Single Regression Research Articles

Relationships between throwing distance and physical strength in female hammer throw: Estimating physical strength requirement corresponding to throwing distance

The purpose of this study was to clarify the relationships between female hammer throwers’ physical strength and their throwing performance and to create standard values corresponding to throwing distance. Eighty-two female hammer throwers, with the throwing distance ranging from 30.31 to 63.82 meters, participated in this study. The questionnaire was designed to collect data on physical strength. Pearson's product-rate correlation coefficient was used to examine the relationship between each item and the throwing distance. A single regression analysis was performed with each item which was significantly correlated to the throwing distance as the dependent variable and the throwing distance as the independent variable to estimate the standard values corresponding to throwing distance. The results showed that all physical strength variables, except for 30 m sprint, were significantly correlated with the throwing distance, with weight training variables having the higher correlation coefficients. In addition, standard values corresponding to throwing distance were obtained using a single regression analysis.

Efficient Automated Model for Predicting and Detecting the Heart Disease Through Machine Learning

Using machine learning, numerous researchers have created a variety of disease prediction models in the contemporary environment. By using prediction models early on, the death rate could be reduced. This work presents the design of an automated database diagnosis model using machine learning. Here, for study purposes, we chose one of the most common diseases, heart illness. This study presents the preparation of a database of patients with heart disease, followed by analysis in the produced database using a pre-trained machine learning model that was trained on the disease detection result that is displayed on the screen. To do the computation for prediction, a single logistic regression is utilized. Determining the risk of heart disease can be aided by early identification and prediction of the condition. Additionally, a comparison study might aid medical professionals in prescribing medication on time.

Statistical analysis of physico-mechanical parameters of sandstones occurring in orogenic settings

In northeastern Sicily (Italy), sandstone rock masses widely crop out as cover deposits over crystalline terrains belonging to the orogenic belt. Despite being part of the same geological formation, these sandstones are characterized by highly different features in terms of texture and physico-mechanical properties. This poses a scientific question on the possibility of tracing these rocks to a single statistical model, which could be representative of their main engineering geological properties. Therefore, it is worth investigating on the possible reasons of such differences, that should be searched either in the current geographical sandstone distribution or in the rock texture. For this study, sandstone samples were collected from different sites and were analyzed at both the hand and thin section scales. Three sandstone types were recognized, characterized by a different texture. Then, the laboratory characterization allowed estimating their main physico-mechanical and ultrasonic properties, such as porosity, density, mechanical strength, deformability, and ultrasonic velocities. The rock mechanical strength proved linked to the rock compactness and to the presence of lithic fragments, while pores and a pseudo-matrix between grains represent weakening features. Rock data were also statistically analyzed by grouping the specimens according to a geographical criterion, with respect to their sampling area, but no link was found between location and rock properties. Finally, with the aim of achieving mathematical laws that could be used to predict some rock properties from others, useful for practical purposes when dealing with such a high property variability, single and multiple regression analyses were carried out. Results show that the Uniaxial Compressive Strength, porosity, and P-wave velocity are the best predictors for a quick, indirect estimation of the main physico-mechanical parameters. The methodological approach developed for this research can be taken as reference to study other worldwide cases, involving rocks characterized by a wide range of physico-mechanical properties and covering large regional territories.

Quantifying the Impact of Alternative Definitions of Sepsis-Associated Acute Kidney Injury on its Incidence and Outcomes: A Systematic Review and Meta-Analysis.

