This paper systematically elaborates on the key points of diagnosis and differential diagnosis of salivary gland tumors characterized by a substantial amount of extracellular mucus as a main or prominent feature, and clarifies the core differential features. The term "mucus-rich" specifically denotes that mucus is a major component of the tumor, rather than a focal or minor one. This phenomenon is associated with distinct histogenetic mechanisms: it may result from specific genetic mutations (e.g., AKT1 E17K in mucinous adenocarcinoma) that drive ductal epithelial differentiation into mucus-secreting cells, or from myoepithelial cells secreting glycosaminoglycans that form a myxoid stroma. Salivary gland tumors with abundant extracellular mucus include mucinous cystadenoma, sialadenoma papilliferum-like intraductal papillary tumors, mucinous myoepithelioma, pleomorphic adenoma with mucin-rich stroma, mucinous adenocarcinoma, low-grade mucoepidermoid carcinoma, mucin-rich salivary duct carcinoma and intestinal-type adenocarcinoma. The diagnosis of these tumors is complicated by the dual nature of extracellular mucus: while it is a defining feature of some entities, it can also obscure key diagnostic architectural features in others, leading to histological overlap and inconspicuous diagnostic areas. Given the frequent histological morphological overlap among these tumors, immunohistochemical findings and molecular characteristics have emerged as crucial differential diagnostic criteria. Core differential diagnostic points include the following: histologically, there must be meticulous identification of typical structures obscured by mucin (such as squamoid cells in mucoepidermoid carcinoma and apocrine features in salivary duct carcinoma); in immunohistochemical staining, CK20 is useful for distinguishing intestinal-type adenocarcinoma (positive) from mucinous adenocarcinoma (negative), while androgen receptor aids in differentiating salivary duct carcinoma (positive) from mucoepidermoid carcinoma (negative); and molecular testing plays a critical role in definitive diagnosis (e.g., the AKT1 E17K mutation for mucinous adenocarcinoma, MAML2 rearrangement for mucoepidermoid carcinoma, and MEF2C::SS18 fusion for microsecretory adenocarcinoma). This paper systematically summarizes the core pathological features and differential diagnostic points of mucin-rich salivary gland tumors, aiming to provide a practical reference for clinical pathological diagnosis.

Bronchiolar adenoma (BA) is a rare type of lung tumor characterized by bilayered epithelial cells having a continuous basal layer and a luminal layer. It resembles mucinous adenocarcinoma (MA) on frozen section, with difficulty in distinguishing the basal layer. Immunohistochemistry is the best choice for verifying the diagnosis. This study aimed to comprehensively characterize three cases of BA-combined carcinoma using clinical, histopathological, and genetic features. BA and carcinoma sections were subjected to next-generation sequencing, respectively. It was hypothesized that while different mutation forms matched different regions, BA and lung adenocarcinoma shared the same gene mutation when they co-occurred in the same location. BA with extensive carcinoma is extremely rare and presents diagnostic challenges due to its overlap with conditions such as MA. Because of its distinctive morphological characteristics, BA may be regarded as a low-grade malignancy, particularly during a confusing evaluation. A multifaceted examination of clinical, radiological, immunohistochemical, and genetic data is necessary for an accurate diagnosis.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

