OBJECTIVE To investigate the effects of subanesthetic dose of esketamine on postoperative anxiety and recovery in patients undergoing laparoscopic cholecystectomy. METHODS A total of 200 patients scheduled for laparoscopic cholecystectomy at Suzhou Ninth Hospital Affiliated to Soochow University from January 2023 to December 2024 were randomly assigned to control group （n＝100） and observation group （n＝100）. One minute before the initiation of anesthesia， patients in the control group received intravenous injections of Propofol emulsion injection， Sufentanil citrate injection， and Succinylcholine chloride injection. On this basis， patients in the observation group received an intravenous injection of Esketamine hydrochloride injection. The anxiety status of patients in both groups was compared， along with their general intraoperative conditions （including sufentanil dosage， duration of pneumoperitoneum， operative time， anesthesia time， and extubation time）， postoperative recovery， incidence of adverse reactions， and the need for dezocine rescue analgesia. Heart rate and mean arterial pressure， entropy index （state entropy and response entropy）， inflammatory marker levels [interleukin-6 （IL-6） and C-reactive protein （CRP）]， numerical rating scale （NRS） for pain intensity were compared between the two groups at different time points. RESULTS No significant differences were found between the two groups in pneumoperitoneum duration， operative time， anesthesia time，extubation time， incidence of postoperative dry mouth， entropy index or length of stay in the post-anesthesia care unit （P＞0.05）. Compared with the control group， the observation group showed significantly lower postoperative STAI-S scores， reduced intraoperative sufentanil consumption， decreased incidence of postoperative nausea， vomiting， and shivering， the need for dezocine rescue analgesia， as well as lower plasma IL-6 and CRP levels at 24 h after surgery， and NRS （P＜0.05）. The heart rate and mean arterial pressure of patients in the observation group at the start of surgery， end of surgery， and during extubation were all significantly higher than those in the control group （P＜0.05）. CONCLUSIONS Subanesthetic dose of esketamine can effectively alleviate postoperative anxiety， reduce intraoperative opioid consumption， suppress postoperative inflammatory response， relieve postoperative pain， and promote recovery in patients undergoing laparoscopic cholecystectomy.

Animal experimentation constitutes a critical link between basic research and clinical application, making its research quality and translational efficiency paramount. Although considerable progress has been made in standardizing operational procedures and ethical guidelines, the standardization of outcome evaluation systems has significantly lagged, creating a key bottleneck that constrains the quality of biomedical research and evidence synthesis. This deficiency is manifested by pronounced heterogeneity in outcome selection across similar studies, incomplete methodological reporting, and disparate criteria for result interpretation, which severely impairs the comparability of findings and the evidence integration. To cope with this challenge, this paper systematically introduces a mature methodological tool from clinical research–the core outcome set (COS)–and explores its construction strategies and application potential in the field of animal experimentation. Given the extensive diversity of animal experiments, a pragmatic strategy of "focusing on key areas, implementing phased pilots, and promoting gradual expansion" should be adopted. This approach prioritizes the development of domain-specific COS for disease areas characterized by high research volume, urgent translational needs, and well-established animal models. A multi-source integration pathway for COS development is detailed, comprising systematic literature searches, methodological appraisals, and expert consensus, with the feasibility of leveraging artificial intelligence (AI) to enhance efficiency also being examined. The development and promotion of such COS are not intended to restrict scientific exploration; rather, they aim to establish a new, tiered evaluation paradigm consisting of "core outcomes" (mandatory), "recommended outcomes" (encouraged), and "exploratory outcomes" (optional). This framework is expected not only to enhance research quality through standardization and to adhere to the "3R" principles but also to accelerate the accumulation of high-quality evidence. This, in turn, provides a solid foundation for higher-level evidence synthesis, ultimately facilitating the effective translation of basic research findings into clinical practice and providing an essential methodological framework for scientific advancement in relevant disciplines.

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.

