Validation Dataset Research Articles

Prediction and mapping of leaf water content in Populus alba var. pyramidalis using hyperspectral imagery

Leaf water content (LWC) encapsulates critical aspects of tree physiology and is considered a proxy for assessing tree drought stress and the risk of forest decline; however, its measurement relies on destructive sampling and is thus less efficient. Advancements in hyperspectral imaging technology present new prospects for noninvasively evaluating LWC and mapping drought severity across forested regions. In this study, leaf samples were obtained from Populus alba var. pyramidalis, a species widely employed for constructing farmland shelterbelts in water-limited regions of northern China but notably susceptible to drought. These samples were dehydrated to varying degrees to generate concurrent LWC measurements and hyperspectral images, enabling the development of narrow-band and multivariate spectral prediction models for LWC estimation. Two visible-spectrum narrow-band indices identified, the single-band index (R627) and the band subtraction index (R437 − R444), demonstrated a strong correlation with LWC. Despite certain influences of variable preprocessing and selection on multivariate model performance, most models exhibited robust predictive accuracy for LWC. The FDRL-UVE-PLSR combination emerged as the optimal multivariate model, with R2 values reaching 0.9925 and 0.9853 and RMSE values below 0.0124 and 0.0264 for the calibration and validation datasets, respectively. Using this optimal model, along with localized spectral smoothing, moisture distribution across leaf surfaces was visualized, revealing lower water retention at the leaf margins compared to central regions. These methodologies provide critical insights into subtle water-associated physiological processes at the leaf scale and facilitate high-frequency, large-scale assessments and monitoring of drought stress levels and the risk of drought-induced tree mortality and forest degradation in drylands.

Plasma extracellular vesicles carry immune system-related peptides that predict human longevity.

Extracellular vesicles (EVs) play crucial roles in aging. In this National Institutes on Aging-funded study, we sought to identify circulating extracellular vesicle (EV) biomarkers indicative of longevity. The plasma EV proteome of 48 older adults (mean age 77.2 ± 1.7 years [range 72-80]; 50% female, 50% Black, 50% < 2-year survival, 50% ≥ 10-year survival) was analyzed by high-resolution mass spectrometry and flow cytometry. The ability of EV peptides to predict longevity was evaluated in discovery (n = 32) and validation (n = 16) datasets with areas under receiver operating characteristic curves (AUCs). Longevity-associated large EV (LEV) plasma subpopulations were mainly related to immune cells (HLA-ABC+, CD9+, and CD31+) and muscle cells (MCAD+ and RyR2+). Of 7960 identified plasma EV peptides (519 proteins), 46.4% were related to the immune system and 10.1% to muscle. Compared with short-lived older adults, 756 EV peptides (131 proteins) had a higher abundance, and 130 EV peptides (78 proteins) had a lower abundance in long-lived adults. Among longevity-associated peptides, 437 (58 proteins) were immune system related, and 12 (2 proteins) were muscle related. Using just three to five plasma EV peptides (mainly complement components C2-C6), we achieved high predictive accuracy for longevity (AUC range 0.91-1 in a hold-out validation dataset). Our findings suggest that immune cells produce longevity-associated plasma EVs and elucidate fundamental mechanisms regulating aging and longevity. EV longevity predictors suggest there may be merit in targeting complement pathways to extend lifespan, for instance, with any one of the multiple complement inhibitors currently available or in clinical development.

