation.

Fluorescence probes offer a sensitive and selective approach for detecting arginine, a biomarker for various biological processes including cancer detection. This study presents a fluorescence turn-on sensor using Cu²⁺-quenched bovine serum albumin-derived carbon dots (BSA@CDs) for arginine detection. The synthesized CDs exhibited a strong blue fluorescence with a quantum yield of 10.15%. The sensing mechanism involved fluorescence quenching of BSA@CDs by Cu²⁺ through static quenching mechanisms including electron transfer and Förster resonance energy transfer (FRET), followed by fluorescence recovery upon arginine binding. A linear correlation of R² = 0.99 was observed, with a limit of detection (LOD) of 25.8 µM. The method demonstrated high selectivity and stability against potential interfering biomolecules and ions. Furthermore, the developed BSA@CDs/Cu probe was applied to detect arginine in biological samples such as urine and saliva, achieving recovery rates ranging from 86 to 105% at different dilution factors. A paper-based assay using Whatman 40 filter paper was also developed as a proof of concept for potential point of care applications. This study highlights the potential of BSA@CDs/Cu as an efficient, economical, and non-intrusive fluorescent probe for arginine detection, paving the way for improved cancer biomarker screening methodologies.

Previous in vitro and in vivo studies have shown an association between BRCA1-defective cancers and the expression of β-hCG, where accelerated tumor progression has been observed in the presence of β-hCG due to its binding to and phosphorylation of TGFβR-II. Given the absence of reported small molecule inhibitors against β-hCG, the present study endeavours to identify the interacting residues of β-hCG with TGFβR-II. Through virtual screening, molecular docking and dynamic simulation studies, the investigation identified six potential small molecule inhibitors, namely, cefotetan, 2′,7′-dichlorofluorescein (DCF), 2′,7′-difluorofluorescein (DFF), F6658-4634, F0922-0590 and F0385-0029. Notably, all inhibitors exhibited interacting residues on β-hCG that coincided with the site to which it binds to TGFβR-II. Further in vitro MTT assays showed a reduction in proliferation in various cell lines, including the cell line developed in our laboratory, which was induced to have BRCA1 promoter hypermethylation using modified CRISPR technology. Binding studies such as Microscale Thermophoresis (MST) and Isothermal Titration Calorimetry (ITC) exhibited the binding of cefotetan to hCG, which could be indicative of the selective cytotoxic effect of cefotetan in β-hCG secreting breast cancer cell lines. The results indicate selective inhibition by these potential inhibitors in BRCA1-defective breast cancer cell lines, with cefotetan being the best among all with an IC50 of 32 μM. This is the first study to report the anticancer activity of cefotetan, an FDA-approved drug used to treat bacterial infections, and this drug could be repurposed as a targeted therapeutic against β-hCG expressing cancers. Findings of this study hold significant implications for developing potential therapeutic interventions, which target β-hCG in the context of BRCA1-defective breast cancers, aligning with the broader goal of advancing precision medicine in oncology.

To enhance the energy density, power density, and cycling stability of supercapacitors, it is imperative to comprehend and control the interplay between electrodes and the electrolyte. Assessment of electrolyte properties plays a crucial role in guaranteeing the best performance of supercapacitors. In this study, electrochemical energy storage performance of symmetric supercapacitors based on 2D WS2 nanosheets has been evaluated in alkaline (1 M KOH), acidic (1 M H2SO4), and neutral (1 M Na2SO4) electrolytes. At a constant specific current of 1A g⁻¹, the supercapcitors exhibit specific capacitance values of 170, 108, and 89 F g⁻¹, respectively in KOH, H2SO4, and Na2SO4 electrolytes. At a power density of 0.8 kW kg⁻¹, the associated energy density values are calculated as 15, 9.6, and 8 Wh kg⁻¹ for alkaline, acidic, and neutral electrolytes, respectively. WS2 nanosheets deliver superior performance in KOH electrolyte by retaining the 100% of the initial capacitance value even after 10,000 continuous charge–discharge cycles.