To derive a pooled estimate of the incidence and outcomes of sepsis-associated acute kidney injury (SA-AKI) in ICU patients and to explore the impact of differing definitions of SA-AKI on these estimates. Medline, Medline Epub, EMBASE, and Cochrane CENTRAL between 1990 and 2023. Randomized clinical trials and prospective cohort studies of adults admitted to the ICU with either sepsis and/or SA-AKI. Data were extracted in duplicate. Risk of bias was assessed using adapted standard tools. Data were pooled using a random-effects model. Heterogeneity was assessed by using a single covariate logistic regression model. The primary outcome was the proportion of participants in ICU with sepsis who developed AKI. A total of 189 studies met inclusion criteria. One hundred fifty-four reported an incidence of SA-AKI, including 150,978 participants. The pooled proportion of patients who developed SA-AKI across all definitions was 0.40 (95% CI, 0.37-0.42) and 0.52 (95% CI, 0.48-0.56) when only the Risk Injury Failure Loss End-Stage, Acute Kidney Injury Network, and Improving Global Outcomes definitions were used to define SA-AKI. There was significant variation in the incidence of SA-AKI depending on the definition of AKI used and whether AKI defined by urine output criteria was included; the incidence was lowest when receipt of renal replacement therapy was used to define AKI (0.26; 95% CI, 0.24-0.28), and highest when the Acute Kidney Injury Network score was used (0.57; 95% CI, 0.45-0.69; p < 0.01). Sixty-seven studies including 29,455 participants reported at least one SA-AKI outcome. At final follow-up, the proportion of patients with SA-AKI who had died was 0.48 (95% CI, 0.43-0.53), and the proportion of surviving patients who remained on dialysis was 0.10 (95% CI, 0.04-0.17). SA-AKI is common in ICU patients with sepsis and carries a high risk of death and persisting kidney impairment. The incidence and outcomes of SA-AKI vary significantly depending on the definition of AKI used.

Relationship between Profitability Indicators and Maximization Market Value A dded a nd I ntr insic for the I ndustr ial Companies

Purpose: This research aims to study the impact of profitability indicators on maximizing the market value added and intrinsic value for industrial companies listed on the Iraq Stock Exchange. Design/methodology/approach: The research was structured using the analysis study method, incorporating various financial indicators, such as Net Profit Margin, Basic Earning Power, Return on Assets, Return on Equity, to examine their impact on market value added and intrinsic value. Findings: The researchers utilized financial data form fourteen industrial companies operating in Iraq ; listed in the financial market, spanning the period from 2011 to 2022. The data underwent analysis employing financial methods and various statistical tools, including single and multiple regression analysis as well as transaction analysis. The results revealed that the profitability indicators adopted by the studied companies exerted varied effects on maximizing the market value or intrinsic value. This variance was observed in accordance with the nature and orientations of each company. The underlying reason for this variation can be attributed to the direct relationship of the financial indicators used directly with the investors, thereby influencing management decisions. Research limitations/implications: The paper focuses on four variables representatives of firm profitability indicators to maximize both the market value added and the intrinsic value; it is necessary expanding the independent variables. Originality/value: This research contributes to the literature on Maximizing value levels for industrial companies. It aids firm leaders and decision-makers understanding the significant role of profitability indicators in maximizing value in financial markets by offering a more comprehensive view of their effectiveness.

Predicting rectal cancer tumor budding grading based on MRI and CT with multimodal deep transfer learning: A dual-center study

ObjectiveTo investigate the effectiveness of a multimodal deep learning model in predicting tumor budding (TB) grading in rectal cancer (RC) patients. Materials and methodsA retrospective analysis was conducted on 355 patients with rectal adenocarcinoma from two different hospitals. Among them, 289 patients from our institution were randomly divided into an internal training cohort (n = 202) and an internal validation cohort (n = 87) in a 7:3 ratio, while an additional 66 patients from another hospital constituted an external validation cohort. Various deep learning models were constructed and compared for their performance using T1CE and CT-enhanced images, and the optimal models were selected for the creation of a multimodal fusion model. Based on single and multiple factor logistic regression, clinical N staging and fecal occult blood were identified as independent risk factors and used to construct the clinical model. A decision-level fusion was employed to integrate these two models to create an ensemble model. The predictive performance of each model was evaluated using the area under the curve (AUC), DeLong's test, calibration curve, and decision curve analysis (DCA). Model visualization Gradient-weighted Class Activation Mapping (Grad-CAM) was performed for model interpretation. ResultsThe multimodal fusion model demonstrated superior performance compared to single-modal models, with AUC values of 0.869 (95% CI: 0.761–0.976) for the internal validation cohort and 0.848 (95% CI: 0.721–0.975) for the external validation cohort. N-stage and fecal occult blood were identified as clinically independent risk factors through single and multivariable logistic regression analysis. The final ensemble model exhibited the best performance, with AUC values of 0.898 (95% CI: 0.820–0.975) for the internal validation cohort and 0.868 (95% CI: 0.768–0.968) for the external validation cohort. ConclusionMultimodal deep learning models can effectively and non-invasively provide individualized predictions for TB grading in RC patients, offering valuable guidance for treatment selection and prognosis assessment.

