Accurate Discrimination Research Articles

The value of radiomics features of the spleen as surrogates for differentiating subtypes of common pediatric lymphomas.

Lymphoma is a common malignant tumor in children. The pathologic subtyping of lymphoma is high complex, and the treatment options vary. The different pathologic subtypes of lymphomas have no significant differences on computed tomography (CT) images. As it is a hematologic disease, patients with lymphoma often show abnormalities in the spleen, and so the aim of this study was to construct a model for differentiating Burkitt lymphoma (BL) from lymphoblastic lymphoma through the extraction of radiomic features of the spleen from CT images. This could provide an efficient, noninvasive method that can differentiate the common pathological subtypes in patients with pediatric lymphoma. The clinical data and imaging data of 48 patients with lymphoblastic lymphoma and 61 patients with BL were retrospectively analyzed. The dataset was divided into a training set (n=76) and a test set (n=33) through complete randomization. Radiomics features of the spleen were separately extracted from CT images in the noncontrast enhanced, arterial, and venous phases. These phase-specific features were integrated to construct fusion models. Three classifiers, quadratic discriminant analysis (QDA), logistic regression (LR), and support vector machine (SVM), were employed to build the models. The fusion model exhibited superior performance compared to individual models. There was no significant difference between the fusion models constructed by QDA and LR in either the training set or the test set. Among the four fusion models constructed with the SVM classifier, SVM_4 emerged as the best performing model. The area under the curve, sensitivity, specificity, and F1-score of the SVM_4 model were 0.967 [95% confidence interval (CI): 0.935-0.998], 0.86, 0.97, and 0.913 in the training set, respectively, and 0.754 (95% CI: 0.584-0.924), 0.611, 0.867, and 0.71 in the test set, respectively. The radiomics features of the spleen demonstrated the capability to distinguish between the two most common lymphoma subtypes in pediatric patients. This noninvasive approach holds promise for efficient and accurate discrimination.

Predictive value of spectral dual-detector computed tomography for PD-L1 expression in stage I lung adenocarcinoma: development and validation of a novel nomogram.

Programmed death ligand-1 (PD-L1) expression serves a predictive biomarker for the efficacy of immune checkpoint inhibitors (ICIs) in the treatment of patients with early-stage lung adenocarcinoma (LA). However, only a limited number of studies have explored the relationship between PD-L1 expression and spectral dual-layer detector-based computed tomography (SDCT) quantification, qualitative parameters, and clinical biomarkers. Therefore, this study was conducted to clarify this relationship in stage I LA and to develop a nomogram to assist in preoperative individualized identification of PD-L1-positive expression. We analyzed SDCT parameters and PD-L1 expression in patients diagnosed with invasive nonmucinous LA through postoperative pathology. Patients were categorized into PD-L1-positive and PD-L1-negative expression groups based on a threshold of 1%. A retrospective set (N=356) was used to develop and internally validate the radiological and biomarker features collected from predictive models. Univariate analysis was employed to reduce dimensionality, and logistic regression was used to establish a nomogram for predicting PD-L1 expression. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curves, and external validation was performed in an independent set (N=80). The proportions of solid components and pleural indentations were higher in the PD-L1-positive group, as indicated by the computed tomography (CT) value, CT at 40 keV (CT40keV; a/v), electron density (ED; a/v), and thymidine kinase 1 (TK1) exhibiting a positive correlation with PD-L1 expression. In contrast, the effective atomic number (Zeff; a/v) showed a negative correlation with PD-L1 expression [r=-0.4266 (Zeff.a), -0.1131 (Zeff.v); P<0.05]. After univariate analysis, 18 parameters were found to be associated with PD-L1 expression. Multiple regression analysis was performed on significant parameters with an area under the curve (AUC) >0.6, and CT value [AUC =0.627; odds ratio (OR) =0.993; P=0.033], CT40keV.a (AUC =0.642; OR =1.006; P=0.025), arterial Zeff (Zeff.a) (AUC =0.756; OR =0.102; P<0.001), arterial ED (ED.a) (AUC =0.641; OR =1.158, P<0.001), venous ED (ED.v) (AUC =0.607; OR =0.864; P<0.001), TK1 (AUC =0.601; OR =1.245; P=0.026), and diameter of solid components (Dsolid) (AUC =0.632; OR =1.058; P=0.04) were found to be independent risk factors for PD-L1 expression in stage I LA. These seven predictive factors were integrated into the development of an SDCT parameter-clinical nomogram, which demonstrated satisfactory discrimination ability in the training set [AUC =0.853; 95% confidence interval (CI): 0.76-0.947], internal validation set (AUC =0.824; 95% CI: 0.775-0.874), and external validation set (AUC =0.825; 95% CI: 0.733-0.918). Decision curve analyses also revealed the highest net benefit for the nomogram across a broad threshold probability range (20-80%), with a clinical impact curve (CIC) indicating its clinical validity. Comparisons with other models demonstrated the superior discriminatory accuracy of the nomogram over any individual variable (all P values <0.05). Quantitative parameters derived from SDCT demonstrated the ability to predict for PD-L1 expression in early-stage LA, with Zeff.a being notably effective. The nomogram established in combination with TK1 showed excellent predictive performance and good calibration. This approach may facilitate the improved noninvasive prediction of PD-L1 expression.

