Objective To assess the clinical utility of single-molecule real-time technology(SMRT)in identifying triplicated α-globin genes and compound variant alleles.Methods A total of 36 samples with tripli-cated α-globin genes were collected.Among them,28 samples were confirmed by PCR flow-through hybridiza-tion and 8 samples were confirmed by Next Generation Sequencing(NGS).These 36 samples included tripli-cated α-globin genes compound variants with cis or trans arrangements unknown,such as αααanti4 2 compoundαcsα(2 cases),αααanti4.2 compound-α3.7(10 cases),and HKαα/--SEA pending confirmation(2 cases),SMRT technology was employed to detect thalassemia gene variants.Additionally,a pedigree with the genotype ofαααanti4.2 compound-α3.7 variant was recruited,including the proband(Ⅱ-1),its father(Ⅰ-1),and mother(Ⅰ-2).PCR flow-through hybridization and SMRT were employed to detect thalassemia gene variants.Results SMRT detected 35 out of 36 samples with triplicated α-globin genes,and 1 sample with quadrupllcated α-globin genes(ααααanti4.2).Among the 2 αααanti4 2 compound αCSα variant samples,both αααanti42 and αCSα were arranged in trans,with a genotype of αααanti4.2/αCSα.Among the 10 αααanti4.2 compound-α3.7 variant samples,9 samples hadαααanti4.2 and-α3.7 in a cis arrangement,with a genotype of HKαα/αα,and 1 sample had αααannti4.2 and-α3.7 in a trans arrangement,with a genotype of αααanti4.2/-α3.7.Compared with PCR flow-through hybridization,SMRT detected one case of a large segment deletion in the β-globin gene and two unknown variants,which led to an increase in the positive detection rate of approximately 10.71％(3/28).The pedigree analysis showed that the proband(Ⅱ-1)inherited αααanti4.2 and-α3.7 variants from his mother(Ⅰ-2),with a genotype of HKαα/αα,con-sistent with the SMRT detection results.Conclusion SMRT can accurately detect triplicated or quadrupllcat-ed α-globin genes,and compound variant alleles.It offers high accuracy,enables one-step identification of cis or trans arrangements,and provides comprehensive coverage of thalassemia gene variations,demonstrating its significant clinical value.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To develop and evaluate a deep learning-based automatic measurement model for panoramic ultrasound biomicroscopy (UBM) images.Methods:A diagnostic test study was conducted.Preoperative UBM examination results of 372 patients who underwent implantable collamer lens (ICL) implantation were collected at the Eye Center of Renmin Hospital of Wuhan University between February 2021 and March 2023.A total of 1 368 panoramic UBM images were obtained to establish an image database.The dataset was divided into a training set (760 images), a validation set (86 images) and an internal test set (522 images).An expert panel consisting of three ophthalmologists annotated the images.The UNet+ + network was used to automatically segment anterior segment tissues, such as the cornea, lens and iris.In addition, image processing techniques and geometric localization algorithms were developed to automatically identify the anatomical landmarks of pupil diameter (PD), anterior chamber depth (ACD), angle-to-angle distance (ATA) and sulcus-to-sulcus distance (STS) to complete the measurement of these parameters.Additionally, 480 panoramic UBM images of 135 patients (240 eyes) from Aier Eye Hospital of Wuhan University were used as an external test set to further evaluate the performance of the model in different centers.The consistency between the measurements from the model and expert panel, the Pentacam system was assessed.Finally, 150 images were randomly selected from the external test set for a human-machine comparison to further evaluate the model's performance.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109) and Aier eye Hospital of Wuhan University (No.2023IRBKY120903).Written informed consent was obtained from each subject.Results:In the internal test dataset and external test dataset, with manual labeling as the reference standard, the model achieved a mean Dice coefficient of not less than 0.882.At least 95.65% of the anatomical landmark localization results had Euclidean distance differences within 250 μm.The intraclass correlation coefficients (ICCs) for the measurements of PD, ACD, angle-to-angle ATA, and STS were at least 0.958, with mean relative errors not exceeding 2.407%.With the Pentacam measurements as the reference standard, the ICCs for PD in the internal and external test sets were 0.540 and 0.466, respectively, while the ICCs for ACD were 0.946 and 0.908, respectively.In the human-machine comparison, the ICCs between the model's measurements and those of senior experts were all not lower than 0.969.Conclusions:The deep learning-based model can automatically measure anterior segment parameters from preoperative panoramic UBM images of patients undergoing ICL surgery.The model demonstrates a consistency comparable to that of senior experts, while providing higher efficiency.In terms of ACD measurement, the model shows good agreement between the measurements obtained from the model and Pentacam system.

