OBJECTIVES: To study the clinical effect of the L-shape technique combined with concentrated growth factor on the horizontal bone defects of maxillary anterior teeth. METHODS: Twenty-five implants from 25 patients who underwent single maxillary anterior tooth implantation with simultaneous bone grafting were selected as the study subjects. Based on the bone grafting techniques, the patients were divided into a test group (L-shaped technique with guided bone regeneration combined with concentrated growth factor, 11 cases) and a control group (traditional guided bone regeneration combined with concentrated growth factor, 14 cases). The early discomfort and wound healing conditions in the two groups at two weeks after surgery were compared. The horizontal bone thickness, vertical bone thickness, and grayscale values in the augmentation area were measured immediately postsurgery and six months after surgery. Implant stability, hard tissue resorption within six months, and grayscale values were compared between the two groups. RESULTS: Differences in early discomfort, wound healing, implant stability, and grayscale values between the two groups were not statistically significant (P>0.05). Vertical bone thickness in the test group was significantly better than that in the control group at six months after surgery (P<0.05). The variation in horizontal bone thickness in the test group was significantly higher than that in the control group (P<0.05). CONCLUSIONS The application of the L-shape technique with concentrated growth factor for horizontal bone defects in the anterior maxillary area yielded satisfactory short-term results in terms of bone augmentation, early discomfort, wound healing, and implant stability at six months after surgery.
Humans ; Maxilla/diagnostic imaging* ; Intercellular Signaling Peptides and Proteins/therapeutic use* ; Wound Healing ; Bone Transplantation/methods* ; Dental Implantation, Endosseous/methods* ; Bone Regeneration ; Male ; Female ; Adult ; Dental Implants, Single-Tooth ; Middle Aged

Objective: To comprehensively evaluate and analyze the transfusion outcomes of patients with acute upper gastrointestinal bleeding (UGIB). Methods: The transfusion management system and hospital information system (HIS) were used to retrospectively collect clinical data of 230 patients with UGIB admitted to Zhejiang Provincial People's Hospital and its branches from June 2018 to June 2021. 101 cases were screened and categorized into transfusion group (n=56) and non-transfusion group (n=45) based on transfusion outcomes. The cohort comprised 68 males and 33 females. A univariate model based on the AIMS65 score, a logistic multiple regression model, and multivariate transfusion models using machine learning methods (including Random Forest, Support Vector Machine, and Artificial Neural Network) were established. The sensitivity, specificity, accuracy, and receiver operating characteristic (ROC) curves of each model were compared. Results: For the univariate model based on the AIMS65 scoring, the optimal threshold was 1.5. This model demonstrated a sensitivity of 0.446, a specificity of 0.822, an AUC of 0.67, an accuracy (ACC) of 0.614, a Kappa value of 0.256, and an F1-score of 0.655. For logistics regression model (optimal critical probability: 0.459), the sensitivity was 0.929, specificity was 0.889, AUC was 0.96, ACC was 0.911, Kappa was 0.819, and F1-score was 0.899. For the Random Forest model (optimal critical probability: 0.458), the sensitivity was 0.964, specificity was 0.956, AUC was 0.99, ACC was 0.960, Kappa was 0.920, and F1-score was 0.956. For the Support Vector Machine model (optimal critical probability: 0.474), the sensitivity was 0.875, specificity was 0.933, AUC was 0.94, ACC was 0.901, Kappa was 0.801, and F1-score was 0.894. For the Artificial Neural Network model (optimal critical probability: 0.797), the sensitivity was 0.804, specificity was 0.956, AUC was 0.96, ACC was 0.871, Kappa was 0.745, and F1-score was 0.869. Ten-fold cross validation also confirmed the reliability of the results. Conclusion: Based on integrated various clinical test indicators of patients, we could establish logistic regression model and multiple machine learning models. These models hold significant value for predicting the need for blood transfusion in patients, indicating a promising application prospect for machine learning algorithms in transfusion prediction.

Objective To observe the effect of traditional Chinese medicine(TCM)syndrome differentiation treatment of insomnia on depressive symptoms and interleukin(IL)-6 level and tumor necrosis factor(TNF)-α level in serum.And then to discuss the effect of TCM syndrome differentiation treatment of insomnia on reducing the occurrence and development of depression.Methods Sixty patients with insomnia disorder in Hangzhou Hospital of Traditional Chinese Medicine from September 2020 to July 2022 were selected as research objects,and they were divided into intervention group and blank group 30 patients for each group,according to their treatment intention and they were treated with sleep hygiene education plus TCM syndrome differentiation treatment and sleep hygiene education alone respectively.The changes of sleep,depression,serum IL-6 level and TNF-α level were compared between two groups.Results After 2 weeks of intervention,the intervention group's Pittsburgh sleep quality index score,patient health questiomnare-9 score,TCM symptom score,and serum IL-6 and TNF-α level were all significantly reduced,and the difference was statistically significant(P＜0.05).Conclusion TCM syndrome differentiation treatment can effectively improve insomnia and depression,thus delaying the occurrence and development of depression,and is an effective method for early intervention of depression.

