Validation Sample Research Articles

Comprehensive propagation of errors for the prediction of woody biomass

Abstract Managing vegetation to sequester carbon in biomass requires estimates to meet standards for accuracy, with methods that are transparent, verifiable and cost‐effective. Allometric models are commonly used to predict biomass from non‐destructive field inventory data. Although a number of studies have addressed biomass error propagation, none have provided a general set of methods for linking errors all the way from initial allometric model development through to the final site‐based biomass prediction, for both above‐ and below‐ground biomass. Error sources in total biomass (above‐ + below‐ground) were quantified using a combination of analytical and Monte Carlo methods, illustrated with four contrasting case studies using either site‐ and‐species‐specific, species‐specific or generalised allometric models. Sampling error was found to be the most important contributor to site‐level biomass uncertainty, arising from the interaction between spatial variability and the field sampling design. The contribution of allometric model covariance to total error was also quantified, with errors in the determination of moisture content during allometric model development identified as a potentially important yet often overlooked error source. Application of different allometric models to the same inventory data suggested the error from generalised models was no greater than that from site‐ or species‐specific models, with increases in the generalised model prediction error balanced by decreases in other error sources associated with the increased sample size on which generalised models are based. Recommendations for reducing errors in predicted biomass include increasing field survey sample size, adopting field survey designs that ensure spatial representativeness and improving moisture content measurement protocols and increasing the moisture content sample size during allometric model development. To reduce costs while maintaining acceptable accuracy, the use of generalised allometric models is recommended, with the caveat that additional biomass sampling for model validation may be required to limit the potential for biased predictions.

Clinical Utility of Induced Sputum and Bronchoalveolar Lavage Cultures in Diagnosing Nontuberculous Mycobacterial Pulmonary Disease

Diagnosing non-tuberculous mycobacterial pulmonary disease (NTM-PD) in patients unable to produce sputum spontaneously requires invasive procedures to obtain valid respiratory specimens. In this retrospective study, we evaluated the results of microbiological tests performed on respiratory samples of 132 patients affected by NTM-PD. In the diagnostic workout, 98 patients performed both induced sputum (IS) and bronchoalveolar lavage (BAL) and were enrolled in our study. A total of 93 out of 98 BAL samples (95%) were culture-positive for mycobacteria, whereas only 67/153 (44%) induced sputum cultures were positive for NTM (p < 0.001). Molecular identification of NTM with real-time polymerase chain reaction (PCR) was positive in 48/64 BAL (75%) and in 47/139 (34%) IS samples (p < 0.001). Patients affected by nodular-bronchiectatic form were 65/98 (66%): BAL culture was positive in 95% of cases (62/65 BAL), while only 30/99 IS cultures were positive (30%; p < 0.001). PCR was positive in 76% of BAL samples examined (26/34) and in 26% of the IS samples (24 out of 91) (p < 0.001). Among 33 patients with a fibro-cavitary radiological pattern, 65% of IS (35/54) were culture-positive for NTM, whereas 94% of cases (31/33) had a positive culture for NTM from BAL (p = 0.002). PCR was positive in 73% of BAL samples tested (22/30) and 48% of IS samples tested (23/48) (p = 0.031). Our results confirm BAL mycobacterial culture as the gold standard for the diagnosis of pulmonary mycobacteriosis. FBS with BAL should be performed in every patient with a strong suspicion of NTM-PD, if other respiratory samples are repeatedly negative. Sputum induction is a useful technique to obtain valid respiratory samples when patients are unable to produce spontaneous sputum, especially in the outpatient setting. However, during the diagnostic workup of NTM-PD, we should not forget that PCR and mycobacterial culture of induced sputum have a lower yield than when performed on BAL, especially in the nodular-bronchiectatic form of the disease.

