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The existing literature consistently finds that emotional experiences and cortisol secretion are linked at the within-person level. Further, relationship partners tend to covary in emotional experience, and in cortisol secretion. However, we are only beginning to understand whether and how an individuals' emotions are linked to their relationship partners' cortisol secretion. In this project, we harmonized data from three intensive measurement studies originating from Canada and Germany to investigate the daily dynamics of emotions and cortisol within 321 older adult couples (age range=56-87 years). Three-level multilevel models accounted for the nested structure of the data (repeated assessments within individuals within couples). Actor-Partner Interdependence Models were used to examine the effect of own emotional experiences (actor effects) and partner emotional experiences (partner effects) on momentary and daily cortisol secretion. Adjusting for age, sex, education, comorbidities, assay version, diurnal cortisol rhythm, time spent together, medication, and time-varying behaviors that may increase cortisol secretion, results suggest that higher relationship partner's positive emotions are linked with lower momentary cortisol and total daily cortisol. Further, this association was stronger for older participants and those who reported higher relationship satisfaction. We did not find within-couple links between negative emotions and cortisol. Overall, our results suggest that one's relationship partner's positive emotional experience may be a protective factor for their physiological responding, and that these more fleeting and day-to-day fluctuations may accumulate over time, contributing to overall relationship satisfaction.

The Taihang Mountains in China span numerous cities, where landslide disasters occur frequently in the mountainous areas, jeopardizing the lives and properties of residents. Consequently, it is of great significance to focus on prevention and control of landslide disasters in the region. Currently, a single model is commonly employed to analyze landslide susceptibility mapping (LSM), but the accuracy of the results fails to meet the demands of early warning, prevention, and control. This paper focuses on the Taihang Mountain area as the research area, organizes the collection of landslide disaster potential points and related influence factor data, and employs the information quantity method to derive a composite machine learning model by coupling with Random Forest (RF) and Extreme Gradient Boosting (XGB), subsequently utilizing the Genetic Optimization Algorithm (GA) to optimize the model. The performance of the composite model is enhanced using the Genetic Algorithm (GA), employing accuracy, regression rate, precision, F1 score, AUC value, and Taylor diagram to evaluate the comprehensive accuracy of the model results, with a susceptibility map generated for comparative analysis. The results demonstrate that the IV-GA-RF model performs optimally (accuracy = 0.956, precision = 0.96, recall = 0.953, F1 score = 0.957, AUC = 0.946 for the testing set, AUC = 0.929 for the training set), with all-around improvement in performance metrics compared to the unoptimized composite model, with metric values improving by 0.044, 0.051, 0.046, 0.044, 0.021 and 0.020, respectively. The IV-GA-RF model exhibits a significant advantage over the IV-GA-XGB algorithm, also optimized using the GA algorithm. The accuracy of the susceptibility map produced by the IV-GA-RF model is superior, as assessed by the Seed Cell Area Index (SCAI) method. The four factors of slope, rainfall, seismicity, and stratigraphic lithology are crucial in determining the occurrence of landslides in the study area. In summary, the IV-GA-RF model can be utilized as an effective model for analyzing landslide disasters, providing a reference for research in this field and contributing scientific insights to disaster prevention and control efforts in the study area; simultaneously, the concept of the composite optimization model introduces new perspectives into this field. [ABSTRACT FROM AUTHOR]

Social desirability bias (SDB) is a common threat to the validity of conclusions from responses to a scale or survey. There is a wide range of person-fit statistics in the literature that can be employed to detect SDB. In addition, machine learning classifiers, such as logistic regression and random forest, have the potential to distinguish between biased and unbiased responses. This study proposes a new application of these classifiers to detect SDB by considering several person-fit indices as features or predictors in the machine learning methods. The results of a Monte Carlo simulation study showed that for a single feature, applying person-fit indices directly and logistic regression led to similar classification results. However, the random forest classifier improved the classification of biased and unbiased responses substantially. Classification was improved in both logistic regression and random forest by considering multiple features simultaneously. Moreover, cross-validation indicated stable area under the curves (AUCs) across machine learning classifiers. A didactical illustration of applying random forest to detect SDB is presented. [ABSTRACT FROM AUTHOR]

Summary: We discuss two novel approaches to inter‐distributional comparisons in the classical two‐sample problem. Our starting point is properly standardised and combined, very popular in several areas of statistics and data analysis, ordinal dominance and receiver characteristic curves, denoted by ODC and ROC, respectively. The proposed new curves are termed the comparison curves. Their estimates, being weighted rank processes on (0,1), form the basis of inference. These weighted processes are intuitive, well‐suited for visual inspection of data at hand and are also useful for constructing some formal inferential procedures. They can be applied to several variants of two‐sample problem. Their use can help improve some existing procedures both in terms of power and the ability to identify the sources of departures from the postulated model. To simplify interpretation of finite sample results, we restrict attention to values of the processes on a finite grid of points. This results in the so‐called bar plots (B‐plots), which readably summarise the information contained in the data. What is more, we show that B‐plots along with adjusted simultaneous acceptance regions provide principled information about where the model departs from the data. This leads to a framework that facilitates identification of regions with locally significant differences. We show an implementation of the considered techniques to a standard stochastic dominance testing problem. Some min‐type statistics are introduced and investigated. A simulation study compares two tests pertinent to the comparison curves to well‐established tests in the literature and demonstrates the strong and competitive performance of the former in many typical situations. Some real data applications illustrate simplicity and practical usefulness of the proposed approaches. A range of other applications of considered weighted processes is briefly discussed too. [ABSTRACT FROM AUTHOR]

BACKGROUND: Previous studies on cervical instability failed to explain the dynamic and static interaction relationship and pathological characteristics changes in the development of cervical lesions under the traditional imaging examination. In recent years, the emerging nuclear magnetic resonance imaging (MRI) radiomics can provide a new way for in-depth research on cervical instability. OBJECTIVE: To investigate the application value of MRI radiomics in the study of cervical instability. METHODS: Through recruitment advertisements and the Second Department of Spine of Wangjing Hospital, China Academy of Chinese Medical Sciences, young cervical vertebra unstable subjects and non-unstable subjects aged 18-45 years were included in the cervical vertebra nuclear magnetic image collection. Five specific regions of interest, including the intervertebral disc region, the facet region, the prevertebral muscle region, the deep region of the posterior cervical muscle group, and the superficial region of the posterior cervical muscle group, were manually segmented to extract and screen the image features. Finally, the cervical instability diagnosis classification model was constructed, and the effectiveness of the model was evaluated using the area under the curve. RESULTS AND CONCLUSION: (1) A total of 56 subjects with cervical instability and 55 subjects with non-instability were included, and 1 688 imaging features were extracted for each region of interest. After screening, 300 sets of specific image feature combinations were obtained, with 60 sets of regions of interest for each group. (2) Five regions of interest diagnostic classification models for cervical instability were initially established. Among them, the support vector machine model for the articular process region and the support vector machine model for the deep cervical muscle group had certain accuracy for the classification of instability and non-instability, and the average area under the curve of ten-fold cross-validation was 0.719 7 and 0.703 3, respectively. (3) The Logistic model in the intervertebral disc region, the LightGBM model in the prevertebral muscle region, and the Logistic model in the superficial posterior cervical muscle region were generally accurate in the classification of instability and non-instability, and the average area under the curve of ten-fold crossvalidation was 0.650 4, 0.620 7, and 0.644 2, respectively. (4) This study proved the feasibility of MRI radiomics in the study of cervical instability, further deepened the understanding of the pathogenesis of cervical instability, and also provided an objective basis for the accurate diagnosis of cervical instability. [ABSTRACT FROM AUTHOR]

