In the field of oral medicine, 3D-printed individualized titanium mesh technology is gradually becoming an important means for the treatment of severe alveolar bone defect augmentation. This article provides a comprehensive analysis of the advantages of this technology, the evaluation of osteogenic effects, and the progress of research in clinical applications. In response to the current issue of variability in bone augmentation outcomes, this paper delves into multiple factors affecting bone augmentation effects, including individualized titanium mesh design (involving the thickness, pore size, pore shape, porosity, contour shape, selection of titanium alloy materials, and 3D printing technology), intraoperative procedures (the accuracy of placement during 3D-printed individualized titanium mesh surgery), and postoperative care (including the prevention of complications, formation of pseudoperiosteum, and stability of the titanium mesh). By integrating the clinical experience and research findings of our team, we propose a series of targeted optimization strategies, including designing, manufacturing, and clinically applying self-positioning individualized titanium meshs (positioning wings + individualized titanium meshs) to improve the positioning accuracy of the titanium mesh; propose individualized treatment processes and titanium mesh design schemes based on specific conditions of alveolar bone defects and soft tissue status; and emphasize the importance of long-term stable fixation of the titanium mesh to reduce the risk of postoperative mesh loosening and displacement. In addition, we appropriately summarize the evaluation methods for the bone augmentation effects of 3D-printed individualized titanium meshes, covering the following key indicators: (1) vertical bone augmentation and horizontal bone augmentation; (2) changes in bone contour morphology; (3) bone volume increase; (4) clinical indicators (surgical success rate, titanium mesh exposure, infection rate, and postoperative recovery); (5) aesthetic effect evaluation; (6) long-term stability; (7) radiological assessment; (8) patient satisfaction; and (9) precision of surgical operation, aiming to assist doctors in comprehensively assessing and in-depth analyzing the surgical outcomes to achieve the best therapeutic effects. The purpose of this article is to provide a reference for the optimization and clinical application of 3D-printed individualized titanium mesh technology and to lay a theoretical foundation for achieving the best osteogenic effects.

OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in breast cancer， and provide a scientific reference for clinical healthcare workers in selecting or developing effective predictive models. METHODS A systematic search was conducted for studies on predictive models of the risk of chemotherapy-induced myelosuppression in breast cancer across the CNKI， VIP， Wanfang， PubMed， Web of Science， Cochrane Library， Embase， and Scopus databases， with a time frame of the establishment of the database to May 7， 2024. Literature was independently screened by 2 investigators， data were extracted according to critical appraisal and data extraction for systematic reviews of predictive model studies， and the risk of bias evaluation tool for predictive model studies was used to analyze the risk of bias and applicability of the included studies. RESULTS There were totally 7 studies， comprising 12 models. Among them， 11 models indicated an area under the subject operating characteristic curve of 0.600-0.908； 2 models indicated calibration. The common predictor variables of the included models were age， pre-chemotherapy neutrophil count， pre-chemotherapy lymphocyte count， and pre-chemotherapy albumin. The overall risk of bias of the 7 studies was high， which was mainly attributed to the flaws in the study design， insufficient sample sizes， inappropriate treatment of variables， non-reporting of missing data， and the lack of indicators for the assessment of the models， but the applicability was good. CONCLUSIONS The predictive performance of risk predictive models for chemotherapy-induced myelosuppression in breast cancer remains to be further enhanced， and the overall risk of model bias is high. Future studies should follow the specifications of model development and reporting， then combine machine learning algorithms to develop risk predictive models with good predictive performance， high stability， and low risk of bias， so as to provide a decision-making basis for the clinic.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

Objective: To evaluate the feasibility of dynamic contrast-enhanced MRI (DCE-MRI) in differentiating tumor deposits (TDs) from metastatic lymph nodes (MLNs) in rectal cancer. Materials and Methods: A retrospective analysis was conducted on 70 patients with rectal cancer, including 168 lesions (70 TDs and 98 MLNs confirmed by histopathology), who underwent pretreatment MRI and subsequent surgery between March 2019 and December 2022. The morphological characteristics of TDs and MLNs, along with quantitative parameters derived from DCE-MRI (K trans , kep, and v e) and DWI (ADCmin, ADCmax, and ADCmean), were analyzed and compared between the two groups.Multivariable binary logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the diagnostic performance of significant individual quantitative parameters and combined parameters in distinguishing TDs from MLNs. Results: All morphological features, including size, shape, border, and signal intensity, as well as all DCE-MRI parameters showed significant differences between TDs and MLNs (all P < 0.05). However, ADC values did not demonstrate significant differences (all P > 0.05). Among the single quantitative parameters, v e had the highest diagnostic accuracy, with an area under the ROC curve (AUC) of 0.772 for distinguishing TDs from MLNs. A multivariable logistic regression model incorporating short axis, border, v e, and ADC mean improved diagnostic performance, achieving an AUC of 0.833 (P = 0.027). Conclusion The combination of morphological features, DCE-MRI parameters, and ADC values can effectively aid in the preoperative differentiation of TDs from MLNs in rectal cancer.

