Kolmogorov-Smirnov Research Articles

Cross-sectional study of psychiatric disorders in patients with chronic musculoskeletal pain and individuals without pain

BackgroundMusculoskeletal chronic pain is a leading cause of global disability and laboral incapacity. However, there is a lack of population-based studies that investigate the relationship between chronic pain and mental disorders with a control group, particularly among low- and middle-income countries. Chronic pain is a serious public health problem in terms of human suffering, and in terms of socioeconomic implications. Frequent association with different mental disorders increases disability, decreases quality of life, and makes diagnosis and treatment challenging. The present study aimed to evaluate the presence of mental disorders in patients with chronic musculoskeletal pain and compare with a control group without pain.MethodsWe selected 100 patients in a regular follow-up at the Musculoskeletal Pain Outpatient Clinic of the University Hospital and compared them with 100 painless individuals from the control group from June 2016 to June 2018. The instruments used were the Mini International Neuropsychiatric Interview (MINI-PLUS) and a structured questionnaire to collect sociodemographic data. Statistical analysis used t-test, chi-square, Fisher’s exact test, Mann-Whitney, Kolmogorov-Smirnov tests, and multiple logistic regression.ResultsIn the sample evaluated, the majority of patients were women (83%), of brown color (54%), with lower-level education (51%), lower salary range (73%) and high absenteeism rate at work (60,7%). Patients with chronic pain had more psychiatric disorders (88% vs. 48% in the control group; p < 0.001). The most frequent diagnoses were anxiety disorders with panic attacks (44%), generalized anxiety (36%), mixed anxiety and depression disorder (33%), social phobia (30%), agoraphobia (29%), suicide risk (28%), and major depression (27%).ConclusionPositive correlations of mental disorders and chronic musculoskeletal pain have been documented. This suggests that psychiatric components must be taken into account in the management of chronic pain syndromes. The use of Mini Plus as a diagnostic tool for psychiatric disorders can contribute to optimizing the diagnosis and treatment of patients with chronic pain and encourage the creation of policies with strategies and criteria for quick access to Multi-professional Services.

Comparative analysis of key factors influencing urban green space in Mashhad, Iran (1988–2018)

This paper analyzed the role of national economic factors, in addition to some key city-level variables, in the variation of the urban green space (UGS) in Mashhad City (Iran) during three decades (1998–2018). The correlation result revealed the effects of the increasing trend of land price, population rate, and construction of built-up areas in the decreasing trend of UGS in the study area (R from − 0.95 to -0.99 at p-value > 95%). Also, the country-level economic factors, i.e., GDP per capita, oil price, export total value, and FDI, represented the overall increasing trend from 1988 to 2018, correlating with the decrease of green space areas (R from − 0.76 to -0.92 at p-value > 75%). Some statistical analyses, such as the run-test, the skewness and kurtosis tests, the Kolmogorov-Smirnov test, and the ANOVA test were done to confirm the normality of the data distribution and reliability of the results. Ultimately, clustering the research variables based on the significance and confidence levels of the estimated correlation results revealed that the change in the oil price and national export values in the petroleum-dependent economy of Iran can be assumed as the lead key economic factors to fluctuate all city-level variables, particularly the UGS variations.

Simultaneous Model Change Detection in Multivariate Linear Regression With Application to Indonesian Economic Growth Data

In this paper, we study asymptotic model change detection in multivariate linear regression by using the Kolmogorov–Smirnov function of the partial sum process of recursive residuals. We approximate the rejection region and also the power function of the test by establishing a functional central limit theorem for the sequence of the partial sum processes of the recursive residuals of the observations. When the assumed model is true, the limit process is given by the standard multivariate Brownian motion which does not depend on the regression functions. However, when the assumed model is not true (some models change), the limit process is represented by a vector of deterministic trend plus the standard multivariate Brownian motion. The finite sample size rejection region and the power of the test are investigated by means of Monte Carlo simulation. The simulation study shows evidence that the proposed test is consistent in the sense that it attains the power larger than the size of the test when the hypothesis is not true. We also demonstrate the application of the proposed test method to Indonesian economic growth data in which we test the adequacy of three-variate low-order polynomial model. The test result shows that the growth of the Indonesian economy is neither simultaneously constant nor linear. The test has successfully detect the appearance of a change in the model which is mainly caused by the COVID-19 pandemic in 2020.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/, date of registration: 30th June 2017.