The paper suggests the use of the functional data analysis approach to study the evolution of well being indicators, visualizing their behaviour over time. Thus, an evolutionary well-being indicator is proposed by complement the original data with information concerning the first derivative. The second task is to provide an overall ranking of the countries over time using two functional tools: the area under the curve and functional depth, which return two distinct rankings. A simulation study is conducted to evaluate the effectiveness of the area in distinguishing groups of countries with different levels of well-being. The proposed method is employed on a real dataset concerning the human development index of European countries. [ABSTRACT FROM AUTHOR]

ABSTRACT Feeding behaviour traits, such as number, duration or intake per feeder visit, have been associated with feed efficiency in dairy cattle. Those traits, however, do not fully capture cows' feeding patterns throughout the day. The goal of this study was to propose a new phenotype for characterising within‐day feeding patterns and estimate its heritability and genetic correlations with dry matter intake (DMI), secreted milk energy, metabolic body weight and residual feed intake. Feeding patterns were evaluated using 4.8 million bunk visits from 1684 midlactation Holstein cows collected from 2009 to 2023 with an Insentec system. Feed efficiency traits were available from 6099 lactating Holstein cows at six research stations across the United States. Daily bunk visits were ordered, with Time 0 designated as the time of first feed delivery. Intake proportions were calculated by visit for each cow by dividing feed intake per visit by the total intake of the cow for that day. Feeding patterns were characterised by the area under the curve of cumulative feed intake proportions for each cow throughout the day. The feeding pattern phenotype per cow was defined as the average of areas under the curve across days, whereas consistency of feeding pattern was calculated as the natural logarithm of variance of daily area under the curve values. Estimates of heritability and genetic correlations were performed using Bayesian inference with an animal model, considering lactation, days in milk and cohort (trial–treatment) as fixed effects and animal as a random effect. Heritability estimates for average area under the curve and variance of daily area under the curve were 0.35 ± 0.05 and 0.16 ± 0.05, respectively. The genetic correlation between average area under the curve and secreted milk energy was −0.30 ± 0.14. Genetic correlations between average area under the curve and DMI, metabolic body weight and residual feed intake were not statistically significant. Variance of daily area under the curve was genetically correlated with DMI (0.47 ± 0.15), secreted milk energy (0.40 ± 0.17) and metabolic body weight (0.28 ± 0.13). The genetic correlation between variance of daily area under the curve and residual feed intake was not significant. Overall, we provided a reliable method to truly characterise feeding patterns in midlactation dairy cows. Feeding pattern and its consistency were heritable, indicating that a significant proportion of phenotypic variation is explained by additive genetic effects. Genetic correlation estimates indicate that cows with more consistent daily feeding patterns have lower DMI, lower secreted milk energy and lower metabolic body weight. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to identify the risk factors and construct the diagnostic model associated with pneumocystis pneumonia (PCP) in pediatric patients. Methods: This retrospective observational study analyzed 34 cases of PCP and 51 cases of other types of pneumonia treated at Children's Hospital Affiliated to Shandong University between January 2021 and August 2023. Multivariate binary logistic regression was used to identify the risk factors associated with PCP. Receiver operating characteristic curves and calibration plots were constructed to evaluate the diagnostic model. Results: Twenty clinical variables significantly differed between the PCP and non-PCP groups. Multivariate binary logistic regression analysis revealed that dyspnea, body temperature>36.5°C, and age<1.46 years old were risk factors for PCP. The area under the curve of the diagnostic model was 0.958, the P -value of Hosmer‐Lemeshow calibration test was 0.346, the R2 of the calibration plot for the actual and predicted probability of PCP was 0.9555 (P <0.001), and the mean Brier score was 0.069. In addition, metagenomic next-generation sequencing revealed 79.41% (27/34) and 52.93% (28/53) mixed infections in the PCP and non-PCP groups, respectively. There was significantly more co-infection with cytomegalovirus and Streptococcus pneumoniae in the PCP group than that in the non-PCP group (p<0.05). Conclusions: Dyspnea, body temperature>36.5°C, and age<1.46 years old were found to be independent risk factors for PCP in pediatric patients. The probability of co-infection with cytomegalovirus and S. pneumoniae in the PCP group was significantly higher than that in the non-PCP group. [ABSTRACT FROM AUTHOR]

Fang Zhou Qu,1 Jiang Ding,2 Xi Feng An,3 Rui Peng,4 Ni He,5 Sheng Liu,1 Xin Jiang5 1Medical School, Xizang Minzu University, Xianyang, People’s Republic of China; 2Institute of Electrical Power Systems, Graz University of Technology, Graz, Austria; 3The First Affiliated Hospital of Jinan University, Guangzhou, People’s Republic of China; 4Affiliated Nanhua Hospital, University of South China, Hengyang, People’s Republic of China; 5Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, People’s Republic of ChinaCorrespondence: Xin Jiang, Department of Cardiology, Shaanxi Provincial People’s Hospital, Xi’an, Shanxi Province, People’s Republic of China, Email 18560453060@163.comPurpose: Heart failure (HF) is a clinical syndrome in which structural or functional abnormalities of the heart result in impaired ventricular filling or ejection capacity. In order to improve the adaptability of models to different patient populations and data situations. This study aims to develop predictive models for HF risk using six machine learning algorithms, providing valuable insights into the early assessment and recognition of HF by clinical features.Patients and Methods: The present study focused on clinical characteristics that significantly differed between groups with left ventricular ejection fractions (LVEF) [≤ 40% and > 40%]. Following the elimination of features with significant missing values, the remaining features were utilized to construct predictive models employing six machine learning algorithms. The optimal model was selected based on various performance metrics, including the area under the curve (AUC), accuracy, precision, recall, and F1 score. Utilizing the optimal model, the significance of clinical features was assessed, and those with importance values exceeding 0.8 were identified as crucial to the study. Finally, a correlation analysis was conducted to examine the relationships between these features and other significant clinical features.Results: The logistic regression (LR) model was determined to be the optimal machine learning algorithm in this study, achieving an accuracy of 0.64, a precision of 0.45, a recall of 0.72, an F1 score of 0.51, and an AUC of 0.81 in the training set and 0.91 in the testing set. In addition, the analysis of feature importance indicated that blood calcium, angiotensin-converting enzyme inhibitors (ACEI) dosage, mean hemoglobin concentration, and survival duration were critical to the study, each possessing importance values exceeding 0.8. Furthermore, correlation analysis revealed a strong relationship between blood calcium and ionized calcium (|cor|=0.99), as well as a significant association between ACEI dosage (|cor|=0.68) and left ventricular metrics (|cor|=0.58); on the other hand, no correlations were observed between mean hemoglobin levels and other clinical characteristics.Conclusion: The present study identified LR as the most effective risk prediction model for patients with HF, highlighting blood calcium, ACEI dosage, and mean hemoglobin level as significant predictors. These findings provide significant insights for the clinical prevention and early intervention of HF.Keywords: left ventricular ejection fractions, logistic regression, blood calcium, area under the curve, correlation analysis

