Insights from multi-omic modeling of neurodegeneration in xeroderma pigmentosum using an induced pluripotent stem cell system.

Xeroderma pigmentosum (XP) is caused by defective nucleotide excision repair of DNA damage. This results in hypersensitivity to ultraviolet light and increased skin cancer risk, as sunlight-induced photoproducts remain unrepaired. However, many XP patients also display early-onset neurodegeneration, which leads to premature death. The mechanism of neurodegeneration is unknown. Here, we investigate XP neurodegeneration using pluripotent stem cells derived from XP patients and healthy relatives, performing functional multi-omics on samples during neuronal differentiation. We show substantially increased levels of 5',8-cyclopurine and 8-oxopurine in XP neuronal DNA secondary to marked oxidative stress. Furthermore, we find that the endoplasmic reticulum stress response is upregulated and reversal of the mutant genotype is associated with phenotypic rescue. Critically, XP neurons exhibit inappropriate downregulation of the protein clearance ubiquitin-proteasome system (UPS). Chemical enhancement of UPS activity in XP neuronal models improves phenotypes, albeit inadequately. Although more work is required, this study presents insights with intervention potential.

Self-powered photodetectors: a device engineering perspective.

Nanoscale self-powered photodetectors that can work without any external source of energy are required for future applications. There is potential demand for these devices in areas like wireless surveillance, weather forecasting, remote monitoring, and places where the availability of power is scarce. This study provides an overview of state of the art research trends and improvements in self-powered photodetectors. A device engineering perspective for improvement in the figures of merit has been presented along with a description of additional effects like pyro-phototronic, piezo-phototronic, and surface plasmonics.

Mechanisms of the initial stage of non-enzymatic oxidation of wine: A mini review.

Non-enzymatic oxidation is a primary factor affecting wine quality during bottling or aging. Although red and white wines exhibit distinct responses to oxidation over time, the fundamental mechanisms driving this transformation remain remarkably uniform. Non-enzymatic oxidation of wine commences with the intricate interplay between polyphenols and oxygen, orchestrating a delicate redox dance with iron and copper. Notably, copper emerges as an accelerant in this process. To safeguard wine integrity, sulfur dioxide (SO2) is routinely introduced to counteract the pernicious effects of oxidation by neutralizing hydrogen peroxide and quinone. In this comprehensive review, the initial stages of non-enzymatic wine oxidation are examined. The pivotal roles played by polyphenols, oxygen, iron, copper, and SO2 in this complex oxidative process are systematically explored. Additionally, the effect of quinone formation on wine characteristics and the intricate dynamics governing oxygen availability are elucidated. The potential synergistic or additive effects of iron and copper are probed, and the precise balance between SO2 and oxygen is scrutinized. This review summarizes the mechanisms involved in the initial stages of non-enzymatic oxidation of wine and anticipates the potential for further research.

Sinomenine protects against atherosclerosis in apolipoprotein E-knockout mice by inhibiting of inflammatory pathway.

Atherosclerosis, a multifaceted and persistent inflammatory condition, significantly contributes to the progression of cardiocerebrovascular disorders, such as myocardial infarctions and cerebrovascular accidents. It involves the accumulation of cholesterol, fatty deposits, calcium and cellular debris in the walls of arteries, leading to the formation of plaques. Our aim is to investigate the potential of sinomenine to counteract atherosclerosis in mice lacking Apolipoprotein E (ApoE-/-) Mice. We employed the high-fat diet-induced method to induce atherosclerosis in ApoE-/- mice, and the mice were treated with sinomenine (5, 10, and 15 mg/kg) and simvastatin (0.5 mg/kg) for 12 weeks. Body weight, water intake, and food intake were assessed. Lipid parameters, oxidative stress, inflammatory cytokines, and mRNA levels were estimated. Sinomenine treatment remarkably (P < 0.001) suppressed body weight, along with food and water intake. Sinomenine altered the levels of total cholesterol (TC), high-density lipoprotein (HDL), triglyceride (TG), low-density lipoprotein (LDL), and very low-density lipoprotein (VLDL), which were modulated in the atherosclerosis group. Sinomenine treatment also altered the levels of oxidative stress parameters such as glutathione peroxidase (GPx), catalase (CAT), malonaldehyde (MDA), superoxide dismutase (SOD) and glutathione (GSH). In addition, it modulated cardiac parameters like C-reactive protein (CRP), endothelin-1 (ET-1), thromboxane B2 (TXB2), nitric oxide (NO), cardiac troponin I (cTnI), lactate dehydrogenase (LDH), and creatinine kinase isoenzymes (CK-MB). Inflammatory cytokines interleukin (IL)-1α, IL-1ß, TNF-α, IL-6, and IL-10 were also affected. Sinomenine further suppressed the mRNA expression of IL-6, IL-17, IL-10, tumor necrosis factor-α (TNF-α), Il-1ß, monocyte chemoattractant protein-1 (MCP-1), MCP-2, MCP-3, transforming Growth Factor-1ß (TGF-1ß), vascular cell adhesion molecule 1 (VCAM-1), and intercellular adhesion molecule-1 (ICAM-1). The results suggest that sinomenine remarkably suppressed the development of atherosclerosis in the early stage.

