High Discrimination Ability Research Articles

A generalised computer vision model for improved glaucoma screening using fundus images.

Worldwide, glaucoma is a leading cause of irreversible blindness. Timely detection is paramount yet challenging, particularly in resource-limited settings. A novel, computer vision-based model for glaucoma screening using fundus images could enhance early and accurate disease detection. To develop and validate a generalised deep-learning-based algorithm for screening glaucoma using fundus image. The glaucomatous fundus data were collected from 20 publicly accessible databases worldwide, resulting in 18,468 images from multiple clinical settings, of which 10,900 were classified as healthy and 7568 as glaucoma. All the data were evaluated and downsized to fit the model's input requirements. The potential model was selected from 20 pre-trained models and trained on the whole dataset except Drishti-GS. The best-performing model was further trained to classify healthy and glaucomatous fundus images using Fastai and PyTorch libraries. The model's performance was compared against the actual class using the area under the receiver operating characteristic (AUROC), sensitivity, specificity, accuracy, precision and the F1-score. The high discriminative ability of the best-performing model was evaluated on a dataset comprising 1364 glaucomatous discs and 2047 healthy discs. The model reflected robust performance metrics, with an AUROC of 0.9920 (95% CI: 0.9920-0.9921) for both the glaucoma and healthy classes. The sensitivity, specificity, accuracy, precision, recall and F1-scores were consistently higher than 0.9530 for both classes. The model performed well on an external validation set of the Drishti-GS dataset, with an AUROC of 0.8751 and an accuracy of 0.8713. This study demonstrated the high efficacy of our classification model in distinguishing between glaucomatous and healthy discs. However, the model's accuracy slightly dropped when evaluated on unseen data, indicating potential inconsistencies among the datasets-the model needs to be refined and validated on larger, more diverse datasets to ensure reliability and generalisability. Despite this, our model can be utilised for screening glaucoma at the population level.

Development of a Short-Form Hwa-Byung Symptom Scale Using Machine Learning Approaches.

Background/Objectives: Hwa-byung (HB), also known as "anger syndrome" or "fire illness", is a culture-bound syndrome primarily observed among Koreans. This study aims to develop a short-form version of the HB symptom scale using machine learning approaches. Methods: Utilizing exploratory factor analysis (EFA) and various machine learning techniques (i.e., XGBoost, Logistic Regression, Random Forest, Support Vector Machine, Decision Tree, and Multi-Layer Perceptron), we sought to create an efficient HB assessment tool. A survey was conducted on 500 Korean adults using the original 15-item HB symptom scale. Results: The EFA revealed two distinct factors: psychological symptoms and somatic manifestations of HB. Statistical testing showed no significant differences between using different numbers of items per factor (ANOVA: F = 0.8593, p = 0.5051), supporting a minimalist approach with one item per factor. The resulting two-item short-form scale (Q3 and Q10) demonstrated high predictive power for the presence of HB. Multiple machine learning models achieved a consistent accuracy (90.00% for most models) with high discriminative ability (AUC = 0.9436-0.9579), with the Multi-Layer Perceptron showing the highest performance (AUC = 0.9579). The models showed balanced performance in identifying both HB and non-HB cases, with precision and recall values consistently around 0.90. Conclusions: The findings of this study highlighted the effectiveness of integrating EFA and artificial intelligence via machine learning in developing practical assessment tools. This study contributes to advancing methodological approaches for scale development and offers a model for creating efficient assessments of Korean medicine.

Clinical validated risk prediction model for development of heart failure after contemporary breast cancer treatment

