Biological role and regulation of circular RNA as an emerging biomarker and potential therapeutic target for cancer.

Circular RNAs (circRNAs) are a unique family of endogenous RNAs devoid of 3' poly-A tails and 5' end caps. These single-stranded circRNAs, found in the cytoplasm, are synthesized via back-splicing mechanisms, merging introns, exons, or both, resulting in covalently closed circular loops. They are profusely expressed across the eukaryotic transcriptome and offer heightened stability against exonuclease RNase R compared to linear RNA counterparts. This review endeavors to provide a comprehensive overview of circRNAs' characteristics, biogenesis, and mechanisms of action. Furthermore, aimed to shed light on the potential of circRNAs as significant biomarkers in various cancer types. It has been performed an exhaustive literature review, drawing on recent studies and findings related to circRNA characteristics, synthesis, function, evaluation techniques, and their associations with oncogenesis. CircRNAs are intricately associated with tumor progression and development. Their multifaceted roles encompass gene regulation through the sponging of proteins and microRNAs, controlling transcription and splicing, interacting with RNA binding proteins (RBPs), and facilitating gene translation. Due to these varied roles, circRNAs have become a focal point in tumor pathology investigations, given their promising potential as both biomarkers and therapeutic agents. CircRNAs, due to their unique biogenesis and multifunctionality, hold immense promise in the realm of oncology. Their stability, widespread expression, and intricate involvement in gene regulation underscore their prospective utility as reliable biomarkers and therapeutic targets in cancer. As our understanding of circRNAs deepens, advanced techniques for their detection, evaluation, and manipulation will likely emerge. These advancements might catalyze the translation of circRNA-based diagnostics and therapeutics into clinical practice, potentially revolutionizing cancer care and prognosis.

Exploring the diversity of CFTR gene mutations in cystic fibrosis individuals of South Asia.

OBJECTIVE: Cystic fibrosis (CF) is an autosomal recessive disorder caused by mutations in the CFTR gene. This study aimed to identify the spectrum of CFTR variants reported in individuals with CF from South Asia (ISA). DATA SOURCES AND STUDY SELECTION: We conducted a PubMed search for CFTR variants reported in ISA. Full text of original articles and case reports was read to compile data on reported variants. To gather additional data, we independently cross-referenced each variant with the CFTR Mutation Database and ClinVar. RESULTS: Our investigation identified a total of 92 CFTR variants reported across 30 articles. The most frequently tested, and reported variant was ΔF508 with a global frequency of 69.74%. Notably, we found 14 pathogenic CFTR mutations shared among ISA, originating from more than one South Asian country: ΔF508, 1525-1 G > A, G542X, S549N, R117H, S549R, R709X, V456A, Y569D, L1077P, 1161delC, 1898 + 1 G > T, G551D, and 2184insA. CONCLUSION: In summary, the higher prevalence of consanguinity and the limited availability of CF diagnostic resources in South Asia considerably contribute to the prevalence of genetic disorders like CF. The spectrum of CFTR mutations exhibits noticeable variations within South Asian and other populations. The inclusion of current study-enlisted CFTR gene variants is highly recommended for CF disease genetic testing in South Asia which may aid in achieving a precise diagnosis, enhancing disease management, and discovering drugs for currently untreatable genetic variants. It is also imperative to conduct a comprehensive study in this region, especially in previously unexplored countries such as Nepal, Bhutan, Maldives, and Bangladesh.

Computation of Entropy Measures for Metal-Organic Frameworks.

Entropy is a thermodynamic function used in chemistry to determine the disorder and irregularities of molecules in a specific system or process. It does this by calculating the possible configurations for each molecule. It is applicable to numerous issues in biology, inorganic and organic chemistry, and other relevant fields. Metal-organic frameworks (MOFs) are a family of molecules that have piqued the curiosity of scientists in recent years. They are extensively researched due to their prospective applications and the increasing amount of information about them. Scientists are constantly discovering novel MOFs, which results in an increasing number of representations every year. Furthermore, new applications for MOFs continue to arise, illustrating the materials' adaptability. This article investigates the characterisation of the metal-organic framework of iron(III) tetra-p-tolyl porphyrin (FeTPyP) and CoBHT (CO) lattice. By constructing these structures with degree-based indices such as the K-Banhatti, redefined Zagreb, and the atom-bond sum connectivity indices, we also employ the information function to compute entropies.

