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Objective The aim of this research was to develop a nomogram that integrates ultrasomics features and clinical factors to non-invasively predict preoperative lymph-vascular space invasion (LVSI) in patients with cervical cancer (CC). Methods A total of 217 patients from three hospitals were retrospectively analyzed (the training set, n  = 122; the test set, n  = 53; and the validation set, n  = 42). Tumor segmentation of the ultrasound(US) images was performed manually, then extracting a multitude of ultrasomics features from the segmented regions of interest (ROIs). After identifying the most significant ultrasomics features via a series of analyses and algorithms, five machine learning (ML) classification algorithms were utilized to develop and compare the ultrasomics models. Besides, we obtained clinically independent predictors for the diagnosis of LVSI and established the clinical model by univariate and multivariate analyses. Next, we compared the predictive capabilities of the clinical, ultrasomics, and combined models in forecasting LVSI in CC. Results Artificial neural networks (ANN) emerged as the top performer among the five ML classification algorithms. International Federation of Gynecology and Obstetrics (FIGO) staging for CC served as the independent predictor of LVSI. The nomogram, incorporating ultrasomics features and FIGO staging, demonstrated the highest diagnostic performance, with area under the curve (AUC) (95% CI) values of 0.911 (0.852–0.957), 0.835 (0.716–0.934), and 0.832 (0.685–0.939) in the training, test, and validation sets, respectively. Furthermore, the nomogram’s calibration curve exhibited excellent agreement between the predicted and actual LVSI outcomes in three datesets. Conclusion The nomogram based on ultrasomics features and FIGO staging is a potential method for non-invasive prediction LVSI of CC.

Deficit irrigation is a common approach in water-scarce regions to balance productivity and water use, whereas drought stress still occurs to various extents, leading to reduced physiological performance and a decrease in yield. Therefore, seeking a rapid and reliable method to identify wheat varieties with drought resistance can help reduce yield loss under water deficit. In this study, we compared ten wheat varieties under three deficit irrigation systems (W0, no irrigation during the growing season; W1, irrigation at jointing; W2, irrigation at jointing and anthesis). UAV thermal imagery, plant physiological traits [leaf area index (LAI), SPAD, photosynthesis (Pn), transpiration (Tr), stomatal conductance (Cn)], biomass and yield were acquired at different growth stages. Wheat drought resistance performance was evaluated through using the canopy temperature extracted from UAV thermal imagery (CT-UAV), in combination with hierarchical cluster analysis (HCA). The CT-UAV of W0 and W1 treatments was significantly higher than in the W2 treatment, with the ranges of 24.8–33.3 °C, 24.3–31.6 °C, and 24.1–28.9 °C in W0, W1 and W2, respectively. We found negative correlations between CT-UAV and LAI, SPAD, Pn, Tr, Cn and biomass under the W0 (R2 = 0.41–0.79) and W1 treatments (R2 = 0.22–0.72), but little relevance for W2 treatment. Under the deficit irrigation treatments (W0 and W1), UAV thermal imagery was less effective before the grain-filling stage in evaluating drought resistance. This study demonstrates the potential of ensuring yield and saving irrigation water by identifying suitable wheat varieties for different water-scarce irrigation scenarios.

Background To investigate the optimal periendometrial zone (PEZ) in ultrasound (US) images and assess the performance of ultrasound radiomics in predicting the outcome of frozen embryo transfer (FET). Methods This prospective study had 422 female participants (training set: n  = 358, external validation set: n  = 64). We delineated the region of interest (ROI) of the endometrium (EN) from ultrasound images of the median sagittal surface of the uteri of patients. We determined the ROIs of PEZ on US images by automatically expanding 2.0, 4.0, 6.0, and 8.0 mm radii surrounding the EN. We determined the radiomics characteristics based on the ROIs of the endometrium and PEZ, then compared the different sizes of PEZ to determine the optimal PEZ. We constructed models of the EN and optimal PEZ using six machine learning algorithms. We developed a combined model using the radiomics characteristics of EN and the optimal PEZ. We evaluated the performance of the three models using the area under the curve (AUC). Results The optimal PEZ was 4.0 mm with a maximum AUC of 0.715 (95% confidence interval (CI): 0.581 – 0.833) in the external validation set. The combined radiomics model (endometrium and PEZ 4.0 mm ) yielded the best predictive performance with AUC = 0.853 (95% CI: 0.811 – 0.890) for the training set and AUC = 0.809 (95% CI: 0.696 – 0.909) for the external validation set. Conclusions PEZ 4.0 mm could be the optimal area for predicting clinical pregnancy after FET. An US-based radiomics model that combines EN and PEZ 4.0 mm demonstrated strong potential in helping clinicians predict FET outcomes more accurately, thereby supporting informed decision-making before treatment.