Preoperative Modified Frailty Index-11 versus EuroSCORE II in Predicting Postoperative Mortality and Complications in Elderly Patients Who Underwent Elective Open Cardiac Surgery: A Retrospective Cohort Study

ObjectivesThis study aimed to compare the sensitivity, specificity, receiver operating characteristic (ROC), and area under the curve (AUC) using modified frailty index (mFI), EuroSCORE II, and combined mFI-11 and EuroSCORE II to predict in-hospital mortality and composite morbidities. DesignRetrospective cohort study SettingSongklanagarind Hospital, a tertiary care center in the Southern of Thailand. ParticipantsElderly patients aged ≥ 60 years who underwent elective open-heart surgical procedures on a pump between January 2017 and December 2022 were included. InterventionsROC curves were constructed to evaluate the discriminatory power of EuroSCORE II and mFI-11 for predicting in-hospital mortality and postoperative complications. Measurements and Main ResultsThe actual in-hospital mortality was 2.5 % for all patients. The discriminative accuracy of mFI-11, EuroSCORE II, and combined mFI-11 with EuroSCORE II for predicting in-hospital mortality was good, with respective AUCs of 0.733 (95% CI 0.6157–0.8499), 0.793 (0.6826–0.9026), and 0.78 (0.6686–0.893). The AUC of mFI-11 for predicting postoperative cardiac, respiratory, neurological, and renal complications was 0.558 (95% CI 0.5101–0.6063), 0.606 (0.5542–0.6581), 0.543 (0.4533–0.6337), and 0.652 (0.5859–0.7179), respectively, and that of EuroSCORE II was 0.553 (0.5038–0.6013), 0.631 (0.578–0.6836), 0.619 (0.5306–0.7076) and 0.702 (0.6378–0.7657), respectively. ConclusionFrailty (mFI-11) and EuroSCORE II demonstrated good discrimination in ROC analysis, with EuroSCORE II showing superior predictive accuracy for in-hospital mortality in elderly elective cardiac surgery patients. However, neither score independently predicted mortality in multiple logistic regression, nor did combining them enhance predictive power significantly. Furthermore, both scores were less effective in predicting postoperative complications.

Exploring perceptual grouping by proximity principle in multistable dot lattices: Dissociation between vision-for-perception and vision-for-action.

Perceptual grouping, a fundamental mechanism in our visual system, significantly influences our interpretation of and interaction with the surrounding world. This study explores the impact of the proximity principle from the perspective of the Two Visual Systems (TVS) model. The TVS model argues that the visual system comprises two distinct streams: the ventral stream, which forms the neural basis for "vision-for-perception," and the dorsal stream, which underlies "vision-for-action." We designed a perceptual grouping task using dot lattices as well as a line-orientation discrimination task. Data were collected using vocal and mouse methods for the vision-for-perception mode, and joystick and pen-paper methods for the vision-for-action mode. Each method, except for vocal, included separate blocks for right and left hands. The proximity data were fitted using exponential and power models. Linear mixed-effects models were used for the statistical analyses. The results revealed similar line-orientation discrimination accuracy across all conditions. The exponential model emerged as the best fit, demonstrating adherence to the Pure Distance Law in both perceptual modes. Sensitivity to the proximity principle was higher in the vision-for-action mode compared to the vision-for-perception. In terms of orientation biases, a strong preference for vertical orientation was observed in the vision-for-perception mode, whereas a noticeable preference toward either of the oblique orientations was detected in the vision-for-action mode. Analysis of free-drawn lines demonstrated an affordance bias in the vision-for-action mode. This suggests a remarkable tendency to perceive organizations within specific orientations that offer more affordances due to the interaction between the body postures and tools.