Satellite or ground-based measurements for air pollutants (PM2.5, PM10, SO2, NO2, O3) data and their health hazards: which is most accurate and why?

Air pollution is growing at alarming rates on regional and global levels, with significant consequences for human health, ecosystems, and change in climatic conditions. The present 12 weeks (4 October 2021, to 26 December 2021) study revealed the different ambient air quality parameters, i.e., PM2.5, PM10, SO2, NO2, and O3 over four different sampling stations of Delhi-NCR region (Dwarka, Knowledge park III, Sector 125, and Vivek Vihar), India, by using satellite remote sensing data (MERRA-2, OMI, and Aura Satellite) and different ground-based instruments. The ground-based observation revealed the mean concentration of PM2.5 in Dwarka, Knowledge park III, Sector 125, and Vivek Vihar as 279 µg m-3, 274 µg m-3, 294 µg m-3, and 365 µg m-3, respectively. The ground-based instrumental concentration of PM2.5 was greater than that of satellite observations, while as for SO2 and NO2, the mean concentration of satellite-based monitoring was higher as compared to other contaminants. Negative and positive correlations were observed among particulate matter, trace gases, and various meteorological parameters. The wind direction proved to be one of the prominent parameter to alter the variation of these pollutants. The current study provides a perception into an observable behavior of particulate matter, trace gases, their variation with meteorological parameters, their health hazards, and the gap between the measurements of satellite remote sensing and ground-based measurements.

Enhancing parasitic organism detection in microscopy images through deep learning and fine-tuned optimizer.

Parasitic organisms pose a major global health threat, mainly in regions that lack advanced medical facilities. Early and accurate detection of parasitic organisms is vital to saving lives. Deep learning models have uplifted the medical sector by providing promising results in diagnosing, detecting, and classifying diseases. This paper explores the role of deep learning techniques in detecting and classifying various parasitic organisms. The research works on a dataset consisting of 34,298 samples of parasites such as Toxoplasma Gondii, Trypanosome, Plasmodium, Leishmania, Babesia, and Trichomonad along with host cells like red blood cells and white blood cells. These images are initially converted from RGB to grayscale followed by the computation of morphological features such as perimeter, height, area, and width. Later, Otsu thresholding and watershed techniques are applied to differentiate foreground from background and create markers on the images for the identification of regions of interest. Deep transfer learning models such as VGG19, InceptionV3, ResNet50V2, ResNet152V2, EfficientNetB3, EfficientNetB0, MobileNetV2, Xception, DenseNet169, and a hybrid model, InceptionResNetV2, are employed. The parameters of these models are fine-tuned using three optimizers: SGD, RMSprop, and Adam. Experimental results reveal that when RMSprop is applied, VGG19, InceptionV3, and EfficientNetB0 achieve the highest accuracy of 99.1% with a loss of 0.09. Similarly, using the SGD optimizer, InceptionV3 performs exceptionally well, achieving the highest accuracy of 99.91% with a loss of 0.98. Finally, applying the Adam optimizer, InceptionResNetV2 excels, achieving the highest accuracy of 99.96% with a loss of 0.13, outperforming other optimizers. The findings of this research signify that using deep learning models coupled with image processing methods generates a highly accurate and efficient way to detect and classify parasitic organisms.

