ObjectiveThe widespread adoption of portable fundus cameras for primary care and community screening is hindered by limitations in current autofocus(AF) technologies. Image-based methods relying on sharpness evaluation require iterative searches, resulting in slow convergence, while projection-based techniques are susceptible to optical artifacts and calibration errors. To address these challenges, this study introduces a novel AF system based on direct wavefront sensing, designed to deliver simultaneous high speed, high precision, and operational robustness within the compact form factor essential for portable ophthalmic devices. MethodsOur approach fundamentally reimagines the AF process by directly measuring the ocular wavefront aberration. We developed a custom portable fundus camera integrating a miniaturized Shack-Hartmann wavefront sensor (SHWS) into the optical path. An 850 nm laser diode projects a point source onto the retina via oblique illumination to minimize corneal reflections. Light scattered from this spot carries the eye’s refractive error through the imaging optics and is directed to the SHWS, positioned at a plane optically conjugate to the primary color CMOS imaging sensor. A microlens array within the SHWS samples the incident wavefront, generating a pattern of focal spots on a CCD. Real-time centroid analysis of these spots provides a map of local wavefront slopes. These measurements are processed through a singular value decomposition (SVD) algorithm to fit a Zernike polynomial basis set, enabling real-time reconstruction of the wavefront phase. The defocus component (S) is extracted from the second-order Zernike coefficients, providing a direct, quantitative measure of the refractive error in diopters. This value serves as a precise error signal in a closed-loop control system, which commands a voice-coil actuated focusing lens to its null position in a single, deterministic step, eliminating the need for iterative search algorithms. ResultsComprehensive evaluation demonstrated the system’s high performance. Testing on a calibrated model eye (OEMI-7) established a highly linear relationship between the computed defocus S and the focusing lens position across a ±20 Diopter (D) compensation range, achievable within a 5 mm mechanical travel. The system achieved a focusing precision of 0.08 D, corresponding to an 18-fold improvement over a conventional projection spot-size method tested under identical conditions. The total focus acquisition time, encompassing wavefront measurement, computation, and lens actuation, averaged under 0.5 s. Clinical validation with 25 human volunteers (50 eyes, refractive range -15 D to +10 D) confirmed practical efficacy. The wavefront-sensing AF succeeded in 92% of attempts with a mean time of 0.5 s, substantially outperforming a projection-based benchmark which achieved only a 32% success rate with an average time of 4.25 s. The system provided instantaneous directional guidance and maintained stability during minor ocular movements. Objective assessment of image quality, via amplitude contrast of retinal vasculature, showed consistent and significant enhancement following AF correction across the entire tested diopter range. ConclusionThis work successfully implements and validates a direct wavefront-sensing autofocus paradigm for portable fundus cameras. By directly quantifying and compensating for the optical defocus aberration, this method bypasses the fundamental limitations of image-processing and projection-based techniques, enabling rapid, precise, and deterministic diopter compensation. The developed system delivers an exceptional combination of a wide operational range (±20 D), high accuracy (0.08 D), fast convergence (0.5 s), and a compact physical footprint. This technology provides a practical and high-performance focusing solution capable of enhancing the reliability, throughput, and diagnostic utility of portable retinal imaging in large-scale screening applications. Future efforts will be directed towards system cost optimization and performance adaptation for diverse ocular conditions.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveThis study aims to investigate the dynamic changes in general body parameters, blood glucose, and blood lipid profiles in obese cynomolgus monkeys, exploring the correlations among these parameters and providing a reference for research on the obese cynomolgus monkey model. Methods30 normal male cynomolgus monkeys aged 5 - 17 years old (with body mass index < 35 kg/m² and glycated hemoglobin content < 4.50%) and 99 spontaneously obese male cynomolgus monkeys (with body mass index ≥35 kg/m² and glycated hemoglobin content < 4.50%) were selected. Over a period of three years, their abdominal circumference, skinfold thickness, body weight, body mass index, fasting blood glucose, glycated hemoglobin, and four blood lipid indicators were monitored. The correlations between each indicator were analyzed using repeated measurement ANOVA, simple linear regression, and multiple linear regression correlation analysis method. Results Compared to the control group, the obese group exhibited significantly higher levels of abdominal circumference, skinfold thickness, body weight, body mass index, and triglyceride (P＜0.05). In the control group, skinfold thickness increased annually, while other indicators remained stable. Compared with the first year, the obese group showed significantly increased abdominal circumference, skinfold thickness, body weight, body mass index, triglyceride, and fasting blood glucose in the second year(P＜0.05), with this increasing trend persisting in the third year (P＜0.05). In the control group, the obesity incidence rates in the second and third years were 16.67% and 23.33%, respectively, while the prevalence of diabetes remained at 16.67%. In the obese group, the diabetes incidence rates were 29.29% and 44.44% in years 2 and 3, respectively. Among the 11-13 year age group, the incidence rates were 36.36% and 44.68%, while for the group older than 13 years, the rates were 28.13% and 51.35%. Correlation analysis revealed significant associations (P＜0.05) between fasting blood glucose and age, abdominal circumference, skinfold thickness, body weight, and triglyceride in the diabetic monkeys. Conclusion Long-term obesity can lead to the increases in general physical indicators and fasting blood glucose levels in cynomolgus monkeys, and an increase in the incidence of diabetes. In diabetic cynomolgus monkeys caused by obesity, there is a high correlation between their fasting blood glucose and age, weight, abdominal circumference, skinfold thickness, and triglyceride levels, which is of some significance for predicting the occurrence of spontaneous diabetes.

