Objective To quantitatively assess the exposure-response association between exposure to heatwave and sudden death, estimate the attributable excess deaths, and identify potential vulnerable subgroups. Methods A time-stratified case-crossover study was conducted among residents who died from sudden death in Jiangsu Province, China between 2015 and 2021. Heatwave events in Jiangsu Province, defined using varying relative temperature thresholds and durations, were identified using temperature data from the China Meteorological Administration Land Data Assimilation System (CLDAS V2.0). Individual heatwave exposure was assessed based on each subject's residential address. The exposure-response association between heatwave and sudden death was evaluated using conditional logistic regression model combined with a Distributed Lag Nonlinear Model(DLNM). Heatwave-attributable excess deaths were estimated. Stratified analyses by sex and age were performed to assess potential effect modifications. Results Under all definitions, exposure to heatwave was significantly associated with an increased risk of sudden death, and the risk increased with the intensity of heatwave. Using the P95_3d definition (temperature exceeding the 95th percentile for ≥3 consecutive days), heatwave was significantlyassociated with a 56% increased risk of sudden death (95% CI: 31%, 86%). The population-attributable fraction of sudden death due to heatwave exposure was 1.45% (95% CI: 0.97%, 1.90%). Stratified analyses indicated no statistically significant differences in the association between heatwave exposure and sudden death across age or sex subgroups. Conclusion Heatwave exposure was associated with an increased risk of sudden death. Reducing heatwave exposure during summer may help lower the occurrence of sudden death.

Oral squamous cell carcinoma (OSCC) is a common head and neck malignancy. Approximately 50% to 60% of patients with OSCC are diagnosed at a locally advanced stage (clinical staging III-IVa). Even with comprehensive and sequential treatment primarily based on surgery, the 5-year overall survival rate remains below 50%, and patients often suffer from postoperative functional impairments such as difficulties with speaking and swallowing. Programmed death receptor-1 (PD-1) inhibitors are increasingly used in the neoadjuvant treatment of locally advanced OSCC and have shown encouraging efficacy. However, clinical practice still faces key challenges, including the definition of indications, optimization of combination regimens, and standards for efficacy evaluation. Based on the latest research advances worldwide and the clinical experience of the expert group, this expert consensus systematically evaluates the application of PD-1 inhibitors in the neoadjuvant treatment of locally advanced OSCC, covering combination strategies, treatment cycles and surgical timing, efficacy assessment, use of biomarkers, management of special populations and immune related adverse events, principles for immunotherapy rechallenge, and function preservation strategies. After multiple rounds of panel discussion and through anonymous voting using the Delphi method, the following consensus statements have been formulated: 1) Neoadjuvant therapy with PD-1 inhibitors can be used preoperatively in patients with locally advanced OSCC. The preferred regimen is a PD-1 inhibitor combined with platinum based chemotherapy, administered for 2-3 cycles. 2) During the efficacy evaluation of neoadjuvant therapy, radiographic assessment should follow the dual criteria of Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 and immune RECIST (iRECIST). After surgery, systematic pathological evaluation of both the primary lesion and regional lymph nodes is required. For combination chemotherapy regimens, PD-L1 expression and combined positive score need not be used as mandatory inclusion or exclusion criteria. 3) For special populations such as the elderly (≥ 70 years), individuals with stable HIV viral load, and carriers of chronic HBV/HCV, PD-1 inhibitors may be used cautiously under the guidance of a multidisciplinary team (MDT), with close monitoring for adverse events. 4) For patients with a poor response to neoadjuvant therapy, continuation of the original treatment regimen is not recommended; the subsequent treatment plan should be adjusted promptly after MDT assessment. Organ transplant recipients and patients with active autoimmune diseases are not recommended to receive neoadjuvant PD-1 inhibitor therapy due to the high risk of immune related activation. Rechallenge is generally not advised for patients who have experienced high risk immune related adverse events such as immune mediated myocarditis, neurotoxicity, or pneumonitis. 5) For patients with a good pathological response, individualized de escalation surgery and function preservation strategies can be explored. This consensus aims to promote the standardized, safe, and precise application of neoadjuvant PD-1 inhibitor strategies in the management of locally advanced OSCC patients.

