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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.

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 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]

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]

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]

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.

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.

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

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]

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]

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]

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]

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

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]

Three simple and affordable UV spectrophotometric methods have been proposed for the simultaneous determination of chlorthalidone and nebivolol in a synthetic mixture, as well as a combined dosage form. Method I use the simultaneous equation methodology and has a linearity range of 5–25 μg/mL for chlorthalidone at 233 nm and 5–90 μg/mL for nebivolol at 280 nm respectively. The linearity ranges for chlorthalidone at 228–238 nm and nebivolol at 275–285 nm were found to be 5–60 and 5–100 μg/mL respectively, using method II, the area under the curve method. The linearity range for method III, the first derivative method, is 10–35 μg/mL for chlorthalidone at 227 nm and 10–35 μg/mL for nebivolol at 275 nm. The two diagnostic plot residuals normal probability plot and residuals versus expected values plot are utilized for the verification of outcome data and found to be optimal for three methods. The method has been validated for accuracy, precision, recovery studies, linearity, specificity, and stability studies according to the International Council of Harmonisation guideline Q2R1. These developed methods have been utilized in routine analysis for the simultaneous determination of chlorthalidone and nebivolol without pre-extraction. [ABSTRACT FROM AUTHOR]

Background Consensus guidelines for dosing and monitoring of vancomycin recommend collection of 2 serum concentrations to estimate an area under the curve/minimum inhibitory concentration ratio (AUC/MIC). Use of Bayesian software for AUC estimation and model-informed precision dosing (MIPD) enables pre–steady state therapeutic drug monitoring using a single serum concentration; however, data supporting this approach are limited. Methods Adult patients with culture-proven gram-positive infections treated with vancomycin ≥72 hours receiving either trough-guided or AUC-guided therapy were included in this retrospective study. AUC-guided therapy was provided using MIPD and single-concentration monitoring. Treatment success, vancomycin-associated acute kidney injury (VA-AKI), and inpatient mortality were compared using a desirability of outcome ranking analysis. The most desirable outcome was survival with treatment success and no VA-AKI, and the least desirable outcome was death. Results The study population (N = 300) was comprised of an equal number of patients receiving AUC-guided or trough-guided therapy. More patients experienced the most desirable outcome in the AUC-guided group compared to the trough-guided group (58.7% vs 46.7%, P =.037). Rates of VA-AKI were lower (21.3% vs 32.0%, P =.037) and median hospital length of stay was shorter (10 days [interquartile range {IQR}, 8–20] vs 12 days [IQR, 8–25]; P =.025) among patients receiving AUC-guided therapy. Conclusions AUC-guided vancomycin therapy using MIPD and single-concentration monitoring improved outcomes in patients with culture-proven gram-positive infections. Safety was improved with reduced incidence of VA-AKI, and no concerns for reduced efficacy were observed. Moreover, MIPD allowed for earlier assessment of AUC target attainment and greater flexibility in the collection of serum vancomycin concentrations. [ABSTRACT FROM AUTHOR]

Purpose: To analyze tumor characteristics derived from pelvic magnetic resonance imaging (MRI) of patients with squamous cell carcinoma of the anus (SCCA) before and during chemoradiotherapy (CRT), and to compare the changes in these characteristics between scans of responders vs. nonresponders to CRT. Methods: We included 52 patients with a pelvic 3T MRI scan prior to CRT (baseline scan); 39 of these patients received an additional scan during week 2 of CRT (second scan). Volume, diameter, extramural tumor depth (EMTD), and external anal sphincter infiltration (EASI) of the tumor were assessed. Mean, kurtosis, skewness, standard deviation (SD), and entropy values were extracted from apparent diffusion coefficient (ADC) histograms. The main outcome was locoregional treatment failure. Correlations were evaluated with Wilcoxon's signed rank-sum test and Pearson's correlation coefficient, quantile regression, univariate logistic regression, and area under the ROC curve (AUC) analyses. Results: In isolated analyses of the baseline and second MRI scans, none of the characteristics were associated with outcome. Comparison between the scans showed significant changes in several characteristics: volume, diameter, EMTD, and ADC skewness decreased in the second scan, although the mean ADC increased. Small decreases in volume and diameter were associated with treatment failure, and these variables had the highest AUC values (0.73 and 0.76, respectively) among the analyzed characteristics. Conclusion: Changes in tumor volume and diameter in an early scan during CRT could represent easily assessable imaging-based biomarkers to eliminate the need for analysis of more complex MRI characteristics. [ABSTRACT FROM AUTHOR]

