Cemental tear is a rare and indetectable condition unless obvious clinical signs present with the involvement of surrounding periodontal and periapical tissues. Due to its clinical manifestations similar to common dental issues, such as vertical root fracture, primary endodontic diseases, and periodontal diseases, as well as the low awareness of cemental tear for clinicians, misdiagnosis often occurs. The critical principle for cemental tear treatment is to remove torn fragments, and overlooking fragments leads to futile therapy, which could deteriorate the conditions of the affected teeth. Therefore, accurate diagnosis and subsequent appropriate interventions are vital for managing cemental tear. Novel diagnostic tools, including cone-beam computed tomography (CBCT), microscopes, and enamel matrix derivatives, have improved early detection and management, enhancing tooth retention. The implementation of standardized diagnostic criteria and treatment protocols, combined with improved clinical awareness among dental professionals, serves to mitigate risks of diagnostic errors and suboptimal therapeutic interventions. This expert consensus reviewed the epidemiology, pathogenesis, potential predisposing factors, clinical manifestations, diagnosis, differential diagnosis, treatment, and prognosis of cemental tear, aiming to provide a clinical guideline and facilitate clinicians to have a better understanding of cemental tear.
Humans ; Dental Cementum/injuries* ; Consensus ; Diagnosis, Differential ; Cone-Beam Computed Tomography ; Tooth Fractures/therapy*

The accurate segmentation of the right ventricle is crucial for cardiac disease research,but its low contrast with surrounding tissues and complex structure make segmentation challenging.To address these issues,a dual-encoder segmentation model combining nested multi-scale feature fusion and feature repurposing modules is proposed.Specifically,the nested multi-scale feature fusion module captures boundary detail features through multi-scale dilated convolutions and reduces the semantic gap between the encoder and decoder using short skip connections,while the feature repurposing module enhances feature extraction ability by leveraging fine-grained features from shallow layers.Ablation experiments show that the inclusion of these two modules improves the Dice similarity coefficient of U-Net by 3.14%.On the ACDC dataset,the proposed model achieves a Dice similarity coefficient of 90.31%and a mean Hausdorff distance of 5.21 mm,outperforming other comparative models.Additionally,its generalization ability is validated on the M&Ms dataset.Experimental results demonstrate the excellent performance and robustness of the proposed model in right ventricle segmentation.

BACKGROUND:The combination of traditional Chinese medicine syndrome and semen quality-related parameters can jointly predict the occurrence of abnormal increase in sperm DNA fragmentation index (DFI) and draw a column chart,which can significantly improve clinical practicality and application efficiency,provide a basis for comprehensive evaluation of semen quality in clinical practice,take active intervention measures to improve clinical outcomes,and formulate personalized medical plans.OBJECTIVE:To explore the prediction model and verification of sperm DNA fragments based on traditional Chinese medicine syndrome and semen quality-related parameters.METHODS:Retrospective analysis was made on 420 infertile patients who received traditional Chinese medicine syndrome diagnosis and sperm DNA fragment rate examination in the Department of Traditional Chinese Medicine Andrology,Nanxishan Hospital of Guangxi Zhuang Autonomous Region from July 2019 to July 2021.According to the Manual of Human Semen Examination and Treatment Laboratories (6th Edition),137 patients with sperm DFI>30％ were included in the group of abnormally high sperm DFI,and 283 patients with sperm DFI ≤ 30％ were taken as the control group.First,univariate analysis was used to screen the influencing factors of the abnormal increase of sperm DFI.Then,the best matching factor was selected by using the collinearity problem of LASSO correction factors.Then,it was included in the multifactor forward stepwise logistic regression to find out its independent influencing factors and draw a nomogram.Finally,the receiver operating characteristic curve,calibration curve,decision curve analysis and clinical impact curve were used to verify the differentiation and accuracy of the prediction model and its clinical application effectiveness.RESULTS AND CONCLUSION:(1) The results of the univariate analysis showed that age,body mass index,forward motion rate,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney sperm deficiency syndrome were the influencing factors for the abnormal increase of sperm DFI (P<0.05).(2) The best matching factors further screened by LASSO regression were age,body mass index,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney essence deficiency syndrome (P<0.05).(3) Multifactor forward stepwise Logistic regression showed that age,body mass index,sperm concentration,total sperm motility,damp heat downpour syndrome,and kidney yang deficiency syndrome were six independent factors that caused the abnormal increase in sperm DFI.(4) Receiver operating characteristic curve showed that the area under the curve of the model group was 0.760(0.713,0.806),and the area under the curve of the validation group was 0.745(0.714,0.776).It showed that the prediction model had good discrimination.(5) The average absolute error of the calibration curve was 0.040,and the Hosmer Lemeshow test (P>0.05),suggesting that there was no significant statistical difference between the probability of the abnormal increase in DFI of spermatozoa predicted by the model and the probability of the abnormal increase in DFI of spermatozoa actually occurred,which confirmed that the model had good accuracy.(6) Decision curve analysis and clinical impact curve showed that the model group and validation group had the maximum clinical net benefit when the threshold probability values were (0.08-0.84) and (0.09-0.78) respectively,and had good clinical application efficiency within the threshold probability range.(7) These findings conclude that age,body mass index,sperm concentration,total sperm viability,damp heat downpour syndrome and kidney yang deficiency syndrome are independent factors that cause the abnormal increase in sperm DFI.The nomogram of the clinical prediction model constructed by them has good clinical prediction value and clinical application efficiency,and can provide the basis for comprehensive clinical evaluation of semen quality and individualized medical service.

