Periodontal bone defects, primarily caused by periodontitis, are highly prevalent in clinical settings and manifest as bone fenestration, dehiscence, or attachment loss, presenting a significant challenge to oral health. In regenerative medicine, harnessing developmental principles for tissue repair offers promising therapeutic potential. Of particular interest is the condensation of progenitor cells, an essential event in organogenesis that has inspired clinically effective cell aggregation approaches in dental regeneration. However, the precise cellular coordination mechanisms during condensation and regeneration remain elusive. Here, taking the tooth as a model organ, we employed single-cell RNA sequencing to dissect the cellular composition and heterogeneity of human dental follicle and dental papilla, revealing a distinct Platelet-derived growth factor receptor alpha (PDGFRA) mesenchymal stem/stromal cell (MSC) population with remarkable odontogenic potential. Interestingly, a reciprocal paracrine interaction between PDGFRA⁺ dental follicle stem cells (DFSCs) and CD31⁺ Endomucin⁺ endothelial cells (ECs) was mediated by Vascular endothelial growth factor A (VEGFA) and Platelet-derived growth factor subunit BB (PDGFBB). This crosstalk not only maintains the functionality of PDGFRA⁺ DFSCs but also drives specialized angiogenesis. In vivo periodontal bone regeneration experiments further reveal that communication between PDGFRA⁺ DFSC aggregates and recipient ECs is essential for effective angiogenic-osteogenic coupling and rapid tissue repair. Collectively, our results unravel the importance of MSC-EC crosstalk mediated by the VEGFA and PDGFBB-PDGFRA reciprocal signaling in orchestrating angiogenesis and osteogenesis. These findings not only establish a framework for deciphering and promoting periodontal bone regeneration in potential clinical applications but also offer insights for future therapeutic strategies in dental or broader regenerative medicine.
Receptor, Platelet-Derived Growth Factor alpha/metabolism* ; Humans ; Neovascularization, Physiologic/physiology* ; Dental Sac/cytology* ; Single-Cell Analysis ; Transcriptome ; Mesenchymal Stem Cells/metabolism* ; Bone Regeneration ; Animals ; Dental Papilla/cytology* ; Periodontium/physiology* ; Stem Cells/metabolism* ; Regeneration ; Angiogenesis

Objective To analyze the influencing factors of tumor-specific mortality in patients with advanced endometrial carcinoma and construct a risk prediction model.Methods A total of 150 patients with advanced endometrial carcinoma admitted to the Fourth Clinical College of Zhejiang Chinese Medical University,Hangzhou First People's Hospital from January 2020 to January 2023 were selected as subjects.Cox regression was employed to analyze factors influencing mortality in advanced endometrial carcinoma.A Logistic regression model was constructed,with receiver operating characteristic(ROC)curve used to evaluate model discrimination,calibration curve for precision assessment,and decision curve for net benefit evaluation.Results Overall 2-year survival rate of 150 patients was 58.67％.The univariate analysis demonstrated that histological grade,lymph node metastasis,tumor maximum diameter,and preoperative carcinoembryonic antigen 125(CA125)levels were all significant risk factors for mortality in advanced endometrial carcinoma(P＜0.05).Multivariate analysis confirmed these parameters as independent prognostic indicators(P＜0.05).A Logistic regression model established using multivariate Cox regression yielded a C index of 0.873(95％CI:0.792-0.951).ROC curve analysis revealed the model's area under the curve for predicting mortality risk factors in advanced endometrial carcinoma was 0.916(95％CI:0.872-0.960).Conclusion The linear chart prediction model constructed based on histological grading,lymph node metastasis,maximum tumor diameter and preoperative CA125 level in this study can effectively predict the death of advanced endometrial carcinoma.

Objective To explore the clinical efficacy and safety of single-port laparoscopic ovarian cyst removal surgery.Methods A total of 145 patients who underwent laparoscopic surgery for ovarian cyst in Wenzhou People's Hospital from January 2023 to June 2024.Among them,single-port laparoscopic ovarian cystectomy+oophoroplasty was performed in 67 cases as single-port group,and traditional multi-port laparoscopic ovarian cystectomy+oophoroplasty was performed in 78 cases as multi-port group.Clinical indicators during and after surgery in two groups were recorded and compared.Results There were significant difference the operation time and pain scores at 1 day after surgery between two groups(P＜0.05).There was no significant difference in intraoperative bleeding,postoperative exhaust time and postoperative hospitalization time between two groups(P＞0.05).Neither of two groups had intraoperative vascular or nerve damage in the abdominal wall,and there were no postoperative complications such as incision infection,intestinal obstruction,or lower limb venous thrombosis,and the incision healed well.Continuing the follow-up for 3 months after discharge,both surgical procedures had no significant effect on menstruation,and there was no short-term recurrence of ovarian cyst in either case.Conclusion The single-port laparoscopic technique for the treatment of ovarian cyst incision is concealed and beautiful,the postoperative pain is lighter.No obvious effect on menstruation,and the curative effect is definite and safe.

