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.

Objective To analyze the efficacy, safety, and learning curve of the three-lobe holmium laser enucleation of the prostate(HoLEP)in the treatment of benign prostatic hyperplasia(BPH)in a county-level hospital.Methods A retrospective analysis was conducted on the clinical data of 65 BPH patients who underwent the three-lobe HoLEP performed by a single surgeon at the Department of Urology, Yining County People's Hospital, during Dec.2023 and Jun.2024.The enucleation efficiency was calculated by dividing the weight of the enucleated prostatic tissue by the enucleation time.A case scatter diagram of enucleation efficiency was plotted according to the chronological order of the operations, and the learning curve was analyzed after fitting.Based on the inflection points of the learning curve, the learning process was divided into the initial learning, mastery, and proficiency phases.The basic clinical data, perioperative indicators, postoperative complications, and follow-up indicators were compared among the different learning phases.Results All 65 procedures were successfully completed.Marked by the enucleation efficiency reaching a plateau, cases 1-20 were defined as the initial learning phase, cases 21-40 as the mastery phase, and cases 41 onwards as the proficiency phase.The prostate volume in the proficiency phase was significantly larger than that in the initial learning and mastery phases(both P＜0.05).There were no statistically significant differences in other baseline characteristics among the three groups(all P＞0.05).The operation time [(105.50±19.12)min vs.(85.25±26.92)min vs.(69.00±23.58)min] and enucleation time [(76.90±14.19)min vs.(63.70±22.24)min vs.(48.80±20.48)min] showed a decreasing trend across the three groups(all P＜0.05).The enucleation efficiency in both the mastery and proficiency phases was significantly higher than that in the initial learning phase [(1.16±0.44)g/min vs.(0.85 ±0.25)g/min, P＜0.05;(1.36±0.49)g/min vs.(0.85±0.25)g/min, P＜0.05].The enucleation efficiency in the proficiency phase was slightly higher than that in the mastery phase, but the difference was not statistically significant(P= 0.389).There was no significant difference in the incidence of perioperative complications among the three groups(all P＞0.05).At the 6-month follow-up, the international prostate symptom score(IPSS), post-void residual(PVR), and maximum urinary flow(Qmax)were significantly improved compared to preoperative values in all three groups(all P＜0.05);however, no significant differences were observed among the three groups(all P＞0.05).Conclusion The three-lobe HoLEP for the treatment of BPH is safe and effective in a county-level hospital setting.Surgeons with some experience in endoscopic surgery can preliminarily master this technique after a learning period of approximately 20 procedures.

Intrahepatic cholangiocarcinoma is a malignant tumor with an extremely poor prognosis， and its pathogenesis is complex and remains unclear. In recent years， more and more studies have focused on the role of bile-gut axis in the development and progression of intrahepatic cholangiocarcinoma. Bile-gut axis refers to the complex interaction between bile and gut microbiota， including bile salt metabolism， dynamic changes of microbiota， inflammatory response， and immune system regulation. This article elaborates on the potential mechanisms of bile-gut axis in intrahepatic cholangiocarcinoma， especially gut microbiota dysbiosis， abnormal bile salt metabolism， chronic inflammatory response， and immune system interaction， this article aims to provide new perspectives and possible therapeutic targets for future research and promote the early diagnosis and effective treatment of intrahepatic cholangiocarcinoma.

