Pearson Correlation Coefficient Research Articles

Left ventricular wall thickness discrepancies at end-diastole and mid-diastole: Reference values for cardiac CT.

To investigate differences in left ventricular wall thickness (LVWT) measurements between end-diastole and mid-diastole using cardiac computed tomography (CCT) and establish LVWT reference values stratified by phase, sex, and region. Subjects who underwent CCT without a history of cardiovascular disease or risk factors were retrospectively included between 2021 and 2024. LVWT was manually measured in each segment according to the American Heart Association's 17-segment model at end-diastole and mid-diastole. Regional LVWT was calculated as the average value of relevant segments. The study included 187 subjects with a mean age of 51±11years, including 77 (41%) men. Global LVWT was lower at end-diastole than at mid-diastole (5.7±0.8 vs. 6.5±0.9mm, P<0.001). Each segmental LVWT correlated significantly between end-diastole and mid-diastole (Pearson's correlation coefficient: 0.79-0.87). Segment 2 was thickest (8.1±1.5mm at end-diastole and 9.1±1.7mm at mid-diastole). LVWT was greater in men than in women (all P<0.001). The upper limits of LVWT were 9.9mm for women and 11.7mm for men at end-diastole, and 11.8mm for women and 13.1mm for men at mid-diastole. LVWT progressively thinned from the base to the apex. Apical LVWT measured on short-axis and long-axis showed a small but statistically significant difference, particularly in Segment 16. This study provides CCT reference values for LVWT at end-diastole and mid-diastole. Mid-diastolic LVWT was slightly greater than end-diastolic LVWT, with a statistically significant difference. Normal LVWT was greater in men than in women, with regional variations observed in both phases.

Prognostic Value of Combined Detection of MMP-7 and ALP Levels in Children With Biliary Atresia Post-Kasai Surgery.

Biliary atresia (BA) remains a prevalent indication for pediatric liver transplantation (LT). We investigated the prognostic value of the serum matrix metalloproteinases 7 (MMP-7) and alkaline phosphatase (ALP) level combined detection for BA children post-Kasai surgery. This study retrospectively enrolled 85 BA children who underwent Kasai surgery. They were divided into the native liver (NL) and LT groups based on their three-year postoperative prognosis. Serum MMP-7 and ALP levels were measured by ELISA. The relationship of intraoperative serum MMP-7 and ALP levels with preoperative gamma-glutamyltransferase (GGT), and their impact on the risk of postoperative LT were analyzed by Pearson's correlation coefficient and Kaplan-Meier curves. The independent risk factors (IRFs) for postoperative LT and the predictive value of the serum MMP-7 and ALP level combined detection for postoperative LT in BA children were analyzed by Cox regression analysis and receiver operating characteristic (ROC) curves. MedCalc software compared the areas under the ROC curves (AUC). Significant differences were observed between the two groups in BA classification, postoperative jaundice clearance rate, and cholangitis occurrence. Intraoperative serum MMP-7 and ALP levels were higher in the LT group and positively correlated with preoperative GGT. High MMP-7 and ALP levels were IRFs for postoperative LT, while significant jaundice clearance was a protective factor. Combined MMP-7 and ALP detection (0.926 AUC, 91.30% sensitivity, 87.18% specificity) significantly improved the prediction for LT. High MMP-7 and ALP levels are IRFs for post-Kasai surgery LT in BA children, with their combination providing superior predictive value.

Assessment of the variability and correlation of biometric measurements in eyes with cataracts.