IntroductionRegulatory agencies generate a vast amount of textual data in the review process. For example, drug labeling serves as a valuable resource for regulatory agencies, such as U.S. Food and Drug Administration (FDA) and Europe Medical Agency (EMA), to communicate drug safety and effectiveness information to healthcare professionals and patients. Drug labeling also serves as a resource for pharmacovigilance and drug safety research. Automated text classification would significantly improve the analysis of drug labeling documents and conserve reviewer resources.MethodsWe utilized artificial intelligence in this study to classify drug-induced liver injury (DILI)-related content from drug labeling documents based on FDA’s DILIrank dataset. We employed text mining and XGBoost models and utilized the Preferred Terms of Medical queries for adverse event standards to simplify the elimination of common words and phrases while retaining medical standard terms for FDA and EMA drug label datasets. Then, we constructed a document term matrix using weights computed by Term Frequency-Inverse Document Frequency (TF-IDF) for each included word/term/token.ResultsThe automatic text classification model exhibited robust performance in predicting DILI, achieving cross-validation AUC scores exceeding 0.90 for both drug labels from FDA and EMA and literature abstracts from the Critical Assessment of Massive Data Analysis (CAMDA).DiscussionMoreover, the text mining and XGBoost functions demonstrated in this study can be applied to other text processing and classification tasks.

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.

Abstract Background This study was conducted to develop and validate an individualized prediction model for spontaneous preterm birth (sPTB) in twin pregnancies. Methods This a retrospective cohort study included 3845 patients who gave birth at the Chongqing Maternal and Child Health Hospital from January 2017 to December 2022. Both univariable and multivariable logistic regression analyses were performed to find factors associated with sPTB. The associations were estimated using the odds ratio (OR) and the 95% confidence interval (CI). Model performance was estimated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results A total of 1313 and 564 cases were included in the training and testing sets, respectively. In the training set, univariate and multivariate logistic regression analysis indicated that age ≥ 35 years (OR, 2.28; 95% CI 1.67–3.13), pre-pregnancy underweight (OR, 2.36; 95% CI 1.60–3.47), pre-pregnancy overweight (OR, 1.67; 95% CI 1.09–2.56), and obesity (OR, 10.45; 95% CI, 3.91–27.87), nulliparity (OR, 0.58; 95% CI 0.41–0.82), pre-pregnancy diabetes (OR, 5.81; 95% CI 3.24–10.39), pre-pregnancy hypertension (OR, 2.79; 95% CI 1.44–5.41), and cervical incompetence (OR, 5.12; 95% CI 3.08–8.48) are independent risk factors for sPTB in twin pregnancies. The AUC of the training and validation set was 0.71 (95% CI 0.68–0.74) and 0.68 (95% CI 0.64–0.73), respectively. And then we integrated those risk factors to construct the nomogram. Conclusions The nomogram developed for predicting the risk of sPTB in pregnant women with twins demonstrated good performance. The prediction nomogram serves as a practical tool by including all necessary predictors that are readily accessible to practitioners.

Abstract Background Pleural effusion (PE) is a common clinical feature that presents a diagnostic challenge for clinicians. In this retrospective study, we aimed to assess the biomarkers, ratios, and multiple indicators in serum and Pleural effusion for the differential diagnosis of tuberculous pleural effusion (TPE) from non-tuberculosis effusion (non-TPE). Methods The participants, who were divided into two groups: TPE and non-TPE (MPE and PPE), from Ningbo First Hospital, were incorporated in this study. The clinical and laboratory features were collected and analyzed using logistic regression analysis. Twelve biomarkers and their ratios in serum and PE were investigated for TPE versus non-TPE. Additionally, the value of multiple indicators for joint diagnosis was estimated. Results Biomarkers and ratios showed good diagnostic performance. The five variables including Serum ADA, IGRA, Effusion ADA, Effusion ADA/Serum ADA and Effusion LDH/Effusion ADA were identified as valuable parameters for differential diagnosis of TPE from non-TPE. The combined diagnosis of the five indexes yielded the highest diagnostic accuracy for TPE with an AUC (0.919), sensitivity (90.30%), and specificity (94.50%). Conclusions The biomarkers and ratios demonstrated strong diagnostic performance, and the utilization of multiple indicators for joint diagnosis can improve the diagnostic efficacy of tuberculous pleurisy.