Synchronization of extraction and chromatographic conditions of diverse physicochemical behavior of pseudoephedrine, loratadine and desloratadine for their simultaneous determination in human plasma: Application to pharmacokinetic study

A selective, sensitive, and rapid LC-MS/MS method has been developed, optimized, and validated for the simultaneous determination of pseudoephedrine (PSE), loratadine (LOR), and LOR active metabolite, desloratadine (DES) in K3EDTA human plasma. A simple liquid–liquid extraction method was adopted for extraction of the 3 analytes and their internal standards; chlorpheniramine (CLP), loratadine D4 (LOR-D4), and desloratadine D4 (DES-D4), respectively, giving consistent recovery results (78.38––88.23 %) using diethyl ether: dichloromethane (80:20, v/v) as the extraction solvent. The chromatographic separation was challenging due to the polarity differences between PES, which is more polar, and LOR and DES, which are less polar that affected their physicochemical properties including the retention of the drugs on C18 column. The separation was achieved using of isocratic elution of 20 mM ammonium formate and 2 mM trifluoracetic acid ammonium salt: acetonitrile containing 0.15 % formic acid (12:88, v/v) as a mobile phase pumped onto a Supelco Discovery® C18 (4.6 mm x 100 mm, 5 µm) at 35 °C with a flow rate of 0.8 mL/min. The use of trifluoracetic acid ammonium salt as an ion pair reagent in the selected mobile phase allowed good retention of the polar PSE on the used C18 column with a reasonable retention time. Positive Electrospray ionization was employed followed by detection using a tandem mass spectrometer in multiple reactions monitoring (MRM) mode. The sample analysis of this study was conducted in compliance with ICH guideline M10 on bioanalytical method validation and study sample analysis. The method was linear over the concentration range of (4–1200) ng/mL for PSE, (50–15000) pg/mL for both LOR, and DES. The developed method was accurate (91.14–110.30 %), precise (0.53–7.86 %), and was applied to study the pharmacokinetic profile of the cited drugs in the plasma of healthy human volunteers after single oral administration of the fixed-dose combination Claritin D® tablet, (240/10 mg) of PSE, and LOR, respectively.

Exploring the antecedents of knowledge transfer among stakeholders in green building projects: From the configuration perspective

With the rapid development of green buildings around the world, knowledge transfer among stakeholders is of great significance in promoting the innovation and application of green building technologies. However, due to the complexity and specificity of green building projects, current studies have not fully established a comprehensive understanding of how elements from various dimensions (i.e., subject, relationship, context, and activity) collectively impact knowledge transfer among stakeholders in green building projects. Therefore, this study aims to explore the key factors that affect knowledge transfer among stakeholders in green building projects and how these factors work together to enhance the effectiveness of knowledge transfer (EKT). Firstly, the key factors influencing knowledge transfer were pinpointed: cognitive differences, connection strength, organizational climate, information technology capability, transmit willingness, receive willingness, perceived policy support, and richness of transfer channels. Then, an examination of the collected valid samples was conducted through configuration analysis utilizing the fuzzy set qualitative comparative analysis (fsQCA) method. The findings reveal five configurations, categorized as strong connection-driven, policy support-driven, and both-driven. Lastly, some combination strategies were outlined for different categories to assist green building companies in enhancing EKT. This study not only enhances the existing green building literature by developing a framework of factors that influence knowledge transfer among stakeholders, but also provides a reference for corporate executives to choose the best strategies to enhance EKT in green building projects.

