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Objective To investigate the effect of different premolar extraction patterns on Bolton index and occlusal relationship of skeletal Class Ⅱ malocclusion. Methods The plaster models of 62 patients with skeletal Class Ⅱ malocclusion were selected and converted into digital models by iTeroElement scanner. Each model was simulated by the four premolar extraction pattrens, and tooth arrangement test was performed after tooth extraction. The Bolton index before and after tooth extraction was calculated, and the occlusal relationship after tooth arrangement was evaluated to explore its law. Results All the four premolar extraction patterns resulted in a significant decrease in the Bolton index over-all ratio in skeletal Class Ⅱ patients, and compared with the extraction patterns of 4/4 and 4/5, the patterns of 5/4 and 5/5 resulted in a greater decrease in the Bolton index over-all ratio(P3 mm), and the difference was statistically significant(P

BackgroundThe purpose of this study is to develop an artificial intelligence (AI)-based automated diabetic retinopathy (DR) grading and training system from a real-world diabetic dataset of China, and in particular, to investigate its effectiveness as a learning tool of DR manual grading for medical students.MethodsWe developed an automated DR grading and training system equipped with an AI-driven diagnosis algorithm to highlight highly prognostic related regions in the input image. Less experienced prospective physicians received pre- and post-training tests by the AI diagnosis platform. Then, changes in the diagnostic accuracy of the participants were evaluated.ResultsWe randomly selected 8,063 cases diagnosed with DR and 7,925 with non-DR fundus images from type 2 diabetes patients. The automated DR grading system we developed achieved accuracy, sensitivity/specificity, and AUC values of 0.965, 0.965/0.966, and 0.980 for moderate or worse DR (95 percent CI: 0.976–0.984). When the graders received assistance from the output of the AI system, the metrics were enhanced in varying degrees. The automated DR grading system helped to improve the accuracy of human graders, i.e., junior residents and medical students, from 0.947 and 0.915 to 0.978 and 0.954, respectively.ConclusionThe AI-based systemdemonstrated high diagnostic accuracy for the detection of DR on fundus images from real-world diabetics, and could be utilized as a training aid system for trainees lacking formal instruction on DR management.

The prevalence of thyroid carcinoma is increasing, and papillary thyroid carcinoma (PTC) is the most frequent subtype. More and more attention is being concentrated on the association between inflammation indicators and malignant tumors. The aim of the present study was to analyze whether the preoperative red blood cell distribution width (RDW) and platelet parameters, including mean platelet volume (MPV) and platelet distribution width (PDW), can be applied to distinguish between patients with PTC or papillary thyroid microcarcinoma (PTMC) and healthy controls, and to explore the associations with clinicopathological characteristics. The study retrospectively compared the RDW, MPV and PDW values of 780 patients with PTC or PTMC against a healthy control group. Receiver operating characteristic (ROC) curves were conducted to determine diagnostic accuracy. Furthermore, the clinicopathological features of the patients with PTC or PTMC were compared between higher and lower platelet parameter groups based on the RDW, MPV and PDW values. Significantly higher preoperative RDW, MPV and PDW values were found in patients with PTC or PTMC compared with those of the healthy group. ROC curve analysis showed that the area under the curve (AUC) plus 95% confidence interval (95% CI) values of RDW, MPV and PDW were 0.808 (0.780–0.835), 0.771 (0.743–0.799) and 0.711 (0.681–0.742), respectively. When RDW and MPV were combined together, the AUC (95% CI) value was enhanced to 0.858 (0.835–0.881) for the patients with PTC. For the patients with PTMC, RDW, MPV and PDW had AUC (95% CI) values of 0.812 (0.783–0.840), 0.779 (0.749–0.808) and 0.718 (0.685–0.751), respectively. When RDW and MPV were combined together, the AUC (95% CI) value was enhanced to 0.858 (0.835–0.881). A higher RDW was significantly associated with being female, deeper tumor infiltration, and normal FT3 and FT4 levels. A higher PDW was significantly associated with elevated thyrotropin receptor antibody levels. In conclusion, as convenient and available inflammation indicators, RDW, PDW and MPV have diagnostic ability and can distinguish between patients with PTC or PTMC and healthy controls. In addition, the combined application of RDW and MPV can improve the diagnostic power. The values of RDW and MPV were associated with clinicopathological characteristics. To the best of our knowledge, this is the first study to prove the usefulness of preoperative RDW combined with MPV in diagnosing patients with PTC or PTMC. [ABSTRACT FROM AUTHOR]

Introduction Methods Results Conclusion Aplastic anemia (AA) and hypoplastic myelodysplastic syndrome (MDS‐h) are bone marrow failure disease and difficult to distinguish merely by morphological analysis. In this study, we investigated the value of flow cytometry (FCM) in the differential diagnosis of AA and MDS‐h.We included 822 patients (626 control, 69 AA, 22 MDS‐h and 105 dilution patients) from January 2017 to December 2022 for a retrospective study. Bone marrow myeloid progenitor (MP) cell and mature lymphocytes proportions were analyzed by FCM. The ratio of MP cell proportion and mature lymphocytes proportion, MPLR, was calculated. Data were compared by Kruskal–Wallis test. Differential diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve. Cutoff value was determined by the maximum Youden index.Bone marrow MP cell proportion and MPLR of MDS‐h patients were higher than AA patients. Mature lymphocytes proportion of MDS‐h patients was lower than AA patients. Area under ROC curve (AUC of ROC) of MP cell proportion, MPLR and mature lymphocytes proportion to distinguish AA from MDS‐h were 0.992, 0.988, and 0.850, respectively. Moreover, MPLR of dilution patients was higher than AA patients but lower than MDS‐h patients. The AUC of ROC curves of MPLR to distinguish MDS‐h and AA from dilution were 0.854 and 0.871, respectively.Bone marrow MP cell proportion and MPLR can effectively discriminate AA from MDS‐h with similar differential efficacy, which is higher than mature lymphocytes proportion. Moreover, MPLR can evaluate the quality of bone marrow aspirates, which would interfere with the differential diagnosis. [ABSTRACT FROM AUTHOR]

Recurrent oral ulcer seriously threatens patients' daily life and health. This study investigated potential genes and pathways that participate in the pathogenesis of recurrent oral ulcer by high throughput bioinformatic analysis. RT-PCR and Western blot were applied to further verify screened interleukins effect. Recurrent oral ulcer related genes were collected from websites and papers, and further found out from Human Genome 280 6.0 microarray data. Each pathway of recurrent oral ulcer related genes were got through chip hybridization. RT-PCR was applied to test four recurrent oral ulcer related genes to verify the microarray data. Data transformation, scatter plot, clustering analysis, and expression pattern analysis were used to analyze recurrent oral ulcer related gene expression changes. Recurrent oral ulcer gene microarray was successfully established. Microarray showed that 551 genes involved in recurrent oral ulcer activity and 196 genes were recurrent oral ulcer related genes. Of them, 76 genes up-regulated, 62 genes down-regulated, and 58 genes up-/down-regulated. Total expression level up-regulated 752 times (60%) and down-regulated 485 times (40%). IL-2 plays an important role in the occurrence, development and recurrence of recurrent oral ulcer on the mRNA and protein levels. Gene microarray can be used to analyze potential genes and pathways in recurrent oral ulcer. IL-2 may be involved in the pathogenesis of recurrent oral ulcer.