Abstract Background Patients receiving breast cancer (BC) treatment are at risk for heart failure and cardiomyopathy (HF/CM). European Society of Cardiology (ESC) cardio-oncology guidelines recommend baseline cardiovascular (CV) assessment, however, there is limited data about cardiotoxicity risk specific to patients with BC. Our aim was to develop and validate model for prediction of HF/CM in women receiving contemporary treatment for invasive early-stage BC. Methods We assembled a cohort women diagnosed with Stage I-III BC from 2008-2020 and followed through 2021 in a large health care plan. Women age >80, with history of HF/CM, and women who received both anthracyclines and trastuzumab were excluded, leaving N=26,044 for analysis. Study cohort was randomly split into derivation (60%) and validation (40%) datasets. Elastic-net penalized Cox proportional hazard model was created using the derivation dataset to select risk factors and to calculate the panelized coefficient which defined the HF/CM risk score. Risk score system was assessed by Hosmer-Lemeshow goodness-of-fit test and area under the ROC curve (AUC). Event rates of <5% constituted low risk; 5-14% moderate; and >15% high risk category. Incident HF/CM events were examined using Kaplan-Meier approach in derivation and validation datasets. Results HF/CM risk score had high discrimination ability and sufficiently good model of fit regarding HF/CM events (P value=0.163 and 0.667 from Hosmer–Lemeshow test;AUC 0.751 and 0.764, in derivation and validation sets, respectively). There were 10% of women in the high risk; 30% in moderate; and 60% in low-risk group. Variables associated with the higher HF/CM risk score included age, receipt of anthracycline-based chemotherapy or anti-HER2 therapy, presence of hypertension, diabetes, or history of other CV conditions at baseline. The score pattern showed major effect of age, with anthracycline and anti-HER2 therapy increasing the risk across all age groups (Figure 1). Kaplan-Meier analysis of incident HF/CM over 14 years after BC diagnosis showed significantly lower number of HF/CM events in low risk compared to moderate and high-risk score groups (p<0.0001, Figure 2). Conclusion Our robust risk prediction model for HF/CM among women with early-stage BC provides a new tool to identify women at higher CV risk and inform cardiac function monitoring during and after BC treatment. Our findings may guide further validation studies and build evidence for the cardiotoxicity risk stratification in patients with BC.Risk Score for HF/CM by Age/ChemoTxK-M curves of freedom from HF/CM

Evaluation of naples prognostic score to predict long-term mortality in patients with pulmonary embolism

Abstract Background/Introduction APE mortality rates are still not satisfactory despite new diagnosis and treatment methods (1). Thus, risk classification is crucial due to the wide range of clinical presentations. Several risk stratifications have been developed such as simplified Pulmonary Embolism Severity Index (sPESI) (2). Nonetheless, there remains a need for an optimal indicator that can be conveniently measured with a high degree of precision to predict clinical outcomes. Naples prognostic score (NPS) is a newly defined index that is developed to determine the prognosis of cancer patients and reflects the inflammatory and nutritional status of the patients (3). Purpose The aim of our study was to determine the long-term prognostic value of NPS in APE patients. Methods Two hundred ninety-three patients diagnosed with APE between November 2016 and February 2022 were included in the study. NPS consists of 4 parameters: serum albumin, total cholesterol (TC) concentration, neutrophil-to-lymphocyte ratio (NLR) and lymphocyte-to-monocyte ratio (LMR). Naples scores of all patients were calculated and the patients were divided into 2 groups according to their NPS and their long-term mortality was compared. The primary endpoint of the study was long-term mortality. Results The long-term mortality was observed in 38 patients out of 293 patients in the mean follow-up of 24 (16-42) months. In the low-Naples risk group (group 1), there were 215 patients, while the high-Naples risk group (group 2) had 78 patients. Multivariate analysis showed that NPS as a categorical parameter (HR: 3.458, 95%CI:1.734-6.896, p<0.001), NPS as a numerical parameter, (HR:1.651, 95%CI:1.217-2.241, p=0.001) and simplified pulmonary embolism severity index (sPESI) (HR:1.887, 95%CI:1.223-2.911), p=0.004) score were independent predictors of long-term mortality. The receiver operating characteristic curve analysis showed that the model 2 with NPS as continuous had higher discriminative ability than the baseline model for detecting patients with mortality (Area under curve (AUC) values = 0.791 vs. 0.723, respectively, p=0.04) (Figure 1A). The long-term predictive capability of NPS was non-inferior than the sPESI score (Figure 1B). Conclusions The current study highlights that NPS, which reflects systemic inflammation and nutritional status may have potential to predict long-term mortality in APE patients. To our knowledge, this is the first study to show the relationship between long-term mortality and NPS in APE patients.

