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The diagnosis tests (DT) under classical statistics are applied under the assumption that all observations in the data are determined. Therefore, these DT cannot be applied for the analysis of the data when some or all observations are not determined. The neutrosophic statistics (NS) which is the extension of classical statistics can be applied for the data having uncertain, unclear, and fuzzy observations. In this paper, we will present the DT, and gold-standard tests under NS are called neutrosophic diagnosis tests (NDT). Therefore, the proposed NDT is the generalization of the existing DT and can be applied under the uncertainty environment. We will present the NDT table and present a real example from the medical field. The use of the proposed method will be more effective and adequate to be used in medical science, biostatistics, decision, and classification analysis.

Alzheimer’s Disease (AD) is the prevailing type of neurodegenerative illness, characterised by the accumulation of amyloid beta plaques. The symptoms associated with AD are memory loss, emotional variability, and a decline in cognitive functioning. To date, the pharmaceuticals currently accessible in the marketplace are limited to symptom management. According to several research, antidepressants have demonstrated potential efficacy in the management of AD. In this particular investigation, a total of 24 anti-depressant medications were selected as ligands, while the Microtubule Affinity Receptor Kinase 4 (MARK4) protein was chosen as the focal point of our study. The selection of MARK4 was based on its known involvement in the advancement of AD and other types of malignancies, rendering it a highly prospective target for therapeutic interventions. The initial step involved doing ADMET analysis, which was subsequently followed by molecular docking of 24 drugs. This was succeeded by molecular dynamics simulation and molecular mechanics generalised Born surface area (MMGBSA) calculations. Upon conducting molecular docking experiments, it has been determined that the binding affinities observed fall within the range of -5.5 kcal/mol to -9.0 kcal/mol. In this study, we selected six anti-depressant compounds (CID ID ‐ 4184, 2771, 4205, 5533, 4543, and 2160) based on their binding affinities, which were determined to be -9.0, -8.7, -8.4, -8.3, -8.2, and -8.2, respectively. Molecular dynamics simulations were conducted for all six drugs, with donepezil serving as the control drug. Various analyses were performed, including basic analysis and post-trajectory analysis such as free energy landscape (FEL), polarizable continuum model (PCM), and MMGBSA calculations. Based on the findings from molecular dynamics simulations and the MMGBSA analysis, it can be inferred that citalopram and mirtazapine exhibit considerable potential as anti-depressant agents. Consequently, these compounds warrant further investigation through in vitro and in vivo investigations in the context of treating AD.

Background This study aimed to evaluate the association between early menarche, lifestyle factors, screen time (TV and mobile phones) and psychological factors among girls aged 8–10 years living in West Bengal, India, with special emphasis on hormonal and psychosocial influences and an urban-rural perspective using the Social Determinants of Health (SDOH) framework. Method A mixed-methods cohort study of 1,200 girls (600 urban, 600 rural) was followed up over three years. Quantitative (hormone assays, anthropometrics) and qualitative (body image, screen use interviews) approaches were used to measure screen time, physical activity, sleep, diet, hormone levels (melatonin, cortisol, GnRH, LH, FSH, estradiol, leptin), anthropometrics (BMI, Tanner staging), and psychological outcomes (stress, anxiety, self-perception). Results Urban girls with > 4 h/day of screen time had ≈ 4.2 months earlier menarche onset than rural girls with < 2 h/day. In urban girls, early menarche was linked to increased cortisol, decreased melatonin, and increased estradiol, associated with greater sedentary activity and stress from social media. Rural girls exhibited resilience amid family stress. Urban girls had body image issues; rural girls used screens as a means of escape. Excessive screen time, hormonal interference, and growth accelerators, from increasingly worse urban diets to the exacerbation of inactivity and presenteeism, help explain the acceleration, which affects urban girls especially, given the activity-inactivity model & the potential for online bullying associated with a reliance on social media. Qualitative data elucidate the protective resilience of rural girls, constructing urban body dissatisfaction, rural escape mobility, and environmental contexts. Conclusion Our findings concluded that screen exposure accelerates menarche through psychobiological mechanisms, especially significant in urban-rural differences. To mitigate screen time effects and protect girls’ health across settings, context-specific interventions are critical. The SDOH framework highlighted how structural factors-like digital access, education stress, and community dynamics-shape puberty in meaningful ways.