The correlation study between the length and angle of ascending aortic and the incidence risk of acute type A aortic dissection.

This study utilized computed tomography angiography (CTA) to assess the risk of acute type A aortic dissection (ATAAD) by analyzing the imaging morphology indicators of the ascending aorta, along with the relevant risk factors associated with aortic dissection. The study utilized a retrospective observational research design. The population consisted of 172 patients who received treatment in the Department of Cardiothoracic Surgery at Qilu Hospital, Shandong University, from January 2018 to December 2022. The patients were divided into two groups: the ATAAD group (n = 97) and the thoracic aortic aneurysm group (TAA, n = 75). Demographic data and ascending aorta CTA measurements were collected from all patients. Single factor and multivariate logistic regression were employed to analyze the statistical differences in clinical data and ascending aorta CTA imaging morphology indicators between the two groups. The variables were included in logistic multivariate analysis for further screening, indicating that the length of the ascending aorta (LAA) before ATAAD (OR = 3.365; 95% CI :1.742-6.500, P<0.001), ascending arch angle (asc-arch angle, OR = 0.902; 95% CI: 0.816-0.996, P = 0.042) and the maximum aortic diameter (MAD) before ATAAD, (OR = 0.614; 95% CI: 0.507-0.743, P<0.001) showed statistically significant differences. This study suggests that increased LAA and MAD, as well as a smaller asc-arch angle may be high-risk factors for the onset of ATAAD.

Patient Characteristics Impacting Adherence to Serial Observation for Vestibular Schwannomas.

To examine patient characteristics that impact serial observation adherence amongvestibular schwannoma (VS) patients. Retrospective chart review. Single tertiary care center. We selected for VS patients from 201 to 2020 who elected for serial observation as initial management. Patients under 18, with previous management, bilateral or intralabyrinthine VS, and neurofibromatosis type 2 were excluded. Demographics, tumor characteristics, and follow-up status were extracted. Single and multiple logistic regression was used to identify patient characteristics impacting follow-up. We identified 507 VS patients who chose serial observation as initial management. Most were female (56.0%), white (73.0%), and married (72.8%). The mean age was 59.3 and most had private insurance (56.4%). Median Charlson Comorbidity Index was 2.00. Mean pure tone audiometry (PTA) average was 41.7 Hz. Average tumor size was 9.04 mm. Of 507 patients, 358 (70.6%) returned for at least one follow-up. On multiple logistic regression analysis, patients with private insurance (odds ratio [OR]: 0.39, confidence interval [CI]: 0.22-0.68; P = .001), racial minority background (OR: 0.54, CI: 0.35-0.83; P = .005), worse PTA averages (OR: 0.99, CI: 0.98-1.00; P = .044), and older age at diagnosis (OR: 0.97, CI: 0.95-1.00; P = .038) were less likely to follow-up. Private health insurance, racial minority background, worse PTA average, and older age were associated with decreased follow-up amongadult VS patients electing serial observation. Patients with these characteristics may require additional support to ensure serial observation adherence.