Gestational anemia significantly elevates the risk of adverse maternal and neonatal outcomes, necessitating early predictive tools for targeted intervention. This study aimed to develop and validate a robust machine learning (ML) framework to forecast perinatal complications and facilitate early risk identification. Perinatal mortality, preterm birth, low birth weight and macrosomia are defined as adverse outcomes. Analyzing a retrospective cohort of 5,710 pregnant women, we identified 22 initial variables using Lasso regression integrated with seven ML-based screening algorithms. Subsequently, eight predictive models were constructed and benchmarked via internal and external validation. Model performance was rigorously evaluated using receiver operating characteristic (ROC), precision‑recall (PR), calibration, and decision curves. Seven key predictors were identified, including gestational hypertension, obstetric history, and hepatic markers (Albumin, Alanine Aminotransferase, Globulin). The XGBoost model consistently outperformed its counterparts, demonstrating superior discriminative power (area under the curve (AUC) and F1-score) and clinical utility, as confirmed by the DeLong test and Kolmogorov‑Smirnov (KS) statistics. Based on XGBoost probabilities, we established a three-tier risk stratification: low-risk (< 0.28), medium-risk (0.28-0.52), and high-risk (≥ 0.53). Our ML-based framework offers a reliable tool for early risk assessment in gestational anemia, enabling clinicians to implement individualized management strategies through precise risk stratification.

Radiotherapy is the primary treatment for patients with nasopharyngeal carcinoma (NPC), and approximately 20% of patients experience treatment failure due to tumour radioresistance. However, the exact regulatory mechanism remains poorly understood. Here, we show that the deubiquitinase USP44 is hypermethylated in NPC, which results in its downregulation. USP44 enhances the sensitivity of NPC cells to radiotherapy in vitro and in vivo. USP44 recruits and stabilizes the E3 ubiquitin ligase TRIM25 by removing its K48-linked polyubiquitin chains at Lys439, which further facilitates the degradation of Ku80 and inhibits its recruitment to DNA double-strand breaks (DSBs), thus enhancing DNA damage and inhibiting DNA repair via non-homologous end joining (NHEJ). Knockout of TRIM25 reverses the radiotherapy sensitization effect of USP44. Clinically, low expression of USP44 indicates a poor prognosis and facilitates tumour relapse in NPC patients. This study suggests the USP44-TRIM25-Ku80 axis provides potential therapeutic targets for NPC patients. Radiotherapy is the mainstay treatment for nasopharyngeal carcinoma (NPC). Here the authors show that the deubiquitinase, USP44, increases radiosensitivity of NPC cells by promoting the degradation of Ku80, and thus enhancing the levels of DNA damage.

Objective To develop and validate a radiomics signature based on magnetic resonance imaging (MRI) from multicenter datasets for preoperative prediction of pathologic response to neoadjuvant chemotherapy (NAC) in patients with osteosarcoma. Methods We retrospectively enrolled 102 patients with histologically confirmed osteosarcoma who received chemotherapy before treatment from 4 hospitals (68 in the primary cohort and 34 in the external validation cohort). Quantitative imaging features were extracted from contrast-enhanced fat-suppressed T1-weighted images (CE FS T1WI). Four classification methods, i.e., the least absolute shrinkage and selection operator logistic regression (LASSO-LR), support vector machine (SVM), Gaussian process (GP), and Naive Bayes (NB) algorithm, were compared for feature selection and radiomics signature construction. The predictive performance of the radiomics signatures was assessed with the area under receiver operating characteristics curve (AUC), calibration curve, and decision curve analysis (DCA). Results Thirteen radiomics features selected based on the LASSO-LR classifier were adopted to construct the radiomics signature, which was significantly associated with the pathologic response. The prediction model achieved the best performance between good and poor responders with an AUC of 0.882 (95% CI, 0.837−0.918) in the primary cohort. Calibration curves showed good agreement. Similarly, findings were validated in the external validation cohort with good performance (AUC, 0.842 [95% CI, 0.793−0.883]) and good calibration. DCA analysis confirmed the clinical utility of the selected radiomics signature. Conclusion The constructed CE FS T1WI-radiomics signature with excellent performance could provide a potential tool to predict pathologic response to NAC in patients with osteosarcoma. Key Points • The radiomics signature based on multicenter contrast-enhanced MRI was useful to predict response to NAC. • The prediction model obtained with the LASSO-LR classifier achieved the best performance. • The baseline clinical characteristics were not associated with response to NAC.