Bioinformatics investigation of the prognostic value and mechanistic role of CD9 in glioma

In recent years, CD9 has been extensively studied as a potential biomarker for cancer. However, the biological role of CD9 in gliomas remains unclear. This study investigates the function of CD9 in gliomas and its molecular mechanisms. Utilizing pan-cancer analysis with TCGA, CGGA, and GEO databases, differential expression of CD9 was observed in 11 tumor types within the TCGA cohort, and it was associated with patient survival rates. Analysis of the CGGA glioma database revealed that patients with high CD9 expression had lower survival rates. The area under the ROC curve (AUC) for GSE16011 was greater than 0.7, indicating a high discriminative ability. Through gene set enrichment analysis (GSEA), immune-related analysis, and CD9 mutation detection, CD9 was found to have the strongest correlation with neutrophil involvement (cor = 0.30, P < 0.05), and the high CD9 expression group exhibited higher rejection responses and TIDE scores, suggesting a lower likelihood of successful immunotherapy. The high CD9 expression group was more sensitive to 81 drugs, indicating potential therapeutic effects for gliomas. Furthermore, overexpression of CD9 in gliomas may be associated with gene mutations. Down-regulation or up-regulation of CD9 expression in the glioblastoma cell line LN229 showed that CD9 could positively regulate the migratory ability of LN229 cells. Further, several marker genes, such as VEGFR-2, TGF-β1, CASP1 and PI3K, were down regulated in CD9 knockdown cell lines and up regulated in CD9 overexpression cell lines, compared with control cell line. This study preliminarily explores the role of CD9 in gliomas and its prognostic value, providing new insights for personalized treatment strategies in glioma therapy.

Construction of a Risk Prediction Model for Ureteral Stricture after Ureteroscopic Holmium Laser Lithotripsy.

Introduction: To analyze the influencing factors of ureteral stenosis after ureteroscopic holmium laser lithotripsy. Methods: The clinical data of 427 patients treated with ureteroscopic holmium laser lithotripsy were selected, and the patients were divided into two groups based on the presence or absence of ureteral stenosis after the operation. Univariate and multivariate logistic regression were used to analyze the independent risk factors for postoperative ureteral stenosis, and R software and regression coefficients were used to construct a predictive model. Results: After a 1-year follow-up of 427 patients, 28 patients (6.56%) developed ureteral stenosis; univariate analysis showed that the occurrence of ureteral stenosis after subureteral holmium laser lithotripsy was related to stone diameter, stone incarceration, degree of hydronephrosis, holmium laser injury of mucosa, and operation time (p < 0.05); further logistic regression analysis showed that a large stone diameter, stone incarceration, and moderate to severe hydronephrosis were independent risk factors for ureteral stenosis after ureteroscopic holmium laser lithotripsy (p < 0.05); According to H-L deviation degree and area under receiver operating characteristic curve test, the results show that the model has high accuracy (χ2 = 2.475, p = 0.613) and differentiation (0.875 [95% confidence interval or CI: 0.817-0.919]), and the external verification of the nomogram prediction model was carried out by the verification group. The results showed that the prediction probability of the calibration curve was close to the actual probability and had a good consistency (area under the curve: 0.873 [95 CI: 0.822-0.914]). Conclusion: The established nomogram model exhibits high accuracy and discriminative ability. It can effectively identify high-risk groups, enabling timely prevention of ureteral stenosis and minimizing the risk of postoperative ureteral stenosis.

An analysis of the clinical significance of the TKI-resistant gene ZNF687 for hepatocellular carcinoma patients.

Novel treatments such as monotherapy and combined immunotherapy significantly extend overall survival (OS) for hepatocellular carcinoma (HCC) patients, but HCC is susceptible to treatment resistance during long-term therapy. The resistance mechanism to targeted drugs in HCC remains ambiguous, making research on HCC drug resistance targets crucial for the development of precision medicine. To investigate the transcriptional features, biological functions and potential clinical value of the tyrosine kinase inhibitor (TKI)-resistant gene ZNF687 in HCC. The TKI-resistant genes of HCC were identified using clustered regularly interspaced short palindromic repeats (CRISPR) in vitro screening. Then, the dependence of HCC cell lines on ZNF687 was investigated in silico. We collected global mRNA datasets of HCC tissue, integrated the mRNA expression characteristics of ZNF687 in HCC and explored the impact of ZNF687 on HCC patient prognoses using the Kaplan-Meier method (in silico). The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analyses were then conducted, and a connectivity map and molecular docking technology were applied to find the underlying agent opposing ZNF687. In vitro, the guide RNA corresponding to ZNF687 was weakly detected in HCC cells, and ZNF687 deficiency was found to inhibit growth in HCC cell lines. ZNF687 mRNA was overexpressed and had a high discriminatory ability for HCC in 2,975 HCC samples, contrasting with 2,340 non-HCC samples. Moreover, an excessive ZNF687 transcript level was related to a worse overall survival (OS) prognosis. Histone modification, spliceosome, transcription coregulator activity, and nucleocytoplasmic transport were the most significant pathways for ZNF687 differential-related gene enrichment. Chaetocin was found to be a candidate compound and presented a strong affinity to ZNF687. ZNF687 represents a TKI-resistant and growth-dependent gene for HCC, the overexpression of which indicates poor OS for HCC patients. Additionally, ZNF687 is expected to be a druggable target for overcoming TKI resistance, and chaetocin may be a candidate therapeutic compound for ZNF687.

