Training Cohort Research Articles

P53 status combined with MRI findings for prognosis prediction of single hepatocellular carcinoma

ObjectTo develop and validate a nomogram for predicting recurrence in individuals suffering single hepatocellular carcinoma (HCC) after curative hepatectomy. Material and methodsA retrospective analysis was conducted on 189 patients with single HCC undergoing curative resection in our center were randomized into training and validation cohorts. P53 status was determined using immunohistochemistry. Clinical data, such as age, and gender were collected. MRI findings, such as tumor size, intratumoral arteries, the presence of peritumoral enhancement and intratumoral necrosis were also recorded. Nomograms were established based on the predictors selected in the training cohort, and receiver operating characteristic (ROC) curve analyses were used to compare the predictive ability among single predictors and nomogram model. The Kaplan-Meier method were used to assess the impact of each predictor and nomogram model on HCC recurrence. The results were validated in the validation cohort. ResultsMultivariate Cox regression analysis showed that P53 (P < 0.001), tumor size (P = 0.009), and intratumoral artery (P = 0.026) were the independent risk factors for HCC recurrence. The nomogram model demonstrated favorable C-index of 0.740 (95 %CI:0.653–0.826) and 0.767 (95 %CI: 0.633–0.900) in the training and validation cohorts, and the area under the curve was 0.740 and 0.752, which was better than the performance of P53 and MR factors alone. Calibration curves indicated a good agreement between observed actual outcomes and predicted values. Kaplan-Meier curves indicated that nomogram model was powerful in discrimination and clinical usefulness. ConclusionsThe integrated nomogram combining P53 status and MRI findings can be a valuable prognostic tool for predicting postoperative recurrence of single HCC.

MiRNAs and Neutrophil-Related Membrane Proteins from Plasma-Derived Extracellular Vesicles for Early Prediction of Organ Dysfunction and Prognosis in Septic Patients.

The pathogenesis of sepsis-induced organ dysfunction remains elusive, and the mortality remains alarmingly high. We sought to investigate the profile of extracellular vesicles (EVs)-mediated communication between plasma and polymorphonuclear neutrophils (PMNs) in sepsis, and to elucidate whether miRNAs and PMN-related membrane proteins from plasma-derived EVs (plasma-EVs) are associated with sepsis-induced organ dysfunction and prognosis. PMN-derived EVs (PMN-EVs) were isolated from the blood samples of healthy controls (N=3) and patients with septic shock (N=3) after ICU admission. We performed miRNA sequencing of the isolated EVs, followed by bioinformatic analysis. A miRNA model for comparing PMN-EVs and plasma-EVs was successfully established in the training cohort. Furthermore, miRNAs and PMN-related membrane proteins from the plasma-EV model were confirmed in the validation cohort. A logistic regression model, receiver operating characteristic (ROC) curves, and Kaplan-Meier analyses were performed to evaluate the efficiency of diagnostic and/or prognostic performance. Further, in vivo and in vitro experiments were conducted to explore the involvement of plasma-EVs in PMNs autophagy. Fifty-five miRNAs from PMN-EVs differed significantly between the healthy controls and patients with septic shock. Furthermore, the plasma-EV model (six miRNAs and eight PMN-related membrane proteins) was confirmed in the validation cohort, demonstrating that miR-34a-5p, miR-503-5p, miR-4772-3p, ITGAM, MPO, and MMP9 serve as sepsis biomarkers for distinguishing lung, liver, and kidney dysfunction. Kaplan-Meier survival analysis showed that miR-34a-5p, miR-4772-3p, ITGAM, and MMP9 were potential prognostic predictors. Finally, we found that plasma-EVs from sepsis patients exert an inhibitory effect on PMNs autophagy, which can be reversed by EV inhibitors such as GW4869 and enoxaparin. These findings suggest that miRNAs and PMN-related membrane proteins from plasma-EVs could be valuable diagnostic tools for identifying sepsis-induced organ dysfunction and predicting prognosis, enabling proactive management of sepsis by physicians and improving the prognosis of sepsis patients.

Development and validation of the MRI-based deep learning classifier for distinguishing perianal fistulizing Crohn’s disease from cryptoglandular fistula: a multicenter cohort study