EQ‑5D‑Y-3L population norms for children and adolescents in Jiangsu, China

ObjectiveThis study aims to establish EQ-5D-Y-3L population norms in Jiangsu, China by conducting a large-scale cross-sectional survey.MethodsChildren and adolescents aged 9–17 from three cities of Jiangsu Province were selected by multistage stratified random sampling to complete the EQ-5D-Y-3L instrument independently. Population norms for Jiangsu, China were determined by calculating statistics based on age and gender. Logistic and Tobit regression models were employed to explain the relationship between HRQoL and factors such as sociodemographic characteristics/recent acute symptoms (experienced fever/cough/sore throat/diarrhea in the past two weeks).ResultsThree cities yielded 37,574 valid samples (a sample validity rate of 95.4%). The EQ-5D-Y-3L utility values (mean ± SD) were 0.964 ± 0.085 for males and 0.958 ± 0.077 for females. Males scored 85.94 ± 19.62 and females scored 84.83 ± 18.45 on the VAS (mean ± SD), while the percentages of respondents reporting full health ranged from 58.3 to 78.8%. The dimension in which most respondents reported having no problems was “feeling worried, sad, or unhappy” (23.0%). And the lowest HRQoL was shown in the 14-year-old age group. Gender, age, board at school, and BMI were found to have an association with HRQoL. In addition, recent acute symptoms also correlate with some aspects of HRQoL.ConclusionsThis study established EQ-5D-Y-3L population norms in Jiangsu, China for the first time. These norms will support resource allocation decision-making and be used as a reference for health evaluation studies.

VALIDATION OF DYNAMIC MESSAGE VARIANT IN ADAPTIVE LOGGING METHOD

Technology, and software technology in particular, is one of the main drivers of progress in human society. It allows us to solve existing tasks more efficiently and find solutions for problems that were previously too complex to deal with. The outreach and importance of its influence on everyday life is hard to overstate. To be able to satisfy ever-growing demand software solutions become more complex and sophisticated, reaching millions users and solving numerous day to day tasks. But as a result new challenges appear, especially related to cybersecurity. And it is not only about integrity, availability and confidentiality aspects, but also – equally important – about control and observability. As the scale of software systems grows more and more, tracking their state and behavior becomes more challenging. This research takes a closer look at observability aspect of cybersecurity, in particular at adaptive logging method for software systems and its dynamic message variant which introduced the ability to have dynamic computations executed before outputting observational information about the system. The flexibility it introduces also makes possible for undesirable side effects to occur as the dynamic nature of such messages allows for execution of virtually any valid code samples. The main purpose of this work is to demonstrate a solution that would allow validation and prevent unwanted code execution. To achieve this an approach that uses analysis of abstract syntax trees with JSON-based validation logic is demonstrated. The natural tree-like structure of ASTs, as well as close resemblance between generated representation and JSON-schemas, allows for precise and easily configurable processing that reduces the possibility of unexpected behaviors. The integration into an existing formal basis of adaptive logging method is also demonstrated with provided justification for required additions and their specifics.

A survey on improving core literacy among impoverished students in Chinese pharmaceutical universities under a development-oriented funding system

BackgroundImpoverished students constitute a group that cannot be overlooked in higher education. It is crucial for pharmaceutical universities worldwide to implement financial assistance programs that promote the development and success of students from poor families. Chinese universities have carried out active exploration of subsidized education, and made important achievements, but there are also problems, which are worth learning from. This study proposed a strategy to improve the core literacy of impoverished pharmaceutical students under the development-oriented funding system.MethodsThe study centers on clinical pharmacy students from a pharmaceutical college as the research subjects. A sequential explanatory mixed-methods approach was employed, with quantitative data collected through a survey questionnaire, supplemented by qualitative data collected through in-depth interviews. Data gathered from these two levels were integrated and subjected to statistical analysis.ResultsThe quantitative survey yielded a total of 397 valid samples, most of whom were females (73.8%), mainly at undergraduate level (89.67%), and from non-urban area (73.81%). Five of them participated in further qualitative interviews. The combined data identified: (a) financial aid—most students were highly satisfied with the financial support; (b) psychological support—most of the students interviewed reported that the scholarship significantly improved their self-confidence and motivation; (c) academic guidance—funded students had clear expectations for career development and academic guidance, demonstrating a strong need for further professional study; and (d) employment assistance—most students wanted career guidance and career planning support.ConclusionsThe financial assistance in pharmaceutical colleges and universities should be enriched to resolve the worries of impoverished clinical pharmacy students through economic assistance, improve the moral cultivation with psychological assistance, strengthen their cultural and scientific literacy, and improve their knowledge and practical ability through academic assistance. Through the integration of pharmacy education, the vocational competence of clinical pharmacy students can be improved by employment assistance, and the reform of higher pharmacy education can be further promoted to improve the training quality of pharmaceutical talents.

