Surgical operation is the main treatment of oral and maxillofacial tumors.Dysphagia is a common postoperative complication.Swal-lowing disorder can not only lead to mis-aspiration,malnutrition,aspiration pneumonia and other serious consequences,but also may cause psychological problems and social communication barriers,affecting the quality of life of the patients.At present,there is no systematic evalua-tion and rehabilitation management plan for the problem of swallowing disorder after oral and maxillofacial tumor surgery in China.Combining the characteristics of postoperative swallowing disorder in patients with oral and maxillofacial tumors,summarizing the clinical experience of ex-perts in the field of tumor and rehabilitation,reviewing and summarizing relevant literature at home and abroad,and through joint discussion and modification,a group of national experts reached this consensus including the core contents of the screening of swallowing disorders,the phased assessment of prognosis and complications,and the implementation plan of comprehensive management such as nutrition management,respiratory management,swallowing function recovery,psychology and nursing during rehabilitation treatment,in order to improve the evalua-tion and rehabilitation of swallowing disorder after oral and maxillofacial tumor surgery in clinic.

Cryoablation therapy with explicit anti-tumor mechanisms and histopathological manifestations has a long history.A large number of clinical practice has shown that cryoablation therapy is safe and effective,making it an ideal tumor treatment method in theory.Previously,its efficacy and clinical application were constrained by the limitations of refrigerants and refrigeration equipment.With the development of the new generation of cryoablation equipment represented by argon helium knives,significant progress has been made in refrigeration efficien-cy,ablation range,and precise temperature measurement,greatly promoting the progression of tumor cryoablation technology.This consensus systematically summarizes the mechanism of cryoablation technology,indications for oral mucosal melanoma(OMM)cryotherapy,clinical treatment process,adverse reactions and management,cryotherapy combination therapy,etc.,aiming to provide reference for carrying out the standardized cryoablation therapy of OMM.

At present,proprotein convertase subtilisin/kexin type 9(PCSK9)inhibitor has been widely used in clinical field as a fast and effective drug to reduce low density lipoprotein cholesterol(LDL-C),in addition to regulating LDL-C to affect the process of atherosclerosis.Clinical data show that PCSK9 is upregulated in ischemic heart,and downregulation of PCSK9 expression can benefit infarct size,post-infarct inflammation and remodeling,and cardiac dysfunction after ische-mia/reperfusion.In subjects with increased cardiovascular risk,PCSK9 inhibition was associated with reduced incidence rates of myocardial infarction,stroke,and coronary revascularization,as well as improved endothelial function.This article reviews the role of PCSK9 in myocardial infarction and ischemia-reperfusion after myocardial infarction.

AIM: To compare the binocular and monocular accommodation among normal group, premyopia group and mild myopia group, and to study the characteristics of accommodation in the premyopia group, thus providing clinical evidence for the delay/prevention of myopia and the effective decrease of the incidence of myopia.METHODS: Cross-sectional descriptive study. A total of 179 children who had abnormal/high-risk visual acuity indicated by the vision screening in school from October 2021 to February 2023 were selected, including 92 males and 87 females, aged from 6 to 12(mean 8.55±1.66)years old, then they were referred to the Juvenile Myopia Prevention and Control Center in Cuizu Community Health Service Center. They were divided into normal group(+0.75 D<SE≤+2.00 D), the premyopia group(-0.50 D<SE≤+0.75 D)and the mild myopia group(-3.00 D≤SE≤-0.50 D)according to the diopters after cycloplegia, and binocular myopia grouping is defined by the eye with lower diopter. Binocular positive relative accommodation(PRA), negative relative accommodation(NRA), accommodative facility(AF), and monocular AF and amplitude of accommodation(AA)were examined. The age, binocular and monocular accommodation of different groups were compared.RESULTS: There were no difference in the sex ratio of different groups(χ2=0.167, P=0.920). There was no difference in age between the normal group and the premyopia group(P=0.310), but there were differences between the mild myopia group and the normal group and premyopia group(P=0.018, <0.01); Binocular NRA, PRA, and AF had significance between the normal group and the premyopia group(P<0.01), while there was no significance between the premyopia group and the mild myopia(P>0.05). Monocular AF had significance between the normal group and the premyopia group(P<0.01), while there was no significance between the premyopia group and the mild myopia group(P>0.05); The monocular AA had significance among the three groups(P<0.05).CONCLUSION: Although the diopters was normal, binocular NRA, PRA, monocular and binocular AF had significantly decreased in the premyopia group, and there was no significant difference compared with mild myopia group; monocular AA had decreased in the premyopia group and it was also significantly different from the mild myopia group. The accommodation function should be examined in premyopic children. Recovering the abnormal visual function through visual training may be a way to prevent and control premyopia from progressing to myopia.