Objective:To evaluate the safety and effectiveness of fuzuloparib for the treatment of ovarian epithelial cancer patients in the real world setting.Methods:A retrospective analysis was conducted on the baseline data of 4 620 ovarian cancer patients who had received fuzuloparib monotherapy or combination therapy. Another 224 ovarian cancer patients who were willing to receive fuzuloparib monotherapy or combination therapy were prospectively enrolled, and their baseline characteristics, drug effectiveness, and safety data were analyzed.Results:(1) Among the 4 620 patients in the retrospective cohort, the median age of patients was 60 years; tumor types: 89.8% (4 149/4 620) had ovarian cancer. Among patients with clearly documented information, the vast majority had a histological type of serous carcinoma (82.9%, 3 770/4 546) and International Federation of Gynecology and Obstetrics (FIGO) staging of Ⅲ-Ⅳ (90.9%, 1 537/1 691). (2) Among the 224 patients in the prospective cohort, the median age of patients was 57 years; tumor types: 83.9% (188/224) had ovarian cancer. Among patients with clearly documented records, the predominant pathologic type was serous carcinoma (91.9%, 193/210), and FIGO stage was Ⅲ-Ⅳ in 79.9% (139/174). (3) Among the 224 prospective patients: 84 patients received first-line fluzoparib maintenance therapy, 92 patients received fluzoparib maintenance therapy after platinum-sensitive recurrence, 23 patients received direct fluzoparib treatment after platinum-sensitive recurrence, 19 patients received direct fluzoparib treatment after platinum-resistant recurrence. The median follow-up durations were 8.5, 8.7, 7.9, and 6.7 months, respectively. The median durations of fluzoparib treatment were 6.7, 4.8, 3.1, and 1.9 months, respectively. The median progression-free survival (PFS) times were not reached during follow-up, 12.6 months, not reached during follow-up, and 4.8 months, respectively. The 1-year PFS rates were 84.1%, 55.0%, 69.8%, and 45.5%, respectively. The remaining 6 patients received other fluzoparib regimens. (4) Among the 224 patients in the prospective dataset, 205 had safety data recorded. Of these, 127 patients (62.0%, 127/205) experienced treatment-related adverse events, with common events including anemia (24.4%, 50/205), thrombocytopenia (21.0%, 43/205), and leukopenia (19.5%, 40/205). Among the 205 patients, 43 (21.0%, 43/205) experienced grade 3 or higher treatment-related adverse events, with common events including anemia (8.3%, 17/205) and thrombocytopenia (8.3%, 17/205).Conclusions:The effectiveness of fuzuloparib in clinical application is generally consistent with other drugs in the same class, with good safety. This study provids new clinical evidence for the treatment of ovarian cancer with fuzuloparib.

Objective To propose a low-dose CT image denoising method based on an improved Corediff model to recover the detailed features of the image and enhance the image quality.Methods An RS-Corediff model was established by modifying the key component U-Net network of the Corediff model.Firstly,the residual module was introduced in the network input stage for feature extraction;secondly,a new downsampling module was designed in the U-Net network encoder,which learned the semantic information of the feature map by convolution and maintained the learning state during the downsampling process so as to fully extract the image features;thirdly,the feature splicing processing was used to further enhance the learning effect during the upsampling process of the U-Net network decoder;finally,the convolutional kernel size was modified to adjust the sensory field during the convolutional process of the whole U-Net network structure so as to obtain rich features.The RS-Corediff model was compared with the residual encoder-decoder convolutional neural network(RED-CNN)model and the Corediff model on the public dataset AAPM 2016 in order to verify its effectiveness for low-dose CT image denoising.Results The RS-Corediff model gained advantages over the RED-CNN and Corediff models with a peak signal-to-noise ratio(PSNR)of 41.269 8,structural similarity(SSIM)of 0.953 4 and root mean square error(RMSE)of 17.568 7.Conclusion The proposed method effectively preserves the texture and details of low-dose CT images during the denoising process to improve the overall quality of the images.[Chinese Medical Equipment Journal,2025,46(5):9-13]

ObjectiveThis study leverages structural data from antigen-antibody complexes of the influenza A virus neuraminidase (NA) protein to investigate the spatial recognition relationship between the antigenic epitopes and antibody paratopes. MethodsStructural data on NA protein antigen-antibody complexes were comprehensively collected from the SAbDab database, and processed to obtain the amino acid sequences and spatial distribution information on antigenic epitopes and corresponding antibody paratopes. Statistical analysis was conducted on the antibody sequences, frequency of use of genes, amino acid preferences, and the lengths of complementarity determining regions (CDR). Epitope hotspots for antibody binding were analyzed, and the spatial structural similarity of antibody paratopes was calculated and subjected to clustering, which allowed for a comprehensively exploration of the spatial recognition relationship between antigenic epitopes and antibodies. The specificity of antibodies targeting different antigenic epitope clusters was further validated through bio-layer interferometry (BLI) experiments. ResultsThe collected data revealed that the antigen-antibody complex structure data of influenza A virus NA protein in SAbDab database were mainly from H3N2, H7N9 and H1N1 subtypes. The hotspot regions of antigen epitopes were primarily located around the catalytic active site. The antibodies used for structural analysis were primarily derived from human and murine sources. Among murine antibodies, the most frequently used V-J gene combination was IGHV1-12*01/IGHJ2*01, while for human antibodies, the most common combination was IGHV1-69*01/IGHJ6*01. There were significant differences in the lengths and usage preferences of heavy chain CDR amino acids between antibodies that bind within the catalytic active site and those that bind to regions outside the catalytic active site. The results revealed that structurally similar antibodies could recognize the same epitopes, indicating a specific spatial recognition between antibody and antigen epitopes. Structural overlap in the binding regions was observed for antibodies with similar paratope structures, and the competitive binding of these antibodies to the epitope was confirmed through BLI experiments. ConclusionThe antigen epitopes of NA protein mainly ditributed around the catalytic active site and its surrounding loops. Spatial complementarity and electrostatic interactions play crucial roles in the recognition and binding of antibodies to antigenic epitopes in the catalytic region. There existed a spatial recognition relationship between antigens and antibodies that was independent of the uniqueness of antibody sequences, which means that antibodies with different sequences could potentially form similar local spatial structures and recognize the same epitopes.