Vaishnavi L Gopaul,&ast; Lacey Winstone,&ast; Beatrice G Gatien,&ast; Braydon D Nault, Sujit Maiti, Reid M Opperman, Mousumi Majumder Department of Biology, Brandon University, Brandon, MB, Canada&ast;These authors contributed equally to this workCorrespondence: Mousumi Majumder, Email majumderm@brandonu.caBackground: YWHAB (14-3-3 Beta) was found in the secretome of miR-526b and miR-655 overexpressed breast cancer (BRCA) cell lines. The potential of YWHAB as a therapeutic target or biomarker for BRCA is investigated here.Methods: After YWHAB was knocked down with siRNA, BRCA cell lines were used for in vitro assays (proliferation, migration, epithelial-to-mesenchymal transition). In silico analysis and in situ validation with BRCA plasma and biopsy tissues were used to test YWHAB’s biomarker potential.Results: YWHAB RNA and protein expression are elevated in aggressive BRCA cell lines, and the knockdown of YWHAB inhibited cell migration, proliferation, and EMT in all subtypes of tumour cell lines. YWHAB expression is significantly higher in BRCA biopsy tissue and blood plasma compared to control tissues and benign plasmas. YWHAB is expressed in all hormonal subtypes of BRCA tumours and has shown increased expression in advanced tumour stages. Its high expression is linked to poor patient survival. YWHAB is a sensitivity tumour marker (AUC of 0.7340, p = 0.0012) but is not a promising blood biomarker. Nevertheless, combined with pri-miR-526b, YWHAB mRNA expression shows potential as a BRCA blood biomarker (AUC of 0.711, p = 0.032), which must be validated in a larger sample set.Conclusion: We elucidate the novel role of YWHAB as a therapeutic target in BRCA, given that its inhibition mitigated aggressive phenotypes across all tumour subtypes, including triple-negative breast cancer. Furthermore, YWHAB emerges as a potential tumour marker, exhibiting high expression in metastatic BRCA and correlating with poor patient survival; however, it is not a sensitive blood biomarker. Keywords: breast cancer, microRNA, miRNA, miR-526b, miR-655, YWHAB, 14-3-3 Beta, biomarker, migration, epithelial to mesenchymal transition, proliferation

BackgroundThere is still a controversy about the superiority of liposomal bupivacaine (LB) over traditional local anesthetics in postoperative analgesia after thoracic surgery. This study aims to determine the effect of LB versus bupivacaine hydrochloride (HCl) for preoperative ultrasound-guided erector spinae plane block (ESPB) on postoperative acute and chronic pain in patients undergoing video-assisted thoracoscopic lung surgery.MethodsThis multicenter, randomized, double-blind, controlled trial will include 272 adult patients scheduled for elective video-assisted thoracoscopic lung surgery. Patients will be randomly assigned, 1:1 and stratified by site, to the liposomal bupivacaine (LB) group or the bupivacaine (BUPI) HCl group. All patients will receive ultrasound-guided ESPB with either LB or bupivacaine HCl before surgery and patient-controlled intravenous analgesia (PCIA) as rescue analgesia after surgery. The numeric rating scale (NRS) score will be assessed after surgery. The primary outcome is the area under the curve of pain scores at rest for 0-72 h postoperatively. The secondary outcomes include the total amount of opioid rescue analgesics through 0-72 h postoperatively, time to the first press on the PCIA device as rescue analgesia, the area under the curve of pain scores on activity for 0-72 h postoperatively, NRS scores at rest and on activity at different time points during the 0-72 h postoperative period, Quality of Recovery 15 scores at 72 h after surgery, and NRS scores on activity on postsurgical day 14 and postsurgical 3 months. Adverse events after the surgery are followed up to the postsurgical day 7, including postoperative nausea and vomiting, fever, constipation, dizziness, headache, insomnia, itching, prolonged chest tube leakage, new-onset atrial fibrillation, severe ventricular arrhythmia, deep venous thrombosis, pulmonary embolism, pulmonary atelectasis, cardiac arrest, ileus, urinary retention, chylothorax, pneumothorax, and organ failure. Analyzes will be performed first according to the intention to treat principle and second with the per-protocol analysis.DiscussionWe hypothesize that LB for preoperative ultrasound-guided ESPB would be more effective than bupivacaine HCl in reducing postoperative pain in video-assisted thoracoscopic lung surgery. Our results will contribute to the optimization of postoperative analgesia regimens for patients undergoing video-assisted thoracoscopic lung surgery.Clinical trial registration:http://www.chictr.org.cn, identifier ChiCTR2300074852.

Objective: Limited sampling strategies are widely employed in clinical practice to minimize the number of blood samples required for the accurate area under the curve calculations, as obtaining these samples can be costly and challenging. Traditionally, the maximum a posteriori Bayesian estimation has been the standard method for the area under the curve estimation based on limited samples. However, machine learning is emerging as a promising alternative for this purpose. Here, we review studies that utilize machine learning approaches to develop limited sampling strategies and compare the strengths and weaknesses of these machine learning methods. Methods: We searched the literature for studies that used machine learning to estimate the area under the curve using a limited sampling strategy approach. Results: We identified ten studies that developed machine learning models to estimate the area under the curve for six different drugs. Several of these models demonstrated good accuracy and precision in area under the curve estimation in reference to the traditional Bayesian approach, highlighting the potential of machine learning models in precision dosing. Conclusions: Despite these promising early results, the development of machine learning for limited sampling strategies is still in its early stages. Further research might be needed to validate machine learning models with larger, high-quality clinical datasets to ensure their reliability and applicability in clinical settings. [ABSTRACT FROM AUTHOR]

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Traditionally, emotions in dreams have been assessed using subjective ratings by human raters (e.g., external raters or dreamers themselves). These methods have extensive support and utility in dream science, yet they have certain innate limitations due to the subjective nature of the rating methodologies. Attempting to circumvent several of these limitations, we aimed to develop a novel method for objectively classifying and quantifying sequential (word-for-word) emotion within a dream report. We investigated whether sentiment analysis, a branch of natural language processing, could be used to generate continuous positive and negative valence ratings across a dream. In this pilot, proof-of-concept study, we used 14 dream reports collected upon awakening following overnight polysomnography. We also collected pre- and post-sleep affective data and personality metrics. Our objectives included demonstrating that (1) valence ratings derived from sentiment analysis (Valence Aware Dictionary for sEntiment Reasoning [VADER]) could be used to visualize (plot) positive and negative emotion fluctuations within a dream, (2) how the visual properties of emotion fluctuations within a dream (peaks and troughs, area under the curve) can be used to generate novel "emotion indicators" as proxies for emotion regulation throughout a dream, and (3) these emotion indicators correlate with sleep, affective, and personality variables known to be associated with dreaming and emotion regulation. We describe 6 novel, objective dream emotion indicators: Total number of Peaks, total number of Troughs, Positive, Negative, and Overall Emotion Intensity (composites from an "area under the curve" method using the trapezoid rule applied to the peaks and troughs), and the Emotion Gradient (a polynomial trendline fitted to the emotion fluctuations in the dream chart). The latter signifies the overall direction of sequential emotion changes within a dream. Results also showed that 5/6 emotion indicators correlated significantly with at least one existing sleep, affective, or personality variable known to be associated with dreaming and emotion regulation. We propose that the novel emotion indicators potentially serve as proxies for emotion regulation processes unfolding within a dream. These preliminary findings provide a methodological foundation for future studies to test and refine the method in larger and more diverse samples. [ABSTRACT FROM AUTHOR]