A Simple Clinical Predictive Model for Arthroscopic Mobility of Osteochondritis Dissecans Lesions of the Knee

Background: Osteochondritis dissecans (OCD) of the knee is a focal idiopathic alteration of subchondral bone and/or its precursor with risk for instability and disruption of adjacent cartilage. Treatment options focused on preventing premature osteoarthritis vary depending on multiple patient and lesion characteristics, including lesion mobility. Purpose: To differentiate lesion mobility before arthroscopy using a multivariable model that includes patient demographic characteristics and physical examination findings. Study Design: Cohort study (Diagnosis); Level of evidence, 2. Methods: Demographic, preoperative physical examination, and radiographic data were collected from a multicenter national prospective cohort of patients with OCD of the knee. Inclusion criteria included patients <19 years of age and patients with arthroscopically confirmed mobility status based on the Research on Osteochondritis Dissecans of the Knee arthroscopy classification. Multivariable logistic regression analysis using stepwise model selection was used to determine factors associated with the likelihood of a mobile versus an immobile lesion. A 75% partition of the data was used for model training, and 25% was used as a validation cohort. Quantitative model fit statistics were computed using the holdout data, including sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC), along with the corresponding 95% CI. Results: A total of 407 patients in the prospective cohort met inclusion criteria, and 62% were male. The mean ± SD age was 13.7 ± 2.2 years, height 161.8 ± 5.3 cm, and weight 59.2 ± 42.2 kg. Arthroscopic evaluation yielded 235 immobile and 172 mobile lesions. Multivariable analysis determined that the best model to predict lesion mobility included chronologic age ≥14 years (P < .001), effusion on physical examination (P < .001), and any loss of range of motion on physical examination (P = .07), while controlling for male sex (P = .38) and weight >54.4 kg (P = .12). In the 25% holdout validation sample (n = 102), a sensitivity of 83%, a specificity of 82%, and an AUC of 0.89 (95% CI, 0.82-0.95) were achieved with these predictive factors. Conclusion: Age, effusion, and loss of motion can predict knee OCD lesion mobility at the time of arthroscopy. Education about lesion mobility can help with surgical planning and patient and family counseling.

A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway

BackgroundExosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them.MethodPeripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs.ResultsThe machine learning model incorporates six small RNAs: piR-36,340, piR-33,161, miR-484, miR-548ah-5p, miR-4282, and miR-6853-3p. The area under the ROC curve (AUC) of the machine learning model in the training set was 0.985 (95% CI = 0.948-1), while the AUC in the test set was 0.972 (95% CI = 0.882–0.995). RT-qPCR was used to detect the expression levels of these key small RNAs in the validation samples, and the results revealed that their expression levels were significantly different between the two groups (P < 0.05). Through target gene prediction and functional enrichment analysis, it was found that the functions of the target genes were enriched mainly in the chemokine signaling pathway.ConclusionThe combination of six plasma exosome small RNAs has good prognostic value for women with positive breast cancer by imaging screening. The chemokine signaling pathway may be involved in the early stage of breast cancer. It is worth further exploring whether small RNAs mediate chemokine signaling pathways in the pathogenesis of breast cancer through the delivery of exosomes.

Empirical Investigation of Digital Collectibles Purchase Intention: The Roles of Value, Risks, Identification, and Scarcity

China’s digital collectibles utilize blockchain technology to create unique digital certificates for specific works and artworks that consumers can purchase, collect, and use. Digital collectibles are localized Chinese digital products, similar to non-fungible token (NFT), but with differences. This study explores the influencing factors and mechanisms of consumer purchase in digital collectibles. Our study developed a research model based on the Stimulus-Organism-Response (SOR) paradigm and the integration of perceived value theory and social identity theory. We used a questionnaire survey method and obtained 333 valid samples. Then, we used PLS-SEM to test the model. The empirical results are as follows: (1) Our study constructs and validates a multidimensional structure of perceived value and perceived risk; (2) consumers’ perceived value influences their purchase intentions through the mediating roles of consumer-digital collectibles cognitive identification and affective identification; (3) both supply-based scarcity and demand-based scarcity have a positive moderating effect on the relationship between cognitive identification and purchase intention. Our study reveals the pathway mechanism of the purchase intention and demonstrates the moderating roles of supply-based and demand-based scarcity. In addition, our study can provide practical suggestions for platforms and industries to conduct marketing activities and avoid risk.