A new coordination compound of Ni(II) with nicotinamide (Nic) and 1,5-naphthalenedisulfonic acid (H2NDS) of formula {[Ni(Nic)2(H2O)4]NDS}.3H2O (NNDSN) has been prepared by gel diffusion technique. SXRD data show that the compound crystallizes in triclinic space group P$\overline{1 }$. In the crystal structure, the Ni(II) ion is coordinated with two nicotinamide units through the nitrogen atom of pyridine ring and four water molecules. The distorted octahedral geometry of the six coordinate Ni(II) compound can be understood from the $\angle$ N–Ni–O (ranges from 86.59(7) to 93.41(7)°), $\angle$ O–Ni–O (88.63(7)° and 91.37(7)°) and difference in bond distances of Ni–O (2.0505(15) and 2.0533(16) Å) and Ni–N (2.1323(17) Å). 1,5-Naphthalenedisulfonate ions present in the crystal lattice balance the charge of Ni(II) ions. In the crystal structure, both coordinated and lattice water molecules, sulfonate groups of 1,5-naphthalenedisulfonate ions and NH2 group of nicotinamide molecules are involved in intermolecular hydrogen bonding. These interactions further stabilize the crystal structure. FT-IR spectral studies show that SO3⁻ group of 1,5-naphthalenedisulfonate ion, C = O and NH2 groups of nicotinamide molecule are not involved in coordinate bond formation. In the UV–vis spectrum, the peaks corresponding to ³A2g → ³T1g (P) and ³A2g → ³T1g (F) transitions are observed at λmax of 388 and 685 nm respectively. TG/DTG studies show that the crystal structure is stable up to 102 °C and the decomposition to NiO takes place through six stages. Photoluminescence studies show that the emission intensity of NNDSN can be quenched by Fe³⁺ ions. This method can be used for the sensing of Fe³⁺ ion at micro level concentration.

Background Persistent diabetes leads to inflammatory changes in liver, heart, and pancreas resulting in pathological alterations. Morin’s ability to reduce inflammation in the pancreas, liver, and heart of streptozotocin (STZ)-induced diabetic rats was assessed. Methods Male Sprague–Dawley rats were given a single intraperitoneal injection of STZ at a dose of 40 mg/kg body weight to develop diabetes. Morin (50 mg/kg b. wt) and metformin (100 mg/kg) were given orally for 45 days. Proinflammatory enzymes such as myeloperoxidase, nitric oxide synthase, lipoxygenase, and cyclooxygenase were analyzed. Expressions of interleukin (IL)-6, (TNF-α), and nuclear factor kappa B (NF-κB) in the heart, liver, and pancreas were also studied. Data were statistically analyzed. Results Blood glucose and glycosylated hemoglobin levels significantly decreased in the treated groups. The activity of proinflammatory enzymes which were increased in the diabetic rats was reduced significantly when treated with morin and metformin. The expression of NF-κB, TNF-α, and IL-6, of heart, liver, and pancreas showed significant downregulation in the morin treated groups in comparison with diabetic control rats. The findings of histopathological analysis suggest that morin is a promising compound in the diabetic studies. Conclusion Morin reduced the levels of inflammatory enzymes, glucose, and HbA 1 C. The STZ-induced diabetic rats exhibited the increased expression levels of proinflammatory cytokines and NF-κB in the heart, liver, and pancreas, which were significantly reduced by morin and metformin.

Background Brain relaxation is attained using several techniques while sleep remains nature’s ultimate remedy. Currently, various machine learning (ML) tools are applied to identify and understand the neural correlates of relaxation from the electroencephalography (EEG) signals. Majority of earlier studies focused on comparing power in the EEG bands during eyes-open and eyes-closed resting state paradigm to train the datasets. However, several Yogic practices are performed using sitting and supine positions. Purpose This study was aimed to elucidate the relaxation correlates in EEG between supine and sitting position during eyes-closed condition using ML classifiers. Methods EEG signals were recorded on five different days from O1, OZ, O2, C3, CZ, C4, F3, FZ and F4 brain region using nine unipolar electrodes for 25 minutes during eyes-closed supine and eyes-closed sitting postures each on, along with electrocardiogram (ECG) for heart rate variability (HRV) analysis in a healthy participant. Relaxation was assessed by extracting the relative power of the alpha and theta waves from the EEG data and corroborated with the alpha and theta lateralisation index (LI) and HRV parameters. These EEG metrics were analysed by leveraging ML classifiers (K-nearest neighbours (KNN), support vector machine(SVM), random forest (RF) and XGBoost) for relaxation states under sitting and supine states. Results Out of all the used classifiers, performance indices of SVM excelled in classifying relaxation states from the EEG alpha and theta band data that was verified with the HRV data and correlated with LI. Conclusion This study demonstrates that ML especially the SVM was effective in classifying the relaxation states during different postures from the EEG. LI and HRV metrics effectively decoded the underlying message in the EEG and ECG respectively.

