Search Results (1,542)

Triple-negative breast cancer (TNBC) represents the most aggressive breast cancer subtype, with its highly heterogeneous tumor microenvironment posing substantial challenges for precision diagnosis and therapy. To address this, we aim to construct a novel prognostic framework based on tumor-immune interactions. Through integrative analysis of single-cell RNA sequencing data from 30 TNBC samples (106,132 cells), we identify key tumor expression metaprograms and uncover their interaction with an immunosuppressive dendritic-cell subset, a process associated with the NECTIN1–NECTIN4 axis. Leveraging these interactions, we developed and validated two immunological prognostic models using multi-cohort transcriptomic data, including the stress response tumor cell and pDC_CLEC4C prognostic model (SPSM) and the immune response tumor cell and pDC_CLEC4C prognostic model (IPSM). These models effectively stratified TNBC patients into distinct risk groups, with the low-risk group characterized by an immunologically active microenvironment and elevated expression of immune checkpoint genes, suggesting a potential responsiveness to immunotherapy. Furthermore, we identified several potential therapeutic agents, including imatinib and bortezomib. Collectively, our dual-model framework provides a tool for risk stratification, offers translational insights for precision treatment, and presents new directions for understanding TNBC heterogeneity and therapeutic development. Full article

To enhance the assessment accuracy of tunnel face instability risks of active collapse during shield tunneling, this study establishes a novel unified analytical framework that couples the effects of unsaturated transient seepage induced by excavation drainage with soil stratification and heterogeneity. Grounded in unsaturated effective stress theory, the framework explicitly incorporates matric suction into the Mohr–Coulomb failure criterion via suction stress and apparent cohesion. By employing a horizontal two-layer nonhomogeneous soil model and solving the one-dimensional vertical Richards’ equation, an analytical solution for the face drainage boundary is derived to quantify the spatiotemporal evolution of suction stress and apparent cohesion. Subsequently, the critical support pressure is evaluated using the upper bound theorem of limit analysis, incorporating a horizontal layer-discretized rotational failure mechanism and the power balance equation. The validity of the proposed framework is confirmed through comparative analyses. Parametric studies reveal that in the upper hard and lower soft strata, the critical support pressure decreases and converges over time, indicating that unsaturated transient seepage exerts a significant influence in the short term that stabilizes over the long term. Additionally, sand–silt stratum exhibits lower overall stability and higher sensitivity to groundwater levels and temporal factors compared to silt–clay stratum. Conversely, silt–clay stratum displays a non-monotonic evolution with increasing cover-to-diameter ratios (C/D), reaching a minimum critical support pressure at approximately C/D = 1.1. Regarding heterogeneity, the internal friction angle of the lower layer exerts dominant control over the critical support pressure compared to seepage velocity, while the influence of other strength parameters remains secondary. These findings provide a theoretical basis for the time-dependent design of tunnel face support pressure under excavation drainage conditions. Full article

Background/Objectives: Ovarian cancer has the highest mortality among gynecological malignancies, with platinum resistance significantly contributing to poor prognosis. We aimed to develop a multimodal model (MMHC-OCPR) to predict platinum response and recurrence risk, enabling earlier personalized treatment and improved outcomes. Methods: This multicenter retrospective study included a combined cohort of 431 patients, comprising 1182 whole slide images (WSIs) curated from two independent datasets. The primary cohort consisted of 376 patients from the National Cancer Center (China), which was further partitioned into training, validation and internal test sets to ensure model development and evaluation. An additional external test cohort was incorporated using publicly available data from TCGA, enhancing the generalizability of our findings. We implemented a weakly supervised multiple instance learning framework to integrate histopathological imaging with clinicopathological variables, further strengthened by the incorporation of the transformer-based pretrained encoder UNI2-h, which enhanced the model’s predictive performance. Results: All patients in the primary cohort had pathology slides collected from primary ovarian tumors and metastatic tumor, along with clinical factors related to prognosis and treatment response. The baseline platinum response classifier using primary WSIs achieved an AUC of 0.896 in the internal test group and 0.876 in the external test group. Integration of metastatic WSIs and clinical data inputs yielded a superior AUC of 0.914 in the internal test set. The recurrence risk model demonstrated a C-index of 0.801, rising to 0.838 after multimodal enhancement. The model stratified patients into low-, intermediate- and high-risk groups with 2-year progression-free survival rates of 77.3%, 48.0% and 2.0%, respectively. Conclusions: Our model enables the early detection of platinum resistance, guiding timely treatment intensification. The recurrence risk stratification supports personalized management by identifying patients with favorable outcomes following surgery and chemotherapy, potentially sparing them from maintenance therapy to reduce associated toxicity, cost, and enhance quality of life. Full article