To assess the variability and correlation of ocular biometric parameters in eyes with cataracts and examine their association with age and gender. A retrospective analysis was performed on biometric data from 7,458 eyes without retinal pathologies, using an optical biometer equipped with low-coherence reflectometry technology. Pearson's correlation coefficients (r) were used to evaluate the relationships between age, gender, and various ocular parameters, including axial length (AL), anterior chamber depth (ACD), lens thickness (LT), central corneal thickness (CCT), corneal refractive power (Km), and white-to-white (WTW) measurement. The mean AL was 23.82 ± 1.83 mm (SD). Among the study cohort, 8.0 % (593 eyes) were categorized as having short AL (AL ≤ 22 mm), 60.5 % (4,520 eyes) as having normal AL (22 mm < AL ≤ 24 mm), 21.2 % (1,579 eyes) as having long AL (24 mm < AL ≤ 26 mm), and 10.3 % (766 eyes) as having a AL (> 26 mm). The mean age of the cohort was 64.18 ± 14.95 years (range: 18 - 98 years), with males averaging 56.53 ± 13.88 years and females averaging 72.19 ± 11.41 years. Mean values for ACD, LT, Km, CCT, WTW, and angle kappa were found to vary with both age and gender. Males demonstrated significantly longer AL, deeper ACD, thicker LT, larger WTW, and lower Km compared to females (P < 0.05). Although males exhibited a greater anterior segment length (ASL), this difference did not reach statistical significance (P > 0.05). No significant correlation was found between ACD and AL in the short and long AL groups, while a positive correlation was observed in the normal and long AL groups. Additionally, LT demonstrated a negative correlation with AL in the normal AL group but a positive correlation in the long AL group. Cataracts develop at a greater age in female patients than in males. Ocular biometric parameters, including ACD, LT, CCT, Km, and (WTW) measurement, are influenced by age and gender. Lens thickness is the primary factor affecting ACD, though CCT, Km, and WTW also play significant roles. Furthermore, a negative correlation between AL and LT was identified, with variations depending on AL classification.

Cross-cultural adaptation, reliability, validity and responsiveness of the hand assessment tool (HAT-Sp) in Spain.

The HAT is a questionnaire designed to measure activity limitations in patients with hand and wrist injuries, without assistance. This study aimed to translate, culturally adapt, and validate the psychometric properties of the HAT-validity, reliability, and responsiveness-for different hand conditions in Spain. Cultural adaptation followed recommendations from the American Academy of Orthopaedic Surgeons (AAOS). Validation adhered to the Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) guidelines and involved 262 hand patients. Reliability was assessed with Cronbach's alpha and intraclass correlation coefficient (ICC) (2,1), for test-retest reliability, the standard error of measurement (SEM) and the smallest detectable change (SDC) were used to assess measurement error. Confirmatory factor analysis (CFA) and construct validity using Pearson's correlation coefficient evaluated validity. Responsiveness was measured using effect size (ES) and standardised response mean (SRM). The adaptation process showed no significant changes, with strong expert agreement (Kendall's W=0.81, p<0.001) and patient difficulty below 15%. Reliability was confirmed with an excellent Cronbach's alpha of 0.91, ICC values of 0.99, and low measurement error (SEM of 0.72 and SDC of 1.41). CFA showed a good model fit (RMSEA of 0.04, SRMR of 0.05). HAT showed a high correlation with DASH (r=0.78, p<0.001) and a weak correlation with EQ-5D (r=-0.07, p>0.05). Responsiveness, assessed in 222 patients, yielded a moderate ES of -0.62 and an SRM of -0.72. The HAT-Sp is a well-adapted and widely accepted tool among both Spanish patients and experts. The study demonstrated its strong reliability, validity, and responsiveness in activity limitation without assistance in patients with various hand and wrist injuries following physiotherapy.

Height development trends among 7-18-year-old school-age children in central plains of China between 2000 and 2019: A serial cross-sectional surveillance study in China.