IntroductionDust is a phenomenon that mainly occurs in arid and semi-arid areas as a result of high wind speed and turbulence on the surface of the soil without vegetation and prone to erosion. Various factors such as wind speed, vegetation cover, soil characteristics, climatic factors, etc. are involved in the creation of wind erosion and the resulting dust, all of which are related to each other and lead to an increase or decrease in wind erosion and dust storms. The problems caused by dust storms are due to the lack of sufficient information about the prevailing conditions in the region, the way these conditions change, and the lack of knowledge of sensitive and prone areas to dust storms. To deal with this phenomenon and provide appropriate management solutions, it is necessary to know the areas prone to dust and the effective factors in the occurrence of this phenomenon. In this regard, remote sensing and modeling can be very effective in investigating the dust phenomenon. Numerous studies have also been conducted to investigate dust storms and dust sources and to model areas sensitive to this phenomenon using remote sensing data and machine learning.Isfahan province is considered one of the most important geographical regions of the country, which is susceptible to successive drought, desertification, and dust storms due to its special geographical location, low rainfall, and proximity to the desert. So, it is necessary to carry out studies that will lead us to a correct understanding of dust-prone areas in this province. Therefore, In the current research, zoning of dust-prone areas in Isfahan province was done using meteorological codes related to dust, Aerosol optical depth values of MODIS sensor of Terra satellite (2001-2022), and machine learning algorithms including RF, BRT, SVM, and CART.Materials and methodsStudy areaIsfahan Province with an area of nearly 107017 km2 (6.4% of Iran area) is located between 30° 43′ to 34° 30′ N and 49° 38′ to 55° 31′ E in central Iran (Fig. 1). The mean annual precipitation of this province is between 40 mm and more than 800 mm and its mean annual temperature varies from 10 °C to 20 °C (Iran Meteorological Organization). According to the Torrent White method, the climate of Isfahan province is dry in 58.73% of its area (eastern, northeastern, and sub-central parts of the province), semi-arid in 28% of its area (central and northern parts of the province), and humid and semi-humid in 13.27% of its area (western and southern parts of the province). MethodologyFirst, using AOD values, the occurrence and non-occurrence points of dust were determined. Ten various factors including land use, temperature, rainfall, erosive wind speed, slope, altitude, albedo, EVI, NDSI, and NDMI were determined as predictive factors. n the next step, the correlation between the predictive factors was calculated using the variance inflation factor (VIF). Using machine learning algorithms, spatial modeling of susceptible areas to dust was done and the importance of predictive factors in zoning was determined using the jackknife test. Finally, using the value of the area under the ROC curve (ROC-AUC), the validation of the model was done.ResultsThe zoning map of dust-prone areas in Isfahan province showed that the low-altitude and flat parts of the north, parts of the northeast, southeast of the province, and the central areas towards the southwest and west of Isfahan province are vulnerable areas against the occurrence of dust.The highest percentage of areas prone to dust in the RF model was in the very low class with a value of 21.36%, in the BRT model was in the medium class with a value of 22.66%, and in the SVM and CART models, it was in very high and low classes with values of 23.92% and 37.6%, respectively. The results of validation illustrated that the RF model with AUC = 0.86 was the most efficient, followed by BRT, CART, and SVM models with AUC values of 0.82, 0.79, and 0.77 respectively. According to the results of the Jackknife test, in RF, BRT, and CART models, rainfall had the most effect in modeling while in the SVM model, temperature and then rainfall had the most effect in the modeling.Discussion and conclusionBased on the results, the most vulnerable areas to dust are assigned to salt land, barren lands, and rangeland with poor quality. These areas, which are mainly located in the northern, central, and parts of the eastern sides of the province, have the lowest amount of surface soil moisture, the lowest amount of rainfall, and the highest temperature, and as a result, they face the lack of vegetation or weak vegetation. Therefore, these areas are exposed to the occurrence of dust, and with winds blowing at a speed exceeding the threshold speed of wind erosion, dust storms will happen. According to the validation results, the RF model has the best performance among the applied models, followed by the BRT, CART, and SVM models. Random forest algorithm is one of the advanced decision tree models used for classification and regression. This algorithm has a much more accurate performance compared to other simple regression trees or parametric statistical methods and is defined based on a large number of decision trees. The results of the jackknife test introduced rainfall as the most important factor in RF, BRT, and CART models. In the RF model, as the best model, after the rainfall factor, the temperature and altitude factors are more important than other factors. Considering the low amount of rainfall in dust-prone areas, it can be said that low rainfall, soil dryness, and, as a result, the reduction of vegetation, increase the conditions for creating wind erosion and dust.