Distribution shift detection for the postmarket surveillance of medical AI algorithms: a retrospective simulation study

Distribution shifts remain a problem for the safe application of regulated medical AI systems, and may impact their real-world performance if undetected. Postmarket shifts can occur for example if algorithms developed on data from various acquisition settings and a heterogeneous population are predominantly applied in hospitals with lower quality data acquisition or other centre-specific acquisition factors, or where some ethnicities are over-represented. Therefore, distribution shift detection could be important for monitoring AI-based medical products during postmarket surveillance. We implemented and evaluated three deep-learning based shift detection techniques (classifier-based, deep kernel, and multiple univariate kolmogorov-smirnov tests) on simulated shifts in a dataset of 130’486 retinal images. We trained a deep learning classifier for diabetic retinopathy grading. We then simulated population shifts by changing the prevalence of patients’ sex, ethnicity, and co-morbidities, and example acquisition shifts by changes in image quality. We observed classification subgroup performance disparities w.r.t. image quality, patient sex, ethnicity and co-morbidity presence. The sensitivity at detecting referable diabetic retinopathy ranged from 0.50 to 0.79 for different ethnicities. This motivates the need for detecting shifts after deployment. Classifier-based tests performed best overall, with perfect detection rates for quality and co-morbidity subgroup shifts at a sample size of 1000. It was the only method to detect shifts in patient sex, but required large sample sizes (>30′000\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$> 30^{\\prime} 000$$\\end{document}). All methods identified easier-to-detect out-of-distribution shifts with small (≤300) sample sizes. We conclude that effective tools exist for detecting clinically relevant distribution shifts. In particular classifier-based tests can be easily implemented components in the post-market surveillance strategy of medical device manufacturers.

Biclustering analysis on tree-shaped time-series single cell gene expression data of Caenorhabditis elegans

BackgroundIn recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results.ResultsA biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function.ConclusionThe results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance.

Characteristic Changes of the Stance-Phase Plantar Pressure Curve When Walking Uphill and Downhill: Cross-Sectional Study.

Monitoring of gait patterns by insoles is popular to study behavior and activity in the daily life of people and throughout the rehabilitation process of patients. Live data analyses may improve personalized prevention and treatment regimens, as well as rehabilitation. The M-shaped plantar pressure curve during the stance phase is mainly defined by the loading and unloading slope, 2 maxima, 1 minimum, as well as the force during defined periods. When monitoring gait continuously, walking uphill or downhill could affect this curve in characteristic ways. For walking on a slope, typical changes in the stance phase curve measured by insoles were hypothesized. In total, 40 healthy participants of both sexes were fitted with individually calibrated insoles with 16 pressure sensors each and a recording frequency of 100 Hz. Participants walked on a treadmill at 4 km/h for 1 minute in each of the following slopes: -20%, -15%, -10%, -5%, 0%, 5%, 10%, 15%, and 20%. Raw data were exported for analyses. A custom-developed data platform was used for data processing and parameter calculation, including step detection, data transformation, and normalization for time by natural cubic spline interpolation and force (proportion of body weight). To identify the time-axis positions of the desired maxima and minimum among the available extremum candidates in each step, a Gaussian filter was applied (σ=3, kernel size 7). Inconclusive extremum candidates were further processed by screening for time plausibility, maximum or minimum pool filtering, and monotony. Several parameters that describe the curve trajectory were computed for each step. The normal distribution of data was tested by the Kolmogorov-Smirnov and Shapiro-Wilk tests. Data were normally distributed. An analysis of variance with the gait parameters as dependent and slope as independent variables revealed significant changes related to the slope for the following parameters of the stance phase curve: the mean force during loading and unloading, the 2 maxima and the minimum, as well as the loading and unloading slope (all P<.001). A simultaneous increase in the loading slope, the first maximum and the mean loading force combined with a decrease in the mean unloading force, the second maximum, and the unloading slope is characteristic for downhill walking. The opposite represents uphill walking. The minimum had its peak at horizontal walking and values dropped when walking uphill and downhill alike. It is therefore not a suitable parameter to distinguish between uphill and downhill walking. While patient-related factors, such as anthropometrics, injury, or disease shape the stance phase curve on a longer-term scale, walking on slopes leads to temporary and characteristic short-term changes in the curve trajectory.