Development and validation of a hierarchical approach for lymphoma classification using immunohistochemical markers.

Accurate lymphoma classification is critical for effective treatment and immunohistochemistry is a cost-effective and time-saving approach. Although several machine learning algorithms showed effective results, they focused on a specific task of classification but not the whole classification workflow, thus impractical to be applied in clinical settings. Thus, we aim to develop an effective and economic machine learning-assisted system that can streamline the lymphoma differential diagnostic workflow using EBER insitu hybridization and immunohistochemical markers. We included pathological reports diagnosed as lymphomas from two cancer centers (Sun Yat-sen University Cancer Center and Peking University Cancer Hospital & Institute). We proposed a hierarchical approach that mimicked the human diagnostic process and employed simplified panels of markers to perform a series of interpretable classification. The diagnostic accuracy for lymphoma pathological subtypes and the markers saving ratio were investigated in both temporal independent population and external medical center. A total of 14,927 patients and corresponding immunohistochemical results from two cancer centers were included. The proposed system had high discriminative ability for differentiating lymphoma pathological subtypes (measured by mean AUC in three validation cohorts, non-Hodgkin and Hodgkin lymphoma: 0.959; non-Hodgkin subtypes: 0.983; B-lymphoma subtypes: 0.868; T-lymphoma subtypes: 0.962; DLBCL subtypes: 0.957). In addition, the system's well selected characteristics can contribute to the development of agreement on panels of markers for differential diagnosis and help minimize cost of immunohistochemical marker techniques (measured by marker saving ratio compared to real clinical settings, internal primary-stage cohort: 16.45% saved, p < 0.001; internal later-stage cohort: 21.73% saved, p < 0.001; external cohort: 3.67% saved, p < 0.001). Machine learning-based hierarchical system using EBER insitu hybridization and IHC markers was developed, which could streamline the workflow by sequentially determining each lymphoma pathological subtype. The proposed system proved to be effective and cost-saving in independent and external validation, thus could be adopted affordably in future clinical practice.

Identifying paediatric patients at risk of severe hearing impairment after treatment for malignancies of the H&N/CNS with proton therapy

Background and purposeA risk calculation model was presented in 2021 by Keilty et al. for determining the likelihood of severe hearing impairment (HI) for paediatric patients treated with photon radiation therapy. This study aimed to validate their risk-prediction model for our cohort of paediatric patients treated with proton therapy (PT) for malignancies of the head and neck (H&N) or central nervous system (CNS). Materials and methodsThis was a single-institution study which extracted data on all patients aged ≤ 18 years treated with PT between Feb 2010 – Feb 2022 for malignancies of the H&N/CNS. The factors required for input into the Keilty model were extracted: age at PT, time since end of PT, mean cochlea dose, and platinum chemotherapy doses. Validation was performed using the statistical software R v 4.3.1, which analysed event discrimination and model calibration. Results587 patients met the criteria. Validation of the model demonstrated excellent discriminative ability, with an “optimal” cut-off value of 16% at a specificity and sensitivity of 82%. However, model calibration was less satisfactory, indicating an overestimation of risk of severe hearing loss (HI) by the model as compared to clinically observed events in our cohort, possibly linked to differences in event scoring between the model developers and this study, and short follow-up time in this study. ConclusionThe published (photon-based) model of Keilty et al. was validated in a PT context, demonstrating a high discriminative ability to determine patients at high risk versus low risk for severe HI. However the overall observed risk was lower than model predictions.

Enhancing Target Tracking: A Novel Grid-Based Beetle Antennae Search Algorithm and Confusion-Aware Detection.