Specific Cytokines Analysis Incorporating Latency-Associated Antigens Differentiates Mycobacterium tuberculosis Infection Status: An Exploratory Study.

Current immunologic methods cannot distinguish Mycobacterium tuberculosis (Mtb) infection statuses, especially to discriminate active tuberculosis (ATB) from latent tuberculosis infection (LTBI). This study explored the potential of latency-associated antigens (Rv1733cSLP and Rv2028c) and multifactorial cytokine detection to distinguish tuberculosis infection states. ATB patients (20), LTBI healthcare workers (25), fever patients (11), and healthy controls (10) were enrolled. Cytokine levels (IFN-γ, TNF-α, IL-2, IL-6, IP-10, IL-1Ra, CXCL-1, and MCP-1) were measured using Luminex with/without MTB-specific virulence factor and latency-associated antigens stimulation. Without antigen stimulation, IL-6, IP-10, MCP-1, and IL-1Ra were higher in the ATB group than in the LTBI group (p<0.05), but no significant differences between the ATB group and the fever group. Stimulated with the four antigens, respectively, the cytokines, including IP-10Esat-6, IP-10CFP-10, IFN-γRv1733cSLP, IFN-γRv2028c, IL-6Esat-6, IL-6Rv1733cSLP, IL-6Rv2028c, IL-2Rv1733cSLP, IL-2 Rv2028c, IL-1RaEsat-6, IL-1RaCFP-10, IL-1RaRv2028c, CXCL-1Esat-6, CXCL-1CFP-10, CXCL-1Rv1733cSLP, CXCL-1Rv2028c, MCP-1Esat-6 and MCP-1CFP-10, demonstrated accurate discrimination between ATB and LTBI (p<0.05). Additive concentrations demonstrated significant secretion differences of IFN-γ, IP-10 and IL-2, primarily by virulence factors in ATB and latency-associated antigens in LTBI. Latency-associated antigens synergized with virulence factors, enhancing TH1-type cytokine diagnostic efficacy for discriminating ATB from LTBI, the AUC for TNF-α increased from 0.696 to 0.820 (p=0.038), IFN-γ increased from 0.806 to 0.962 (p=0.025), and IL-2 increased from 0.565 to 0.868 (p=0.007). Model selected by forward likelihood method indicated combined detection of IFN-γCFP-10, IFN-γRv1733cSLP, IP-10Rv1733cSLP, and CXCL-1Rv1733cSLP achieved ATB diagnosis (AUC=0.996) and ATB-LTBI differentiation (AUC=0.992). Combined detection of IFN-γCFP-10 and IFN-γRv1733cSLP achieved tuberculosis infection diagnosis (AUC=0.943). Latency-associated antigens enhance multiple cytokine discriminatory ability, particularly TH1-type cytokines, for differentiating Mtb infection statuses.

Validation of Variables for Use in Pediatric Obesity Risk Score Development in Demographically and Racially Diverse United States Cohorts