Membrane fouling, originating from a diverse range of sources such as organic matter, inorganic particulates, biological agents, and industrial contaminants, continues to pose a significant challenge in water purification processes. This fouling results from complex nonspecific interactions between the membrane surface and foulants, leading to a substantial decline in filtration performance, including reduced permeability, selectivity, and operational lifespan. To address these limitations, there is an urgent need to engineer advanced membranes with integrated antibacterial, catalytic, and antifouling functionalities to enable efficient and sustainable water treatment. In this context, we developed an innovative approach to mitigate membrane fouling of polyethersulfone (PES) membrane by coating with silver-decorated reduced graphene oxide (rGO). This coating imparts exceptional antibacterial efficacy, catalytic dye degradation properties, antifouling performance and remarkable filtration capacity to the PES membrane. The antibacterial assessments conducted against Staphylococcus aureus (S. aures) and Escherichia coli (E. coli) bacteria revealed that increasing concentrations of silver in rGO composites resulted in a pronounced inhibitory effect on bacterial growth, with the most significant activity observed for membranes with higher silver loadings (rGO A500). Moreover, catalytic studies performed on the rGO A500 membrane emphasize the degradation of Congo Red, Methyl-Orange, and as well as the conversion of Nitrophenol to Aminophenol, occurring within 46 min, 25 min, and 23 min, respectively. Furthermore, the rGO A500 membrane exhibits notable antifouling properties, evidenced by a flux recovery ratio of 98% and a minimal irreversible fouling ratio of 1.7% during Bovine Serum Albumin (BSA) protein filtration. Additionally, the composite membrane demonstrates an impressive water flux of 50 L m⁻² h⁻¹ along with dye rejection efficiency of 92% for Congo Red, 86% for Rhodamine-B, and 81% for Methylene Blue. Overall, the findings underscore the multifunctional performance of the rGO A500 composite membrane, showcasing its antibacterial, catalytic and antifouling capabilities, and positioning it as a robust and practical solution for next-generation wastewater treatment technologies. Graphical abstract

Rapid urbanization and increased tourism in urban catchments cause a greater input of nutrients and organic matter into lacustrine systems, leading to ecological degradation. The sixth Sustainable Development Goal, which focuses on reducing pollution and improving water quality in lakes and reservoirs, may only be achieved by identifying the sources of pollutants and depositions in the waterbody. Veli-Akkulam is a well-known tourist destination and environmentally significant location in Thiruvananthapuram. It is a small urban lake that borders the Arabian Sea, having an average length of 3.2 km and a depth of 2.4 m. The purpose of this study is to determine the sources of organic pollution in Veli-Akkulam Lake by analysing the spatio-temporal variations in the concentration of biogenic elements and TOC/N of the lake surface sediment samples taken from eight selected stations. The study’s findings indicate that silt and clay predominate the eastern portion of the lake, where concentrations of total carbon, total organic carbon, total nitrogen, and total phosphorus were 6.26%, 5.1%, 0.9%, and 14.5%, respectively. During the pre-monsoon, monsoon, and post-monsoon seasons, the TOC/N varied between 0.95 and 5.04, 0.18 and 4, and 1.01 and 30, respectively, indicating the presence of both autochthonous and allochthonous sources of organic matter. There was a substantial positive correlation between the biogenic elements and the TOC/N ratio. According to the findings, the lake has been affected by eutrophication caused by organic contaminants. Dredging, sediment elution, sewage treatment, and other management techniques must be implemented so as to sustainably manage the lake.