ObjectiveTo improve the quality and service performance of community visual health services in Shanghai, and to establish a set of reasonable and effective evaluation index system for community visual health services. MethodsCentered on the national and Shanghai-based visual health policies and based on the current status and development trends of community visual health service program in Shanghai, the candidate indicators were formed through literature review and expert interviews, firstly. The framework of an evaluation index system was formulated through qualitative research successively, which was further revised and perfected using the Delphi method. Coefficient weights were calculated using the analytic hierarchy process (AHP), culminating in the establishment of the community visual health evaluation index system, lastly. ResultsA total of 22 visual health experts from district-level center for disease control, hospital ophthalmology and leaders in charging of visual health service in community health centers participated in the Delphi questionnaire survey, with a questionnaire recovery rate of 100% and an expert authority coefficient of 0.86, indicating high credibility. After a round of correspondence to experts’ importance ratings and discussions, a comprehensive evaluation index system comprising 3 primary indicators, 12 secondary indicators, and 47 tertiary indicators, along with 5 additional indicators, was finalized. ConclusionAn index system tailored to effective evaluation for community visual health initiatives was drawn up in this study, which can promote the capacity building in community eye health services, facilitating the high-quality development of visual health courses, and enhancing residents’ eye health.

OBJECTIVE To construct a prediction model for the efficacy of serotonin-norepinephrine reuptake inhibitor （SNRI） in inpatients with moderate and severe depression by using a machine learning method. METHODS The case records of inpatients with moderate and severe depression treated with SNRI antidepressants were collected from a third-grade class-A hospital in Xinjiang from January 2022 to October 2024； those patients were divided into effective group and ineffective group based on the Hamilton depression scale-24 score reduction rate. After screening the characteristic variables related to the therapeutic efficacy of SNRI drugs through LASSO regression， five prediction models including support vector machine， k-nearest neighbor， random forest， lightweight gradient boosting machine and extreme gradient boosting were constructed using the training set. Bayesian optimization was used to adjust the hyperparameters of these models. The performance of the models was evaluated in the validation set to select the optimal model. The Shapley additive explanations method was used to perform explainable analysis on the best model. RESULTS The medical records from 355 hospitalized patients with moderate and severe depression were collected， comprising 285 cases in the effective group and 70 cases in the ineffective group， resulting in an overall therapeutic response rate of 80.28%. After feature variable screening， five characteristic variables for therapeutic efficacy were obtained， including Hamilton anxiety scale， blood urea nitrogen， combination of anti-anxiety drugs， drinking history， and first onset of the disease. Compared with other models， the random forest model performed the best. The area under the receiver operating characteristic curve was 0.85， the area under the precision-recall curve was 0.87， the accuracy was 0.74， and the recall rate value was 0.75. CONCLUSIONS The random forest model constructed based on five characteristic variables demonstrates potential for predicting the therapeutic efficacy of SNRI antidepressants in hospitalized patients with moderate and severe depression.

Polyunsaturated fatty acids (PUFAs) have diverse health-promoting effects, such as potentially protecting in immune, nervous, and cardiovascular systems by targeting a variety of sites, including most ion channels. Voltage-gated potassium channels of the K_V7 family and large-conductance Ca²⁺- and voltage-activated K⁺ (BK_Ca) channels are expressed in many tissues, therefore, their physiological importance is evident from the various disorders linked to dysfunctional K_V7 channels and BK_Ca channels. Thus, it is extremely important to learn how potassium channels are regulated by PUFAs. The aim of this review is to provide an overview of the effects of PUFAs on K_V7 channels and BK_Ca channels functions, as well as the mechanisms underlying these effects. In summarizing reported effects of PUFAs on K_V7 and BK_Ca channels mediated currents, we generally conclude that PUFAs increase the current amplitude, meanwhile, differential molecular and biophysical mechanisms are associated with the current increase. In K_V7 channels the currents increasement are associated with a shift in the voltage dependence of channel opening and increased maximum conductance in K_V7 channels, while in BK_Ca channels, they are associated with destabilization the pore domain closed conformation. Furthermore, PUFA effects are influenced by auxiliary subunits of K_V7 and BK_Ca channels, associate with channels in certain tissues. although findings are conflicting. A better understanding of how PUFAs regulate K_V7 and BK_Ca channels may offer insight into their physiological regulation and may lead to new therapeutic strategies and approaches.