Unveiling the shared genes between systemic sclerosis and lung cancer

The risk of lung cancer is significantly increased in patients with systemic sclerosis (SSc), yet the specific genes underlying this association remain unexplored. Our study aims to identify genes shared by SSc and lung cancer. We identified differentially expressed genes (DEGs) from SSc and lung adenocarcinoma (LUAD) datasets (SSc: GSE95065, LUAD: GSE136043) in the GEO database. We found shared genes by intersecting top genes in protein–protein interaction networks by the STRING database. The area under the ROC curve (AUC) was calculated for each shared gene in validation datasets (SSc: GSE231692; LUAD: GSE43458), identifying PRKG2 as the core shared gene. We used the UALCAN platform to assess PRKG2 expression in LUAD patients at various stages and lymph node metastasis states, and compared disease-free survival (DFS) between low and high PRKG2 expression LUAD groups. PRKG2 was overexpressed in A549 cells to study its impact on lung cancer cell proliferation and invasion in vitro. We identified seven shared genes (SCN7A, AGTR1, WIF1, PRKG2, LTF, AQP4, COL10A1), with the AUC for PRKG2 exceeding 0.93 in both diseases (SSc AUC = 0.973; LUAD AUC = 0.939). The PRKG2 expression levels of LUAD patients with different clinical stages and lymph node metastasis states were consistently lower than those observed in normal individuals. The DFS of LUAD patients in the high PRKG2 expression group was higher than that in the low expression group (p = 0.028). In vitro experiments confirmed elevated PRKG2 expression inhibits the proliferation and invasion of lung cancer cells. PRKG2 is one of the genes shared by SSc and lung cancer, affecting the proliferation and invasion of lung cancer cells.

Development and internal validation of El-Shazly-Buchholz's nomogram to predict postoperative complications after PCNL: A multicenter study.

A model to predict the risk of surgical complications following percutaneous nephrolithotomy (PCNL) could be a useful tool to guide clinical decision-making. The aim of this study was to develop a simple and widely applicable stratification tool to be used for patient counseling, surgical planning, evaluation of outcomes, and academic reporting. Data of patients who underwent PCNL were retrieved from the database of the collaborating centers including demographics of patients, characteristics of their stones and urinary tracts, and perioperative data. The primary outcome was the development of postoperative complications. Data were randomly split into a training dataset (85%) and a validation dataset (15%). A univariate and multivariate logistic regression analysis of the training dataset was performed to identify independent predictors of postoperative complications. Model variables were used to construct a nomogram that was internally validated on the testing dataset by measuring calibration, discrimination, and plotting the decision curve. Six hundred thirty one patients (245 Males) with a median (IQR) age of 49 (37-56) years were included. Post-operative complications occurred in 147 (23.3%) patients. Significant predictors of complications included preoperative urine culture (p < 0.001), largest stone diameter (p = 0.02), and intraoperative blood loss (p = 0.002). A nomogram was developed from the predictors and applied to the validation dataset showing an area under the curve (95%CI) of 66.4% (52.2;80.6). This new scoring system emphasized patient characteristics and operative details rather than stone characters to predict the morbidity of PCNL. Furthermore, it should facilitate risk adjustment, enabling physicians to better define the nephrolithiasis disease continuum and identify patients who should be referred to tertiary care centers.

A light-weight factorized convolutions based dual-input fuzzy-CNN for efficient motor bearing fault diagnosis

ABSTRACT Efficient and timely identification of bearing faults is imperative to ensure operational normalcy, reduced down-times and health hazards in motor fault tolerant control systems. This paper proposes a fault diagnosis method that combines the vibration information with time-varying rotational speed for effective fault diagnosis under non-stationary conditions. The complex wavelet transform is used to encode both the vibrational and rotational signals in 2d representations for spatial feature extraction. An efficient algorithm is proposed to select the mother wavelet with the least average entropy. Moreover, a spatial decomposition-based approach using factorised convolutions is used to create a light-weight fuzzy convolutional neural network named Split-Operation Fuzzy Convolutional Neural Network (SOF-CNN) for semi rule-based feature extraction and classification. The performance was evaluated on the University of Ottawa (UOO) dataset for multiple speed conditions and cross-condition validation with the highest accuracy yield being 99.92% from the fourth condition and 3rd trial acquired via averaged 10-Fold Cross Validation. The average accuracy yield across all the scenario was 99.89% with 64.73% being the highest accuracy for cross-condition validation. The performance was evaluated across a diverse range of evaluation criterion including both quantitative and statistical tests.