Objective By simulating the etiology of abnormal uterine bleeding-ovulatory dysfunction(AUB-O)and establishing a rat model of abnormal uterine bleeding(AUB),this study aims to provide an experimental platform for investigating pathological mechanisms and developing therapeutic drugs for AUB.Methods After acclimation,24 adult(10-week-old)female SD rats were randomly divided into a normal control group(6 rats)and a model group(18 rats).The normal control group was housed in a barrier environment,while the model group underwent bilateral ovariectomy via dorsal approach in the same environment and rested for one week before starting to receive modeling drugs.In the model group,from Days 1 to 3 of modeling,each rat received a daily subcutaneous injection of 0.5 mg estradiol into the dorsal region.From Days 4 to 7,a daily subcutaneous injection of 5.0 mg progesterone was administered.On Day 6,rats received bilateral injections of 0.5 mL soybean oil per uterine cavity(total 1.0 mL)via the same dorsal surgical incision.On Day 8,mifepristone(10 mg/kg)was administered via oral gavage.The estrous cycle stage and its dynamic changes were continuously monitored during modeling.Uterine bleeding was recorded during the 48-hour observation period post-modeling.Serum and uterine tissue samples were collected from the model group at 0,12,24,36,and 48 h after mifepristone administration,while the normal control group was sampled at 36 h.The samples were subjected to HE staining,serum sex hormone ELISA,immunohistochemistry,TUNEL apoptosis staining,Western blotting,transcriptome sequencing,and bioinformatics analysis for comprehensive evaluation of the AUB rat model.Results The AUB rats exhibited uterine bleeding,endometrial detachment and injury,incomplete uterine restoration,inflammatory cell infiltration in the endometrium,enhanced tissue apoptosis,and structural damage of the stroma,glands,and vasculature.Compared with the normal control group,the levels of serum follicle-stimulating hormone(FSH),estradiol,and luteinizing hormone(LH)were significantly increased in the AUB rats(P＜0.05).The vascular density of the endometrium was significantly reduced(P＜0.05).The expression of vascular endothelial growth factor(VEGF)was qualitatively observed to be markedly enhanced at the site of endometrial detachment but significantly decreased around the stromal blood vessels(P＜0.01).Matrix metalloproteinase-9(MMP-9)expression was qualitatively observed to be strongly upregulated at the site of endometrial injury but significantly reduced in the non-detached stroma and glands(P＜0.01).Endometrial stromal cell apoptosis was significantly enhanced(P＜0.01).The expression levels of fibroblast growth factor 2(FGF2)and endothelin-1(ET-1)in uterine tissues were significantly decreased(P＜0.05).After comparing the transcriptome sequencing results of uterine tissues between AUB and normal rats,a total of 4 723 differentially expressed genes were identified,including 2 191 up-regulated genes and 2 532 down-regulated genes.KEGG enrichment analysis revealed that these differentially expressed genes were significantly enriched in pathways related to inflammation,immune apoptosis,cell signal transduction,proliferation and differentiation,and muscle contraction,among others.Conclusion An AUB rat model can be successfully established using a sequential administration protocol of estrogen,progesterone,and mifepristone to simulate the etiology of AUB-O.In this model,endometrial injury is associated with inflammation and apoptosis,with pathological manifestations influenced by abnormal vasoconstriction and impaired endometrial regeneration.This rat model closely recapitulates pathological characteristics of non-structural AUB observed in clinical practice,making it a validated experimental platform for exploring the pathological mechanisms and therapeutic interventions of non-structural AUB.

Objective:To evaluate the diagnostic performance of artificial intelligence-assisted diagnostic systems in cervical cytopathological examination.Methods:Cervical cytology slide data were retrospectively collected from four hospitals for the external validation of the developed artificial intelligence-assisted diagnostic system. Subsequently, prospective data collection was conducted for human-machine assisted studies.Results:In the retrospective study, a total of 3 162 valid samples were collected as external validation data. The system showed an area under the curve (AUC) of 0.890 (95% CI: 0.878-0.902), accuracy of 0.885 (95% CI: 0.873-0.896), sensitivity of 0.928 (95% CI: 0.914-0.941), and specificity of 0.852 (95% CI: 0.834-0.867). In the prospective study, 212 valid samples were collected, and five junior cytologists participated in the human-machine assisted study. Without artificial intelligence assistance, the average AUC for the five cytologists was 0.686 (95% CI: 0.650-0.722), the accuracy was 0.699 (95% CI: 0.671-0.727), the sensitivity was 0.653 (95% CI: 0.599-0.703), the specificity was 0.719 (95% CI: 0.685-0.750), the Fleiss κ value was 0.510, and the reading time was 223 seconds. With artificial intelligence assistance, the AUC, accuracy, sensitivity, and specificity increased by 0.166, 0.143, 0.225, and 0.107, respectively. Additionally, Fleiss κ was 0.730 and the reading time decreased by 188 seconds. All differences were statistically significant (all P<0.001). Conclusions:Artificial intelligence-assisted diagnosis system shows excellent performance and good generalizability, significantly improving the diagnostic accuracy, consistency, and efficiency of junior cytologists. It can be an effective auxiliary tool for junior cytologists in clinical practice.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