The aim of this study was to analyze changes in height of 7-18-year-old school-age children in China during the year of 2000-2019. We used the survey data from the Chinese National Survey on Students' Constitution and Health in Henan Province for the years 2000, 2005, 2010, 2014, and 2019. Data were categorized into subgroups based on geographic location, gender, and age; mean, standard deviation, and Pearson's correlation coefficient were used to analyze trends in height change among children and adolescents and the correlation between socioeconomic indicators and height change. The height of children and adolescents in Henan has shown a continuous upward trend. The height difference between urban and rural areas has gradually narrowed but has not disappeared. The correlation of height development trends between neighbouring urban areas was higher than in other areas. The level of medical care was significantly associated with the change in height for both boys (r = 0.950, p = 0.013) and girls (r = 0.897, p = 0.039); GDP per capita (r = 0.940，p = 0.018) was significantly associated with the change in height for boys only. The height of Chinese children and adolescents will continue to maintain a positive growth trend, but we need to pay attention to the health status and nutritional intake of children and adolescents in economically disadvantaged areas in order to narrow the height disparity between different socio-economic groups.

Exploring factors influencing body acceptance in women with breast cancer: A cross-sectional study.

Body acceptance in women with breast cancer is a crucial factor related to their physical, psychological health, and quality of life. However, research on the factors influencing body acceptance in this population is lacking. This study aims to identify these factors to provide insights for developing tailored interventions. We conducted a cross-sectional study with 258 participants via an online survey in September 2023. Variables measured included general and disease-related characteristics, self-esteem, resilience, social support, objectified body consciousness, and body acceptance. We analyzed the data using descriptive statistics, frequency analysis, independent t-test, one-way ANOVA, Scheffé's test, Pearson's correlation coefficient, and hierarchical regression analysis. Among participants' characteristics, we identified age, religion, menstruation status, elapsed period after diagnosis, cancer stage at diagnosis, and current treatment as factors influencing body acceptance. Additionally, higher resilience (β=0.35, p<.001), increased social support (β=0.13, p=.037), and lower objectified body consciousness (β=-0.31, p<.001) positively influenced body acceptance. Collectively, these variables accounted for 78.9% of variance in body acceptance. This study offers valuable insights into the factors affecting body acceptance in women with breast cancer. The findings highlight the need for tailored interventions that enhance resilience, foster social support, and address objectified body consciousness. Future research should validate these findings across diverse populations and refine personalized care strategies in clinical practice.

Bayesian optimization enhanced FKNN model for Parkinson’s diagnosis

A progressive neurodegenerative condition that adversely impacts motor skills, speech, and cognitive abilities is Parkinson’s disease (PD). Research has revealed that verbal impediments manifest in the early of PD, making them a potential diagnostic marker. This study introduces an innovative approach, leveraging Bayesian Optimization (BO) to optimize a fuzzy k-nearest neighbor (FKNN) model, enhancing the detection of PD. BO-FKNN was validated on a speech datasets. To comprehensively evaluate the efficacy of the proposed model, BO-FKNN was compared against five commonly used parameter optimization methods, including FKNN based on Particle Swarm Optimization, FKNN based on Genetic algorithm, FKNN based on Bat algorithm, FKNN based on Artificial Bee Colony algorithm, and FKNN based on Grid search. Moreover, to further boost the diagnostic accuracy, a hybrid feature selection method based on Pearson Correlation Coefficient (PCC) and Information Gain (IG) was employed prior to the BO-FKNN method, consequently the PCCIG-BO-FKNN was proposed. The experimental outcomes highlight the superior performance of the proposed system, boasting an impressive classification accuracy of 98.47%.

Early detection of high blood pressure from natural speech sounds with graph diffusion network.

This study presents an innovative approach to cuffless blood pressure prediction by integrating speech and demographic features. With a focus on non-invasive monitoring, especially in remote regions, our model harnesses speech signals and demographic data to accurately estimate blood pressure. We found a strong correlation between our predictive model and early-stage high blood pressure, highlighting its potential for early detection. Central to our investigation is the Graph Diffusion Network (GDN) model, achieving exceptional performance with an R2 score of 0.96 and a Pearson correlation coefficient (PCC) of 0.98. In early-stage hypertension detection, the GDN model achieved an F1-Score of 0.8735±0.10 and accuracy of 0.8896±0.11. Additionally, without considering demographic features, the model still performed well, with an R2 of 0.740 and PCC of 0.764 when used alone. These results emphasize the value of combining speech and demographic features, offering a promising, non-invasive solution for blood pressure monitoring.