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure

We propose GrainGNN, a surrogate model for the evolution of polycrystalline grain structure under rapid solidification conditions in metal additive manufacturing. High fidelity simulations of solidification microstructures are typically performed using multicomponent partial differential equations (PDEs) with moving interfaces. The inherent randomness of the PDE initial conditions (grain seeds) necessitates ensemble simulations to predict microstructure statistics, e.g., grain size, aspect ratio, and crystallographic orientation. Currently such ensemble simulations are prohibitively expensive and surrogates are necessary.In GrainGNN, we use a dynamic graph to represent interface motion and topological changes due to grain coarsening. We use a reduced representation of the microstructure using hand-crafted features; we combine pattern finding and altering graph algorithms with two neural networks, a classifier (for topological changes) and a regressor (for interface motion). Both networks have an encoder-decoder architecture; the encoder has a multi-layer transformer long-short-term-memory architecture; the decoder is a single layer perceptron.We evaluate GrainGNN by comparing it to high-fidelity phase field simulations for in-distribution and out-of-distribution grain configurations for solidification under laser power bed fusion conditions. GrainGNN results in 80%–90% pointwise accuracy; and nearly identical distributions of scalar quantities of interest (QoI) between phase field and GrainGNN simulations compared using Kolmogorov-Smirnov test. GrainGNN's inference speedup (PyTorch on single x86 CPU) over a high-fidelity phase field simulation (CUDA on a single NVIDIA A100 GPU) is 150×–2000× for 100-initial grain problem. Further, using GrainGNN, we model the formation of 11,600 grains in 220 seconds on a single CPU core.

Correlation of serum fibroblast growth factor-23 levels and calcium phosphate products levels in chronic kidney disease; sub analysis of chronic kidney disease-mineral and bone disorder study

&lt;em data-sider-select-id="0e59b1fc-fce5-41b4-b987-316daa778de4"&gt;Introduction: The body produces fibroblast growth factor-23 (FGF-23) to maintain normal phosphate levels when hyperphosphatemia occurs. Production of FGF-23 indirectly causes hypocalcemia. Phosphate and calcium disturbances also occur in chronic kidney disease (CKD), therefore this adaptation mechanism applies. This situation; however, only manifests in the early stages of CKD; if the estimated glomerular filtration rate (eGFR) is less than 30% of normal. This adaptation is no longer adequate and levels of calcium-phosphate (Ca×P) products and FGF-23 still rise. &lt;em data-sider-select-id="f0dc3bf1-1598-419e-9de6-fe837eadc8e6"&gt;Objectives: In this study, the correlation between both the serum levels of FGF-23 and Ca×P products in CKD was analyzed. &lt;em data-sider-select-id="a367eeb1-7c32-4bf0-98ef-e6ae52522dc7"&gt;Patients and Methods: A cross-sectional study including 78 subjects with CKD stages 3 to 5 dialysis was conducted. Serum FGF-23 levels were determined using the enzyme-linked immunosorbent assay (ELISA) method and Ca×P product levels were calculated using the formula calcium (mg/ dL) × phosphate (mg/dL). The Kolmogorov-Smirnov test and Spearman’s test were conducted in the statistical study. If the P value is less than 0.05, the statistical findings are significant. &lt;em data-sider-select-id="e286c166-6c09-432e-b832-c902b1a13b81"&gt;Results: Serum FGF-23 levels and Ca×P product levels were shown to be significantly correlated. This analysis of the two correlations was independent of age and diabetes mellitus (DM). Based on stages of CKD, serum FGF-23 levels and Ca×P product levels were discovered to be significantly correlated only at stage 5 of non-dialysis. &lt;em data-sider-select-id="9b3534d4-deaa-4ccf-8b82-745bb5380661"&gt;Conclusion: Increasing serum FGF-23 levels were correlated with increased Ca×P product levels, particularly in CKD stage 5 non-dialysis subjects. This correlation was independent of age and DM.

The influence of brand awareness on brand equity: an investigation among dentists and their prescription behavior

Purpose This study aims to examine the influence of brand awareness on brand equity with the mediating role of brand association and perceived quality. Design/methodology/approach This research is applied in terms of the objective and descriptive survey in terms of methodology because its objective is to describe the studied conditions or phenomena and also the relationship between research variables that have been conducted in the field method. Findings The results explained that 53% of respondents are female and 47% of respondents are male. Most respondents are between 30 and 40 years old, and the youngest respondents are less than 30 years old. Most respondents were specialists, and the 134 respondents were subspecialists. The results explained that the significance level of the Kolmogorov–Smirnov test for research variables is less than 0.05 and has an abnormal distribution. The abnormality of the data in this study directed the authors to use PLS software to analyze the data. Originality/value To the best of the authors’ knowledge, it is the first time that research is prepared to study the impact of these marketing parameters on dentists’ prescription behavior.

Assessing reliability of naïve respondent-driven sampling samples by using repeated surveys among people who inject drugs (PWID) in New Jersey

IntroductionRespondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings. MethodsWe assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance – People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses. ResultsThe study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples. ConclusionsIn conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments.