Abstract Background Juvenile Dermatomyositis (JDM) is a rare autoimmune disease characterized by skin and muscle inflammation. The loss of nail fold capillary end row loops (ERL) is evidence of small vessel involvement in JDM. This study aimed to examine the specific association of ERL over the disease course with evidence of JDM disease damage. Methods We analyzed data from 68 initially treatment-naïve JDM children who had been observed for at least five years with multiple ERL density assessments. The JDM disease course were categorized into monocyclic short, monocyclic long, polycyclic, and chronic. The ERL capillary count was cumulatively evaluated using the area under the curve (AUC) method. Results The mean ERL density for the treatment-naive JDM was significantly lower than that of their healthy age-matched controls (4.8 ± 1.6 /mm vs. 7.9 ± 0.9 /mm; p

Background: Many studies have showed that adenosine deaminase (ADA) and alkaline phosphatase (ALP) values were highly supportive in the diagnosis of tuberculosis but comparison between ADA and ALP has not been much documented in pleural fluid (PF) in eastern India. Hence, the main aim of the study was to identify the relation between ADA and ALP in diagnosis of tuberculosis if any. Aims and Objectives: This study was conducted to estimate and compare the levels of ADA and ALP of PF in tubercular and non-tubercular diseases. Materials and Methods: This study was hospital-based, cross-sectional, and observational study during the period of 1 year. A total of 90 patients have been analyzed among them after excluding few outliers, 82 patients had been taken whose ADA and ALP were measured in PFs. Results: Kolmogorov–Smirnov and Shapiro–Wilk tests for statistical analysis were used to determine whether continuous variables were normally distributed or not. In the comparison of tubercular and non-tubercular effusion groups, P-value for ADA in independent t-test was 0.000, and P-value for ADA and ALP in Mann–Whitney U-test was 0.000 and 0.000, respectively. Receiver operating characteristic (ROC) curve showed area under the curve (AUC) of 0.965 and 0.751 in ADA and ALP for diagnosing tuberculosis. Cutoff values of 31.87 IU/L for ADA were determined by ROC curve analysis with 100% of sensitivity and 82.3% of specificity and cutoff values of 40.5 IU/L for ALP were also determined by ROC curve analysis with 75.6% of sensitivity and 68.3% of specificity for diagnosis of tuberculosis. Conclusion: Although ADA is the best parameter for diagnosing tubercular pleural effusions, analysis of ALP is cost-effective.

