Statistically Research Articles

Genetic overlap between inflammatory bowel disease and iridocyclitis: insights from a genome-wide association study in a European population.

Inflammatory bowel disease (IBD) is occasionally associated with ophthalmic diseases, including iridocyclitis (IC). The co-occurrence of IBD and IC has been increasingly observed, possibly due to shared genetic structures. A three-part analysis was executed utilizing genome-wide association study (GWAS) data on IBD and IC. First, the overall genetic correlation between the two traits was observed using linkage disequilibrium score regression (LDSC). Subsequent to this, a local genetic correlation analysis was conducted utilizing the heritability estimation from summary statistics (HESS) methodology. Finally, the conditional/conjunctional false discovery rate (cond/conjFDR) statistical framework was utilized to ascertain the degree of genetic overlap between the two traits. Positive overall correlations were observed among IBD, ulcerative colitis (UC), and IC, encompassing both acute/subacute and chronic IC presentations. While a significant correlation was identified between Crohn's disease (CD) and IC, it was not evident for acute/subacute or chronic IC (P > 0.05). Notably, IBD (encompassing CD and UC) demonstrated local genetic correlations with IC and acute/subacute IC, with pronounced enrichment notably on chromosomes 1 and 6, though such correlations were not observed with chronic IC. The conjFDR analysis confirmed the genetic overlap between the two diseases. The shared genes overlapping between IBD (encompassing CD and UC) and IC were IL23R, GPR35, and ERAP1. For acute/subacute IC and chronic IC, there were six overlapping genes (GPR35, RPL23AP12, IL23R, SNAPC4, ERAP1, and INAVA) and one overlapping gene (INAVA), respectively. This study confirms the existence of a shared genetic structure between IBD and IC, providing a biological basis for their comorbidity. Additionally, this finding has significant implications for preventing and treating these two diseases.

Effect of rPET Content and Preform Heating/Cooling Conditions in the Stretch Blow Molding Process on Microcavitation and Solid-State Post-Condensation of vPET-rPET Blend: Part I-Research Methodology and Results.

Polyethylene terephthalate (PET) is widely used in bottle production due to its cost-effectiveness and low environmental impact. The first part of this article describes the research and statistical analysis methodology of the influence of the virgin PET (vPET) and recycled PET (rPET) content in the vPET-rPET blend, as well as the preform heating/cooling conditions in the stretch blow molding (SBM) process on the microscopic bottle properties. Microscopic properties such as crystallinity, density, viscosity, relaxation degree of the amorphous phase, and microcavitation in PET were examined. This study reveals that microcavity and solid-state post-condensation effects occur during PET deformation in the SBM process. The increase in free volume, indicating microcavitation, was confirmed by measuring positron annihilation lifetime spectroscopy (PALS). PALS and density of the amorphous phase studies prove a reduction in the dimensions of the free volumes, with a simultaneous significant increase in their number and ellipsoidization. It can be associated with crystallite rotation in a temperature-dependent non-crystalline matrix. The occurrence of solid-state post-condensation effects was confirmed by measuring the intrinsic viscosity. The conclusions resulting from the analysis of the microstructure affecting the mechanical strength of the material were validated by pressure resistance tests of the bottles.

Decoding Multidrug Resistance: Genetic Architecture and Codon Usage Patterns in ESKAPE Pathogens

Introduction: The escalating challenge of multidrug resistance among ESKAPE pathogens has become a prominent concern for global healthcare providers, leading to amplified morbidity and mortality rates. Method: Therefore, we conducted this study to elucidate the genetic architecture of ESKAPE constituents with the intent of ameliorating pathogenicity and facilitating drug development efforts. A comprehensive array of computational tools and statistical methodologies were employed to scrutinize the genomes of ESKAPE pathogens, revealing that translational selection profoundly influences the codon usage bias within this pathogenic cohort. Notably, leucine emerged as the predominantly amino acid, except in the case of Acinetobacter baumannii, where arginine exhibited preeminence. There was a universal preference for at least one histidine codon across all ESKAPE pathogens. GpC emerged as the most prominently overrepresented dinucleotide at the codon pair junction in all ESKAPE pathogens. Result: Furthermore, a comparison of gyrB gene sequences and phylogenic tree construction showed a distinct evolutionary relationship between AT-rich and GC-rich ESKAPE pathogens. Additionally, identification, characterization, and phylogenetic analysis of multiple antibiotic resistance genes revealed distinct evolutionary relationships. Conclusion: It was discerned that despite substantial variability amongst antibioticresistance genes, leucine emerged as the predominant amino acid. conclusion: Ultimately, we can say that codon and amino acid usage bias in ESKAPE pathogens and their ARGs genes has contributed the evolution of antibiotic resistance nature if the pathogens. Therefore, our study underscores the importance of continued surveillance and research efforts to address the growing threat of antibiotic resistance in ESKAPE pathogens .

