Cancer cell plasticity: from cellular, molecular, and genetic mechanisms to tumor heterogeneity and drug resistance.

Cancer is a complex disease displaying a variety of cell states and phenotypes. This diversity, known as cancer cell plasticity, confers cancer cells the ability to change in response to their environment, leading to increased tumor diversity and drug resistance. This review explores the intricate landscape of cancer cell plasticity, offering a deep dive into the cellular, molecular, and genetic mechanisms that underlie this phenomenon. Cancer cell plasticity is intertwined with processes such as epithelial-mesenchymal transition and the acquisition of stem cell-like features. These processes are pivotal in the development and progression of tumors, contributing to the multifaceted nature of cancer and the challenges associated with its treatment. Despite significant advancements in targeted therapies, cancer cell adaptability and subsequent therapy-induced resistance remain persistent obstacles in achieving consistent, successful cancer treatment outcomes. Our review delves into the array of mechanisms cancer cells exploit to maintain plasticity, including epigenetic modifications, alterations in signaling pathways, and environmental interactions. We discuss strategies to counteract cancer cell plasticity, such as targeting specific cellular pathways and employing combination therapies. These strategies promise to enhance the efficacy of cancer treatments and mitigate therapy resistance. In conclusion, this review offers a holistic, detailed exploration of cancer cell plasticity, aiming to bolster the understanding and approach toward tackling the challenges posed by tumor heterogeneity and drug resistance. As articulated in this review, the delineation of cellular, molecular, and genetic mechanisms underlying tumor heterogeneity and drug resistance seeks to contribute substantially to the progress in cancer therapeutics and the advancement of precision medicine, ultimately enhancing the prospects for effective cancer treatment and patient outcomes.

The Truncated Burr X-G Family of Distributions: Properties and Applications to Actuarial and Financial Data.

In this article, the "truncated-composed" scheme was applied to the Burr X distribution to motivate a new family of univariate continuous-type distributions, called the truncated Burr X generated family. It is mathematically simple and provides more modeling freedom for any parental distribution. Additional functionality is conferred on the probability density and hazard rate functions, improving their peak, asymmetry, tail, and flatness levels. These characteristics are represented analytically and graphically with three special distributions of the family derived from the exponential, Rayleigh, and Lindley distributions. Subsequently, we conducted asymptotic, first-order stochastic dominance, series expansion, Tsallis entropy, and moment studies. Useful risk measures were also investigated. The remainder of the study was devoted to the statistical use of the associated models. In particular, we developed an adapted maximum likelihood methodology aiming to efficiently estimate the model parameters. The special distribution extending the exponential distribution was applied as a statistical model to fit two sets of actuarial and financial data. It performed better than a wide variety of selected competing non-nested models. Numerical applications for risk measures are also given.

A New Generator of Probability Models: The Exponentiated Sine-G Family for Lifetime Studies.

In this article, we propose the exponentiated sine-generated family of distributions. Some important properties are demonstrated, such as the series representation of the probability density function, quantile function, moments, stress-strength reliability, and Rényi entropy. A particular member, called the exponentiated sine Weibull distribution, is highlighted; we analyze its skewness and kurtosis, moments, quantile function, residual mean and reversed mean residual life functions, order statistics, and extreme value distributions. Maximum likelihood estimation and Bayes estimation under the square error loss function are considered. Simulation studies are used to assess the techniques, and their performance gives satisfactory results as discussed by the mean square error, confidence intervals, and coverage probabilities of the estimates. The stress-strength reliability parameter of the exponentiated sine Weibull model is derived and estimated by the maximum likelihood estimation method. Also, nonparametric bootstrap techniques are used to approximate the confidence interval of the reliability parameter. A simulation is conducted to examine the mean square error, standard deviations, confidence intervals, and coverage probabilities of the reliability parameter. Finally, three real applications of the exponentiated sine Weibull model are provided. One of them considers stress-strength data.

Discrimination of sunflower seeds using multispectral and texture dataset in combination with region selection and supervised classification methods.