A singular reliable modality for early distinguishing perianal fistulizing Crohn's disease (PFCD) from cryptoglandular fistula (CGF) is currently lacking. We aimed to develop and validate an MRI-based deep learning classifier to effectively discriminate between them. The present study retrospectively enrolled 1054 patients with PFCD or CGF from three Chinese tertiary referral hospitals between January 1, 2015, and December 31, 2021. The patients were divided into four cohorts: training cohort (n=800), validation cohort (n=100), internal test cohort (n=100) and external test cohort (n=54). Two deep convolutional neural networks (DCNN), namely MobileNetV2 and ResNet50, were respectively trained using the transfer learning strategy on a dataset consisting of 44871MR images. The performance of the DCNN models was compared to that of radiologists using various metrics, including receiver operating characteristic curve (ROC) analysis, accuracy, sensitivity, and specificity. Delong testing was employed for comparing the area under curves (AUCs). Univariate and multivariate analyses were conducted to explore potential factors associated with classifier performance. A total of 532 PFCD and 522 CGF patients were included. Both pre-trained DCNN classifiers achieved encouraging performances in the internal test cohort (MobileNetV2 AUC: 0.962, 95% CI 0.903-0.990; ResNet50 AUC: 0.963, 95% CI 0.905-0.990), as well as external test cohort (MobileNetV2 AUC: 0.885, 95% CI 0.769-0.956; ResNet50 AUC: 0.874, 95% CI 0.756-0.949). They had greater AUCs than the radiologists (all p≤0.001), while had comparable AUCs to each other (p=0.83 and p=0.60 in the two test cohorts). None of the potential characteristics had a significant impact on the performance of pre-trained MobileNetV2 classifier in etiologic diagnosis. Previous fistula surgery influenced the performance of the pre-trained ResNet50 classifier in the internal test cohort (OR 0.157, 95% CI 0.025-0.997, p=0.05). The developed DCNN classifiers exhibited superior robustness in distinguishing PFCD from CGF compared to artificial visual assessment, showing their potential for assisting in early detection of PFCD. Our findings highlight the promising generalized performance of MobileNetV2 over ResNet50, rendering it suitable for deployment on mobile terminals. National Natural Science Foundation of China.

Derivation and external validation of prediction model for hypertensive disorders of pregnancy in twin pregnancies: a retrospective cohort study in southeastern China

ObjectiveWe aimed to develop and validate an effective prediction model for hypertensive disorder of pregnancy (HDP) in twin pregnancies after 28 weeks of gestation.DesignRetrospective cohort study.SettingMaternity hospital.ParticipantsWe recruited twin pregnancies...

Utilization and validation of dried blood spot-based metabolomics in plasma-derived diagnostic models

Metabolomics has rapidly advanced in life sciences, enhancing our understanding of physiological conditions. Plasma stands out as a predominant sample type in metabolomics studies. Dried blood spot (DBS) has been recognized as a valuable strategy due to its unique characteristics, such as minimal invasiveness, small sample volume, and easy transport. Most large cohort studies construct the diagnostic model using plasma-based metabolomics rather than DBS. However, the comparability of metabolite measurements between DBS and plasma samples, as well as the integration of DBS-based metabolomics into established plasma-derived diagnostic models, remains unclear. We collected plasma and DBS samples from the same 12 healthy controls under fasting and non-fasting conditions and performed ultra-high performance liquid chromatography-mass spectrometry (UPLC-MS)-based metabolomics. We identified 277 metabolites present in both plasma and DBS samples, with 229 (82.7 %) showing a strong correlation between the two sample types. Furthermore, pre-processing datasets from plasma and DBS samples before combining them effectively diminished inconsistencies between plasma- and DBS-based metabolomics without compromising the inherent classification within the data. For the clinical application, the plasma samples were obtained from the gastric cancer patients (n = 204) and the healthy controls (n = 128) to serve as the training cohorts. Additionally, DBS samples were collected from different participants of gastric cancer (n = 19) and healthy controls (n = 12) to validate the diagnostic model. Finally, DBS-based metabolomics were integrated into established plasma-derived gastric cancer diagnostic models, yielding an area under the ROC curve (AUC) of 0.934. Overall, incorporating DBS-based metabolomics into an established plasma-derived diagnostic model holds promise, offering valuable opportunities and insights for diagnostic research and clinical practice.

Development of a Combined Oxidative Stress and Endoplasmic Reticulum Stress-Related Prognostic Signature for Hepatocellular Carcinoma.

Oxidative stress and endoplasmic reticulum stress are important components of the cellular stress process, which plays a critical role in tumor initiation and progression. First, the correlation between oxidative stress and endoplasmic reticulum stress was detected in 68 human hepatocellular carcinoma (HCC) tissue microarray samples by immunohistochemistry. Differentially expressed oxidative stress- and endoplasmic reticulum stressrelated genes (OESGs) then were screened in HCC. Next, an OESGs prognostic signature was constructed for HCC in the training cohort (TCGA-LIHC from The Cancer Genome Atlas), by least absolute shrinkage and selection operator Cox and stepwise Cox regression analyses, and was verified in the external cohort (GSE14520 from the Gene Expression Omnibus). The MCP counter was employed to evaluate immune cell infiltration. The C-index was used to evaluate the predictive power of prognostic signature. Finally, a prognostic nomogram model was constructed to predict the survival probability of patients with HCC based on the results of Cox regression analysis. We demonstrated a positive correlation between oxidative stress and endoplasmic reticulum stress in human HCC samples. We then identified five OESGs as a prognostic signature consisting of IL18RAP, ECT2, PPARGC1A, STC2, and NQO1 for HCC. Related risk scores correlated with tumor stage, grade, and response to transcatheter arterial chemoembolization therapy, and the higher risk score group had less T cells, CD8+ T cells, cytotoxic lymphocytes and natural killer cell infiltration. The C-index of our OESGs prognostic signature was superior to four previously published signatures. Furthermore, we developed a nomogram based on the OESGs prognostic signature and clinical parameters for patients with HCC that is an effective quantitative analysis tool to predict patient survival. The OESGs signature showed excellent performance in predicting survival and therapeutic responses for patients with HCC.