Despite successful revascularization, patients with non-ST elevation myocardial infarction (NSTEMI) remain at higher risk of mortality and morbidity. Accurately predicting mortality risk in this cohort can improve outcomes through timely interventions. This study for the first time predicts 1-year all-cause mortality in an NSTEMI cohort using features extracted primarily from the aortic pressure (AP) signal recorded during cardiac catheterization. We analyzed data from 497 NSTEMI patients (66.3 ± 12.9 years, 187 (37.6%) females) retrospectively. We developed three survival models, the multivariate Cox proportional hazards, DeepSurv, and random survival forest, to predict mortality. Then, used Shapley additive explanations (SHAP) to interpret the decision-making process of the best survival model. Using 5-fold stratified cross-validation, DeepSurv achieved an average C-index of 0.935, an IBS of 0.028, and a mean time-dependent AUC of 0.939, outperforming the other models. Ejection systolic time, ejection systolic period, the difference between systolic blood pressure and dicrotic notch pressure (DesP), skewness, the age-modified shock index, and myocardial oxygen supply/demand ratio were identified by SHAP as the most characteristic AP features. In conclusion, AP signal features offer valuable prognostic insight for predicting 1-year all-cause mortality in the NSTEMI population, leading to enhanced risk stratification and clinical decision-making. Full article

Background and Objectives: The optimal pharmacological strategy to mitigate progressive vertebral collapse during the acute phase of osteoporotic vertebral compression fractures (OVCFs) remains a subject of debate. This initial 3-month window is the most critical period for evaluating the structural stability of the fracture, as the majority of progressive height loss occurs before solid bone union is achieved, directly influencing the decision to continue conservative management or transition to surgical intervention. Materials and Methods: In this retrospective study, 123 patients were allocated to control (n = 26), denosumab (n = 35), teriparatide (n = 30), or romosozumab (n = 32) groups. Treatment choice was non-randomized, driven by clinical pragmatism and patient preference. Serial changes in vertebral compression rate (VCR) and pain (VAS) were analyzed over 3 months using linear mixed models (LMMs) specifically adjusted for baseline imbalances in initial VCR. Results: In the unadjusted analysis, DMAB appeared to show a slower progression of compression compared to the control group. However, after adjusting for the initial VCR, no significant structural benefit was observed in any medication group (p > 0.05), with all groups showing small effect sizes (Cohen’s d < 0.4). In contrast, unstable fracture morphology was identified as the most potent driver of vertebral collapse (β = 2.758, 95% CI: 1.51–4.01, p < 0.001). Clinically, the RM group showed significantly lower overall pain levels throughout the follow-up period compared to the control group (p = 0.014). Conclusions: Short-term osteoporosis medication does not significantly mitigate vertebral collapse during the acute phase of OVCFs. Practically, these findings suggest that unstable fracture morphology and the baseline VCR—reflecting a potential ‘floor effect’ where less initially collapsed vertebrae may undergo more significant progression—are more informative predictors of acute collapse than medication choice. Consequently, early imaging-based risk stratification is crucial to identify patients at high risk for progressive deformity, regardless of their pharmacological regimen. Full article