The purpose of this study is to discriminate sunflower seeds with the help of a dataset having spectral and textural features. The production of crop based on seed purity and quality other hand sunflower seed used for oil content worldwide. In this regard, the foundation of a dataset categorizes sunflower seed varieties (Syngenta CG, HS360, S278, HS30, Armani, and High Sun 33), which were acquired from the agricultural farms of The Islamia University of Bahawalpur, Pakistan, into six classes. For preprocessing, a new region-oriented seed-based segmentation was deployed for the automatic selection of regions and extraction of 53 multi-features from each region, while 11 optimized fused multi-features were selected using the chi-square feature selection technique. For discrimination, four supervised classifiers, namely, deep learning J4, support vector machine, random committee, and Bayes net, were employed to optimize the multi-feature dataset. We observe very promising accuracies of 98.2%, 97.5%, 96.6%, and 94.8%, respectively, when the size of a region is (180 × 180).

The Truncated Cauchy Power Family of Distributions with Inference and Applications.

As a matter of fact, the statistical literature lacks of general family of distributions based on the truncated Cauchy distribution. In this paper, such a family is proposed, called the truncated Cauchy power-G family. It stands out for the originality of the involved functions, its overall simplicity and its desirable properties for modelling purposes. In particular, (i) only one parameter is added to the baseline distribution avoiding the over-parametrization phenomenon, (ii) the related probability functions (cumulative distribution, probability density, hazard rate, and quantile functions) have tractable expressions, and (iii) thanks to the combined action of the arctangent and power functions, the flexible properties of the baseline distribution (symmetry, skewness, kurtosis, etc.) can be really enhanced. These aspects are discussed in detail, with the support of comprehensive numerical and graphical results. Furthermore, important mathematical features of the new family are derived, such as the moments, skewness and kurtosis, two kinds of entropy and order statistics. For the applied side, new models can be created in view of fitting data sets with simple or complex structure. This last point is illustrated by the consideration of the Weibull distribution as baseline, the maximum likelihood method of estimation and two practical data sets wit different skewness properties. The obtained results show that the truncated Cauchy power-G family is very competitive in comparison to other well implanted general families.

Statistical Inference of the Half-Logistic Inverse Rayleigh Distribution.

The inverse Rayleigh distribution finds applications in many lifetime studies, but has not enough overall flexibility to model lifetime phenomena where moderately right-skewed or near symmetrical data are observed. This paper proposes a solution by introducing a new two-parameter extension of this distribution through the use of the half-logistic transformation. The first contribution is theoretical: we provide a comprehensive account of its mathematical properties, specifically stochastic ordering results, a general linear representation for the exponentiated probability density function, raw/inverted moments, incomplete moments, skewness, kurtosis, and entropy measures. Evidences show that the related model can accommodate the treatment of lifetime data with different right-skewed features, so far beyond the possibility of the former inverse Rayleigh model. We illustrate this aspect by exploring the statistical inference of the new model. Five classical different methods for the estimation of the model parameters are employed, with a simulation study comparing the numerical behavior of the different estimates. The estimation of entropy measures is also discussed numerically. Finally, two practical data sets are used as application to attest of the usefulness of the new model, with favorable goodness-of-fit results in comparison to three recent extended inverse Rayleigh models.

Estimation of Entropy for Inverse Lomax Distribution under Multiple Censored Data.

The inverse Lomax distribution has been widely used in many applied fields such as reliability, geophysics, economics and engineering sciences. In this paper, an unexplored practical problem involving the inverse Lomax distribution is investigated: the estimation of its entropy when multiple censored data are observed. To reach this goal, the entropy is defined through the Rényi and q-entropies, and we estimate them by combining the maximum likelihood and plugin methods. Then, numerical results are provided to show the behavior of the estimates at various sample sizes, with the determination of the mean squared errors, two-sided approximate confidence intervals and the corresponding average lengths. Our numerical investigations show that, when the sample size increases, the values of the mean squared errors and average lengths decrease. Also, when the censoring level decreases, the considered of Rényi and q-entropies estimates approach the true value. The obtained results validate the usefulness and efficiency of the method. An application to two real life data sets is given.

Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image.

The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR-that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones-were acquired from 500 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 × 256) for each DR stage and a total of 2500 (500 × 5) datasets of the five DR stages were acquired. This research introduces the novel clustering-based automated region growing framework. For texture analysis, four types of features-histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)-were extracted, and various ML classifiers were employed, achieving 77.67%, 80%, 89.87%, and 96.33% classification accuracies, respectively. To improve classification accuracy, a fused hybrid-feature dataset was generated by applying the data fusion approach. From each image, 245 pieces of hybrid feature data (H, W, COM, and RLM) were observed, while 13 optimized features were selected after applying four different feature selection techniques, namely Fisher, correlation-based feature selection, mutual information, and probability of error plus average correlation. Five ML classifiers named sequential minimal optimization (SMO), logistic (Lg), multi-layer perceptron (MLP), logistic model tree (LMT), and simple logistic (SLg) were deployed on selected optimized features (using 10-fold cross-validation), and they showed considerably high classification accuracies of 98.53%, 99%, 99.66%, 99.73%, and 99.73%, respectively.

TIAs pathway genes and associated miRNA identification in Vinca minor: supporting aspidosperma and eburnamine alkaloids linkage via transcriptomic analysis.

V. minor contains monomeric eburnamine-type of indole alkaloids having utilization as a neuro-medicinal plant. The biosynthetic pathway studies using miRNAs has been the focal point for plant genomic research in recent years and this technique is utilized to get an insight into a possible pathway level study in V. minor as understanding of genes in this prized medicinal plant is meagrely understood. The de novo transcriptomic analysis using Illumina Next gen sequencing has been performed in glasshouse shifted plant and transformed roots to elucidate the possible non confirmed steps of terpenoid indole alkaloids (TIAs) pathway in V. minor. A putative TIA pathway is elucidated in the study including twelve possible TIAs biosynthetic genes. The specific miRNA associated with TIAs pathway were identified and their roles were discussed for the first time in V. minor. The comparative analysis of transcriptomic data of glasshouse shifted plant and transformed roots showed that the raw reads of transformed roots were higher (83,740,316) compared to glasshouse shifted plant (67,733,538). The EST-SSR prediction showed the maximum common repeats among glasshouse shifted plant and transformed roots, although small variation was found in trinucleotide repeats restricted to glasshouse shifted plant. The study reveals overall 37 miRNAs which were observed to be true and can have a role in pathway as they can regulate the growth and alkaloid production. The identification of putative pathway genes plays an important role in establishing linkage between Aspidosperma and Eburnamine alkaloids.

The influence of organophosphate and carbamate on sperm chromatin and reproductive hormones among pesticide sprayers.

This study is aimed at evaluating the association between occupational exposure to organophosphate (OP) and carbamate (CB) pesticides and semen quality as well as levels of reproductive and thyroid hormones of pesticide sprayers in Malihabad, Lucknow, Uttar Pradesh, India. Thirty-five healthy men (unexposed group) and 64 male pesticide sprayers (exposed group) were recruited for clinical evaluation of fertility status. Fresh semen samples were evaluated for sperm quality and analyzed for DNA fragmentation index (DFI) by flow cytometry. Pesticide exposure was assessed by measuring erythrocyte acetylcholinesterase and plasma butyrylcholinesterase (BuChE) with a Test-mate ChE field kit. Serum levels of total testosterone (Tt), prolactin (PRL), follicle-stimulating hormone (FSH), luteinizing hormone (LH), thyroid-stimulating hormone (TSH), and free thyroxine (FT4) were analyzed using enzyme immunoassay kits. Evidence of pesticide exposure was found in 88.5% of sprayers and significant increments were observed in sperm DFI with significant decrease in some semen parameters. DFI was negatively correlated with BuChE, sperm concentration, morphology, and vitality in these pesticide sprayers. The levels of Tt, PRL, FT4, and TSH appeared to be normal; however, there was a tendency for increased LH and FSH levels in exposed workers. The results confirm the potential impact of chronic occupational exposure to OP and CB pesticides on male reproductive function, which may cause damage to sperm chromatin, decrease semen quality, and produce alterations in reproductive hormones, leading to adverse reproductive health outcomes.