Uncertainty-aware automatic TNM staging classification for [18F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning

Background[18F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text. Accordingly, we developed a multi-task ‘TNMu’ classifier to classify the presence/absence of tumour, node, metastasis (‘TNM’) findings (as defined by The Eight Edition of TNM Staging for Lung Cancer). This is combined with an uncertainty classification task (‘u’) to account for studies with ambiguous TNM status.Methods2498 reports were annotated by a nuclear medicine physician and split into train, validation, and test datasets. For additional evaluation an external dataset (n = 461 reports) was created, and annotated by two nuclear medicine physicians with agreement reached on all examples. We trained and evaluated eleven publicly available PLMs to determine which is most effective for PET-CT reports, and compared multi-task, single task and traditional machine learning approaches.ResultsWe find that a multi-task approach with GatorTron as PLM achieves the best performance, with an overall accuracy (all four tasks correct) of 84% and a Hamming loss of 0.05 on the internal test dataset, and 79% and 0.07 on the external test dataset. Performance on the individual TNM tasks approached expert performance with macro average F1 scores of 0.91, 0.95 and 0.90 respectively on external data. For uncertainty an F1 of 0.77 is achieved.ConclusionsOur ‘TNMu’ classifier successfully extracts TNM staging information from internal and external PET-CT reports. We concluded that multi-task approaches result in the best performance, and better computational efficiency over single task PLM approaches. We believe these models can improve PET-CT services by assisting in auditing, creating research cohorts, and developing decision support systems. Our approach to handling uncertainty represents a novel first step but has room for further refinement.

Learning to Train and to Explain a Deep Survival Model with Large-Scale Ovarian Cancer Transcriptomic Data

Background/Objectives: Ovarian cancer is a complex disease with poor outcomes that affects women worldwide. The lack of successful therapeutic options for this malignancy has led to the need to identify novel biomarkers for patient stratification. Here, we aim to develop the outcome predictors based on the gene expression data as they may serve to identify categories of patients who are more likely to respond to certain therapies. Methods: We used The Cancer Genome Atlas (TCGA) ovarian cancer transcriptomic data from 372 patients and approximately 16,600 genes to train and evaluate the deep learning survival models. In addition, we collected an in-house validation dataset of 12 patients to assess the performance of the trained survival models for their direct use in clinical practice. Despite deceptive generalization capabilities, we demonstrated how our model can be interpreted to uncover biological processes associated with survival. We calculated the contributions of the input genes to the output of the best trained model and derived the corresponding molecular pathways. Results: These pathways allowed us to stratify the TCGA patients into high-risk and low-risk groups (p-value 0.025). We validated the stratification ability of the identified pathways on the in-house dataset consisting of 12 patients (p-value 0.229) and on the external clinical and molecular dataset consisting of 274 patients (p-value 0.006). Conclusions: The deep learning-based models for survival prediction with RNA-seq data could be used to detect and interpret the gene-sets associated with survival in ovarian cancer patients and open a new avenue for future research.

Construction and validation of a readmission risk prediction model for elderly patients with coronary heart disease

BackgroundTo investigate the risk factors for readmission of elderly patients with coronary artery disease, and to construct and validate a predictive model for readmission risk of elderly patients with coronary artery disease within 3 years by applying machine learning method.MethodsWe selected 575 elderly patients with CHD admitted to the Affiliated Lu’an Hospital of Anhui Medical University from January 2020 to January 2023. Based on whether patients were readmitted within 3 years, they were divided into two groups: those readmitted within 3 years (215 patients) and those not readmitted within 3 years (360 patients). Lasso regression and multivariate logistic regression were used to compare the predictive value of these models. XGBoost, LR, RF, KNN and DT algorithms were used to build prediction models for readmission risk. ROC curves and calibration plots were used to evaluate the prediction performance of the model. For external validation, 143 patients who were admitted between February and June 2023 from a different associated hospital in Lu'an City were also used.ResultsThe XGBoost model demonstrated the most accurate prediction performance out of the five machine learning techniques. Diabetes, Red blood cell distribution width (RDW), and Triglyceride glucose-body mass index (TyG-BMI), as determined by Lasso regression and multivariate logistic regression. Calibration plot analysis demonstrated that the XGBoost model maintained strong calibration performance across both training and testing datasets, with calibration curves closely aligning with the ideal curve. This alignment signifies a high level of concordance between predicted probabilities and observed event rates. Additionally, decision curve analysis highlighted that both decision trees and XGBoost models achieved higher net benefits within the majority of threshold ranges, emphasizing their significant potential in clinical decision-making processes. The XGBoost model's area under the ROC curve (AUC) reached 0.903, while the external validation dataset yielded an AUC of 0.891, further validating the model's predictive accuracy and its ability to generalize across different datasets.ConclusionTyG-BMI, RDW, and diabetes mellitus at the time of admission are the factors affecting readmission of elderly patients with coronary artery disease, and the model constructed based on the XGBoost algorithm for readmission risk prediction has good predictive efficacy, which can provide guidance for identifying high-risk patients and timely intervention strategies.

