Bibliometric analysis of hotspots and trends of global myopia research.

AIM: To gain insights into the global research hotspots and trends of myopia. METHODS: Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software. RESULTS: A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers. CONCLUSION: Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

Calibrate the Inter-observer Segmentation Uncertainty via Diagnosis-first Principle.

Many of the tissues/lesions in the medical images may be ambiguous. Therefore, medical segmentation is typically annotated by a group of clinical experts to mitigate personal bias. A common solution to fuse different annotations is the majority vote, e.g., taking the average of multiple labels. However, such a strategy ignores the difference between the grader expertness. Inspired by the observation that medical image segmentation is usually used to assist the disease diagnosis in clinical practice, we propose the diagnosis-first principle, which is to take disease diagnosis as the criterion to calibrate the inter-observer segmentation uncertainty. Following this idea, a framework named Diagnosis-First segmentation Framework (DiFF) is proposed. Specifically, DiFF will first learn to fuse the multi-rater segmentation labels to a single ground-truth which could maximize the disease diagnosis performance. We dubbed the fused ground-truth as Diagnosis-First Ground-truth (DF-GT). Then, the Take and Give Model (T&G Model) to segment DF-GT from the raw image is proposed. With the T&G Model, DiFF can learn the segmentation with the calibrated uncertainty that facilitate the disease diagnosis. We verify the effectiveness of DiFF on three different medical segmentation tasks: optic-disc/optic-cup (OD/OC) segmentation on fundus images, thyroid nodule segmentation on ultrasound images, and skin lesion segmentation on dermoscopic images. Experimental results show that the proposed DiFF can effectively calibrate the segmentation uncertainty, and thus significantly facilitate the corresponding disease diagnosis, which outperforms previous state-of-the-art multi-rater learning methods.

Open Fundus Photograph Dataset with Pathologic Myopia Recognition and Anatomical Structure Annotation.

Pathologic myopia (PM) is a common blinding retinal degeneration suffered by highly myopic population. Early screening of this condition can reduce the damage caused by the associated fundus lesions and therefore prevent vision loss. Automated diagnostic tools based on artificial intelligence methods can benefit this process by aiding clinicians to identify disease signs or to screen mass populations using color fundus photographs as inputs. This paper provides insights about PALM, our open fundus imaging dataset for pathological myopia recognition and anatomical structure annotation. Our databases comprises 1200 images with associated labels for the pathologic myopia category and manual annotations of the optic disc, the position of the fovea and delineations of lesions such as patchy retinal atrophy (including peripapillary atrophy) and retinal detachment. In addition, this paper elaborates on other details such as the labeling process used to construct the database, the quality and characteristics of the samples and provides other relevant usage notes.

Predicting Implantable Collamer Lens Vault Using Machine Learning Based on Various Preoperative Biometric Factors.

Purpose: To predict the vault size after Implantable Collamer Lens (ICL) V4c implantation using machine learning methods and to compare the predicted vault with the conventional manufacturer's nomogram. Methods: This study included 707 patients (707 eyes) who underwent ICL V4c implantation at the Department of Ophthalmology, Peking Union Medical College Hospital, from September 2019 to January 2022. Random Forest Regression (RFR), XGBoost, and linear regression (LR) were used to predict the vault size 1 week after ICL V4c implantation. The mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and Bland-Altman plot were utilized to compare the prediction performance of these machine learning methods. Results: The dataset was divided into a training set of 180 patients (180 eyes) and a test set of 527 patients (527 eyes). XGBoost had the lowest prediction error, with mean MAE, RMSE, and SMAPE values of 121.70 µm, 148.87 µm, and 19.13%, respectively. The Bland‒Altman plots of RFR and XGBoost showed better prediction consistency than LR. However, XGBoost showed narrower 95% limits of agreement (LoA) than RFR, ranging from -307.12 to 256.59 µm. Conclusions: XGBoost demonstrated better predictive performance than RFR and LR, as it had the lowest prediction error and the narrowest 95% LoA. Machine learning may be applicable for vault prediction, and it might be helpful for reducing the complications and the secondary surgery rate. Translational Relevance: Using the proposed machine learning model, surgeons can consider the postoperative vault to reduce the surgical complications.