Objective To analyze the epidemiological characteristics of newly diagnosed occupational diseases in Guangdong Province from 2019 to 2023. Methods Data on newly diagnosed occupational diseases reported in Guangdong Province from 2019 to 2023 were collected from the national occupational disease network reporting system. The spectrum of occupational diseases and their distribution by region, industry, and population were analyzed. Results A total of 4 136 newly diagnosed occupational disease cases were reported in Guangdong Province from 2019 to 2023, showing an overall downward trend. Newly diagnosed cases were classified into eight categories and 53 types of occupational diseases. In terms of the number of cases, the top five categories were occupational diseases of the ear, nose, throat and oral cavity;occupational pneumoconiosis and other respiratory diseases; occupational diseases caused by physical factors; occupational chemical poisoning; and occupational tumors, accounting for 98.62% of all cases. The top ten specific disease types were occupational noise-induced deafness, occupational silicosis, occupational other pneumoconiosis, occupational chronic benzene poisoning, occupational heatstroke, occupational hand-arm vibration disease, occupational coal workers′ pneumoconiosis, occupational welders′ pneumoconiosis, occupational tumor (leukemia caused by benzene exposure), and occupational chronic n-hexane poisoning, accounting for 94.85% of all cases. Most of the cases were distributed in the Pearl River Delta region, accounting for 89.19%; as well as manufacturing industry, accounting for 84.89%. Male cases accounted for 87.02%. Most diagnoses occurred in individuals aged >40-60 years, accounting for 74.73%. Conclusion Newly diagnosed occupational diseases in Guangdong Province from 2019 to 2023 showed the following characteristics: concentration of categories and disease types, polarization of regional distribution, industry clustering, and population difference. The disease spectrum is evolving from a dual-disease predominance toward a multi-disease predominance.

background Current assessment of noise-induced hearing loss relies on the hearing threshold statistical distribution table of ISO 7029-2017 standard (ISO 7029), which is based on foreign population data and lacks a hearing threshold distribution table derived from pure-tone audiometry data of the Chinese population, hindering accurate evaluation of hearing loss in this group. Objective To establish a statistical distribution table of hearing threshold level (HTL) for otologically normal Chinese adults and to provide a scientific basis for revising the diagnostic criteria of occupational noise-induced deafness in China. Methods A total of 1271 otologically normal Chinese adults aged 18-60 years were divided into four groups with 10-year age intervals. Based on the pure tone audiometry data and power function operation formula, the relevant parameters of the median and percentile calculation formula of ISO 7029 were adjusted. Based on the adjusted parameters, a statistical distribution table of HTL of normal Chinese adults in otology was preliminarily constructed according to the calculation formula of ISO 7029. A nonparametric rank sum test was used to compare the difference between the adjusted median deviation value and the ISO 7029 calculation. Results Except for a few frequencies (2000, 3000, and 8000 Hz), the value of parameter α adjusted for the auditory threshold deviation of the Chinese population (α_CHN-md) increased with the increase of frequency, while the value of parameter β adjusted for the auditory threshold deviation of the Chinese population (β_CHN-md) decreased with the increase of frequency. The preliminary distribution table of otologically normal Chinese adults' median hearing threshold deviation (ΔH_CHN-md) showed that the hearing threshold deviation increased with age and frequency. At 8000 Hz, the increase in ΔH_CHN-md with age was the greatest for both men and women, from 0 dB to 23 dB and 21 dB respectively. The minimal threshold elevation was noted at 500, 1000, and 2000 Hz, which increased from 0 dB to 2 dB. In otologically normal Chinese adults, the maximum increase of ΔH_CHN-md with frequency was observed at age of 50 years, which increased from 2 dB at 500 Hz to 23 dB at 8000 Hz in men and from 2 dB at 500 Hz to 21 dB at 8000 Hz in women. The comparison results with the ISO 7029 calculation showed that the trend was consistent. The median hearing threshold deviation value of the adjusted Chinese population was lower than that of the ISO 7029 calculation (ΔH_md). However, they had no significant statistical difference (P>0.05). For otologically normal normal Chinese men and women, the maximum difference of ΔH_md between the 50-year-old group and the ISO calculation at 8000 Hz was 7 dB and 9 dB, respectively. Conclusion This preliminary analysis of hearing threshold deviations in otologically normal Chinese adults suggests that current standards may potentially underestimate hearing loss at frequencies below 8000 Hz while possibly overestimating it at 8000 Hz. These findings require further validation through expanded sample sizes to more accurately assess the characteristics of hearing thresholds in the Chinese population and their discrepancies with existing standards.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