Abstract 1072: The EsomiR project: A case control study to assess the role of exosomal miRNAs in breast cancer cancerogenesis

Abstract Introduction: One of the unmet needs in Breast Cancer (BC) prevention is how to identify women at higher risk of developing this disease, who might benefit from periodic surveillance and/or from chemoprevention. The goal of this retrospective study is to assess the role miRNAs isolated from extracellular vesicles (EV-miRNAs) as possible early biomarkers for identifying patients with higher risk of developing BC, in order to personalize their screening programme. To this goal, EV-miRNAs from cyst fluid derived from women with Gross Cyst Disease of the Breast (GCDB - a benign disease of the mammary gland associated with a 2-4-fold increase in the risk of developing BC) have been analysed. Methodology: Cyst fluids have been selected among samples from a cohort of 600 patients diagnosed with GCDB between 1985 and 1993, some of whom developed BC during follow-up. EV-miRNAs have been extracted from cyst fluid using the exoRNeasy midi kit (Qiagen), qualitatively evaluated by Bioanalyzer (Agilent) and quantified by Qubit™ using the microRNA Assay Kit (Thermo Fisher Scientific). The MiRNome profiles have been assessed by microarray using an optimised protocol on Agilent platform (labelling and hybridization on SurePrint Human miR Microarrays 8 × 60 K and images acquisition by G2565CA scanner). Results: A total of 117 samples (58 cases and 59 controls, paired on the bases of clinical and pathological variables) have been analysed. Single variable logistic regression analysis on the most expressed EV-miRNAs (logFC &amp;gt;= 6 (n=329)), selected six EV-miRNAs (miR-6076, miR-202-3p, miR-6872-3p, miR-769-3p, miR-5195-3p, miR-4443) and one (miR-4713-3p) negatively and positively associated with BC development, respectively (raw p-value &amp;lt;0.05). A risk score combining the seven EV-miRNAs was derived by multivariable logistic regression modelling and reported an AUC=0.73 (95% [CI] 0.6427-0.8231). The model was refined including clinical variables and applying step-wise logistic regression analysis. The combination of 3 EV-miRNAs (miR-6076, miR-6872-3p, miR-5195-3p) with menopausal status, familiarity and type of cyst yielded an AUC of 0.8 (95% [CI] 0.7015-0.8847). Logistic regression was also performed on Type I cysts (n=78), that are correlated with a higher risk of developing BC, pointing in evidence 3 EV-miRNAs with a negative (miR-6076, miR-6872-3p) or positive (miR-4515) correlation with BC development risk (AUC = 0.74, 95% [CI] 0.6278-0.8475). In the model comprehensive of clinical variables, two of those EV-miRNAs (miR-6872-3p, miR-4515) together with familiarity and menopausal status were selected, giving an AUC of 0.8 (95% [CI] 0.6961-0.8991). Conclusion: This study allowed the identification of a EV-miRNA-based BC risk signature obtained by liquid biopsy on cyst fluid. The final purpose of this project is to transfer this signature in the clinic by testing it prospectively on plasma samples. Citation Format: Barbara Cardinali, Patrizia Piccioli, Francesco Boccardo, Alessandra Rubagotti, Linda Zinoli, Andrea Sciutto, Simona Coco, Giovanna Chiorino, Paola Ostano, Emir Sehovic, Zita Cavalieri, Cristina Bruzzo, Silvia Marconi, Roberta Tasso, Rodolfo Quarto, Davide Ceresa, Paolo Malatesta, Lucia Del Mastro. The EsomiR project: A case control study to assess the role of exosomal miRNAs in breast cancer cancerogenesis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1072.

Longitudinal Associations Between Pain, Risk for Posttraumatic Stress Disorder, Posttraumatic Stress Symptoms, and Pain Characteristics in Children After Unintentional Injury.