GAMMA challenge: Glaucoma grAding from Multi-Modality imAges.

Glaucoma is a chronic neuro-degenerative condition that is one of the world's leading causes of irreversible but preventable blindness. The blindness is generally caused by the lack of timely detection and treatment. Early screening is thus essential for early treatment to preserve vision and maintain life quality. Colour fundus photography and Optical Coherence Tomography (OCT) are the two most cost-effective tools for glaucoma screening. Both imaging modalities have prominent biomarkers to indicate glaucoma suspects, such as the vertical cup-to-disc ratio (vCDR) on fundus images and retinal nerve fiber layer (RNFL) thickness on OCT volume. In clinical practice, it is often recommended to take both of the screenings for a more accurate and reliable diagnosis. However, although numerous algorithms are proposed based on fundus images or OCT volumes for the automated glaucoma detection, there are few methods that leverage both of the modalities to achieve the target. To fulfil the research gap, we set up the Glaucoma grAding from Multi-Modality imAges (GAMMA) Challenge to encourage the development of fundus & OCT-based glaucoma grading. The primary task of the challenge is to grade glaucoma from both the 2D fundus images and 3D OCT scanning volumes. As part of GAMMA, we have publicly released a glaucoma annotated dataset with both 2D fundus colour photography and 3D OCT volumes, which is the first multi-modality dataset for machine learning based glaucoma grading. In addition, an evaluation framework is also established to evaluate the performance of the submitted methods. During the challenge, 1272 results were submitted, and finally, ten best performing teams were selected for the final stage. We analyse their results and summarize their methods in the paper. Since all the teams submitted their source code in the challenge, we conducted a detailed ablation study to verify the effectiveness of the particular modules proposed. Finally, we identify the proposed techniques and strategies that could be of practical value for the clinical diagnosis of glaucoma. As the first in-depth study of fundus & OCT multi-modality glaucoma grading, we believe the GAMMA Challenge will serve as an essential guideline and benchmark for future research.

Advances in biological functions and mechanisms of histone variants in plants.

Nucleosome is the basic subunit of chromatin, consisting of approximately 147bp DNA wrapped around a histone octamer, containing two copies of H2A, H2B, H3 and H4. A linker histone H1 can bind nucleosomes through its conserved GH1 domain, which may promote chromatin folding into higher-order structures. Therefore, the complexity of histones act importantly for specifying chromatin and gene activities. Histone variants, encoded by separate genes and characterized by only a few amino acids differences, can affect nucleosome packaging and stability, and then modify the chromatin properties. Serving as carriers of pivotal genetic and epigenetic information, histone variants have profound significance in regulating plant growth and development, response to both biotic and abiotic stresses. At present, the biological functions of histone variants in plant have become a research hotspot. Here, we summarize recent researches on the biological functions, molecular chaperons and regulatory mechanisms of histone variants in plant, and propose some novel research directions for further study of plant histone variants research field. Our study will provide some enlightens for studying and understanding the epigenetic regulation and chromatin specialization mediated by histone variant in plant.

Transcriptomic analysis reveals the functions of H2S as a gasotransmitter independently of Cys in Arabidopsis.