Temporal Variations in Land Surface Temperature within an Urban Ecosystem: A Comprehensive Assessment of Land Use and Land Cover Change in Kharkiv, Ukraine

Remote sensing technologies are critical for analyzing the escalating impacts of global climate change and increasing urbanization, providing vital insights into land surface temperature (LST), land use and cover (LULC) changes, and the identification of urban heat island (UHI) and surface urban heat island (SUHI) phenomena. This research focuses on the nexus between LULC alterations and variations in LST and air temperature (Tair), with a specific emphasis on the intensified SUHI effect in Kharkiv, Ukraine. Employing an integrated approach, this study analyzes time-series data from Landsat and MODIS satellites, alongside Tair climate records, utilizing machine learning techniques and linear regression analysis. Key findings indicate a statistically significant upward trend in Tair and LST during the summer months from 1984 to 2023, with a notable positive correlation between Tair and LST across both datasets. MODIS data exhibit a stronger correlation (R2 = 0.879) compared to Landsat (R2 = 0.663). The application of a supervised classification through Random Forest algorithms and vegetation indices on LULC data reveals significant alterations: a 70.3% increase in urban land and a decrement in vegetative cover comprising a 15.5% reduction in dense vegetation and a 62.9% decrease in sparse vegetation. Change detection analysis elucidates a 24.6% conversion of sparse vegetation into urban land, underscoring a pronounced trajectory towards urbanization. Temporal and seasonal LST variations across different LULC classes were analyzed using kernel density estimation (KDE) and boxplot analysis. Urban areas and sparse vegetation had the smallest average LST fluctuations, at 2.09 °C and 2.16 °C, respectively, but recorded the most extreme LST values. Water and dense vegetation classes exhibited slightly larger fluctuations of 2.30 °C and 2.24 °C, with the bare land class showing the highest fluctuation 2.46 °C, but fewer extremes. Quantitative analysis with the application of Kolmogorov-Smirnov tests across various LULC classes substantiated the normality of LST distributions p &gt; 0.05 for both monthly and annual datasets. Conversely, the Shapiro-Wilk test validated the normal distribution hypothesis exclusively for monthly data, indicating deviations from normality in the annual data. Thresholded LST classifies urban and bare lands as the warmest classes at 39.51 °C and 38.20 °C, respectively, and classifies water at 35.96 °C, dense vegetation at 35.52 °C, and sparse vegetation 37.71 °C as the coldest, which is a trend that is consistent annually and monthly. The analysis of SUHI effects demonstrates an increasing trend in UHI intensity, with statistical trends indicating a growth in average SUHI values over time. This comprehensive study underscores the critical role of remote sensing in understanding and addressing the impacts of climate change and urbanization on local and global climates, emphasizing the need for sustainable urban planning and green infrastructure to mitigate UHI effects.

Multi-nutrients and periodontal disease - a new adjunct to improving treatment outcomes? A randomised placebo-control clinical trial.