Objective:To predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer based on preoperative inflammatory and immune nutritional indicators,and to construct a nomo-gram prediction model,providing a basis and tool for preoperative diagnosis of lymph node metastasis in cervical cancer.Methods:A retrospective analysis was conducted on the clinical data of 307 patients preoperatively diag-nosed with early-stage cervical cancer who underwent surgical treatment at the Obstetrics and Gynecology De-partment of the Second Affiliated Hospital of Zhengzhou University from January 2018 to July 2023.R software was used to randomize the groups into a training set(n=231)and a validation set(n=76)in a 3∶1 ratio.Uni-variate and multivariate logistic regression analyses were employed to identify factors influencing lymph node me-tastasis in patients preoperatively diagnosed with early-stage cervical cancer.R software was used to establish a nomogram prediction model and draw receiver operating characteristic(ROC)curves and calibration curves for validation.Results:① The results of univariate logistic regression analysis showed that positive lymphovascular invasion,platelet-to-lymphocyte ratio(PLR)≥151.70,neutrophil-to-white blood cell ratio(NWR)≥0.65,plate-let-to-albumin ratio(PAR)≥ 4.94,preoperative systemic immune-inflammation index(SII)≥604.03,and sys-temic inflammatory response index(SIRI)≥ 1.05 were associated with lymph node metastasis(P＜0.05).②Multivariate logistic regression analysis found that positive lymphovascular invasion,NWR≥0.65,and PAR≥4.94 were independent risk factors for lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer(OR＞1,P＜0.05).③ A nomogram was constructed to predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer.The ROC curve shows an area under the train-ing set curve(AUC)of 0.821 and a validation set AUC of 0.858.The calibration curve shows an average abso-lute error of 0.024 for the training set and 0.059 for the validation set.Conclusions:The prediction model for lymph node metastasis in cervical cancer constructed using preoperative inflammatory and immune nutritional indi-cators such as NWR,PAR is helpful for gynecological oncologists to predict lymph node metastasis in cervical cancer patients before surgery.

Objective This study aimed to evaluate the clinical outcomes of immediate implantation of single maxil-lary central incisor and explore factors affecting post-implant bone volume.Methods Clinical data and imaging records from pre-surgery,the day of surgery,and 6 months post-surgery of 100 patients(100 implants)with non-salvageable maxillary central incisors who underwent immediate implantation were collected.Bone thickness at the cervical,middle,and apical regions of the implant's labial and palatal sides were measured immediately post-surgery and at 6 months,and bone volume changes were observed.A regression analysis model was used to assess predictive factors for labial and pal-atal bone plate thickness.Results At 6 months post-surgery,the labial bone thicknesses at the cervical,middle,and api-cal regions were 2.35,2.29,and 3.28 mm,respectively,and those of the palatal side were 0.00,2.40,and 6.05 mm,re-spectively.The cervical region had the highest alveolar crest collapse rates,with 32.87%on the labial side and 62.20%on the palatal side.The regression model indicated that factors influencing the thickness of bone at the cervical labial side of the implant included initial bone thickness,the implant center to adjacent tooth center angle,implant diameter,and the type of implant closure(P<0.05).The initial bone thickness on the palatal side was the sole predictor for bone thickness on the palatal side(P<0.05).Conclusion Immediate implantation of single maxillary central incisors yields effective clinical results.The thickness of new bone around the implant is influenced by multiple factors.A comprehensive consideration of these factors in the plan-ning of immediate implantation is necessary to achieve optimal therapeutic outcomes.