Background: Interleukin-38 (IL-38) is an anti-inflammatory IL-1—family cytokine implicated in limiting tissue injury by its anti-inflammatory character. We evaluated the diagnostic discrimination and prognostic relevance in steatotic liver disease (SLD). Methods: We conducted a prospective, monocentric cohort analysis of 184 patients with SLD (n = 176) and healthy controls (n = 8). We tested group differences using Mann–Whitney U or Kruskal–Wallis; determined diagnostic quality using ROC curves. Logistic regression was used to assess the relationship with decompensation. Associations with MELD and routine laboratory parameters were modeled using Spearman correlation and linear regression. We analyzed survival using Kaplan–Meier and Cox regression. Findings: IL-38 concentrations were found to be higher in decompensated (n = 94) than in compensated patients (n = 82) (p < 0.001). MELD was positively associated with IL-38 (p < 0.001; 95% CI 0.057–0.120). This corresponds to a 9.2% increase in IL-38 per 1-point increase in MELD (95% CI 5.9–12.7%). IL-38 correlated positively with the MELD score (p < 0.001) and with bilirubin/AST/LDH. In the combination model (MELD + IL-38 ± CRP), a very good AUC ≈ 0.92 was achieved. Conclusion: IL-38 reflects the severity of steatotic liver disease and is therefore a potentially predictive biomarker for early risk stratification and therapy monitoring. Full article

Surgical resection combined with chemotherapy offers the best chance of survival in pancreatic ductal adenocarcinoma (PDAC), but many will experience recurrence and early mortality. We examined soluble AXL (sAXL), a blood protein, for its ability to predict 6-month mortality after resection and compared it to CA19-9. Fifty-four patients with PDAC who underwent tumour resection were analyzed to assess biomarker performance and identify optimal cut-off levels. The cut-off for sAXL was 40.26 ng/mL (sensitivity 0.729; specificity 0.643), while it 253.3 U/mL for CA19-9 (sensitivity 0.591; specificity 0.621). Patients with sAXL > 40.26 ng/mL had a non-significant trend toward worse survival (log-rank p = 0.088). Univariate Cox regression revealed that high tumour grade (3 + 4) and positive resection margin significantly predicted early mortality. Multivariate Cox regression showed that sAXL > 40.26 ng/mL remained associated with 6-month mortality (hazard ratio 2.42, bootstrap 95% CI 1.15–5.65, p = 0.020), independent of high tumour grade (hazard ratio 4.02, bootstrap 95% CI 1.68–13.2, p = 0.002). These findings suggest that a preoperative blood test (sAXL) has utility for predicting futile surgery beyond the current standard, CA19-9, and can be incorporated into larger models to assist in risk stratification and follow-up planning. Full article

Objective: To identify risk factors for intrapartum cesarean delivery (CD) among women with obesity (BMI ≥ 30) and to evaluate whether a machine learning model (XGBoost) can improve prediction of this outcome compared with a previously developed regression-based risk score. Methods: A retrospective cohort study at a single university-affiliated tertiary medical center was conducted. All women with a pre-pregnancy BMI ≥ 30 who initiated a trial of labor between 2012 and 2024 were included. Women who underwent elective CD or had missing outcome data were excluded. Maternal, obstetric, and intrapartum characteristics were compared between women who delivered vaginally and those who required an intrapartum CD. Predictors were evaluated using extreme gradient boosting (XGBoost), and model performance was assessed using receiver operating characteristic (ROC) analysis and SHAP-based interpretability. Results: Among 146,999 women who delivered during the study period, 10,248 (7.0%) had a pre-pregnancy BMI ≥ 30. A total of 7236 obese women attempted a trial of labor, of whom 1031 (14.5%) underwent an intrapartum CD. Key predictors included limited cervical dilatation at admission, epidural anesthesia, nulliparity, maternal BMI and age, oxytocin use, birthweight, inflammatory markers (white blood count and neutrophils to lymphocytes ratio), and previous cesarean scar. The XGBoost model demonstrated excellent discriminatory ability with an AUC of 0.945 (95%CI 0.930–0.960, DeLong), and exceeded the performance of our previous regression-based score, and provided detailed insight into nonlinear effects through SHAP analysis. In a secondary analysis restricted to variables available at admission, a pre-labor model retained a strong discriminatory performance across BMI categories, supporting its applicability for early risk stratification prior to labor onset. Conclusions: A machine learning-based model accurately predicts intrapartum cesarean delivery in women with obesity and may serve as a valuable tool to support individualized counseling and delivery planning. Full article