Approximately 20% of children demonstrate persistent posttraumatic stress symptoms (PTSS) after unintentional injury, with more severe pain intensity predicting concurrent and later PTSS. Examining additional pain characteristics like pain behaviors, impairment related to pain, and subjective experiences of pain might provide additional insight into the mechanisms that reinforce relationships between risk for posttraumatic stress disorder (PTSD), PTSS, and pain. During hospitalization for unintentional injury, the Screening Tool for Predictors of PTSD (STEPP) was administered and the highest pain score was collected. One month later, the Child PTSD Symptom Scale and PROMIS questionnaires assessed PTSS and pain characteristics respectively, including intensity, interference, behaviors, and quality. Correlations between PTSS and PROMIS questionnaires were significant. STEPP predicted future PTSS and all PROMIS questionnaires. The highest pain score predicted future PTSS, as well as pain interference and pain behavior, and did not predict pain intensity and pain quality. When STEPP and highest pain score were combined into a single regression, STEPP and highest pain score predicted future PTSS but only STEPP continued to predict all PROMIS questionnaires. PTSD risk significantly predicted PTSS and pain characteristics 1 month later. The highest pain score predicted future PTSS and several pain characteristics but no longer had predictive value for pain-related outcomes when combined with PTSD risk. These results indicate that risk factors for PTSD are stronger predictors than pain-related risk factors in predicting pain outcomes. Addressing PTSD risk, as well as pain intensity during hospitalization, may result in improved outcomes for children with unintentional injury.

Relationship between the number of public health nurses and the total number of people receiving home-visit nursing services for mental health/intractable diseases, and those numbers' regional differences

Objectives The primary aim of this study was to clarify the relationship between the number of public health nurses (PHNs) and the total number of people who received home-visit nursing services for mental health or intractable diseases. The secondary aim was to clarify the extent of regional differences in the number of PNHs and mental health or intractable diseases.Methods This study used the total number of people who received home-visit nursing services for mental health or intractable diseases in 2019 from the Portal Site of Official Statistics of Japan (e-Stat) and population and area data in January 2020. Single and multiple regression analyses (covariates: population and area) were performed on the relationship between the number of PHNs per 100,000 population (abbreviated as "ratios of PHNs") and the total number of people who received home-visit nursing services for mental health or intractable diseases per 100,000 population (abbreviated as "mental health/intractable disease achievements"). Regional differences in ratios of PHNs and mental health/intractable disease achievement were examined using mean, standard deviation, maximum/minimum values, and Gini coefficients. Analyses were performed for each of the five units: the prefectures as a whole, prefectural public health centers, municipalities within the jurisdiction of prefectural public health centers, and cities in which public health centers are established (including or not including special wards).Results Regression analyses indicated a positive relationship between the ratios of PHNs and mental health/intractable disease achievements. Multiple regression analysis indicated that both achievements were positively associated with population size and negatively associated with area size. The largest regression coefficients between the ratios of PHNs and achievements were 34.07 and 5.48 regarding mental health achievements and intractable disease achievements, respectively. For regional differences, the smallest Gini coefficient was the ratios of PHNs, and the largest was intractable disease achievements. The smallest and largest coefficient of the prefectures as a whole was 0.15 and 0.34, respectively. The maximum/minimum values of the prefectures as a whole also indicated that the smallest was 3.8 in the ratio of PHNs and the largest was 30.0 in intractable disease achievement.Conclusions Increasing number of PHNs is needed to provide more home-visit nursing services for mental health and intractable diseases. It is particularly important to fill up the larger number of PHNs in smaller populations or larger area prefectures. Due to regional differences in the home-visit nursing service, it is important to promote the increase in the level of these activities.

