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.

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.

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.

To address the growing challenge of an increasing number of elderly lung cancer patients amidst China's aging population and to fill the gap in quality control standards for surgical treatment in this special population, this study aimed to develop a standardized surgical evaluation system for elderly lung cancer patients tailored to China's national conditions. The system was established through a literature review, integrated the pathophysiological characteristics of elderly patients, and was constructed following review, feedback, and revision by experts from multiple thoracic surgery centers. Employing a 100-point scoring system, it comprises three primary domains: physical infrastructure and geriatric adaptability foundational conditions (10 points); management level and perioperative care models (20 points); and technical proficiency and clinical outcomes (70 points). The system places a strong emphasis on geriatric adaptability, proposing specific, quantifiable indicators for age-friendly facility modifications, control of elderly-specific complications, multidisciplinary collaboration, and standardized perioperative management. It provides a convenient and measurable assessment tool for quality control in the surgical treatment of elderly lung cancer in China, which is expected to promote the standardization and homogenization of diagnosis and treatment.

Lung cancer constitutes the most prevalent and lethal malignant tumor in China. Approximately 85% of lung cancer diagnoses correspond to the non-small cell histological subtype [non-small cell lung cancer (NSCLC)]. Despite surgery being the mainstay for early-stage disease, postoperative recurrence remains high and adjuvant chemotherapy offers limited benefit. In recent years, targeted therapy has demonstrated substantial advantages in driver mutation-positive NSCLC. To this end, the Lung Cancer Medical Education Committee of the Chinese Medical Education Association developed guidelines based on a systematic review of evidence through November 2025, using the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) approach and a modified Delphi method. Focusing on epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK), and addressing ROS proto-oncogene 1 (ROS1), B-Raf proto-oncogene serine/threonine kinase (BRAF) V600E mutation, and mesenchymal-epithelial transition factor (MET) exon 14 (METex14) skipping, the guideline covers molecular testing, neoadjuvant/adjuvant therapy, perioperative strategies, minimal residual disease monitoring, and postoperative surveillance. It defines testing requirements, specifies stage-directed and subtype-specific treatments, and standardizes minimal residual disease monitoring. These recommendations emphasize precision and feasibility to improve survival and quality of life.

Objective To understand the situation of dental cone beam computed tomography (CBCT) quality control testing phantoms in radiation health technical service institutions in Guangdong province, analyze the differences among different phantoms, and provide a reference for dental CBCT quality control testing. Methods The testing phantoms of 49 radiation health technical service institutions were used as the research objects. The designated CBCT equipment was used for scanning and imaging. The Z-score method was used to evaluate the high-contrast resolution, low-contrast resolution, and distance measurement deviation of each phantom. Results The satisfaction rates of various items for the phantoms in 49 institutions ranged from 85.7% to 100%. The distance measurement deviations of four institutions were “suspicious”, and the high-contrast resolution of four institutions and the distance measurement deviation of one institution were “unsatisfactory”. Conclusion The overall performance of dental CBCT quality control testing phantoms in radiological health technical service institutions in Guangdong province is satisfactory. However, there are still some phantoms with poor results in items such as distance measurement deviation and high-contrast resolution. The structural design, material selection, and manufacturing process of the phantom may all affect the results of quality control testing. Therefore, appropriate phantoms, optimized exposure conditions, and suitable reconstruction algorithms should be used in CBCT quality control testing to ensure accurate and reliable measurements.