Landslide susceptibility mapping using combined geospatial, FR and AHP models: a case study from Ethiopia’s highlands

This study performed landslide susceptibility mapping in Awabel Woreda, situated in the east Gojjam zone of the Amhara region in Ethiopia. The occurrence of landslides and slope instability is widespread in Awabel Woreda, leading to the devastation of agricultural fields, crops, and residences, the demise of animal life, and the forced relocation of local inhabitants from their dwellings. The present investigation utilized remote sensing and GIS techniques in conjunction with the Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) models to map landslide risk zones. For the landslide susceptibility mapping of this study, nine causative factors, such as “elevation, slope, aspect, drainage density, lineament density, land use and land cover (LULC), soil texture, rainfall, and lithology”, were considered. A total of 130 past landslide events were identified via field survey and Google Earth image, of which 70% (91) of them were used as training datasets and 30% (39) were used as validation datasets of the proposed models (i.e., FR and AHP). The nine contributing variables and their classes were evaluated, and factor weights were computed using the IDRISI Selva 17.0 expert programme. The landslide susceptibility indexes (LSI) of the FR and AHP models were calculated and categorized into five relative zones using ArcGIS 10.7. The landslide susceptibility map (LSM) produced by the FR model shows that 93.2 km2 (14.52%) of the study area is classified as very low susceptibility, 167.51 km2 (26.09%) as low susceptibility, 174.10 km2 (27.12%) as moderate susceptibility, 137.03 km2 (21.34%) as high susceptibility, and 70.30 km2 (10.93%) as very high susceptibility to landslides. Based on the AHP model's LSM, different landslide susceptibility zones were identified in the study area. Specifically, 140.46 km2 (21.88%) of the region falls into the very low susceptibility zone, 116.78 km2 (18.59%) falls into the low susceptibility zone, 147.94 km2 (23.04%) falls into the moderate susceptibility zone, 154.04 km2 (23.99%) falls into the high susceptibility zone, and 82.78 km2 (12.89%) falls into the very high susceptibility zone. The validation investigation demonstrated that the FR and AHP models had accuracy rates of 89.73 and 87.18%, respectively. The FR model exhibited marginally more accurate results than AHP, primarily because of the direct correlation between previous and current occurrences of landslides. Nevertheless, the AHP model’s effectiveness relies on the individual’s expertise and the characteristics of the components that cause the outcome. The landslide susceptibility maps generated through these models provide valuable insights for land management and disaster mitigation efforts, with delineated zones indicating very low to very high susceptibility areas.

Metabolomic studies reveal and validate potential biomarkers of diabetic retinopathy in two Chinese datasets with type 2 diabetes: a cross-sectional study