Modelling Stock Prices of Energy Sector using Supervised Machine Learning Techniques

This paper aims at comparing the performance of the different state-of-the-art machine learning techniques in anticipating the performance of stock prices of the energy sector. The data collected cover the period from January 2020 to February 2023 with a daily frequency for the three most imported refined petroleum products in Morocco and trained four regression machines learning (linear regression, lasso regression, ridge regression, and SVR) and four classifiers machine learning (logistic regression, decision tree, extra tree and Random Forest) so that anticipating one day ahead prices direction can take place no matter whether they are negative or positive prices. The performance of regression algorithm is then evaluated using different evaluation metrics, especially MSE, RMSE, MAE, MAPE and R2 to evaluate the performance of regression algorithm while precision, recall and F1 scores are used to evaluate the performance of classifiers algorithm. The outcomes propose that the performance of linear regression and ridge regression takes place equally and outperform other single regression that is lasso regression and SVR for-one-day predictions as a whole. In addition to that, we have come to find that in the classifiers, algorithms group all machine learning display similar predictive accuracy, this is on one hand. On the other hand, the best of them is the logistic regression. In brief, this study suggests that all performance metrics are significantly improved by ensemble learning. Therefore, this study proves that critical information affecting stock movement can be captured by utilizing historical transactions.

Analysis of the composition and influencing factors of hospitalization expenses for 1517 patients with lung malignant tumors in Beijing.

This study aimed to analyze the composition of the total hospitalization expenses of patients with lung cancer in Beijing TongRen Hospital from January 2018 to December 2020 before and after the implementation of the "Beijing Medical Consumption Linkage Comprehensive Reform Implementation Plan" (hereinafter referred to as "Reform"). The SPSS 25.0 statistical software was used to perform descriptive statistics on the total hospitalization costs of selected 1517 patients with lung malignant tumors, and single factor and multivariate regression analysis were used to clarify the influencing factors of the patients' total hospitalization costs. From 2018 to 2020, the total hospitalization costs of patients with lung malignant tumors increased year by year (P < .05), and the average length of hospital stay decreased year by year (P < .05). The total hospitalization expenses of patients with lung malignant tumors mainly include material expenses, surgical expenses, inspection expenses, inspection expenses and medicine expenses. After the implementation of the "Reform," the proportion of medicine, inspection, nursing and other expenses in the total hospitalization expenses of patients with lung malignant tumors has been significantly reduced (P < .05), and the proportion of surgical expenses has been significantly increased (P < .05). The results of the univariate analysis showed that gender, age, length of stay in the hospital, surgery, and tumor location were the main factors affecting the total hospitalization expenses of patients (P < .05). The results of multivariate analysis showed that gender (female), age (<40 years old), length of stay (≥15 days), surgery (yes), and tumor location (right lung) are the main factors affecting the total hospitalization cost of patients with malignant tumors (P < .05). Under the premise of ensuring the efficacy of patients, the economic burden of patients is reduced by reducing the cost of materials, shortening the length of hospitalization, strengthening hospital management, and controlling the continuous growth of hospitalization costs.

Learning single and multi-scene camera pose regression with transformer encoders

Contemporary state-of-the-art localization methods perform feature matching against a structured scene model or learn to regress the scene 3D coordinates. The resulting matches between 2D query pixels and 3D scene coordinates are used to estimate the camera pose using PnP and RANSAC, requiring the camera intrinsics for both the query and reference images. An alternative approach is to directly regress the camera pose from the query image. Although less accurate, absolute camera pose regression does not require any additional information at inference time and is typically lightweight and fast. Recently, Transformers were proposed for learning multi-scene camera pose regression, employing encoders to attend to spatially varying deep features while using decoders to embed multiple scene queries at once. In this work, we show that Transformer Encoders can aggregate and extract task-informative latent representations for learning both single- and multi- scene camera pose regression, without Transformer-Decoders. Our approach is shown to reduce the runtime and memory of previous Transformer-based multi-scene solutions, while comparing favorably with contemporary pose regression schemes and achieving state-of-the-art accuracy on multiple indoor and outdoor regression benchmarks. In particular, to the best of our knowledge, our approach is the first absolute regression approach to attain sub-meter average accuracy across outdoor scenes. We make our code publicly available at: https://github.com/yolish/transposenet.