The influence of a 21 kDa Kunitz-type trypsin inhibitor from nonhost madras thorn, Pithecellobium dulce, seeds on H. armigera (Hübner) (Lepidoptera: Noctuidae).

A trypsin inhibitor purified from the seeds of the Manila tamarind, Pithecellobium dulce (PDTI), was studied for its effects on growth parameters and developmental stages of Helicoverpa armigera. PDTI exhibited inhibitory activity against bovine trypsin (∼86%; ∼1.33 ug/ml IC50). The inhibitory activity of PDTI was unaltered over a wide range of temperature, pH, and in the presence of dithiothreitol. Larval midgut proteases were unable to digest PDTI for up to 12 h of incubation. Dixon and Lineweaver-Burk double reciprocal plots analysis revealed a competitive inhibition mechanism and a Ki of ∼3.9 × 10(-8) M. Lethal dose (0.50% w/w) and dosage for weight reduction by 50% (0.25% w/w) were determined. PDTI showed a dose-dependent effect on mean larval weight and a series of nutritional disturbances. In artificial diet at 0.25% w/w PDTI, the efficiency of conversion of ingested food, of digested food, relative growth rate, and growth index declined, whereas approximate digestibility, relative consumption rate, metabolic cost, consumption index, and total developmental period were increased in larvae. This is the first report of antifeedant and antimetabolic activities of PDTI on midgut proteases of H. armigera.

Kinetic assessment and effect on developmental physiology of a trypsin inhibitor from Eugenia jambolana (Jambul) seeds on Helicoverpa armigera (Hübner).

A trypsin inhibitor was purified from the seeds of Eugenia jambolana (Jambul) with a fold purification of 14.28 and a yield recovery of 2.8%. Electrophoretic analysis of E. jambolana trypsin inhibitor (EjTI) revealed a molecular weight of approximately 17.4 kDa on 12% denaturing polyacrylamide gel electrophoresis with or without reduction. EjTI exhibited high stability over a wide range of temperatures (4-80 °C for 30 min) and pH (3.0-10.0) and inhibited trypsin-like activities of the midgut proteinases of fourth instar Helicoverpa armigera larvae by approximately 86%. Feeding assays containing 0.05, 0.15, and 0.45 (% w/w) EjTI on functionally important fourth-instar larvae indicated a dose-dependent downfall in the larval body weight as well as on extent of survival. The nutritional analysis suggests that EjTI exerts toxic effects on H. armigera. Dixon plot analysis revealed competitive inhibition of larval midgut proteinases by EjTI, with an inhibition constant (Ki ) of approximately 3.1 × 10(-9) M. However, inhibitor kinetics using double reciprocal plots for trypsin inhibition demonstrated a mixed inhibition pattern. These observations suggest the potential of E. jambolana trypsin inhibitor protein in insect pest management.

Bio-potency of a 21 kDa Kunitz-type trypsin inhibitor from Tamarindus indica seeds on the developmental physiology of H. armigera.