Unmanned aerial vehicle target tracking is a complex task that encounters challenges in scenarios involving limited computing resources, real-time requirements, and target confusion. This research builds on previous work and addresses challenges by proposing a grid-based beetle antennae search algorithm and designing a lightweight multi-target detection and positioning method, which integrates interference-sensing mechanisms and depth information. First, the grid-based beetle antennae search algorithm's rapid convergence advantage is combined with a secondary search and rollback mechanism, enhancing its search efficiency and ability to escape local extreme areas. Then, the You Only Look Once (version 8) model is employed for target detection, while corner detection, feature point extraction, and dictionary matching introduce a confusion-aware mechanism. This mechanism effectively distinguishes potentially confusing targets within the field of view, enhancing the system's robustness. Finally, the depth-based localization of the target is performed. To verify the performance of the proposed approach, a series of experiments were conducted on the grid-based beetle antennae search algorithm. Comparisons with four mainstream intelligent search algorithms are provided, with the results showing that the grid-based beetle antennae search algorithm excels in the number of iterations to convergence, path length, and convergence speed. When the algorithm faces non-local extreme-value-area environments, the speed is increased by more than 89%. In comparison with previous work, the algorithm speed is increased by more than 233%. Performance tests on the confusion-aware mechanism by using a self-made interference dataset demonstrate the model's high discriminative ability. The results also indicate that the model meets the real-time requirements.

Metabolomic profiles differentiate between porto-sinusoidal vascular disorder, cirrhosis, and health

Background & AimsPorto-sinusoidal vascular disorder (PSVD) is a rare and diagnostically challenging vascular liver disease. This study aimed to identify distinct metabolomic signatures in patients with PSVD, cirrhosis, and healthy volunteers (HV) to facilitate non-invasive diagnosis and elucidate perturbed metabolic pathways. MethodsSerum samples from 20 HV and patients with histologically confirmed PSVD or cirrhosis were analyzed. Metabolites were measured using liquid chromatography-mass spectrometry (LC-MS). Differential abundance was evaluated with Limma’s moderated t-statistics. Artificial neural network (ANN) and support vector machine (SVM) models were developed to classify PSVD against cirrhosis or HV metabolomic profiles. An independent cohort was used for validation. ResultsA total of 283 metabolites were included for downstream analysis. Clustering effectively separated PSVD metabolomes from HV, although a subset of PSVD patients (n=5, 25%) overlapped with cirrhosis. Differential testing revealed significant PSVD-linked metabolic perturbations, including taurocholic and adipic acids, distinguishing PSVD from both HV and cirrhosis. Alterations in pyrimidine, glycine, serine, and threonine pathways were exclusively associated with PSVD. Machine learning models utilizing selected metabolic signatures reliably differentiated PSVD from HV or cirrhosis using only 4 to 6 metabolites. Validation in an independent cohort demonstrated the high discriminative ability of taurocholic acid (AUROC 0.899) for PSVD vs HV and the taurocholic acid/aspartic acid ratio (AUROC 0.720) for PSVD vs cirrhosis. ConclusionsHigh-throughput metabolomics enabled the identification of distinct metabolic profiles for differentiating PSVD, cirrhosis, and healthy individuals. Unique alterations in the glycine, serine, and threonine pathways suggest their potential involvement in PSVD pathogenesis. Impact And ImplicationsPorto-sinusoidal vascular disorder (PSVD) is a vascular liver disease that can lead to pre-sinusoidal portal hypertension in the absence of cirrhosis, with poorly understood pathophysiology and no established treatment. Our study demonstrates that analyzing the serum metabolome could reveal distinct metabolic signatures in PSVD patients, including alterations in the pyrimidine, glycine, serine and threonine pathways and potentially shedding light on the disease's underlying pathways. These findings hold potential for earlier and non-invasive diagnosis of PSVD, potentially reducing reliance on invasive procedures like liver biopsy and guiding diagnostic pathways.