Automated detection and recognition system for chewable food items using advanced deep learning models.

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.

Probiotics as modulators of gut-brain axis for cognitive development.

Various microbial communities reside in the gastrointestinal tract of humans and play an important role in immunity, digestion, drug metabolism, intestinal integrity, and protection from pathogens. Recent studies have revealed that the gut microbiota (GM) is involved in communication with the brain, through a bidirectional communication network known as the gut-brain axis. This communication involves humoral, immunological, endocrine, and neural pathways. Gut dysbiosis negatively impacts these communication pathways, leading to neurological complications and cognitive deficits. Both pre-clinical and clinical studies have demonstrated that probiotics can restore healthy GM, reduce intestinal pH, and reduce inflammation and pathogenic microbes in the gut. Additionally, probiotics improve cell-to-cell signaling and increase blood-brain-derived neurotrophic factors. Probiotics emerge as a potential approach for preventing and managing neurological complications and cognitive deficits. Despite these promising findings, the safety concerns and possible risks of probiotic usage must be closely monitored and addressed. This review article provides a brief overview of the role and significance of probiotics in cognitive health.

Modulation of Physical and Thermal Properties in Wild Banana (Musa balbisiana Colla) Seed Powder by Moisture Variations.

Engineering and flow properties of banana seed powder as a function of moisture content are important for processing, handling, packaging, and transport processes. The bulk density, tapped density, and porosity increased from 377.37 to 427.36 kg m-3, 622.08 to 746.33 kg m-3, and 38.99-43.74%, respectively, within the increasing moisture content range. The Hausner ratio (Hr) and Carr's compressibility index (CI) significantly (p < 0.05) increased with an increase in moisture content (6.16-19.56% db) of banana seed powder, whereas HR fell in the range of 1.4-2.0, indicating cohesive characteristics of banana seed powder. The angle of repose, angle of spatula, and angle of fall exhibited a linear increase, ranging from 40.6° to 49°, 33.4° to 39.4°, and 35.6° to 42.6°, respectively, with increasing moisture content. The static coefficient of friction was found to be highest for aluminium and glass surfaces and least for stainless steel. The water activity and swelling power of banana seed powder showed a significant increase, while the solubility and oil absorption capacity exhibited a significant decrease within the range of increasing moisture content. The thermal characteristics of wild banana seed powder, such as thermal conductivity (0.16 to 0.20 Wm-1 K-1) and volumetric specific heat (0.58 to 0.99 MJm-3 K-1), demonstrated an increasing trend as the moisture content increased. However, the thermal diffusivity showed a decrease from 0.31 to 0.19 (×10-6 m2s-1) with the increase in moisture content.

Machine Learning-based Deep Analysis of Human Blood using NIR Spectrophotometry Signatures.

BACKGROUND: Non-invasive bio-diagnostics are essential for providing patients with safer treatment. In this subject, significant growth is attained for noninvasive anaemia detection in terms of Hb concentration by means of spectroscopic and image analysis. The lower satisfaction rate is found due to inconsistent results in various patient settings. OBJECTIVE: This observational study aims to present an adaptable point-of-care Near-Infrared (NIR) spectrophotometric approach with a constructive Machine Learning (ML) algorithm for monitoring Haemoglobin (Hb) concentration by considering dominating influencing factors into account. METHODS: To accomplish this objective, 121 subjects (19.2-55.4 years) were enrolled in the study, having a wide range of Hb concentrations (8.2-17.4 g/dL) obtained from two standard Laboratory analyzers. To inspect the performance, the unique dimensionality reduction approaches are applied with numerous regression models using 5-fold cross-validation. RESULTS: The optimum accuracy is found using support vector regression (SVR) and mutual information having 3 independent features i.e. Pearson correlation (r)= 0.79, standard deviation (SD)= 1.07 g/dL, bias=-0.13 g/dL and limits of agreement (LoA)=-2.22 to 1.97 g/dL. Additionally, comparability between two standard laboratory analyzers is found as; r=0.97, SD=0.50 g/dL, bias=0.21 g/dL, and LoA= -0.77 to 1.19 g/dL. CONCLUSION: The precision of ±1 g/dL in 5-fold cross-validation ensures the same performance irrespective of different age groups, gender, BMI, smoking level, drinking level, and skin type. The outcomes with the offered NIR sensing system and an exclusive ML algorithm can accelerate its' requirement at remote locative rural areas and critical care units where continuous Hb monitoring is compulsory.