Архітектура інтелектуальної системи управління ризиками та розпізнавання видів грибів

The article presents the development of an intelligent system for recognising mushroom species that provides high accuracy and ease of use. To train the model, a large dataset ‘Mushrooms classification’ from the Kaggle platform was used, which provided the necessary diversity of images and achieved a classification accuracy of 85%. Data pre-processing included image quality checks, standardisation, and division into training, validation, and test samples, which contributed to efficient model training. The recognition algorithm is based on the ResNet convolutional neural network, which has demonstrated an accuracy advantage over other architectures.

Comparative Quantitative and Discriminant Analysis of Wheat Flour with Different Levels of Chemical Azodicarbonamide Using NIR Spectroscopy and Hyperspectral Imaging.

This study investigated and comprehensively compared the performance of spectra (950-1660 nm) acquired respectively from NIR and HSI in the rapid and non-destructive quantification of azodicarbonamide (ADA) content (0-100 mg/kg) in WF and simultaneously identified WF containing excessive ADA (>45 mg/kg). The raw spectra were preprocessed using 14 methods and then mined by the partial least squares (PLS) algorithm to fit ADA levels using different numbers of WF samples for training and validation in five datasets (NTraining/Validation = 189/21, 168/42, 147/63, 126/84, 105/105), yielding better abilities of NIR Savitzky-Golay 1st derivative (SG1D) spectra-based PLS models and raw HSI spectra-based PLS models in quantifying ADA with higher determination coefficients and lower root-mean-square errors in validation (R2V & RMSEV), as well as establishing 100% accuracy in PLS discriminant analysis (PLS-DA) models for identifying excessive ADA-contained WF in each dataset. Twenty-four wavelengths selected from a NIR SG1D spectra in a 168/42 dataset and 23 from a raw HSI spectra in a 147/63 dataset allowed for the better performance of quantitative models in ADA determination with higher R2V and RMSEV in validation (R2V > 0.98, RMSEV < 3.87 mg/kg) and for discriminant models in WF classification with 100% accuracy. In summary, NIR technology may be sufficient if visualization is not required.

Pathway analysis of clinical nurse educator's intention to use virtual reality technology based on the UTAUT model.

This study aims to investigate the willingness of clinical nurse educator to adopt virtual reality technology, while also examining the underlying mechanisms that influence this willingness through the lens of the Unified Theory of Acceptance and Use of Technology (UTAUT). A convenience sampling method was employed to select 225 clinical nurse educator, all of whom possess a professional qualification certificate as nurse practitioners, from a tertiary hospital in Wuhan City, Hubei Province. The study utilized an adapted UTAUT model theory-based design to develop several questionnaires: the performance expectancy questionnaire (11 items), the effort expectancy questionnaire (4 items), the social influence questionnaire (6 items), the facilitating conditions questionnaire (7 items), and the behavioral intention questionnaire (4 items). These instruments were designed to assess the clinical nurse educators' willingness to adopt VR technology. Furthermore, a regression model was established to analyze the factors influencing this willingness, utilizing SPSS 26.0 for statistical analysis and validating the model through path analysis with AMOS 24.0, where a p-value of less than 0.05 was considered statistically significant. The questionnaire demonstrated strong reliability and validity, yielding a total of 222 valid samples, comprising 209 females (94.14%) and 13 males (5.86%). Among the clinical nurse educators, 163 (73.42%) reported a willingness to use virtual reality technology, with scores of 4 or higher. Pearson correlation analysis revealed positive correlations between performance expectancy, effort expectancy, social influence, and facilitating conditions with behavioral intention (p < 0.05). Furthermore, regression analysis indicated that performance expectancy, effort expectancy, social influence, and facilitating conditions had a positive impact on behavioral intention (p < 0.05). The path model exhibited a good fit, and the results were consistent with the regression analysis, showing that the effects of performance expectancy, effort expectancy, and social influence on the behavioral intention to use virtual reality technology were 0.231, 0.150, 0.236, and 0.247, respectively. Clinical nurse educators exhibit a robust willingness to engage with VR technology. Moreover, improving factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions can substantially enhance their readiness to adopt this technology.