In decision-making problems, the researchers’ application of parametric tests is the first choice due to their wide applicability, reliability, and validity. The common parametric tests require the validation of the normality assumption even for large sample sizes in some cases. Jarque-Bera test is among one of the methods available in the literature used to serve the purpose. One of the Jarque-Bera test restrictions is the computational limitations available only for the data in exact form. The operational procedure of the test is helpless for the interval-valued data. The interval-valued data generally occurs in situations under fuzzy logic or indeterminate state of the outcome variable and is often called neutrosophic form. The present research modifies the existing statistic of the Jarque-Bera test for the interval-valued data. The modified design and operational procedure of the newly proposed Jarque-Bera test will be useful to assess the normality of a data set under the neutrosophic environment. The proposed neutrosophic Jarque-Bera test is applied and compared with its existing form with the help of a numerical example of real gold mines data generated under the fuzzy environment. The study’s findings suggested that the proposed test is effective, informative, and suitable to be applied in indeterminacy compared to the existing Jarque–Bera test.

Quality control (QC) is a systematic approach to ensuring that products and services meet customer requirements. It is an essential part of manufacturing and industry, as it helps to improve product quality, customer satisfaction, and profitability. Quality practitioners generally apply control charts to monitor the industrial process, among many other statistical process control tools, and to detect changes. New developments in control charting schemes for high-quality monitoring are the need of the hour. In this paper, we have enhanced the performance of the mixed homogeneously weighted moving average (HWMA)-cumulative sum (CUSUM) control chart by using the auxiliary information-based (AIB) regression estimator and named it MHC AIB . The proposed MHC AIB chart provided an unbiased and more efficient estimator of the process location. The various measures of the run length are used to judge the performance of the proposed MHC AIB and to compare it with existing AIB charts like CUSUM AIB , EWMA AIB , MEC AIB (mixed AIB EWMA-CUSUM), and HWMA AIB . The Run length (RL) based performance comparisons indicate that the MHC AIB chart performs relatively better in monitoring small to moderate shifts over its competitor’s charts. It is shown that the chart’s performance improves with the increase in correlation between the study variable and the auxiliary variable. An illustrative application of the proposed MHC AIB chart is also provided to show its implementation in practical situations.

Colorectal cancer (CRC) is the third most frequently detected type of cancer, and the second most common cause of cancer-related mortality globally. The American Cancer Society predicted that approximately 147,950 individuals would be diagnosed with CRC, out of which 53,200 individuals would succumb to the disease in the USA alone in 2020. CRC-related mortality ranks third among both males and females in the USA. CRC arises from 3 major pathways: i) The adenoma-carcinoma sequence; ii) serrated pathway; and iii) the inflammatory pathway. The majority of cases of CRC are sporadic and result from risk factors, such as a sedentary lifestyle, obesity, processed diets, alcohol consumption and smoking. CRC is also a common preventable cancer. With widespread CRC screening, the incidence and mortality from CRC have decreased in developed countries. However, over the past few decades, CRC cases and mortality have been on the rise in young adults (age, <50 years). In addition, CRC cases are increasing in developing countries with a low gross domestic product (GDP) due to lifestyle changes. CRC is an etiologically heterogeneous disease classified by tumor location and alterations in global gene expression. Accumulating genetic and epigenetic perturbations and aberrations over time in tumor suppressor genes, oncogenes and DNA mismatch repair genes could be a precursor to the onset of colorectal cancer. CRC can be divided as sporadic, familial, and inherited depending on the origin of the mutation. Germline mutations in APC and MLH1 have been proven to play an etiological role, resulting in the predisposition of individuals to CRC. Genetic alterations cause the dysregulation of signaling pathways leading to drug resistance, the inhibition of apoptosis and the induction of proliferation, invasion and migration, resulting in CRC development and metastasis. Timely detection and effective precision therapies based on the present knowledge of CRC is essential for successful treatment and patient survival. The present review presents the CRC incidence, risk factors, dysregulated signaling pathways and targeted therapies.

