Environmental pollution is a significant public health challenge worldwide, and investigating the causal relationship between environmental exposure and population health outcomes is a key objective of environmental epidemiology research. In recent years, the complexity of environmental exposures has increasingly come to the forefront, making it challenging for observational studies that dominate environmental epidemiology to accurately estimate causal effects. Causal inference methods are particularly advantageous in controlling for confounding factors, thus holding great potential in environmental epidemiology research. Researchers can use appropriate causal inference methods to simulate the process of randomization, providing strong support for revealing the causal relationship between environmental exposure and health outcomes. However, there is a lack of reviews on the application of causal inference methods in environmental epidemiology studies in China. Therefore, this study introduced the basic principles of common causal inference statistical methods in environmental epidemiology, summarized the applicable conditions, advantages and disadvantages of various methods, and provided R software implementation codes for these methods, aiming to offer guidance for optimizing research design and practicing causal inference statistical methods.

Myosteatosis is one of the common complications in patients with end-stage liver disease, which is significantly associated with poor outcomes after liver transplantation. Currently, diagnostic criteria of myosteatosis have not been established, and CT is the most commonly used for diagnosis. The pathogenesis of myosteatosis is multifactorial, and the pathophysiological mechanisms linking it to end-stage liver disease are not fully understood. An increasing number of scholars have recognized that the severity of myosteatosis is closely related to its clinical consequences, but there are no effective treatment options available. This article reviews the pathophysiological mechanisms and diagnostic methods of myosteatosis, and its impact on the prognosis of liver transplant recipients, and discusses current treatment strategies to provide references for the perioperative management of liver transplant recipients.

Objective: To explore the application method and clinical efficacy of endoscopic and computerized navigation technology in maxillofacial foreign body removal surgery, and to provide a reference for the clinical application of this technology. Methods: This study, which was approved by the Medical Ethics Committee of the hospital, retrospectively analyzed the data of five patients with maxillofacial foreign bodies who were admitted to Sun Yat-sen Memorial Hospital, Sun Yat-sen University from January 2018 to December 2024. All patients underwent preoperative CT scanning. Intraoperatively, endoscopic and computer navigation techniques were used in combination or separately according to the location, size, and adjacency of the foreign body to important neurovascular vessels. The foreign body was precisely localized by endoscopic magnification and direct visualization, and the optimal surgical path was designed and verified under the real-time guidance of computerized navigation to accurately remove the foreign body. The type of foreign body, location, length and diameter, duration of surgery, length of incision, success rate of foreign body removal, postoperative complications, and follow-up were recorded and analyzed. Results: The foreign body was successfully removed in all five patients with a success rate of 100%. The intraoperative computerized navigation system was accurate in positioning, and the alignment stability was not significantly affected by mandibular movement; the endoscope provided good illumination and exposure of the operative field. All surgical incisions were small, and no serious complications, such as foreign body residue, important neurovascular injury, or infection, occurred after surgery. One month after the operation, the patients were followed up and recovered well. Conclusion The combination of endoscopy and computer navigation or separately assisted technology can provide a clear field and real-time positioning for maxillofacial foreign body removal, effectively avoiding important anatomical structures, thus realizing safe and complete foreign body removal with minimized trauma. This assistive technology significantly improves the accuracy and safety of the operation and has clinical promotion value.

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.

Ethanol detection plays an important role in food industry,environmental monitoring,medical health monitoring,prevention of drunk driving,etc.The development of low-cost,high-performance ethanol sensors has important application value.In this study,a ZnO/FeS nano heterojunction ethanol sensor was prepared on commercial ceramic silver electrode substrate.The sensing characteristics of the sensor for ethanol gas were systematically studied.The results showed that ZnO had a nanowire structure,and the FeS was coated on the ZnO nanowire in the form of nanosheets.The sensor performed well for ethanol detection in environments with relative humidity ranging from 30%to 60%,with a detection range from 0.2 mg/m3 to 50 mg/m3.At the optimum operating temperature of 300℃,the response of ZnO/FeS nano heterojunction sensor to 50 mg/m3 ethanol was 15.6,the response time was 5.0 s,and the detection limit was as low as 0.101 mg/m3,which was obviously better than that of commercial ethanol sensor.This sensor was highly selective for ethanol compared to other gases such as CO,NH3,acetone,etc,and could steadily work for 30 days.The fabricated sensor had good development potential in the field of low-cost and high-performance ethanol gas detection.

In the field of substance detection,ion dissociation techniques have become crucial for enhancing qualitative accuracy.By applying external energy to induce dissociation of ions in the substance being analyzed,the internal structural information can be obtained,thereby improving qualitative capabilities.Current research on ion dissociation techniques primarily focuses on tandem mass spectrometry,which typically requires a vacuum environment.However,research on ambient ion dissociation techniques is less developed,with some progress made in the field of tandem ion mobility spectrometry.Recently,the development of field-induced dissociation(FID)in this area has enabled ambient dissociation of various explosive and volatile alcohol ions.Nevertheless,the limitation imposed by the maximum breakdown field of air restricts the energy of the electric field,making it challenging to dissociate ions with high energy requirements,such as those of drugs.To address this issue,in this work,an ambient heat-induced dissociation(HID)technique based on high temperatures was proposed,in which an ambient ion heat-induced dissociation unit was developed and integrated into a home-made ion trap mass spectrometer.Experiments were conducted on four representative drug samples,e.g.methamphetamine,heroin,cocaine,and ketamine.The parent ions mass spectra,low vacuum collision-induced dissociation(CID)mass spectra and ambient HID mass spectra for each sample were obtained.By analyzing and comparing the fragmentation products from ambient and low vacuum dissociation,the feasibility of the ambient HID technique was verified.This technique provided a method for ion dissociation in single mass analyzers without tandem mass spectrometry capability and offered a new research direction for the future development of tandem ion mobility spectrometry.