BackgroundDiabetic retinopathy (DR) is a major microvascular complication of diabetes mellitus and causes vision impairment and blindness. The presence of major risk factors for DR, such as high levels of HbA1c, does not predict all DR pathogenesis in the clinic, which suggests that uncovering the underlying mechanisms and identifying novel markers are needed. Previous evidence has shown that the serum metabolic signature of DR is unique and detectable compared with that of diabetes mellitus (DM). Here, we aimed to identify serum metabolites as reliable biomarkers for the presence of DR in type 2 DM (T2DM) patients.MethodsWe performed untargeted and targeted metabolomic studies using liquid chromatography‒mass spectrometry (LC‒MS) and multiple reaction monitoring (MRM) methods on the serum samples of T2DM patients. For the discovery dataset, 39 DR patients and 39 non-DR (NDR) patients were included. For the validation dataset, 95 DR patients and 95 non-DR (NDR) patients were included. Receiver operating characteristic curve analysis was performed to evaluate the discriminating power of the metabolites. Binary logistic regression models were fit to evaluate the associations of metabolite peak areas or neurotransmitter concentrations with the presence of DR and adjusted for known risk factors.ResultsA total of 7123 metabolites were tested. The 39 DR patients had a mean age of 56 years with an average diabetes duration of 12 years, and the 39 NDR patients had a mean age of 57 years with an average diabetes duration of 11 years. Nine serum candidate markers were further identified. Six out of nine markers were associated with DR after we adjusted for covariates, including blood pressure, HbA1c, diabetes duration, fasting blood glucose, triglyceride, eGFR etc. Among them, eicosapentaenoic acid (EPA) and L-tyrosine were validated in an independent, risk factor-matched sample set. The serum L-tyrosine concentration was decreased in DR group by 47% (-0.22 ± 0.87 vs. 0.48 ± 1.05, P < 0.001), of which the cutoff value was 0.10 mg/ml, with 86% sensitivity and 40% specificity (AUC = 0.62, 95% CI = 0.54–0.70, P = 0.005).ConclusionsLow levels of circulating L-Tyrosine indicate retinopathy occurrence in T2DM population.

A Novel Effective Models for Identifying BRCA Patients and Optimizing Clinical Treatments.

This study aimed to develop an effective model that identifies high-risk breast cancer (BRCA) patients and optimizes clinical treatments. This study includes five public datasets, TCGA-BRCA as the training dataset and other cohorts as the validation datasets. Machine learning algorithms for finding key tumor-associated immune gene pairs (TAIGPs). These TAIGPs were used to construct tumor-associated immune gene pair index (TAIGPI) by multivariate analysis and further validated on the validation datasets. In addition, the differences in clinical prognosis, biological characteristics, and treatment benefits between high and low TAIGPI groups were further analyzed. The TAIGPI was established by 36 TAIGPs. Better clinical outcomes in the low TAIGPI patients, with consistent results, were also obtained in the validation datasets. The study showed that patients in the low TAIGPI group had a high infiltration of immune cells and low proliferative activity of tumor cells. In contrast, patients in the high TAIGPI group exhibited low infiltration of immune cells and high proliferative activity of tumor cells. In addition, patients in the low TAIGPI group are more likely to benefit from chemotherapy, adjuvant chemotherapy, or immunotherapy. The TAIGPI can be an effective predictive strategy for the clinical prognosis of breast cancer patients, providing new insights into personalized treatment options for breast cancer patients.

Use Self-Training Random Forest for Predicting Winter Wheat Yield

The effectiveness of supervised ML heavily depends on having a large, accurate, and diverse annotated dataset, which poses a challenge in applying ML for yield prediction. To address this issue, we developed a self-training random forest algorithm capable of automatically expanding the annotated dataset. Specifically, we trained a random forest regressor model using a small amount of annotated data. This model was then utilized to generate new annotations, thereby automatically extending the training dataset through self-training. Our experiments involved collecting data from over 30 winter wheat varieties during the 2019–2020 and 2021–2022 growing seasons. The testing results indicated that our model achieved an R2 of 0.84, RMSE of 627.94 kg/ha, and MAE of 516.94 kg/ha in the test dataset, while the validation dataset yielded an R2 of 0.81, RMSE of 692.96 kg/ha, and MAE of 550.62 kg/ha. In comparison, the standard random forest resulted in an R2 of 0.81, RMSE of 681.02 kg/ha, and MAE of 568.97 kg/ha in the test dataset, with validation results of an R2 of 0.79, RMSE of 736.24 kg/ha, and MAE of 585.85 kg/ha. Overall, these results demonstrate that our self-training random forest algorithm is a practical and effective solution for expanding annotated datasets, thereby enhancing the prediction accuracy of ML models in winter wheat yield forecasting.