Comparative Study of Molecular Descriptors of Pent-Heptagonal Nanostructures Using Neighborhood M-Polynomial Approach.

In this article, a novel technique to evaluate and compare the neighborhood degree molecular descriptors of two variations of the carbon nanosheet C5C7(a,b) is presented. The conjugated molecules follow the graph spectral theory, in terms of bonding, non-bonding and antibonding Ruckel molecular orbitals. They are demonstrated to be immediately determinable from their topological characteristics. The effort of chemical and pharmaceutical researchers is significantly increased by the need to conduct numerous chemical experiments to ascertain the chemical characteristics of such a wide variety of novel chemicals. In order to generate novel cellular imaging techniques and to accomplish the regulation of certain cellular mechanisms, scientists have utilized the attributes of nanosheets such as their flexibility and simplicity of modification, out of which carbon nanosheets stand out for their remarkable strength, chemical stability, and electrical conductivity. With efficient tools like polynomials and functions that can forecast compound features, mathematical chemistry has a lot to offer. One such approach is the M-polynomial, a fundamental polynomial that can generate a significant number of degree-based topological indices. Among them, the neighborhood M-polynomial is useful in retrieving neighborhood degree sum-based topological indices that can help in carrying out physical, chemical, and biological experiments. This paper formulates the unique M-polynomial approach which is used to derive and compare a variety of neighborhood degree-based molecular descriptors and the corresponding entropy measures of two variations of pent-heptagonal carbon nanosheets. Furthermore, a regression analysis on these descriptors has also been carried out which can further help in the prediction of various properties of the molecule.

Computational Analysis of Some More Rectangular Tessellations of Kekulenes and Their Molecular Characterizations.

Cycloarene molecules are benzene-ring-based polycyclic aromatic hydrocarbons that have been fused in a circular manner and are surrounded by carbon-hydrogen bonds that point inward. Due to their magnetic, geometric, and electronic characteristics and superaromaticity, these polycyclic aromatics have received attention in a number of studies. The kekulene molecule is a cyclically organized benzene ring in the shape of a doughnut and is the very first example of such a conjugated macrocyclic compound. Due to its structural characteristics and molecular characterizations, it serves as a great model for theoretical research involving the investigation of π electron conjugation circuits. Therefore, in order to unravel their novel electrical and molecular characteristics and foresee potential applications, the characterization of such components is crucial. In our current research, we describe two unique series of enormous polycyclic molecules made from the extensively studied base kekulene molecule, utilizing the essential graph-theoretical tools to identify their structural characterization via topological quantities. Rectangular kekulene Type-I and rectangular kekulene Type-II structures were obtained from base kekulene molecules arranged in a rectangular fashion. We also employ two subcases for each Type and, for all of these, we derived ten topological indices. We can investigate the physiochemical characteristics of rectangular kekulenes using these topological indices.

Entropy Related to K-Banhatti Indices via Valency Based on the Presence of C6H6 in Various Molecules.

Entropy is a measure of a system's molecular disorder or unpredictability since work is produced by organized molecular motion. Shannon's entropy metric is applied to represent a random graph's variability. Entropy is a thermodynamic function in physics that, based on the variety of possible configurations for molecules to take, describes the randomness and disorder of molecules in a given system or process. Numerous issues in the fields of mathematics, biology, chemical graph theory, organic and inorganic chemistry, and other disciplines are resolved using distance-based entropy. These applications cover quantifying molecules' chemical and electrical structures, signal processing, structural investigations on crystals, and molecular ensembles. In this paper, we look at K-Banhatti entropies using K-Banhatti indices for C6H6 embedded in different chemical networks. Our goal is to investigate the valency-based molecular invariants and K-Banhatti entropies for three chemical networks: the circumnaphthalene (CNBn), the honeycomb (HBn), and the pyrene (PYn). In order to reach conclusions, we apply the method of atom-bond partitioning based on valences, which is an application of spectral graph theory. We obtain the precise values of the first K-Banhatti entropy, the second K-Banhatti entropy, the first hyper K-Banhatti entropy, and the second hyper K-Banhatti entropy for the three chemical networks in the main results and conclusion.

Fundamental Aspects of Skin Cancer Drugs via Degree-Based Chemical Bonding Topological Descriptors.