Development and Validation of a New Model Including Inflammation Indexes for the Long-Term Prognosis of Hepatitis B-Related Acute-On-Chronic Liver Failure.

Acute-on-chronic liver failure (ACLF) is a severe condition characterized by a systemic inflammatory response and associated with high mortality. Currently, there is no reliable prediction model for long-term prognosis in ACLF. This study aimed to develop and validate a prognostic model incorporating inflammation indexes to predict the long-term outcome of patients with hepatitis B virus-related ACLF (HBV-ACLF). A retrospective analysis of clinical data from HBV-ACLF patients (n = 986) treated at the Third Affiliated Hospital of Sun Yat-sen University between January 2014 and December 2018 was conducted. Patients were randomly divided into training (n = 690) and validation (n = 296) cohorts. The Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses were used to identify independent risk factors for long-term mortality. The following variables were identified as independent predictors of long-term mortality: age, cirrhosis, hepatic encephalopathy, total bilirubin (TBIL), international normalized ratio (INR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-platelet ratio (NPR). A novel nomogram was established by assigning weights to each variable. The C-index of the nomogram was 0.777 (95% confidence interval [CI]: 0.752-0.802). In the training set, the area under the curve (AUC) for predicting mortality at 1, 3, and 12 months was 0.841 (95% CI: 0.807-0.875), 0.827 (95% CI: 0.796-0.859), and 0.829 (95% CI: 0.798-0.859), respectively. The nomogram demonstrated superior predictive performance for 12-month survival compared to the model for end-stage liver disease (MELD) score (0.767, 95% CI: 0.730-0.804, p < 0.001) and the clinical overt sepsis in acute liver failure clinical practice Guidelines-ACLF II score (0.807, 95% CI: 0.774-0.840, p = 0.028). Finally, calibration curves and decision curve analysis (DCA) confirmed the clinical utility of the nomogram. The novel inflammation-based scoring system, incorporating MLR and NPR, effectively predicts long-term mortality in HBV-ACLF patients.

Development and validation of a deep learning pipeline to diagnose ovarian masses using ultrasound screening: a retrospective multicenter study

Ovarian cancer has the highest mortality rate among gynaecological malignancies and is initially screened using ultrasound. Owing to the high complexity of ultrasound images of ovarian masses and the anatomical characteristics of the deep pelvic cavity, subjective assessment requires extensive experience and skill. Therefore, detecting the ovaries and ovarian masses and diagnose ovarian cancer are challenging. In the present study, we aimed to develop an automated deep learning framework, the Ovarian Multi-Task Attention Network (OvaMTA), for ovary and ovarian mass detection, segmentation, and classification, as well as further diagnosis of ovarian masses based on ultrasound screening. Between June 2020 and May 2022, the OvaMTA model was trained, validated and tested on a training and validation cohort including 6938 images and an internal testing cohort including 1584 images which were recruited from 21 hospitals involving women who underwent ultrasound examinations for ovarian masses. Subsequently, we recruited two external test cohorts from another two hospitals. We obtained 1896 images between February 2024 and April 2024 as image-based external test dataset, and further obtained 159 videos for the video-based external test dataset between April 2024 and May 2024. We developed an artificial intelligence (AI) system (termed OvaMTA) to diagnose ovarian masses using ultrasound screening. It includes two models: an entire image-based segmentation model, OvaMTA-Seg, for ovary detection and a diagnosis model, OvaMTA-Diagnosis, for predicting the pathological type of ovarian mass using image patches cropped by OvaMTA-Seg. The performance of the system was evaluated in one internal and two external validation cohorts, and compared with doctors' assessments in real-world testing. We recruited eight physicians to assess the real-world data. The value of the system in assisting doctors with diagnosis was also evaluated. In terms of segmentation, OvaMTA-Seg achieved an average Dice score of 0.887 on the internal test set and 0.819 on the image-based external test set. OvaMTA-Seg also performed well in ovarian mass detection from test images, including healthy ovaries and masses (internal test area under the curve [AUC]: 0.970; external test AUC: 0.877). In terms of classification diagnosis prediction, OvaMTA-Diagnosis demonstrated high performance on image-based internal (AUC: 0.941) and external test sets (AUC: 0.941). In video-based external testing, OvaMTA recognised 159 videos with ovarian masses with AUC of 0.911, and is comparable to the performance of senior radiologists (ACC: 86.2 vs. 88.1, p=0.50; SEN: 81.8 vs. 88.6, p=0.16; SPE: 89.2 vs. 87.6, p=0.68). There was a significant improvement in junior and intermediate radiologists who were assisted by AI compared to those who were not assisted by AI (ACC: 80.8 vs. 75.3, p=0.00015; SEN: 79.5 vs. 74.6, p=0.029; SPE: 81.7 vs. 75.8, p=0.0032). General practitioners assisted by AI achieved an average performance of radiologists (ACC: 82.7 vs. 81.8, p=0.80; SEN: 84.8 vs. 82.6, p=0.72; SPE: 81.2 vs. 81.2, p>0.99). The OvaMTA system based on ultrasound imaging is a simple and practical auxiliary tool for screening for ovarian cancer, with a diagnostic performance comparable to that of senior radiologists. This provides a potential tool for screening ovarian cancer. This work was supported by the National Natural Science Foundation of China (Grant Nos. 12090020, 82071929, and 12090025) and the R&D project of the Pazhou Lab (Huangpu) (Grant No. 2023K0605).