ObjectiveTo investigate how vitamin B₁₂ eye drops regulate MMP-9 and TNF-α expression levels in the corneas of dry eye disease （DED） mouse model induced by a combination of an artificially simulated dry environment （air humidity 30%） and subcutaneous injection of hyoscine hydrobromide. MethodsFifty C57BL/6 female mice were used in the study. 10 mice formed A group （blank control）， while the remaining 40 mice were subjected to dry eye disease induction through subcutaneous injection of scopolamine hydrobromide in a controlled environment with 30% air humidity. All mice underwent schirmer I test （SIT）， tear break-up time （TBUT）， and corneal fluorescence staining score（CFS）evaluations. Mice meeting modeling criteria were randomly assigned to three groups： B group （model group）， C group （PBS control）， and D group （vitamin B₁₂ group）. 0， 7， 14， and 21 days after treatment， each group received TBUT， SIT， CFS， and corneal inflammation assessments. Macroscopic analysis evaluated the efficacy of vitamin B₁₂ eye drops in treating dry eye. After 21 days of treatment， all mice were euthanized via cervical dislocation and their corneas analyzed using HE staining to examine morphological changes. ELISA and immunohistochemical methods were employed to detect MMP-9 and TNF-α expression levels in corneal tissues across groups. ResultsIn dry environments （air humidity 30%）， subcutaneous injection of scopolamine hydrobromide induced changes in dry eye-related indicators in mice： SIT significantly decreased， TBUT significantly shortened， corneal fluorescein staining increased， and CFS all significantly elevated （P 0.05）， indicating successful establishment of the dry eye model. A total of 30 dry eye mice were enrolled. When the dry eye model was successfully established without drug intervention， no statistically significant differences were observed between B group， C group and D group in terms of TBUT， SIT， corneal fluorescein staining， or CFS. Compared with A group， B group， C group and D group showed statistically significant differences in reduced SIT， shortened TBUT， increased corneal fluorescein staining， and elevated CFS （P 0.05）. After drug intervention， D group （vitamin B₁₂ group） exhibited significantly increased SIT， prolonged TBUT， reduced corneal fluorescein staining， clearer corneas， decreased fluorescein staining， and lower CFS compared with B and C groups（P 0.05）. ELISA and immunohistochemical staining results showed that compared with A group， B and C groups exhibited significantly increased expression of MMP-9 and TNF-α in corneal tissues （P 0.05）. D group showed decreased expression of MMP-9 and TNF-α compared with B and C groups but remained higher than A group （P 0.05）. ConclusionThe expression of MMP-9 and TNF-α in the corneal tissues of mice with dry eye is elevated， suggesting that both inflammatory factors， MMP-9 and TNF-α， are involved in the development of dry eye. Vitamin B₁₂ eye drops may improve the symptoms of ocular surface discomfort by promoting the repair of the corneal epithelium. Vitamin B₁₂ eye drops may increase tear secretion， prolong tear film break up time， reduce corneal fluorescein staining， and alleviate the inflammatory reaction of the cornea by decreasing the expression of MMP-9 and TNF-α in the corneal tissues of the mice， thus playing a therapeutic role in the treatment of dry eye disease.

This paper provides a systematic review of the policies, regulations, and management requirements in the development and use of in vitro diagnostic reagents by healthcare institutions, along with their current implementation status both domestically and internationally, and includes a case analysis of the pilot program at Peking Union Medical College Hospital (PUMCH). The Medical Affairs Department of PUMCH, serving as the leading and operational management unit, established a multi-department collaborative governance structure and developed a full-lifecycle management model encompassing project selection, manufacturing approach determination, regulatory submission and approval, batch production, and clinical implementation. Under this management model, the hospital successfully completed five projects within just one year, achieving the highest number of project filings nationwide. Economic benefit evaluation of the first approved reagent, which is urgently required by clinical practice and named allergen-specific immunoglobulin E detection assay for Fraxinus pennsylvanica pollen, demonstrates that the project realized controllable risk and predictable commercial value, and proves that proactive implementation of systematic management model and project systematic construction is the key to successfully advancing laboratory-developed test programs. The experiences provide both theoretical insights and practical guidance for peer medical institutions seeking to develop and implement self-developed in vitro diagnostic reagents.