AIM:To investigate the effects of memantine(Mem),an N-methyl-D-aspartate(NMDA)recep-tor antagonist,on sevoflurane(Sev)anesthetic depth and perioperative neurocognitive disorders in mice,and to explore the possible mechanisms involved.METHODS:Mouse electroencephalogram(EEG)monitoring and cognitive disorder models were established.For EEG monitoring,male C57BL/6J mice were randomly divided into control group,Sev group,and Mem+Sev group.The EEG monitoring electrodes were implanted in the heads of the mice 7 d before anesthe-sia.On the day of anesthesia,the mice in Mem+Sev group received an intraperitoneal injection of 20 mg/kg Mem dissolved in normal saline,while those in control and Sev groups received intraperitoneal injection of an equivalent volume of normal saline based on body weight.Thirty minutes later,the mice in Sev and Mem+Sev groups were anesthetized with 400 mL/min O2+3%Sev for 5 h,while those in control group were treated with 400 mL/min O2 for 5 h.The EEG monitoring was ter-minated after the righting reflex was restored in Sev and Mem+Sev groups.The time of disappearance and recovery of the righting reflex was recorded,and changes in EEG burst suppression ratio and relative power of each frequency band were analyzed.For the cognitive disorder part,another batch of male C57BL/6J mice were selected and divided into the same groups as before.The mice underwent water maze spatial navigation training for 6 d before anesthesia.On the day of anes-thesia,the mice in Mem+Sev group received an intraperitoneal injection of 20 mg/kg Mem dissolved in normal saline,while those in control and Sev groups received intraperitoneal injection of an equivalent volume of normal saline based on body weight.Thirty minutes later,the mice in Sev and Mem+Sev groups were anesthetized with 400 mL/min O2+3%Sev for 5 h,and those in control group were treated with 400 mL/min O2 for 5 h.Spatial navigation and exploration tests were conducted 3 d after anesthesia.After the tests,the mice were sacrificed,and their hippocampal tissues were collected.The levels of interleukin-1β(IL-1β),tumor necrosis factor-α(TNF-α)and acetylcholine(ACh)in the hippocampal tis-sues were detected by ELISA.The concentration of Ca2+in the hippocampal tissues was measured using a calcium assay kit.Pathological changes in the hippocampal CA3 region were observed by HE staining,and the protein levels of NMDA receptor GluN1 subunit,GABAA receptor,amyloid β-protein(Aβ),and p-tau were detected by Western blot.RE-SULTS:Compared with control group,the mice in Sev group had increased burst suppression ratio at all time points dur-ing anesthesia and prolonged escape latency and reduced platform crossings 3 d after anesthesia(P<0.05).The levels of IL-1β and TNF-α in the hippocampal tissues increased,while the level of ACh decreased,and the concentration of Ca2+in-creased.The protein levels of GluN1 subunit,Aβ and p-tau were elevated(P<0.05).Compared with Sev group,the mice in Mem+Sev group had shortened anesthesia induction time and increased burst suppression ratio at all time points during anesthesia,with elevated relative power of slow waves and δ waves(P<0.05).The escape latency was shortened,and the platform crossings increased 3 d after anesthesia(P<0.05).The levels of IL-1β and TNF-α in the hippocampal tissues decreased,while the levels of ACh increased,and the protein levels of GluN1 subunit,Aβ and p-tau were reduced(P<0.05).There was no significant difference in anesthesia recovery time among the groups(P>0.05).CONCLU-SION:Memantine,in combination with Sev anesthesia,accelerates anesthesia induction and deepens anesthetic depth,which may be related to the increased relative power of δ EEG waves,but has no significant effect on recovery time.Me-mantine intervention alleviates Sev anesthesia-induced cognitive disorders by inhibiting the overexpression of NMDA recep-tors,Aβ and p-tau,and attenuating neuroinflammation.