Analysis and Validation of a Diagnostic Nomogram for Predicting the Risk of Acute Respiratory Failure for Non-HIV Related Pneumocystis Jirovecii Pneumonia Patients.

Pneumocystis Pneumonia (PCP), primarily affecting individuals with weakened immune systems, is a severe respiratory infection caused by pneumocystis jirovecii and can lead to acute respiratory failure (ARF). In this article, we explore the risk factors of ARF and propose a prognostic model of ARF for PCP patients. In this multi-center, retrospective study in 6secondary or tertiary academic hospitals in China, 120 PCP patients were screened from the Dryad database for the development of a predictive model. A total of 49 patients from Peking University People's Hospital were collected for external validation. Crucial clinical features of these patients are selected applying univariate and multivariate logistic regression analysis. We established an intuitive nomogram. Receiver operating characteristic (ROC) curve, calibration curve, decision curve analysis (DCA) and clinical impact curve (CIC) were plotted to evaluate the model's performance. A cohort of 120 patients formed the training cohort for the development of the model, with 49 patients constituting the test cohort. Univariate and multivariate logistic regression analysis identified five risk factors associated with ARF, which are age, fever, dyspnea, high neutrophil count and use of antibiotics. A nomogram was then proposed based on these factors. The area under the ROC curve (AUROC) in the development group has reached 0.8576, while the validation group has an AUROC of 0.7372, indicating commendable ability for predicting ARF. In addition, results for Hosmer-Lemeshow test indicate the effectiveness of our model. Furthermore, DCA and CIC curves demonstrate excellent clinical benefit. We present a nomogram for predicting ARF in non-HIV related PCP patients. The prognostic model may provide references in clinical medicine, promote timely treatment and improve therapeutic outcomes of PCP patients.

TGF-β Score based on Silico Analysis can Robustly Predict Prognosis and Immunological Characteristics in Lower-grade Glioma: The Evidence from Multicenter Studies.

Nowadays, mounting evidence shows that variations in TGF-β signaling pathway-related components influence tumor development. Current research has patents describing the use of anti-TGF-β antibodies and checkpoint inhibitors for the treatment of proliferative diseases. Importantly, TGF-β signaling pathway is significant for lower-grade glioma (LGG) to evade host immunity. Loss of particular tumor antigens and shutdown of professional antigenpresenting cell activity may render the anti-tumor response ineffective in LGG patients. However, the prognostic significance of TGF-β related genes in LGG is still unknown. We collected RNA-seq data from the GTEx database (normal cortical tissues), the Cancer Genome Atlas database (TCGA-LGG), and the Chinese Glioma Genome Atlas database (CGGA-693 and CGGA-325) for conducting our investigation. In addition, previous publications were explored for the 223 regulators of the TGF-β signaling pathway, and 30 regulators with abnormal expression in TCGA and GTEx database were identified. In order to identify hub prognostic regulators, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis were used to screen from differentially expressed genes (DEGs). On the basis of 11 genes from LASSO-Cox regression analysis (NEDD8, CHRD, TGFBR1, TP53, BMP2, LRRC32, THBS2, ID1, NOG, TNF, and SERPINE1), TGF-β score was calculated. Multiple statistical approaches verified the predictive value of the TGF-β score for the training cohort and two external validation cohorts. Considering the importance of the TGF-β signaling pathway in immune regulation, we evaluated the prediction of the TGF-β score for immunological characteristics and the possible application of the immunotherapeutic response using six algorithms (TIMER, CIBERSORT, QUANTISEQ, MCP-counter, XCELL and EPIC) and three immunotherapy cohorts (GSE78820, Imvigor-210 and PRJEB23709). Notably, we compared our risk signature with the signature in ten publications in the meta-cohort (TCGA-LGG, CGGA-693 and CGGA-325), and the TGF-β score had the best predictive efficiency (C-index =0.812). In conclusion, our findings suggest that TGF-β signaling pathway-related signatures are prognostic biomarkers in LGG and provide a novel tool for tumor microenvironment (TME) assessment.

Analysis of Risk Factors and Establishment of a Risk Prediction Model for Severe Postpartum Haemorrhage.