Development of Two Methods for Estimating High-Dimensional Data in the Case of Multicollinearity and Outliers

High-dimensional problems involve datasets or models characterized by a substantial number of variables or parameters prevalent across various domains such as statistics, machine learning, optimization, physics, and engineering. Challenges in these scenarios include computational complexity, data sparsity, over-fitting, and the curse of dimensionality. This study introduces two innovative techniques that combine the Random Forest machine learning approach with both the least absolute shrinkage and selection operator and the elastic net, which are statistical methodologies tailored to address high-dimensional challenges. We compared performance evaluations of these hybrid methods against traditional statistical approaches and standalone machine learning methods. The assessment is conducted using goodness-of-fit measures and involves both Monte Carlo simulation and a real-world application. The findings show that the strategies proposed in this study exhibit superior performance compared to conventional approaches when tackling high-dimensional challenges.

Statistical characterization of electricity use profile: Leveraging data analytics for stochastic simulation in a smart campus

On the path to energy transition, advanced metering infrastructures have been installed in distribution systems to support sustainability goals, generating a substantial volume of electricity consumption data that are essential for planning and management studies. Additionally, given the stochastic nature of electricity consumption, understanding and quantifying statistical properties such as data distribution, normality, stationarity, and autocorrelation are crucial for the development of more sustainable systems and the enhancement of building performance. In this context, this paper presents a statistical methodology for assessing key aspects of electricity consumption in buildings on a smart campus, which is an initiative originated on university campuses that integrates sustainable energy systems, efficient electrical infrastructure, and data-driven technologies to establish a sustainable learning environment. Using 28 months of electricity consumption data from a Brazilian smart campus, Electricity Use Profile models are developed and several hypothesis tests and probability distribution fittings are conducted to extract statistical features from the models of 128 buildings. The results indicate that each building exhibits unique statistical properties that cannot be generalized, emphasizing the need for data analysis for each building before using the data in decision-making processes.

A prototype photoplethysmography-based cuffless device shows promising results in tracking changes in blood pressure.

Non-invasive cuffless blood pressure devices have shown promising results in accurately estimating blood pressure when comparing measurements at rest. However, none of commercially available or prototype cuffless devices have yet been validated according to the appropriate standards. The aim of the present study was to bridge this gap and evaluate the ability of a prototype cuffless device, developed by Aidee Health AS, to track changes in blood pressure compared to a non-invasive, continuous blood pressure monitor (Human NIBP or Nexfin) in a laboratory set up. The performance was evaluated according to the metrics and statistical methodology described in the ISO 81060-3:2022 standard. However, the present study is not a validation study and thus the study was not conducted according to the ISO 81060-3:2022 protocol, e.g., non-invasive reference and distribution of age not fulfilled. Data were sampled continuously, beat-to-beat, from both the cuffless and the reference device. The cuffless device was calibrated once using the reference BP measurement. Three different techniques (isometric exercise, mental stress, and cold pressor test) were used to induce blood pressure changes in 38 healthy adults. The mean difference (standard deviation) was 0.3 (8.7) mmHg for systolic blood pressure, 0.04 (6.6) mmHg for diastolic blood pressure, and 0.8 (7.9) mmHg for mean arterial pressure, meeting the Accuracy requirement of ISO 81060-3:2022 (≤6.0 (10.0) mmHg). The corresponding results for the Stability criteria were 1.9 (9.2) mmHg, 2.9 (8.1) mmHg and 2.5 (9.5) mmHg. The acceptance criteria for the Change requirement were achieved for the 85th percentile of ≤50% error for diastolic blood pressure and mean arterial pressure but were higher than the limit for systolic blood pressure (56% vs. ≤50%) and for all parameters for the 50th percentile (32%-39% vs. ≤25%). The present study demonstrated that the cuffless device could track blood pressure changes in healthy adults across different activities and showed promising results in achieving the acceptance criteria from ISO 81060-3:2022.