Objective:To explore the clinical application value of a deep learning algorithm-based ultrasound biomicroscopy (UBM) image analysis system for primary angles-closure glaucoma (PACG) anterior located ciliary body.Methods:A diagnostic test study was conducted.A total of 2 132 UBM images from 726 eyes of 378 PACG patients who underwent UBM examination were collected at Renmin Hospital of Wuhan University from August 2022 to December 2023.The dataset was divided into a training set of 1 599 images and a test set of 533 images, and a deep learning algorithm was employed to construct a model.An additional 334 UBM images from 101 eyes of 69 PACG patients treated at Huangshi Aier Eye Hospital were selected to conduct external testing.A separate set of another 110 UBM images were selected for a human-machine competition to compare the accuracy and speed between anterior located ciliary body evaluation system and three senior ophthalmologists.Furthermore, eight junior ophthalmologists assessed the 110 UBM images independently without and with the assistance of the model, and the differences between the two evaluations were analyzed to assess the assisstance effect of the model.This study adhered to the Declaration of Helsinki.The study protocol was approved by the Ethics Committee of Renmin Hospital of Wuhan University (No.WDRY-2022-K109).Results:The model achieved an accuracy of 93.43% for anterior located ciliary body identification in the internal test set, with a sensitivity of 84.30% and a specificity of 97.78%.The model also performed well on the external test set with an accuracy of 92.81%.In the human-machine competition, the model's accuracy was comparable to that of the senior ophthalmologists and outperformed two of the three senior ophthalmologists.The average total time of the three senior ophthalmologists was 726.73 seconds, approximately 12.47 times longer than the model's 58.30 seconds.With model assistance, the diagnostic accuracy of the eight junior ophthalmologists was 86.71%, which was significantly higher than 76.25% without model assistance ( χ2=-7.550, P<0.001).And the image evaluation time was (714.91±213.82)seconds, which was significantly lower than (987.90±238.56)seconds without model assistance ( t=2.774, P<0.05). Conclusions:The UBM image analysis system based on a deep learning algorithm demonstrates high accuracy in diagnosing anterior located ciliary body in PACG and provides a strong support for the UBM image recognition training of junior ophthalmologists.

ObjectiveTo investigate the efficacy and safety of cinobufagin tablets combined with thalidomide/dexamethasone （TD） regimen in the treatment of newly diagnosed multiple myeloma （NDMM） with phlegm and stasis obstruction. MethodsThe clinical data of 50 patients with NDMM of phlegm and stasis obstruction who were hospitalized at the Jiangsu Province Hospital of Chinese Medicine from June 1st， 2015 to July 31th， 2019 were retrospectively analyzed， and they were divided into a control group （bortezomib/dexamethasone-containing regimen， 27 cases） and an observation group （cinobufagin tablets combined with TD regimen， 23 cases）. The clinical efficacy and safety were compared between the two groups after two or three courses of treatment. The primary outcomes were clinical remission rate including overall response rate and deep remission rate， one-year and two-year overall survival rate， and adverse effects. The secondary outcomes were the proportion of plasma cells in bone marrow， hemoglobin， β2-microglobulin， lactate dehydrogenase， serum creatinine， blood urea nitrogen， bone pain score， and KPS functional status score （KPS score） before and after treatment. ResultsIn terms of clinical efficacy， there was no statistically significant difference （P＞0.05） in the overall response rate ［the observation group 69.57%（16/23） vs the control group 70.37% （19/27）］ and deep remission rate ［the observation group 56.52% （13/23） vs the control group 55.56% （15/27）］ between groups after the treatment. The one-year overall survival rates of the observation group and the control group were 90.9% and 92.4%， and the two-year overall survival rates were 81.8% and 80.9% respectively， with no statistically significant differences between groups （P＞0.05）. During the treatment， no renal function injury occurred in both groups. The incidence of peripheral nerve injury in the observation group was 8.70%， which was lower than 48.15% in the control group （P＜0.01）. After the treatment， the proportion of myeloma plasma cells， β2-microglobulin， serum creatinine level， and bone pain score decreased， while the hemoglobin level and KPS score increased in both groups （P＜0.05 or P＜0.01）. Compared between groups after treatment， the bone pain score of the observation group was lower than that of the control group， while the KPS score was higher than that of the control group （P＜0.05）. ConclusionThe clinical efficacy of cinobufagin tablets combined with TD in the treatment of NDMM is equivalent to bortezomib/dexamethasone-containing regimen， but the former is more helpful in relieving the pain and improving the quality of life， and has better safety.

Objective:To investigate the tumor-specific neoantigen for primary plasma cell leukemia (PCL) using gene sequencing technology combined with bioinformatic analysis. Methods: Peripheral blood samples of one patient with primary PCL during relapse and remission periods were collected. HLA molecular typing was performed using polymerase chain reaction with sequencing-based typing; whole-exome and transcriptome were sequenced by next-generation sequencing method; and bioinformatics software NetMHCpan was used to predict neoantigens. Results: Six tumor-specific missense mutations were found in the patient's peripheral blood during relapse period, located in genes FRG1, MLL3, SVIL, MYOM1, ZDHHC11 and RFPL4A.Considering patient's HLA sub-types, 43 neoantigens were predicted via bioinformatics. Considering that FRG1 and MLL3 had relatively high gene expression levels, 20 neoantigens derived from mutations of the two genes were preferentially selected, among which four neoantigens had high affinity with the patient's HLA molecules and thus had potential clinical application value. Conclusion: The study has completed a tumor neoantigen screen and prediction for primary PCL. This practice demonstrates that predicting neoantigen based on tumor-specific somatic mutation is feasible for primary PCL.