A study on the impact of marital status on the survival status of prostate cancer patients based on propensity score matching

Marital status is an independent prognostic factor for survival in many types of cancers, but its prognostic impact on patients with prostate cancer (PCa) has not been established. The aim of this study was to explore the independent prognostic factors of PCa and to investigate the effect of marital status on survival outcomes in patients with different stratified by PCa. Using the surveillance, epidemiology, and end results (SEER) database, we collected data on 584,655 PCa patients diagnosed between 1975 and 2019. Marital status was classified as married, divorced, widowed, and single. We used the Kaplan–Meier analysis and single multivariate Cox proportional hazards regression analysis to determine the effect of marital status on overall survival (OS) and cancer-specific survival (CSS). In addition, we performed subgroup analyses for different ages, Gleason score and PSA values, and performed a 1:1 propensity score matching (PSM) to reduce the impact of confounding factors to obtain more accurate matching results. According to our findings, marital status was an independent prognostic factor for the survival of PCa patients and a better prognosis of married patients. Moreover, we also found that factors such as age, TNM stage, Gleason score, and PSA concentration were also considered as important predictors for the prognosis of PCa. The above findings can facilitate early detection and treatment of high-risk PCa patients, prolong their life and reduce family burden.

Association between stress and catecholamines with painful TMD in children.

Studying biomarkers in children with temporomandibular disorders (TMD) such as epinephrine, norepinephrine, and dopamine may reveal factors like screen time or sleep loss that affect these biomarkers and predict TMD-related pain, offering new research opportunities. To determine the association between stress and catecholamines with myofascial pain and headache related to TMD in children. Sixty-six 9- to 11-year-old children assisting at the clinics of Pediatric Dentistry of Universidad CES participated in the study. Myofascial pain and headache attributed to TMD were determined according to the Diagnostic Criteria for TMD (DC/TMD) Axis I. Stress was evaluated with the Perceived Stress Scale-Children (PSS-C), and a 24-h urine sample was analyzed using liquid chromatography to assay catecholamines. Single and multiple regression analyses were performed. Children with a mean age of 10.3 years participated in the study. The mean score of stress was 29 ± 4. Perceived stress, dopamine, epinephrine, and norepinephrine were statistically significant predictors of myofascial pain and headache attributed to TMD in the single- and multiple variable logistic regression analyses. Stressful states and its biological biomarkers increase the probability of developing myofascial pain and headache attributed to TMD in children.

The Relationship of the Teachers’ Growth Mindset to the Students’Mutual Cooperation Character in Primary School

The teacher's mindset will influence learning strategies such as designing or implementing learning. Furthermore, the strategies created by teachers will influence student character development. This research is motivated by the absence of other research that discusses the relationship between elementary school teachers' growth mindset and the cooperation character of elementary school students. This research aims to describe the growth mindset profile of elementary school teachers and find the relationship between elementary school teachers' growth mindset and students' cooperation character. The research was an ex post facto correlational design involving 30 elementary school teachers as research subjects. Data was collected through non-test methods, using questionnaires and checklist sheets. Data analysis used quantitative descriptive and single predictor regression analysis techniques. The research results show that most teachers have a growth mindset with some fixed ideas. A correlation coefficient of 0.769 was also obtained, meaning there is a strong and unidirectional relationship between the growth mindset of elementary school teachers and the students' cooperative character. This research implies that more training can be held to shape the growth mindset of teachers so that they can take more of a role in improving students' cooperative character.

Scheduling and Vendor Consistency Improves Turnover Time Efficiency in Total Joint Arthroplasty