TestCompareR: an R package to compare two binary diagnostic tests using paired data.

Binary diagnostic tests are commonly used in medicine to answer a question about a patient's clinical status, most commonly, do they or do they not have some disease. Recent advances in statistical methodologies for performing inferential statistics to compare commonly used test metrics for two diagnostic tests have not yet been implemented in a robust statistical package. Up-to-date statistical methods to compare the test metrics achieved by two binary diagnostic tests are implemented in the new R package testCompareR. The output and efficiency of testCompareR is compared to the only other available package which performs this function, DTComPair, as well as an open-source program, compbdt, using a motivating example. testCompareR achieves similar results to DTComPair using statistical methods with improved coverage and asymptotic performance. Further, testCompareR is faster than the currently available package and requires fewer pre-processing steps in order to produce accurate results. testCompareR provides a new tool to compare the test metrics for two binary diagnostic tests compared with the gold standard. This tool allows flexible inputs, which minimises the need for data pre-processing, and operates in very few steps, so that it is easy to use even for those less experienced with R. testCompareR achieves results comparable to those computed by DTComPair, using optimised statistical methods and with improved computational efficiency.

A Comprehensive Review of Statistical Methods in Microbiology and epidemiology: A Data-Driven Approach

The rapid growth of computational power and big data analytics has transformed the landscape of statistical methods in microbiology and epidemiology. Advanced techniques such as machine learning, Bayesian inference, and network modeling are now being used alongside traditional statistical approaches. These methods offer unprecedented precision in identifying microbial patterns, tracking infectious diseases, and predicting outbreaks. This review highlights recent advances in statistical tools used in microbiology and epidemiology, particularly in analyzing antibiotic resistance, viral mutation rates, and epidemiological forecasting models for global pandemics such as COVID-19. The integration of novel statistical methodologies has improved our understanding of complex microbial interactions, resistance evolution, and the dynamics of infectious disease spread, enabling more informed public health interventions.

In Vivo Effects of a GHR Synthesis Inhibitor during Prolonged Treatment in Dogs

Background: The activation of the growth hormone receptor (GHR) is a major determinant of body growth. Defective GHR signaling, as seen in human Laron dwarfism, resulted in low plasma IGF-1 concentrations and limited growth, but also marked absence in the development of breast cancer and type 2 diabetes. In vitro, we identified a small molecule (C#1) that inhibits the translation of GHR mRNA to receptor protein. Methods: Before its application in humans as a potential anticancer drug, C#1 was tested in animals to evaluate whether it could be administered to achieve a plasma concentration in vivo that inhibits cell proliferation in vitro without causing unwanted toxicity. To evaluate the efficacy and toxicity of C#1, a group of six intact female Beagle dogs was treated daily each morning for 90 days with an oral solution of C#1 in Soiae oleum emulgatum at a dose of 0.1 mg/kg body weight. During treatment, dogs were closely monitored clinically, and blood samples were taken to measure plasma C#1 concentrations, complete blood counts (CBC), clinical chemistry, and endocrinology. At the end of the treatment, dogs were euthanized for gross and histopathological analysis. An additional group of six female Beagle dogs was included for statistical reasons and only evaluated for efficacy during treatment for 30 days. Results: Daily administration of C#1 resulted in a constant mean plasma concentration of approximately 50 nmol/L. In both groups, two out of six dogs developed decreased appetite and food refusal after 4–5 weeks, and occasionally diarrhea. No significant effects in CBC or routine clinical chemistry were seen. Plasma IGF-1 concentrations, used as biomarkers for defective GHR signaling, significantly decreased by 31% over time. As plasma growth hormone (GH) concentrations decreased by 51% as well, no proof of GHR dysfunction could be established. The measured 43% decrease in plasma acylated/non-acylated ghrelin ratios will also lower plasma GH concentrations by reducing activation of the GH secretagogue receptor (GHSR). C#1 did not directly inhibit the GHSR in vivo, as shown in vitro. There were no significant effects on glucose, lipid, or folate/homocysteine metabolism. Conclusions: It is concluded that with daily dosing of 0.1 mg C#1/kg body weight, the induction of toxic effects prevented further increases in dosage. Due to the concomitant decrease in both IGF-1 and GH, in vivo inhibition of GHR could not be confirmed. Since the concept of specific inhibition of GHR synthesis by small molecules remains a promising strategy, searching for compounds similar to C#1 with lower toxicity should be worthwhile.