Objective To observe the value of ultrasound radiomics based on convolutional neural network(CNN)for predicting effect of neoadjuvant chemotherapy(NAC)for breast cancer.Methods Totally 164 women with breast cancer were retrospectively enrolled and divided into effective group(n=68)and ineffective group(n=96)according to the treatment response,also randomly divided into training set(n=131)and validation set(n=33)at the ratio of 8∶2.Based on ultrasound before NAC,radiomics features of breast cancer were extracted and screened with CNN,radiomics models were constructed with logistic regression(LR),support vector machine(SVM),K-nearest neighbor(KNN),random forest(RF)and multilayer perceptron(MLP),respectively.The best radiomics model was selected,deep learning score(DL-Score)was calculated,and the nomogram was drawn combined with clinical features.Results Among 5 radiomics models,MLP model had the best comprehensive efficacy for predicting effect of NAC for breast cancer,and its sensitivity,specificity and area under the curve(AUC)in training set was 77.78％,92.21％and 0.929,respectively,which in validation set was 78.57％,84.21％and 0.921,respectively.The estrogen or progesterone receptor,human epidermal growth factor receptor 2 and DL-Score were all independent predictors of NAC effect for breast cancer(all P＜0.05).The sensitivity,specificity and AUC of nomogram drawn based on the above independent predictors was 83.30％,92.21％and 0.953 in training set,85.71％,94.74％and 0.955 in validation set,respectively.AUC of the nomogram was slightly higher than that of MLP model,but no significant difference was found(both P＞0.05).The integrated discrimination improvement index showed that adding clinical features(i.e.the above-mentioned immunohistochemically indicators)could improve the predictive performance of radiomics models(P＜0.001).Conclusion Ultrasound radiomics based on CNN could be used to predict effect of NAC for breast cancer.Combining with immunohistochemically indicators might improve their efficacy.

Objective:To investigate the value and significance of α-methylacyl-CoA racemase (AMACR), p63 and Ki-67 immunohistochemical detection in the pathological diagnosis of prostate cancer.Methods:A total of 156 archived prostate biopsy paraffin samples collected from Qidong Hospital of Traditional Chinese Medicine from June 2022 to August 2023 were selected as research materials. The expression of AMACR, p63 and Ki-67 in 48 cases of prostate cancer, 32 cases of high-grade prostatic intraepithelial neoplasia, 26 cases of low-grade prostatic intraepithelial neoplasia and 50 cases of prostatic hyperplasia were detected by immunohistochemistry. The correlation between the expression of AMACR, p63 and Ki-67 and the clinical and pathological features of prostate cancer patients, as well as their diagnostic efficacy for prostate cancer were analyzed.Results:There were statistically significant differences in the positive expression rates of AMACR, p63 and Ki-67 among the samples of different groups (all P<0.05). The positive expression rate of AMACR in the biopsy samples of prostate cancer group was 100%, and the high expression rate was 81.25%, which was significantly higher than that of the other three groups. The negative expression rate of p63 was 97.92%, which was significantly higher than the other three groups. The positive expression rate of Ki-67 was 81.25% and the high expression rate was 54.17%, which was significantly higher than the other three groups. The expression of AMACR was related to tumor length, TNM stage, differentiation degree and Gleason score of prostate cancer patients. The high expression rate of AMACR in patients with tumor size ≥1.5 cm, stage Ⅱ to Ⅲ, medium and highly differentiated tumors with Gleason score 8-10 was significantly higher than those with tumor size <1.5 cm, stage Ⅰ, low differentiation, and Gleason score 2-7 (all P<0.05). Ki-67 expression was correlated with tumor length, differentiation degree, lymph node metastasis and Gleason score in prostate cancer patients. The high expression rate of Ki-67 in patients with tumor size ≥1.5 cm, medium and highly differentiated tumors with lymph node metastasis and Gleason score 8-10 was significantly higher than that in patients with tumor size<1.5 cm, low differentiation, no lymph node metastasis and Gleason score 2-7 (all P<0.05). The expression of p63 was not correlated with the clinical and pathological features of patients with prostate cancer (all P>0.05). The sensitivity and negative predictive value of AMACR positive/p63 negative/Ki-67 positive combined diagnosis of prostate cancer were 100.00%, and the specificity was 81.82%. Conclusions:AMACR, p63 and Ki-67 in prostate biopsy samples can be used as good biomarkers for the diagnosis or exclusion of prostate cancer, and the combined detection can improve the diagnosis accuracy of prostate cancer.