Factors Influencing the Firm Performance of Digital Merchandising Businesses in Thailand

Thailand's national strategy focuses on making the country more competitive by helping modern entrepreneurs succeed both within the country and internationally. This research aims for development a new framework that shows how critical elements like market capabilities, IT capabilities, digitalization, and innovation contribute to the success of digital businesses in Thailand. The study uses a mixed method research, combining an integration of qualitative and quantitative techniques are utilized. Within qualitative research, we gathering insights from 20 experts, by using e-Rough Set Delphi method. In quantitative research, The research was based on a collection of 611 valid datasets, which were gathered from digital merchandising business owners in Thailand via online questionnaires. The results indicate that market capabilities, IT capabilities, digitalization, and innovation all have an influential and meaningful effect on business performance. Research also emphasizes importance of investing in IT infrastructure, adopting advanced IT strategies, and improving market capabilities to stay competitive in the fast-changing digital market. The insights from this study are valuable for strengthening the digital economy, as adopting digital technologies and innovations can help businesses grow sustainably and positively impact the overall economy and society.

Secondary size distributions for single drop impacts at high wall superheat

The impingement of liquid sprays on hot walls is used extensively in both spray-cooling systems and in combustor fuel injection applications. At low and moderate wall temperatures, the secondary size distributions have been reported in the literature. For high wall superheat conditions, particularly for real multicomponent fuels, this secondary size distribution has received less attention. Understanding the resultant size distribution for a spray-wall impact is key to capturing vaporization and local mixture for fuel-spray impingement. In this study, single drop impacts for a range of single-component (n-decane) and multicomponent jet fuel (F-24) are characterized through dual-view imaging. Secondary droplets are captured for impact Weber numbers of 100–600 and wall temperatures spanning the nucleate and film boiling (Leidenfrost) regimes. Imaging through a transparent sapphire substrate is used to capture the impact phenomena and impact-induced breakup of impacting drops. We report empirical correlations for the secondary droplet size for single-component (n-decane) and multicomponent (F-24) liquid fuels with varying wall temperature to provide validation datasets for spray-wall simulations.

Normalized Difference Vegetation Index Prediction for Blueberry Plant Health from RGB Images: A Clustering and Deep Learning Approach

In precision agriculture (PA), monitoring individual plant health is crucial for optimizing yields and minimizing resources. The normalized difference vegetation index (NDVI), a widely used health indicator, typically relies on expensive multispectral cameras. This study introduces a method for predicting the NDVI of blueberry plants using RGB images and deep learning, offering a cost-effective alternative. To identify individual plant bushes, K-means and Gaussian Mixture Model (GMM) clustering were applied. RGB images were transformed into the HSL (hue, saturation, lightness) color space, and the hue channel was constrained using percentiles to exclude extreme values while preserving relevant plant hues. Further refinement was achieved through adaptive pixel-to-pixel distance filtering combined with the Davies–Bouldin Index (DBI) to eliminate pixels deviating from the compact cluster structure. This enhanced clustering accuracy and enabled precise NDVI calculations. A convolutional neural network (CNN) was trained and tested to predict NDVI-based health indices. The model achieved strong performance with mean squared losses of 0.0074, 0.0044, and 0.0021 for training, validation, and test datasets, respectively. The test dataset also yielded a mean absolute error of 0.0369 and a mean percentage error of 4.5851. These results demonstrate the NDVI prediction method’s potential for cost-effective, real-time plant health assessment, particularly in agrobotics.

Machine learning approach to identify phenotypes in patients with ischaemic heart failure with reduced ejection fraction.