TestCompareR: an R package to compare two binary diagnostic tests using paired data

Background Binary diagnostic tests are commonly used in medicine to answer a question about a patient’s clinical status, most commonly, do they or do they not have some disease. Recent advances in statistical methodologies for performing inferential statistics to compare commonly used test metrics for two diagnostic tests have not yet been implemented in a robust statistical package. Methods Up-to-date statistical methods to compare the test metrics achieved by two binary diagnostic tests are implemented in the new R package testCompareR. The output and efficiency of testCompareR is compared to the only other available package which performs this function, DTComPair, as well as an open-source program, compbdt, using a motivating example. Results testCompareR achieves similar results to DTComPair using statistical methods with improved coverage and asymptotic performance. Further, testCompareR is faster than the currently available package and requires fewer pre-processing steps in order to produce accurate results. Conclusions testCompareR provides a new tool to compare the test metrics for two binary diagnostic tests compared with the gold standard. This tool allows flexible inputs, which minimises the need for data pre-processing, and operates in very few steps, so that it is easy to use even for those less experienced with R. testCompareR achieves results comparable to those computed by DTComPair, using optimised statistical methods and with improved computational efficiency.

Better data for budgetary surveillance: EU law on statistics as key to unlocking the potential of public sector accounting

The quality of the data available for monitoring budgetary discipline of the Member States has been a hot topic within the European Union (EU) institutions and in the literature ever since the beginning of the economic crisis in 2008. The COVID-19 pandemic has shown the inadequacy, not only of the data, but also of the policy instruments available for coordinating efforts and decision-making. In this paper, we attempt to unlock the potential of public sector accounting (PSA) for contributing to the improvement of EU surveillance mechanisms. We look beyond the idea of standardised accounting rules for the entire EU public sector (European Public Sector Accounting Standards) into a less controversial area of EU competence, and we explore the conditions for introducing reporting obligations based on PSA data as part of EU law on statistics. Points for practitioners The current succession of crises might create momentum for a change in EU fiscal policy-making. New instruments for the monitoring of the budgetary situation in the Member States could become necessary. The EU institutions cannot impose the European Public Sector Accounting Standards. However, a new standardised reporting instrument could become binding EU law if it would come in the shape of a statistical methodology. Basic principles of EU law guarantee that any new statistical reporting obligations are limited to what is necessary, proportionate and cost-effective.

Women may not benefit from repeated frozen embryo transfers: a retrospective analysis of the cumulative live birth rate of 43972 women.