Nepenthes pitchers are leaf‐evolved biological traps containing high levels of CO 2 within them. Extrafloral nectar (EFN) secreted by these pitchers has long been regarded as the major reward to visiting arthropods, but its chemical constituents and their role in prey capture are least explored. In this study Nepenthes EFNs were isolated, and their sugars, amino acids, proteins, vitamin C and fatty acids were analysed using HPTLC‐densitometry, UFLC, and biochemical assays. C:N ratio, minerals and volatiles of EFNs were determined by CHNS, ICP‐OES and headspace‐GC–MS, respectively; metabolic profiling was carried out using LC–MS. Peristome/lid EFNs and their active constituent were subjected to acetylcholinesterase (AChE) inhibition assays; the AChE inhibitor was characterized by bioactivity guided isolation and spectroscopy. Here we demonstrate Nepenthes EFN as a sugar (glucose–fructose–sucrose) mix with high C:N ratio, minimal amino acids, proteins, and vitamin C. N. khasiana peristome and lid EFNs displayed strong AChE inhibition; the naphthoquinone derivative, (+)‐isoshinanolone, was identified as the AChE inhibitor. Plumbagin, the major volatile naphthoquinone in Nepenthes , also showed strong AChE inhibition. Direct EFN‐ and (+)‐isoshinanolone‐feeding bioassays showed symptoms of cholinergic toxicity in ants. Nepenthes EFN is a toxic bait which hinders neuronal activity in visiting arthropods. Their pitchers adopt various deceptive strategies for prey capture, and our study abolishes the notion that Nepenthes EFN is a reward to visiting ants and other arthropods. Moreover, our findings suggest that elevated CO 2 levels within the pitchers play a crucial role in regulating the growth, metabolism, herbivory, and carnivory in Nepenthes .

A surge in electronic device markets has generated considerable demand for thin-film microbatteries for practical usage in everyday electronics. Due to the accommodation constraints and safety issues of batteries that use liquid electrolytes, solid-state electrolytes are highly sought after. However, poor interfacial contacts and expansion strain-related mechanical failures are major setbacks for the direct implementation of solid-state batteries. Upon downsizing, i.e. fabrication in the 2D regime, the aforementioned issues can be easily addressed. Therefore, thin-film solid-state batteries are the most suited architecture for everyday electronics, IoT and medical implants. Several different techniques have been utilized to fabricate electrodes and solid electrolytes for all-solid-state thin-film batteries. However, the conventional techniques do not provide a platform to overcome challenges, such as particle agglomeration, formation of off-stoichiometric phases, and surface morphological inconsistencies. In this regard, RF magnetron sputtering stands out as the most adaptable deposition technique. RF sputtering provides a platform to fabricate a wide variety of materials, including conventional electrodes and solid-state electrolytes. In addition, it is possible to control the size, structure, composition and morphology easily by simply altering the synthesis parameters. RF sputtering is industrially scalable for fabricating large volumes of thin-film batteries as it serves as a facile method to engineer the electrodes, solid electrolytes and their interfaces. The present review focuses on the recent developments and prospects of cathode, electrolyte and anode material thin films grown via RF magnetron sputtering and their optimization for their potential application as commercial batteries for microelectronic devices. Graphical Abstract

Numerous contemporary computer-aided disease detection methodologies predominantly depend on feature engineering techniques; yet, they possess several drawbacks, including the presence of redundant features and excessive time consumption. Conventional feature engineering necessitates considerable manual effort, resulting in issues from superfluous features that diminish the model’s performance potential. In contrast to recent effective deep-learning models, these may address these issues while concurrently obtaining and capturing intricate structures inside extensive medical image datasets. Deep learning models autonomously develop feature extraction abilities but require substantial computational resources and extensive datasets to yield significant abstraction methods. The dimensionality problem is a key challenge in healthcare research. Despite the hopeful advancements in illness identification with deep learning architectures in recent years, attaining high performance remains notably tough, particularly in scenarios with limited data or intricate feature spaces. This research endeavors to elucidate the integration of bio-inspired optimization techniques that improve disease diagnostics through deep learning models. The targeted feature selection of bio-inspired methods enhances computational efficiency and operational efficacy by minimizing model redundancy and computational costs, particularly when data availability is constrained. These algorithms employ natural selection and social behavior models to efficiently explore feature spaces, enhancing the robustness and generalizability of deep learning systems. This paper seeks to elucidate the efficacy of deep learning models in medical diagnostics by employing concepts and strategies derived from biological system ontologies, such as genetic algorithms, particle swarm optimization, ant colony optimization, artificial immune systems, and swarm intelligence. Bio-inspired methodologies have exhibited significant potential in addressing critical challenges in illness detection across many data types. It seeks to tackle the problem by creating bio-inspired optimization methods to enhance efficient and equitable deep learning for illness diagnosis. This work assists researchers in selecting the most effective bio-inspired algorithm for disease categorization, prediction, and the analysis of high-dimensional biomedical data.