Image-based Chronic Kidney Disease Diagnosis Using 2D Convolutional Neural Networks in the Context of a Comprehensive Artificial Intelligence-Driven Healthcare System

Reports published by the World Health Organization (WHO) indicate that noncommunicable diseases (NCDs) including chronic kidney disease (CKD) are among the top ten causes of mortality worldwide. Accurate and early diagnosis of chronic kidney disease could save lives, ameliorate deleterious effects and dramatically improve quality of life. This paper presents a system that harnesses convolutional neural networks (CNNs) that could be incorporated into a comprehensive artificial intelligence (AI)-driven healthcare system for the automated diagnosis of chronic kidney disease. Utilizing publicly available image datasets featuring images representing normal kidney states, cysts, tumors and kidney stones split into training and validation samples, the system achieves an accuracy approximating 97% on the training and validation datasets.

PICZL : Image-based photometric redshifts for AGN

Computing reliable photometric redshifts (photo-z) for active galactic nuclei (AGN) is a challenging task, primarily due to the complex interplay between the unresolved relative emissions associated with the supermassive black hole and its host galaxy. Spectral energy distribution (SED) fitting methods, while effective for galaxies and AGN in pencil-beam surveys, face limitations in wide or all-sky surveys with fewer bands available, lacking the ability to accurately capture the AGN contribution to the SED, hindering reliable redshift estimation. This limitation is affecting the many tens of millions of AGN detected in existing datasets, such as those AGN clearly singled out and identified by SRG/eROSITA. Our goal is to enhance photometric redshift performance for AGN in all-sky surveys while simultaneously simplifying the approach by avoiding the need to merge multiple data sets. Instead, we employ readily available data products from the 10th Data Release of the Imaging Legacy Survey for the Dark Energy Spectroscopic Instrument, which covers &gt; 20,000 deg of extragalactic sky with deep imaging and catalog-based photometry in the grizW1-W4 bands. We fully utilize the spatial flux distribution in the vicinity of each source to produce reliable photo-z. We introduce PICZL a machine-learning algorithm leveraging an ensemble of convolutional neural networks. Utilizing a cross-channel approach, the algorithm integrates distinct SED features from images with those obtained from catalog-level data. Full probability distributions are achieved via the integration of Gaussian mixture models. On a validation sample of 8098 AGN PICZL achieves an accuracy $ NMAD $ of 4.5&lt;!PCT!&gt; with an outlier fraction eta of 5.6&lt;!PCT!&gt;. These results significantly outperform previous attempts to compute accurate photo-z for AGN using machine learning. We highlight that the model's performance depends on many variables, predominantly the depth of the data and associated photometric error. A thorough evaluation of these dependencies is presented in the paper. Our streamlined methodology maintains consistent performance across the entire survey area, when accounting for differing data quality. The same approach can be adopted for future deep photometric surveys such as LSST and Euclid, showcasing its potential for wide-scale realization. With this paper, we release updated photo-z (including errors) for the XMM-SERVS W-CDF-S, ELAIS-S1 and LSS fields.

Influence of Insect-Based Diets on Rural Leisure, Tourism, and Public Consumption-A Case Study from Yunnan, China.