Which specific groups of women would not benefit from repeated frozen embryo transfers (FETs)? Women over 45 years of age should stop treatment after three FET attempts due to the absence of further benefits, while women aged 40-45 years and those with a diminished ovarian reserve and other causes of infertility have a lower chance of improving their cumulative live birth rate (CLBR) within five FET cycles and experience fewer advantages from repeated transfers. In real-life scenarios of ART, women who fail to achieve a live birth often choose to undergo repeated FETs via the freeze-all strategy. This retrospective study included 43972 women who underwent 86496 oocyte retrieval cycles and 82022 FET cycles between January 2010 and March 2023 under the freeze-all strategy. We categorized the population based on the female's age at the first oocyte pick-up (OPU) cycle (Groups 1-6: <30, 30-34, 35-39, 40-42, 43-44, and ≥45 years of age), number of retrieved oocytes at the first OPU cycle (Groups 1-5: 1-5, 6-10, 11-15, 16-20, and >20 oocytes), and causes of infertility (Groups 1-9: tubal factor, male factor, polycystic ovary syndrome, diminished ovarian reserve, endometriosis, other uterine factors, combined factors, unexplained infertility, and other infertility) to analyse their CLBRs within different FET cycles via Kaplan-Meier analysis (optimistic method) and the competing risk method (conservative method). We utilized multivariate Cox and Fine-Gray models to examine the associations between the CLBR and age, the number of retrieved oocytes, and nine causes of infertility. The CLBR decreased with increasing female age over five FET cycles (Groups 1-6: optimistic method: 96.4%, 94.2%, 86.0%, 50.2%, 23.1%, and 10.1%; conservative method: 87.1%, 82.0%, 67.8%, 33.9%, 13.8%, and 3.5%, respectively). Moreover, there was an increasing trend in the number of retrieved oocytes (Groups 1-5: optimistic method: 82.5%, 91.7%, 93.6%, 94.1%, and 96.2%; conservative method: 58.6%, 76.7%, 84.8%, 88.0%, and 92.5%, respectively). Furthermore, the CLBR varied across different causes of infertility (Groups 1-9: optimistic method: 91.7%, 93.1%, 96.6%, 79.2%, 89.9%, 76.1%, 90.0%, 92.9%, and 35.4%; conservative method: 77.3%, 79.4%, 88.9%, 46.7%, 72.7%, 62.1%, 74.4%, 78.8%, and 20.1%, respectively). Calculating the actual CLBR for each person is difficult because some patients have remaining embryos that have not been transferred; additionally, the current statistical methodology uses both optimistic and conservative methods to calculate the CLBR, and in real life, the CLBR falls between the optimistic and conservative curves. Our study is the first to identify specific subgroups of women who fail to benefit from repeated FETs and who require rational discontinuation of treatment following unsuccessful transfer. This study was financially supported by grants from the National Natural Science Foundation of China (grant numbers: 82271732 to Y.K., 82071603 to L.W., 82001502 to Y.L., and 82201888 to X.S.). The authors declare that they have no conflicts of interest in the present study. N/A.

Not all that glitters is green: empirical evidence from the Eurostoxx600 on stakeholders’ perception of greenwashing

AbstractGreenwashing refers to the deceptive practice where a company exaggerates or misrepresents the sustainability of its actions or projects. Given the ambiguity surrounding the methodologies behind conventional ratings, we enquire their robustness through the implementation of an alternative comprehensive measure entailing both internally disclosed and externally generated data. We address a notorious critique in greenwashing research—that the entire voluntary CSR approach inadvertently facilitates the diffusion of greenwashing. We make a unique contribution to the statistical methodology by breaking down the difference between internal and external perception of sustainability through regression analysis. We claim that only when the presence of CSR Committees is coupled with tangible initiatives boosting sustainability, both external and internal stakeholders are found to positively evaluate the sustainable commitment of a company.

The Fuzzified Log-Rank Test Procedure for Comparing More Than Two Groups

Objectives: To extend the conventional log-rank test to handle fuzzy data, accommodating the inherent uncertainty and vagueness in illustrative data. To create a statistical procedure that can compare survival distributions across more than two groups. Methods: The log-rank test is a familiar non-parametric methodology used to compare the survival experiences of two or more groups of subjects. This study applies fuzzification procedures to survival data, transforming precise survival times and event indicators into fuzzy numbers. Findings: When fuzziness is attributed to the survival data, the proposed method shows less potential and is unable to produce reliable results. However, the fuzzified log-rank test outperforms conventional methods when dealing with uncertain or imprecise data, providing more reliable results. Novelty: Unlike conventional log-rank tests that are typically limited to two groups, the proposed method can simultaneously compare survival distributions across three groups, providing a more comprehensive survival analysis. This article suggests a new procedure that can accommodate the fuzziness in the data. The fuzzified log-rank test enhances the analytical capability of survival analysis and represents a novel contribution to statistical methodologies in medical and survival research. Keywords: Fuzziness, Survival Data, Log-Rank Test, Three Groups, Defuzzification

Systematic review of clinical prediction models for psychosis in individuals meeting At Risk Mental State criteria.