Objective:To predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer based on preoperative inflammatory and immune nutritional indicators,and to construct a nomo-gram prediction model,providing a basis and tool for preoperative diagnosis of lymph node metastasis in cervical cancer.Methods:A retrospective analysis was conducted on the clinical data of 307 patients preoperatively diag-nosed with early-stage cervical cancer who underwent surgical treatment at the Obstetrics and Gynecology De-partment of the Second Affiliated Hospital of Zhengzhou University from January 2018 to July 2023.R software was used to randomize the groups into a training set(n=231)and a validation set(n=76)in a 3∶1 ratio.Uni-variate and multivariate logistic regression analyses were employed to identify factors influencing lymph node me-tastasis in patients preoperatively diagnosed with early-stage cervical cancer.R software was used to establish a nomogram prediction model and draw receiver operating characteristic(ROC)curves and calibration curves for validation.Results:① The results of univariate logistic regression analysis showed that positive lymphovascular invasion,platelet-to-lymphocyte ratio(PLR)≥151.70,neutrophil-to-white blood cell ratio(NWR)≥0.65,plate-let-to-albumin ratio(PAR)≥ 4.94,preoperative systemic immune-inflammation index(SII)≥604.03,and sys-temic inflammatory response index(SIRI)≥ 1.05 were associated with lymph node metastasis(P＜0.05).②Multivariate logistic regression analysis found that positive lymphovascular invasion,NWR≥0.65,and PAR≥4.94 were independent risk factors for lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer(OR＞1,P＜0.05).③ A nomogram was constructed to predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer.The ROC curve shows an area under the train-ing set curve(AUC)of 0.821 and a validation set AUC of 0.858.The calibration curve shows an average abso-lute error of 0.024 for the training set and 0.059 for the validation set.Conclusions:The prediction model for lymph node metastasis in cervical cancer constructed using preoperative inflammatory and immune nutritional indi-cators such as NWR,PAR is helpful for gynecological oncologists to predict lymph node metastasis in cervical cancer patients before surgery.

Objective To investigate the expression,synergistic relationship and clinical significance of alcohol de-hydrogenase(ADH1A)and vascular endothelial growth factor-A(VEGFA)in hepatocellular carcinoma(HCC).Methods The expression and correlation of ADH1A and VEGFA in HCC and adjacent normal tissues were ana-lyzed by GEPIA.TCGA and GSEA were used to analyze the pathway of ADH1A in HCC.The clinical and patho-logical data of 84 patients with HCC were collected,and 54 patients with paracancer normal tissue samples were se-lected as controls to analyze the correlation between ADH1A and VEGFA and clinicopathological parameters of HCC.Immunohistochemistry was used to detect the protein expression of ADH1A and VEGFA in cases and con-trols,and the correlation between the expression of ADH1A and VEGFA and the clinical progression and prognosis of patients with HCC was analyzed based on clinical pathological parameters and Kaplan-Meier.Results Bioinfor-matics analysis found that ADH1A was low-expressed in HCC and VEGFA was highly expressed in HCC,and there was a negative correlation between the two(P<0.001);immunohistochemical detection results showed that the expression of ADH1A in HCC tissue was lower than that in normal tissue adjacent to cancer(P<0.01)while the expression rate of VEGFA in HCC tissue was significantly higher than that of normal tissue adjacent to cancer(P<0.01);The recurrence rate of vascular thrombus and HCC patients in HCC group with high expression of ADH1A was lower(P<0.05).The proportion of tumor diameter>5 cm,high TNM stage,microsatellite and G2-G3 dif-ferentiation in HCC tissues in VEGFA high expression group was higher(P<0.05).Kaplan-Meier survival analy-sis showed that patients with high ADH1A expression and low VEGFA expression had a higher five-year survival rate.Conclusion Low expression of ADH1A and high expression of VEGFA in tumor tissues of patients with HCC indicate tumor progression and can be used as one of the prognostic evaluation indicators for patients with HCC.

Objective:To predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer based on preoperative inflammatory and immune nutritional indicators,and to construct a nomo-gram prediction model,providing a basis and tool for preoperative diagnosis of lymph node metastasis in cervical cancer.Methods:A retrospective analysis was conducted on the clinical data of 307 patients preoperatively diag-nosed with early-stage cervical cancer who underwent surgical treatment at the Obstetrics and Gynecology De-partment of the Second Affiliated Hospital of Zhengzhou University from January 2018 to July 2023.R software was used to randomize the groups into a training set(n=231)and a validation set(n=76)in a 3∶1 ratio.Uni-variate and multivariate logistic regression analyses were employed to identify factors influencing lymph node me-tastasis in patients preoperatively diagnosed with early-stage cervical cancer.R software was used to establish a nomogram prediction model and draw receiver operating characteristic(ROC)curves and calibration curves for validation.Results:① The results of univariate logistic regression analysis showed that positive lymphovascular invasion,platelet-to-lymphocyte ratio(PLR)≥151.70,neutrophil-to-white blood cell ratio(NWR)≥0.65,plate-let-to-albumin ratio(PAR)≥ 4.94,preoperative systemic immune-inflammation index(SII)≥604.03,and sys-temic inflammatory response index(SIRI)≥ 1.05 were associated with lymph node metastasis(P＜0.05).②Multivariate logistic regression analysis found that positive lymphovascular invasion,NWR≥0.65,and PAR≥4.94 were independent risk factors for lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer(OR＞1,P＜0.05).③ A nomogram was constructed to predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer.The ROC curve shows an area under the train-ing set curve(AUC)of 0.821 and a validation set AUC of 0.858.The calibration curve shows an average abso-lute error of 0.024 for the training set and 0.059 for the validation set.Conclusions:The prediction model for lymph node metastasis in cervical cancer constructed using preoperative inflammatory and immune nutritional indi-cators such as NWR,PAR is helpful for gynecological oncologists to predict lymph node metastasis in cervical cancer patients before surgery.