Objective To observe the value of ultrasound radiomics based on convolutional neural network(CNN)for predicting effect of neoadjuvant chemotherapy(NAC)for breast cancer.Methods Totally 164 women with breast cancer were retrospectively enrolled and divided into effective group(n=68)and ineffective group(n=96)according to the treatment response,also randomly divided into training set(n=131)and validation set(n=33)at the ratio of 8∶2.Based on ultrasound before NAC,radiomics features of breast cancer were extracted and screened with CNN,radiomics models were constructed with logistic regression(LR),support vector machine(SVM),K-nearest neighbor(KNN),random forest(RF)and multilayer perceptron(MLP),respectively.The best radiomics model was selected,deep learning score(DL-Score)was calculated,and the nomogram was drawn combined with clinical features.Results Among 5 radiomics models,MLP model had the best comprehensive efficacy for predicting effect of NAC for breast cancer,and its sensitivity,specificity and area under the curve(AUC)in training set was 77.78％,92.21％and 0.929,respectively,which in validation set was 78.57％,84.21％and 0.921,respectively.The estrogen or progesterone receptor,human epidermal growth factor receptor 2 and DL-Score were all independent predictors of NAC effect for breast cancer(all P＜0.05).The sensitivity,specificity and AUC of nomogram drawn based on the above independent predictors was 83.30％,92.21％and 0.953 in training set,85.71％,94.74％and 0.955 in validation set,respectively.AUC of the nomogram was slightly higher than that of MLP model,but no significant difference was found(both P＞0.05).The integrated discrimination improvement index showed that adding clinical features(i.e.the above-mentioned immunohistochemically indicators)could improve the predictive performance of radiomics models(P＜0.001).Conclusion Ultrasound radiomics based on CNN could be used to predict effect of NAC for breast cancer.Combining with immunohistochemically indicators might improve their efficacy.

Objective To explore the clinical efficacy and safety of single-port laparoscopic ovarian cyst removal surgery.Methods A total of 145 patients who underwent laparoscopic surgery for ovarian cyst in Wenzhou People's Hospital from January 2023 to June 2024.Among them,single-port laparoscopic ovarian cystectomy+oophoroplasty was performed in 67 cases as single-port group,and traditional multi-port laparoscopic ovarian cystectomy+oophoroplasty was performed in 78 cases as multi-port group.Clinical indicators during and after surgery in two groups were recorded and compared.Results There were significant difference the operation time and pain scores at 1 day after surgery between two groups(P＜0.05).There was no significant difference in intraoperative bleeding,postoperative exhaust time and postoperative hospitalization time between two groups(P＞0.05).Neither of two groups had intraoperative vascular or nerve damage in the abdominal wall,and there were no postoperative complications such as incision infection,intestinal obstruction,or lower limb venous thrombosis,and the incision healed well.Continuing the follow-up for 3 months after discharge,both surgical procedures had no significant effect on menstruation,and there was no short-term recurrence of ovarian cyst in either case.Conclusion The single-port laparoscopic technique for the treatment of ovarian cyst incision is concealed and beautiful,the postoperative pain is lighter.No obvious effect on menstruation,and the curative effect is definite and safe.

Objective To analyze the influencing factors of tumor-specific mortality in patients with advanced endometrial carcinoma and construct a risk prediction model.Methods A total of 150 patients with advanced endometrial carcinoma admitted to the Fourth Clinical College of Zhejiang Chinese Medical University,Hangzhou First People's Hospital from January 2020 to January 2023 were selected as subjects.Cox regression was employed to analyze factors influencing mortality in advanced endometrial carcinoma.A Logistic regression model was constructed,with receiver operating characteristic(ROC)curve used to evaluate model discrimination,calibration curve for precision assessment,and decision curve for net benefit evaluation.Results Overall 2-year survival rate of 150 patients was 58.67％.The univariate analysis demonstrated that histological grade,lymph node metastasis,tumor maximum diameter,and preoperative carcinoembryonic antigen 125(CA125)levels were all significant risk factors for mortality in advanced endometrial carcinoma(P＜0.05).Multivariate analysis confirmed these parameters as independent prognostic indicators(P＜0.05).A Logistic regression model established using multivariate Cox regression yielded a C index of 0.873(95％CI:0.792-0.951).ROC curve analysis revealed the model's area under the curve for predicting mortality risk factors in advanced endometrial carcinoma was 0.916(95％CI:0.872-0.960).Conclusion The linear chart prediction model constructed based on histological grading,lymph node metastasis,maximum tumor diameter and preoperative CA125 level in this study can effectively predict the death of advanced endometrial carcinoma.