ObjectiveTo evaluate the performance of childhood obesity prediction models in four independent cohorts in the United States, using previously validated variables obtained easily from medical records as measured in different clinical settings. Study designData from four prospective cohorts, Latinx, Eating, and Diabetes (LEAD); Stress in Pregnancy Study (SIPS); Project Viva; and Center for the Health Assessment of Mothers and Children of Salinas (CHAMACOS) were used to test childhood obesity risk models and predict childhood obesity by ages 4 through 6, using five clinical variables (maternal age, maternal pre-pregnancy body mass index, birth weight Z-score, weight-for-age Z-score change, and breastfeeding), derived from a previously validated risk model and as measured in each cohort’s clinical setting. Multivariable logistic regression was performed within each cohort, and performance of each model was assessed based on discrimination and predictive accuracy. ResultsThe risk models performed well across all four cohorts, achieving excellent discrimination. The area under the receiver operator curve (AUROC) was 0.79 for CHAMACOS and Project Viva, 0.83 for SIPS, and 0.86 for LEAD. At a 50th percentile threshold, the sensitivity of the models ranged from 12 to 53%, and specificity was greater than or equal to 90%. The negative predictive values (NPV) were ≥ 80% for all cohorts, and the positive predictive values (PPV) ranged from 62-86%. ConclusionAll four risk models performed well in each independent and demographically diverse cohort, demonstrating the utility of these five variables for identifying children at high risk for developing early childhood obesity in the United States.

Changes in precipitation phases based on the multi-discrimination method in the Tibetan Plateau

Global warming is rapidly changing the precipitation structure, and the shift in precipitation phases is even more pronounced in the Tibetan Plateau (TP), a region sensitive to global climate change. The accuracy of precipitation phase discrimination is essential for studying precipitation variability. Based on the observed data from 112 meteorological stations in the study area obtained from the China Meteorological Administration (CMA) and the National Climatic Data Center (NCDC) before 1980, we compared and assessed six methods for distinguishing precipitation phases and the optimal method was determined to complete the precipitation phase data after 1980 in the study area. Subsequently, the changes in precipitation amount, intensity, and frequency were analyzed by classifying them into light, moderate, heavy, and extreme precipitation using the percentile threshold discrimination method. It found that the dynamic threshold temperature method (presented by Ding et al., 2014) was the most effective to distinguish precipitation phases in the TP, and its performance was further improved from 80.9% to 86.1% after using frequency intersection and probability guarantee methods. Precipitation was mainly concentrated in July. Meanwhile, the proportion of snowfall to total precipitation (S/P) was approximately 9%. From 1961 to 2020, the amount of total precipitation, rainfall and snowfall in the TP exhibited increasing trends, while the sleet demonstrated a persistent decline. Spatial and temporal heterogeneity existed in the changes of precipitation of different phases. The average single precipitation amount increased. Moreover, the changes in the amount and frequency of extreme precipitation were more pronounced. Increased frequency and amount of both rainfall and snowfall were distributed in the central and eastern parts of the study area with a more significant proportion. In contrast, the peripheral areas witnessed a decrease. The spatial distribution of frequency and amount of different precipitation intensities was comparable to that of the total precipitation. This study offers novel procedure into changes of precipitation phases at high altitudes, which will inform future research on climate change and water resource management.

Effect of segmentation dimension on radiomics analysis for MGMT promoter methylation status in gliomas

Introduction and objective: We investigated the impact of 2D (2D_seg) and 3D (3D_seg) segmentation on the accuracy of prediction models in the radiomics analysis to determine the presence or absence of methylation in the O6-methylguanine DNA methyltransferase (MGMT) gene promoter region of gliomas. Materials and methods: Magnetic resonance imaging images of gliomas were obtained from the Cancer Imaging Archive for 50 methylated and 50 unmethylated cases respectively. For each case, 2D_seg and 3D_seg were performed, and 788 radiomics features, including wavelet transform, were obtained. Ten features were selected by LASSO regression. The coefficients of determination (R2) and root mean squared error (RMSE) were calculated by multiple regression analysis. Discriminant boundaries to discriminate methylation were created by linear discriminant analysis, and the sensitivity and specificity of each method were calculated. The discriminant accuracy of both methods was evaluated by receiver operating characteristics (ROC) analysis. Results: The R2 value and RMSE were 0.72/0.28 and 0.73/0.33 for 2D_seg and 3D_seg, respectively. Similarly, sensitivity and specificity were 82.5/67.5% and 85/62.5%, respectively. The area under the curve determined by ROC analysis was 0.80 and 0.79, respectively, i.e. slightly larger for 2D_seg. The p-value by the DeLong method was 0.73. Conclusions: In the radiomics analysis using 2D_seg and 3D_seg, no difference in discriminant accuracy was observed between them. Therefore, 2D segmentation should be chosen because it is easier to segment.