Background Metals and metalloids in fine particulate matter (PM_2.5) may cause damage to the respiratory and circulatory systems of the human body, and long-term exposure is prone to causing chronic poisoning, cancer, and other adverse effects. Objective To assess the distribution characteristics of metals and metalloids in outdoor PM_2.5 in a southern city of China, conduct source apportionment, and evaluate the associated health risks, thereby providing theoretical support for further pollution control measures. Methods PM_2.5 samples were collected in districts A, B, and C of a southern China city, and the concentrations of 17 metals and metalloids were detected by inductively coupled plasma-mass spectrometry (ICP-MS). Pollution sources were assessed through enrichment factor and principal components analysis, and the main pollution sources were quantified using absolute principal component scores-multivariate linear regression (APCS-MLR). Health risks were evaluated based on the Technical guide for environmental health risk assessment of chemical exposure (WS/T777—2021). Results The ambient air PM_2.5 concentrations in the city were higher in winter and spring, and lower in summer and autumn. The annual average concentrations of ambient PM_2.5 in districts A, B, and C were 36.7, 31.9, and 24.4 μg·m⁻³, respectively. The ambient PM_2.5 levels in districts B and C were below the second-grade limit set by the Ambient air quality standards (GB 3095—2012). The enrichment factors of cadmium (Cd), aluminum (Al), and antimony (Sb) were greater than 10, those of copper (Cu), lead (Pb), arsenic (As), nickel (Ni), mercury (Hg), and molybdenum (Mo) fell between 1 and 10, and those of manganese (Mn), vanadium (V), chromium (Cr), cobalt (Co), barium (Ba), beryllium (Be), and uranium (U) were below or equal to 1. The comprehensive evaluation of source analysis showed that the main pollution sources in districts A and C and the whole city were coal-burning. In district B, the main pollution source was also coal combustion, followed by industrial process sources and dust sources. The carcinogenic risks of As and Cr were between 1×10⁻⁶ and 1×10⁻⁴. However, the hazard quotients for 15 metals and metalloids in terms of non-carcinogenic risk were below 1. Conclusion Cr and As in the atmospheric PM_2.5 of the city present a certain risk of cancer and should be paid attention to. In addition, preventive control measures should be taken against relevant pollution sources such as industrial emission, dust, and coal burning.

As a physical treatment technique, modified electro-convulsive therapy (MECT) is used to treat mental and certain neurological disorders by causing seizures with short, suitable electrical currents applied to the brain while the patient is under general anesthesia and muscle relaxants. MECT is recognized for its therapeutic efficacy and clinical safety, rendering it one of the most prevalent interventions in psychiatric care. To enhance clinical outcomes and minimize adverse effects, this consensus document delineates the indications, therapeutic parameters, therapeutic procedures, potential adverse effects, and associated management strategies for MECT. These guidelines are informed by the latest clinical research and expert consensus, integrating evidence-based medicine methodologies. The objective is to furnish clinicians with precise operational guidelines and to advance the standardization of MECT practices in clinical settings.