Background: Hydroxyurea (HU), also known as hydroxycarbamide, is an oral ribonucleotide reductase inhibitor. In 1999, the United States Food and Drug Administration (FDA) approved HU for the treatment of sickle cell disease (SCD). Since then, it has become the cornerstone in the management of SCD patients, helping to reduce vaso-occlusive crises, acute chest syndrome, the need for blood transfusions, hospitalizations and mortality. There is considerable variability among individuals in HU pharmacokinetic (Pk) parameters that can influence treatment efficacy and toxicity. The objective of this work is part of a clinical study aimed at investigating HU Pk and determining the optimal sampling time to estimate the Area Under the Curve (AUC) in SCD patients. Methods: HU plasma concentration in 80 patients at various time points (2, 4, 6, 24 h) following a 48-h drug washout was quantified using High-Pressure Liquid Chromatography (HPLC) coupled with an ultraviolet (UV) detection method previously described in the literature and adapted to new conditions with partial modifications. Results: The mean HU administered dose was 19.5 ± 5.1 mg/kg (range: 7.7–37.5 mg/kg). The median AUC quantified in plasma patients was 101.3 mg/L/h (Interquartile Range (IQR): 72.5–355.9) and it was not influenced by the weight-based dose. However, there was a strong positive correlation between AUC and Body Mass Index (BMI) as well as dose per Body Surface Area (BSA). Along with a three-point approach for AUC determination present in the literature, we show results obtained from a four-point sampling strategy, which is more useful and effective for better optimizing dose escalation to the maximum tolerated dose (MTD). Moreover, we observed that most patients achieved the maximum HU plasma concentration two hours after drug administration, regardless of age differences. Conclusions: HU treatment, which represents a milestone in the treatment of SCD due to its ability to reduce disease complications and improve patients' quality of life, requires careful monitoring to optimize the individual dose for saving potential side effects and/or adverse events. [ABSTRACT FROM AUTHOR]

Integrin beta 4 (ITGB4) is a vital factor for numerous cancers. However, no reports regarding ITGB4 in small cell lung carcinoma (SCLC) have been found in the existing literature. This study systematically investigated the expression and clinical value of ITGB4 in SCLC using multi-center and large-sample (n = 963) data. The ITGB4 expression levels between SCLC and control tissues were compared using standardized mean difference and Wilcoxon rank-sum test. The clinical significance of the gene in SCLC was observed using Cox regression and Kaplan–Meier curves. ITGB4 is overexpressed in multiple cancers and represents significant value in distinguishing among cancer samples (AUC = 0.91) and predicting the prognoses (p < 0.05) of patients with different cancers. In contrast, decreased ITGB4 mRNA expression was determined in SCLC (SMD < 0), and this finding was further confirmed at protein levels using in-house specimens (p < 0.05). This decrease in expression may be attributed to the regulatory role of estrogen receptor 1. ITGB4 may participate in the progression of SCLC by affecting several signaling pathways (e.g., tumor necrosis factor signaling pathway) and a series of immune cells (e.g., dendritic cells) (p < 0.05). The gene may serve as a potential marker for predicting the disease status (AUC = 0.97) and prognoses (p < 0.05) of patients with SCLC. Collectively, ITGB4 was identified as an identification and prognosis marker associated with immune infiltration in SCLC. [ABSTRACT FROM AUTHOR]

Objective No available scale, at the time of initial evaluation for necrotizing enterocolitis (NEC), accurately predicts, that is, with an area under the curve (AUC) ≥0.9, which preterm infants will undergo surgery for NEC stage III or die within a week. Study Design This is a retrospective cohort study (n = 261) of preterm infants with <33 weeks' gestation or <1,500 g birth weight with either suspected or with definite NEC born at Parkland Hospital between 2009 and 2021. A prediction model using the new HASOFA score (Hyperglycemia, Hyperkalemia, use of inotropes for Hypotension during the prior week, Acidemia, Neonatal Sequential Organ Failure Assessment [nSOFA] score) was compared with a similar model using the nSOFA score. Results Among 261 infants, 112 infants had NEC stage I, 68 with NEC stage II, and 81 with NEC stage III based on modified Bell's classification. The primary outcome, surgery for NEC stage III or death within a week, occurred in 81 infants (surgery in 66 infants and death in 38 infants). All infants with pneumoperitoneum or abdominal compartment syndrome either died or had surgery. The HASOFA and the nSOFA scores were evaluated in 254 and 253 infants, respectively, at the time of the initial workup for NEC. Both models were internally validated. The HASOFA model was a better predictor of surgery for NEC stage III or death within a week than the nSOFA model, with greater AUC 0.909 versus 0.825, respectively, p < 0.001. Combining HASOFA at initial assessment with concurrent or later presence of abdominal wall erythema or portal gas improved the prediction surgery for NEC stage III or death with AUC 0.942 or 0.956, respectively. Conclusion Using this new internally validated prediction model, surgery for NEC stage III or death within a week can be accurately predicted at the time of initial assessment for NEC. Key Points No available scale, at initial evaluation, accurately predicts which preterm infants will undergo surgery for NEC stage III or die within a week. In this retrospective cohort study of 261 preterm infants with either suspected or definite NEC we developed a new prediction model (HASOFA score). The HASOFA-model had high discrimination (AUC: 0.909) and excellent calibration and was internally validated. [ABSTRACT FROM AUTHOR]

Background: The utility of preoperative abdominal ultrasonography (US) in evaluating patients with obesity before metabolic bariatric surgery (MBS) remains ambiguously defined. Method: Retrospective analysis whereby patients were classified into four groups based on ultrasound results. Group 1 had normal findings. Group 2 had non-significant findings that did not affect the planned procedure. Group 3 required additional or follow-up surgeries without changing the surgical plan. Group 4, impacting the procedure, needed further investigations and was subdivided into 4A, delaying surgery for more assessments, and 4B, altering or canceling the procedure due to critical findings. Machine learning techniques were utilized to identify variables. Results: Four thousand four hundred eighteen patients' records were analyzed. Group 1 was 45.7%. Group 2, 35.7%; Group 3, 17.0%; Group 4, 1.5%, Group 4A, 0.8%; and Group 4B, 0.7%, where surgeries were either canceled (0.3%) or postponed (0.4%). The hyperparameter tuning process identified a Decision Tree classifier with a maximum tree depth of 7 as the most effective model. The model demonstrated high effectiveness in identifying patients who would benefit from preoperative ultrasound before MBS, with training and testing accuracies of 0.983 and 0.985. It also showed high precision (0.954), recall (0.962), F1 score (0.958), and an AUC of 0.976. Conclusion: Our study found that preoperative ultrasound demonstrated clinical utility for a subset of patients undergoing metabolic bariatric surgery. Specifically, 15.9% of the cohort benefited from the identification of chronic calculous cholecystitis, leading to concomitant cholecystectomy. Additionally, surgery was postponed in 1.4% of the cases due to other findings. While these findings indicate a potential benefit in certain cases, further research, including a cost–benefit analysis, is necessary to fully evaluate routine preoperative ultrasound's overall utility and economic impact in this patient population. [ABSTRACT FROM AUTHOR]