Design and Application of an Onboard Particle Identification Platform Based on Convolutional Neural Networks

Space radiation particle detection plays a crucial role in scientific research and engineering practice, especially in particle species identification. Currently, commonly used in-orbit particle identification techniques include telescope methods, electrostatic analysis time of flight (ESA × TOF), time-of-flight energy (TOF × E), and pulse shape discrimination (PSD). However, these methods usually fail to utilize the full waveform information containing rich features, and their particle identification results may be affected by the random rise and fall of particle deposition and noise interference. In this study, a low-latency and lightweight onboard FPGA real-time particle identification platform based on full waveform information was developed utilizing the superior target classification, robustness, and generalization capabilities of convolutional neural networks (CNNs). The platform constructs diversified input datasets based on the physical features of waveforms and uses Optuna and Pytorch software architectures for model training. The hardware platform is responsible for the real-time inference of waveform data and the dynamic expansion of the dataset. The platform was utilized for deep learning training and the testing of the historical waveform data of neutron and gamma rays, and the inference time of a single waveform takes 4.9 microseconds, with an accuracy rate of over 97%. The classification expectation FOM (figure-of-merit) value of this CNN model is 133, which is better than the traditional pulse shape discrimination (PSD) algorithm’s FOM value of 0.8. The development of this platform not only improves the accuracy and efficiency of space particle discrimination but also provides an advanced tool for future space environment monitoring, which is of great value for engineering applications.

Validation of Lung Ultrasound for Coronavirus Disease 2019 Prognostication in an International Multicenter Cohort Study.

Despite many studies evaluating lung ultrasound (LUS) for coronavirus disease 2019 (COVID-19) prognostication, the generalizability and utility across clinical settings are uncertain. Adults (≥18 years of age) with COVID-19 were enrolled at 2 military hospitals, an emergency department, home visits, and a homeless shelter in the United States, and in a referral hospital in Uganda. Participants had a 12-zone LUS scan performed at time of enrollment and clips were read off-site. The primary outcome was progression to higher level of care after the ultrasound scan. We calculated the cross-validated area under the curve for the validation cohort for individual LUS features. We enrolled 191 participants with COVID-19 (57.9% female; median age, 45.0 years [interquartile range, 31.5-58.0 years]). Nine participants clinically deteriorated. The top predictors of worsening disease in the validation cohort measured by cross-validated area under the curve were B-lines (0.88 [95% confidence interval {CI}, .87-.90]), discrete B-lines (0.87 [95% CI, .85-.88]), oxygen saturation (0.82 [95%, CI, .81-.84]), and A-lines (0.80 [95% CI, .78-.81]). In an international multisite point-of-care ultrasound cohort, LUS parameters had high discriminative accuracy. Ultrasound can be applied toward triage across a wide breadth of care settings during a pandemic.

Colorimetric sensor array for identifying antioxidants based on pyrolysis-free synthesis of Fe–N/C single-atom nanozymes

Iron-anchored nitrogen/doped carbon single-atom nanozymes (Fe–N/C), which possess homogeneous active sites and adjustable catalytic environment, represent an exemplary model for investigating the structure-function relationship and catalytic activity. However, the development of pyrolysis-free synthesis technique for Fe–N/C with adjustable enzyme-mimicking activity still presents a significant challenge. Herein, Fe–N/C anchored three carrier morphologies were created via a pyrolysis-free approach by covalent organic polymers. The peroxidase-like activity of these Fe–N/C nanozymes was regulated via the pores of the anchored carrier, resulting in varying electron transfer efficiency due to disparities in contact efficacy between substrates and catalytic sites within diverse microenvironments. Additionally, a colorimetric sensor array for identifying antioxidants was developed: (1) the Fe–N/C catalytically oxidized two substrates TMB and ABTS, respectively; (2) the development of a colorimetric sensor array utilizing oxTMB and oxABTS as sensing channels enabled accurate discrimination of antioxidants such as ascorbic acid (AsA), glutathione (GSH), cysteine (Cys), gallic acid (GA), and caffeic acid (CA). Subsequently, the sensor array underwent rigorous testing to validate its performance, including assessment of antioxidant mixtures and individual antioxidants at varying concentrations, as well as target antioxidants and interfering substances. In general, the present study offered valuable insights into the active origin and rational design of nanozyme materials, and highlighting their potential applications in food analysis.