Patients experiencing ischaemic heart failure with reduced ejection fraction (HFrEF) represent a diverse group. We hypothesize that machine learning clustering can help separate distinctive patient phenotypes, paving the way for personalized management. A total of 8591 ischaemic HFrEF patients pooled from the EPHESUS and CAPRICORN trials (64 ± 12 years; 28% women) were included in this analysis. Clusters were identified using both clinical and biological variables. Association between clusters and the composite of (i) heart failure hospitalization or all-cause death, (ii) cardiovascular (CV) hospitalization or all-cause death, and (iii) major adverse CV events was assessed. The derived algorithm was applied in the COMMANDER-HF trial (n = 5022) for external validation. Five clinical distinctive clusters were identified: Cluster 1 (n = 2161) with the older patients, higher prevalence of atrial fibrillation and previous CV events; Cluster 2 (n = 1376) with the higher prevalence of older hypertensive women and smoking habit; Cluster 3 (n = 1157) with the higher prevalence of diabetes and peripheral artery disease; Cluster 4 (n = 2073) with relatively younger patients, mostly men and with the higher left ventricular ejection fraction; Cluster 5 (n = 1824) with the younger patients and lower CV events burden. Cluster membership was efficiently predicted by a random forest algorithm. Clusters were significantly associated with outcomes in derivation and validation datasets, with Cluster 1 having the highest risk, and Cluster 4 the lowest. Mineralocorticoid receptor antagonist benefit on CV hospitalization or all-cause death was magnified in clusters with the lowest risk of events (Clusters 2 and 4). Clustering reveals distinct risk subgroups in the heterogeneous array of ischaemic HFrEF patients. This classification, accessible online, could enhance future outcome predictions for ischaemic HFrEF cases.

Development and Validation of a Nomogram for Predicting Systemic Inflammatory Response Syndrome Following Percutaneous Nephrolithotomy.

Objective: To develop and validate a nomogram for predicting the occurrence of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL), aiming to enhance clinical decision-making and treatment planning. Methods: Clinical data of 1,047 patients undergoing PCNL at a single-center hospital between 2017 and 2023 were retrospectively analyzed. Independent risk factors influencing SIRS occurrence were identified through multi-variable logistic regression analysis, and a predictive model was constructed. The model's accuracy and reliability were evaluated through internal training and validation set. Results: Multi-variable regression analysis identified six key predictive factors: gender, diabetes, urine culture results, stone surface, staghorn stones, and operative time, leading to the establishment of a nomogram predictive model. Internal validation and validation set data demonstrated the model's high predictive accuracy and reliability, with areas under the receiver operating characteristic curve of 0.718 and 0.723, respectively. Conclusions: A nomogram predictive model for assessing SIRS following PCNL was successfully developed and validated. This model provides clinicians with a valuable tool for personalized treatment planning and implementing preventive measures.

Evaluating microvascular invasion in hepatitis B virus-related hepatocellular carcinoma based on contrast-enhanced computed tomography radiomics and clinicoradiological factors.

Microvascular invasion (MVI) is a significant indicator of the aggressive behavior of hepatocellular carcinoma (HCC). Expanding the surgical resection margin and performing anatomical liver resection may improve outcomes in patients with MVI. However, no reliable preoperative method currently exists to predict MVI status or to identify patients at high-risk group (M2). To develop and validate models based on contrast-enhanced computed tomography (CECT) radiomics and clinicoradiological factors to predict MVI and identify M2 among patients with hepatitis B virus-related HCC (HBV-HCC). The ultimate goal of the study was to guide surgical decision-making. A total of 270 patients who underwent surgical resection were retrospectively analyzed. The cohort was divided into a training dataset (189 patients) and a validation dataset (81) with a 7:3 ratio. Radiomics features were selected using intra-class correlation coefficient analysis, Pearson or Spearman's correlation analysis, and the least absolute shrinkage and selection operator algorithm, leading to the construction of radscores from CECT images. Univariate and multivariate analyses identified significant clinicoradiological factors and radscores associated with MVI and M2, which were subsequently incorporated into predictive models. The models' performance was evaluated using calibration, discrimination, and clinical utility analysis. Independent risk factors for MVI included non-smooth tumor margins, absence of a peritumoral hypointensity ring, and a high radscore based on delayed-phase CECT images. The MVI prediction model incorporating these factors achieved an area under the curve (AUC) of 0.841 in the training dataset and 0.768 in the validation dataset. The M2 prediction model, which integrated the radscore from the 5 mm peritumoral area in the CECT arterial phase, α-fetoprotein level, enhancing capsule, and aspartate aminotransferase level achieved an AUC of 0.865 in the training dataset and 0.798 in the validation dataset. Calibration and decision curve analyses confirmed the models' good fit and clinical utility. Multivariable models were constructed by combining clinicoradiological risk factors and radscores to preoperatively predict MVI and identify M2 among patients with HBV-HCC. Further studies are needed to evaluate the practical application of these models in clinical settings.

