Oral squamous cell carcinoma(OSCC)is a malignant lesion originating from the oral mucosal squamous epithelium,account-ing for over 80％of oral and maxillofacial malignancies.Key etiological factors include tobacco,alcohol abuse,and betel quid chewing.In China,its incidence has shown an overall upward trend,posing a significant threat to public health.OSCC exhibits high local invasive-ness,making early diagnosis critical for improving prognosis.Its clinical management requires close multidisciplinary collaboration among oral and maxillofacial surgery,head and neck surgery,radiation oncology,medical oncology,reconstructive surgery,radiology,patholo-gy,and nutritional support teams.Given the increasing disease burden of OSCC and rapid development of multidisciplinary collaborative models,an expert panel has formulated this integrated management consensus based on evidence-based medicine and extensive deliber-ation.Centered on the'Prevention-Screening-Diagnosis-Treatment-Rehabilitation'framework,the consensus provides comprehensive guidance for the entire disease course of OSCC patients,aiming to standardize clinical practice.

OBJECTIVE: To establish a genotyping method for Diego, MNS and Kell blood groups based on quantitative real-time PCR (qPCR) technology, and preliminarily apply it to the screening of rare blood groups in blood donors. METHODS: Blood group gene standards containing heterozygous and homozygous alleles were prepared by blood group serological and PCR-SBT methods. Specific amplification primers and hybridization probes were designed, and explore to establish the qPCR method for detecting Diego, MNS, and Kell blood group genotypes. Then the established qPCR method was used to identify blood group genotypes of 186 blood donor samples. RESULTS: A method based on qPCR technology was established to identify Dia/Dib, S/s and K/k blood group antigens. The genotyping results of the gene standard samples were consistent with the serological testing results and genotypes detected by PCR-SBT. qPCR testing of 186 samples identified 11 cases of DI*A/B heterozygosity and 19 cases of GYPB*S/s heterozygosity, and the rest were DI*B/B, GYPB*s/s, KEL*02/02 homozygosity. No rare blood group genotypes of DI*A/A, GYPB*S/S, KEL*01.01/01.01 were found. CONCLUSION The established qPCR method is suitable for genotyping on Diego, MNS and Kell blood group, and it can be used for batch screening of blood donors and the establishment of rare blood group bank.
Humans ; Genotype ; Genotyping Techniques/methods* ; Real-Time Polymerase Chain Reaction/methods* ; Blood Group Antigens/genetics* ; Kell Blood-Group System/genetics* ; Blood Donors ; Blood Grouping and Crossmatching/methods* ; Erythrocytes ; MNSs Blood-Group System/genetics*

Given the lack of annotations for key lung organs and tissues in existing public datasets,this study collected 863 cases of chest CT scan images and constructed the first comprehensive dataset containing annotations of pulmonary vessels,airways,and nodules using a semi-automated method that combines computer vision algorithms with manual corrections by radiologists.On this basis,a lung nodule segmentation model based on multi-task learning is proposed.By incorporating annotations of pulmonary vessels(pulmonary arteries and veins)and the trachea to enhance model's ability to learn lung features,the proposed model reduces the false discovery rate in lung nodule detection,and improves generalization ability.Additionally,the use of larger image patches further optimizes model performance.The trained VAAN_128 model achieves the best performance,with a Dice coefficient of 0.694 and a false discovery rate of 0.210 for lung nodule segmentation.Moreover,it simultaneously provides accurate segmentation results of pulmonary vessels and the trachea,assisting in the formulation of more precise diagnosis and treatment plans.Based on the VAAN_128 model,a software system for navigation and localization in biopsy procedures is developed.In clinical practice,this system can assist physicians in accurately locating lung nodules,distinguishing critical tissues,and improving preoperative planning efficiency.This provides precise and efficient technical support for early diagnosis and disease monitoring of lung diseases,and is of great significance for path planning in clinical navigation system and future lung imaging research.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.

Oropharyngeal squamous cell carcinoma(OPSCC)is a malignant tumor originating from the squamous epithelium of the oro-pharyngeal mucosa,accounting for more than 90％of oropharyngeal malignancies.In recent years,human papillomavirus(HPV)infec-tion has become one of the primary etiological factors of oropharyngeal squamous carcinoma.The incidence of HPV-associated oropharyn-geal squamous carcinoma has been rising annually,with a noticeable trend toward younger populations,posing a significant threat to hu-man health.Due to the distinct biological behavior and clinical characteristics of HPV-associated oropharyngeal squamous carcinoma com-pared to its non-HPV-related counterpart,the diagnostic and treatment strategies for oropharyngeal squamous carcinoma have undergone substantial changes.Prevention and screening for oropharyngeal squamous carcinoma are of critical importance.The diagnostic and treat-ment process involves multi-disciplinary collaboration,including oral and maxillofacial surgery,otolaryngology,head and neck surgery,oncology,radiology and pathology.Based on evidence from clinical practice,a comprehensive,integrated diagnostic and therapeutic ap-proach has been established,centered around the concept of"prevention,screening,diagnosis,treatment,and rehabilitation",covering the entire patient lifecycle and providing a valuable reference for clinical practice.

