Objective:To explore the differences in bacterial communities within tissues during the process of oral mucosal carcinogenesis, and analyze the relationship between the high-abundance species Prevotella intermedia (Pi) and the occurrence and development of oral mucosal carcinogenesis. Methods:Fresh tissue samples were collected from patients diagnosed with oral leukoplakia (OLK), oral squamous cell carcinoma (OSCC), and healthy controls (HC) at Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, from January 2022 to November 2024, following strict inclusion criteria. Bacterial DNA was extracted from these specimens, and the 2bRAD sequencing for microbiome (2bRAD-M) was employed to analyze and compare the α and β diversity, as well as the community composition of bacteria within tissues, aiming to identify specifically expressed bacteria. Subsequently, paraffin-embedded clinical specimens were collected: 15 cases in the OLK group (including 4 cases of simple hyperplasia, 6 cases of mild dysplasia, and 5 cases of moderate to severe dysplasia), 12 cases in the OSCC group, and 5 cases in the HC group. A 4-nitroquinoline N-oxide (4NQO)-induced OLK progression mouse model was also constructed. Mice were randomly divided into three groups using a random number table, with six in each group. The negative control group was given distilled water to drink; Group 1 was given distilled water containing 4NQO to drink until week 12, while Group 2 was given distilled water containing 4NQO to drink until week 22. After the mice were sacrificed, their tongue tissue were collected and fixed. Fluorescence in situ hybridization (FISH) with specific probes was used to validate the presence of Pi in human and mouse tissue sections, analyzing the correlation between histopathological grading and the invasion depth of Pi.Results:The 2bRAD-M microbial analysis revealed that the relative abundance of Pi in OSCC tissues (10.80%) was significantly higher than in the HC group (0.50%) ( P=0.001) and OLK group (0.70%) ( P=0.002). FISH probe detection showed that the fluorescence intensity of Pi in human OSCC tissues [123.50 (101.00, 142.30)] was higher than in the HC group [0.00 (0.00, 28.50)], simple hyperplasia OLK [0.00 (0.00, 35.25)], and mild dysplasia OLK [24.50 (0.00, 55.50)] groups, with statistically significant differences respectively ( P=0.002, P=0.003, P=0.005). However, there was no significant difference compared to moderate to severe dysplasia OLK [56.00 (28.00, 62.50)] ( P=0.210). The fluorescence area of Pi in human OSCC tissues [8 615.00 (7 439.00, 11 084.00)] was significantly larger than in the HC group [0.00 (0. 00, 45.00)], simple hyperplasia OLK group [0.00 (0.00, 81.00)], mild dysplasia [49.00 (0.00, 151.00)], and moderate to severe dysplasia groups [1 450.00 (454.00, 2 892.00)], with highly significant differences ( P<0.001). There was a significant correlation between the invasive depth of Pi and the degree of histopathological grading ( P<0.001). In mice, the fluorescence intensity of Pi in OSCC tissues [120.00 (110.00, 127.00)] was significantly higher than in the HC group [0.00 (0.00, 12.25)] ( P<0.01), but showed no significant difference compared with the OLK group [50.00 (0.00, 58.00)] ( P>0.05). The fluorescence area of Pi in mice OSCC tissues [11 020.00 (6 790.00, 12 102.00)] was significantly larger than in the HC group [0.00 (0.00, 56.75)] and the OLK group [0.00 (0.00, 751.50)] ( P=0.006, P=0.043). There is a significant correlation between the depth of invasion of Pi and the degree of histopathological grading ( P<0.01). Conclusions:This study suggests that Pi in oral mucosal tissue may be a potential biomarker for early detection of OSCC and play an important role in the carcinogenesis process of oral mucosa.