This study analyzed the effects of insect-based diets on rural leisure activities, tourism, and consumption. A questionnaire survey was conducted, with 900 valid samples being obtained. The collected data were then analyzed through descriptive statistical analysis, a t-test, and a Pearson's product-moment correlation coefficient test by using the SPSS 26.0 statistical software. Subsequently, semistructured interviews were conducted to collect expert and public opinions on the analysis results. Finally, literature findings and the collected questionnaire and interview data were reviewed through multivariate validation analysis. According to the results, the public agrees that entomophagy culture and insect cuisine can enhance leisure and tourism appeal. Entomophagy culture can also stimulate rural economic, social, and environmental development as well as rural consumption. However, modern public infrastructure and medical facilities are still required to increase tourism accessibility. Local governments must improve public sanitation, moral standards, and the overall quality of tourism and living environments. They must also attract corporate investment and cultivate entrepreneurship among residents to develop new insect-based tourism and leisure activities, food, and products. These measures can enhance the influence of insects on the sustainable development of leisure activities, tourism, and economies in rural regions.

Adaptation and validation of the Adolescent Psychotic-like Symptom Screener (APSS-6) in Spain: evidence from adult population

BackgroundPsychotic-like experiences are common in the general population and an important indicator of psychological vulnerability. One of the used instruments for their assessment is the Adolescent Psychotic-Like Symptom Screener (APSS). However, there are few studies on the APSS psychometric characteristics in different cultural contexts. The aim of this study is to adapt the instrument in the Spanish context and analyze its psychometric properties in an adult population.MethodsUpon the translation and adaptation of the APSS to Spanish, a sample of 287 participants (19–60 years) completed the questionnaire online together with the Symptom Assessment-45 Questionnaire (SA-45) and an ad-hoc survey on clinical and sociodemographic variables. The sample was then randomly split into two halves, the first being the calibration (n = 144) and the second the validation sample (n = 143). An exploratory factor analysis was performed with the former, and a confirmatory factor analysis was performed with the latter, together with internal consistency and convergent validity analyses.ResultsThe APSS Spanish version presents a unifactorial structure comprising 6 out of the 7 original items with adequate fit and good internal consistency. This single factor structure is invariant across age, sex and history of self-reported psychopathology. The instrument also shows significant positive correlations with the SA-45 psychoticism and paranoid ideation subscales.ConclusionsTo the best of our knowledge, this is the first study that has conducted a psychometric analysis of the APSS in adult population. The Spanish version of the APSS shows adequate reliability, construct and convergent validity in adults, therefore provides a handy tool to be used for the screening of psychotic-like experiences in the Spanish general population.

Development of a Symptom Validity Index for the Beck Anxiety Inventory

Objective: Anxiety disorders are the most prevalent psychiatric disorders experienced by individuals in the United States, and anxiety is often assessed with anxiety symptom inventories. At present, though, there are no anxiety symptom inventories that include symptom validity indices. The present study sought to develop a symptom validity index for the most commonly used anxiety symptom inventory in neuropsychological practice, the Beck Anxiety Inventory (BAI). Method: A sample of 244 veterans seeking outpatient neuropsychological assessments were included in the study. Participants were divided into valid and invalid groups based on external criterion symptom validity tests. The valid participants were then divided into clinical subgroups (Current Anxiety, Lifetime Anxiety, and No Anxiety). A validity index derived from the BAI total score was examined for the total sample and all subgroups. Results: A cutoff of ≥ 29 was identified when utilizing the Valid Full Sample, which resulted in a 0.91 specificity rate and 0.54 sensitivity rate. However, cutoffs had to be adjusted when applied to the clinical subgroups. The Valid Current Anxiety subgroup required the largest cutoff increase (i.e. ≥ 36), which resulted in a 0.91 specificity rate and a 0.42 sensitivity rate. Conclusions: This is the first published article to develop a symptom validity index for the BAI. To minimize false positive errors, a BAI total score of ≥ 36 is recommended.