This study by Laky et al. is a single-centre, double-blinded randomised placebo-controlled clinical trial, examining the effects of micronutrient supplementation in combination with non-surgical hygiene phase therapy (nsHPT)for Stage III and IV periodontal disease. The study was carried out at the Medical University of Vienna (Austria), School of Dentistry. The patients enrolled in the study were randomised to receive a commercially available multi-nutrient supplement (containing: vitamin C, vitamin E, zinc, selenium, alpha-lipoic-acid, cranberry extract, grapeseed extract, and coenzyme Q10) or a cellulose placebo to take for 2 months, alongside a course of non-surgical hygiene phase therapy. At baseline, the patients had a periodontal index screening (PSI) performed. Patients scoring >3 and meeting the inclusion criteria were enrolled in the trial. Oral hygiene instruction and patient education was performed, followed by 1 or more sessions of nsHPT depending on the clinical assessment of each patient, and a full periodontal probing depth (PPD) chart was recorded. The patients were then randomised to receive either the multi-nutrient supplement or the placebo to take twice daily for 8 weeks. Following this 8-week period the PPD as well as bleeding on probing (BOP), gingival recession (GR) and clinical attachment loss (CAL) were measured. The randomisation was carried out by a study-independent member of staff at the centre using a computer programme. A total of 149 patients were assessed for eligibility for this trial. Out of these patients, 83 were deemed to be unsuitable for the trial as they failed to meet the inclusion criteria, and 24 declined to participate. A total of 42 patients were then randomised to each arm of the study. The inclusion criteria included patients with Stage III and IV periodontal disease. Patients were deemed eligible if they met the following criteria: presence of 18 or more scorable teeth (excluding 3rd molars); >4 teeth with PPD of >6 mm; CAL > 5 mm, and no previous periodontal treatment in the past. Percentage bone loss at the most severe site, smoking status, and presence of diabetes and diabetic control were all taken into consideration. Patients were deemed ineligible for the trial if they were pregnant or under 18 years old. Exclusion criteria also included: recent periodontal abscess; a past or current co-morbidity which may confound the results of the study or endanger patients partaking in the study (i.e. malignancy, rheumatoid arthritis, and other severe systemic conditions); and obesity. PPD was used as the primary variable in this study. Other measured outcomes such as GR, CAL, BOP and other measurements taken, such as papillary bleeding index (PBI) and papillary inflamed surface area (PISA), were considered as secondary outcome variables. A variety of tests were used in order to assess the collected data. The Kolmogorov‒Smirnov test and Levene test were used for the equality of variances, and ICC and 95% CI were used when comparing periodontal chartings for PPD (in mm). Chi-square or Fisher tests were used for categorical data between intervention and placebo. The baseline and reevaluation data was compared using the McNemar test. All data was analysed using statistical software. Significant data is indicated by a p value < 0.05. The number of patients included for analysis was reduced to n = 20 for the intervention group and n = 19 for the placebo group. Patients were lost due to a variety of reasons including need for antibiotics and lost to follow-up. The authors concluded that in each arm of the trial, periodontal and hygiene parameters improved overall. The results show that the intervention group had a significantly higher reduction in PPD; however, following data analysis, these findings were found to be statistically insignificant. The authors conclude that the addition of a multi-nutrient supplement, alongside nsHPT for patients undergoing treatment for Stage III and IV periodontal disease, showed a reduction in PPD and BOP when compared with a placebo and nsHPT.

Abstract PO1-22-03: Radiation Toxicity and Treatment Outcomes in Breast Cancer Patients with ATM Pathogenic and VUS Mutations: Implications for Personalized Therapy

Abstract Introduction: Previous studies have shown that ATM (ataxia-telangiectasia mutated) mutations may be associated with increased radiation sensitivity and altered treatment response. However, there is a limited understanding of the clinical implications and optimal management strategies for breast cancer patients with ATM mutations receiving radiotherapy (RT). The aim of our study is to assess the impact of ATM mutations on radiation response and treatment outcomes in breast cancer patients undergoing RT. Material and Methods: A total of 79 female patients diagnosed with breast cancer were included in the study, with 31 (39.3%) having pathogenic (P) and likely pathogenic (LP) variants (all of the variants were heterozygous) and 48 (60.8%) having VUS variants detected through whole ATM gene sequencing screening for patients in accordance with NCCN clinical practice guideline. Statistical analysis, including descriptive, graphical, and inferential methods, such as the Kolmogorov-Smirnov test, Mann-Whitney U Test, and Chi-square tests, was conducted using SPSS version 25.0 to evaluate the normal distribution of continuous variable scores and perform quantitative and qualitative comparisons between groups at a 95% confidence level (p &amp;lt; 0.05). Results: The median age of the patients was 44 years (range: 24-74), with 75% of them being 50 years old or younger .The rate of breast cancer in patients with VUS variants was significantly higher in those aged 50 and below compared to those with P/LP variants (83% vs. 61%, p=0.028). There was no statistically significant difference in clinical stage, tumour histological type, and molecular subtype classification based on ATM gene variant type (P/LP vs. VUS) among the patients (p&amp;gt;0.05), but the rate of high tumour grade was significantly higher in the VUS variant group (65% vs. 40%, p=0.034). The rate of patients who received RT was 87%, with 26 patients receiving radiation to the breast or chest wall and 43 patients receiving additional regional lymph node irradiation. There were no statistically significant differences in surgical and systemic treatment types and RT dose based on ATM gene variant type (P/LP vs. VUS) (p&amp;gt;0.05). Among the patients who received RT, 33 (48%) experienced early-stage skin reactions, and 15 (22%) experienced late-stage skin reactions according to Common Terminology Criteria for Adverse Events (CTCAE) v5. No Grade 3, 4 and 5 early and late toxicity were observed . The rate of early (83% vs. 23%; p&amp;lt; 0.001) and late (35% vs. 13%; p=0.029) skin reactions was significantly higher in the P/LP variant group compared to the VUS variant group. Grade I lymphedema was observed in 2 (2.5%) patients, 1 (3.2%) in the P/LP group and 1 (2.1%) in the VUS group (p&amp;gt;0.05). Among the 23 patients who underwent breast reconstruction, 1 (4%) had implant revision and 1 (4%) experienced implant loss, both of which were observed in the pathogenic variant group (p&amp;gt;0.05). Conclusion: Limited clinical data exists on RT toxicity in breast cancer patients with ATM gene mutations, and clear guidelines for decision-making are lacking. Our study revealed higher rates of acute and chronic skin reactions in the P/LP variant group, highlighting the need for further investigation into the impact of these variants on treatment outcomes and patient management. However, it is crucial to manage these reactions effectively and not allow them to hinder treatment. Additional research and interventions are warranted to better understand and address these reactions, aiming to enhance the overall treatment experience and outcomes for patients with ATM gene mutations. Citation Format: Evrim Tezcanli, Seda Eroz, Ugur Ozbek, Ahmet Yesilyurt, CIHAN URAS, Nuran Bese. Radiation Toxicity and Treatment Outcomes in Breast Cancer Patients with ATM Pathogenic and VUS Mutations: Implications for Personalized Therapy [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-22-03.