Aims/Background Severe postpartum haemorrhage (PPH) is a dangerous condition, characterized by rapid progression and poor prognosis. It remains the leading preventable cause of maternal death worldwide. This study aimed to investigate the risk factors for severe PPH and establish a prediction model to identify severe PPH early, allowing for early intervention reduce maternal death. Methods Clinical data were collected from 784 patients diagnosed with PPH and delivered at the Second Affiliated Hospital of Anhui Medical University between December 2018 and December 2023. These cases were categorized into the training cohort. Severe PPH was diagnosed in 234 patients based on the criterion of the volume of vaginal bleeding volume exceeding 1000 mL within 24 hours after delivery; these patients were assigned to the experimental group. The remaining 550 patients with nonsevere PPH were assigned to the control group. Data from an additional 338 postpartum women from the same period were selected and classified into the validation cohort. Univariate and multivariate logistic regression analyses were performed to pinpoint the determinants associated with severe PPH. Additionally, these analyses were instrumental for developing and assessing a prediction model to forecast the risk of such complications. Results Most of the PPH cases in this study stemmed from uterine atony, the leading aetiology. The second most common factor was soft birth canal lacerations and haematoma formation, accounting for 7.26% of the subjects in experimental group and 6.55% of those in the control group. Uterine rupture, uterine inversion, and amniotic fluid embolism were exclusively observed in the experimental group. In the univariate analysis, notable disparities were identified across various parameters, including maternal age, gravidity, parity, abortion frequency, gestational week at delivery, prothrombin time (PT), activated partial thromboplastin time (APTT), in vitro fertilisation, foetal position, caesarean delivery, pregnancy with anaemia, and hypertensive disorders of pregnancy (p < 0.05). A multivariate logistic regression model revealed that a parity of two or more, two or more abortions, in vitro fertilisation, gestational weeks at delivery, foetal position, caesarean delivery, pregnancy with anaemia, and hypertensive disorders of pregnancy were independent predictors of severe PPH (p < 0.05). The predictive model demonstrated excellent calibration for the training and validation datasets, with the areas under the curve (AUC) for receiver operating characteristic (ROC) curves measuring 0.799 and 0.759, respectively. Clinical decision curve analysis (DCA) confirmed a significant threshold exceeding 0.9, signifying a substantial net benefit and model precision. Conclusion Parity ≥2 times, abortion ≥2 times, in vitro fertilisation, gestational week at delivery, abnormal foetal position, caesarean delivery, pregnancy with anaemia, and hypertensive disorders of pregnancy are independent risk factors for severe PPH. The predictive model established in this study accurately predicts the occurrence of severe PPH and has significant value for clinical application.

Use of a Pathomics Nomogram to Predict Postoperative Liver Metastasis in Patients with Stage III Colorectal Cancer.

Approximately 25% of patients with stage III colorectal cancer experience liver metastasis after radical resection; however, there is currently a lack of methods to predict liver metastasis. This study aims to develop and validate a pathomics nomogram to predict liver metastasis in patients with stage III colorectal cancer. A total of 318 enrolled patients were divided into three cohorts: a training cohort (n=139), a validation cohort (n=69), and an external cohort (n=110). A competitive risk nomogram was established by the pathomics signature and clinicopathological characteristics and assessed by calibration, discrimination, and clinical usefulness. A significant correlation between the pathomics signature and liver metastasis in stage III colorectal cancer was found. Multivariate Fine-Gray analysis indicated that preoperative carcinoembryonic antigen level, postoperative chemotherapy, and pathomics signature were independent predictors of liver metastasis. A competitive risk nomogram was developed to predict liver metastasis in patients with stage III colorectal cancer. The predicting nomogram shows good discrimination and calibration, with C-indexes of 0.811 (95% confidence interval [CI] 0.651-0.971), 0.759 (95% CI 0.531-0.987), and 0.845 (95% CI 0.641-0.999), with area under the receiver operating characteristic (AUROC) curves at 5 years of 0.833 (95% CI 0.742-0.925), 0.760 (95% CI 0.652-0.893), and 0.812 (95% CI 0.692-0.931) in the training, validation, and external cohorts, respectively. Compared with the clinicopathological nomogram, the nomogram combined with the pathomics signature had better performance (AUROC 0.823 [95% CI 0.764-0.881] vs. 0.678 [95% CI 0.606-0.751]; p<0.001). The pathomics signature is a predictive indicator for liver metastasis in patients with stage III colorectal cancer, and the integrated nomogram can be used to predict liver metastasis better than the clinicopathological nomogram alone.

Development and Validation of a Screening Tool for Generalized Anxiety and Major Depressive Disorder in Patients with Chronic Obstructive Pulmonary Disease.