Background: Atrial Fibrillation (AF) is associated with a prothrombotic state and increased risk of ischemic stroke. Type 2 diabetes mellitus (T2DM) is a major cardiometabolic comorbidity in AF and independently increases thromboembolic risk. D-dimer is a well-established biomarker of coagulation activation and fibrin turnover, but the specific contribution of T2DM to D-dimer levels in AF remains insufficiently characterized in real-world cohorts. Methods: We conducted a retrospective, observational, single-center study including 300 adult patients with non-valvular AF evaluated at a tertiary university hospital. Patients were stratified according to the presence of T2DM (150 with T2DM and 150 without diabetes). Plasma D-dimer levels were compared between groups and analyzed across clinically relevant thresholds and CHA₂DS₂-VASc categories. Multivariable linear and logistic regression models were used to assess the independent association between T2DM and D-dimer levels after adjustment for demographic factors, comorbidities, renal function, prior stroke, CHA₂DS₂-VASc score components, and oral anticoagulation. Results: Patients with T2DM exhibited significantly higher D-dimer levels than non-diabetic patients (median 0.94 vs. 0.63 µg/mL FEU, p < 0.001). T2DM was independently associated with higher log-transformed D-dimer levels (adjusted β = 0.19, p < 0.001) and with increased odds of elevated D-dimer above both 0.5 µg/mL and 1.0 µg/mL thresholds. Across all CHA₂DS₂-VASc categories, patients with T2DM consistently showed higher D-dimer concentrations. Findings remained robust in sensitivity analyses restricted to anticoagulated patients. Conclusions: In patients with atrial fibrillation, type 2 diabetes mellitus is associated with increased coagulation activity as reflected by higher D-dimer levels, independent of clinical thromboembolic risk. These results support the concept of a diabetes-associated hypercoagulable AF phenotype and highlight the potential role of coagulation biomarkers in refining risk stratification. Full article

Background/Objectives: Necrotizing enterocolitis (NEC) is one of the most devastating gastrointestinal emergencies in premature neonates, with particularly high mortality among those requiring surgical intervention. Early identification of high-risk patients remains challenging. This study aimed to evaluate the prognostic value of complete blood count-derived inflammatory indices for predicting mortality in premature infants undergoing surgery for NEC. Methods: A total of 74 premature neonates with Bell stage II or III NEC who underwent surgical treatment between 2018 and 2023 were retrospectively analyzed. Preoperative and postoperative hematologic inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and platelet-to-neutrophil ratio (PNR), were recorded. Receiver operating characteristic (ROC) curve analysis was used to assess predictive performance. Variables with p < 0.10 in univariate analysis were entered into multivariate logistic regression models. Results: Overall mortality was 35.1%. Non-survivors had significantly lower gestational age and birth weight and a higher prevalence of advanced disease. Preoperatively, NLR was higher and PNR was lower in non-survivors. Postoperatively, NLR and C-reactive protein levels increased, while PNR showed a marked decline in infants who died. ROC analysis identified postoperative PNR as the strongest predictor of mortality, followed by preoperative SII and postoperative NLR. Multivariate analysis demonstrated that lower gestational age, advanced disease stage, and reduced postoperative PNR were independently associated factors for mortality. Conclusions: Postoperative reduction in platelet-to-neutrophil ratio appears to be a practical, inexpensive, and easily obtainable biomarker for early risk stratification in surgically treated NEC. Incorporating routine hematologic inflammatory indices into postoperative monitoring may support timely identification of high-risk infants and guide individualized clinical management in neonatal intensive care units. Full article