BackgroundMaximizing operative room (OR) efficiency is important for hospital efficiency, patient care, and positive surgeon and staff morale. Reducing turnover time (TOT) has become a popular focus to improve OR efficiency. The present study evaluated if TOT is influenced by changing case type, implant vendor, and/or laterality. MethodsIn total, 444 turnovers from January to July 2023 were retrospectively analyzed. All turnovers were same-surgeon turnovers between primary arthroplasty cases in dedicated, overlapping rooms. Single linear regression models tested the predictability of TOT based on case type, vendor, and laterality. A multivariate multiple regression and 1-way Analyses of Variance analyzed variables against each other. Independent sample t-tests evaluated TOTs when all variables were the same or different. ResultsChanging versus keeping the same case type increased TOT by 2.4 minutes (95% confidence interval [CI] = 0.7, 4.0; P = .004). Changing vendors increased TOT by 2.9 minutes (95% CI = 1.1, 4.7; P = .002). Laterality did not affect TOT, with a change of 0.9 minutes (95% CI = −0.6, 2.5; P = .229). Vendor (P = .030) independently predicted TOT when analyzed as a covariate with case type (P = .410). The TOT with same case type and vendor (mean 38.2 minutes; range, 22 to 62) was less than that of different case types and vendors (mean 41.4 minutes; range, 26 to 73) (P = .017). Mean TOT differed by 5.5 minutes when keeping all variables the same versus all different (P = .018). ConclusionsMaintaining a consistent case type, vendor, and laterality had a synergistic effect in reducing TOT in arthroplasty ORs with the same primary surgeon running 2 overlapping rooms. Changing vendor representatives was found to independently predict TOT increases, which is likely attributed to a disruption in workflow and collaboration of the multidisciplinary OR team. Level of EvidenceLevel III.

The Risk of Cervical Cancer in Women Among Han, Bai, Dai and Hani Ethnic Minorities in Yunnan Province of China.

Research on the risk factors for cervical cancer in Yunnan Province's four characteristic ethnic groups (Han, Bai, Dai, and Hani) is lacking. To study the risk factors of cervical cancer in four ethnic women in Yunnan Province, and to provide evidence for its prevention. The cervical cancer patients of Han, Bai, Dai and Hani ethnic groups in Yunnan Province who were first diagnosed in the Third Affiliated Hospital of Kunming Medical University (Yunnan Cancer Hospital, Yunnan Cancer Center) from January 2011 to December 2020 were selected as the research objects. The 1:1 matched case-control study method was used, and single factor and conditional logistic regression were used for statistical analysis. HPV types 16, 18 and 58 are mostly related with cervical cancer, the younger the age of the last pregnancy, the more times of pregnancy, childbirth and abortion, especially the younger the first marriage age of Bai and Dai, are the risk factors of cervical cancer; the infection of genital tract bacteria, mycoplasma and chlamydia is closely related to the incidence of cervical cancer in four ethnicities. Multifactorial analysis showed that demographic characteristics and environment/behavior were not included in the influencing factors of cervical cancer; among Han, Bai, Dai and Hani ethnic minorities, contraception (OR=0.29, OR=0.03, OR=0.09, OR=0.16, P<0.05) was positive factor, HPV infection (OR=64.77, OR=128.71, OR=71.89, OR=40.07, P<0.01) was a causative factor of cervical cancer. Risk of high parity with cervical cancer could be due to a complex interplay of factors, it is very important to formulate prevention strategies and measures in line with the cervical cancer of Han, Bai, Dai and Hani ethnic groups women in Yunnan Province.

A YOLOV3-Based Method for Detecting Deepfake Manipulated Facial Images

With the advancement of technology and the development of applications that make it easier to transfer images, sounds and videos to the virtual environment, it has become much easier to access people's personal information, videos and images. Deepfake technology produces fakes of authentic images or sounds using deep learning and artificial intelligence techniques. Today, in addition to being used in the entertainment and film industries, it is also used in situations such as creating fake news and discrediting people. Different studies have been conducted in the literature to detect deepfake images and videos to prevent these situations. In this study, a comprehensive literature review was conducted. Real and fake images were collected and labelled from different datasets or videos, and a dataset was created by applying the necessary pre-processing steps. With the created dataset, training was carried out with YOLOv3 technology, which calculates class probabilities differently from traditional methods using Convolutional Neural Networks (CNN) and handles all operations in a single regression problem, which can make fast and high-accurate detection, and the modelling process is explained. With the tests performed in the study, the model that can detect fake images produced with deepfake technology with 95% accuracy was obtained.