Background Myocardial infarction (MI) continues to pose a significant global healthcare burden despite advances in treatment options and their effectiveness. The incidence, prevalence, and mortality rates associated with MI are rising, emphasizing the need for improved therapeutic strategies. Traditional invasive surgical methods, aimed at recanalizing blood flow to the coronary arteries, have proven insufficient in fully addressing the complexities of MI. This ongoing challenge necessitates the exploration of novel approaches to enhance treatment efficacy and outcomes for MI patients. Main text One promising approach is the use of nanoparticle delivery systems for targeted therapy to the infarct site. When conventional methods fail to achieve adequate permeability and retention, nanoparticle strategies offer a potential solution. Functionalizing nanoparticles is a particularly effective technique, allowing these particles to conjugate with specific ligands. These ligands possess the intrinsic ability to selectively bind to receptors that are overexpressed or uniquely present at the infarct site, thereby conferring “smartness” to the nanoparticle constructs. This review delves into the various strategies employed in nanoparticle-ligand functionalization, highlighting the versatility and potential of these approaches. It provides a detailed cross section of several ligand classes, each with unique properties and binding affinities that make them suitable for targeted delivery in the context of MI. The focus is on identifying ligands that are either unique to the infarcted myocardium or significantly upregulated during MI, ensuring precise and efficient targeting of therapeutic agents. Conclusion In summary, while traditional surgical methods for restoring blood flow in MI patients remain important, they are not sufficient on their own. By leveraging the specificity of these ligands, nanoparticles can be directed precisely to the infarct site, enhancing the delivery and efficacy of therapeutic agents. This review underscores the need for continued research into nanoparticle-ligand functionalization strategies, aiming to improve outcomes for MI patients and reduce the global burden of this condition.

Silsesquioxane‐mediated morphology transition of nano‐gold is reported. The silsesquioxane obtained through the hydrolysis and condensation of 3‐aminopropyltriethoxysilane and vinyltriethoxysilane in ethanol–water mixture was used as the reducing‐cum‐stabilizing agent to synthesize nano‐gold. A transition of morphology of nano‐gold from predominantly nanoprisms (GNPMs) to quasi‐spherical nanoparticles (GNPs) was observed between silsesquioxane concentrations of 10.12 and 16.8 mM. Unhydrolyzed silane mixture of the same composition as that of the silsesquioxane or hydrolyzed silane with aminopropyl groups alone, or ethanol/water ratio below 2.3:1 and above 4:1 failed to produce stable colloidal gold and the morphology transition. Therefore, it is proposed that hydrophobic interaction of the vinyl groups with ethanol, hydrophilic interaction of amino groups with water, steric factors due to the rigid cage‐like structures of the silsesquioxanes, and variation of the reduction time with increasing concentration of silsesquioxanes were the reasons for the observed morphology transition of the nano‐gold from GNPMs to GNPs. The GNPs and GNPMs were modified with doxorubicin intercalated laponite to obtain composite nanoparticles. In vitro evaluation of the doxorubicin‐intercalated laponite composites of GNPs and GNPMs using the human cervical cancer cell line (HeLa) indicated a concentration‐dependent cytotoxicity profile.