An Unusual Case of Hoarseness of Voice and Dysphagia in Multiple Myeloma with Amyloidosis.

How to cite this article: Dash S, Pandalanghat S, Kumar Y. An Unusual Case of Hoarseness of Voice and Dysphagia in Multiple Myeloma with Amyloidosis. Indian J Crit Care Med 2024;28(2):175-176.

Flexible Linker-Based Triazine-Functionalized 2D Covalent Organic Frameworks for Supercapacitor and Gas Sorption Applications.

Covalent organic frameworks (COFs) having a large surface area, porosity, and substantial amounts of heteroatom content are recognized as the ideal class of materials for energy storage and gas sorption applications. In this work, we have synthesized four different porous COF materials by the polycondensation of a heteroatom-rich flexible triazine-based trialdehyde linker, namely 2,4,6-tris(4-formylphenoxy)-1,3,5-triazine (TPT-CHO), with four different triamine linkers. Triamine linkers were chosen based on differences in size, symmetry, planarity, and heteroatom content, leading to the synthesis of four different COF materials named IITR-COF-1, IITR-COF-2, IITR-COF-3, and IITR-COF-4. IITR-COF-1, synthesized within 24 h from the most planar and largest amine monomer, exhibited the largest Brunauer-Emmett-Teller (BET) surface area of 2830 m2 g-1, superior crystallinity, and remarkable reproducibility compared to the other COFs. All of the synthesized COFs were explored for energy and gas storage applications. It is shown that the surface area and redox-active triazene rings in the materials have a profound effect on energy and gas storage enhancement. In a three-electrode setup, IITR-COF-1 achieved an electrochemical stability potential window (ESPW) of 2.0 V, demonstrating a high specific capacitance of 182.6 F g-1 with energy and power densities of 101.5 Wh kg-1 and 298.3 W kg-1, respectively, at a current density of 0.3 A g-1 in 0.5 M K2SO4 (aq) with long-term durability. The symmetric supercapacitor of IITR-COF-1//IITR-COF-1 exhibited a notable specific capacitance of 30.5 F g-1 and an energy density of 17.0 Wh kg-1 at a current density of 0.12 A g-1. At the same time, it demonstrated 111.3% retention of its initial specific capacitance after 10k charge-discharge cycles. Moreover, it exhibited exceptional CO2 capture capacity of 25.90 and 10.10 wt % at 273 and 298 K, respectively, with 2.1 wt % of H2 storage capacity at 77 K and 1 bar.

Exfoliated MXene-AuNPs hybrid in sensing and multiple catalytic hydrogenation reactions.

The increasing use of nanomaterials in consumer products is expected to lead to environmental contamination sometime soon. As water pollution is a pressing issue that threatens human survival and impedes the promotion of human health, the search for adsorbents for removing newly identified contaminants from water has become a topic of intensive research. The challenges in the recyclability of contaminated water continue to campaign the development of highly reusable catalysts. Although exfoliated 2D MXene sheets have demonstrated the capability towards water purification, a significant challenge for removing some toxic organic molecules remains a challenge due to a need for metal-based catalytic properties owing to their rapid response. In the present study, we demonstrate the formation of hybrid structure AuNPs@MXene (Mo2CTx) during the sensitive detection of Au nanoparticle through MXene sheets without any surface modification, and subsequently its applications as an efficient catalyst for the degradation of 4-nitrophenol (4-NP), methyl orange (MO), and methylene blue (MB). The hybrid structure (AuNPs@MXene) reveals remarkable reusability for up to eight consecutive cycles, with minimal reduction in catalytic efficiency and comparable apparent reaction rate constant (Kapp) values for 4-NP, MB, and MO, compared to other catalysts reported in the literature.