This study aims to review studies developing or validating a prediction model for transition to psychosis in individuals meeting At Risk Mental State (ARMS) criteria focussing on predictors that can be obtained as part of standard clinical practice. Prediction of transition is crucial to facilitating identification of patients who would benefit from cognitive behavioural therapy and, conversely, those that would benefit from less costly and less-intensive regular mental state monitoring. The review aims to determine whether prediction models rated as low risk of bias exist and, if not, what further research is needed within the field. Bibliographic databases (PsycINFO, Medline, EMBASE, CINAHL) were searched using index terms relating to the clinical field and prognosis from 1994, the initial year of the first prospective study using ARMS criteria, to July 2024. Screening of titles, abstracts, and subsequently full texts was conducted by two reviewers independently using predefined criteria. Study quality was assessed using the Prediction model Risk Of Bias ASessment Tool (PROBAST). Studies in any setting were included. The primary outcome for the review was the identification of prediction models considering transition risk and a summary of their risk of bias. Forty-eight unique prediction models considering risk of transition to psychosis were identified. Variables found to be consistently important when predicting transition were age, gender, global functioning score, trait vulnerability, and unusual thought content. PROBAST criteria categorised four unique prediction models as having an overall low-risk bias. Other studies were insufficiently powered for the number of candidate predictors or lacking enough information to draw a conclusion regarding risk of bias. Two of the 48 identified prediction models were developed using current best practice statistical methodology, validated their model in independent data, and presented low risk of bias overall in line with the PROBAST guidelines. Any new prediction model built to evaluate the risk of transition to psychosis in people meeting ARMS criteria should be informed by the latest statistical methodology and adhere to the TRIPOD reporting guidelines to ensure that clinical practice is informed by the best possible evidence. External validation of such models should be carefully planned particularly considering generalisation across different countries. https://www.crd.york.ac.uk/PROSPEROFILES/108488_PROTOCOL_20191127.pdf, identifier CRD42018108488.

Hybrid CNC–MXene Nanolubricant for Tribological Application: Characterization, Prediction, and Optimization of Thermophysical Properties Evaluation

In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p &lt; 0.001) and the quadratic effect of temperature (p &lt; 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings.

Spatiotemporal functional permutation tests for comparing observed climate behavior to climate model projections

Abstract. Comparisons of observed and modeled climate behavior often focus on central tendencies, which overlook other important distributional characteristics related to quantiles and variability. We propose two permutation procedures, standard and stratified, for assessing the accuracy of climate models. Both procedures eliminate the need to model cross-correlations in the data, encouraging their application in a variety of contexts. By making only slightly stronger assumptions, the stratified procedure dramatically strengthens the ability to detect a difference in the distribution of observed and climate model data. The proposed procedures allow researchers to identify potential model deficiencies over space and time for a variety of distributional characteristics, providing a more comprehensive assessment of climate model accuracy, which will hopefully lead to further model refinements. The proposed statistical methodology is applied to temperature data generated by the state-of-the-art North American Coordinated Regional Climate Downscaling Experiment (NA-CORDEX).

Expression, Characteristics, and Clinical Target Prediction of PIK3C3/ vps34 in Gastric Cancer.

This study aimed to investigate the expression pattern of phosphatidylinositol 3-kinase class III (PIK3C3/vps34) in gastric cancer (GC) tissues and their juxtaposed normal counterparts and its correlation with the clinicopathological attributes and prognostic outlook of afflicted individuals. Immunohistochemical (IHC) staining was used to ascertain the expression levels of PIK3C3/vps34 across 60 GC tissues juxtaposed with their normal counterparts. Statistical methodologies were used to scrutinize the correlation between PIK3C3/vps34 expression and clinicopathological features, along with prognostic implications for GC patients. In GC tissues, the positive expression rate of PIK3C3/vps34 was 23.3% (14/60), which contrasted sharply with the markedly elevated rate of 66.7% (40/60) observed in adjacent tissues. The positive expression proportion of PIK3C3/vps34 within GC tissues exhibited a notable decrease than in adjacent tissues (P<0.05). The expression of PIK3C3/vps34 inverse-ly correlated with tumor size, degree of tissue differentiation, depth of tumor infiltration, and incidence of lymph node metastasis (P<0.05), whereas no significant associations were found with patient sex, age, tumor location, TNM staging, or distant metastasis (P > 0.05). As the tumor diameter increases, the degree of tissue differentiation diminishes, tumor infiltration depth intensifies, lymph node metastasis emerges, the TNM stage progresses, and PIK3C3/vps34 expression level within GC tissues declines correspondingly. Kaplan-Meier survival analysis unveiled a prolonged survival duration among GC patients exhibiting heightened PIK3C3/vps34 expression than in their counterparts with diminished expression (HR=0.66, 95% CI: 0.55-0.80), demonstrating statistical significance (P<0.05). Protein interaction analysis revealed noteworthy interactions involving PIK3C3 with Beclin 1, UVRAG, and ATG14. PIK3C3/vps34 is downregulated in GC tissues, exerting a pivotal role in tumorigenesis, and is intimately linked with the prognostic trajectory of GC patients. It may serve as a significant biomarker for prognostic evaluation and a promising molecular therapeutic target for GC.

