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*

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

Objective To investigate the classification capability of a deep learning classification model based on biparametric mag-netic resonance imaging(bpMRI)for clinically significant prostate cancer(csPCa)and clinically insignificant prostate cancer(cisPCa).Methods A retrospective analysis was conducted on the data of 565 prostate bpMRI patients.A deep learning classification model was established for csPCa.The patients were randomly divided into training set(452 cases)and internal test set(113 cases)at a ratio of 8︰2.Internal validation was performed,followed by external validation(external validation set)using data from 120 patients across four different hospitals.The area under the curve(AUC)of the receiver operating characteristic(ROC)curve,F1 score,precision,sensi-tivity,specificity,accuracy,and calibration curves were used to evaluate the model.Decision curve analysis(DCA)was also applied to assess the clinical benefit of the model.Results The deep learn-ing classification model for csPCa classification demonstrated the following performance across the training set,internaltest set,and external validation set:sensitivity of 0.986,0.887,and 0.750;specificity of 0.967,0.850,and 0.976;precision of 0.963,0.839,and 0.818;accuracy of 0.974,0.862,and 0.792;F1 score of 0.974,0.862,and 0.783;and AUC of 0.998,0.896,and 0.883,respec-tively.The calibration curves for all three datasets showed high consistency between predicted and actual probabilities.DCA indicated that the highest net benefit threshold probabilities for the training set,internal test set,and external validation set were 0.2-0.7,0.2-0.6,and 0.2-0.5,respectively.Conclusion The deep learning classification model demonstrated excellent performance in classifying csPCa and exhibited good generalizability,which is worhty of clinical application.

OBJECTIVETo investigate the HLA-A, -B allele polymorphism in the Luoba ethnic population.

METHODSHLA-A, -B DNA types in 92 healthy individuals of Luoba nationality in the Linzhi area, Tibet Autonomous Region, were investigated by polymerase chain reaction-sequence specific oligo-nucleotide (PCR-SSO).

RESULTSTen alleles at HLA-A locus, and 19 alleles at HLA-B locus in Luoba ethnic group were detected. Of the 10 HLA-A alleles detected, the three most common alleles were HLA-A*11(allele frequency: 36.40%), -A*02 (25.50%), -A*24 (23.90%), and they covered 85.80% of the total HLA-A alleles detected from the Luoba ethnic group. Of the 19 HLA-B alleles detected, the three most common alleles were HLA-B*40 (27.20%), -B*15 (11.40%) and -B*38(10.90%), and they covered 49.50% of the total -B alleles detected in the Luoba ethnic group.

CONCLUSIONThe distribution of HLA-A, -B allele polymorphism in the Luoba nationality is distinctive, but some of the gene distribution in the Luoba group is nearer to that in the Tibetan group. These are consistent with the results of ethnological, historical and sociological researches.

Alleles ; Ethnic Groups ; genetics ; Gene Frequency ; HLA Antigens ; genetics ; HLA-B Antigens ; genetics ; Humans ; Polymerase Chain Reaction ; Polymorphism, Genetic ; genetics ; Tibet

To obtain an initial overview of gene diversity and expression pattern in porcine thymus, 11,712 ESTs (Expressed Sequence Tags) from 100-day-old porcine thymus (FTY) were sequenced and 7,071 cleaned ESTs were used for gene expression analysis. Clustered by the PHRAP program, 959 contigs and 3,074 singlets were obtained. Blast search showed that 806 contigs and 1,669 singlets (totally 5,442 ESTs) had homologues in GenBank and 1,629 ESTs were novel. According to the Gene Ontology classification, 36.99% ESTs were cataloged into the gene expression group, indicating that although the functional gene (18.78% in defense group) of thymus is expressed in a certain degree, the 100-day-old porcine thymus still exists in a developmental stage. Comparative analysis showed that the gene expression pattern of the 100-day-old porcine thymus is similar to that of the human infant thymus.
Animals ; Computational Biology ; Expressed Sequence Tags ; Fetus ; metabolism ; Gene Expression Profiling ; Genetic Variation ; Sequence Analysis, DNA ; Sus scrofa ; genetics ; metabolism ; Thymus Gland ; metabolism