Anxiety and depression are often underdiagnosed in patients with chronic obstructive pulmonary disease (COPD). This study aimed to develop and validate a screening tool for anxiety and depression in COPD patients. Stable COPD patients were consecutively recruited from November 2021 to October 2023 and underwent a psychiatric interview to diagnose generalized anxiety disorder (GAD) and/or major depressive disorder (MDD) based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. Patients were split into training and validation sets according to their recruitment time. We assessed known risk factors and used core items from the Patient Health Questionnaire Anxiety and Depression Scale (PHQ-ADS) to develop a prediction nomogram. Multivariable logistic regression with least absolute shrinkage and selection operator (LASSO) were used to construct the nomogram. Among the enrolled COPD patients (n = 329), 58 (25.6%) in the training cohort and 33 (32.4%) in the validation cohort were diagnosed with GAD and/or MDD. Three variables were identified in the prediction nomogram: COPD Assessment Test score and two core items from PHQ-ADS. The under the curve (AUC) value for the nomogram was 0.826 (95% CI: 0.755-0.897) and 0.855 (95% CI: 0.767-0.942) in the training and validation cohorts, respectively. The calibration curve was close to the diagonal. The discriminatory power of the screening nomogram was comparable to that of PHQ-ADS (AUC: 0.826 vs. 0.831, P = 0.832). The new screening tool for GAD and MDD in COPD patients is concise and valid, with discriminatory power comparable to existing anxiety/depression screening questionnaires.

Identification of neural alterations in patients with Crohn’s disease with a novel multiparametric brain MRI-based radiomics model

ObjectivesGut-brain axis dysfunction has emerged as a key contributor to the pathogenesis of Crohn’s disease (CD). The elucidation of neural alterations may provide novel insights into its management. We aimed to develop a multiparameter brain MRI-based radiomics model (RM) for characterizing neural alterations in CD patients and to interpret these alterations using multiomics traits.MethodsThis prospective study enrolled 230 CD patients and 46 healthy controls (HCs). Participants voluntarily underwent brain MRI and psychological assessment (n = 155), blood metabolomics analysis (n = 260), and/or fecal 16S rRNA sequencing (n = 182). The RM was developed using 13 features selected from 13,870 first-order features extracted from multiparameter brain MRI in training cohort (CD, n = 75; HCs, n = 32) and validated in test cohort (CD, n = 34; HCs, n = 14). Multiomics data (including gut microbiomics, blood metabolomics, and brain radiomics) were compared between CD patients and HCs.ResultsIn the training cohort, area under the receiver operating characteristic curve (AUC) of RM for distinguishing CD patients from HCs was 0.991 (95% confidence interval (CI), 0.975–1.000). In test cohort, RM showed an AUC of 0.956 (95% CI, 0.881–1.000). CD-enriched blood metabolites such as triacylglycerol (TAG) exhibited significant correlations with both brain features detected by RM and CD-enriched microbiota (e.g., Veillonella). One notable correlation was found between Veillonella and Ctx-Lh-Middle-Temporal-CBF-p90 (r = 0.41). Mediation analysis further revealed that dysbiosis, such as of Veillonella, may regulate the blood flow in the middle temporal cortex through TAG.ConclusionWe developed a multiparameter MRI-based RM that characterized the neural alterations of CD patients, and multiomics data offer potential evidence to support the validity of our model. Our study may offer clues to help provide potential therapeutic targets.Critical relevance statementOur brain-gut axis study developed a novel model using multiparameter MRI and radiomics to characterize brain changes in patients with Crohn’s disease. We validated this model’s effectiveness using multiomics data, making it a potential biomarker for better patient management.Key PointsUtilizing multiparametric MRI and radiomics techniques could unveil Crohn’s disease’s neurophenotype.The neurophenotype radiomics model is interpreted using multiomics data.This model may serve as a novel biomarker for Crohn’s disease management.Graphical

Development and performance evaluation of a clinical prediction model for sepsis risk in burn patients.

Sepsis is a common and severe complication in burn patients and remains one of the leading causes of mortality. This retrospective study aimed to develop a predictive model for the risk of in-hospital sepsis among burn patients treated at Guangzhou Red Cross Hospital between January 2022 and January 2024, with the goal of improving clinical outcomes through early prevention based on risk stratification. A total of 302 eligible patients were randomly divided into training and validation cohorts in a 7:3 ratio for model development and validation, respectively. Predictive factors were initially selected using LASSO regression, followed by logistic regression analysis to establish the prediction model and construct a nomogram. The final model incorporated 4 independent predictors: burn area (odds ratio [OR] = 1.043, 95% confidence interval [CI]: 1.026-1.062/1%), hemoglobin (OR = 0.968, 95% CI: 0.954-0.980/1 g/L), diabetes (OR = 10.91, 95% CI: 2.563-56.62), and potassium (OR = 3.091, 95% CI: 1.635-6.064/1 mmol/L). The areas under the receiver operating characteristic curve were 0.875 and 0.861 for the training and validation cohorts, with Youden indexes of 0.634 and 0.600, respectively. The calibration curve and decision curve analysis demonstrated good predictive accuracy and clinical utility of the model. These findings suggest that our developed model exhibits robust predictive performance for the risk of in-hospital sepsis in burn patients, and early prevention strategies based on risk stratification may potentially improve clinical outcomes.