Objective:To report construction of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) using the Delphi method.Methods:Literature related to the study of adult distal radius fractures was fully searched for and evaluated. An expert group was established from representative experts from all over the nation. The related clinical issues were established by consulting the experts in the form of electronic questionnaires, strictly following the Delphi research method. After the first draft of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) was written, an expert consultation questionnaire was designed for the recommendation opinions to determine the recommendation strength.Results:The clinical issues were determined by 2 rounds of correspondence based on the Delphi method. For the both rounds of correspondence, the questionnaire recovery rates were respectively 88.68% (47/53) and 98.11% (52/53), and the expert authority coefficients >0.7. According to the screening criteria based on the importance of clinical issues (mean importance score <3.5 points or a coefficient of variation ≥0.25 points and a full score ratio <30%) and expert opinions, a total of 40 clinical issues were deleted in the first round of determination of clinical issues, and a total of 5 clinical issues deleted in the second round of determination of clinical issues. The reliability analysis of the results of the 2 rounds of questionnaires showed that the Cronbach α coefficient was >0.9. In the questionnaire to determine the recommendation strength, according to the screening criteria for the consistency of recommendation strength (consistency ≥ 70%) and expert opinions, a total of 26 recommendations were screened in the first round. In the second round when the remaining 4 recommendations were investigated, one recommendation reached the consistency of recommendation strength ≥ 70%. Eventually, 27 recommendations were formed.Conclusion:The Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) constructed using the Delphi method shows good scientific validity, authority, and reliability, providing methodological references for guideline development and research.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To evaluate the value of ultrasound radiomics combined with machine learning for early diagnosis of seronegative Hashimoto’s thyroiditis (SN-HT) .Methods:This retrospective study included 164 patients from Liaoning Provincial People’s Hospital , Lixin County People’s Hospital, Linghai Dalinghe Hospital, Fengcheng Phoenix Hospital, who underwent thyroidectomy for solitary nodules with normal thyroid function between Nov. 2016 and Jan. 2024. Postoperative pathology confirmed Hashimoto’s thyroiditis (HT) in some cases, who were further categorized into antibody-positive and antibody-negative groups based on serum antibody status. Patients without Hashimoto’s thyroiditis served as the control group. A total of 298 ultrasound images were analyzed. Radiomics features were extracted from hypoechoic non-nodular areas within 0.5 cm surrounding the tumor. Two senior pathologists and two senior ultrasound physicians independently assessed lymphocytic infiltration, eosinophilic changes of follicular epithelium, and the proportion of hypoechoic areas in pathology and ultrasound images, respectively. A machine learning model, CCH-NET, was developed using linear regression and t-distributed stochastic neighbor embedding (t-SNE) techniques. The dataset was divided into a training set (80%) and a validation set (20%) to compare the diagnostic accuracy of CCH-NET with that of senior ultrasound physicians. Results:In internal validation, CCH-NET achieved a diagnostic accuracy of 88.89% for both antibody-positive and antibody-negative groups, significantly higher than the 66.67% accuracy of senior ultrasound physicians ( P<0.01). In external validation, CCH-NET achieved 75.00% and 66.67% accuracy for the two groups, compared to 50.00% by senior ultrasound physicians. For the control group, both methods achieved 93.33% accuracy. The AUC of CCH-NET was 0.848, outperforming senior ultrasound physicians (0.681) ,demonstrating superior diagnostic performance. Conclusion:The radiomics-based CCH-NET model, using non-nodular hypoechoic areas as a specific indicator, can accurately identify early SN-HT in euthyroid patients. It significantly outperforms senior ultrasound physicians, improving diagnostic accuracy and reducing missed diagnoses.

Objective:To explore the differences in bacterial communities within tissues during the process of oral mucosal carcinogenesis, and analyze the relationship between the high-abundance species Prevotella intermedia (Pi) and the occurrence and development of oral mucosal carcinogenesis. Methods:Fresh tissue samples were collected from patients diagnosed with oral leukoplakia (OLK), oral squamous cell carcinoma (OSCC), and healthy controls (HC) at Nanjing Stomatological Hospital, Affiliated Hospital of Medical School, Nanjing University, from January 2022 to November 2024, following strict inclusion criteria. Bacterial DNA was extracted from these specimens, and the 2bRAD sequencing for microbiome (2bRAD-M) was employed to analyze and compare the α and β diversity, as well as the community composition of bacteria within tissues, aiming to identify specifically expressed bacteria. Subsequently, paraffin-embedded clinical specimens were collected: 15 cases in the OLK group (including 4 cases of simple hyperplasia, 6 cases of mild dysplasia, and 5 cases of moderate to severe dysplasia), 12 cases in the OSCC group, and 5 cases in the HC group. A 4-nitroquinoline N-oxide (4NQO)-induced OLK progression mouse model was also constructed. Mice were randomly divided into three groups using a random number table, with six in each group. The negative control group was given distilled water to drink; Group 1 was given distilled water containing 4NQO to drink until week 12, while Group 2 was given distilled water containing 4NQO to drink until week 22. After the mice were sacrificed, their tongue tissue were collected and fixed. Fluorescence in situ hybridization (FISH) with specific probes was used to validate the presence of Pi in human and mouse tissue sections, analyzing the correlation between histopathological grading and the invasion depth of Pi.Results:The 2bRAD-M microbial analysis revealed that the relative abundance of Pi in OSCC tissues (10.80%) was significantly higher than in the HC group (0.50%) ( P=0.001) and OLK group (0.70%) ( P=0.002). FISH probe detection showed that the fluorescence intensity of Pi in human OSCC tissues [123.50 (101.00, 142.30)] was higher than in the HC group [0.00 (0.00, 28.50)], simple hyperplasia OLK [0.00 (0.00, 35.25)], and mild dysplasia OLK [24.50 (0.00, 55.50)] groups, with statistically significant differences respectively ( P=0.002, P=0.003, P=0.005). However, there was no significant difference compared to moderate to severe dysplasia OLK [56.00 (28.00, 62.50)] ( P=0.210). The fluorescence area of Pi in human OSCC tissues [8 615.00 (7 439.00, 11 084.00)] was significantly larger than in the HC group [0.00 (0. 00, 45.00)], simple hyperplasia OLK group [0.00 (0.00, 81.00)], mild dysplasia [49.00 (0.00, 151.00)], and moderate to severe dysplasia groups [1 450.00 (454.00, 2 892.00)], with highly significant differences ( P<0.001). There was a significant correlation between the invasive depth of Pi and the degree of histopathological grading ( P<0.001). In mice, the fluorescence intensity of Pi in OSCC tissues [120.00 (110.00, 127.00)] was significantly higher than in the HC group [0.00 (0.00, 12.25)] ( P<0.01), but showed no significant difference compared with the OLK group [50.00 (0.00, 58.00)] ( P>0.05). The fluorescence area of Pi in mice OSCC tissues [11 020.00 (6 790.00, 12 102.00)] was significantly larger than in the HC group [0.00 (0.00, 56.75)] and the OLK group [0.00 (0.00, 751.50)] ( P=0.006, P=0.043). There is a significant correlation between the depth of invasion of Pi and the degree of histopathological grading ( P<0.01). Conclusions:This study suggests that Pi in oral mucosal tissue may be a potential biomarker for early detection of OSCC and play an important role in the carcinogenesis process of oral mucosa.