Nasopharyngeal carcinoma (NPC) is characterized by a high rate of local invasion and early distant metastasis. Increasing evidence indicates that epigenetic abnormalities play important roles in NPC development. However, the epigenetic mechanisms underlying NPC metastasis remain unclear. Here we investigate aberrantly methylated transcription factors in NPC tissues, and we identify the HOP homeobox HOPX as the most significantly hypermethylated gene. Consistently, we find that HOXP expression is downregulated in NPC tissues and NPC cell lines. Restoring HOPX expression suppresses metastasis and enhances chemosensitivity of NPC cells. These effects are mediated by HOPX-mediated epigenetic silencing of SNAIL transcription through the enhancement of histone H3K9 deacetylation in the SNAIL promoter. Moreover, we find that patients with high methylation levels of HOPX exhibit poor clinical outcomes in both the training and validation cohorts. In summary, HOPX acts as a tumour suppressor via the epigenetic regulation of SNAIL transcription, which provides a novel prognostic biomarker for NPC metastasis and therapeutic target for NPC treatment. HOPX is a transcription factor epigenetically silenced in several cancers. Here the authors, by analysing methylation profiles, identify HOPX as a suppressor of metastasis in nasopharyngeal carcinoma: mechanistically HOPX inhibits SNAIL transcription through deacetylation-mediated silencing.

Nasopharyngeal carcinoma (NPC) progression is regulated by genetic, epigenetic, and epitranscript modulation. As one of the epitranscript modifications, the role of N6-Methyladenosine (m 6 A) has not been elucidated in NPC. In the present study, we found that the poorly methylated gene ZNF750 (encoding zinc finger protein 750) was downregulated in NPC tumor tissues and cell lines. Ectopic expression of ZNF750 blocked NPC growth in vitro and in vivo. Further studies revealed that m 6 A modifications maintained the low expression level of ZNF750 in NPC. Chromatin immunoprecipitation sequencing identified that ZNF750 directly regulated FGF14 (encoding fibroblast growth factor 14), ablation of which reversed ZNF750’s tumor repressor effect. Moreover, the ZNF750-FGF14 signaling axis inhibited NPC growth by promoting cell apoptosis. These findings uncovered the critical role of m 6 A in NPC, and stressed the regulatory function of the ZNF750-FGF14 signaling axis in modulating NPC progression, which provides theoretical guidance for the clinical treatment of NPC.

Mitochondria are essential organelles in balancing oxidative stress and cell death during cancer cell proliferation. Rapid tumor growth induces tremendous stress on mitochondria. The mammalian tumor necrosis factor-α-induced protein 8-likes (TIPEs) family plays critical roles in balancing cancer cell death and survival. Yet, the roles of TIPEs in HNSCC tumorigenesis and mitochondria stress maintenance is unclear. Based on an integrative analysis of public HNSCC datasets, we identified that the downregulation of TIPE3 via its promoter hypermethylation modification is the major event of TIPEs alterations during HNSCC tumorigenesis. Low expression levels of TIPE3 were correlated with high malignancy and poor clinical outcomes of HNSCC patients. Restoring TIPE3 represses HNSCC proliferation, migration, and invasion in vitro and in vivo, while silencing TIPE3 acted on an opposite way. Mechanistically, TIPE3 band to the PGAM5 and electron transport chain (ETC) complex. Restoring TIPE3 promoted PGAM5 recruiting BAX and dephosphorylating p-DRP1(Ser637), which triggered mitochondrial outer membrane permeabilization and fragmentation. Ultimately, TIPE3 induced ETC damage and oxygen consumption rate decrease, ROS accumulation, mitochondrial membrane potential depolarization, and cell apoptosis. Collectively, our work reveals that TIPE3 plays critical role in maintaining mitochondrial stress and cancer cell progression in HNSCC, which might be a potential therapeutic target for HNSCC patients.