Abstract PO1-11-07: Do factors other than type of surgery performed for breast cancer affect quality of life and sexual functioning in breast cancer survivors? An observational study from Northern India

Abstract INTRODUCTION There is an increasing focus of the treating clinicians on issues related to quality of life and sexual functioning in breast cancer survivors. This is even more important in developing countries like India as the incidence of breast cancer in young in also significant here. However, despite this, there is little data from India on the factors affecting quality of life and sexual functioning on breast cancer survivors. Hence, we decided to perform this study. METHODS Patients who had undergone surgery for breast cancer within the last 2-5 years were included. Patients having significant psychiatric illness were excluded and so were patients who developed metastasis or local recurrence during the follow up. Questions on demographics, clinical history, standard of living index, quality of life (SF-36 questionnaire) and sexual function (CSFQ questionnaire) were asked. Data was analyzed using SPSS v22. Normality of continuous data was tested by Kolmogorov–Smirnov and Shapiro–Wilk tests. As a test of significance, chi square was used for qualitative data and t-test, Mann-Whitney U, and Kruskal-Wallis were used (as applicable) for quantitative data. Bonferroni adjustment was made wherever significance was found, if more than 1 group were involved. Multiple linear regression was carried out for quantitative dependent variables where significant risk factors were found. Independent variables with significant collinearity were excluded from linear regression. RESULTS A total of 120 patients were enrolled. In QOL scores, both physical health (B = -13.76, p = 0.007) and emotional problems (B = -10.65, p = 0.035) were affected adversely by endocrine therapy. Patients who had received radiotherapy had worse emotional well-being scores (B = -14.61, p = &amp;lt; 0.001). Patients who received chemotherapy or were married had significantly better social functioning score (B = 15.27, p = 0.020, B = 17.38, p = 0.041 respectively), whereas it was worse in patients who received trastuzumab (B = -18.32, p = 0.024). Pain scores were higher in premenopausal patients (B = 11.95, p = 0.021) and lower in patients having co-morbidities (B = -10.95, p = 0.039). None of the factors affected physical functioning, energy/fatigue, general health or health change scores. In sexual functioning scores, pleasure score (B = 1.27, p = 0.011) frequency scores (B = 1.97, p = 0.012), orgasm score (B = 3.43, p = 0.019) and total score (B = 12.30, p = 0.006) were higher in married women. Pleasure score was found to be lower in patients with co-morbidities (B = -0.82, p = 0.003). None of the factors affected desire or arousal. CONCLUSION Even though sexual functioning depended a lot on having a long-term partner, most of the factors affecting quality of life scores were treatment related. Actively addressing these concerns may help in improving quality of life and in turn, may improve compliance to treatment and follow-up. Citation Format: SIDDHANT KHARE, Devesh Dhamor, R N NAGA SANTOSH IRRINKI, Bijaya Padhi. Do factors other than type of surgery performed for breast cancer affect quality of life and sexual functioning in breast cancer survivors? An observational study from Northern India [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-11-07.