A trypsin inhibitor purified from the seeds of Tamarindus indica by Sephadex G-75, DEAE-Sepharose and Trypsin-Sepharose CL-4B columns was studied for its antifeedant, larvicidal, pupicidal and growth inhibitory activities against Helicoverpa armigera larvae. Tamarindus trypsin inhibitor (TTI) exhibited inhibitory activity towards total gut proteolytic enzymes of H. armigera (~87%) and bovine trypsin (~84%). Lethal doses which caused mortality and weight reduction by 50% were 1% w/w and 0.50% w/w, respectively. IC50 of TTI against Helicoverpa midgut proteases and bovine trypsin were ~2.10 µg/ml and 1.68 µg/ml respectively. In larval feeding studies the 21 kDa Kunitz-type protein was found to retard growth and development, prolonged the larval-pupal development durations along with adversely affecting the fertility and fecundity of H. armigera. In artificial diet at 0.5% w/w TTI, the efficiency of conversion of ingested food as well as of digested food, relative growth rate, growth index declined whereas approximate digestibility, metabolic cost, relative consumption rate, consumption index and total developmental period enhanced for H. armigera larvae. These results suggest that TTI has toxic and adverse effect on the developmental physiology of H. armigera and could be useful in controlling the pest H. armigera.

Comparative Transcriptomics Data Profiling Reveals E2F Targets as an Important Biological Pathway Overexpressed in Intellectual Disability Disorder.

Intellectual disability (ID) is an early childhood neurodevelopmental disorder that is characterized by impaired intellectual functioning and adaptive behavior. It is one of the major concerns in the field of neurodevelopmental disorders across the globe. Diversified approaches have been put forward to overcome this problem. Among all these approaches, high throughput transcriptomic analysis has taken an important dimension. The identification of genes causing ID rapidly increased over the past 3 to 5 years owing to the use of sophisticated high throughput sequencing platforms. Early monitoring and preventions are much important for such disorder as their progression occurs during fetal development. This study is an attempt to identify differentially expressed genes (DEGs) and upregulated biological processes involved in development of ID patients through comparative analysis of available transcriptomics data. A total of 7 transcriptomic studies were retrieved from National Center for Biotechnology Information (NCBI) and were subjected to quality check and trimming prior to alignment. The normalization and differential expression analysis were carried out using DESeq2 and EdgeR packages of Rstudio to identify DEGs in ID. In progression of the study, functional enrichment analysis of the results obtained from both DESeq2 and EdgeR was done using gene set enrichment analysis (GSEA) tool to identify major upregulated biological processes involved in ID. Our findings concluded that monitoring the level of E2F targets, estrogen, and genes related to oxidative phosphorylation, DNA repair, and glycolysis during the developmental stage of an individual can help in the early detection of ID disorder.

RNA-Seq data analysis reveals novel nonsense mutations in the NPR3 gene leading to the progression of intellectual disability disorder.

Intellectual disability (ID) is a progressive disorder that affects around 1-3% of the world's population. The heterogeneity of intellectual disability makes it difficult to diagnose as a complete disease. Genetic factors and major mutations play a noticeable role in the development and progression of ID. There is a high need to explore novel variants that may lead to new insights into the progressive aspects of ID. In the current course of study, 31 samples of ID from different studies available on GEO (GSE77742, GSE74263, GSE90682, GSE98476, GSE108887, GSE145710, and PRJEB21964) datasets were taken for the study. These datasets were analyzed for differential gene expression and single nucleotide polymorphism (SNPs). The SNPs of high impact were compared with the differentially expressed genes. Comparison leads to the identification of the priority gene ie NPR3 gene. The identified priority gene further was evaluated for the effect of the mutation using a Mutation Taster. Structure comparison analysis of the wild and mutated proteins of the NPR3 gene was further carried out by UCSF Chimera. Structural analysis reveals the anomalies in protein expression affecting the regulations of the NPR3 gene. These findings identified a novel nonsense mutation (E222*) in the downregulated NPR3 gene that leads to anomalies in the regulation of its protein expression. This missense mutation reveals a major role in causing ID. Our study concludes that the decrease in the expression of the NPR3 gene causes delayed sensory, motor, and physiological functions of the human brain leading to neurodevelopmental delay that causes ID.

A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance.

BACKGROUND: Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches. METHODOLOGY: To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series). RESULTS: The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts. CONCLUSION: In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.

Modelling the GDP of KSA using linear and non-linear NNAR and hybrid stochastic time series models.