Objective This study established a qualitative and quantitative detection method for 9 new synthetic cannabinoids in e-cigarette oil using liquid chromatography-high resolution mass spectrometry.Methods E-cigarette oil was extracted with methanol,concentrated by nitrogen blowing under a water bath at 60℃,and separated using an Acquity UPLC? HSS T3(100 mm×2.1 mm,1.8 μm)column.Gradient elution was performed with ammonium acetate buffer solution(20 mmol/L)containing 0.1%formic acid and acetonitrile as the mobile phase.Detection was carried out in the positive ion mode of the HESI ion source using DDA(FullScan+dd-MS2)mode.Quantification was performed using the external standard method based on precise precursor ion peak areas,while qualitative analysis relied on retention times and characteristic fragment ions.Results The results showed that the standard curves of all 9 synthetic cannabinoids in blank e-cigarette oil matrix exhibited good linearity with R2 values>0.999.The limits of detection ranged from 0.0001 to 0.01 μg/g,and the limits of quantification ranged from 0.0003 to 0.03 μg/g.Recovery rates at low,medium,and high spiking levels in blank e-cigarette oil matrix ranged from 75.7%to 115%,with relative deviations<8.5%.Conclusion This method features a short instrument detection time of only 10 min,relatively simple operation,low detection limits,high accuracy,and good precision,making it applicable for rapid screening and quantitative analysis of synthetic cannabinoids in e-cigarette oil.

Objective:To carry out a systematic review of the development, introduction or validation of drug literacy assessment tools for adults at home and abroad, and to summarize and analyze the characteristics of the assessment tools.Methods:The research framework of the scoping review was used to systematically search 8 Chinese and English databases, such as China National Knowledge Infrastructure, China Biology Medicine disc, PubMed, Web of Science, and Embase. The search period was from database establishment to October 31, 2023. Studies on adult drug literacy assessment tools were screened and included, tool characteristics were extracted and analyzed, and ultimately the extracts were standardized for reporting.Results:A total of 31 articles that met the criteria were included, of which 26 were on the development and validation of assessment tools, three were on the localization and application of assessment tools, and two were on the revision of assessment tools, covering 33 assessment tools.Conclusions:It is needed to recognize the importance of drug literacy assessment, actively explore the diversity of drug literacy assessment tools, clarify the limitations of existing drug literacy assessment tools, further improve the reliability and validity of existing tools, continue to develop and introduce assessment tools suitable for China's national conditions, so as to increase the accuracy of drug literacy assessment.

Objective This study established a qualitative and quantitative detection method for 9 new synthetic cannabinoids in e-cigarette oil using liquid chromatography-high resolution mass spectrometry.Methods E-cigarette oil was extracted with methanol,concentrated by nitrogen blowing under a water bath at 60℃,and separated using an Acquity UPLC? HSS T3(100 mm×2.1 mm,1.8 μm)column.Gradient elution was performed with ammonium acetate buffer solution(20 mmol/L)containing 0.1%formic acid and acetonitrile as the mobile phase.Detection was carried out in the positive ion mode of the HESI ion source using DDA(FullScan+dd-MS2)mode.Quantification was performed using the external standard method based on precise precursor ion peak areas,while qualitative analysis relied on retention times and characteristic fragment ions.Results The results showed that the standard curves of all 9 synthetic cannabinoids in blank e-cigarette oil matrix exhibited good linearity with R2 values>0.999.The limits of detection ranged from 0.0001 to 0.01 μg/g,and the limits of quantification ranged from 0.0003 to 0.03 μg/g.Recovery rates at low,medium,and high spiking levels in blank e-cigarette oil matrix ranged from 75.7%to 115%,with relative deviations<8.5%.Conclusion This method features a short instrument detection time of only 10 min,relatively simple operation,low detection limits,high accuracy,and good precision,making it applicable for rapid screening and quantitative analysis of synthetic cannabinoids in e-cigarette oil.