A recent genome-wide association study of age-related macular degeneration (AMD) identified new AMD-associated risk variants. These variants now can be incorporated into an updated polygenic risk score (PRS). This study aimed to assess the performance of an updated PRS, PRS2023, in an independent cohort of older individuals with retinal imaging data and to compare performance with an older PRS, PRS2016. Cross-sectional study. A total of 4175 participants of European ancestry, 70 years of age or older, with genotype and retinal imaging data. We used logistic regression models and area under the receiver operating characteristic curve (AUC) to assess the performance of PRS2023 compared with PRS2016. AMD status and severity were graded using color fundus photography. Association of PRS2023 and PRS2016 with AMD risk at baseline. At enrollment among 4175 participants, 2605 participants (62.4%) had no AMD and 853 participants (20.4%), 671 participants (16.1%), and 46 participants (1.1%) had early, intermediate, and late-stage AMD, respectively. More than 27% of the participants with a high PRS2023 (top quartile) had intermediate or late-stage AMD, compared with < 15% for those in the middle 2 quartiles and less than 13% for those in the lowest quartile. Both PRS2023 and PRS2016 were associated significantly with AMD after adjustment for age, sex, smoking status, and lipid levels, with increasing odds ratios (ORs) for worsening AMD grades. PRS2023 outperformed PRS2016 (P = 0.03 for all AMD and P = 0.03 for late AMD, DeLong test comparing AUC). PRS2023 was associated with late-stage AMD with an adjusted OR of 5.05 (95% confidence interval [CI], 3.41–7.47) per standard deviation. The AUC of a model containing conventional or nongenetic risk factors and PRS2023 was 91% (95% CI, 87%–95%) for predicting late-stage AMD, which improved 12% over the model without the PRS (AUC, 79%; P < 0.001 for difference). A new PRS, PRS2023, for AMD outperforms a previous PRS and predicts increasing risk for late-stage AMD (with stronger association for more severe imaging-confirmed AMD grades). Our findings have clinical implications for the improved prediction and risk stratification of AMD. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. [ABSTRACT FROM AUTHOR]

Supervised learning has been the cornerstone of many successful medical imaging applications. However, its reliance on large labeled datasets poses significant challenges, especially in the medical domain, where data annotation is time-consuming and expensive. In response, self-supervised learning (SSL) has emerged as a promising alternative, leveraging unlabeled data to learn meaningful representations without explicit supervision. This paper provides a detailed overview of supervised learning and its limitations in medical imaging, underscoring the need for more efficient and scalable approaches. The study emphasizes the importance of the area under the curve (AUC) as a key evaluation metric in assessing SSL performance. The AUC offers a comprehensive measure of model performance across different operating points, which is crucial in medical applications, where false positives and negatives have significant consequences. Evaluating SSL methods based on the AUC allows for robust comparisons and ensures that models generalize well to real-world scenarios. This paper reviews recent advances in SSL for medical imaging, demonstrating their potential to revolutionize the field by mitigating challenges associated with supervised learning. Key results show that SSL techniques, by leveraging unlabeled data and optimizing performance metrics like the AUC, can significantly improve the diagnostic accuracy, scalability, and efficiency in medical image analysis. The findings highlight SSL's capability to reduce the dependency on labeled datasets and present a path forward for more scalable and effective medical imaging solutions. [ABSTRACT FROM AUTHOR]

In the past decade, scientific research in the area of Nephrology has focused on evaluating the clinical utility and performance of various biomarkers for diagnosis, risk stratification and prognosis. Before implementing a biomarker in everyday clinical practice for screening a specific disease context, specific statistic measures are necessary to evaluate the diagnostic accuracy and performance of this biomarker. Receiver Operating Characteristic (ROC) Curve analysis is an important statistical method used to estimate the discriminatory performance of a novel diagnostic test, identify the optimal cut-off value for a test that maximizes sensitivity and specificity, and evaluate the predictive value of a certain biomarker or risk, prediction score. Herein, through practical examples, we aim to present a simple methodological approach to explain in detail the principles and applications of ROC curve analysis in the field of nephrology pertaining diagnosis and prognosis. [ABSTRACT FROM AUTHOR]

Abstract Background The management of pain presents a significant challenge in healthcare, particularly in cases where conventional therapies prove inadequate. In response to this need, this study aims to devise two innovative UV spectrophotometric techniques rooted in the principles of green analytical chemistry for the analysis of Aceclofenac (ACE), Paracetamol (PAR), and Tramadol (TRM) in both bulk and tablet forms. Results Utilizing advanced mathematical methodologies such as the double divisor ratio spectra method and area under the curve, the concentrations of these drugs were accurately determined. Validation of the developed methods adhered to the guidelines outlined by the International Council for Harmonisation in the Q2 (R1), revealing linear calibration curves for ACE (8–12 µg/mL), PAR (22.75–35.75 µg/mL), and TRM (2.62–4.12 µg/mL). Furthermore, statistical analyses employing Student’s t test and F test were conducted to ensure the robustness of the proposed method. The evaluation of environmental impact through green metric tools confirmed the eco-friendliness of the proposed methodologies. Conclusion The assessment performed utilizing green metric tools has substantiated the environmental sustainability of the proposed approach. Thus, this methodology offers accurate and reliable outcomes for the determination of three drugs, as indicated by the complete overlap observed in the zero-order spectra.

Objective To evaluate the value of mean reticulated hemoglobin content (Mchr) in diagnosing iron deficiency anemia (IDA) and assessing its severity. Methods This study included 302 patients with IDA from January 2021 to December 2021, recruited from Ruijin Hospital, Shanghai Jiao Tong University School of Medicine (North), Xinhua Hospital, and Ruijin Hospital. The cohort comprised 118 patients with mild anemia, 159 with moderate anemia, and 25 with severe anemia. In addition, 365 non-IDA patients (encompassing those with thalassemia, megaloblastic anemia, pure red cell aplastic anemia, hemolytic anemia, and aplastic anemia) and 138 healthy controls were included. Venous blood samples were collected from all participants for analysis of hemoglobin (Hb), hematocrit (HCT), Mchr, mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), serum iron (Fe), transferrin saturation (TS), ferritin, and total iron-binding capacity (TIBC). Mchr levels were compared between the IDA and non-IDA patient groups and between different degrees of IDA severity. Receiver operating characteristic (ROC) curves were plotted to evaluate the diagnostic value of Mchr in IDA. Results Compared with the non-IDA group, the IDA cohort exhibited significantly reduced levels of Mchr, Hb, MCV, MCH, MCHC, HCT, Fe, TS, and ferritin, while TIBC was markedly elevated, with all differences being statistically significant (P65.8 μmol/L) were 90.76% and 94.70% respectively, and the AUC was 0.9839(95%CI: 0.9772-0.9905). Conclusions Mchr can serve as a potential clinical marker for screening IDA and its severity. Its combination with iron metabolism indicators adds diagnostic value for IDA, providing a strong basis for whether further invasive diagnosis is needed.