Assessing the Use of the Triglyceride-Glycemic Index (TyG), Neutrophil-Lymphocyte Ratio (NLR), and Platelet-Lymphocyte Ratio (PLR) in Distinguishing Benign and Malignant Tumors Among Patients With Complaints of Breast Mass

IntroductionBreast cancer is a prevalent global health concern characterized by uncontrolled cell growth in breast tissue. In 2020, approximately 2.3 million cases were reported worldwide, with 162,468 new cases and 87,090 fatalities documented in India in 2018. Early diagnosis is crucial for reducing mortality. Our study focused on the use of markers such as the triglyceride-glycemic index and hematological markers to distinguish between benign and malignant breast masses. MethodsA prospective cross-sectional study included female patients with breast mass complaints. The target sample size was 200. Data collection included medical history, clinical breast examination, mammography, cytological assessment via fine-needle aspiration cytology (FNAC), and blood sample collection. The analyzed parameters included neutrophil-to-lymphocyte Ratio (NLR), platelet-to-lymphocyte Ratio (PLR), and triglyceride-glycemic index (TyG). Histopathological examination confirmed the FNAC results. Statistical analysis including propensity score matching, Kolmogorov–Smirnov tests, Mann–Whitney U tests, receiver's operator curve (ROC) analysis, and logistic regression models was conducted using SPSS and R Software. Additional validation was performed on 25 participants. ResultsThis study included 200 participants. 109 had benign tumors and 91 had malignant tumors. Propensity score matching balanced covariates. NLR did not significantly differ between the groups, while PLR and TyG index differed significantly. NLR correlated strongly with the breast cancer stage, but not with the BI-RADS score. PLR and TyG index showed moderate positive correlations with the BI-RADS score. ROC analysis was used to determine the optimal cutoff values for PLR and TyG index. Logistic regression models combining PLR and TyG index significantly improved malignancy prediction. ConclusionsTyG index and PLR show potential as adjunctive markers for distinguishing breast masses. NLR correlated with cancer stage but not lesion type. Combining TyG and PLR improves prediction, aiding clinical decisions, but large-scale multicenter trials and long-term validation are required for clinical implementation.

Studying the phenomenon of self-compassion in the context of preventing academic procrastination

Introduction. Currently, the presence of procrastination causes contradictions in modern conditions of society, which places special demands on the independence, initiative, motivation and responsibility of the individual. Scientific research describes in sufficient detail the factors that cause procrastination. There is less research on the relationship between procrastination and such an aspect of personality as self-compassion. The purpose of our study is to examine the relationship between procrastination and self-compassion among university students. Materials and methods. The sample consisted of 157 students of the St. Petersburg State University of Architecture and Civil Engineering, of which 77 boys and 80 girls aged 19 to 22 years. The assumption that procrastination depends on self-compassion and self-esteem was tested using the Self-Compassion Scale by K. Neff, adapted by K.A. Chistopolskaya et al., test «Degree of severity of procrastination» (SVP) by M.A. Kiseleva, N.E. Shustova, O.V. Karina and the test «Personal Self-Esteem» by O.I. Motkova. The obtained empirical results were verified using the Kolmogorov–Smirnov test, Student's t-test, Pearson correlation analysis, and regression analysis. Results. It was revealed that 28% of students have a high level of self-compassion, 69% have an average level, 3% do not show self-compassion. The level of self-compassion among girls was significantly higher (79.3±10.0) than among boys (75.5±12.2). There is a negative relationship between indicators of self-compassion and indicators of procrastination: with a high level of self-compassion in a student, motivational insufficiency (r=- 0.24; p≤0.05), procrastination (r=-0.21; p≤0.05), anxiety (r=-0.31; p≤0.05). A positive relationship between self-compassion and self-esteem indicators was found. As a result of regression analysis, two components of self-compassion influencing procrastination were identified: self-isolation (B = -0.44) and excessive identification with negative emotions (B = -0.24). Conclusion. The results obtained show the need to develop self-compassion as a factor reducing personal procrastination.

Cyto R-CNN and CytoNuke Dataset: Towards reliable whole-cell segmentation in bright-field histological images

Background and objective:Cell segmentation in bright-field histological slides is a crucial topic in medical image analysis. Having access to accurate segmentation allows researchers to examine the relationship between cellular morphology and clinical observations. Unfortunately, most segmentation methods known today are limited to nuclei and cannot segment the cytoplasm. Methods:We present a new network architecture Cyto R-CNN that is able to accurately segment whole cells (with both the nucleus and the cytoplasm) in bright-field images. We also present a new dataset CytoNuke, consisting of multiple thousand manual annotations of head and neck squamous cell carcinoma cells. Utilizing this dataset, we compared the performance of Cyto R-CNN to other popular cell segmentation algorithms, including QuPath’s built-in algorithm, StarDist, Cellpose and a multi-scale Attention Deeplabv3+. To evaluate segmentation performance, we calculated AP50, AP75 and measured 17 morphological and staining-related features for all detected cells. We compared these measurements to the gold standard of manual segmentation using the Kolmogorov–Smirnov test. Results:Cyto R-CNN achieved an AP50 of 58.65% and an AP75 of 11.56% in whole-cell segmentation, outperforming all other methods (QuPath 19.46/0.91%; StarDist 45.33/2.32%; Cellpose 31.85/5.61%, Deeplabv3+ 3.97/1.01%). Cell features derived from Cyto R-CNN showed the best agreement to the gold standard (D¯=0.15) outperforming QuPath (D¯=0.22), StarDist (D¯=0.25), Cellpose (D¯=0.23) and Deeplabv3+ (D¯=0.33). Conclusion:Our newly proposed Cyto R-CNN architecture outperforms current algorithms in whole-cell segmentation while providing more reliable cell measurements than any other model. This could improve digital pathology workflows, potentially leading to improved diagnosis. Moreover, our published dataset can be used to develop further models in the future.