Objective:To predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer based on preoperative inflammatory and immune nutritional indicators,and to construct a nomo-gram prediction model,providing a basis and tool for preoperative diagnosis of lymph node metastasis in cervical cancer.Methods:A retrospective analysis was conducted on the clinical data of 307 patients preoperatively diag-nosed with early-stage cervical cancer who underwent surgical treatment at the Obstetrics and Gynecology De-partment of the Second Affiliated Hospital of Zhengzhou University from January 2018 to July 2023.R software was used to randomize the groups into a training set(n=231)and a validation set(n=76)in a 3∶1 ratio.Uni-variate and multivariate logistic regression analyses were employed to identify factors influencing lymph node me-tastasis in patients preoperatively diagnosed with early-stage cervical cancer.R software was used to establish a nomogram prediction model and draw receiver operating characteristic(ROC)curves and calibration curves for validation.Results:① The results of univariate logistic regression analysis showed that positive lymphovascular invasion,platelet-to-lymphocyte ratio(PLR)≥151.70,neutrophil-to-white blood cell ratio(NWR)≥0.65,plate-let-to-albumin ratio(PAR)≥ 4.94,preoperative systemic immune-inflammation index(SII)≥604.03,and sys-temic inflammatory response index(SIRI)≥ 1.05 were associated with lymph node metastasis(P＜0.05).②Multivariate logistic regression analysis found that positive lymphovascular invasion,NWR≥0.65,and PAR≥4.94 were independent risk factors for lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer(OR＞1,P＜0.05).③ A nomogram was constructed to predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer.The ROC curve shows an area under the train-ing set curve(AUC)of 0.821 and a validation set AUC of 0.858.The calibration curve shows an average abso-lute error of 0.024 for the training set and 0.059 for the validation set.Conclusions:The prediction model for lymph node metastasis in cervical cancer constructed using preoperative inflammatory and immune nutritional indi-cators such as NWR,PAR is helpful for gynecological oncologists to predict lymph node metastasis in cervical cancer patients before surgery.

Objective:To predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer based on preoperative inflammatory and immune nutritional indicators,and to construct a nomo-gram prediction model,providing a basis and tool for preoperative diagnosis of lymph node metastasis in cervical cancer.Methods:A retrospective analysis was conducted on the clinical data of 307 patients preoperatively diag-nosed with early-stage cervical cancer who underwent surgical treatment at the Obstetrics and Gynecology De-partment of the Second Affiliated Hospital of Zhengzhou University from January 2018 to July 2023.R software was used to randomize the groups into a training set(n=231)and a validation set(n=76)in a 3∶1 ratio.Uni-variate and multivariate logistic regression analyses were employed to identify factors influencing lymph node me-tastasis in patients preoperatively diagnosed with early-stage cervical cancer.R software was used to establish a nomogram prediction model and draw receiver operating characteristic(ROC)curves and calibration curves for validation.Results:① The results of univariate logistic regression analysis showed that positive lymphovascular invasion,platelet-to-lymphocyte ratio(PLR)≥151.70,neutrophil-to-white blood cell ratio(NWR)≥0.65,plate-let-to-albumin ratio(PAR)≥ 4.94,preoperative systemic immune-inflammation index(SII)≥604.03,and sys-temic inflammatory response index(SIRI)≥ 1.05 were associated with lymph node metastasis(P＜0.05).②Multivariate logistic regression analysis found that positive lymphovascular invasion,NWR≥0.65,and PAR≥4.94 were independent risk factors for lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer(OR＞1,P＜0.05).③ A nomogram was constructed to predict lymph node metastasis in patients preoperatively diagnosed with early-stage cervical cancer.The ROC curve shows an area under the train-ing set curve(AUC)of 0.821 and a validation set AUC of 0.858.The calibration curve shows an average abso-lute error of 0.024 for the training set and 0.059 for the validation set.Conclusions:The prediction model for lymph node metastasis in cervical cancer constructed using preoperative inflammatory and immune nutritional indi-cators such as NWR,PAR is helpful for gynecological oncologists to predict lymph node metastasis in cervical cancer patients before surgery.