Objective: To assess parental expectations for the development of children with autism spectrum disorder (ASD) and identify factors associated with these expectations, so as to provide references for improving rehabilitation outcomes and long term development in children with ASD. Methods: From June 2024 to January 2026, a total of 877 children with ASD and their parents were recruited from 27 rehabilitation institutions in Hefei. The children had been diagnosed by chief physicians at tertiary level hospitals. Demographic characteristics and data on parental expectations were collected by the Parental Expectations Questionnaire. Differences in parental expectations between groups were assessed using the Mann-Whitney U- test and Kruskal-Wallis H-test, and associated factors were examined using quantile regression. Results: The mean item score for overall parental expectations among parents of children with ASD was (4.55±0.47), and scores for all dimensions were higher than those among parents of typically developing children ( t =11.11-31.14, all P <0.01). Child age, school enrollment, and maternal personality were associated with daily living expectation scores, learning and special talents and strengths expectation scores, and special talents and strengths expectation scores, respectively ( H =6.16-8.18, all P <0.05). Parents of preterm children had higher expectations for social interaction, physical and mental well being, and daily living than parents of non-preterm children ( Z =-2.56, -2.48, -2.39, all P <0.05). Multivariate quantile regression analysis showed that children’s age was associated with the expected level of self care ability; school enrollment status was associated with the expected level of learning and specialty strengths; maternal personality was associated with the expected levels of social competence, physical and mental health, self care ability, and specialty strengths; parental age was associated with the expected levels of physical and mental health and specialty strengths; and parental educational attainment was associated with the expected level of self care ability ( β =-0.36-0.41, all P <0.05). Conclusion Parents of children with ASD in Hefei generally have high expectations for their children s development across the domains of learning, daily living, social interaction, and physical and mental well being.

Poloxamer， as a non-ionic surfactant， exhibits a unique triblock [polyethylene oxide-poly （propylene oxide）-polyethylene oxide] structure， which endows it with broad application potential in various fields， including solid dispersion technology， nanotechnology， gel technology， biologics， gene engineering and 3D printing. As a carrier， it enhances the solubility and bioavailability of poorly soluble drugs. In the field of nanotechnology， it serves as a stabilizer etc.， enriching preparation methods. In gel technology， its self-assembly behavior and thermosensitive properties facilitate controlled drug release. In biologics， it improves targeting efficiency and reduces side effects. In gene engineering， it enhances delivery efficiency and expression levels. In 3D printing， it provides novel strategies for precise drug release control and the production of high-quality biological products. As a versatile material， poloxamer holds promising prospects in the pharmaceutical field.

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.

ObjectiveBased on the AMP-activated protein kinase （AMPK）/mammalian target of rapamycin （mTOR） signaling pathway， this study aimed to observe the effect of the Huazhuo Jiedu prescription on renal fibrosis in 5/6 nephrectomy rats and explore its underlying mechanism. MethodsA total of 67 SPF-grade male SD rats were used， of which 11 were randomly selected as the normal group. A chronic renal failure （CRF） model was established using 5/6 nephrectomy. The successfully modeled rats were randomly assigned to the model group， losartan potassium group （4.5 mg·kg^-1）， and low- （1.175 g·kg^-1）， medium- （2.35 g·kg^-1） and high-dose （4.7 g·kg^-1） Huazhuo Jiedu prescription groups， with 9 rats per group. Each group received an equivalent volume of saline or the corresponding concentration of Huazhuo Jiedu prescription by gavage once daily for 8 weeks. Hematoxylin-eosin （HE） and Masson staining were used to observe renal tissue pathological changes. Transmission electron microscopy examined renal ultrastructure. Immunohistochemistry （IHC） detected expressions of α-smooth muscle actin （α-SMA） and transforming growth factor-β₁ （TGF-β₁）. Western blot analyzed expression levels of microtubule-associated protein Ⅰ light chain 3Ⅱ （LC3Ⅱ）， Beclin1， p62， AMPK， phosphorylated AMPK （p-AMPK）， mTOR， and phosphorylated mTOR （p-mTOR）. ResultsCompared with the normal group， the model group exhibited glomerular shrinkage， mesangial and interstitial thickening， and tubular vacuolar degeneration， with no evident autophagosomes or autophagolysosome structures. Expression levels of α-SMA and TGF-β₁ were significantly increased （P0.01）， while p-AMPK/AMPK， Beclin1， and LC3Ⅱ were significantly decreased （P0.01）， and p-mTOR/mTOR and p62 were significantly increased （P0.01）. Compared with the model group， the medium- and high-dose Huazhuo Jiedu prescription groups and the losartan potassium group showed varying degrees of pathological improvement. Autophagosomes with double- or multiple-layer membranes and autophagolysosomes with monolayer membranes containing undegraded organelles were observed. Renal α-SMA and TGF-β₁ protein expression levels were markedly reduced （P0.05， P0.01）， p-mTOR/mTOR and p62 were significantly decreased （P0.05， P0.01）， and p-AMPK/AMPK， Beclin1， and LC3Ⅱ expression levels were significantly increased （P0.05， P0.01）. ConclusionHuazhuo Jiedu prescription may improve renal fibrosis in 5/6 nephrectomy rats by regulating the AMPK/mTOR signaling pathway and enhancing autophagy.