ObjectiveTo explore the material basis of the "interaction of blood stasis and toxin" mechanism in cerebral ischemia-reperfusion injury， as well as the protective role of Huoxue Huayu Jiedu prescription （HXHYJDF） against ferroptosis. MethodsSixty SPF-grade male SD rats were randomly divided into six groups： sham group， model group， deferoxamine （DFO） group （100 mg·kg^-1）， low-dose HXHYJDF group （4.52 g·kg^-1）， medium-dose HXHYJDF group （9.04 g·kg^-1）， and high-dose HXHYJDF group （18.07 g·kg^-1）， with ten rats in each group. Except for the sham group， the other groups were used to replicate the model of focal cerebral ischemia-reperfusion in the middle cerebral artery of rats by the reforming Longa method. Neurological function was assessed at 1^st， 3^rd， 5^th， and 7^th days post-reperfusion using the modified neurological severity scores （m-NSS）. Brain tissue pathology and the morphology of mitochondria were observed using hematoxylin-eosin （HE） staining and transmission electron microscopy. The contents of malondialdehyde （MDA）， glutathione （GSH）， divalent iron ions （Fe²⁺）， and reactive oxygen species （ROS） in the ischemic cerebral tissue were detected using enzyme-linked immunosorbent assay （ELISA）. Immunohistochemistry and Western blot （WB） were used to detect the expression of iron death marker proteins glutathione peroxidase 4 （GPX4）， ferroportin-1 （FPN1）， transferrin receptor protein 1 （TfR1）， and ferritin mitochondrial （FtMt） in brain tissue. ResultsCompared with the sham group， the mNSS score of the model group was significantly increased （P<0.01）. HE staining showed that the number of neurons in the cortex of brain tissue was seriously reduced， and the intercellular space was widened. The nucleus was fragmented， and the cytoplasm was vacuolated. The results of transmission electron microscopy showed that the mitochondria in the cytoplasm contracted and rounded， and the mitochondrial cristae decreased. The matrix was lost and vacuolated， and the density of the mitochondrial bilayer membrane increased. The results of ELISA showed that the content of GSH decreased significantly （P<0.01）， and the contents of MDA， Fe²⁺， and ROS increased significantly （P<0.01）. The results of immunohistochemistry and WB showed that the expression of GPX4 and FPN1 proteins was significantly decreased （P<0.01）， and the expression of FtMt and TfR1 proteins was significantly increased （P<0.01）. Compared with those of the model group， the m-NSS scores of the high-dose and medium-dose HXHYJDF groups began to decrease on the 3^rd and 5^th days， respectively （P<0.05， P<0.01）. The results of HE and transmission electron microscopy showed that the intervention of HXHYJDF improved the pathological changes of neurons and mitochondria. The results of ELISA showed that the content of GSH in the medium-dose and high-dose HXHYJDF groups increased significantly （P<0.01）， and the contents of MDA， Fe²⁺， and ROS decreased significantly （P<0.05， P<0.01）. The content of GSH in the low-dose HXHYJDF group increased significantly （P<0.01）， and the contents of MDA and ROS decreased significantly （P<0.01）. The results of immunohistochemistry showed that the expression of GPX4 and FPN1 in the high-dose HXHYJDF group increased significantly （P<0.01）， and the expression of FtMt and TfR1 decreased significantly （P<0.01）. The expression of GPX4 and FPN1 in the medium-dose HXHYJDF group increased significantly （P<0.05）， and the expression of TfR1 decreased significantly （P<0.01）. WB results showed that the expression levels of FPN1 and GPX4 proteins in the high-dose， medium-dose， and low-dose HXHYJDF groups were significantly up-regulated （P<0.01）， and the expression levels of FtMt and TfR1 proteins were significantly down-regulated （P<0.01）. ConclusionHXHYJDF can significantly improve neurological dysfunction symptoms in rats with cerebral ischemia-reperfusion injury， improve the pathological morphology of the infarcted brain tissue， and protect the brain tissue of rats with cerebral ischemia-reperfusion injury to a certain extent. Neuronal ferroptosis is involved in cerebral ischemia-reperfusion injury， with increased levels of MDA， Fe²⁺， ROS， and TfR1 and decreased levels of FtMt， FPN1， GPX4， and GSH potentially constituting the material basis of the interaction of blood stasis and toxin mechanism in cerebral ischemia-reperfusion injury. HXHYJDF may exert brain-protective effects by regulating iron metabolism-related proteins， promoting the discharge of free iron， reducing brain iron deposition， alleviating oxidative stress， and inhibiting ferroptosis.