The accurate segmentation of the right ventricle is crucial for cardiac disease research,but its low contrast with surrounding tissues and complex structure make segmentation challenging.To address these issues,a dual-encoder segmentation model combining nested multi-scale feature fusion and feature repurposing modules is proposed.Specifically,the nested multi-scale feature fusion module captures boundary detail features through multi-scale dilated convolutions and reduces the semantic gap between the encoder and decoder using short skip connections,while the feature repurposing module enhances feature extraction ability by leveraging fine-grained features from shallow layers.Ablation experiments show that the inclusion of these two modules improves the Dice similarity coefficient of U-Net by 3.14%.On the ACDC dataset,the proposed model achieves a Dice similarity coefficient of 90.31%and a mean Hausdorff distance of 5.21 mm,outperforming other comparative models.Additionally,its generalization ability is validated on the M&Ms dataset.Experimental results demonstrate the excellent performance and robustness of the proposed model in right ventricle segmentation.

BACKGROUND:The combination of traditional Chinese medicine syndrome and semen quality-related parameters can jointly predict the occurrence of abnormal increase in sperm DNA fragmentation index (DFI) and draw a column chart,which can significantly improve clinical practicality and application efficiency,provide a basis for comprehensive evaluation of semen quality in clinical practice,take active intervention measures to improve clinical outcomes,and formulate personalized medical plans.OBJECTIVE:To explore the prediction model and verification of sperm DNA fragments based on traditional Chinese medicine syndrome and semen quality-related parameters.METHODS:Retrospective analysis was made on 420 infertile patients who received traditional Chinese medicine syndrome diagnosis and sperm DNA fragment rate examination in the Department of Traditional Chinese Medicine Andrology,Nanxishan Hospital of Guangxi Zhuang Autonomous Region from July 2019 to July 2021.According to the Manual of Human Semen Examination and Treatment Laboratories (6th Edition),137 patients with sperm DFI>30％ were included in the group of abnormally high sperm DFI,and 283 patients with sperm DFI ≤ 30％ were taken as the control group.First,univariate analysis was used to screen the influencing factors of the abnormal increase of sperm DFI.Then,the best matching factor was selected by using the collinearity problem of LASSO correction factors.Then,it was included in the multifactor forward stepwise logistic regression to find out its independent influencing factors and draw a nomogram.Finally,the receiver operating characteristic curve,calibration curve,decision curve analysis and clinical impact curve were used to verify the differentiation and accuracy of the prediction model and its clinical application effectiveness.RESULTS AND CONCLUSION:(1) The results of the univariate analysis showed that age,body mass index,forward motion rate,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney sperm deficiency syndrome were the influencing factors for the abnormal increase of sperm DFI (P<0.05).(2) The best matching factors further screened by LASSO regression were age,body mass index,total sperm motility,sperm concentration,sperm morphology,kidney yang deficiency syndrome,damp heat downpour syndrome,and kidney essence deficiency syndrome (P<0.05).(3) Multifactor forward stepwise Logistic regression showed that age,body mass index,sperm concentration,total sperm motility,damp heat downpour syndrome,and kidney yang deficiency syndrome were six independent factors that caused the abnormal increase in sperm DFI.(4) Receiver operating characteristic curve showed that the area under the curve of the model group was 0.760(0.713,0.806),and the area under the curve of the validation group was 0.745(0.714,0.776).It showed that the prediction model had good discrimination.(5) The average absolute error of the calibration curve was 0.040,and the Hosmer Lemeshow test (P>0.05),suggesting that there was no significant statistical difference between the probability of the abnormal increase in DFI of spermatozoa predicted by the model and the probability of the abnormal increase in DFI of spermatozoa actually occurred,which confirmed that the model had good accuracy.(6) Decision curve analysis and clinical impact curve showed that the model group and validation group had the maximum clinical net benefit when the threshold probability values were (0.08-0.84) and (0.09-0.78) respectively,and had good clinical application efficiency within the threshold probability range.(7) These findings conclude that age,body mass index,sperm concentration,total sperm viability,damp heat downpour syndrome and kidney yang deficiency syndrome are independent factors that cause the abnormal increase in sperm DFI.The nomogram of the clinical prediction model constructed by them has good clinical prediction value and clinical application efficiency,and can provide the basis for comprehensive clinical evaluation of semen quality and individualized medical service.