BACKGROUND: Gross domestic product (GDP) serves as a crucial economic indicator for measuring a country's economic growth, exhibiting both linear and non-linear trends. This study aims to analyze and propose an efficient and accurate time series approach for modeling and forecasting the GDP annual growth rate (%) of Saudi Arabia, a key financial indicator of the country. METHODOLOGY: Stochastic linear and non-linear time series modeling, along with hybrid approaches, are employed and their results are compared. Initially, conventional linear and nonlinear methods such as ARIMA, Exponential smoothing, TBATS, and NNAR are applied. Subsequently, hybrid models combining these individual time series approaches are utilized. Model diagnostics, including mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE), are employed as criteria for model selection to identify the best-performing model. RESULTS: The findings demonstrated that the neural network autoregressive (NNAR) model, as a non-linear approach, outperformed all other models, exhibiting the lowest values of MAE, RMSE and MAPE. The NNAR(5,3) projected the GDP of 1.3% which is close to the projection of IMF benchmark (1.9) for the year 2023. CONCLUSION: The selected model can be employed by economists and policymakers to formulate appropriate policies and plans. This quantitative study provides policymakers with a basis for monitoring fluctuations in GDP growth from 2022 to 2029 and ensuring the sustained progression of GDP beyond 2029. Additionally, this study serves as a guide for researchers to test these approaches in different economic dynamics.

A unified exponential-H family for modeling real-life data: Properties and inference.

The exponential distribution is one of the most widely used statistical distribution for reliability issues. In this paper, we introduce a novel family based on the exponential model, called the new exponential-H (NEx-H) family. The sub-models of the NEx-H family are capable of accommodating variable failure rates, as well as unimodal, bimodal, left-skewed, symmetric, right-skewed, and J-shape densities. The mathematical features of the NEx-H family are derived. The parameters of the NEx-Weibull distribution are estimated by using seven estimation methods. Detailed numerical simulations are presented. Based on our study, the maximum likelihood is the best estimation method for estimating the NEx-Weibull parameters. Three real-life data sets are fitted using the NEx-Weibull distribution. The NEx-Weibull model provides better fit as compared to some competing Weibull models.

Modeling COVID-19 data with a novel neutrosophic Burr-III distribution.

In this study, we have presented a novel probabilistic model called the neutrosophic Burr-III distribution, designed for applications in neutrosophic surface analysis. Neutrosophic analysis allows for the incorporation of vague and imprecise information, reflecting the reality that many real-world problems involve ambiguous data. This ability to handle vagueness can lead to more robust and realistic models especially in situation where classical models fall short. We have also explored the neutrosophic Burr-III distribution in order to deal with the ambiguity and vagueness in the data where the classical Burr-III distribution falls short. This distribution offers valuable insights into various reliability properties, moment expressions, order statistics, and entropy measures, making it a versatile tool for analyzing complex data. To assess the practical relevance of our proposed distribution, we applied it to real-world data sets and compared its performance against the classical Burr-III distribution. The findings revealed that the neutrosophic Burr-III distribution outperformed than the classical Burr-III distribution in capturing the underlying data characteristics, highlighting its potential as a superior modeling toolin various fields.

The development of an extended Weibull model with applications to medicine, industry and actuarial sciences.

This paper delves into the theoretical and practical exploration of the complementary Bell Weibull (CBellW) model, which serves as an analogous counterpart to the complementary Poisson Weibull model. The study encompasses a comprehensive examination of various statistical properties of the CBellW model. Real data applications are carried out in three different fields, namely the medical, industrial and actuarial fields, to show the practical versatility of the CBellW model. For the medical data segment, the study utilizes four data sets, including information on daily confirmed COVID-19 cases and cancer data. Additionally, a Group Acceptance Sampling Plan (GASP) is designed by using the median as quality parameter. Furthermore, some actuarial risk measures for the CBellW model are obtained along with a numerical illustration of the Value at Risk and the Expected Shortfall. The research is substantiated by a comprehensive numerical analysis, model comparisons, and graphical illustrations that complement the theoretical foundation.