Objective: To explore the association between negative life events and smartphone addiction among middle school students, so as to provide theoretical support and practical guidance for prevention and intervention of smartphone addiction among middle school students. Methods: Using cluster sampling, 8 890 students were selected to survey from 27 junior high schools and 3 senior high schools in a district of Shenzhen in 2022 (baseline) and 2023 (followup). Data were collected through selfresigned questionnaires on basic information, the Smartphone Addiction Scale-Short Version, and the Adolescent Selfrating Life Events Checklist. Mixedeffects models were employed to analyze the association. Results: Compared to 2022, the punishment scores of middle school students in 2023 [1.00 (0.00, 6.00) and 1.00 (0.00, 6.00)] decreased (Z=4.27), while the scores of interpersonal stress, learning stress and adaptation [4.00(0.00, 8.00), 4.00(0.00, 8.00); 4.00(1.00, 8.00), 5.00(2.00, 9.00); 2.00 (0.00, 6.00), 3.00 (0.00, 7.00)] increased (Z=-3.04, -8.36, -6.80) (P<0.01). Mixedeffects models revealed a positive doseresponse relationship between negative life events and smartphone addiction (OR=1.08-1.17, P<0.01). Stepwise regression showed independent positive effects of interpersonal stress (OR=1.05), academic stress (OR=1.03), and adaptation stress (OR=1.11) on smartphone addiction (P<0.01). Subgroup analysis of nonaddicted students in 2022 confirmed persistent associations for academic stress (OR=1.03) and adaptation (OR=1.07) (P<0.01). Conclusion Negative life events exhibit a positive doseresponse relationship with smartphone addiction, particularly interpersonal stress, academic stress, and adaptationrelated events.

Objective: To explore the potential causal association between adolescent compulsive behaviour and smartphone addiction based on longitudinal data, so as to provide reference for the establishment of adolescent smartphone addiction interventions. Methods: A preliminary survey and follow-up were conducted on 8 907 middle and high school students in a district of Shenzhen in 2022 and 2023, respectively. Compulsive behaviours were measured by using the Mental Health Inventory for Middle School Students-60 Items (MMHI-60), smartphone addiction was assessed by using the Smartphone Addiction Scale-Short Version ( SAS- SV), and the associations between compulsive behaviours and smartphone addiction were analysed by using multilevel mixed-effects models and subgroup analyses. Results: Smartphone addiction detection rates among middle school students were significantly associated with genders, father s education level, mother s education level, study load subgroups, and whether or not they were single-parent families, and there were statistical differences ( χ 2=17.21-175.34, P <0.05). Students with compulsive behaviours were 2.98 times more likely to develop smartphone addiction than those without compulsive behaviours ( OR=2.98, 95%CI=2.77-3.22, P <0.05). Subgroup analysis of middle school students without smartphone addiction in the first year found that compulsive behaviours significantly predicted smartphone addiction ( OR= 1.76 , 95%CI=1.54-2.01, P <0.05). Conclusion There is a potential causal association between obsessive-compulsive behaviours and smartphone addiction in middle school students, and obsessive-compulsive behaviours in middle school students could significantly predicted the occurrence of smartphone addiction.

OBJECTIVE To provide technical support for improving recognition rate of adverse drug events （ADEs） related to traditional Chinese medicine （TCM） formula granules and decoction pieces among inpatient patients. METHODS By referencing the global trigger tool （GTT） whitepaper， literature on adverse reactions to TCM， and expert review opinions， ADE trigger items for TCM formula granules and decoction pieces used in the inpatients were established. GTT was applied to analyze ADEs in inpatients who had used TCM formula granules and decoction pieces in our hospital from August 2013 to August 2023， utilizing the Chinese Hospital Pharmacovigilance System. The effectiveness of GTT and the characteristics of these ADEs were analyzed. RESULTS A total of forty-eight triggers were established， including thirty-two laboratory test indexes， thirteen clinical symptoms， and three antidotes. Among the 1 682 patients included， GTT identified 652 potential ADEs， 284 true positive ADEs，with a trigger rate of 38.76% and a positive predictive value of 43.56%. After review by the auditor， 278 cases of ADEs were finally confirmed， with an incidence rate of 16.53%， significantly higher than the number of spontaneously reported ADEs during the same period （0）. The 278 cases of ADEs were mostly grade 1 （223 cases）， mainly involving hepatobiliary system， gastrointestinal system， blood- lymphatic system， etc；a total of 219 types of TCMs are involved，and the top five suspected TCMs used at a frequency higher than 1% were Poria cocos， Codonopsis pilosula， Atractylodes macrocephala， fried Glycyrrhiza uralensis， and Scutellaria baicalensis. CONCLUSIONS The established GTT can improve the recognition rate of ADEs for hospitalized patients using traditional Chinese medicine formula granules and decoction pieces.