Due to significant advancements being made in the field of drug design, the use of topological descriptors remains the primary approach. When combined with QSPR models, descriptors illustrate a molecule's chemical properties numerically. Numbers relating to chemical composition topological indices are structures that link chemical composition to physical characteristics. This research concentrates on the analysis of curvilinear regression models and degree-based topological descriptors for thirteen skin cancer drugs. The physicochemical characteristics of the skin cancer drugs are examined while regression models are built for computed index values. An analysis is performed for several significant results based on the acquired data.

An empirical method for geometric calibration of a photon counting detector-based cone beam CT system.

BACKGROUND: Geometric calibration is essential in developing a reliable computed tomography (CT) system. It involves estimating the geometry under which the angular projections are acquired. Geometric calibration of cone beam CTs employing small area detectors, such as currently available photon counting detectors (PCDs), is challenging when using traditional-based methods due to detectors' limited areas. OBJECTIVE: This study presented an empirical method for the geometric calibration of small area PCD-based cone beam CT systems. METHODS: Unlike the traditional methods, we developed an iterative optimization procedure to determine geometric parameters using the reconstructed images of small metal ball bearings (BBs) embedded in a custom-built phantom. An objective function incorporating the sphericities and symmetries of the embedded BBs was defined to assess performance of the reconstruction algorithm with the given initial estimated set of geometric parameters. The optimal parameter values were those which minimized the objective function. The TIGRE toolbox was employed for fast tomographic reconstruction. To evaluate the proposed method, computer simulations were carried out using various numbers of spheres placed in various locations. Furthermore, efficacy of the method was experimentally assessed using a custom-made benchtop PCD-based cone beam CT. RESULTS: Computer simulations validated the accuracy and reproducibility of the proposed method. The precise estimation of the geometric parameters of the benchtop revealed high-quality imaging in CT reconstruction of a breast phantom. Within the phantom, the cylindrical holes, fibers, and speck groups were imaged in high fidelity. The CNR analysis further revealed the quantitative improvements of the reconstruction performed with the estimated parameters using the proposed method. CONCLUSION: Apart from the computational cost, we concluded that the method was easy to implement and robust.

Efficacy of rhizobacteria for degradation of profenofos and improvement in tomato growth.

Pesticides are widely used for managing pathogens and pests for sustainable agricultural output to feed around seven billion people worldwide. After their targeted role, residues of these compounds may build up and persist in soils and in the food chain. This study evaluated the efficiency of bacterial strains capable of plant growth promotion and biodegradation of profenofos. To execute this, bacteria were isolated from an agricultural area with a history of repeated application of profenofos. The profenofos degrading bacterial strains with growth-promoting characteristics were identified based on biochemical and molecular approaches through partial 16S ribosomal rRNA gene sequencing. The results revealed that one strain, Enterobacter cloacae MUG75, degraded over 90% profenofos after 9 days of incubation. Similarly, plant growth was significantly increased in plants grown in profenofos (100 mg L-1) contaminated soil inoculated with the same strain. The study demonstrated that inoculation of profenofos degrading bacterial strains increased plant growth and profenofos degradation. Novelty statementPesticides are extensively applied in the agriculture sector to overcome pest attacks and to increase food production to fulfill the needs of the growing world population. Residues of these pesticides can persist in the environment for long periods, may enter the groundwater reservoirs and cause harmful effects on living systems highlighting the need for bioremediation of pesticide-contaminated environments. Microbes can use pesticides as a source of carbon and energy and convert them into less toxic and non-toxic products. Application of profenofos degrading rhizobacteria in interaction with the plants in the rhizosphere can remediate the pesticide-contaminated soils and minimize their uptake into the food chain. Hence, this approach can improve soil health and food quality without compromising the environment.

Connecting SiO4 in Silicate and Silicate Chain Networks to Compute Kulli Temperature Indices.

A topological index is a numerical parameter that is derived mathematically from a graph structure. In chemical graph theory, these indices are used to quantify the chemical properties of chemical compounds. We compute the first and second temperature, hyper temperature indices, the sum connectivity temperature index, the product connectivity temperature index, the reciprocal product connectivity temperature index and the F temperature index of a molecular graph silicate network and silicate chain network. Furthermore, a QSPR study of the key topological indices is provided, and it is demonstrated that these topological indices are substantially linked with the physicochemical features of COVID-19 medicines. This theoretical method to find the temperature indices may help chemists and others in the pharmaceutical industry forecast the properties of silicate networks and silicate chain networks before trying.