AIM:To evaluate visual outcomes and visual function in nonamblyopic adults with myopic anisometropia treated with small incision lenticule extraction(SMILE).METHODS:Prospective comparative cohort study. The consecutive patients who underwent SMILE for the treatment of myopia or myopic astigmatism at Zhongshan Ophthalmic Center(Guangzhou, China)between October 2015 and January 2016 were included. They were divided into two groups based on the bilateral difference of a spherical equivalent(SE)refraction ≥1.50 D: the anisometropic myopia group(interocular SE difference ≥1.50 D)and non-anisometropic myopia group(interocular SE difference<1.50 D). Refractive status, uncorrected and corrected distance visual acuity(UDVA and CDVA), and visual function parameters including fusional vergence amplitude, stereoacuity and horizontal phoria were measured preoperatively and at 1 wk,1,3 and 6 mo after surgery.RESULTS:A total of 49 cases(98 eyes)were included in the study, and 19 cases(38 eyes)in the anisometropic group, including 11 males and 8 females, with a mean age of 25.4±6.2 y, and 30 cases(60 eyes)in the non-anisometropic myopia group, including 19 males and 11 females, with a mean age of 26.8±4.6 y. The CDVA of the non-anisometropia group was significantly better than that of the anisometropia group 6 mo postoperatively(P=0.036). However, the safety and efficacy indexes of the two groups did not show significant differences. The fusional vergence(break point and recovery point)of the anisometropia group decreased(P=0.005 and P=0.03)and was significantly lower than that in the non-anisometropia group at 6 mo post operatively(P=0.029 and P=0.046). Both groups showed a significant improvement in distance and near stereopsis at 1, 3 and 6 mo in comparison with the preoperative baseline and 1 wk postoperatively(all P<0.05). No clinically significant change in the amount of ocular alignment in terms of distance and near deviation postoperatively in either groups.CONCLUSION: SMILE is a predictable, effective, and safe method for correcting myopic anisometropia in adults without amblyopia. Although the fusional vergence amplitudes changed, stereopsis can be improved after surgery.

OBJECTIVE To explore and establish a full-cycle management model for drug traceability codes that aligns with national policy requirements and the practical needs of healthcare institutions， thereby enhancing the refinement of drug management and the level of medication safety. METHODS A tripartite strategy integrating “hardware deployment， system transformation， and process re-engineering” was adopted. This involved the introduction of intelligent identification devices （personal digital assistant， high-definition industrial reader）， the modification of the hospital information system interface， and the re-engineering of workflows （drug warehousing， dispensing and distribution， drug withdrawal， uploading to the insurance platform） to achieve comprehensive， informatized collection and association of drug traceability codes throughout all stages. RESULTS A full-cycle management model for drug traceability codes was successfully established， realizing the goals of making drugs “traceable to their source， trackable in their distribution， and accountable in their responsibility”. The patient waiting time for medication dispensing before and after the implementation was [3.08（1.67，5.58）] min and [3.28（1.77，5.98）] min， respectively. Among them， the patient waiting time under the pre-preparation mode was [3.60（2.13，6.35）] min and [3.50（2.03，6.30）] min， respectively； the patient waiting time under the real-time mode was [2.05（0.83，4.03）] min and [2.78（1.18，5.38）] min， respectively； the number of dispensing errors was 3， 0， respectively； the staffing of relevant positions had not been increased. CONCLUSIONS The drug traceability code management model constructed from a full-cycle perspective effectively meets national policy requirements. It provides data support for refined hospital management and offers solid technical and procedural safeguards for ensuring patient medication safety and strengthening medical insurance fund supervision， demonstrating practical value.

High myopia has become a major public health concern worldwide, particularly in China and Southeast Asia. It is associated not only with a variety of fundus diseases but also with earlier onset and greater severity of cataracts, resulting in significant visual impairment. Phacoemulsification is currently the main surgical treatment for cataracts. However, intraoperative fluctuations in perfusion pressure and exposure to ultrasonic energy may affect the vitreoretinal structures, potentially accelerating the progression of fundus pathology after surgery in highly myopic eyes. This article summarizes current evidence on the progression of posterior scleral staphyloma, myopic maculopathy, and retinal detachment following phacoemulsification in highly myopic eyes.

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.

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.