Numerous studies have revealed the gasotransmitter functions of hydrogen sulfide (H2S) in various biological processes. However, the involvement of H2S in sulfur metabolism and/or Cys synthesis makes its role as a signaling molecule ambiguous. The generation of endogenous H2S in plants is closely related to the metabolism of Cys, which play roles in a variety of signaling pathway occurring in various cellular processes. Here, we found that exogenous H2S fumigation and Cys treatment modulated the production rate and content of endogenous H2S and Cys to various degrees. Furthermore, we provided comprehensive transcriptomic analysis to support the gasotransmitter role of H2S besides as a substrate for Cys synthesis. Comparison of the differentially expressed genes (DEGs) between H2S and Cys treated seedlings indicated that H2S fumigation and Cys treatment caused different influences on gene profiles during seedlings development. A total of 261 genes were identified to respond to H2S fumigation, among which 72 genes were co-regulated by Cys treatment. GO and KEGG enrichment analysis of the 189 genes, H2S but not Cys regulated DEGs, indicated that these genes mainly involved in plant hormone signal transduction, plant-pathogen interaction, phenylpropanoid biosynthesis, and MAPK signaling pathway. Most of these genes encoded proteins having DNA binding and transcription factor activities that play roles in a variety of plant developmental and environmental responses. Many stress-responsive genes and some Ca2+ signal associated genes were also included. Consequently, H2S regulated gene expression through its role as a gasotransmitter, rather than just as a substrate for Cys biogenesis, and these 189 genes were far more likely to function in H2S signal transduction independently of Cys. Our data will provide insights for revealing and enriching H2S signaling networks.

Lens structure segmentation from AS-OCT images via shape-based learning.

BACKGROUND AND OBJECTIVES: The lens is one of the important refractive media in the eyeball. Abnormality of the nucleus or cortex in the lens can lead to ocular disorders such as cataracts and presbyopia. To achieve an accurate diagnosis, segmentation of these ocular structures from anterior segment optical coherence tomography (AS-OCT) is essential. However, weak-contrast boundaries of the object in the images present a challenge for accurate segmentation. The state-of-the-art (SOTA) methods, such as U-Net, treat segmentation as a binary classification of pixels, which cannot handle pixels on weak-contrast boundaries well. METHODS: In this paper, we propose to incorporate shape prior into a deep learning framework for accurate nucleus and cortex segmentation. Specifically, we propose to learn a level set function, whose zero-level set represents the object boundary, through a convolutional neural network. Moreover, we design a novel shape-based loss function, where the shape prior knowledge can be naturally embedded into the learning procedure, leading to improvement in performance. We collect a high-quality AS-OCT image dataset with precise annotations to train our model. RESULTS: Abundant experiments are conducted to verify the effectiveness of the proposed framework and the novel shape-based loss. The mean Intersection over Unions (MIoUs) of the proposed method for lens nucleus and cortex segmentation are 0.946 and 0.957, and the mean Euclidean Distance (MED) measure, which can reflect the accuracy of the segmentation boundary, are 6.746 and 2.045 pixels. In addition, the proposed shape-based loss improves the SOTA models on the nucleus and cortex segmentation tasks by an average of 0.0156 and 0.0078 in the MIoU metric and 1.394 and 0.134 pixels in the MED metric. CONCLUSION: We transform the segmentation from a classification task to a regression task by making the model learn the level set function, and embed shape information in deep learning by designing loss functions. This allows the proposed method to be more efficient in the segmentation of the object with weak-contrast boundaries. CONCISE ABSTRACT: We propose to incorporate shape priors into a deep learning framework for accurate nucleus and cortex segmentation from AS-OCT images. Specifically, we propose to learn a level set function, where the zero-level set represents the boundary of the target. Meanwhile, we design a novel shape-based loss function in which additional convex shape prior can be embedded in the learning process, leading to an improvement in performance. The IOUs for nucleus and cortex segmentation are 0.946 and 0.957, while the MED that reflects the accuracy of the boundary are 6.746 and 2.045 pixels. The proposed shape-based loss improves the SOTA model for nucleus and cortex segmentation by an average of 0.0156 and 0.0078 in IOU, and 1.394 and 0.134 pixels in MED. We transform segmentation from classification to regression by making the model learn a level set function, resulting in improved performance at the boundary with weak contrast.

Gasotransmitter H2S accelerates seed germination via activating AOX mediated cyanide-resistant respiration pathway.