Some Novel Results Involving Prototypical Computation of Zagreb Polynomials and Indices for SiO4 Embedded in a Chain of Silicates.

A topological index as a graph parameter was obtained mathematically from the graph's topological structure. These indices are useful for measuring the various chemical characteristics of chemical compounds in the chemical graph theory. The number of atoms that surround an atom in the molecular structure of a chemical compound determines its valency. A significant number of valency-based molecular invariants have been proposed, which connect various physicochemical aspects of chemical compounds, such as vapour pressure, stability, elastic energy, and numerous others. Molecules are linked with numerical values in a molecular network, and topological indices are a term for these values. In theoretical chemistry, topological indices are frequently used to simulate the physicochemical characteristics of chemical molecules. Zagreb indices are commonly employed by mathematicians to determine the strain energy, melting point, boiling temperature, distortion, and stability of a chemical compound. The purpose of this study is to look at valency-based molecular invariants for SiO4 embedded in a silicate chain under various conditions. To obtain the outcomes, the approach of atom-bond partitioning according to atom valences was applied by using the application of spectral graph theory, and we obtained different tables of atom-bond partitions of SiO4. We obtained exact values of valency-based molecular invariants, notably the first Zagreb, the second Zagreb, the hyper-Zagreb, the modified Zagreb, the enhanced Zagreb, and the redefined Zagreb (first, second, and third). We also provide a graphical depiction of the results that explains the reliance of topological indices on the specified polynomial structure parameters.

A Paradigmatic Approach to Find the Valency-Based K-Banhatti and Redefined Zagreb Entropy for Niobium Oxide and a Metal-Organic Framework.

Entropy is a thermodynamic function in chemistry that reflects the randomness and disorder of molecules in a particular system or process based on the number of alternative configurations accessible to them. Distance-based entropy is used to solve a variety of difficulties in biology, chemical graph theory, organic and inorganic chemistry, and other fields. In this article, the characterization of the crystal structure of niobium oxide and a metal-organic framework is investigated. We also use the information function to compute entropies by building these structures with degree-based indices including the K-Banhatti indices, the first redefined Zagreb index, the second redefined Zagreb index, the third redefined Zagreb index, and the atom-bond sum connectivity index.

Intracranial hemorrhage complicating anticoagulant prophylactic therapy in three hospitalized COVID-19 patients.

SARS-CoV2 has led to a global pandemic affecting almost 3 million people in almost over 3 months. Various clinical presentations have been reported so far and no definite therapy is established. Anticoagulation is recommended by several experts to address the potential prothrombotic complications from COVID-19, but its safety and regimen need further clinical trials and safety and efficacy profile. Here, we present three cases of intracranial hemorrhage in three critically ill patients with COVID-19 and discuss their course in relation to various regimens of anticoagulation used.

Analysis of toll-like receptors-9 (TLR9) gene polymorphism (rs5743836) in Pakistani patients with HCV.

Toll-like receptors (TLRs) are innate immune receptors that mediate the inflammatory response during HCV infections. The goal of this study was to evaluate the association of TLR9 gene polymorphism (rs5743836) in Pakistani patients infected with genotype 3a of HCV. Total 500 subjects were recruited, 400 HCV patients and 100 healthy individuals. Genotyping of TLR9 (-1237T/C, rs5743836) was carried out in 400 HCV patients (323 interferon responders and 77 interferon non responder) and control group by applying High resolution melting (HRM) curve assay. No remarkable differences in distribution of genotype between HCV (p<0.0001; OR= 3.21, 95% CI= (2.514.12) and control groups (p<0.0001; OR=0.092, 95%CI= (0.0580.14) were observed. In conclusion TLR9-1237T/C gene polymorphism may not be considered as a molecular risk for patients with HCV in Pakistan.

rs12603332 is associated with male asthma patients specifically in urban areas of Lahore, Pakistan.