Development of a deep learning model for cancer diagnosis by inspecting cell-free DNA end-motifs

Accurate discrimination between patients with and without cancer from cfDNA is crucial for early cancer diagnosis. Herein, we develop and validate a deep-learning-based model entitled end-motif inspection via transformer (EMIT) for discriminating individuals with and without cancer by learning feature representations from cfDNA end-motifs. EMIT is a self-supervised learning approach that models rankings of cfDNA end-motifs. We include 4606 samples subjected to different types of cfDNA sequencing to develop EIMIT, and subsequently evaluate classification performance of linear projections of EMIT on six datasets and an additional inhouse testing set encopassing whole-genome, whole-genome bisulfite and 5-hydroxymethylcytosine sequencing. The linear projection of representations from EMIT achieved area under the receiver operating curve (AUROC) values ranged from 0.895 (0.835–0.955) to 0.996 (0.994–0.997) across these six datasets, outperforming its baseline by significant margins. Additionally, we showed that linear projection of EMIT representations can achieve an AUROC of 0.962 (0.914–1.0) in identification of lung cancer on an independent testing set subjected to whole-exome sequencing. The findings of this study indicate that a transformer-based deep learning model can learn cancer-discrimative representations from cfDNA end-motifs. The representations of this deep learning model can be exploited for discriminating patients with and without cancer.

Logging Lithology Discrimination with Enhanced Sampling Methods for Imbalance Sample Conditions

In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is of great significance to precise fine-scale reservoir characterization. This study employed data over-sampling and under-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN) to process well logging dataset. To achieve automatic and precise lithology discrimination on enhanced sampled well logging dataset, support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were trained using cross-validation and grid search methods. Aimed to objectively evaluate the performance of different models on different sampling results from multiple perspectives, the lithology discrimination results were evaluated and compared based on the Jaccard index and F1 score. By comparing the predictions of eighteen lithology discrimination workflows, a new discrimination process containing ADASYN, ENN, and RF has the most precise lithology discrimination result. This process improves the discrimination accuracy of fine-scale reservoir interval lithology, has great generalization ability, and is feasible in a variety of different geological environments.

Detection of cytotoxic effects of carboplatin in human nasopharyngeal carcinoma epithelioid cell line CNE-1 by Raman spectroscopy combined with multivariate statistical analysis

Raman spectroscopy combined with multivariate analysis was used to examine the molecular mechanism of carboplatin toxicity in human nasopharyngeal carcinoma epithelioid cell line CNE-1. Multivariate analysis was performed through principal component analysis combined with linear discriminant analysis, through which a diagnostic algorithm that could discriminate between the control and treatment groups was generated. Compared with the control group (0 μmol/L, 0 hour), the 2 protein Raman peaks (1002 cm−1 and 1659 cm−1) and the 2 deoxyribonucleic acid Raman peaks (1303 cm−1 and 1338 cm−1) in the treatment group decreased with increasing carboplatin concentrations as well as with increasing treatment durations. Using principal component analysis combined with linear discriminant analysis, the discrimination accuracy, sensitivity, and specificity between the control group and the cell groups treated at 12 and 16 μmol/L carboplatin for 24 hours were all 100%, suggesting that 16 μmol/L is suitable as the therapeutic dosing. Using the same method, the discrimination accuracy, sensitivity, and specificity between the control group and the cell groups that were treated with 12 μmol/L carboplatin for 24 and 36 hours were all 100%, indicating that 24 hours could be suitable treatment duration. These results suggest that Raman spectroscopy combined with multivariate statistical analysis could be a useful tool for assessing carboplatin-induced cytotoxicity in human nasopharyngeal carcinoma cells.