Predicting Long-Term Clinical Outcomes of Patients With Recurrent Pericarditis

BackgroundRecurrent pericarditis (RP) is a complex condition associated with significant morbidity. Prior studies have evaluated which variables are associated with clinical remission. However, there is currently no established risk-stratification model for predicting outcomes in these patients. ObjectivesWe developed a risk stratification model that can predict long-term outcomes in patients with RP and enable identification of patients with characteristics that portend poor outcomes. MethodsWe retrospectively studied a total of 365 consecutive patients with RP from 2012 to 2019. The primary outcome was clinical remission (CR), defined as cessation of all anti-inflammatory therapy with complete resolution of symptoms. Five machine learning survival models were used to calculate the likelihood of CR within 5 years and stratify patients into high-risk, intermediate-risk, and low-risk groups. ResultsAmong the cohort, the mean age was 46 ± 15 years, and 205 (56%) were women. CR was achieved in 118 (32%) patients. The final model included steroid dependency, total number of recurrences, pericardial late gadolinium enhancement, age, etiology, sex, ejection fraction, and heart rate as the most important parameters. The model predicted the outcome with a C-index of 0.800 on the test set and exhibited a significant ability in stratification of patients into low-risk, intermediate-risk, and high-risk groups (log-rank test; P < 0.0001). ConclusionsWe developed a novel risk-stratification model for predicting CR in RP. Our model can also aid in stratifying patients, with high discriminative ability. The use of an explainable machine learning model can aid physicians in making individualized treatment decision in RP patients.

Predictors of 24-month onset of macular fibrosis in type 3 macular neovascularisation

AimsTo explore prognostic multimarker models for progression to macular fibrosis (MF) over 24 months specific to type 3 macular neovascularisation (T3 MNV).MethodsThis retrospective, exploratory, single-centre, cohort study comprised 65 eyes...

Comparison of subjective cognitive decline and polygenic risk score in the prediction of all-cause dementia, Alzheimer’s disease and vascular dementia

BackgroundPolygenic risk scores (PRS) and subjective cognitive decline (SCD) are associated with the risk of developing dementia. It remains to examine whether they can improve the established cardiovascular risk factors aging and dementia (CAIDE) model and how their predictive abilities compare.MethodsThe CAIDE model was applied to a sub-sample of a large, population-based cohort study (n = 5,360; aged 50–75) and evaluated for the outcomes of all-cause dementia, Alzheimer’s disease (AD) and vascular dementia (VD) by calculating Akaike’s information criterion (AIC) and the area under the curve (AUC). The improvement of the CAIDE model by PRS and SCD was further examined using the net reclassification improvement (NRI) method and integrated discrimination improvement (IDI).ResultsDuring 17 years of follow-up, 410 participants were diagnosed with dementia, including 139 AD and 152 VD diagnoses. Overall, the CAIDE model showed high discriminative ability for all outcomes, reaching AUCs of 0.785, 0.793, and 0.789 for all-cause dementia, AD, and VD, respectively. Adding information on SCD significantly increased NRI for all-cause dementia (4.4%, p = 0.04) and VD (7.7%, p = 0.01). In contrast, prediction models for AD further improved when PRS was added to the model (NRI, 8.4%, p = 0.03). When APOE ε4 carrier status was included (CAIDE Model 2), AUCs increased, but PRS and SCD did not further improve the prediction.ConclusionsUnlike PRS, information on SCD can be assessed more efficiently, and thus, the model including SCD can be more easily transferred to the clinical setting. Nevertheless, the two variables seem negligible if APOE ε4 carrier status is available.

A predictive model for ischemic colitis: Integrating clinical and laboratory parameters

AbstractObjectiveWe aimed to develop a predictive model for the clinical diagnosis of ischemic colitis (IC).MethodsClinical data were collected from patients with acute IC lesions who were diagnosed and admitted to Beijing Tsinghua Changgung Hospital from January 2016 to December 2022. These patients were included in the IC case group in this retrospective observational study. The control group comprised patients aged ≥40 years who were diagnosed with abdominal pain during the same period, excluding those with IC. All patients were divided into a training and test sets based on the time window. Least absolute shrinkage and selection operator regression was used to screen risk factors for the occurrence of IC. Logistic stepwise regression (maximum likelihood ratio method) was performed in multifactorial analysis, and a diagnostic prediction model for IC was established using R language. The area under the receiver operating characteristic (ROC) curve (AUC) was examined to assess differentiation using working ROC curves. We used bootstrap resampling (1000 times) for internal validation. Model calibration curves and decision curve analysis (DCA) were also applied.ResultsOur study indicates that constipation, hematochezia, neutrophil counts, and specific abdominal computed tomography (CT) (plain scan) findings, including intestinal wall edema and thickening, intestinal lumen stenosis, and dilation, are independent predictors of IC. The predictive model exhibited high discriminative ability with an AUC of 0.9788 in the training set, and the calibration and DCA curves demonstrated excellent model performance. After validation, the AUC remained robust at 0.9868, underscoring the model's reliability in predicting IC.ConclusionAccording to our model, constipation accompanied by hematochezia necessitates careful consideration of IC. Abdominal CT (plain scan) is an effective diagnostic tool for IC, and it is common for patients to exhibit elevated neutrophil counts. The predictive model, demonstrating high discriminative ability and accuracy, shows promise for practical application in clinical settings, aiding in the early diagnosis and management of IC.