Objective:To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer.Methods:Specimens were collected from 209 breast cancer patients who received NAT at Renmin Hospital of Wuhan University from October 2019 to June 2023. TSR levels in pre-NAT biopsy specimens were automatically computed using a deep learning algorithm and categorized into low stroma (TSR≤30%), intermediate stroma (TSR 30% to ≤60%), and high stroma (TSR>60%) groups. Residual cancer burden (RCB) grading of post-NAT surgical specimens was determined to compare the relationship between TSR expression levels and RCB grades. The correlation of TSR with NAT efficacy was analyzed, and the association between TSR expression and patient prognosis was further investigated.Results:There were 85 cases with low stroma (TSR≤30%), 93 cases with intermediate stroma (TSR 30% to ≤60%), and 31 cases with high stroma (TSR>60%). Different TSR expression levels showed significant differences between various RCB grades ( P<0.05). Logistic univariate and multivariate analyses showed that TSR was a risk factor for obtaining a complete pathological remission from neoadjuvant therapy for breast cancer when it was used as a continuous variable ( P<0.05); COX regression and survival analyses showed that the lower the percentage of tumorigenic mesenchyme was, the better the prognosis of the patient was ( P<0.05). Conclusions:The deep learning-based model enables automatic and accurate quantification of TSR. A lower pre-treatment tumoral stroma is associated with a lower RCB score and a higher rate of pathologic complete response, indicating that TSR can predict the efficacy of neoadjuvant therapy in breast cancer and thus holds prognostic significance. Therefore, TSR may serve as a biomarker for predicting therapeutic outcomes in breast cancer neoadjuvant therapy.

Objective:To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer.Methods:Specimens were collected from 209 breast cancer patients who received NAT at Renmin Hospital of Wuhan University from October 2019 to June 2023. TSR levels in pre-NAT biopsy specimens were automatically computed using a deep learning algorithm and categorized into low stroma (TSR≤30%), intermediate stroma (TSR 30% to ≤60%), and high stroma (TSR>60%) groups. Residual cancer burden (RCB) grading of post-NAT surgical specimens was determined to compare the relationship between TSR expression levels and RCB grades. The correlation of TSR with NAT efficacy was analyzed, and the association between TSR expression and patient prognosis was further investigated.Results:There were 85 cases with low stroma (TSR≤30%), 93 cases with intermediate stroma (TSR 30% to ≤60%), and 31 cases with high stroma (TSR>60%). Different TSR expression levels showed significant differences between various RCB grades ( P<0.05). Logistic univariate and multivariate analyses showed that TSR was a risk factor for obtaining a complete pathological remission from neoadjuvant therapy for breast cancer when it was used as a continuous variable ( P<0.05); COX regression and survival analyses showed that the lower the percentage of tumorigenic mesenchyme was, the better the prognosis of the patient was ( P<0.05). Conclusions:The deep learning-based model enables automatic and accurate quantification of TSR. A lower pre-treatment tumoral stroma is associated with a lower RCB score and a higher rate of pathologic complete response, indicating that TSR can predict the efficacy of neoadjuvant therapy in breast cancer and thus holds prognostic significance. Therefore, TSR may serve as a biomarker for predicting therapeutic outcomes in breast cancer neoadjuvant therapy.