Concordance between PD-L1 assays 28-8 and SP263 and their respective scoring algorithms in procured upper gastrointestinal adenocarcinoma samples.

479 Background: PD-L1 is being studied as a predictive immunohistochemical (IHC) biomarker for gastroesophageal junction (GEJ), gastric (GC), and esophageal (EAC) adenocarcinomas, and anti-PD-(L)1 therapies are used to treat them. Here, we compare the VENTANA PD-L1 (SP263) IHC assay and its tumor area positivity (TAP) algorithm with the clinically validated Agilent PD-L1 IHC 28-8 pharmDX combined positive score (CPS) algorithm in GEJ, GC, and EAC samples. The degree of concordance between SP263 TAP ≥ 5% and 28-8 CPS ≥ 5 is important for interpreting real-world settings and emerging clinical trial data. Methods: GEJ, GC, and EAC tumor samples procured from company-sponsored clinical trials (NCT02545504, NCT02862535, NCT02864381) and external vendors with informed consent were analyzed 4 ways: SP263 TAP, SP263 CPS, 28-8 TAP, and 28-8 CPS. Readouts were evaluated by a single pathologist and were analyzed with Pearson correlation coefficients. Results: 286 patient samples were analyzed (GEJ, n = 106; GC, n = 88; EAC, n = 92). The agreement among the SP263 TAP ≥ 5% and 28-8 CPS ≥ 5 prevalence is shown in the Table. The overall correlation coefficient between 28-8 CPS and SP263 TAP was high ( R = 0.95; P &lt; 2.2 × 10 −16 ). In addition, when applying the same scoring algorithm to both assays (SP263 TAP vs 28-8 TAP or SP263 CPS vs 28-8 CPS), the correlation coefficients remained high ( R = 0.97; P &lt; 2.2 × 10 −16 ). When both algorithms were applied to the same IHC assay (SP263 TAP vs SP263 CPS or 28-8 TAP vs 28-8 CPS), the correlation coefficients were also high ( R = 0.98; P &lt; 2.2 × 10 −16 ). Conclusions: In this controlled experiment the PD-L1 assays 28-8 and SP263 concordance was high. The scoring algorithms for GEJ, GC, and EAC samples were observed as highly correlated; minor differences were likely driven by the distinct PD-L1 monoclonal antibody clones utilized in the IHC assays. These data suggest that the 2 assays are comparable for evaluating PD-L1 expression at the TAP ≥ 5% and CPS ≥ 5 cutoffs. Clinical trial information: NCT02545504 , NCT02862535 , NCT02864381 . Concordance of SP263 TAP ≥ 5% with 28-8 CPS ≥ 5. Positive percentage agreement: 0.96Negative percentage agreement: 0.95Overall percentage agreement: 0.95 28-8 CPS ≥ 5: Positive 28-8 CPS ≥ 5: Negative SP263 TAP ≥ 5%: Positive 116 9 SP263 TAP ≥ 5%: Negative 5 156

The influence of transcranial alternating current stimulation on EEG spectral power during subsequent sleep: A randomized crossover study.

To evaluate the instant impact of transcranial alternating current stimulation (tACS) on sleep brain oscillations. Thirty-six healthy subjects were randomly assigned to receive tACS and sham stimulation in a crossover design separated by a one-week washout period. After stimulation, a 2-h nap polysomnography (PSG) was performed to obtain Electroencephalogram (EEG) data and objective sleep variables, and self-reported subjective sleep parameters were collected at the end of the nap. EEG spectral analyses were conducted on the EEG data to obtain the absolute and relative power for each sleep stage during the nap. The associations between power values and objective and subjective measurements were analyzed using Spearman or Pearson correlation coefficients. The tACS group presented higher power in slow wave activity (SWA) and delta frequency bands and lower alpha, sigma and beta power values compared to the sham group during the N2 and N3 sleep stages. SWA and delta power were positively associated with sleep duration and sleep efficiency relevant parameters; while alpha, sigma and beta power were positively associated with prolonged sleep latency and wakefulness related variables. PSG, self-reported and sleep diary measured objective and subjective sleep parameters were comparable between the tACS and the sham groups. Our results support that tACS could promote sleep depth in microstructure of sleep EEG, manifesting as an increase in EEG spectral power in low frequency bands and a decrease in high frequency bands. The registration number of this study is ChiCTR2200063729.