Hydrogen sulfide (H2S) has been witnessed as a crucial gasotransmitter involving in various physiological processes in plants. H2S signaling has been reported to involve in regulating seed germination, but the underlying mechanism remains poorly understood. Here, we found that endogenous H2S production was activated in germinating Arabidopsis seeds, correlating with upregulated both the transcription and the activity of L-cysteine desulfhydrase (EC 4.4.1.28, LCD and DES1) responsible for H2S production. Moreover, seed germination could be significantly accelerated by exogenous NaHS (the H2S donor) fumigation and over-expressing DES1, while H2S-generation defective (lcd/des1) seeds exhibited decreased germination speed. We also confirmed that the alternative oxidase (AOX), a cyanide-insensitive terminal oxidase, can be stimulated by imbibition. Furthermore, exogenous H2S fumigation and over-expressing DES1 could significantly reinforced imbibition induced increase of both the AOX1A expression and AOX protein abundance, while this increase could be obviously weakened in lcd/des1. Additionally, exogenous H2S fumigation mediated post-translational modification to keep AOX in its reduced and active state, which might involve H2S induced improvement of the reduced GSH content and the cell reducing power. The promotive effect of H2S on germination was clearly impaired by inducing aox1a mutation, indicating that AOX acts downstream of H2S signaling to accelerate seed germination. Consequently, H2S signaling was activated during germination then acted as a trigger to induce AOX mediated cyanide-resistant respiration to accelerate seed germination. Our study correlates H2S signaling to cyanide-resistant respiration, providing evidence for more extensive studies of H2S signaling.

An atlas of plant full-length RNA reveals tissue-specific and monocots-dicots conserved regulation of poly(A) tail length.

Poly(A) tail is a hallmark of eukaryotic messenger RNA and its length plays an essential role in regulating mRNA metabolism. However, a comprehensive resource for plant poly(A) tail length has yet to be established. Here, we applied a poly(A)-enrichment-free, nanopore-based method to profile full-length RNA with poly(A) tail information in plants. Our atlas contains over 120 million polyadenylated mRNA molecules from seven different tissues of Arabidopsis, as well as the shoot tissue of maize, soybean and rice. In most tissues, the size of plant poly(A) tails shows peaks at approximately 20 and 45 nucleotides, while the poly(A) tails in pollen exhibit a distinct pattern with strong peaks centred at 55 and 80 nucleotides. Moreover, poly(A) tail length is regulated in a gene-specific manner-mRNAs with short half-lives in general have long poly(A) tails, while mRNAs with long half-lives are featured with relatively short poly(A) tails that peak at ~45 nucleotides. Across species, poly(A) tails in the nucleus are almost twice as long as in the cytoplasm. Our comprehensive dataset lays the groundwork for future functional and evolutionary studies on poly(A) tail length regulation in plants.

A Multicenter Clinical Study of the Automated Fundus Screening Algorithm.

Purpose: To evaluate the effectiveness of automated fundus screening software in detecting eye diseases by comparing the reported results against those given by human experts. Results: There were 1585 subjects who completed the procedure and yielded qualified images. The prevalence of referable diabetic retinopathy (RDR), glaucoma suspect (GCS), and referable macular diseases (RMD) were 20.4%, 23.2%, and 49.0%, respectively. The overall sensitivity values for RDR, GCS, and RMD diagnosis are 0.948 (95% confidence interval [CI], 0.918-0.967), 0.891 (95% CI, 0.855-0.919), and 0.901 (95% CI-0.878, 0.920), respectively. The overall specificity values for RDR, GCS, and RMD diagnosis are 0.954 (95% CI, 0.915-0.965), 0.993 (95% CI-0.986, 0.996), and 0.955 (95% CI-0.939, 0.968), respectively. Methods: We prospectively enrolled 1743 subjects at seven hospitals throughout China. At each hospital, an operator records the subjects' information, takes fundus images, and submits the images to the Image Reading Center of Zhongshan Ophthalmic Center, Sun Yat-Sen University (IRC). The IRC grades the images according to the study protocol. Meanwhile, these images will also be automatically screened by the artificial intelligence algorithm. Then, the analysis results of automated screening algorithm are compared against the grading results of IRC. The end point goals are lower bounds of 95% CI of sensitivity values that are greater than 0.85 for all three target diseases, and lower bounds of 95% CI of specificity values that are greater than 0.90 for RDR and 0.85 for GCS and RMD. Conclusions: Automated fundus screening software demonstrated a high sensitivity and specificity in detecting RDR, GCS, and RMD from color fundus imaged captured using various cameras. Translational Relevance: These findings suggest that automated software can improve the screening effectiveness for eye diseases, especially in a primary care context, where experienced ophthalmologists are scarce.