Background/Objectives: The National Comprehensive Cancer Network (NCCN) classifies prostate cancer with PSA > 20 ng/mL as high risk; however, outcomes within this group are heterogeneous. Emerging data suggest that men with PSA > 20 ng/mL as the sole high-risk feature may have more favorable disease biology. We evaluated outcomes of men with prostate cancer treated with definitive radiation therapy (RT), focusing on the prognostic significance of individual high-risk factors. Methods: We analyzed 742 men with prostatic adenocarcinoma treated with curative-intent RT between 2005 and 2021, including 282 meeting traditional NCCN high-risk criteria. Treatment consisted of dose-escalated RT (median 78 Gy), with androgen deprivation therapy (ADT) administered to 94% (median duration 28 months). Primary endpoints were freedom from biochemical failure (FFBF) and distant metastasis (FFDM). Outcomes were assessed using Kaplan–Meier methods and Cox proportional hazards modeling. Results: At 5 years, high-risk patients demonstrated FFBF of 83% and FFDM of 89%, with significantly worse outcomes among very high-risk subgroups. Men with PSA > 20 ng/mL as their only high-risk feature (n = 49) achieved superior outcomes compared with other high-risk patients (5-year FFBF 94% vs. 74%; FFDM 97% vs. 82%; both p = 0.05), comparable to intermediate-risk disease. On multivariable analysis, Gleason score and clinical T-stage independently predicted poorer outcomes, whereas PSA >20 alone did not. Conclusions: PSA > 20 ng/mL as an isolated high-risk feature is associated with favorable outcomes following definitive RT and appears to be the weakest NCCN high-risk criterion. These findings support refined risk stratification and raise the possibility of treatment de-escalation in select patients. Full article

Artificial intelligence (AI) is rapidly reshaping adult cardiac surgery, enabling more accurate diagnostics, personalized risk assessment, advanced surgical planning, and proactive postoperative care. Preoperatively, deep-learning interpretation of ECGs, automated CT/MRI segmentation, and video-based echocardiography improve early disease detection and refine risk stratification beyond conventional tools such as EuroSCORE II and the STS calculator. AI-driven 3D reconstruction, virtual simulation, and augmented-reality platforms enhance planning for structural heart and aortic procedures by optimizing device selection and anticipating complications. Intraoperatively, AI augments robotic precision, stabilizes instrument motion, identifies anatomy through computer vision, and predicts hemodynamic instability via real-time waveform analytics. Integration of the Hypotension Prediction Index into perioperative pathways has already demonstrated reductions in ventilation duration and improved hemodynamic control. Postoperatively, machine-learning early-warning systems and physiologic waveform models predict acute kidney injury, low-cardiac-output syndrome, respiratory failure, and sepsis hours before clinical deterioration, while emerging closed-loop control and remote monitoring tools extend individualized management into the recovery phase. Despite these advances, current evidence is limited by retrospective study designs, heterogeneous datasets, variable transparency, and regulatory and workflow barriers. Nonetheless, rapid progress in multimodal foundation models, digital twins, hybrid OR ecosystems, and semi-autonomous robotics signals a transition toward increasingly precise, predictive, and personalized cardiac surgical care. With rigorous validation and thoughtful implementation, AI has the potential to substantially improve safety, decision-making, and outcomes across the entire cardiac surgical continuum. Full article

Triple-negative breast cancer (TNBC) is an aggressive malignancy that disproportionately affects young women. The integration of immune checkpoint inhibitors (ICIs) has significantly improved outcomes in both early-stage and metastatic TNBC, shifting attention toward long-term survivorship issues, particularly endocrine function and fertility. However, the reproductive safety profile of ICIs remains insufficiently characterized. This narrative review synthesizes current preclinical and clinical evidence on ICI-associated reproductive toxicity, focusing on both direct immune-mediated gonadal injury and indirect disruption of the hypothalamic–pituitary–gonadal axis. Experimental models consistently demonstrate immune cell infiltration of ovarian and testicular tissue, cytokine-driven inflammatory cascades, follicular atresia, impaired spermatogenesis, and altered steroidogenesis following PD-1/PD-L1 and CTLA-4 blockade. Emerging clinical data report cases of immune-related orchitis, azoospermia, testosterone deficiency, diminished ovarian reserve, and premature ovarian insufficiency. Secondary hypogonadism due to immune-mediated hypophysitis represents an additional and frequently underdiagnosed mechanism. We further discuss the oncofertility challenges faced by young patients with TNBC treated with chemoimmunotherapy, emphasizing the uncertainty of fertility risk stratification and the importance of early fertility counseling and individualized fertility preservation strategies. To illustrate the potential clinical impact, we present the case of a 34-year-old nulliparous woman who developed premature ovarian insufficiency two years after neoadjuvant chemoimmunotherapy including atezolizumab, despite ovarian suppression. In conclusion, while ICIs have transformed the therapeutic landscape of TNBC, their potential long-term impact on reproductive and endocrine health represents a clinically significant concern. A precautionary, multidisciplinary oncofertility approach and prospective clinical registries are essential to define the true incidence and mechanisms of ICI-associated reproductive toxicity. Full article