Identification of gene signatures relevant to the efficacy of immune checkpoint inhibitors in non-small cell lung cancer.

Despite significant advancements in the treatment of non-small cell lung cancer (NSCLC) through immunotherapy, many patients still exhibit resistance to this approach. This study aims to identify the characteristics of individuals who can benefit from immunotherapy, especially immune checkpoint inhibitors (ICIs), and to investigate optimal strategies for patients who experience resistance to it. Data on gene expression patterns and clinical information from NSCLC patients who underwent immunotherapy were obtained from the Gene Expression Omnibus databases. A predictive signature for immunotherapy prognosis was developed using a training dataset and validated with validation datasets. Immune landscape and immunotherapy responsiveness analyses were conducted to assess the risk signature. Additionally, data from a study on immunotherapy were used to evaluate the correlation between MNX1 mutation and the effectiveness of ICIs, including clinical data and whole exome sequencing data. We identified 7 genes in NSCLC using RNA-seq data that were significantly associated with the efficacy of immunotherapy. Based on these genes, a risk signature was created to predict the efficacy of ICIs. Patients in the low-risk group had better outcomes compared to those in the high-risk group after receiving ICIs. Additionally, our analysis of the immune landscape revealed a significant association between the high-risk signature and an immunosuppressive state. We also discovered an unexpected role of tumor-specific MNX1 and HOXD1 in suppressing the immune response against cancer. Notably, NSCLC patients with MNX1 mutations experienced prolonged progression-free survival. Furthermore, we identified several medications that exhibited increased sensitivity in patients with high MNX1 expression, with topoisomerase inhibitors showing the highest level of sensitivity. This could be a potential strategy to improve the efficacy of ICIs. The risk signature has demonstrated its effectiveness in forecasting the prognosis of NSCLC treated with ICIs, enabling better patient stratification and more accurate prediction of immunotherapy response. Moreover, MNX1 and HOXD1 have been identified as key molecules related to immunotherapy resistance. Inhibition of these molecules, combined with current ICIs, offers novel strategies for the management of NSCLC patients.

Identification of genetic associations between acute myocardial infarction and non-small cell lung cancer

IntroductionA growing body of evidence suggests a potential connection between myocardial infarction (MI) and lung cancer (LC). However, the underlying pathogenesis and molecular mechanisms remain unclear. This research aims to identify common genes and pathways between MI and LC through bioinformatics analysis.MethodsTwo public datasets (GSE166780 and GSE8569) were analyzed to identify differentially expressed genes (DEGs). Common DEGs were enriched using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Hub genes were identified and their diagnostic performance was evaluated. Gene co-expression networks, as well as regulatory networks involving miRNA-hub genes and TF-hub genes, were also constructed. Finally, candidate drugs were predicted.ResultsAmong the datasets, 34 common trend DEGs were identified. Enrichment analysis linked these DEGs to key biological processes, cellular components, and molecular functions. Eight hub genes (CEBPA, TGFBR2, EZH2, JUNB, JUN, FOS, PLAU, COL1A1) were identified, demonstrating promising diagnostic accuracy. Key transcription factors associated with these hub genes include SP1, ESR1, CREB1, ETS1, NFKB1, and RELA, while key miRNAs include hsa-mir-101-3p, hsa-mir-124-3p, hsa-mir-29c-3p, hsa-mir-93-5p, and hsa-mir-155-5p. Additionally, potential therapeutic drugs were identified, with zoledronic acid anhydrous showing potential value in reducing the co-occurrence of the two diseases.DiscussionThis study identified eight common signature genes shared between NSCLC and AMI. Validation datasets confirmed the diagnostic value of key hub genes COL1A1 and PLAU. These findings suggest that shared hub genes may serve as novel therapeutic targets for patients with both diseases. Ten candidate drugs were predicted, with zoledronic acid showing potential for targeting dual hub genes, offering a promising therapeutic approach for the comorbidity of lung cancer and myocardial infarction.