ADAM Challenge: Detecting Age-Related Macular Degeneration From Fundus Images.

Age-related macular degeneration (AMD) is the leading cause of visual impairment among elderly in the world. Early detection of AMD is of great importance, as the vision loss caused by this disease is irreversible and permanent. Color fundus photography is the most cost-effective imaging modality to screen for retinal disorders. Cutting edge deep learning based algorithms have been recently developed for automatically detecting AMD from fundus images. However, there are still lack of a comprehensive annotated dataset and standard evaluation benchmarks. To deal with this issue, we set up the Automatic Detection challenge on Age-related Macular degeneration (ADAM), which was held as a satellite event of the ISBI 2020 conference. The ADAM challenge consisted of four tasks which cover the main aspects of detecting and characterizing AMD from fundus images, including detection of AMD, detection and segmentation of optic disc, localization of fovea, and detection and segmentation of lesions. As part of the ADAM challenge, we have released a comprehensive dataset of 1200 fundus images with AMD diagnostic labels, pixel-wise segmentation masks for both optic disc and AMD-related lesions (drusen, exudates, hemorrhages and scars, among others), as well as the coordinates corresponding to the location of the macular fovea. A uniform evaluation framework has been built to make a fair comparison of different models using this dataset. During the ADAM challenge, 610 results were submitted for online evaluation, with 11 teams finally participating in the onsite challenge. This paper introduces the challenge, the dataset and the evaluation methods, as well as summarizes the participating methods and analyzes their results for each task. In particular, we observed that the ensembling strategy and the incorporation of clinical domain knowledge were the key to improve the performance of the deep learning models.

Reprogramming of Histone H3 Lysine Methylation During Plant Sexual Reproduction.

Plants undergo extensive reprogramming of chromatin status during sexual reproduction, a process vital to cell specification and pluri- or totipotency establishment. As a crucial way to regulate chromatin organization and transcriptional activity, histone modification can be reprogrammed during sporogenesis, gametogenesis, and embryogenesis in flowering plants. In this review, we first introduce enzymes required for writing, recognizing, and removing methylation marks on lysine residues in histone H3 tails, and describe their differential expression patterns in reproductive tissues, then we summarize their functions in the reprogramming of H3 lysine methylation and the corresponding chromatin re-organization during sexual reproduction in Arabidopsis, and finally we discuss the molecular significance of histone reprogramming in maintaining the pluri- or totipotency of gametes and the zygote, and in establishing novel cell fates throughout the plant life cycle. Despite rapid achievements in understanding the molecular mechanism and function of the reprogramming of chromatin status in plant development, the research in this area still remains a challenge. Technological breakthroughs in cell-specific epigenomic profiling in the future will ultimately provide a solution for this challenge.

The association of adjusted plasma valproic acid concentration with CYP2C9 gene polymorphism in patients with epilepsy: a systematic review and meta-analysis.