Association of bodyweight loss with changes in lipids, blood pressure, and fasting serum glucose following tirzepatide treatment in Japanese participants with type 2 diabetes: Apost hoc analysis of the SURPASS J-mono trial.

In the SURPASS J-mono trial, tirzepatide demonstrated significant improvements in bodyweight and several metabolic parameters in Japanese participants with type 2 diabetes. This post hoc analysis evaluated the potential relationships between weight loss and metabolic improvements in SURPASS J-mono. Metabolic parameter data from tirzepatide-treated participants were analyzed by weight loss subgroups and compared to dulaglutide 0.75 mg. Correlations between changes from baseline to week 52 in weight loss and each metabolic parameter were assessed; Pearson correlation coefficients were derived. Mediation analyses were conducted to evaluate weight loss-associated and -unassociated effects of tirzepatide vs dulaglutide 0.75 mg. This analysis included 548 participants (tirzepatide: n = 411, dulaglutide: n = 137). Weight loss subgroups showed greater improvement in metabolic parameters with greater bodyweight loss. Significant (P < 0.05) but weak correlations between changes in bodyweight and triglycerides (r = 0.18-0.25), high-density lipoprotein cholesterol (r = -0.37 to -0.29), and systolic blood pressure (r = 0.19-0.41) were observed across treatment groups; in diastolic blood pressure in the tirzepatide 5-mg (r = 0.28), pooled tirzepatide (r = 0.20), and dulaglutide 0.75-mg (r = 0.23) groups; and in fasting serum glucose in the dulaglutide 0.75-mg (r = 0.18) and pooled tirzepatide (r = 0.13) groups. Weight loss was associated with treatment differences between tirzepatide and dulaglutide 0.75 mg to varying degrees across metabolic parameters, with improvements in fasting serum glucose having the lowest association with weight loss (36.6%-43.5%). In this post hoc analysis, non-glycemic and glycemic parameter improvements appeared differentially associated with weight loss, suggesting both weight loss-associated and -unassociated effects of tirzepatide.

Abstract TMP118: Validation and Reliability of the SPAN Score for Assessing Neurological Function in Mouse Stroke Models following MCAO

Introduction: Neuroanatomy-based functional examinations are crucial for understanding the extent and effects of infarcts in stroke studies. Despite the importance of correlating neurological function with brain anatomy, a comprehensive neurological score that correlates with infarction volume and location following middle cerebral artery occlusion (MCAO) in mice is lacking. During the pilot phase of the Stroke Preclinical Assessment Network (SPAN), a new scoring system—SPAN score—was developed. This study aims to evaluate the reliability and validity of the SPAN score, a 9-item tool rated on a 0-3 Likert scale. Methods: The SPAN 2.0 pilot database was utilized for analysis. Reliability was assessed using Cronbach's alpha (α) and intraclass correlation coefficient (ICC) for individual items and the total score. Multivariate regression was performed for item selection and to determine the weight of each item. Concurrent validity was examined by correlating post-operative day 2 infarction volume, as measured by MRI, with the SPAN score. Results: The total SPAN score demonstrated acceptable reliability, with Cronbach's α and ICC both at 0.751. Item-total correlation coefficients of each item ranged from 0.092 to 0.638. The Pearson correlation coefficient between the total SPAN score and infarction volume was 0.539. Notably, spontaneous activity showed a reversed relationship with other items; excluding this item increased Cronbach's α to 0.766. Conclusion: The SPAN score proves to be a reliable and valid tool for neuroanatomy-based functional assessment in mouse stroke models following MCAO. Ongoing analysis will focus on refining the score by reducing the number of items, adjusting the Likert scale, incorporating lesion area, and further exploring item relationships.