Valorization of guinea grass seed (Megathyrsus maximus): Synthesis and utilization of cellulose microfiber to reinforce esterified and cross-linked guinea starch films.

The present study extracts starch from guinea grass seed and fiber from the starch extraction residue. The fibrous residue was chemically converted into cellulose microfiber (CMF) and used to reinforce the native, esterified and crosslinked guinea starch films. The films were developed with 5 % starch, 40 % glycerol and 0, 2.5, 5, and 10 % CMF based on the dry matter of starch. SEM images of all film samples showed good compatibility of CMF with starch molecules, and no fractures or pores were observed. Adding filler materials to modified starch films slightly increased the film thickness (0.24 to 0.30 mm) due to the high dimensions of CMF, which comprise a significant amount of the composite's volume. A synergetic effect of starch modification and CMF in films decreased the moisture content (21.98 to 9.21 %), water solubility (25.65 to 15.47 %), water vapor permeability (6.96×10-7 to 1.65×10-7g∙mm2∙day∙Pa), and elongation at the break (33.51 to 16.79 %) while increasing the tensile strength (1.84 to 3.85 MPa) and Young's modulus (5.49 to 22.93 MPa). The L* and a* values of the films decreased, and the b* and opacity values of the films increased with the addition of CMF. The XRD graph showed that all films have semicrystalline structures with peaks at 18°, 20°, and 22°, and the degree of crystallinity increases (32.3 to 55.1 %) with CMF. All film samples showed good thermal stability up to 315 °C. In conclusion, esterified starch-based films exhibited superior barrier properties and flexibility. On the contrary, cross-linked starch films demonstrated higher tensile strength and lower water solubility.

Cracking the Code of Lumpy Skin Disease: Identifying Causes, Symptoms and Treatment Options for Livestock Farmers.

The novel bovine viral infection known as lumpy skin disease is common in most African and Middle Eastern countries, with a significant likelihood of disease transfer to Asia and Europe. Recent rapid disease spread in formerly disease-free zones highlights the need of understanding disease limits and distribution mechanisms. Capripox virus, the causal agent, may also cause sheeppox and Goatpox. Even though the virus is expelled through several bodily fluids and excretions, the most common causes of infection include sperm and skin sores. Thus, vulnerable hosts are mostly infected mechanically by hematophagous arthropods such as biting flies, mosquitoes, and ticks. As a result, milk production lowers, abortions, permanent or temporary sterility, hide damage, and mortality occur, contributing to a massive financial loss for countries that raise cattle. These illnesses are economically significant because they affect international trade. The spread of Capripox viruses appears to be spreading because to a lack of effectual vaccinations and poverty in rural areas. Lumpy skin disease has reached historic levels; as a consequence, vaccination remains the only viable option to keep the illness from spreading in endemic as well as newly impacted areas. This study is intended to offer a full update on existing knowledge of the disease's pathological characteristics, mechanisms of spread, transmission, control measures, and available vaccinations.

Long-term application of agronomic management strategies effects on soil organic carbon, energy budgeting, and carbon footprint under rice-wheat cropping system.

In the plains of western North India, traditional rice and wheat cropping systems (RWCS) consume a significant amount of energy and carbon. In order to assess the long-term energy budgets, ecological footprint, and greenhouse gas (GHG) pollutants from RWCS with residual management techniques, field research was conducted which consisted of fourteen treatments that combined various tillage techniques, fertilization methods, and whether or not straw return was present in randomized block design. By altering the formation of aggregates and the distribution of carbon within them, tillage techniques can affect the dynamics of organic carbon in soil and soil microbial activity. The stability of large macro-aggregates (> 2 mm), small macro-aggregates (2.0-2.25 mm), and micro-aggregates in the topsoil were improved by 35.18%, 33.52%, and 25.10%, respectively, over conventional tillage (0-20 cm) using tillage strategies for conservation methods (no-till in conjunction with straw return and organic fertilizers). The subsoil (20-40 cm) displayed the same pattern. In contrast to conventional tilling with no straw returns, macro-aggregates of all sizes and micro-aggregates increased by 24.52%, 28.48%, and 18.12%, respectively, when conservation tillage with organic and chemical fertilizers was used. The straw return (aggregate-associated C) also resulted in a significant increase in aggregate-associated carbon. When zero tillage was paired with straw return, chemical, and organic fertilizers, the topsoil's overall aggregate-associated C across all aggregate proportions increased. Conversely, conventional tillage, in contrast to conservation tillage, included straw return as well as chemical and organic fertilizers and had high aggregate-associated C in the subsurface. This study finds that tillage techniques could change the dynamics of microbial biomass in soils and organic soil carbon by altering the aggregate and distribution of C therein.