Background Kruskal-Wallis H test from the bank of classical statistics tests is a well-known nonparametric alternative to a one-way analysis of variance. The test is extensively used in decision-making problems where one has to compare the equality of several means when the observations are in exact form. The test is helpless when the data is in an interval form and has some indeterminacy. Methods The interval-valued data often contain uncertainty and imprecision and often arise from situations that contain vagueness and ambiguity. In this research, a modified form of the Kruskal-Wallis H test has been proposed for indeterminacy data. A comprehensive theoretical methodology with an application and implementation of the test has been proposed in the research. Results The proposed test is applied on a Covid-19 data set for application purposes. The study results suggested that the proposed modified Kruskal-Wallis H test is more suitable in interval-valued data situations. The application of this new neutrosophic Kruskal-Wallis test on the Covid-19 data set showed that the proposed test provides more relevant and adequate results. The data representing the daily ICU occupancy by the Covid-19 patients were recorded for both determinate and indeterminate parts. The existing nonparametric Kruskal-Wallis H test under Classical Statistics would have given misleading results. The proposed test showed that at a 1% level of significance, there is a statistically significant difference among the average daily ICU occupancy by corona-positive patients of different age groups. Conclusions The findings of the results suggested that our proposed modified form of the Kruskal-Wallis is appropriate in place of the classical form of the test in the presence of the neutrosophic environment.

The COVID-19 survivors are under a great deal of financial stress due to high medical costs, income lost during illness, and ongoing medical expenses. Many survivors borrow money, deplete their savings, or become more economically vulnerable. The situation becomes worse for the marginalized groups who do not receive enough support. The pandemic has also had a significant financial and social impact on survivors in Pakistan. The present research aims to quantify the economic implications of COVID-19 on survivors from Punjab, Pakistan. A cross-sectional study of 5045 survivors from Punjab, Pakistan, was conducted, and the economic burden of COVID-19 on survivors was quantified using a 27-item self-administered scale. Descriptive statistics, structural equation models, and odds ratios were computed to quantify the study's objectives. The economic burden was classified into six constructs, representing the financial and social burdens of the disease on survivors. About 59.2% of the survivors were male, with an average age of 45.4 years, and 65.8% were employed in some capacity. The results depicted that the survivors aged over 45 years (OR=2.15, p < 0.01), admitted to hospital (OR=2.32, p < 0.01), infected severely (OR=2.11, p < 0.01), maximum secondary level of education (OR=2.18, p < 0.01), and disease duration up to 14 days (OR=5.05, p < 0.01) have an elevated financial burden than the opponent groups. The average duration of the disease was 12.1 days, and the daily cost of living and morbidity was estimated at 4763 PKR and 21286 PKR, respectively. The financial burden of the disease not only affects survivors but also strains families and communities, underscoring the necessity of comprehensive support systems and policies to address the social and economic impacts of the pandemic on survivors. Policymakers should target healthcare cost containment, income support, and financial support to vulnerable groups such as women, unemployed individuals, and those infected with severe diseases.

This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

Redox homeostasis plays an important role in the osteogenic differentiation of human mesenchymal stem cells (hMSCs) for bone engineering. Oxidative stress (OS) is believed to induce osteoporosis by changing bone homeostasis. Selenium nanoparticles (SeNPs), an antioxidant with pleiotropic pharmacological activity, prevent bone loss. However, the molecular mechanism underlying the osteogenic activity during hMSC-SeNP interaction is unclear. This study assessed the effects of different concentrations (25, 50, 100, and 300 ng/mL) of SeNPs on the cell viability and differentiation ability of human embryonic stem cell-derived hMSCs. In addition, we analyzed OS markers and their effect on mitogen-activated protein kinase (MAPK) and Forkhead box O3 (FOXO3) during osteogenesis. SeNPs increased the cell viability of hMSCs and induced their differentiation toward an osteogenic over an adipogenic lineage by enhancing osteogenic transcription and mineralization, while inhibiting Nile red staining and adipogenic gene expression. By preventing excessive reactive oxygen species accumulation, SeNPs increased antioxidant levels in hMSCs undergoing osteogenesis compared to untreated cells. In addition, SeNPs significantly upregulated the gene and protein expression of phosphorylated c-Jun N-terminal kinase (JNK) and FOXO3a, with no significant change in the expression levels of extracellular signal-related kinase (ERK) and p38 MAPK. The results approved that low concentrations of SeNPs might enhance the cell viability and osteogenic potential of hMSCs by moderating OS. Increased JNK and FOXO3a expression shows that SeNPs might enhance osteogenesis via activation of the JNK/FOXO3 pathway. In addition, SeNP co-supplementation might prevent bone loss by enhancing osteogenesis and, thus, can be an effective candidate for treating osteoporosis through cell-based therapy.