Genetic Homogeneity of Francisella tularensis subsp. mediasiatica Strains in Kazakhstan.

Tularemia is an acute febrile disease caused by the Gram-negative bacillus Francisella tularensis. Based on genetic and phenotypic characteristics, three subspecies are distinguished: tularensis, holarctica, and mediasiatica. F. tularensis subsp. mediasiatica remains the least studied subspecies. Over the past decade, new foci of distribution of F. tularensis subsp. mediasiatica have been discovered in Russia (Siberia), expanding the possible distribution area by thousands of kilometers. This article provides whole genome single nucleotide polymorphism (wgSNP) and polymorphic tandem repeats (MLVA) analyses of 28 mediasiatica strains isolated between 1965 and 2004 in Kazakhstan. Despite high genetic homogeneity, MLVA with eleven loci (MLVA11) demonstrates a high discriminatory ability (diversity index, 0.9497). The topological structure of the trees based on wgSNP and MLVA is not comparable; however, clustering remains congruent for most outbreaks, with the exception of two strains from one outbreak that are identical in terms of wgSNP but differ at three tandem repeat loci. Based on wgSNP, the strains are assigned to one of the three currently known mediasiatica sublineages, lineage M.I, together with other historical strains maintained in collections in Russia and Sweden. wgSNP shows limited previously unknown genetic diversity, with the M.I lineage size being only 118 SNPs. The wgSNP genotype is not strongly correlated with year and place of isolation.

Stability analysis of male sterile-derived pearl millet (Pennisetum glaucum) hybrids under variable growing condition

The identification and deployment of high-yielding pearl millet [Pennisetum glaucum (L.) R.Br.] hybrids adapted to various stress agro-ecologies are crucial for enhancing food and nutrition security in northern India. A study was carried out during the rainy (kharif) seasons of 2019 and 2020 at CCS Haryana Agricultural University, Hisar, Haryana in different growing situations (early-sown and late-sown patterns and rainfed to irrigated conditions) to investigate the genotype-environment interaction (GEI) of 59 pearl millet restorer lines, male sterile lines and their derived hybrids. Pooled analysis of variance for genotype, environment and GEI was significant for days to 50% flowering, seed yield and harvest index. These results indicate the significant differences among the genotypes, various environments and response of GEI. By combining the results of regression analysis and additive main effects and multiplicative interaction (AMMI) biplot, the genotypes HMS58 A1×EMRL-14/105, HMS54 A5×EMRL-14/127 and EMRL-15/109 exhibited high mean seed yield and high stability across environments. Among the tested environments, irrigated late-sown condition for seed yield and harvest index; and rainfed early-sown condition for days to 50% flowering had the highest discrimination ability. Hence, this study can help for the grouping of pearl millet potential hybrids and allow multi-year trials for identifying the best genotypes in the northern India.

The Serum NLRP1 Level and Coronary Artery Calcification: From Association to Development of a Risk-Prediction Nomogram.

To investigate the correlation between inflammasomes and coronary artery calcification (CAC), and develop and validating a nomogram for predicting the risk of CAC in patients with coronary artery disease (CAD). A total of 626 patients with CAD at the Affiliated Hospital of Xuzhou Medical University were enrolled in this study. The patients were divided into the calcification group and the non-calcification group based on the assessment of coronary calcification. We constructed a training set and a validation set through random assignment. The least absolute shrinkage and selection operator (LASSO) regression and multivariate analysis were performed to identify independent risk factors of CAC in patients with CAD. Based on these independent predictors, we developed a web-based dynamic nomogram prediction model. The area under the receiver operating characteristic curve (AUC-ROC), calibration curves, and decision curve analysis (DCA) were used to evaluate this nomogram. Age, smoking, diabetes mellitus (DM), hyperlipidemia, the serum level of nucleotide-binding oligomerization domain (NOD)-like receptor protein 1 (NLRP1), alkaline phosphatase (ALP) and triglycerides (TG) were identified as independent risk factors of CAC. The AUC-ROC of the nomogram is 0.881 (95% confidence interval (CI): 0.850-0.912) in the training set and 0.825 (95% CI: 0.760-0.876) in the validation set, implying high discriminative ability. Satisfactory performance of this model was confirmed using calibration curves and DCA. The serum NLRP1 level is an independent predictor of CAC. We established a web-based dynamic nomogram, providing a more accurate estimation and comprehensive perspective for predicting the risk of CAC in patients with CAD.