Nine short tandem repeat (STR) markers (D3S1358, VWA, FGA, THO1, TPOX, CSFIPO, D5S818, D13S317, and D7S820) and a sex-identification marker (Amelogenin locus) were amplified with multiplex PCR and were genotyped with a four-color fluorescence method in samples from 174 unrelated Han individuals in North China. The allele frequencies, genotype frequencies, heterozygosity, probability of discrimination powers, probability of paternity exclusion and Hardy-Weinberg equilibrium expectations were determined. The results demonstrated that the genotypes at all these STR loci in Han population conform to Hardy-Weinberg equilibrium expectations. The combined discrimination power (DP) was 1.05 x 10(-10) within nine STR loci analyzed and the probability of paternity exclusion (EPP) was 0.9998. The results indicate that these nine STR loci and the Amelogenin locus are useful markers for human identification, paternity and maternity testing and sex determination in forensic sciences.
Amelogenin ; China ; Dental Enamel Proteins ; genetics ; Electrophoresis ; Ethnic Groups ; genetics ; Forensic Medicine ; methods ; Gene Frequency ; Genetics, Population ; Genotype ; Heterozygote ; Humans ; Polymerase Chain Reaction ; Polymorphism, Genetic ; Sex Determination Analysis ; methods ; Tandem Repeat Sequences ; genetics

The corona-like spikes or peplomers on the surface of the virion under electronic microscope are the most striking features of coronaviruses. The S (spike) protein is the largest structural protein, with 1,255 amino acids, in the viral genome. Its structure can be divided into three regions: a long N-terminal region in the exterior, a characteristic transmembrane (TM) region, and a short C-terminus in the interior of a virion. We detected fifteen substitutions of nucleotides by comparisons with the seventeen published SARS-CoV genome sequences, eight (53.3%) of which are non-synonymous mutations leading to amino acid alternations with predicted physiochemical changes. The possible antigenic determinants of the S protein are predicted, and the result is confirmed by ELISA (enzyme-linked immunosorbent assay) with synthesized peptides. Another profound finding is that three disulfide bonds are defined at the C-terminus with the N-terminus of the E (envelope) protein, based on the typical sequence and positions, thus establishing the structural connection with these two important structural proteins, if confirmed. Phylogenetic analysis reveals several conserved regions that might be potent drug targets.
Amino Acid Sequence ; Antigens, Viral ; immunology ; Base Composition ; Computational Biology ; Enzyme-Linked Immunosorbent Assay ; Membrane Glycoproteins ; genetics ; Molecular Sequence Data ; Mutation ; genetics ; Phylogeny ; Protein Structure, Tertiary ; SARS Virus ; genetics ; immunology ; Sequence Analysis, DNA ; Sequence Homology ; Spike Glycoprotein, Coronavirus ; Viral Envelope Proteins ; genetics ; metabolism

Human leukocyte antigen (HLA) system is the most polymorphic region known in the human genome. In the present study, we analyzed for the first time the HLA-A gene polymorphisms defined by the high-resolution typing methods--sequence-based typing (SBT) in 161 Northern Chinese Han people. A total of 74 different HLA-A gene types and 36 alleles were detected. The most frequent alleles were A*110101 (GF=0.2360), A*24020101 (GF=0.1646), and A*020101 (GF=0.1553); followed by A*3303 (GF=0.1180), A*3001 (GF=0.0590),and A*310102 (GF=0.0404). The frequencies of following alleles, A*0203, A*0205,A*0206, A*0207, A*030101, A*2423, A*2601, A*3201, and A*3301, are all higher than 0.0093. The homozygous alleles include A*020101, A*110101, A*24020101 and A*310102. Heterozygosity (H), polymorphism information content (PIC), discrimination power (DP) and probability of paternity exclusion (PPE) of HLA-A in the samples were calculated and their values were 0.8705, 0.8491, 0.6014, and 0.9475, respectively. These results by SBT analysis of HLA-A polymorphism in Northern Chinese Han population, especially the allele subtypes character, will be of great interest for clinical transplantation, disease-associated study and forensic identification. Implementation of high-resolution typing methods allows a significantly wider spectrum of HLA variation including rare alleles. This spectrum will further be extensively utilized in many fields.