Purpose: The purpose of this study was to investigate whether the clinically "healthy subjects," identified as being at "risk" of developing diabetes, show significant changes in retinal thickness using optical coherence tomography(OCT) and foveal slope patterns. Methods: A cross-sectional, comparative study was conducted among the subjects who attended the hospital from December 2022 to April 2023. Subjects were included after obtaining written informed consent and were divided into three groups: Type 2 DM, healthy subjects with a family history (high-risk group), and those without a family history of diabetes. Foveal, parafoveal, and perifoveal thicknesses were measured using OCT. The fovea slope pattern was calculated using Image J software. Descriptive statistics, Pearson correlation, and ANOVA were performed for statistical analysis. Results: The study group had a mean age of 45.7 years, 95% of whom were female. The mean central foveal thickness (CFT) for the three groups was (CFTDM: 270 ± 33 μm; CFT FHD+: 254±19.6 μm; CFTFHD−: 255.4 ±19.2 μm; P = 0.118). The mean AUC (ILM-RPE) for (DM: 94577 ± 118905) pixel2 was significantly different from healthy control (FHD−:183705 ±156139 pixel2; P = 0.030) but was insignificant (P = 1.000) from subjects with family history (FHD+: 112749 ± 130451 pixel2). Males with diabetes had a greater foveal thickness than females (male: 277.8 ± 39.5 μm vs. female: 242.0 ± 41.7 μm, P = 0.05); however, no significant gender disparity was reported in the foveal slope. Conclusion: Decreased ILM-RPE thickness and foveal configuration were identified in subjects with family histories who do not meet the clinical criteria of diabetes but showed trends similar to diabetics. [ABSTRACT FROM AUTHOR]

Objectives: To compare image quality of diffusion-weighted imaging (DWI) and contrast-enhanced breast MRI (DCE-T1) stratified by the amount of fibroglandular tissue (FGT) as a measure of breast density. Methods: Retrospective, multi-reader, bicentric visual grading analysis study on breast density (A–D) and overall image and fat suppression quality of DWI and DCE-T1, scored on a standard 5-point Likert scale. Cross tabulations and visual grading characteristic (VGC) curves were calculated for fatty breasts (A/B) versus dense breasts (C/D). Results: Image quality of DWI was higher in the case of increased breast density, with good scores (score 3–5) in 85.9% (D) and 88.4% (C), compared to 61.6% (B) and 53.5% (A). Overall image quality of DWI was in favor of dense breasts (C/D), with an area under the VGC curve of 0.659 (p < 0.001). Quality of DWI and DCE-T1 fat suppression increased with higher breast density, with good scores (score 3–5) for 86.9% and 45.7% of density D, and 90.2% and 42.9% of density C cases, compared to 76.0% and 33.6% for density B and 54.7% and 29.6% for density A (DWI and DCE-T1 respectively). Conclusions: Dense breasts show excellent fat suppression and substantially higher image quality in DWI images compared with non-dense breasts. These results support the setup of studies exploring DWI-based MR imaging without IV contrast for additional screening of women with dense breasts. Clinical relevance statement: Our findings demonstrate that image quality of DWI is robust in women with an increased amount of fibroglandular tissue, technically supporting the feasibility of exploring applications such as screening of women with mammographically dense breasts. Key Points: • Image and fat suppression quality of diffusion-weighted imaging are dependent on the amount of fibroglandular tissue (FGT) which is closely connected to breast density. • Fat suppression quality in diffusion-weighted imaging of the breast is best in women with a high amount of fibroglandular tissue. • High image quality of diffusion-weighted imaging in women with a high amount of FGT in MRI supports that the technical feasibility of DWI can be explored in the additional screening of women with mammographically dense breasts. [ABSTRACT FROM AUTHOR]

Borderline Personality Disorder (BPD) symptoms include inappropriate control of anger and severe emotional dysregulation after rejection in daily life. Nevertheless, when using the Cyberball paradigm, a tossing game to simulate social exclusion, the seven basic emotions (happiness, sadness, anger, surprise, fear, disgust, and contempt) have not been exhaustively tracked out. It was hypothesized that these patients would show anger, contempt, and disgust during the condition of exclusion versus the condition of inclusion. When facial emotions are automatically detected by Artificial Intelligence, "blending", -or a mixture of at least two emotions- and "masking", -or showing happiness while expressing negative emotions- may be most easily traced expecting higher percentages during exclusion rather than inclusion. Therefore, face videos of fourteen patients diagnosed with BPD (26 ± 6 years old), recorded while playing the tossing game, were analyzed by the FaceReader software. The comparison of conditions highlighted an interaction for anger: it increased during inclusion and decreased during exclusion. During exclusion, the masking of surprise; i.e., displaying happiness while feeling surprised, was significantly more expressed. Furthermore, disgust and contempt were inversely correlated with greater difficulties in emotion regulation and symptomatology, respectively. Therefore, the automatic detection of emotional expressions during both conditions could be useful in rendering diagnostic guidelines in clinical scenarios. [ABSTRACT FROM AUTHOR]

Background: The management of pain presents a significant challenge in healthcare, particularly in cases where conventional therapies prove inadequate. In response to this need, this study aims to devise two innovative UV spectrophotometric techniques rooted in the principles of green analytical chemistry for the analysis of Aceclofenac (ACE), Paracetamol (PAR), and Tramadol (TRM) in both bulk and tablet forms. Results: Utilizing advanced mathematical methodologies such as the double divisor ratio spectra method and area under the curve, the concentrations of these drugs were accurately determined. Validation of the developed methods adhered to the guidelines outlined by the International Council for Harmonisation in the Q2 (R1), revealing linear calibration curves for ACE (8–12 µg/mL), PAR (22.75–35.75 µg/mL), and TRM (2.62–4.12 µg/mL). Furthermore, statistical analyses employing Student's t test and F test were conducted to ensure the robustness of the proposed method. The evaluation of environmental impact through green metric tools confirmed the eco-friendliness of the proposed methodologies. Conclusion: The assessment performed utilizing green metric tools has substantiated the environmental sustainability of the proposed approach. Thus, this methodology offers accurate and reliable outcomes for the determination of three drugs, as indicated by the complete overlap observed in the zero-order spectra. [ABSTRACT FROM AUTHOR]

The area under the receiver‐operating characteristic curve (AUC) has become a popular index not only for measuring the overall prediction capacity of a marker but also the strength of the association between continuous and binary variables. In the current considered study, the AUC was used for comparing the association size of four different interventions involving impulsive decision making, studied through an animal model, in which each animal provides several negative (pretreatment) and positive (posttreatment) measures. The problem of the full comparison of the average AUCs arises therefore in a natural way. We construct an analysis of variance (ANOVA) type test for testing the equality of the impact of these treatments measured through the respective AUCs and considering the random‐effect represented by the animal. The use (and development) of a post hoc Tukey's HSD‐type test is also considered. We explore the finite‐sample behaviour of our proposal via Monte Carlo simulations, and analyse the data generated from the original problem. An R package implementing the procedures is provided in the supporting information. [ABSTRACT FROM AUTHOR]