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

This study was aimed at establishing a method of ultra-fast quantitative PCR for Brucella detection.We used an exogenous recombinant plasmid as the internal reference and targeted the T4SS secretion system,an important Brucella viru-lence factor,to design specific primers and probes.The sensitivity,specificity,and repeatability of this method were evaluated,and a standard curve was constructed.The coincidence rate of detection findings with this method versus quantitative PCR was determined.This method markedly decreased the detection time to only 10 minutes.The standard curve demonstrated a good linear relationship(Y=-3.410 7x+38.357,R2=0.998 5)with a low minimum detection limit of 10 copies/μL.The method exhibited good specificity and did not specifically amplify several common clinical bacteria other than Brucella.The de-tection of three concentrations of positive plasmids yielded coefficients of variation(CVs)of 0.20％to 0.91％,thus demonstra-ting the method's excellent repeatability.Furthermore,140 clinical samples were analyzed concurrently with the fluorescence PCR method,which yielded a 100％compliance rate and consistent results.Our findings indicated that the Brucella ultra-fast quantitative PCR was ultrafast;had high sensitivity,high specificity,and good specificity;and can be used for the clinical de-tection of Brucella and emergency investigation of epidemics.Therefore,this method is valuable for the early diagnosis of Bru-cella.

Gastric contrast-enhanced ultrasound includes oral contrast-enhanced ultrasound (OCUS) and double contrast-enhanced ultrasound (DCEUS),which can provide valuable clinical information about tumor morphology,vascular characteristics,and treatment responses.OCUS can clearly identify the gastric wall structure and the extent and depth of lesions by applying oral contrast agents.DCEUS,based on OCUS combined with venography,can display the anatomical and perfusion characteristics of lesions.In recent years,gastric contrast agents and imaging techniques have developed rapidly.However,the clinical application of gastric contrast-enhanced ultrasound is still in the developmental stage.This article reviews the clinical status of OCUS and DCEUS in the screening,diagnosis,staging,pathological typing,and treatment evaluation of gastric cancer.Studies have shown that gastric contrast-enhanced ultrasound has high sensitivity and specificity in the assessment of diagnosis and T-staging of gastric cancer.Furthermore,gastric contrast-enhanced ultrasound has the advantages of being cost-effective,convenient,non-invasive,free from radiation exposure,real-time,and easy to repeat.In the diagnosis and treatment of gastric cancer,it is expected to become one of the important imaging assessment tools.
Humans ; Stomach Neoplasms/diagnostic imaging* ; Contrast Media ; Ultrasonography/methods*

Immunotherapy has emerged as a revolutionary approach to treat immune-related diseases. Dendritic cells (DCs) play a pivotal role in orchestrating immune responses, making them an attractive target for immunotherapeutic interventions. Modulation of gene expression in DCs using genome editing techniques, such as the CRISPR-Cas system, is important for regulating DC functions. However, the precise delivery of CRISPR-based therapies to DCs has posed a significant challenge. While lipid nanoparticles (LNPs) have been extensively studied for gene editing in tumor cells, their potential application in DCs has remained relatively unexplored. This study investigates the important role of cholesterol in regulating the efficiency of BAMEA-O16B lipid-assisted nanoparticles (BLANs) as carriers of CRISPR/Cas9 for gene editing in DCs. Remarkably, BLANs with low cholesterol density exhibit exceptional mRNA uptake, improved endosomal escape, and efficient single-guide RNA release capabilities. Administration of BLAN_mCas9/gPD-L1 results in substantial PD-L1 gene knockout in conventional dendritic cells (cDCs), accompanied by heightened cDC1 activation, T cell stimulation, and significant suppression of tumor growth. The study underscores the pivotal role of cholesterol density within LNPs, revealing potent influence on gene editing efficacy within DCs. This strategy holds immense promise for the field of cancer immunotherapy, offering a novel avenue for treating immune-related diseases.