Objective:To investigate the effect of dulagopeptide on physical examination indexes,plasma glucose metabolism and islet function in type 2 diabetes mellitus(T2DM)patients with poorly controlled plasma glucose.Methods:135 T2DM patients with poorly controlled plasma glucose who were admitted in our hospital from January 2023 to July 2024 were selected.A prospective randomized controlled design was adopted,they were divided into control group 1(received treatment with sitagliptin,n=45),control group 2(received treatment with insulin glargine,n=45),and observation group(received treatment with dulaglutide,n=45)according to the random number table method.Physical examination indexes,plasma glucose indicators,islet function,and incidence of adverse reactions were compared among the three groups.Results:12 weeks after treatment,body mass index(BMI),waist circumference,fasting plasma glucose(FPG),glycated hemoglobin(HbA1c),and postprandial 2-hour plasma glucose(2hPG)in the observation group were lower than those in control group 1 and control group 2(P＜0.05).12 weeks after treatment,the observation group had the highest HbA1c compliance rate,reaching 71.1％(P＜0.05).12 weeks after treatment,the fasting C-peptide(FC-P)and HOMA-islet(CP-DM)levels in the observation group were higher than those in control group 1 and control group 2(P＜0.05).Conclusion:Dulagopeptide can effectively improve physical examination indexes,plasma glucose indicators,and islet function in T2DM patients with poorly controlled plasma glucose.

Objective To investigate the correlation between G protein-coupled receptor family C group 6 member A(GPRC6A)gene polymorphisms and their expression and pulmonary infections in elderly patients with chronic heart failure(CHF).Methods 138 elderly CHF patients admitted to the Xianyang First People's Hospital from January 2021 to January 2024 were selected as the research subjects,and were divided into an infected group(n=42)and an uninfected group(n=96)based on their lung infection status.Polymerase chain reaction(PCR)was used to detect polymorphisms at the rs6901250 and rs1606365 loci of the GPRC6A gene.The allele and genotype frequency distributions of the infected and uninfected groups were compared.Logistic regression modeling was used to analyze the s6901250 and rs1606365 loci under three genetic models(co-dominant,dominant and reces-sive)and lung infections in elderly patients with CHF.Real-time fluorescence quantitative PCR(RT-qPCR)method was used to detect the expression level of GPRC6A gene.The predictive value of the mRNA expression level of the GPRC6A gene for the development of pulmonary infections in elderly patients with CHF was analyzed by applying the receiver operator characteristic(ROC)curve.Results The distribution of genotypes at loci rs6901250 and rs1606365 of the GPRC6A gene in both the infected and uninfected groups of the lungs of elderly CHF patients conformed to the Hardy-Weinberg equilibrium law(χ2=0.199～0.376,all P>0.05),which was representative of the population.Compared with the uninfected group,the frequency of allele A at locus rs6901250(57.14%vs 41.67%)was significantly higher in the infected group,Allele G(54.76%vs.37.50%)and genotype GG(14.06%vs 29.99%)frequencies were significantly higher at locus rs1606365,and the differences were statistically significant(χ2=5.628,7.114,6.849,all P<0.05).At locus rs6901250,in the co-dominant model(GG vs AA)and the dominant model(GA+AA vs GG),the elderly CHF patients with AA genotype the risk of lung infection was higher than that of GG genotype(OR=1.753,1.546,all P<0.05);.rs1606365 locus showed that the risk of lung infection was higher than that of CC genotype in el-derly CHF patients with GG genotype under all three genetic models of co-dominant model(CC vs GG),dominant model(CG+GG vs CC)and recessive model(CG+CC vs GG)(OR=1.833,1.741,0.695,all P<0.05).The mRNA expression level of GPR-C6A gene in the lung-infected group of elderly CHF patients(1.43±0.35)was significantly higher than that in the uninfected group(1.02±0.21),and the difference was statistically significant(t=8.515,P<0.001).The results of the ROC curve analysis showed that the GPRC6A gene expression level predicted lung infection in elderly CHF patients with an AUC value of 0.895,a cut-offvalue of 1.37,and sensitivity and specificity of 85.7%and 66.7%,respectively.Conclusion The AA genotype at the rs6901250 locus and the GG genotype at the rs1606365 locus of the GPRC6A gene increased the risk of developing lung infec-tions in elderly patients with CHF.MRNA expression levels of the GPRC6A gene were elevated in the infected group,and its ex-pression level could be used as a predictive indicator for the development of lung infections in elderly patients with CHF.