Objective: The aim of the study is to predict employees' long-term sick leave due to psychiatric disorders using the national psychological stress screening program. Methods: University employees who took long-term psychiatric sick leave in 2016-2018 were assigned as cases. Those who were present at work and matched for sex, age, and occupation type were assigned as controls. Answers in a 57-item questionnaire were analyzed by multivariable regression, and a prediction model was developed. It was validated in cases and matched controls in 2019. Results: Six items were identified as independent predictors by multivariable regression and included in a prediction model. The area under the receiver-operating characteristics curve was 0.768 (95% confidence interval: 0.723-0.813). This finding was similar to that in the validation sample. Conclusions: The performance of the prediction model was modest and the national Stress Check Program should be further refined. [ABSTRACT FROM AUTHOR]

ObjectivesThis study aimed to identify the risk factors and construct the diagnostic model associated with pneumocystis pneumonia (PCP) in pediatric patients.MethodsThis retrospective observational study analyzed 34 cases of PCP and 51 cases of other types of pneumonia treated at Children’s Hospital Affiliated to Shandong University between January 2021 and August 2023. Multivariate binary logistic regression was used to identify the risk factors associated with PCP. Receiver operating characteristic curves and calibration plots were constructed to evaluate the diagnostic model.ResultsTwenty clinical variables significantly differed between the PCP and non-PCP groups. Multivariate binary logistic regression analysis revealed that dyspnea, body temperature>36.5°C, and age36.5°C, and age

IntroductionPulmonary arterial hypertension (PAH) is a severe cardiovascular condition characterized by pulmonary vascular remodeling, increased resistance to blood flow, and eventual right heart failure. Right heart catheterization (RHC) is the gold standard diagnostic technique, but due to its invasiveness, it poses risks such as vessel and valve injury. In recent years, machine learning (ML) technologies have offered non-invasive alternatives combined with ML for improving the diagnosis of PAH.ObjectivesThe study aimed to evaluate the diagnostic performance of various methods, such as electrocardiography (ECG), echocardiography, blood biomarkers, microRNA, chest x-ray, clinical codes, computed tomography (CT) scan, and magnetic resonance imaging (MRI), combined with ML in diagnosing PAH.MethodsThe outcomes of interest included sensitivity, specificity, area under the curve (AUC), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR). This study employed the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool for quality appraisal and STATA V.12.0 for the meta-analysis.ResultsA comprehensive search across six databases resulted in 26 articles for examination. Twelve articles were categorized as low-risk, nine as moderate-risk, and five as high-risk. The overall diagnostic performance analysis demonstrated significant findings, with sensitivity at 81% (95% CI = 0.76–0.85, p

Abstract To quantitatively assess the diagnostic efficacy of multiple parameters derived from multi-b-value diffusion-weighted imaging (DWI) using turbo spin echo (TSE)–based acquisition techniques in patients with solitary pulmonary lesions (SPLs). A total of 105 patients with SPLs underwent lung DWI using single-shot TSE–based acquisition techniques and multiple b values. The apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM) parameters, and lesion-to-spinal cord signal intensity ratio (LSR), were analyzed to compare the benign and malignant groups using the Mann–Whitney U test and receiver operating characteristic analysis. The Dstar values observed in lung cancer were slightly lower than those observed in pulmonary benign lesions (28.164 ± 31.950 versus 32.917 ± 34.184; Z = -2.239, p = 0.025). The LSR values were significantly higher in lung cancer than in benign lesions (1.137 ± 0.581 versus 0.614 ± 0.442; Z = − 4.522, p

• For elexacaftor-tezacaftor-ivacaftor (ETI) it is unclear whether drug exposure should be monitored by assessment of C min levels or AUC. • A correlation between C min and AUC was found, with correlation coefficients of 0.963, 0.908 and 0.860 for ETI, respectively. • Exposure of ETI may be monitored by assessment of C min levels. Therapeutic drug monitoring (TDM) of elexacaftor, tezacaftor, ivacaftor (ETI) could be a useful tool to increase efficacy and decrease the risk of adverse effects in people with Cystic Fibrosis (pwCF). It is however unclear whether drug exposure should be monitored by assessment of trough (C min) levels or determination of the area under the curve (AUC). Hence, in this study the correlation between measured C min concentration and AUC was evaluated. Serial plasma samples, including C min , were drawn after administration of ETI in order to calculate the AUC and assess the correlation between the two parameters. A linear correlation between C min and AUC 0–24h was found, with Pearson's r correlation coefficients of 0.963, 0.908 and 0.860 for elexacaftor, tezacaftor and ivacaftor, respectively. Exposure of ETI may be monitored by assessment of C min levels. [ABSTRACT FROM AUTHOR]

Background/Objectives: Vancomycin is a reserve antibiotic that is frequently prescribed for central venous catheter (CVC)-associated infections in hemodialysis patients. Hemodialysis patients are very fragile patients and the presence of CVCs increases the risk of sepsis. We conducted a prospective study, evaluating the needs of changes in vancomycin dosing for treatment based on the use of the new 2020 vancomycin dosing guidelines, to increase drug safety (preventing subtherapeutic or supratherapeutic doses and offering therapeutic concentrations of the drug) in a particular group of patients with sepsis caused by catheter infections and being on intermittent hemodialysis. Methods: This prospective study included patients with sepsis caused by catheter infections and being on intermittent hemodialysis, treated with vancomycin, admitted in the nephrology department and intensive care unit (ICU). Vancomycin levels were adjusted according to the 2020 vancomycin guidelines. Results: In our study, nine (45%) patients had a vancomycin AUC between 400 and 600 mcg × h/mL, five (25%) patients had a subtherapeutic AUC, and six (30%) patients had a supratherapeutic AUC. It is important to mention that in 10 (50%) of the patients included in the study, the loading and maintenance doses mentioned in the protocol were respected, but 50% of them had a supratherapeutic AUC. We observed that a supratherapeutic AUC occurred when the loading dose was 1500 mg or 2000 mg, and in one case at 1000 mg with a low BMI. Conclusions: a therapeutic level of vancomycin can often be difficult to achieve because of different reasons, mainly in hemodialysis patients.

Objectives: To investigate if delta-radiomics features have the potential to predict the major pathological response (MPR) to neoadjuvant chemoimmunotherapy in non-small cell lung cancer (NSCLC) patients. Methods: Two hundred six stage IIA-IIIB NSCLC patients from three institutions (Database1 = 164; Database2 = 21; Database3 = 21) who received neoadjuvant chemoimmunotherapy and surgery were included. Patients in Database1 were randomly assigned to the training dataset and test dataset, with a ratio of 0.7:0.3. Patients in Database2 and Database3 were used as two independent external validation datasets. Contrast-enhanced CT scans were obtained at baseline and before surgery. The delta-radiomics features were defined as the relative net change of radiomics features between baseline and preoperative. The delta-radiomics model and pre-treatment radiomics model were established. The performance of Immune-Related Response Evaluation Criteria in Solid Tumors (iRECIST) for predicting MPR was also evaluated. Results: Half of the patients (106/206, 51.5%) showed MPR after neoadjuvant chemoimmunotherapy. For predicting MPR, the delta-radiomics model achieved a satisfying area under the curves (AUCs) values of 0.768, 0.732, 0.833, and 0.716 in the training, test, and two external validation databases, respectively, which showed a superior predictive performance than the pre-treatment radiomics model (0.644, 0.616, 0.475, and 0.608). Compared with iRECIST criteria (0.624, 0.572, 0.650, and 0.466), a mixed model that combines delta-radiomics features and iRECIST had higher AUC values for MPR prediction of 0.777, 0.761, 0.850, and 0.670 in four sets. Conclusion: The delta-radiomics model demonstrated superior diagnostic performance compared to pre-treatment radiomics model and iRECIST criteria in predicting MPR preoperatively in neoadjuvant chemoimmunotherapy for stage II-III NSCLC. Clinical relevance statement: Delta-radiomics features based on the relative net change of radiomics features between baseline and preoperative CT scans serve a vital support tool in accurately identifying responses to neoadjuvant chemoimmunotherapy, which can help physicians make more appropriate treatment decisions. Key Points: • The performances of pre-treatment radiomics model and iRECIST model in predicting major pathological response of neoadjuvant chemoimmunotherapy were unsatisfactory. • The delta-radiomics features based on relative net change of radiomics features between baseline and preoperative CT scans may be used as a noninvasive biomarker for predicting major pathological response of neoadjuvant chemoimmunotherapy. • Combining delta-radiomics features and iRECIST can further improve the predictive performance of responses to neoadjuvant chemoimmunotherapy. [ABSTRACT FROM AUTHOR]