Combined seawater Hf and Nd isotope compositions have been applied as reliable tracers of water mass mixing and weathering input changes.The establishment of standardized synchronous extraction methods for Fe-Mn oxyhydroxide in sediments,which serve as effective carriers for isotopic signals of ancient seawater,is a prerequisite and key for paleoceanography research.The research material of this study was the surface sediments of the Western Arctic Ocean obtained from China's 7th Arctic Scientific Expedition,and a reliable simultaneous Nd-Hf isotope extraction method in Fe-Mn oxyhydroxide fraction was established through systematic optimization of pretreatment procedures and key extraction parameters.This research focused on analyzing the effects of pretreatment(Milli-Q water pre-wash)and key extraction parameters(leaching time,solution/solid ratios,concentration of EDTA-2Na,and concentration of mixed solution of hydroxyamine hydrochloride(HH)and acetic acid(HAc))on element extraction efficiency and isotopic composition.The experimental results indicated that the Milli-Q water pre-wash was not necessary,Fe-Mn oxyhydroxide extraction could be directly performed.Nd extraction amount in Fe-Mn oxyhydroxide fraction was not sensitive to changes in extraction conditions,while the Hf extraction amount was significantly affected by leaching time,solution/solid ratios and concentration of EDTA-2Na.The optimal conditions for synchronously extracting Nd-Hf isotopes were as follows:adding 20 mL of a mixed solution of 0.005 mol/L HH+1.5%HAc+0.03 mol/L EDTA-2Na per gram of sample,and reaction at room temperature for 3 h under shaking.Through systematic verification of 87Sr/86Sr ratio,εNd value,εHf value,Al/Nd ratio and Al/Hf ratio,it was confirmed that this method could effectively avoid interferences from residual components and accurately obtain Nd-Hf isotope signals of ancient seawater preserved in sediments.The standardized method established in this work provided reliable technical support for reconstructing water mass mixing between the Arctic,North Atlantic,and North Pacific Oceans and riverine input histories.It also provided important reference for the extraction methods of Nd and Hf isotopes from marine sources in sediments from other sea areas around the world.

Background: Diabetic macrovascular and microvascular complications often coexist and may share similar risk factors and pathological pathways. We aimed to investigate whether 10-year atherosclerotic cardiovascular disease (ASCVD) risk, which is commonly assessed in diabetes management, can predict incident diabetic nephropathy (DN) and retinopathy (DR) in patients with type 2 diabetes mellitus (T2DM). Methods: This prospective cohort study enrolled 2,891 patients with clinically diagnosed T2DM who were free of ASCVD, nephropathy, or retinopathy at baseline in the Guangzhou (2017–2022) and Shaoguan (2019–2021) Diabetic Eye Study in southern China. The 10-year ASCVD risk was calculated by the Prediction for ASCVD Risk in China (China-PAR) equations. Multivariable- adjusted Cox proportional hazard models were developed to estimate hazard ratios (HRs) with 95% confidence intervals (CIs). The area under the receiver operating characteristic curve (AUC) was used to evaluate predictive capability. Results: During follow-up, a total of 171 cases of DN and 532 cases of DR were documented. Each 1% increment in 10-year ASCVD risk was associated with increased risk of DN (pooled HR, 1.122; 95% CI, 1.094 to 1.150) but not DR (pooled HR, 0.996; 95% CI, 0.979 to 1.013). The model demonstrated acceptable performance in predicting new-onset DN (pooled AUC, 0.670; 95% CI, 0.628 to 0.715). These results were consistent across cohorts and subgroups, with the association appearing to be more pronounced in women. Conclusion Ten-year ASCVD risk predicts incident DN but not DR in our study population with T2DM. Regular monitoring of ASCVD risk in routine diabetes practice may add to the ability to enhance population-based prevention for both macrovascular and microvascular diseases, particularly among women.

Fungal keratitis represents a significant cause of blindness, with current therapeutic approaches yielding limited success. The disease's onset and progression are primarily driven by fungal virulence factors and the host's immune response. The innate immune system is the first to respond, with neutrophils playing a pivotal role in the antifungal defense. Although neutrophils are critical for pathogen clearance, their excessive or abnormal activation can lead to tissue damage, exacerbating the disease. Thus, elucidating the mechanisms underlying neutrophil activity in fungal keratitis is crucial for refining treatment strategies. This article aims to systematically review the principal antimicrobial mechanisms employed by neutrophils, including phagocytosis, degranulation, and the formation of neutrophil extracellular traps(NETs). Furthermore, it explores the crosstalk between neutrophils and macrophages, alongside their collective impact and underlying mechanisms in the context of fungal keratitis. Exploration of the mechanisms of fungal keratitis facilitates precise intervention and enhances the efficacy of treatment.