Statistical equivalence of metrics for meteor dynamical association

Meteor showers, originating as a result of the activity of comets or the disruption of large objects, provide a unique window into the composition and dynamics of our Solar System. While modern meteor detection networks have amassed extensive data, distinguishing sporadic meteors from those belonging to specific meteor showers remains challenging. In this study, we statistically evaluate and compare four orbital similarity criteria within five-dimensional parameter space (DSH,DD,DH, and ϱ2) to study dynamical associations using the already classified meteors (manually by a human) in CAMS database as a benchmark. In addition, we assess various distance metrics typically used in Machine Learning with two different vectors: ORBIT, grounded in heliocentric orbital elements, and GEO, predicated on geocentric observational parameters. To estimate their degree of correlation and efficacy, the Kendall rank correlation coefficient and the Top-k accuracy are employed. The statistical equivalence of the Top-1 results is examined using the Kolmogorov–Smirnov test and the percentage of Top-1 agreement is calculated on an event-by-event basis. Additionally, we compute the optimal cut-offs for all methods for distinguishing sporadic background events. Our findings demonstrate the superior performance of the sEuclidean metric in conjunction with the GEO vector. Within the scope of D-criteria, DSH emerged as the preeminent metric, closely followed by ϱ2. The Bray-Curtis metric displayed an advantage compared to the other distance metrics when paired with the ORBIT vector for Top-k accuracy, however, the Cityblock metric is more effective when considering the sporadic background. ϱ2 stands out as the most equivalence to the distance metrics when utilizing the GEO vector and the most compatible with GEO and ORBIT simultaneously, whereas DD aligns more closely when using the ORBIT vector. The stark contrast in DD’s behavior compared to other D-criteria highlights potential inequivalence. Our results suggest that geocentric features provide a more robust basis than orbital elements for meteor dynamical association. Most distance metrics associated with the GEO vector surpass the D-criteria when differentiating the meteoroid background. Accuracy displayed a dependence on solar longitude with a pronounced decrease around 180° matching an apparent increase in the meteoroid background activity, tentatively associated with the transition from the Perseids to the Orionids. Considering lately identified meteor showers, ∼27% of meteors in CAMS would have different associations. This work unveils that Machine Learning distance metrics can rival or even exceed the performance of tailored orbital similarity criteria for meteor dynamical association.

Water-soluble vitamin-E for enhancing fluorescence diagnosis in infected human dentine treated with NaOCl

IntroductionBacterial fluorescence methods are of interest in endodontics for informing endpoints for debridement.This study explored potential fluorescence quenching reversal effects of a water-soluble vitamin E conjugate (D-α-Tocopherol polyethylene glycol 1000 succinate, TPGS) when applied to polymicrobial biofilms grown on dentine that had been exposed to sodium hypochlorite (NaOCl) to cause quenching. MethodExtracted human teeth were debrided, embedded in transparent acrylic resin and sectioned. After smear layer removal, tooth dentine sections were inoculated with a polymicrobial inoculum, and cultured for 7 days to create biofilms. Samples (n=8 per group) were exposed to 1% or 4% NaOCl for 2 or 4 minutes, and then treated with TPGS. Bacterial fluorescence readings under laser excitation at 655 nm were assessed over 10 min using a calibrated DIAGNOdent device. All data were assessed for normality (Kolmogorov-Smirnov test) and analysed with ANOVA followed by Bonferroni post-hoc tests. ResultsNaOCl at both concentrations quenched fluorescence readings of biofilms grown on dentine samples, with a maximal reduction of 40.4% at 5 min after 4% NaOCl. Treatment with TPGS gave faster recovery of fluorescence readings compared to the control at 5 and 10 min. ConclusionThe water-soluble antioxidant TPGS partially reversed fluorescence quenching caused by NaOCl. This agent may have value clinically for reducing the time needed for fluorescence readings to recover when NaOCl is used as an irrigant. This will facilitate more accurate assessment of endpoints for canal debridement.