Objective To investigate T cell subtypes and their functions in liver immune microenvironments among patients with alveolar echinococcosis (AE) using single-cell RNA sequencing (scRNA-seq). Methods Four AE patients that were admitted to Qinghai Provincial People’s Hospital in 2023 for hepatic surgery for the first time were enrolled, and liver specimens were sampled 1 cm (peri-lesion, PL group) and > 5 cm from AE lesions (distal lesion, DL group) among each patient. Finally, a total of eight liver specimens were sampled from four AE patients for scRNA-seq analysis. Genome and transcriptome data of liver specimens were processed using the software Cell Ranger and R package. Differentially expressed genes (DEGs) and their biological functions were analyzed using gene ontology (GO) enrichment analysis and Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis, and the primary intercellular communication patterns and interaction mechanisms were identified among T cell subtypes in liver specimens using the CellChat package. In addition, the developmental stages of T cells were subjected to trajectory analysis with the monocle package to investigate the expression of genes associated with cell growth and tumor transformation, and to predict the developmental trajectories of T cells. Results All four AE patients were female, with a mean age of (25.00 ± 9.06) years, and there were three cases from Jiuzhi County, Golog Tibetan Autonomous Prefecture and one case from Chengduo County, Yushu Tibetan Autonomous Prefecture, Qinghai Province. The viability of single-cell samples from eight liver specimens was 90.41% to 96.33%, and a total of 81 763 cells were analyzed, with 19 cell types annotated. Of these cell types, 13 were immune cells (87.60%), and T cells (33.13%), neutrophils (15.40%), and natural killer cells (11.92%) were the three most common cell types. Re-clustering of 27 752 T cells and proliferative T cells identified 10 distinct T cell subtypes, with CD8⁺ cytotoxic T cells (23.43%), CD8⁺ naive T cells (12.80%), and CD4⁺ effector memory T cells (17.73%) as dominant cell types. The proportions of T helper 2 (Th2) cells (5.19% vs. 3.63%; χ² = 38.35, P < 0.01) and CD4⁺ effector memory T cells (21.59% vs. 13.67%; χ² = 244.70, P < 0.01) were significantly higher in liver specimens in the PL group than in the DL group, and the proportion of CD4⁺ helper T cells was significantly lower in the PL group than in the DL group (7.50% vs. 14.75%; χ² = 330.52, P < 0.01). KEGG pathway analysis revealed that Th2 cells were significantly enriched in cell apoptosis and multiple cancer-associated pathways, and CD4⁺ effector memory T cells were significantly enriched in the regulation of cytokines and chronic inflammation, while CD4⁺ helper T cells were significantly enriched in immune responses regulation. Trajectory analysis of T cells showed that CD4⁺ helper T cells were at an earlier developmental stage relative to Th2 cells and CD4⁺ effector memory T cells, and the expression of inhibitor of DNA binding 3 (ID3), thioredoxin interacting protein (TXNIP), Bcl2-associated athanogene 3 (BAG3) and heat shock protein family B (small) member 1 (HSPB1) genes appeared a tendency towards a decline over time. Conclusions CD4⁺ effector memory T cells and CD8⁺ cytotoxic T cells are primary interacting cells in the liver specimens of AE patients. Reduced expression of Th2 cells and CD4⁺ helper T cells contributes to an inhibitory immune microenvironment, which promotes immune evasion by Echinococcus multilocularis, and Th2 cells are significantly enriched in multiple cancer-associated pathways, which may be linked to the invasive growth of E. multilocularis.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.

Objective:To establish a menstrual prediction model after the first course of hormone replacement therapy(HRT)in premature ovarian insufficiency(POI)patients after allogeneic hematopoietic stem cell transplan-tation(allo-HSCT),and to provide certain reference value for formulating HRT plans.Methods:The retrospective analysis recruited 154 POI patients after allo-HSCT in the First Affiliated Hospital of Soochow University from Jan-uary 2017 to October 2022.They were divided into ideal menstruation group(n=116)and unideal menstruation group(n=38)according to menstruation after the first course of HRT.Basic characteristics and clinical data were compared in single-factor analysis to select predictive factors.Patients were randomly divided into training set and test set.The menstrual prediction model was developed based on random forest algorithm on the training set and the prediction efficiency was verified by the test set.Finally,we made a user interaction interface and deployed to the server for sharing.Results:The single-factor analysis suggested statistic difference of age of visit,body mass index(BMI),gravidity,parity,hematologic diseases,transplantation age,donor gender,follicle-stimulating hormone(FSH),Luteinizing Hormone(LH),lumbar bone mineral density(BMD)and HRT plan(P<0.05).According to mean decrease accuracy,the predictive factors included visit age,transplantation age,BMI,FSH,HRT plans,gravidity and parity.After the initial establishment of the random forest model,we improved it by adjusting ntree to 500,mtry to 6 and training/test set division to 80%/20% .We also used tenfold cross validation to reduce over-fitting.The area under curve(AUC)of the final constructed menstrual prediction model was 0.768,a sensitiv-ity of 0.695 and a specificity of 0.735.Conclusions:This study successfully established a menstrual prediction model for amenorrhea patients after allo-HSCT when finished the first course of HRT.The false positive rate was low,suggesting that if the prediction result of the model is non-ideal menstruation,we may consider adjusting HRT plans to promote menstruation in time.