The patients with skeletal Class Ⅱ high-angle malocclusion are frequently complicated by idiopathic condylar resorption(ICR),which may lead to temporomandibular joint(TMJ)dysfunction and dentofacial deformities.This article reports the diagnosis and treatment process of a 24-year-old female patient with skeletal Class Ⅱ high-angle malocclusion accompanied by ICR.The patient's chief complaints were anterior open bite and TMJ pain,and was diagnosed with ICR through clinical examination and imaging.After stabilizing condylar resorption with occlusal splint therapy,combined orthodontic-orthognathic treatment was performed.The 42-month follow-up revealed:well-aligned dentition with complete closure of diastemas,significant improvement of protrusive facial profile(ANB angle reduced by 4.2°),complete resolution of TMJ pain and clicking,and establishment of stable Class Ⅰ occlusion.Three-dimensional CT demonstrated satisfactory condylar bone remodeling and normalized joint space.Through multidisciplinary treatment,both occlusal function and facial aesthetics were significantly improved.This case demonstrates that orthodontic-orthognathic treatment should be performed after condylar stabilization in ICR patients,and occlusal splint therapy serves as an effective preoperative intervention.

Objective:To establish the reference intervals (RIs) of systemic immune inflammatory index (SII), platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR) and monocyte to lymphocyte ratio (MLR) in healthy pregnant women in Henan province, China.Methods:A retrospective analysis was conducted on the data of the healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from August 2016 to February 2019. A total of 4 016 healthy pregnant women were selected for establishing RIs. Data from healthy adult control group were derived from the healthy adult cohort in Henan established earlier by our team, and the Propensity Score Matching analysis was used and 3 595 healthy adult women and 3 595 healthy pregnant women to compare the indicators between the two groups. The RIs of the above indicators were established using the indirect method with a 95% confidence interval. The Tukey Rule was used to identify and remove outliers. The RIs were stratified and grouped based on the differences in each indicator during the pregnancy: SII: 3 929 cases, including 712 in the first trimester, 1 947 in the second trimester, and 1, 270 in the third trimester; PLR: 3 927 cases, no grouping; NLR: 3 925 cases, including 712 in the first trimester and 3 213 in the second and third trimesters; LMR: 3 925 cases, including 723 in the first trimester, 1 942 in the second trimester, and 1 260 in the third trimester; MLR: 3 904 cases, including 721 in the first trimester, 1 928 in the second trimester, and 1 255 in the third trimester. After the RIs were established, another 396 healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from February to April 2019 were selected for the validation of the RIs.Results:SII, NLR, LMR, MLR, and PLR differ significantly between healthy adult women and healthy pregnant women. There were significant differences in SII, LMR, and MLR among the three trimesters ( P<0.05). NLR in the first trimester was significantly lower than that in the second and third trimesters ( P<0.05), while there was no significant difference between the second and third trimester ( P=0.124). PLR only showed significant differences between the second and third trimester ( P<0.05), while no significant differences were found among the other groups. Based on the above results, the stratified RIs of each index in healthy pregnant population were established and verified. SII: first trimester (341-1 426)×10 9/L, second trimester (437-1 680)×10 9/L, third trimester (379-1 580)×10 9/L; PLR: 73-215; NLR: first trimester 1.78-5.60, second and third trimester 2.21-6.74; LMR: first trimester 2.20-6.61, second trimester 1.85-5.42, third trimester 1.63-4.82; MLR: first trimester 0.14-0.42, second trimester 0.17-0.49, third trimester 0.18-0.55. The rejection rate of 396 cases was less than 10%. Conclusions:The RIs of SII, NLR, LMR, MLR and PLR for healthy pregnant women in Hernan province of China were established and validated, and4 could be used in clinical practice.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Pigeon circovirus(PiCV)is globally widespread and is considered a potential cause of young pigeon sickness syndrome(YPDS),which leads to severe immunosuppression and high mortality.Due to the inability of PiCV to be cultured in cells,the development of traditional vac-cines is severely limited,and no effective vaccines is currently available.To develop a novel PiCV DNA candidate vaccine,we cloned the △Cap gene lacking a nuclear localization signal(NLS),and fused it at its C-terminus with the transmembrane and cytoplasmic regions of the Newcastle dis-ease virus(NDV)F protein(△Cap-TMCT).Two DNA vaccine candidates were constructed:pCAGG-△Cap,targeting intracellular expression,and pCAGG-△Capt,for cell surface expression,respectively.The results of indirect immunofluorescence and Western blot analyses confirmed suc-cessful expression of both recombinant plasmids in DF1 cells.Immunization studies in mice re-vealed that pCAGG-△Capt induced significantly higher levels of specific IgG antibodies,T-cell re-sponses,and cytokine secretion compared to pCAGG-△Cap,as assessed by ELISA,flow cytome-try,and ELISpot assays.These findings suggest that targeting △Cap-TMCT fusion protein to the cell surface can effectively enhance its immunogenicity,highlighting its potential as a PiCV DNA vaccine candidate.This study provides new strategies and theoretical foundations for the design and development of PiCV DNA vaccines.