Ali Riza Koksal,1 Nergiz Ekmen,2 Yucel Aydin,2 Kelley Nunez,3 Tyler Sandow,4 Molly Delk,2 Martin Moehlen,2 Paul Thevenot,3 Ari Cohen,3,5 Srikanta Dash1,2,6 1Department of Pathology and Laboratory Medicine, Tulane University Health Sciences Center, New Orleans, LA, USA; 2Department of Gastroenterology and Hepatology, Tulane University Health Sciences Center, New Orleans, LA, USA; 3Department of Gastroenterology and Hepatology, Institute of Translational Research, Ochsner Health, New Orleans, LA, USA; 4Department of Radiology, Institute of Translational Research, Ochsner Health, New Orleans, LA, USA; 5Multi-Organ Transplant Institute, Ochsner Health, New Orleans, LA, USA; 6Southeast Louisiana Veterans Health Care System, New Orleans, LA, USACorrespondence: Srikanta Dash, Department of Pathology and Laboratory Medicine, Tulane University Health Sciences Center, 1430 Tulane Avenue, New Orleans, LA, 70112, USA, Tel +1 504-988-2519, Fax +1 504-988-7389, Email sdash@tulane.eduIntroduction: Extracellular vesicles could serve as a non-invasive biomarker for early cancer detection. However, limited methods to quantitate cancer-derived vesicles in the native state remain a significant barrier to clinical translation.Aim: This research aims to develop a rapid, one-step immunoaffinity approach to quantify HCC exosomes directly from a small serum volume.Methods: HCC-derived exosomes in the serum were captured using fluorescent phycoerythrin (PE)-conjugated antibodies targeted to GPC3 and alpha-fetoprotein (AFP). Total and HCC-specific exosomes were then quantified in culture supernatant or patient-derived serums using fluorescence nanoparticle tracking analysis (F-NTA). The performance of HCC exosome quantification in the serum was compared with the tumor size determined by MRI.Results: Initially we tested the detection limits of the F-NTA using synthetic fluorescent and non-fluorescent beads. The assay showed an acceptable sensitivity with a detection range of 104– 108 particles/mL. Additionally, the combination of immunocapture followed by size-exclusion column purification allows the isolation of smaller-size EVs and quantification by F-NTA. Our assay demonstrated that HCC cell culture releases a significantly higher quantity of GPC3 or GPC3+AFP positive EVs (100– 200 particles/cell) compared to non-HCC culture (10– 40 particles/cell) (p< 0.01 and p< 0.05 respectively). The F-NTA enables absolute counting of HCC-specific exosomes in the clinical samples with preserved biological immunoreactivity. The performance of F-NTA was clinically validated in serum from patients ± cirrhosis and with confirmed HCC. F-NTA quantification data show selective enrichment of AFP and GPC3 positive EVs in HCC serum compared to malignancy-free cirrhosis (AUC values for GPC3, AFP, and GPC3/AFP were found 0.79, 0.71, and 0.72 respectively). The MRI-confirmed patient cohort indicated that there was a positive correlation between total tumor size and GPC3-positive exosome concentration (r:0.78 and p< 0.001).Conclusion: We developed an immunocapture assay that can be used for simultaneous isolation and quantification of HCC-derived exosomes from a small serum volume with high accuracy.Keywords: hepatocellular carcinoma, HCC, extracellular vesicles, EVs, glypican 3, GPC3, alpha-fetoprotein, AFP, magnetic resonance imaging, MRI, area under the curve, AUC

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.

Background: This study was conducted to develop and validate an individualized prediction model for spontaneous preterm birth (sPTB) in twin pregnancies. Methods: This a retrospective cohort study included 3845 patients who gave birth at the Chongqing Maternal and Child Health Hospital from January 2017 to December 2022. Both univariable and multivariable logistic regression analyses were performed to find factors associated with sPTB. The associations were estimated using the odds ratio (OR) and the 95% confidence interval (CI). Model performance was estimated using sensitivity, specificity, accuracy, area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA). Results: A total of 1313 and 564 cases were included in the training and testing sets, respectively. In the training set, univariate and multivariate logistic regression analysis indicated that age ≥ 35 years (OR, 2.28; 95% CI 1.67–3.13), pre-pregnancy underweight (OR, 2.36; 95% CI 1.60–3.47), pre-pregnancy overweight (OR, 1.67; 95% CI 1.09–2.56), and obesity (OR, 10.45; 95% CI, 3.91–27.87), nulliparity (OR, 0.58; 95% CI 0.41–0.82), pre-pregnancy diabetes (OR, 5.81; 95% CI 3.24–10.39), pre-pregnancy hypertension (OR, 2.79; 95% CI 1.44–5.41), and cervical incompetence (OR, 5.12; 95% CI 3.08–8.48) are independent risk factors for sPTB in twin pregnancies. The AUC of the training and validation set was 0.71 (95% CI 0.68–0.74) and 0.68 (95% CI 0.64–0.73), respectively. And then we integrated those risk factors to construct the nomogram. Conclusions: The nomogram developed for predicting the risk of sPTB in pregnant women with twins demonstrated good performance. The prediction nomogram serves as a practical tool by including all necessary predictors that are readily accessible to practitioners. [ABSTRACT FROM AUTHOR]