Objective:To investigate the value and significance of α-methylacyl-CoA racemase (AMACR), p63 and Ki-67 immunohistochemical detection in the pathological diagnosis of prostate cancer.Methods:A total of 156 archived prostate biopsy paraffin samples collected from Qidong Hospital of Traditional Chinese Medicine from June 2022 to August 2023 were selected as research materials. The expression of AMACR, p63 and Ki-67 in 48 cases of prostate cancer, 32 cases of high-grade prostatic intraepithelial neoplasia, 26 cases of low-grade prostatic intraepithelial neoplasia and 50 cases of prostatic hyperplasia were detected by immunohistochemistry. The correlation between the expression of AMACR, p63 and Ki-67 and the clinical and pathological features of prostate cancer patients, as well as their diagnostic efficacy for prostate cancer were analyzed.Results:There were statistically significant differences in the positive expression rates of AMACR, p63 and Ki-67 among the samples of different groups (all P<0.05). The positive expression rate of AMACR in the biopsy samples of prostate cancer group was 100%, and the high expression rate was 81.25%, which was significantly higher than that of the other three groups. The negative expression rate of p63 was 97.92%, which was significantly higher than the other three groups. The positive expression rate of Ki-67 was 81.25% and the high expression rate was 54.17%, which was significantly higher than the other three groups. The expression of AMACR was related to tumor length, TNM stage, differentiation degree and Gleason score of prostate cancer patients. The high expression rate of AMACR in patients with tumor size ≥1.5 cm, stage Ⅱ to Ⅲ, medium and highly differentiated tumors with Gleason score 8-10 was significantly higher than those with tumor size <1.5 cm, stage Ⅰ, low differentiation, and Gleason score 2-7 (all P<0.05). Ki-67 expression was correlated with tumor length, differentiation degree, lymph node metastasis and Gleason score in prostate cancer patients. The high expression rate of Ki-67 in patients with tumor size ≥1.5 cm, medium and highly differentiated tumors with lymph node metastasis and Gleason score 8-10 was significantly higher than that in patients with tumor size<1.5 cm, low differentiation, no lymph node metastasis and Gleason score 2-7 (all P<0.05). The expression of p63 was not correlated with the clinical and pathological features of patients with prostate cancer (all P>0.05). The sensitivity and negative predictive value of AMACR positive/p63 negative/Ki-67 positive combined diagnosis of prostate cancer were 100.00%, and the specificity was 81.82%. Conclusions:AMACR, p63 and Ki-67 in prostate biopsy samples can be used as good biomarkers for the diagnosis or exclusion of prostate cancer, and the combined detection can improve the diagnosis accuracy of prostate cancer.

Objective To develop and validate a prediction model for severe disability or death(SDD)in acute ischemic stroke(AIS)patients who underwent endovascular treatment(EVT).Methods Based on the dataset of ANGEL-ACT study who received EVT for AIS between november 2017 and march 2019,a retrospective analysis was performed on 1 677 patients,including 1 111 males and 566 females,aged ≥ 18 years.Patients were divided into two groups according to whether SDD occurred(mRS 5-6 scores 90 days after surgery):SDD group(n=478)and non-SDD group(n=1 199).Risk factors that might influence SDD after EVT in AIS patients were screened and analyzed by multifactorial analysis,LAS-SO regression,and RF-RFE methods.A nomogram was developed after evaluating the model performance and the execution of internal validation.Results SDD occurred in 380(28.1％)patients in the develop-ment cohort and 98(30.2％)patients in the validation cohort.Combining the three variable screening meth-ods,10 risk factors were selected for inclusion in the final model:age,NIHSS score,whether successful re-canalization,glucose level,hemoglobin,hematocrit,onset to puncture time,systolic blood pressure,AS-PECT score,and whether have treatment-related serious adverse events.A two-stage model means that model 1 contains pre-treatment variables(7 in total)and model 2 contains pre-treatment and post-treatment variables(10 in total).The area under the curve(AUC)of model 1 in the development cohort was 0.705(95％CI 0.674-0.736)and 0.731(95％CI 0.701-0.760)in model 2.Both models had good calibration with aslope of 1.000,and the decision curve analysis showed good clinical applicability.The results of the validation cohort were similar to those of the development cohort.Conclusion Age,admission NIHSS score,whether successful recanalization,admission glucose level,hemoglobin content,erythrocyte pressure volume,onset to puncture time,admission systolic blood pressure,ASPECT score,and whether have treat-ment-related serious adverse events are risk factors for SDD in patients with acute ischemic stroke.The two prediction models based on the above factors were used before and after endovascular treatment to predict SDD occurrence better.