BACKGROUND: Valproic acid (VPA) is a common antiepileptic drug used to treat both generalized and partial epilepsy. Although there is increasing evidence to suggest that CYP2C9 gene polymorphisms are associated with interindividual variability of VPA metabolism, the results are debatable. Therefore, in the present study, we conducted a meta-analysis to evaluate the correlation between CYP2C9 gene polymorphisms and adjusted plasma VPA concentration. METHODS: The EMBASE, MEDLINE, and Cochrane Library databases were searched to obtain relevant studies. Eligible articles were reviewed, and data extraction was performed. We calculated 95% confidence intervals (CIs) and mean differences (MDs) to assess the strength of the relationship of CYP2C9 gene polymorphisms with adjusted plasma VPA concentration. RESULTS: The meta-analysis included 6 studies involving 847 patients with epilepsy. The pooled analysis showed that the CYP2C9 A1075C (AA vs. AC) polymorphism was related to the adjusted plasma concentration of VPA (P=0.02, I2= 82%). Additionally, the AC phenotype statistically significantly increased the adjusted plasma VPA concentration in children compared with the mixed age subgroup (P=0.04, I2= 48%). A similar association was observed between the AC phenotype for Asians (P<0.00001, I2=0%) but not for Caucasians (P=0.34, I2=87%). DISCUSSION: Age might be a crucial covariate influencing the dosage-adjusted VPA concentration in patients with epilepsy. A reduced VPA dosage may be recommendable for children, particularly Asian children, who are CYP2C9 A1075C AC carriers. Further studies could provide high-quality evidence to confirm the correlation between VPA pharmacokinetics and CYP2C9 A1075C polymorphisms.

Stenosis-DetNet: Sequence consistency-based stenosis detection for X-ray coronary angiography.

BACKGROUND: The automatic detection of coronary artery stenosis on X-ray images is important in coronary heart disease diagnosis. Conventional methods cannot accurately detect all stenosis areas because of heartbeat, respiratory movements and weak vascular features in single-frame contrast images. METHOD: This paper proposes the use of Stenosis-DetNet, which is a method based on object detection networks. A sequence feature fusion module and a sequence consistency alignment module are designed to maximize temporal information to achieve accurate detection results. The sequence feature fusion module fuses all candidate box features and uses the temporal information to enhance these features. The sequence consistency alignment module optimizes the initial results by using the coronary artery displacement information and image features of the adjacent images and leads to the final detection of coronary artery stenosis. RESULTS: In the experiment, 166 X-ray image sequences were used for training and testing. Compared with the three existing stenosis detection methods, the precision and sensitivity of Stensis-DetNet were 94.87 % and 82.22 %, respectively, which were better than those of the other three methods. CONCLUSION: Our proposed method effectively suppressed the false positive and false negative results of stenosis detection in sequence angiography images. It was superior to the state-of-art methods.

Motion-flow-guided recurrent network for respiratory signal estimation of x-ray angiographic image sequences.

Motion compensation can eliminate inconsistencies of respiratory movement during image acquisitions for precise vascular reconstruction in the clinical diagnosis of vascular disease from x-ray angiographic image sequences. In x-ray-based vascular interventional therapy, motion modeling can simulate the process of organ deformation driven by motion signals to display a dynamic organ on angiograms without contrast agent injection. Automatic respiratory signal estimation from x-ray angiographic image sequences is essential for motion compensation and modeling. The effects of respiratory motion, cardiac impulses, and tremors on structures in the chest and abdomen bring difficulty in extracting accurate respiratory signals individually. In this study, an end-to-end deep learning framework based on a motion-flow-guided recurrent network is proposed to address the aforementioned problem. The proposed method utilizes a convolutional neural network to learn the spatial features of every single frame, and a recurrent neural network to learn the temporal features of the entire sequence. The combination of the two networks can effectively analyze the image sequence to realize respiratory signal estimation. In addition, the motion-flow between consecutive frames is introduced to provide a dynamic constraint of spatial features, which enables the recurrent network to learn better temporal features from dynamic spatial features than from static spatial features. We demonstrate the advantages of our approach on designed datasets which contain coronary and hepatic angiographic sequences with diaphragm structures, and coronary angiographic sequences without diaphragm structures. Our method improves over state-of-the-art manifold-learning-based methods by 85.7%, 81.5% and 75.3% in respiratory signal accuracy metric on these datasets. The results demonstrate that the proposed method can effectively estimate respiratory signals from multiple motion patterns.

Efficacy and safety of ibrutinib in diffuse large B-cell lymphoma: A single-arm meta-analysis.