Investigating the molecular mechanisms underlying the co-occurrence of Parkinson’s disease and inflammatory bowel disease through the integration of multiple datasets

Parkinson’s disease (PD) and inflammatory bowel disease (IBD) are chronic diseases affecting the central nervous system and gastrointestinal tract, respectively. Recent research suggests a bidirectional relationship between neurodegeneration in PD and intestinal inflammation in IBD. PD patients may experience gastrointestinal dysfunction over a decade before motor symptom onset, and IBD may increase the risk of developing PD. Despite the “gut-brain axis” concept, the underlying pathophysiological mechanisms of this potential association remain unclear. This study aimed to investigate the biological mechanisms of differentially expressed genes in PD and IBD using bioinformatics tools, providing novel insights into the co-diagnosis and treatment of these diseases. We constructed a gene marker for disease diagnosis and identified five important genes (BTK, NCF2, CRH, FCGR3A and SERPINA3). Through nomogram and decision tree analyses, we found that both the IBD and PD required only the expression levels of BTK and NCF2 for accurate discrimination. Additionally, small molecule drugs RO-90-7501 and MST-312 may be useful for the treatment of both IBD and PD. These findings offer new perspectives on the co-diagnosis and treatment of PD and IBD, and suggest that targeting BTK may be a promising therapeutic strategy for both diseases.

Can ChatGPT Fool the Match? Artificial Intelligence Personal Statements for Plastic Surgery Residency Applications: A Comparative Study.

Introduction: Personal statements can be decisive in Canadian residency applications. With the rise in AI technology, ethical concerns regarding authenticity and originality become more pressing. This study explores the capability of ChatGPT in producing personal statements for plastic surgery residency that match the quality of statements written by successful applicants. Methods: ChatGPT was utilized to generate a cohort of personal statements for CaRMS (Canadian Residency Matching Service) to compare with previously successful Plastic Surgery applications. Each AI-generated and human-written statement was randomized and anonymized prior to assessment. Two retired members of the plastic surgery residency selection committee from the University of British Columbia, evaluated these on a 0 to 10 scale and provided a binary response judging whether each statement was AI or human written. Statistical analysis included Welch 2-sample t tests and Cohen's Kappa for agreement. Results: Twenty-two personal statements (11 AI-generated by ChatGPT and 11 human-written) were evaluated. The overall mean scores were 7.48 (SD 0.932) and 7.68 (SD 0.716), respectively, with no significant difference between AI and human groups (P = .4129). The average accuracy in distinguishing between human and AI letters was 65.9%. The Cohen's Kappa value was 0.374. Conclusions: ChatGPT can generate personal statements for plastic surgery residency applications with quality indistinguishable from human-written counterparts, as evidenced by the lack of significant scoring difference and moderate accuracy in discrimination by experienced surgeons. These findings highlight the evolving role of AI and the need for updated evaluative criteria or guidelines in the residency application process.

Genetic Risk Stratification of Primary Open-Angle Glaucoma in Japanese Individuals

To assess the impact of genetic risk estimation for primary open-angle glaucoma (POAG) in Japanese individuals. Cross-sectional analysis. Genetic risk scores (GRSs) were constructed based on a genome-wide association study (GWAS) of POAG in Japanese people. A total of 3625 Japanese individuals, including 1191 patients and 2434 controls (Japanese Tohoku), were used for the model selection. We also evaluated the discriminative accuracy of constructed GRSs in a dataset comprising 1034 patients and 1147 controls (the Japan Glaucoma Society Omics Group [JGS-OG] and the Genomic Research Committee of the Japanese Ophthalmological Society [GRC-JOS]) and 1900 participants from a population-based study (Hisayama Study). We evaluated 2 types of GRSs: polygenic risk scores using the pruning and thresholding procedure and a GRS using variants associated with POAG in the GWAS of the International Glaucoma Genetics Consortium (IGGC). We selected the model with the highest areas under the receiver operating characteristic curve (AUC). In the population-based study, we evaluated the correlations between GRS and ocular measurements. Proportion of patients with POAG after stratification according to the GRS. We found that a GRS using 98 variants, which showed genome-wide significance in the IGGC, showed the best discriminative accuracy (AUC, 0.65). In the Japanese Tohoku, the proportion of patients with POAG in the top 10% individuals was significantly higher than that in the lowest 10% (odds ratio [OR], 6.15; 95% confidence interval [CI], 4.35-8.71). In the JGS-OG and GRC-JOS, we confirmed similar impact of POAG GRS (AUC, 0.64; OR [top vs. bottom decile], 5.81; 95% CI, 3.79-9.01). In the population-based study, POAG prevalence was significantly higher in the top 20% individuals of the GRS compared with the bottom 20% (9.2% vs. 5.0%). However, the discriminative accuracy was low (AUC, 0.56). The POAG GRS was correlated positively with intraocular pressure (r= 0.08: P= 4.0× 10-4) and vertical cup-to-disc ratio (r= 0.11; P= 4.0× 10-6). The GRS showed moderate discriminative accuracy for POAG in the Japanese population. However, risk stratification in the general population showed relatively weak discriminative performance. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Perception and adaptation of receptive prosody in autistic adolescents