Objectives: To identify factors associated with postnatal growth failure (PGF) in very-low-birth-weight (VLBW) infants and to develop a model for the early identification of neonates at risk. Methods: This retrospective cohort study included VLBW infants born between 2014 and 2024. PGF was defined using the 2013 Fenton growth chart. Multivariate logistic regression was used to identify predictors of PGF, and a weighted risk score was derived from their relative contributions. Model performance was evaluated using a receiver operating characteristic (ROC) curve. Results: Among 481 VLBW infants, 334 (69.4%) had PGF. Independent predictors were birth weight < 750 g (adjusted odds ratio [aOR] 8.11; 95% confidence interval [CI], 3.01–21.83), birth weight 750–1000 g (aOR 2.39; 95% CI, 1.35–4.21), multiple births (aOR 2.82; 95% CI, 1.71–4.67), pregnancy-induced hypertension (PIH) (aOR 3.32; 95% CI, 2.02–5.46), oligohydramnios (aOR 4.08; 95% CI, 1.68–9.92), no antenatal corticosteroid exposure (aOR 2.97; 95% CI, 1.65–5.36), and formula or mixed feeding (aOR 1.69; 95% CI, 1.08–2.64). The model showed good discrimination for scores ≥2 (area under the ROC curve, 0.736; sensitivity, 71.6%; specificity, 64.5%). Conclusions: Birth weight < 1000 g, multiple births, PIH, oligohydramnios, no antenatal corticosteroid exposure, and formula or mixed feeding were significant predictors of PGF. The score may support early risk stratification and prompt closer nutritional surveillance. Full article

Background: Radiation therapy is an important treatment method for non-small-cell lung cancer (NSCLC). However, predicting patient prognosis remains challenging due to considerable interpatient heterogeneity. The TP53 signaling pathway, implicated in tumor radiosensitivity and treatment outcomes, represents a promising predictive biomarker. Accordingly, in this study, we aimed to identify TP53-signaling pathway-related genes and develop a novel prognostic model for risk stratification for NSCLC patients undergoing radiation therapy. Methods: Publicly available NSCLC transcriptomic datasets were obtained from the GEO and TCGA databases. Utilizing bioinformatics approaches, we identified differentially expressed genes (DEGs) associated with the TP53 signaling pathway. Feature selection was performed using LASSO regression, followed by the construction of a multivariate-Cox-regression-based prognostic prediction model. In vitro validation was performed using a cell viability assay, colony formation, cell cycle analysis, apoptosis detection, γH2AX immunofluorescence staining and comet electrophoresis. In vivo validation was performed utilizing a subcutaneous tumor-bearing mouse model, where radiosensitivity was assessed by monitoring tumor volume post-irradiation. Results: We constructed a robust prognostic prediction model based on five TP53-signaling-pathway-related genes (MDM2, THBS1, TP53I3, ATM, and SESN3), achieving a 5-year AUC of 0.828 in the training set and a 3-year AUC of 0.824 in the validation set. The model exhibited a significant ability to stratify patients into distinct high- and low-risk groups, demonstrating good predictive performance. The poor prognosis observed in the high-risk group was associated with lower infiltration of anti-tumor immune cells but higher infiltration of immunosuppressive cells. Both in vitro and in vivo experiments demonstrated that TP53I3 knockdown significantly enhanced the radiosensitivity of NSCLC through increased DNA damage, cell cycle arrest and apoptosis. Conclusions: In this study, a five-gene signature derived from the TP53 signaling pathway was developed, and the model was shown to effectively predict the prognoses of NSCLC patients undergoing radiotherapy. This signature has the potential to be developed into a clinically applicable tool for personalizing radiotherapy regimens for NSCLC. Full article