A Nomogram to Predict Intracranial Hypertension in Moderate Traumatic Brain Injury Patients

ObjectivePatients with moderate traumatic brain injury (TBI) are under the threat of intracranial hypertension (IHT). However, it is unclear which moderate TBI patient will develop IHT and should receive intracranial pressure (ICP)-lowering treatment or invasive ICP monitoring after admission. The purpose of the present study was to develop and validate a prediction model that estimates the risk of IHT in moderate TBI patients. MethodsBaseline data collected on admission of 296 moderate TBI patients with Glasgow Coma Scale (GCS) score of 9-11 was collected and analyzed. Multi-variable logistic regression modeling with backward stepwise elimination was used to develop a prediction model for IHT. The discrimination efficacy, calibration efficacy, and clinical utility of the prediction model were evaluated. Finally, the prediction model was validated in a separate cohort of 122 patients from 3 hospitals. ResultsFour independent prognostic factors for IHT were identified: GCS score, Marshall head computed tomography score, injury severity score and location of contusion. The C-statistic of the prediction model in internal validation was 84.30% (95% confidence interval [CI]: 0.794–0.892). The area under the curve for the prediction model in external validation was 82.80% (95% CI: 0.747∼0.909). ConclusionsA prediction model based on baseline parameters was found to be highly sensitive in distinguishing moderate TBI patients with GCS score of 9-11 who would suffer IHT. The high discriminative ability of the prediction model supports its use in identifying moderate TBI patients with GCS score of 9-11 who need ICP-lowering therapy or invasive ICP monitoring.

Axiological criteria for predicting the success of socio-psychological adaptation of schoolchildren of younger adolescence

Introduction. The system of personal value orientations is considered the main determinant of the processes of socio-psychological adaptation. The value structure has a significant impact on both the development and formation of a personality, as well as on the regulation of its self-awareness, cognitive and emotional sphere and behavior. In adolescence, the maximum stress of adaptation processes occurs and the most pronounced intolerance to negative environmental influences is manifested. In this regard, the analysis of value-intentional mechanisms and strategies of sociopsychological adaptation at this age seems extremely relevant.Materials and Methods. The study involved 300 boys and girls of early adolescence, who, using a refined technique, had Schwartz and the K. Rogers and R. Diamond questionnaire "Sociopsychological adaptation" assessed the system of value orientations and the level of adaptation. Statistical processing of the materials included the calculation of the Mann-Whitney U-test and discriminant analysis (step-by-step Wilkes method).Results. The system of value orientations (includes 19 parameters) and the level of social adaptation of adolescents are studied. The comparison of value and adaptation parameters in boys and girls was carried out. Using statistical procedures, respondents with high and low levels of adaptability were identified. The comparison of value indicators was carried out both between respondents with different degrees of adaptation, and between adolescents of different genders. Canonical functions with a high discriminative ability for boys and girls are calculated, which allow using axiological variables to divide the respondents according to their degree of social adaptation.Discussion and Conclusions. It is shown that the effectiveness of the processes of social adaptation of adolescents improves as the degree of differentiation of value orientations increases, manifested by an increase in the "spread" of their positions in the "field" of value consciousness. It is established that the most important factor in the successful social adaptation of adolescents is the dominance of the main group-centered value orientations and the diminution of the role of egocentric intentions. The ambivalence of the influence of hedonistic intention on adaptation has been revealed, the increasing importance of which, in combination with the development of groupcentered values, contributes to successful social adaptation, and in combination with the increasing role of other egocentric values, becomes a factor of low tolerance to social frustration. The maladaptive influence of the value orientations "reputation" (in boys) and "independence" (in girls) on the processes of social adaptation has been established. The axiological criteria are calculated.