Impact of pre-stroke cognitive decline on rehabilitation effectiveness and cognitive-functional status of patients in the acute period of ischemic stroke

Summary. The aim was to assess the influence of premorbid cognitive deficit on the effectiveness of rehabilitation using VR technologies and cognitive-functional status of patients in the acute period of ischemic stroke based on statistical methodology. Materials and methods: 120 patients diagnosed with ischemic stroke were examined. The IQCODE questionnaire was used to identify the patient’s cognitive decline before and the Pandas and SciPy libraries. Results: all patients had a multifunctional type of cognitive impairment with a predominant decline in the areas of executive function, memory, speech and perception. Patients of the 2nd group had a more pronounced deficit in the areas of constructive praxis, episodic memory, semantic information processing and perception. Assessment of the dynamics of cognitive impairment during therapy revealed a significant improvement in memory loss (p=0, 0034), executive dysfunction (p&lt;0.0001) and attention impairment (p=0.0001) in patients of 1st group and regression cognitive deficit in almost all cognitive areas in patients of 2nd group. Factors impeding rehabilitation in 1st group of patients were attention (r = -0,325) and perceptual disorders (r = -0,425); in 2nd group: semantic aphasia (r = -0,425), constructive apraxia (r = -0,325), speech impairment (r = — 0,375), object agnosia (r = -0,323) and IQCODE (r = -0,395). Conclusion: pre-stroke cognitive impairments are predictors of the development and progression of post-stroke cognitive deficits and reduce the possibilities of rehabilitation and cognitive recovery. The introduction of IQCODE in the diagnosis of cognitive dysfunction in the acute period of ischemic stroke is a useful tool in predicting the course and development of cognitive deficit and studying the prospects of rehabilitation programmes using VR technologies in patients with pre-stroke cognitive impairment.

A protocol for systematic review and meta-analysis on the efficacy and safety of nutraceuticals with special reference to Ahara Dravya (food articles) in type 2 diabetes mellitus

Different Aushadi (drugs) are indicated for Prameha (diabetes mellitus) in Ayurveda, with some serving as nutraceuticals. Still, their efficacy and safety need to be investigated. A systematic review will be conducted, including both published and relevant unpublished randomized controlled trials of selected nutraceuticals in diabetes mellitus. For the meta-analysis, only published studies will be considered. Searches will be executed in PubMed, Cochrane Central Register of Controlled Trials, Shodhganga: A reservoir for Indian theses, Digital Helpline for Ayurveda Research Articles, and AYUSH Research Portal, along with dissertation and thesis works in the public domain, unpublished studies from websites of university/institution, as well as other possible sources. Two reviewers will independently select studies, extract data, and synthesize results. A third reviewer will address disagreement. Robinson Risk of Bias 2 tool will be used to evaluate bias. Meta-analysis of extracted data will be performed through Review Manager Web software focusing on fasting blood sugar levels and glycosylated hemoglobin and reported adverse events if any. The strength of the shreds of evidence and reliability will be analyzed using the Grading of Recommendations, Assessment, Development, and Evaluation system. Findings will be presented using appropriate statistical methodology and interpreted accordingly. This study will comprehensively assess the selected nutraceuticals in type 2 diabetes mellitus (T2DM), demonstrating their efficacy and safety in preventing and managing T2DM. The conclusion may be drawn based on the resultant outcome of the impact of selected nutraceuticals on T2DM. PROSPERO registration number: CRD42024529448.