Chemoprotective effect of baicalin against cyclophosphamide induced ovarian toxicity in mice via inhibition of TGF-ß.

Ovarian toxicity is most common gynecologically related malignancy, arising for most cases owing to the advanced stage of diagnosis. The aim of the current study was to explore the anticancer potential of baicalin against cyclophosphamide (CP) induced ovarian toxicity in mice and explore the possible mechanism. ovarian cancer cells (Hey, SKOv3ip and HO891PM) were treated with different doses of baicalin and examined via flow cytometry and cell proliferation assay. Subcutaneous administration of CP (200 mg/kg) was used to induce the ovary toxicity and mice were received the oral administration of baicalin. Oxidative, pro-inflammatory, inflammatory, apoptosis parameters, progesterone, estrogen hormones and histopathological were also estimated at end of the study. Baicalin increased the apoptosis and caused the cell cycle arrest at the G2/M stage in ovarian cancer cells. Baicalin significantly (P < 0.001) reduced the level of TGF-ß in the HO8910PM, SKOv3ip and Hey cell lines. Baicalin significantly (P < 0.001) increased the body weight and reduced the tumor volume in mice. Baicalin significantly (P < 0.001) increased the level of estrogen and progesterone. Baicalin significantly (P < 0.001) reduced the level of malonaldehyde (MDA) and increased the level of superoxide dismutase (SOD) and catalase (CAT). Baicalin significantly (P < 0.001) decreased the level of pro-inflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1ß (IL-1ß), interleukin-6 (IL-6) and inflammatory parameter such as nuclear kappa B factor (NF-κB), respectively. Baicalin significantly (P < 0.001) reduced the level of the caspase-3. Baicalin, act as the potential agent against the ovarian toxicity by alteration of TGF-ß and inflammatory pathways.

The Impacts and Changes Related to the Cancer Drug Resistance Mechanism.

BACKGROUND: Cancer drug resistance remains a difficult barrier to effective treatment, necessitating a thorough understanding of its multi-layered mechanism. OBJECTIVE: This study aims to comprehensively explore the diverse mechanisms of cancer drug resistance, assess the evolution of resistance detection methods, and identify strategies for overcoming this challenge. The evolution of resistance detection methods and identification strategies for overcoming the challenge. METHODS: A comprehensive literature review was conducted to analyze intrinsic and acquired drug resistance mechanisms, including altered drug efflux, reduced uptake, inactivation, target mutations, signaling pathway changes, apoptotic defects, and cellular plasticity. The evolution of mutation detection techniques, encompassing clinical predictions, experimental approaches, and computational methods, was investigated. Strategies to enhance drug efficacy, modify pharmacokinetics, optimizoptimizee binding modes, and explore alternate protein folding states were examined. RESULTS: The study comprehensively overviews the intricate mechanisms contributing to cancer drug resistance. It outlines the progression of mutation detection methods and underscores the importance of interdisciplinary approaches. Strategies to overcome drug resistance challenges, such as modulating ATP-binding cassette transporters and developing multidrug resistance inhibitors, are discussed. The study underscores the critical need for continued research to enhance cancer treatment efficacy. CONCLUSION: This study provides valuable insights into the complexity of cancer drug resistance mechanisms, highlights evolving detection methods, and offers potential strategies to enhance treatment outcomes.