Pigeon circovirus(PiCV)is globally widespread and is considered a potential cause of young pigeon sickness syndrome(YPDS),which leads to severe immunosuppression and high mortality.Due to the inability of PiCV to be cultured in cells,the development of traditional vac-cines is severely limited,and no effective vaccines is currently available.To develop a novel PiCV DNA candidate vaccine,we cloned the △Cap gene lacking a nuclear localization signal(NLS),and fused it at its C-terminus with the transmembrane and cytoplasmic regions of the Newcastle dis-ease virus(NDV)F protein(△Cap-TMCT).Two DNA vaccine candidates were constructed:pCAGG-△Cap,targeting intracellular expression,and pCAGG-△Capt,for cell surface expression,respectively.The results of indirect immunofluorescence and Western blot analyses confirmed suc-cessful expression of both recombinant plasmids in DF1 cells.Immunization studies in mice re-vealed that pCAGG-△Capt induced significantly higher levels of specific IgG antibodies,T-cell re-sponses,and cytokine secretion compared to pCAGG-△Cap,as assessed by ELISA,flow cytome-try,and ELISpot assays.These findings suggest that targeting △Cap-TMCT fusion protein to the cell surface can effectively enhance its immunogenicity,highlighting its potential as a PiCV DNA vaccine candidate.This study provides new strategies and theoretical foundations for the design and development of PiCV DNA vaccines.

Objective:To establish the reference intervals (RIs) of systemic immune inflammatory index (SII), platelet to lymphocyte ratio (PLR), neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR) and monocyte to lymphocyte ratio (MLR) in healthy pregnant women in Henan province, China.Methods:A retrospective analysis was conducted on the data of the healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from August 2016 to February 2019. A total of 4 016 healthy pregnant women were selected for establishing RIs. Data from healthy adult control group were derived from the healthy adult cohort in Henan established earlier by our team, and the Propensity Score Matching analysis was used and 3 595 healthy adult women and 3 595 healthy pregnant women to compare the indicators between the two groups. The RIs of the above indicators were established using the indirect method with a 95% confidence interval. The Tukey Rule was used to identify and remove outliers. The RIs were stratified and grouped based on the differences in each indicator during the pregnancy: SII: 3 929 cases, including 712 in the first trimester, 1 947 in the second trimester, and 1, 270 in the third trimester; PLR: 3 927 cases, no grouping; NLR: 3 925 cases, including 712 in the first trimester and 3 213 in the second and third trimesters; LMR: 3 925 cases, including 723 in the first trimester, 1 942 in the second trimester, and 1 260 in the third trimester; MLR: 3 904 cases, including 721 in the first trimester, 1 928 in the second trimester, and 1 255 in the third trimester. After the RIs were established, another 396 healthy pregnant women without a history of adverse pregnancy events who participated in health check-ups from February to April 2019 were selected for the validation of the RIs.Results:SII, NLR, LMR, MLR, and PLR differ significantly between healthy adult women and healthy pregnant women. There were significant differences in SII, LMR, and MLR among the three trimesters ( P<0.05). NLR in the first trimester was significantly lower than that in the second and third trimesters ( P<0.05), while there was no significant difference between the second and third trimester ( P=0.124). PLR only showed significant differences between the second and third trimester ( P<0.05), while no significant differences were found among the other groups. Based on the above results, the stratified RIs of each index in healthy pregnant population were established and verified. SII: first trimester (341-1 426)×10 9/L, second trimester (437-1 680)×10 9/L, third trimester (379-1 580)×10 9/L; PLR: 73-215; NLR: first trimester 1.78-5.60, second and third trimester 2.21-6.74; LMR: first trimester 2.20-6.61, second trimester 1.85-5.42, third trimester 1.63-4.82; MLR: first trimester 0.14-0.42, second trimester 0.17-0.49, third trimester 0.18-0.55. The rejection rate of 396 cases was less than 10%. Conclusions:The RIs of SII, NLR, LMR, MLR and PLR for healthy pregnant women in Hernan province of China were established and validated, and4 could be used in clinical practice.

Objective:To develop an artificial intelligence (AI)-based platelet review strategy to identify abnormal platelet histograms with no significant difference between initial impedance platelet count (PLT-I) and PLT-F results.Methods:This study included 5 119 routine blood analysis in Nanfang Hospital of Southern Medical University and its Ganzhou branch from July 2023 and March 2024. Specimens exhibiting abnormal platelet histograms and an initial platelet count >40×10?/L underwent review using the fluorescent platelet count (PLT-F) channel. Consistency of the results was defined as a difference between impedance platelet count (PLT-I) and PLT-F less than ±20% of the PLT-F results. A deep learning model was developed using platelet and red blood cell histogram data from a training set of 3 807 specimens. The model′s diagnostic performance was evaluated on an independent external validation set ( n=805) using receiver operating characteristic (ROC) curve analysis. Changes in the number of reviewed samples and sample turnaround time were analyzed to assess its clinical utility. Results:The deep learning model based on platelet and red blood cell histograms achieved an area under the ROC curve (AUC) of 0.854 in the training set. At a cutoff value of 0.1, the sensitivity was 0.954 and specificity was 0.358. The model could reduce review by 16.80% (190/1 131). In the validation set, the AUC was 0.805, with a sensitivity of 0.955 and specificity of 0.307, corresponding to a reduction of 17.41% (47/270) in reviewed specimens.Conclusion:The platelet review prediction model developed based on deep learning technology can efficiently identify samples with consistent results before and after review, reducing unnecessary reviews and shortening specimen testing time, thereby improving the efficiency of platelet test.