Objective:To report construction of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) using the Delphi method.Methods:Literature related to the study of adult distal radius fractures was fully searched for and evaluated. An expert group was established from representative experts from all over the nation. The related clinical issues were established by consulting the experts in the form of electronic questionnaires, strictly following the Delphi research method. After the first draft of Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) was written, an expert consultation questionnaire was designed for the recommendation opinions to determine the recommendation strength.Results:The clinical issues were determined by 2 rounds of correspondence based on the Delphi method. For the both rounds of correspondence, the questionnaire recovery rates were respectively 88.68% (47/53) and 98.11% (52/53), and the expert authority coefficients >0.7. According to the screening criteria based on the importance of clinical issues (mean importance score <3.5 points or a coefficient of variation ≥0.25 points and a full score ratio <30%) and expert opinions, a total of 40 clinical issues were deleted in the first round of determination of clinical issues, and a total of 5 clinical issues deleted in the second round of determination of clinical issues. The reliability analysis of the results of the 2 rounds of questionnaires showed that the Cronbach α coefficient was >0.9. In the questionnaire to determine the recommendation strength, according to the screening criteria for the consistency of recommendation strength (consistency ≥ 70%) and expert opinions, a total of 26 recommendations were screened in the first round. In the second round when the remaining 4 recommendations were investigated, one recommendation reached the consistency of recommendation strength ≥ 70%. Eventually, 27 recommendations were formed.Conclusion:The Evidence-Based Guidelines for the Diagnosis and Treatment of Distal Radius Fractures in Adults (2024) constructed using the Delphi method shows good scientific validity, authority, and reliability, providing methodological references for guideline development and research.

Objective:To investigate the laboratory detection methods for immune hemolytic transfusion reactions caused by anti-tigecycline antibody and the clinical diagnosis and treatment of one patient.Methods:The correlation between hemolysis-related laboratory indexes of the patient and the duration of medication was analyzed. Blood samples of the patient were tested using direct anti-human globulin test, free antibody test, and release test. Erythrocyte sensitization method and immune complexome analysis were used to detect the antibody against tigecycline in the serum of the patient. The properties and the titers of anti-tigecycline antibody were analyzed.Results:Anti-tigecycline antibody was found in the patient through the erythrocyte sensitization method and the immune complexome analysis, and the result of the direct anti-human globulin test was positive. The clinical symptoms and physical signs of the patient improved rapidly after withdrawal of tigecycline and blood transfusion. The patient was discharged after 14-day treatment with immunoglobulin and hormone.Conclusions:Tigecycline can cause hemolytic transfusion reactions. Serological tests are essential for the diagnosis of drug-induced hemolytic anemia. Withdrawal of medications and symptomatic treatment should be conduceted immediately when patients develop drug-related hemolytic anemia.

Objective To analyze the innate circuit qi endowment of patients with seasonal allergic rhinitis(SAR)in Renyin year(Ren-Yin stem-branch year of Chinese lunar calendar).Methods A retrospective study was conducted in 8 918 SAR patients who were treated in the Department of Otorhinolaryngology of Xiyuan Hospital,China Academy of Chinese Medical Sciences in pollen seasons of the spring and autumn(i.e.,the period of March 5 to May 21,and the period of August 7 to September 23)in 2022(Renyin year).The characteristics of circuit qi at birth date of the SAR patient were analyzed.Results The occurrence of SAR in Renyin year was related to the suiyun(yearly circuit phase),sitian(the first half of the alterable yearly circuit qi),dominant qi,and circuit qi combination.The two-factor model analysis showed that the occurrence of SAR in Renyin year was closely related with suiyun-sitian and dominant qi-guest qi.High morbidity of SAR was shown in the patients born in the year of heavenly-stem Ren and earthly-branch Xu,and with the circuit qi features of five circuits being wood circuit,Suiyun being excessive wood circuit,sitian of yangming dryness-gold,zaiquan(the second half of the alterable yearly circuit qi)of shaoyin monarch-fire,dominant qi being yangming dryness-gold,and circuit qi combination being disharmony.The two-factor model analysis showed that the morbidity of SAR was increased in the year with the circuit qi features of suiyun-sitian being excessive wood circuit and taiyang cold-water,and the combination of dominant qi and guest qi being yangming dryness-gold and taiyang cold-water.Conclusion Among the innate circuit qi factors,suiyun,sitian,dominant qi,and circuit qi combination have an impact on the occurrence of SAR.The interaction between innate circuit qi and circuit qi at onset may be one of the important mechanisms leading to the occurrence of SAR.