Development and external validation of a Nomogram to predict obstructive sleep apnea in Children

To develop a nomogram model to predict obstructive sleep apnea in children and perform an external validation. 864 children who underwent polysomnography at our hospital were randomly assigned to a training cohort and an internal validation cohort (7:3) and 292 children at another hospital were enrolled for external validation. Logistic regression analyses were performed to explore the risk factors for obstructive sleep apnea. Features selected by least absolute shrinkage and selection operator logistic regression were enrolled to develop a predictive nomogram, which was assessed by discrimination, calibration, and clinical benefit in three cohorts and six subgroups from the all development cohort: age, gender, nonobese children, and term children. Furthermore, the nomogram was compared with the Obstructive Sleep Apnea-18 questionnaire. Six features were selected to construct the nomogram: adenoid hypertrophy, tonsil size 2, tonsil size 3, birth weight, total sleep time, and the lowest oxygen saturation. The areas under the curve of the nomogram in the training cohort [0.784, 95%CI (0.746–0.821)], internal validation cohort [0.780 (0.721–0.840)], and external validation cohort [0.782 (0.726–0.838)] were higher than those of the OSA-18 and remained stable in subgroups. Calibration curves and decision curve analysis both exhibited favorable performance.Conclusion: The nomogram with satisfactory performance was constructed as a robust tool for evaluating the risk of obstructive sleep apnea in children.What is Known:• Obstructive sleep apnea (OSA) is common in children and is closely related to multiple system dysfunction, while still remains underdiagnosed and undertreated because of the limited usage of polysomnography.• A prediction model for accurately identifying OSA in children is warranted in a subsequent recommendation of essential PSG to provide proper treatment and delay the occurrence of OSA complications.What is New:• Adenoid hypertrophy, tonsil size 2, tonsil size 3, birth weight, total sleep time, and the lowest oxygen saturation were risk factors of children with OSA.• This nomogram with satisfactory performance was a robust tool for evaluating the risk of OSA in children.

Development of a Novel Nomogram Based on Ultrasonic Radiomics for Predicting Intrauterine Pregnancy After Frozen Embryo Transfer Cycle.

This study aimed to develop a nomogram for predicting intrauterine pregnancy after an in vitro frozen embryo transfer cycle using endometrial ultrasound radiomics. A total of 211 patients who underwent ultrasound examination on the day of endometrial transformation before the frozen embryo transfer cycle were enrolled. The patients were divided into an intrauterine pregnancy group and a pregnancy failure group based on ultrasound results. Clinical characteristics and radiomic features were analyzed using univariate and multivariate logistic regression analyses. A nomogram prediction model was established based on radiomic signatures and significant clinical factors. The model's robustness was assessed in training and external validation cohorts. Nine radiomic features were selected using least absolute shrinkage and selection operator (LASSO), and the radiomics score (Rad-score) was calculated as the sum of each feature multiplied by the nonzero coefficient from LASSO. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve based on the Rad-score was 0.72, 0.65, and 0.69 in the training, validation, and combined cohorts, respectively. To improve diagnostic efficiency, the Rad-score was further integrated with clinical factors to form a novel predictive nomogram. The results indicated that the AUC increased to 0.81, 0.67, and 0.77 in the training, validation, and combined cohorts, respectively. Decision curve analysis showed that the radiomics nomogram was clinically useful. The radiomics and clinical predictive nomogram can effectively predict intrauterine pregnancy after in vitro frozen embryo transfer and can be further applied in clinical strategy.

Deciphering potential molecular mechanisms in clear cell renal cell carcinoma based on the ubiquitin-conjugating enzyme E2 related genes: Identifying UBE2C correlates to infiltration of regulatory T cells.

Renal clear cell carcinoma (ccRCC) is a highly aggressive and common form of kidney cancer, with limited treatment options for advanced stages. Recent studies have highlighted the importance of the ubiquitin-proteasome system in tumor progression, particularly the role of ubiquitin-conjugating enzyme E2 (UBE2) family members. However, the prognostic significance of UBE2-related genes (UBE2RGs) in ccRCC remains unclear. In this study, bulk RNA-sequencing and single-cell RNA-sequencing data from ccRCC patients were retrieved from the Cancer Genome Atlas and Gene Expression Omnibus databases. Differential expression analysis was performed to identify UBE2RGs associated with ccRCC. A combination of 10 machine learning methods was applied to develop an optimal prognostic model, and its predictive performance was evaluated using area under the curve (AUC) values for 1-, 3-, and 5-year overall survival (OS) in both training and validation cohorts. Functional enrichment analyses of gene ontology and Kyoto Encyclopedia of Genes and Genomes were conducted to explore the biological pathways involved. Correlation analysis was conducted to investigate the association between the risk score and tumor mutational burden (TMB) and immune cell infiltration. Immunotherapy and chemotherapy sensitivity were assessed by immunophenoscore and tumor immune, dysfunction, and exclusion scores to identify potential predictive significance. In vitro, knockdown of the key gene UBE2C in 786-O cells by specific small interfering RNA to validate its impact on apoptosis, migration, cell cycle, migration, invasion of tumor cells, and induction of regulatory T cells (Tregs). Analysis of sc-RNA revealed that UBE2 activity was significantly upregulated in malignant cells, suggesting its role in tumor progression. A three-gene prognostic model comprising UBE2C, UBE2D3, and UBE2T was constructed by Lasoo Cox regression and demonstrated robust predictive accuracy, with AUC values of 0.745, 0.766, and 0.771 for 1-, 3-, and 5-year survival, respectively. The model was validated as an independent prognostic factor in ccRCC. Patients in the high-risk group had a worse prognosis, higher TMB scores, and low responsiveness to immunotherapy. Additionally, immune infiltration and chemotherapy sensitivity analyses revealed that UBE2RGs are associated with various immune cells and drugs, suggesting that UBE2RGs could be a potential therapeutic target for ccRCC. In vitro experiments confirmed that the reduction of UBE2C led to an increase in apoptosis rate, as well as a decrease in tumor cell invasion and metastasis abilities. Additionally, si-UBE2C cells reduced the release of the cytokine Transforming Growth Factor-beta 1 (TGF-β1), leading to a decreased ratio of Tregs in the co-culture system. This study presents a novel three-gene prognostic model based on UBE2RGs that demonstrates significant predictive value for OS, immunotherapy, and chemotherapy in ccRCC patients. The findings underscore the potential of UBE2 family members as biomarkers and therapeutic targets in ccRCC, warranting further investigation in prospective clinical trials.