Researchers are often interested in examining between-individual differences in within-individual processes. If the process under investigation is tracked for a long time, its trajectory may show a certain degree of nonlinearity, so that the rate of change is not constant. A fundamental goal of modeling such nonlinear processes is to estimate model parameters that reflect meaningful aspects of change, including the parameters related to change and other parameters that shed light on substantive hypotheses. However, if the measurement occasion is unstructured, existing models cannot simultaneously estimate these two types of parameters. This article has three goals. First, we view the change over time as the area under the curve (AUC) of the rate of change versus time ( r - t ) graph. Second, using the instantaneous rate of change midway through a time interval to approximate the average rate of change during that interval, we propose a new specification to describe longitudinal processes. In addition to obtaining the individual change-related parameters and other parameters related to specific research questions, the new specification allows for unequally spaced study waves and individual measurement occasions around each wave. Third, we derive the model-based interval-specific change and change from baseline, two common measures to evaluate change over time. We evaluate the proposed specification through a simulation study and a real-world data analysis. We also provide OpenMx and Mplus 8 code for each model with the novel specification. [ABSTRACT FROM AUTHOR]

Aims: To determine the Oral Health Assessment Tool (OHAT) critical score in palliative care patients and the optimal timing for predicting mortality using time‐dependent receiver operating characteristic (ROC) curves. Methods and Results: A retrospective observational study was conducted on 176 patients treated by the palliative care team of our medical center between April 2017 and March 2020. Oral health was assessed using the OHAT. Prediction accuracy was evaluated using the area under the curve (AUC) analysis, sensitivity, and specificity, using time‐dependent ROC curves. Overall survival (OS) was compared using Kaplan‐Meier curves with the log‐rank test; hazard ratios (HRs) adjusted for covariates were calculated using a Cox proportional hazard model. A OHAT score of 6 was shown to best predict 21‐day OS (AUC 0.681, sensitivity 42.2%, specificity 80.0%). The median OS was significantly shorter in patients with total OHAT scores ≥6 than in patients with scores < 6 (21 days vs. 43 days, p =.017). For individual OHAT items, the unhealthy status of the lips and tongue was associated with decreased OS (HR = 1.91; 95% confidence interval [CI], 1.19–3.05 and adjusted HR = 1.48; 95% CI, 1.00–2.20). Conclusion: Predicting disease prognosis based on patient oral health can enable clinicians to provide timely treatment. [ABSTRACT FROM AUTHOR]

In recent years, the study of Receiver Operating Characteristic (ROC) curve analysis has gained significant attention as a means of accurately assessing test performance and determining optimal cutoff points. Traditionally, ROC models have been developed for bi-distributional univariate and multivariate data, such as Bi-normal, Bi-Exponential, Multivariate ROC models, and so forth. However, in current practical scenarios, the prevalence of high-dimensional matrix variate data poses a challenge for accurate test evaluation. To address this issue, this paper presents a novel ROC model that incorporates matrix variate normal distribution to effectively explain the accuracy of a test in the context of matrix data. Further, the accuracy measure, Area under the Curve (AUC) is derived, which helps in explaining the variability of the curve and provides the sensitivity at a particular value of specificity and vice versa. The proposed methodology is supported by a real data set and simulation studies. [ABSTRACT FROM AUTHOR]

Abbreviated breast magnetic resonance imaging (AB-MRI) has emerged as a supplementary screening tool, though protocols have not been standardized. The purpose of this study was to compare the diagnostic performance of modified and classic AB-MRI and determine MRI features affecting their diagnostic performance. Classic AB-MRI included one pre- and two post-contrast T1-weighted imaging (T1WI) scans, while modified AB-MRI included a delayed post-contrast axial T1WI scan and an axial T2-weighted interpolated scan obtained between the second and third post-contrast T1WI scans. Four radiologists (two specialists and two non-specialists) independently categorized the lesions. The MRI features investigated were lesion size, lesion type, and background parenchymal enhancement (BPE). The Wilcoxon rank-sum test, Fisher's exact test, and bootstrap-based test were used for statistical analysis. The average area under the curve (AUC) for modified AB-MRI was significantly greater than that for classic AB-MRI (0.76 vs. 0.70, p = 0.010) in all reader evaluations, with a similar trend in specialist evaluations (0.83 vs. 0.76, p = 0.004). Modified AB-MRI demonstrated increased AUCs and better diagnostic performance than classic AB-MRI, especially for lesion size > 10 mm (p = 0.018) and mass lesion type (p = 0.014) in specialist evaluations and lesion size > 10 mm (p = 0.003) and mild (p = 0.026) or moderate BPE (p = 0.010) in non-specialist evaluations. [ABSTRACT FROM AUTHOR]

INTRODUCTION: This study aimed to investigate the influence of the overall Alzheimer's disease (AD) genetic architecture on Down syndrome (DS) status, cognitive measures, and cerebrospinal fluid (CSF) biomarkers. METHODS: AD polygenic risk scores (PRS) were tested for association with DS‐related traits. RESULTS: The AD risk PRS was associated with disease status in several cohorts of sporadic late‐ and early‐onset and familial late‐onset AD, but not in familial early‐onset AD or DS. On the other hand, lower DS Mental Status Examination memory scores were associated with higher PRS, independent of intellectual disability and APOE (PRS including APOE, PRSAPOE, p = 2.84 × 10−4; PRS excluding APOE, PRSnonAPOE, p = 1.60 × 10−2). PRSAPOE exhibited significant associations with Aβ42, tTau, pTau, and Aβ42/40 ratio in DS. DISCUSSION: These data indicate that the AD genetic architecture influences cognitive and CSF phenotypes in DS adults, supporting common pathways that influence memory decline in both traits. Highlights: Examination of the polygenic risk of AD in DS presented here is the first of its kind.AD PRS influences memory aspects in DS individuals, independently of APOE genotype.These results point to an overlap between the genes and pathways that leads to AD and those that influence dementia and memory decline in the DS population.APOE ε4 is linked to DS cognitive decline, expanding cognitive insights in adults. [ABSTRACT FROM AUTHOR]