AIM To investigate the effects of Congrong San on neuronal apoptosis and the Bax/Bcl-2/Caspase3 signaling pathway in a rat model of Alzheimer's disease(AD).METHODS A total of 60 2-month-old SD male rats were randomly divided into the blank group,the model group,the memantine hydrochloride group(0.025 g/kg)and low-dose,medium-dose and high-dose Congrong San groups(4.62,9.24,18.48 g/kg).All groups except the control group received stereotactic intracerebral injection of Aβ1-42 to establish AD models.Following the successful modeling,each group received its corresponding intragastric administration once daily for 28 consecutive days.After the administration,the rats had their learning and memory ability detected by the morris water maze test;their hippocampal neuronal morphology observed with HE and Nissl staining;their hippocampal neuronal apoptosis observed with TUNEL staining;and their hippocampal expressions of amyloid precursor protein(APP),β-site APP-cleaving enzyme 1(BACE1),and apoptosis-related proteins Bax,Bcl-2 and Caspase3 detected with Western blot assay.RESULTS Compared with the model group,the groups intervened with medium-dose and high-dose Congrong San exhibited improved learning and memory performance,alleviated hippocampal neuronal damage,increased Nissl body count(P＜0.01),reduced hippocampal apoptosis rate(P＜0.05,P＜0.01),decreased protein expressions of APP,BACE1,Bax and cleaved-Caspase3/Caspase3 ratio(P＜0.05,P＜0.01),and elevated Bcl-2 expression(P＜0.01).CONCLUSION Congrong San mitigates cognitive impairment,hippocampal neuronal damage,and apoptosis in AD rats,probably through inhibition of the Bax/Bcl-2/Caspase3 signaling pathway activation.

OBJECTIVE To explore the association of the expressions of tissue forkhead box transcription factor(FOX)F2,angiopoietin-like protein 4(ANGPTL4)and FOXP3 with the pathological features and recurrence of the cervical cancer patients with human papillomavirus(HPV)infection undergoing radical hysterectomy.METHODS Totally 69 cervical cancer patients with HPV infection who underwent radical hysterectomy in Danzhou People's Hospital from Nov.2018 to Jul.2021 were randomly assigned as the cervical cancer group,meanwhile,72 patients who had cervical intraepithelial neoplasia(CIN)were chosen as the CIN group.The patients of the cer-vical cancer group were divided into the recurrence group with 25 cases and the no recurrence group with 44 cases according to the prognosis.The levels of tissue FOXF2,ANGPTL4 and FOXP3 were compared among the groups.The levels of FOXF2,ANGPTL4 and FOXP3 were compared among the cervical cancer patients with dif-ferent clinical pathological features,and the pathological features were compared among the patients with different treatment outcomes.The values of the three indexes in diagnosis of the postoperative recurrence in the cervical cancer patients with HPV infection undergoing radical resection were analyzed.RESULTS There were significant differences in the levels of tissue FOXF2 mRNA,ANGPTL4 mRNA and FOXP3 mRNA between the cervical cancer group and the CIN group(P＜0.05).There were significant differences in the levels of FOXF2 mRNA,ANGPTL4 mR-NA and FOXP3 mRNA among the cervical cancer patients with HPV infection who varied in federation international of gynecology and obstetrics(FIGO)stage,lymphatic metastasis and infiltration depth(P＜0.05).There were significant differences in the FIGO stage,lymphatic metastasis,infiltration depth,FOXF2 mRNA level,ANGPTL4 mRNA level and FOXP3 mRNA level between the recurrence group and the no recurrence group(P＜0.05).The area under the curve(AUC)value of the joint detection of FOXF2 mRNA,ANGPTL4 mRNA and FOXP3 mRNA was higher than that of the single detection in diagnosis of the postoperative recurrence in the cervical cancer patients with HPV infection under-going radical resection(P＜0.05).CONCLUSIONS The cervical cancer patients with HPV infection undergoing radi-cal resection show the abnormal expressions of tissue FOXF2 mRNA,ANGPTL4 mRNA and FOXP3 mRNA.There is association between the three indexes and the FIGO stage,lymphatic metastasis and infiltration depth.The joint detection of the three indexes has high value in diagnosis of the postoperative recurrence.