Aim: Describe the utility of an inverse reinforcement learning pathway to develop a novel model to predict and manage the spread of COVID-19. Materials & methods: Convolutional neural network (CNN) with multilayer perceptron (MLP) modeling functions utilized inverse reinforcement learning to predict COVID-19 outcomes based on a comprehensive array of factors. Results: Our model demonstrates a sensitivity of 0.67 in the receiver operating characteristic curve and can correctly identify approximately 67% of the positive cases. Conclusion: We demonstrate the ability to augment clinical decision-making with a novel artificial intelligence (AI) solution that accurately predicted the susceptibility of transplant patients to COVID-19. This enables physicians to administer treatment and take appropriate preventative measures based on patients' risk factors. Artificial intelligence (AI) is about creating intelligent machines that can replicate human cognitive functions like learning and problem-solving. These AI systems can help to predict and address problems, and their power lies in learning from large datasets. AI can help to understand and respond to COVID-19 risks, particularly for certain groups of patients, like heart transplant patients. Figuring out how COVID-19 affects heart transplant patients is tough because we don't have much information about it. Combining medical knowledge with AI seems to be an effective way to solve problems during the pandemic. However, using AI in medicine is challenging as the use and adoption of AI in clinical practice remains limited, with many AI algorithms at the design and development stage. Considerable progress has been made, but continued research is needed for better monitoring and diagnosis. [ABSTRACT FROM AUTHOR]

With the recent rise in violent crime, the real-time situation analysis capabilities of the prevalent closed-circuit television have been employed for the deterrence and resolution of criminal activities. Anomaly detection can identify abnormal instances such as violence within the patterns of a specified dataset; however, it faces challenges in that the dataset for abnormal situations is smaller than that for normal situations. Herein, using datasets such as UBI-Fights, RWF-2000, and UCSD Ped1 and Ped2, anomaly detection was approached as a binary classification problem. Frames extracted from each video with annotation were reconstructed into a limited number of images of 3 × 3 , 4 × 3 , 4 × 4 , 5 × 3 sizes using the method proposed in this paper, forming an input data structure similar to a light field and patch of vision transformer. The model was constructed by applying a convolutional block attention module that included channel and spatial attention modules to a residual neural network with depths of 10, 18, 34, and 50 in the form of a three-dimensional convolution. The proposed model performed better than existing models in detecting abnormal behavior such as violent acts in videos. For instance, with the undersampled UBI-Fights dataset, our network achieved an accuracy of 0.9933, a loss value of 0.0010, an area under the curve of 0.9973, and an equal error rate of 0.0027. These results may contribute significantly to solve real-world issues such as the detection of violent behavior in artificial intelligence systems using computer vision and real-time video monitoring. [ABSTRACT FROM AUTHOR]

Systematic Biopsy (SBx) has been and continues to be the standard staple for detecting prostate cancer. The more expensive MRI guided biopsy (MRITBx) is a better way of detecting cancer. The prostatectomy can provide an accurate condition of the prostate. The goal is to assess how reliable SBx and MRITBx are vis à vis prostatectomy. Graded Gleason scores are used for comparison. Cohen's Kappa index and logistic regression after binarization of the graded Gleason scores are some of the methods used to achieve our goals. Machine learning methods, such as classification trees, are employed to improve predictability clinically. The Cohen's Kappa index is 0.31 for SBx versus prostatectomy, which means a fair agreement. The index is 0.34 for MRITBx versus prostatectomy, which again means a fair agreement. A direct comparison of SBx versus prostatectomy via binarized graded scores gives sensitivity 0.83 and specificity 0.50. On the other hand, a direct comparison of MRITBx versus prostatectomy gives sensitivity 0.78 and specificity 0.67, putting MRITBx on a higher level of accuracy. The SBx and MRITBx do not yet match the findings of prostatectomy completely, but they are useful. We have developed new biomarkers, considering other pieces of information from the patients, to improve the accuracy of SBx and MRITBx. From a clinical point of view, we provide a prediction model for prostatectomy Gleason grades using classification tree methodology. [ABSTRACT FROM AUTHOR]