A fundamental aspect of language processing is inferring others’ minds from subtle variations in speech. The same word or sentence can often convey different meanings depending on its tempo, timing, and intonation–features often referred to as prosody. Although autistic children and adults are known to experience difficulty in making such inferences, the science remains unclear as to why. We hypothesize that detail-oriented perception in autism may interfere with the inference process if it lacks the adaptivity required to cope with the variability ubiquitous in human speech. Using a novel prosodic continuum that shifts the sentence meaning gradiently from a statement (e.g., “It’s raining”) to a question (e.g., “It’s raining?”), we have investigated the perception and adaptation of receptive prosody in autistic adolescents and two groups of non-autistic controls. Autistic adolescents showed attenuated adaptivity in categorizing prosody, whereas they were equivalent to controls in terms of discrimination accuracy. Combined with recent findings in segmental (e.g., phoneme) recognition, the current results provide the basis for an emerging research framework for attenuated flexibility and reduced influence of contextual feedback as a possible source of deficits that hinder linguistic and social communication in autism.

Quality non-destructive sorting of large yellow croaker based on image recognition

The YOLOv7 algorithm was used to establish a fast and non-destructive quality identification model for large yellow croaker (Larimichthys crocea) in this study. The cross multi-head attention mechanism in the swin transformer was incorporated into the neck of YOLOv7 architecture to enhance the recognition performance. The freshness classification model based on the total volatile basic nitrogen value was evaluated by freshness indicators, visual features, and texture profile analysis (TPA). The findings indicated that the enhanced model achieved an accuracy rate of 98.6% in freshness classification, which was higher than 85.6% of the original model. Visual features (fish-eye plumpness and turbidity) were highly correlated with all freshness indexes (all above 0.8). The accuracy of freshness discrimination in different lighting environments was also greater than 90%. These results collectively indicate the potential for the eye region images to serve as a reliable indicator for the sorting of freshness in large yellow croakers.

Optimal bowel diameter thresholds for diagnosing small bowel obstruction and surgical intervention with point-of-care ultrasound

ObjectivesA bowel diameter threshold of ≥2.5 cm, originally derived from the research using computed tomography, is frequently used for diagnosing small bowel obstruction (SBO) with point-of-care ultrasound (POCUS). We sought to determine the optimal bowel diameter threshold for diagnosing SBO using POCUS and its accuracy in predicting surgical intervention. MethodsWe conducted a secondary analysis using individual patient-level data from a previous systematic review on POCUS for SBO diagnosis across five academic EDs. Patient data were collected, including imaging results, surgical findings, and final diagnosis. The measured diameter of the small bowel using POCUS was recorded. ROC area under the receiver operating characteristic curves (AUC) were constructed to determine the optimal threshold for bowel diameter in predicting SBO diagnosis and surgical intervention. Subgroup analyses were performed based on sex and age. ResultsA total of 403 patients had individual patient-level data available, with 367 patients included in the final analysis. The most accurate bowel diameter overall for predicting SBO was 2.75 cm (AUC = 0.76, 95% CI 0.71–0.81). A bowel diameter of ≤1.7 cm had 100% sensitivity with no miss rate, while a bowel diameter of ≥4 cm had 90.7% specificity in confirming SBO. Patients under 65 had an optimal threshold of 2.75 cm versus 2.95 cm in patients over 65. Females had an optimal threshold of 2.75 cm, while males had a value of 2.95 cm. There was no significant correlation between bowel diameter thresholds and surgical intervention. ConclusionA bowel diameter threshold of 2.75 cm on POCUS is more discriminative diagnostic accuracy for diagnosing SBO. Patients' age and sex may impact diagnostic accuracy, suggesting that tailored approaches may be needed.