Objective: To explore the effect of different miniscrew placement heights on the distribution of biological forces produced by clear aligner combined with intramaxillary traction for mandibular molar distalization, to identify the miniscrew location that is conducive to the protection of lower anterior tooth anchorage and to provide a reference that can be used when designing clinical treatments. Methods: Mimics, GeomagicStudio 2017, SolidWorks 2016, and Ansys workbench were used to establish finite element analysis models and perform mechanical analysis under the following six working conditions: working condition 1 was the control group without miniscrews; working conditions 2 to 5 had miniscrew in the buccal bone cortex between the first and second molars of the lower jaw 10 mm, 7 mm, 4 mm, and 1 mm from the top of the alveolar crest, respectively; working condition 6 had the miniscrew in the center of the buccal tongue at the anterior edge of the ascending branch of the lower jaw 5 mm above the occlusal plane. Results: On the sagittal axis, miniscrew anchorage caused distal displacement of all teeth. Compared to the control group, in the miniscrew group, the displacement of the anterior molars exceeded that of the second molars. On the vertical axis, the result in the control group was similar to backward bending; the results in the miniscrew groups resembled the effect of a lever, lowering the lateral incisors and canines and raising the central incisors and first premolars. On the coronal axis, the second premolars and the first molars showed lingual displacement in the control group, and only the premolars and first molars showed lingual displacementin the miniscrew groups. The canines were the teeth that were most strongly affected by the change in miniscrew placement height. Conclusion The higher the miniscrew position is, the stronger the protective effect on the anterior anchorage. According to the miniscrew placement height, the mandibular arch should be properly narrowed, the central incisors and first premolars should be lowered, and the lateral incisors and canines should be raised when designing clinical treatments.

Objective To observe the value of preoperative CT radiomics models for predicting composition of in vivo urinary calculi.Methods Totally 543 urolithiasis patients were retrospectively enrolled and divided into calcium oxalate monohydrate stone group(group A,n=373),anhydrous uric acid stone group(group B,n=86),carbonate apatite group(group C,n=30),ammonium urate stone group(group D,n=28)and ammonium magnesium phosphate hexahydrate stone group(group E,n=26)according to the composition of calculi,also divided into training set and test set at the ratio of 7:3.Radiomics features were extracted and screened based on plain CT images of urinary system.Five binary task models(model A-E corresponding to group A-E)and a quinary task model were constructed using least absolute shrinkage and selection operator algorithm for predicting the composition of calculi in vivo.Then receiver operating characteristic curves were drawn,and the area under the curves(AUC)were calculated to evaluate the predictive efficacy of binary task models,while the accuracy,precision,recall and F1 score were used to evaluate the predictive efficacy of the quinary task model.Results All binary task models had good efficacy for predicting the composition of urinary calculi in vivo,with AUC of 0.860-0.948 in training set and of 0.856-0.933 in test set.The accuracy,precision,recall and F1 score of the quinary task model for predicting the composition of in vivo urinary calculi was 82.25％,83.79％,46.23％and 0.596 in training set,respectively,while was 80.63％,75.26％,43.48％and 0.551 in test set,respectively.Conclusion Binary task radiomics models based on preoperative plain CT had good efficacy for predicting the composition of in vivo urinary calculi,while the quinary task radiomics model had high accuracy but relatively poor stability.

Objective To explore the effect of CT slice thickness on the reproducibility of radiomic features in urinary stones.Methods A retrospective analysis was performed on 30 cases of stones diagnosed by urinary CT.Thin-slice(1 mm)images were reconstructed into two groups of images with thicknesses of 2 mm and 5 mm.Two radiologists conducted manual segmentation independently and then extracted features.The reproducibility of the radiomic features was calculated using the concordance correlation coefficient(CCC).Results Under different slice thickness conditions by the same radiologist,the average CCC of all groups was less than 0.85(P＜0.05).For radiologist A,the proportion of stone features with CCC＜0.85 were 50.13％,79.91％,and 82.38％in the 1 mm vs 2 mm group,2 mm vs 5 mm group,and 1 mm vs 5 mm group,respectively.Corresponding values for radiologist B were 44.55％,79.47％and 82.32％,respectively.Among the seven categories of radiomic features,the morphological features with CCC＜0.85 was 100％in the 1 mm vs 5 mm group.Conclusion CT slice thickness significantly affected the reproducibility of radiomic features in urinary stones,with the greatest effect observed in morphological features.Thin-slice imaging demonstrated more stable reproducibility of stone characteristics.