Logistic regression models are widely applied in daily practice. Hence, it is necessary to ensure they have an adequate predictive performance, which is usually estimated by means of the receiver operating characteristic (ROC) curve and the area under it (area under the curve [AUC]). Traditional estimators of these parameters are thought to be applied to simple random samples but are not appropriate for complex survey data. The goal of this work is to propose new weighted estimators for the ROC curve and AUC based on sampling weights which, in the context of complex survey data, indicate the number of units that each sampled observation represents in the population. The behaviour of the proposed estimators is evaluated and compared with the traditional unweighted ones by means of a simulation study. Finally, weighted and unweighted ROC curve and AUC estimators are applied to real survey data in order to compare the estimates in a real scenario. The results suggest the use of the weighted estimators proposed in this work in order to obtain unbiassed estimates for the ROC curve and AUC of logistic regression models fitted to complex survey data. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to build radiomic feature-based machine learning models to predict pathological clinical response (pCR) of neoadjuvant chemoradiation therapy (nCRT) for esophageal squamous cell carcinoma (ESCC) patients. Methods: A total of 112 ESCC patients who underwent nCRT followed by surgical treatment from January 2008 to December 2018 were recruited. According to pCR status (no visible cancer cells in primary cancer lesion), patients were categorized into primary cancer lesion pCR (ppCR) group (N = 65) and non-ppCR group (N = 47). Patients were also categorized into total pCR (tpCR) group (N = 48) and non-tpCR group (N = 64) according to tpCR status (no visible cancer cells in primary cancer lesion or lymph nodes). Radiomic features of pretreatment CT images were extracted, feature selection was performed, machine learning models were trained to predict ppCR and tpCR, respectively. Results: A total of 620 radiomic features were extracted. For ppCR prediction models, radiomic model had an area under the curve (AUC) of 0.817 (95% CI: 0.732–0.896) in the testing set; and the combination model that included rad-score and clinical features had a great predicting performance, with an AUC of 0.891 (95% CI: 0.823–0.950) in the testing set. For tpCR prediction models, radiomic model had an AUC of 0.713 (95% CI: 0.613–0.808) in the testing set; and the combination model also had a great predicting performance, with an AUC of 0.814 (95% CI: 0.728–0.881) in the testing set. Conclusion: This study built machine learning models for predicting ppCR and tpCR of ESCC patients with favorable predicting performance respectively, which aided treatment plan optimization. Clinical relevance statement: This study significantly improved the predictive value of machine learning models based on radiomic features to accurately predict response to therapy of esophageal squamous cell carcinoma patients after neoadjuvant chemoradiation therapy, providing guidance for further treatment. Key Points: • Combination model that included rad-score and clinical features had a great predicting performance. • Primary tumor pCR predicting models exhibit better predicting performance compared to corresponding total pCR predicting models. [ABSTRACT FROM AUTHOR]

The objective of this study was to conduct a comprehensive evaluation of binary classification algorithms within data lakes, employing a diverse array of metrics. Binary classification algorithms, which categorize inputs into one of two distinct classes, were scrutinized to determine their efficacy. The research focused on the evaluation techniques applicable to these algorithms. Methods for assessing algorithmic efficiency were investigated, including logistic regression, error function, regularization, and ancillary training tools within the dataset. A detailed analysis of the parameters pertinent to classifier evaluation was performed, encompassing accuracy, confusion matrix, precision, recall, decision threshold, F1 score, and the Receiver Operating Characteristic (ROC) curve. A critical comparison between the ROC and Precision-Recall (PR) curves was conducted, with particular attention to the Area Under the Curve (AUC) metric. The study's methodology involved training a classifier on the UCI Machine Learning Repository's Breast Cancer Wisconsin dataset, followed by the calibration of the precision/recall ratio. The findings of this study offer an in-depth examination of various evaluation metrics and threshold optimization techniques, thereby augmenting the comprehension of binary classifier performance. Practitioners are provided with guidance to select suitable metrics and thresholds tailored to specific contexts. Furthermore, the study's insights into the strengths and limitations of these metrics across heterogeneous datasets promote refined practices in machine learning and data analysis, facilitating more strategic model selection and deployment. [ABSTRACT FROM AUTHOR]