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.

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.

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 provide alternative delivery methods of novel oral antitumor drugs for patients with dysphagia.Methods The retrieval range was determined according to the"Guidelines for Clinical Application of Novel Antitumor Drugs(2022 edition)".By consulting the instructions and searching databases such as PubMed,Micromedex,UpToDate,etc.,the preparation,stability,storage,and related clinical information of temporary liquid drug formulations were obtained.Results Seventy novel oral anticancer drugs were included in the literature search.Thirty-three drugs had relevant literature or data supporting alternative administration methods,only eight had information on alternative administration in the instructions,and the level of evidence for other drugs varied.Conclusion The evidence levels were low for most temporary liquid formulations,and medical teams should fully consider the advantages and disadvantages of using these products outside the instructions and use them with caution.

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.

The incidence of early-onset gastric cancer (EOGC) is consistently increasing, and its etiology is notably complex. This increase may be attributed to distinctive factors that differ from those associated with late-onset gastric cancer (LOGC), including genetic predispositions, dietary factors, gastric microbiota dysbiosis, and screening of high-risk cases. These factors collectively contribute to the onset of cancer. EOGC significantly differs from LOGC in terms of clinicopathological and molecular characteristics. Moreover, multiple differences in prognosis and clinical management also exist. This study aimed to systematically review the latest research advancements in the epidemiological characteristics, etiological factors, clinicopathological and molecular features, prognosis, and treatment modalities of EOGC.