Abstract 4119992: 12-lead electrocardiograms predict adverse cardiovascular outcomes of emergency department patients

Introduction: Chest pain is a common cause for presentation to the emergency department (ED). Identifying patients at increased risk of adverse cardiovascular outcomes may help guide testing. Aims: We sought to determine if deep learning enabled 12-lead electrocardiogram (ECG) analysis may improve prediction of adverse cardiovascular outcomes of ED patients presenting with chest pain compared to conventional approaches. Methods: A pretrained convolutional neural network was applied to derive numerical representations of the index ECG waveforms from patients presenting to the Brigham and Women’s Hospital (BWH) ED with chest pain. We trained a neural network model (‘CP-AI’) to predict 30-day major adverse cardiovascular events (MACE) defined as myocardial infarction (MI), pulmonary embolism (PE), aortic dissection (AD), or mortality using age, sex, cardiac biomarkers (troponin and d-dimer), and the numerical ECG representations as inputs ( Figure 1 ). In a holdout sample of Massachusetts General Hospital (MGH) ED patients, we compared the performance of the CP-AI model to alternative models fit on (1) demographics (‘Age and Sex’) (2) demographics + cardiac biomarkers (‘Biomarker Model’), and (3) demographics + numerical ECG representations (‘ECG Model’). Results: The BWH derivation sample included 18,811 individuals (51% female, mean age 65 ± 16 years). The MGH validation sample included 14,476 individuals (45% female, mean age 65 ± 15 years). The CP-AI model significantly outperformed the comparison models for prediction of 30-day MACE with an area under the receiver operating characteristic curve (AUROC) of 0.82 [95% CI 0.81-0.83] ( Figure 1 ). CP-AI also significantly outperformed the comparison models for classification of the 30-day MACE components including MI (AUROC 0.91 [95% CI 0.90-0.92]), PE (AUROC 0.74 [95% CI 0.72-0.76]), and mortality (AUROC 0.86 [95% CI 0.85-0.87]), but not aortic dissection (0.71 [95% CI 0.68-0.75]). Conclusions: The integration of deep learning ECG analysis with conventional assessment improved prediction of adverse cardiovascular of ED patients presenting with chest pain.

DNA methylation classifier to diagnose pancreatic ductal adenocarcinoma metastases from different anatomical sites

BackgroundWe have recently constructed a DNA methylation classifier that can discriminate between pancreatic ductal adenocarcinoma (PAAD) liver metastasis and intrahepatic cholangiocarcinoma (iCCA) with high accuracy (PAAD-iCCA-Classifier). PAAD is one of the leading causes of cancer of unknown primary and diagnosis is based on exclusion of other malignancies. Therefore, our focus was to investigate whether the PAAD-iCCA-Classifier can be used to diagnose PAAD metastases from other sites.MethodsFor this scope, the anomaly detection filter of the initial classifier was expanded by 8 additional mimicker carcinomas, amounting to a total of 10 carcinomas in the negative class. We validated the updated version of the classifier on a validation set, which consisted of a biological cohort (n = 3579) and a technical one (n = 15). We then assessed the performance of the classifier on a test set, which included a positive control cohort of 16 PAAD metastases from various sites and a cohort of 124 negative control samples consisting of 96 breast cancer metastases from 18 anatomical sites and 28 carcinoma metastases to the brain.ResultsThe updated PAAD-iCCA-Classifier achieved 98.21% accuracy on the biological validation samples, and on the technical validation ones it reached 100%. The classifier also correctly identified 15/16 (93.75%) metastases of the positive control as PAAD, and on the negative control, it correctly classified 122/124 samples (98.39%) for a 97.85% overall accuracy on the test set. We used this DNA methylation dataset to explore the organotropism of PAAD metastases and observed that PAAD liver metastases are distinct from PAAD peritoneal carcinomatosis and primary PAAD, and are characterized by specific copy number alterations and hypomethylation of enhancers involved in epithelial-mesenchymal-transition.ConclusionsThe updated PAAD-iCCA-Classifier (available at https://classifier.tgc-research.de/) can accurately classify PAAD samples from various metastatic sites and it can serve as a diagnostic aid.