Hazardous gas emissions from drop-in biofuels: mutagenicity, cytotoxicity, and unregulated pollutants

This study investigates cancer-related mutations (TA98 and YG5185 strains/Ames test), cell death (human A549 cell line/MTT assay) and unregulated pollutants (16 PAH, 13 carbonyls) from the gas exhaust emissions from a last-mile delivery vehicle following the WLTC driving cycle, operating with hydrotreated vegetable oil and biodiesel. Both biofuels were used pure and blended 20% by volume with diesel fuel. Gas phase samples were collected using XAD-2 Amberlite® resin. Total carbonyl emission factors for the different fuels ranged from 9.4±0.4 (HVO100) to 14.8±1.6mg/km (B20), while PAH emission factors ranged from 1.8±0.5 (B100) to 4.3±0.9mg/km (HVO20). The ester group in biodiesel demonstrated a significant impact on increasing carbonyl emissions. All fuels were cytotoxic at the highest concentration of exhaust gases, causing more than 30% cell death in human cell line A549 (HVO100 ≈ HVO20 > B100 ≈ B20 > ULSD). No significant correlation was found between cytotoxicity and most of PAH and carbonyls. A strong correlation between PAH and mutagenicity (Pearson correlation coefficient higher than 0.6 for PAH with 3 or more rings) was observed with strain YG5185, particularly when using the metabolic activator. These results indicate that the exhaust gases from the tested biofuels pose potential health risks, particularly in chronic exposure scenarios.

Spatiotemporal characteristics of human activity and land use on ecosystem service functions in mountainous areas of Northeast Guizhou, Southwest China

With the rapid advancement of urbanization and industrialization, intense land utilization in mountainous areas has led to ecological and environmental issues, and also causing a gradual degradation of ecosystem services. Investigating the spatiotemporal characteristics of human activities on land use and ecosystem services in mountainous areas holds significant practical importance for local ecological conservation and land management. This study utilizes human activity and land use models to evaluate the complex relationship between humans and land in the northeastern Guizhou, employs the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) and the Modified Universal Soil Loss Equation (RUSLE) models to conduct an in-depth investigation into the spatiotemporal distribution of soil conservation (SC), water yield (WY), carbon sequestration (CS), habitat quality (HQ), and landscape aesthetics (LA). Besides, we utilized the Pearson correlation coefficient and spatial bivariate models to scrutinize the spatial heterogeneity and interactions among these ecosystem services. The results revealed a significant escalation in the intensity of human activity and land use within the study area, with values rising from 0.483 and 316 in 2000 to 0.695 and 351 in 2020, respectively. For 2000–2020, the added value of SC and WY experienced a change of 1.179 t‧hm−2 and 118.503 mm, respectively, while the CS, HQ, and LA have largely remained stable. The spatial distribution of ecosystem service functions exhibited distinct patterns of trade-offs and synergies. The ecosystem's multifunctional benefits were predominantly concentrated in the northern sector of the study area, with the maximum migration distance of class IV hotspots recorded at 70.431 km. Over time, the impact of human activities and land use on ecosystem services has been escalating. Studying the interactions between human activities and land use and their impact on ecosystem services can help us develop land use policies and ecological management strategies.

Translation, Cross-Cultural Adaptation and Validation of the Karaduman Chewing Performance Scale for the Italian Paediatric Population.