Prognostic prediction for HER2-low breast cancer patients using a novel machine learning model

BackgroundsTo develop a machine learning (ML) model for predicting the prognosis of breast cancer (BC) patients with low human epidermal growth factor receptor 2 (HER2) expression, and to investigate the association between clinicopathological characteristics and outcomes in HER2-low BC (HLBC) patients.MethodsA retrospective analysis was conducted on data from 998 female HLBC patients treated at the Breast Center of the Fourth Hospital of Hebei Medical University (Hebei, China) between January 1, 2017, and December 31, 2020. To address class imbalance, the synthetic minority over-sampling technique was applied. Feature selection was performed using the least absolute shrinkage and selection operator, followed by construction of the prediction model using the random forest algorithm. Model performance, including specificity and accuracy, was assessed using the receiver operating characteristic (ROC) curve and confusion matrix, comparing it against other ML models. Additionally, the log-rank test was employed to examine the relationship between selected features and patient outcomes in HLBC.ResultsThe random survival forest model demonstrated superior accuracy and specificity in predicting survival outcomes for HLBC patients. Compared with other ML models, it achieved more precise predictions of Disease-Free Survival (DFS) at 1, 2, and 3 years, with the area under the ROC curve (AUC) in the test and training cohorts measured at 0.726 and 0.819, 0.712 and 0.776, and 0.685 and 0.774, respectively. The analysis further identified a strong correlation between poor prognosis in HLBC patients and factors such as axillary lymph node dissection, family history, elevated topoisomerase (TOPO)-2 expression, advanced clinical stage, negative progesterone receptor status, P53 mutation, and increased Ki67 expression, observed across both cohorts.ConclusionsA novel ML model was developed for accurate prognosis prediction in HLBC patients, offering valuable insights into prognostic risk factors. This model equips clinicians with enhanced data to guide treatment decisions, ultimately contributing to improved patient outcomes.

Prediction of acute pancreatitis severity based on early CT radiomics

BackgroundThis study aims to develop and validate an integrated predictive model combining CT radiomics and clinical parameters for early assessment of acute pancreatitis severity.MethodsA retrospective cohort of 246 patients with acute pancreatitis was analyzed, with a 70%-30% split for training and validation groups. CT image segmentation was performed using ITK-SNAP, followed by the extraction of radiomics features. The stability of the radiomics features was assessed through inter-observer Intraclass Correlation Coefficient analysis. Feature selection was carried out using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation. A radiomics model was constructed through logistic regression to compute the radiomics score. Concurrently, univariate and multivariate logistic regression were employed to identify independent clinical risk factors for the clinical model. The radiomics score and clinical variables were integrated into a combined model, which was visualized with a nomogram. Model performance and net clinical benefit were evaluated through the area under the receiver operating characteristic curve (AUC), the DeLong test, and decision curve analysis.ResultsA total of 913 radiomics features demonstrated satisfactory consistency. Eight features were selected for the radiomics model. Serum calcium, C-reactive protein, and white blood cell count were identified as independent clinical predictors. The AUC of the radiomics model was 0.871 (95% CI, 0.793–0.949) in the training cohort and 0.859 (95% CI, 0.751–0.967) in the validation cohort. The clinical model achieved AUCs of 0.833 (95% CI, 0.756–0.910) and 0.810 (95% CI, 0.692–0.929) for the training and validation cohorts, respectively. The combined model outperformed both the radiomics and clinical models, with an AUC of 0.905 (95% CI, 0.837–0.973) in the training cohort and 0.908 (95% CI, 0.824–0.992) in the validation cohort. The DeLong test confirmed superior predictive performance of the combined model over both the radiomics and clinical models in the training cohort, and over the clinical model in the validation cohort. Decision curve analysis further demonstrated that the combined model provided greater net clinical benefit than the radiomics or clinical models alone.ConclusionThe clinical-radiomics model offers a novel tool for the early prediction of acute pancreatitis severity, providing valuable support for clinical decision-making.