We investigate the transport feature of an inertial chiral active Ornstein-Uhlenbeck particle moving on a two-dimensional surface. Using both analytical approach and numerical simulations, we have explored the particle’s transient and steady-state behavior by analyzing the simulated particle trajectories, probability distribution functions for position and velocity, mean square displacement, mean square velocity, and effective kinetic temperature of the medium. From the mean square displacement calculations, we observe that, unlike an inertial active Brownian particle, a chiral active particle manifests an initial ballistic, intermediate transient sub-diffusive to non-diffusive, and the conventional long-time diffusive behavior. The intermediate transient sub-diffusive to non-diffusive behavior is prominent for the self-propulsion of an overdamped particle. It can be understood by chirality-induced transient self-trapping, which persists for short time intervals and diffuses away in the time asymptotic limit or at the steady state. This behavior is further complemented by the exact calculation of mean square velocity or effective kinetic temperature of the medium, which is a decreasing function of the magnitude of chirality. Moreover, in the inertial regime, the steady-state MSD and MSV are found to have a dependence on both the chirality and the activity time scale and hence can be controlled by tuning the persistent duration of activity or strength of the chirality of the particle.

Methionine is an essential sulfur‐containing amino acid that plays a pivotal role in cancer biology due to its abnormal metabolism in malignant cells. Elevated methionine levels serve as potential biomarkers for various cancers, highlighting their diagnostic significance. This study presents a bimetallic sensing platform using L‐cysteine‐capped copper–tin nanoclusters (Cu–SnNCs) for the selective and sensitive detection of methionine. The Cu–SnNCs are synthesized via a one‐pot hydrothermal process, exhibiting strong blue fluorescence, high stability, and significant optical properties. Fe³⁺ is employed as a quencher, leveraging its paramagnetic nature to suppress fluorescence, which is subsequently restored upon the addition of methionine. The sensing mechanism demonstrates a linear response over a methionine concentration range of 0.18–1.62 mm, with a detection limit of 1.69 μm. The probe's potential is further validated with a preliminary paper strip test for detecting methionine in biological samples. These findings underscore the utility of Cu–SnNCs as a noninvasive, economical diagnostic tool for cancer detection and screening.

The fundamental role of biotechnology in the breakthrough of cancer drugs is a sign of a transformative influence that has markedly elevated our intellectual capacity and strategies for dealing with cancer. Biotechnology utilizes modern methods and technologies to make sense of the complexities of cancer biology, identify novel targets, and establish various innovative therapies. Recognizing these issues enables the prompt implementation of measures to augment the possibility of success. A choice of essential basics emphasizes the consequences of biotechnology in this field. Despite the extensive scientific data accumulated in the field of cancer research, this infirmity continues to set as a noteworthy global cause of mortality. It compels a substantial societal burden due to its allied comorbidities. In this context, it is decisive to improve cancer therapy approaches by focusing on advancing drug discovery technologies, optimizing them, and rapidly developing bioinformatics programs. The landscape of cancer research and drug development has entered a new phase, marked by the real-time materialization of advanced technologies like whole-genome profiling, proteome profiling, and exome sequencing. These innovative approaches have yielded groundbreaking information. Utilizing artificial intelligence, there may also be an advanced approach for identifying novel anticancer targets and uncovering novel drugs inside biological networks. This is achievable as these networks efficiently conserve and enumerate the interactions between components of cell systems that underlie human diseases, including cancer. It covers a wide range of topics, from understanding cancer biology putting into practice using high-tech technologies. It also accentuates advancements achieved, complications encountered, and the optimistic outlook for cancer combat through biotech-driven strategies. Biotechnology plays a pivotal and groundbreaking role in the exploration of cancer drugs, employing a comprehensive approach that involves unraveling the molecular intricacies of cancer, developing targeted therapies, and advancing personalized medicine. The realms of drug discovery and personalized medicine are closely interconnected, with advancements in personalized medicine significantly influencing the drug discovery journey. Utilizing state-of-the-art genomic and proteomic techniques, biotechnology aims to decipher intricate resistance mechanisms within oncological scenarios. This reveals molecular abnormalities and crucial signaling cascades that are central to evading therapeutic interventions. The enduring advancement of biotechnological tools holds the potential for additional breakthroughs in the ongoing fight against cancer.