Chewing is a fundamental motor activity, but there is no specific assessment tool in Italian for paediatric rehabilitation. The Karaduman Chewing Performance Scale (KCPS) is a performance-based assessment tool that allow to classify chewing performance in childhood. To translate, culturally adapt and assess reliability, criterion validity and cross-cultural validity of the KCPS into Italian in a paediatric population. Following international guidelines, the KCPS was translated and culturally adapted into Italian. Inter-rater reliability was measured using the intraclass correlation coefficient (ICC), the criterion validity using the Pearson correlation coefficient comparing KCPS score with the Paediatric Screening-Priority Evaluation Dysphagia (PS-PED), and cross-cultural validity was examined across diagnostic groups. The study included 165 children with a mean age of 6.33 with different health conditions, namely autism spectrum disorder, cerebral palsy and genetic syndromes. The analysis revealed that KCPS was reliable measure with a ICC 0.93, and a moderate positive linear correlation with the PS-PED (Pearson 0.48) was found. In each diagnostic group, chewing performance disorders were found, highlighting specific characteristics. Despite limited sample in reliability analysis and the need of exploring the relationship with chewing abilities and severity of diseases, the KCPS was found a reliable and valid tool for determining the level of chewing performance in paediatric population. Now Italian clinicians can use it with more confidence in their clinical practice and research.

Association Between Lung Ultrasound Score and PaO2/FiO2 Ratio in Neonatal Pneumonia: A Prospective Cohort Study.

Lung ultrasonography (LUS) is increasingly used to assess pulmonary diseases due to its safety and convenience. This study aimed to find out the association between the LUS score and the PaO2/FiO2 ratio in neonatal pneumonia. This study prospectively enrolled 57 neonates with suspected pneumonia. LUS score (range 0-48) was evaluated by assessing 12 lung regions. The PaO2/FiO2 ratio was calculated by dividing the arterial oxygen partial pressure by the fraction of inspired oxygen from blood gas analysis. Correlations were analyzed using Pearson's correlation coefficient. Birth weight ranged 1 100-4 500 g (median 2 925 g), gestational age 27-41 weeks (median 37 weeks), and age 1-28 days (median 1 day). The LUS score was significantly correlated with the PaO2/FiO2 ratio (r = 0.758, p < 0.001), arterial oxygen partial pressure (r = 0.731, p < 0.001), partial pressure of carbon dioxide (r = 0.213, p < 0.001), and oxygen saturation (r = 0.196, p < 0.001). Our study demonstrates significant correlations between the LUS score and blood gas analysis indices in neonatal pneumonia.

Development of machine learning-based models for vault prediction in implantable collamer lens surgery according to implant orientation.

The main objective was to develop a prediction model based on machine learning to calculate the postoperative vault as well as the ideal implantable collamer lens (ICL) size, considering for the first time the implantation orientation in a Caucasian population. Arruzafa Ophthalmological Hospital (Cordoba, Spain) and Barraquer Ophthalmology Center (Barcelona, Spain). Multicenter, randomized, retrospective study. Anterior segment biometric data from 235 eyes of patients who underwent ICL lens implantation surgery were collected using the anterior segment optical coherence tomography (AS-OCT) CASIA II, to train and validate five types of multiple regression models based on advanced machine learning techniques. To perform an external validation a dataset of 45 observations was used. The Pearson correlation coefficient between observed and predicted values was similar in the five models in the external validation, with least absolute shrinkage and selection operator (LASSO) regression being the highest (r = 0.62, p < 0.001), followed by random forest regression model (r = 0.60, p < 0.001) and backward stepwise regression (r = 0.58, ρ < 0.001). In addition, the predictions generated by the different models showed closer agreement with the actual vault compared with the Nakamura formulas. Using the new methods, about 70% of the observations had a prediction error below 150 µm. Advanced forms of regressions models based on machine learning allow satisfactory calculation of the ideal lens size, offering greater precision to surgeons customizing surgery according to implant orientation.

Persian Version of the Modified Constant Score in Overhead Athletes: A Reliability and Validation Study.