OBJECTIVE: rs12603332, an important regulatory site variant, is known to alter the regulatory motif E2A that is involved in the maturation of B-lymphocytes. The study was designed to check whether different environmental exposures alter its risk allele association with asthma or not. METHODS: 200 Physician-diagnosed asthma patients and 108 healthy individuals were enrolled from hospitals of Lahore. After quantitation of DNA extracted from peripheral blood, amplification of genomic region with rs12603332, followed by single base extension (SBE), was performed. Allele and genotype frequencies were calculated by SHEsis and Haploview software packages. Statistical analyses on PLINK were also performed, taking different factors as covariates. HaploReg analysis was done to predict the effect of risk allele on different regulatory motifs. RESULTS: Risk allele for rs12603332 i.e., "C" allele was found to be significantly associated with male patients residing in urban localities. CONCLUSION: The finding suggests that on exposure with urban environment, risk allele carriers tend to develop asthma symptoms via epigenetic regulation of motif associated with maturation of B-lymphocytes.

Genetic variants of ADAM33 are associated with asthma susceptibility in the Punjabi population of Pakistan.

OBJECTIVE: A disintegrin and metalloproteinase 33 (ADAM33) gene has been considered as an asthma susceptibility gene due to its possible role in airway remodeling, abnormal cell proliferation, and differentiation. Association of this gene with asthma has been reported in several genetic studies on various populations. The current study aims to evaluate the association of ADAM33 gene polymorphisms with the risk of asthma in the Punjabi population of Pakistan. METHOD: A total of 101 asthma patients and 102 age-matched healthy controls from Lahore, a city in Punjab, were recruited. ADAM33 single nucleotide polymorphisms (SNPs) T + 1[rs2280089], T2[rs2280090], T1[rs2280091], ST + 5[rs597980], ST + 4[rs44707], S2[rs528557], Q - 1[rs612709], and F + 1[rs511898] were genotyped in both patients and controls using single base extension and capillary electrophoresis-based genetic analyzer. The basic allelic and genotypic model was analyzed for association of the SNPs with asthma using SHEsis software. Haploview software was used to calculate pairwise linkage disequilibrium (LD) among six of the genotyped SNPs. RESULTS: Of the 8 SNPs genotyped, only S2[rs528557] showed significant association with asthma (Allele p = 0.0189, Genotype p = 0.021). SNPs T + 1[rs2280089], T2[rs2280090], T1[rs2280091], ST + 4[rs44707], S2[rs528557], and Q - 1[rs612709] were found to be in moderate to strong LD. The significantly higher frequency of haplotype "AAGTCG" in healthy controls suggests a protective effect against asthma risk in the studied population (p = 0.0059). CONCLUSION: These findings suggest that genetic variants of ADAM33 gene may play important roles in asthma susceptibility in the Punjabi population of Pakistan.

Sequence variants on 17q21 are associated with the susceptibility of asthma in the population of Lahore, Pakistan.

OBJECTIVE: Single nucleotide polymorphisms (SNPs) on 17q21 are known to be associated with asthma disease in multiple populations. This study was designed to know whether this region is associated with asthma in Lahore region population or not. METHODS: A total of 200 asthma patients and 100 healthy controls were enrolled from different hospitals of Lahore, Pakistan. Twelve SNPs from chromosomal region 17q21 were analyzed in cases and controls by single base extension method and capillary-based genetic analyzers. Associations with asthma were checked using basic allelic model, genotypic model, and results were adjusted by logistic regression analysis using PLINK v1.9. Pair-wise linkage disequilibrium among the SNPs was analyzed by using Haploview software. RESULTS: SNP rs3816470 showed a strong association (p = 8.89 × 10(-5), Odd Ratio = 3.082 [1.755-5.41]) with asthma, whereas rs3859192 and rs6503525 also showed a significant association with the development of asthma, especially in the case of positive family history. In LD block1 (93 kb) consisting of six SNPs (rs12936231, rs7216389, rs7216558, rs9894164, rs1007654 and rs7212938), none of the haplotypes show any significant association with asthma except the haplotype "CCTCAG", which is a significant protective factor against asthma having frequency 0.051 in controls while 0.017 in cases (p = 3.56 × 10(-2), χ2 = 4.415). CONCLUSION: The present study reports that the polymorphic genomic variant rs3816470 is significantly and independently associated with asthma in the studied population, while the variants, rs6503525 and rs3859192, also indicate a significant association with asthma in this population when family history of the disease is taken as a covariate.

Evaluating spectral performance for quantitative contrast-enhanced breast CT with a GaAs based photon counting detector: a simulation approach.