In recent decades,the incidence of human papillomavirus(HPV)-associated oropharyngeal squamous cell carcinoma(OPSCC)has shown a marked increase.Significant changes have also occurred in the OPSCC diagnosis and treatment paradigm.Deter-mining HPV status prior to treatment is now essential,and radiotherapy/chemotherapy,immunotherapy,and minimally invasive surgical techniques have progressively emerged as key modalities for managing OPSCC.However,alongside these paradigm shifts,a comprehen-sive technical consensus guiding the entire diagnostic and therapeutic process for OPSCC patients is currently lacking.Given China's large population base and the rising incidence of OPSCC,an expert panel convened to develop a clinical technical consensus on OPSCC diagno-sis and management tailored to China's specific context.This consensus aims to further enhance and standardize understanding of OPSCC management techniques among relevant healthcare professionals.

Astragalus membranaceus(A. membranaceus), a traditional tonic, contains flavonoids as one of its main bioactive components and key indicators for quality standard detection. These compounds predominantly exist in glycosylated forms after glycosylation modification within the plant. The catalytic products of flavonoid glycosyltransferases in A. membranaceus have been reported to be mostly monoglycosides, and only AmUGT28 catalyzes luteolin to form diglycosides. In this study, we cloned a glycosyltransferase gene, AmGT90, from A. membranaceus, with an ORF length of 1 335 bp, encoding 444 amino acids, and the protein had a relative molecular mass of 50.5 kDa. Phylogenetic tree analysis indicated that AmGT90 belongs to the UGT74 family. In vitro enzymatic reaction showed that AmGT90 had broad substrate specificity and could catalyze the glycosylation of various flavonoids, including isoflavones, flavones, flavanones, and chalcones. AmGT90 not only catalyzed the formation of monoglycosides but also diglycosides. In addition, the mechanism of AmGT90 catalyzing the formation of diglycosides from luteolin was preliminarily explored. The experimental results showed that AmGT90 may preferentially recognize C4'-OH of luteolin and then recognize C7-OH to form diglycosides. This study reported a glycosyltransferase from A. membranaceus capable of converting flavonoids into monoglycosides and diglycosides. This finding not only enhances our understanding of the biosynthetic pathways of flavonoid glycosides in A. membranaceus but also introduces a new component for glycoside production through synthetic biology.
Glycosyltransferases/chemistry* ; Flavonoids/chemistry* ; Astragalus propinquus/classification* ; Phylogeny ; Glycosylation ; Plant Proteins/chemistry* ; Substrate Specificity ; Cloning, Molecular ; Amino Acid Sequence

Thoracic anesthesia with spontaneous respiration represents a form of precision anesthesia meticulously customized to individual patients.Considering the more stringent requirements this anesthesia approach imposes on the regulation of respiratory function,the writing group of the"Consensus of Experts on the Management of Thoracic Anesthesia with Spontaneous Respiration"has formulated elaborate guidelines regarding indications and contraindications,preoperative evaluation,anesthesia implementation,common complications,and treatment strategies.This was accomplished by referencing relevant domestic and international literature and integrating it with actual clinical requirements.The objective is to standardize the rational application of this anesthesia method.

Given the lack of annotations for key lung organs and tissues in existing public datasets,this study collected 863 cases of chest CT scan images and constructed the first comprehensive dataset containing annotations of pulmonary vessels,airways,and nodules using a semi-automated method that combines computer vision algorithms with manual corrections by radiologists.On this basis,a lung nodule segmentation model based on multi-task learning is proposed.By incorporating annotations of pulmonary vessels(pulmonary arteries and veins)and the trachea to enhance model's ability to learn lung features,the proposed model reduces the false discovery rate in lung nodule detection,and improves generalization ability.Additionally,the use of larger image patches further optimizes model performance.The trained VAAN_128 model achieves the best performance,with a Dice coefficient of 0.694 and a false discovery rate of 0.210 for lung nodule segmentation.Moreover,it simultaneously provides accurate segmentation results of pulmonary vessels and the trachea,assisting in the formulation of more precise diagnosis and treatment plans.Based on the VAAN_128 model,a software system for navigation and localization in biopsy procedures is developed.In clinical practice,this system can assist physicians in accurately locating lung nodules,distinguishing critical tissues,and improving preoperative planning efficiency.This provides precise and efficient technical support for early diagnosis and disease monitoring of lung diseases,and is of great significance for path planning in clinical navigation system and future lung imaging research.

Purpose To explore the value of machine learning models based on synthetic MRI,dynamic contrast-enhanced MRI(DCE-MRI)and diffusion weighted imaging(DWI)parameters in identifying molecular subtypes of breast cancer.Materials and Methods A retrospective analysis was conducted on the data of 292 patients who underwent synthetic MRI,DCE-MRI and DWI examinations from September 2020 to September 2024 in Ningxia Medical University General Hospital before surgery and were pathologically confirmed to have breast cancer postoperatively.Patients were randomly divided into training and test sets using a ratio of 7:3.Multiple parameters were obtained from the synthetic MRI,DCE-MRI and DWI images.Variance analysis were used to screen the characteristic parameters among molecular subtype groups.Five machine learning models were established based on the selected characteristic parameters,and receiver operating characteristic curves were plotted to calculate the area under the curve among the molecular subtype groups.Results The support vector machine model exhibited the highest overall performance,with an area under the curve of 0.972,accuracy of 82.5％,specificity of 94.76％and sensitivity of 82.14％in the test set.This model's area under the curve values for differentiating luminal A,luminal B,human epidermal growth factor receptor-2 overexpression,and triple-negative groups in the training set were 0.979,0.925,0.971 and 0.982,respectively;in the test set,the area under the curve values were 0.973,0.873,0.956 and 0.955,respectively.Conclusion Machine learning models based on multimodal MRI parameters can assist clinicians in preoperatively determining the molecular subtypes of breast cancer and the support vector machine model shows relatively high comprehensive performance.