Shoulder impingement syndrome (SIS) is the most common shoulder injury among overhead athletes. The Constant score (CS) is a combined scoring system to evaluate functional status of the shoulder in patients with shoulder pain. Cross-sectionalstudy. Cultural adaptation of the Persian version of CS was conducted using Beaton's guideline. Sixty-three overhead athletes (23.57 [7.50] y old) with SIS completed the final version for psychometric assessment. After 5 to 7 days, a group of 33 patients completed the Persian CS again to assess test-retest reliability. Internal consistency, floor/ceiling effects, and item-total correlation were evaluated in patients with SIS. Concurrent validity was assessed through comparison with the disability of the arm, shoulder, and hand questionnaire scores. Twenty-five healthy athletes (31.80 [7.32] y old) participated for determining discriminant validity. The Persian CS demonstrated good test-retest reliability, with subjective and objective intraclass correlation coefficients of .79 and .80, respectively. The internal consistency was satisfactory (Cronbach alpha = .74). No ceiling or floor effects were noted. The Pearson correlation coefficient between CS and disability questionnaire was -.68. The mean total score for patients was 67.02, while for the healthy group was 96.99 (P = .000). The standard error of measurement was 1.51 for patients and 0.7 for healthy group. The smallest detectable change was 16.95 for patients and 5.71 for healthy group. The translation and cross-cultural adaptation of CS into Persian were successful. The Persian version demonstrates sufficient reliability and validity to evaluate shoulder function in overhead athletes with SIS.

Application of correlation-based recurrent neural network in porosity prediction for petroleum exploration

Abstract Porosity prediction serves as a key metric to determine the characteristics of reservoirs and to reduce the exploration efforts, which in turn lead to enhancing the production planning and making economic vital decisions. In this sense, one can use low costly and low time-consumptive method by conducting experimental tests on various soil samples. Predicting porosity can be effectively utilized for identifying suitable drilling locations for petroleum exploration, thus reducing the need for extensive laboratory experiments beforehand. The seismic behavior has a direct impact on porosity features. This study works to find a mapping relationship between well-log porosity and seismic attributes. A Pearson correlation coefficient method has been proposed based on diverse scales to determine the most significant seismic attributes and to reduce data dimensionality. The recurrent prediction neural network models have been proposed for learning and prediction. Three neural network models are introduced, represented by Nonlinear Autoregressive (NARX) network, the Long Short-Term Memory (LSTM) network, and the Gated Recurrent Unit (GRU) network. All these proposed models are trained to associate the nonlinear relationship between the selected attributes and porosity. The seismic attributes are assigned as the inputs to the neural models, while the porosity represents the target of neural structure. Moreover, the correlated and uncorrelated strategies are involved to test the effective attributes based on seven scenarios with the proposed neural structures. The results showed that the correlated NARX network consistently delivers better prediction accuracy and minimum errors as compared to other models for different correlation scales. Consequently, correlated NARX network is the best choice for porosity prediction.

Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture

Chatter, a type of self-excited vibration, deteriorates surface quality and reduces tool life and machining efficiency. Chatter detection serves as an effective approach to achieve stable cutting. To address the low accuracy in chatter detection caused by the limitations of both one-dimensional temporal and two-dimensional image modal information, this study proposes a multi-modal denoised data-driven milling chatter detection method using an optimized hybrid neural network architecture. A data denoising model combining Complementary Ensemble Empirical Mode Decomposition (CEEMD) and Singular Value Decomposition (SVD) is established. The Ivy algorithm is employed to optimize the hyperparameters of CEEMD-SVD. Multi-modal data features of different machining states are then obtained using time–frequency domain methods and Markov transition field methods. Sensitivity analysis of time–frequency domain features is conducted using Pearson correlation coefficient analysis. A hybrid neural network model (DBMA) for chatter detection is constructed by integrating dual-scale parallel convolutional neural networks, bidirectional gated recurrent units, and multi-head attention mechanisms. The Ivy algorithm is utilized to optimize the hyperparameters of DBMA. The t-SNE algorithm is employed to visualize features extracted from different network layers of the chatter detection model. Results demonstrate that effective denoising of machining signals and the use of multi-modal data can significantly improve the accuracy of state detection. Compared with other methods, the proposed model exhibits superior stability and robustness.