Quantitative contrast-enhanced breast computed tomography (CT) has the potential to improve the diagnosis and management of breast cancer. Traditional CT methods using energy-integrated detectors and dual-exposure images with different incident spectra for material discrimination can increase patient radiation dose and be susceptible to motion artifacts and spectral resolution loss. Photon Counting Detectors (PCDs) offer a promising alternative approach, enabling acquisition of multiple energy levels in a single exposure and potentially better energy resolution. Gallium arsenide (GaAs) is particularly promising for breast PCD-CT due to its high quantum efficiency and reduction of fluorescence x-rays escaping the pixel within the breast imaging energy range. In this study, the spectral performance of a GaAs PCD for quantitative iodine contrast-enhanced breast CT was evaluated. A GaAs detector with a pixel size of 100µm, a thickness of 500µm was simulated. Simulations were performed using cylindrical phantoms of varying diameters (10 cm, 12 cm, and 16 cm) with different concentrations and locations of iodine inserts, using incident spectra of 50, 55, and 60 kVp with 2 mm of added aluminum filtration and and a mean glandular dose of 10 mGy. We accounted for the effects of beam hardening and energy detector response using TIGRE CT open-source software and the publicly available Photon Counting Toolkit (PcTK). Material-specific images of the breast phantom were produced using both projection and image-based material decomposition methods, and iodine component images were used to estimate iodine intake. Accuracy and precision of the proposed methods for estimating iodine concentration in breast CT images were assessed for different material decomposition methods, incident spectra, and breast phantom thicknesses. The results showed that both the beam hardening effect and imperfection in the detector response had a significant impact on performance in terms of Root Mean Squared Error (RMSE), precision, and accuracy of estimating iodine intake in the breast. Furthermore, the study demonstrated the effectiveness of both material decomposition methods in making accurate and precise iodine concentration predictions using a GaAs-based photon counting breast CT system, with better performance when applying the projection-based material decomposition approach. The study highlights the potential of GaAs-based photon counting breast CT systems as viable alternatives to traditional imaging methods in terms of material decomposition and iodine concentration estimation, and proposes phantoms and figures of merit to assess their performance.

Comprehensive review on glucose 6 phosphate dehydrogenase: A critical immunometabolic and redox switch in insects.

Mounting an active immune response is energy intensive and demands the reallocation of nutrients to maintain the body's resistance and tolerance against infections. Central to this metabolic adaptation is Glucose-6-phosphate dehydrogenase (G6PDH), a housekeeping enzyme involve in pentose phosphate pathway (PPP). PPP play an essential role in generating ribose, which is critical for nicotinamide adenine dinucleotide phosphate (NADPH). It is vital for physiological and cellular processes such as generating nucleotides, fatty acids and reducing oxidative stress. The G6PDH is extremely conserved enzyme across species in PP shunt. The deficiency of enzymes leads to serious consequences on organism, particularly on adaptation and development. Acute deficiency can lead to impaired cell development, halted embryonic growth, reduce sensitivity to insulin, hypertension and increase inflammation. Historically, research focusing on G6PDH and PPP have primarily targeted diseases on mammalian. However, our review has investigated the unique functions of the G6PDH enzyme in insects and greatly improved mechanistic understanding of its operations. This review explore how G6PDH in insects plays a crucial role in managing the redox balance and immune related metabolism. This study aims to investigate the enzyme's role in different metabolic adaptations.

Review and Assessment of Material, Method, and Predictive Modeling for Fiber-Reinforced Polymer (FRP) Partially Confined Concrete Columns.

The repairing and strengthening of concrete structures using external and internal partial confinements are inevitable in the construction industry due to the new standards and rapid developments. The conventional materials and methods of confinement are unable to meet modern safety and functional standards. The fiber-reinforced polymer (FRP) enhances the strength and ductility of deteriorating and new concrete columns by reducing lateral confinement pressure and resistance against seismic shocks. The precise methods of partial confinement are inevitable for effective FRP-concrete bonding, durability, and cost-effectiveness under different loading conditions and to cope with external environmental factors. Predictive modeling and simulation techniques are pivotal for the optimization of confinement materials and methods by investigating the FRP-concrete novel confinement configurations, stress-strain responses, and failure modes. The novel materials and methods for concrete columns' partial confinement lack high compressive strength, ductility, chemical attack resistivity, and different fiber orientation impacts. This review provides an overview of recent confinement materials, novel methods, and advanced modeling and simulation techniques with a critical analysis of the research gaps for partial FRP confinement of concrete columns. The current challenges and future prospects are also presented.