This study aimed to evaluate the benefits and risks of patients with diffuse large B-cell lymphoma (DLBCL) treated with ibrutinib. PubMed, Embase, the Cochrane Library, and ClinicalTrials.gov were searched for relevant studies. The pooled overall response (OR), complete response (CR), and partial response (PR) were 57.9 %, 35.0 %, and 20.1 %, respectively. The pooled OR and CR of ibrutinib monotherapy were 41.6 % and 15.2 % and of ibrutinib + rituximab-based therapy were 72.0 % and 47.5 %, respectively. The relapsed/refractory DLBCL achieved a pooled OR and CR of 49.7 % and 27.7 %, respectively. The pooled OR and CR were 64.2 % and 56.9 % in non-germinal center B-cell-like (non-GCB) DLBCL and 68.3 % and 36.0 % in relapsed/refractory central nervous system lymphoma (CNSL), respectively. The pooled median progression-free and overall survival were 4.54 months and 11.5 months, respectively. 70.7 % and 52.6 % patients experienced ≥ grade 3 adverse events and serious adverse events. The ibrutinib-contained therapy was well tolerated and showed the potential to improve tumor response of patients with non-GCB DLBCL and relapsed/refractory CNSL.

The effect of ABCB1 polymorphism on sirolimus in renal transplant recipients: a meta-analysis.

BACKGROUND: Sirolimus (SRL) is an immunosuppressive drug and substrate of the P-glycoprotein (P-GP) encoded by ABCB1. The relationship between ABCB1 polymorphism and the pharmacokinetics of SRL in different studies were conflicting in renal transplant recipients. Thus, this meta-analysis aims to investigate the influence of ABCB1 C3435T, C1236T, and G2677T/A polymorphisms on the dose-adjusted trough level (C/D) of SRL in renal transplant recipients. METHODS: PubMed, Embase, and the Cochrane Library were searched for relevant studies. The quality of each eligible study was assessed according to Newcastle-Ottawa Scale. The STATA 15.0 was adopted to perform the meta-analysis. The fixed-effects model was used for pooled results with low heterogeneity (I2 ≤50%); otherwise, the random-effects model was used. RESULTS: A total of 6 studies were included in the meta-analysis. Results of pooled analysis showed no significant association of SRL C/D ratio with ABCB1 C3435T polymorphism. The subgroup analysis based on different ethnic groups and different time-points after SRL initiation in renal transplant recipients were also conducted. No significant association was observed in these subgroups. Significant associations were showed between ABCB1 C1236T polymorphism and the C/D ratio of SRL in the homozygous model (TT vs. CC; WMD: -45.54; 95% CI: -75.15, -15.94; P=0.003), and also in subgroup of Caucasian (TT vs. CC; WMD: -46.57; 95% CI: -91.90, -1.25; P=0.044 and TT vs. CC + CT; WMD: -52.10; 95% CI: -95.38, -8.82; P=0.018). Significant differences were found in association between the ABCB1 G2677T/A polymorphism and the C/D ratio of SRL, including the homozygous model (TT vs. GG; WMD: -76.47; 95% CI: -126.37, -26.58; P= 0.003), the heterozygous model (GT vs. GG,WMD: 178.62; 95% CI: 125.03, 232.22; P= 0.000), the dominant model (GT + TT vs. GG; WMD: 82.23; 95% CI: 36.28, 128.17; P=0.000), the recessive model (TT vs. GG + GT; WMD: -179.38; 95% CI: -283.33, -75.42; P=0.001), and the over-dominant model (GT vs. GG + TT; WMD: 199.44; 95% CI: 84.84, 314.05; P=0.001). CONCLUSIONS: No significant association exists between ABCB1 C3435T polymorphism and the C/D ratio of SRL in renal transplant recipients. To achieve target therapeutic concentrations, ABCB1 C1236T homozygous mutant TT genotype will require a higher dose of sirolimus than wild type GG, especially in Caucasian renal transplant recipients. ABCB1 G2677T/A TT genotype will also need a higher dose of sirolimus genotype. Genotyping of ABCB1 might help to improve the individualization of SRL for renal transplant recipients. Further studies are expected to provide high-quality evidence.

Solid dispersion-based pellet for colon delivery of tacrolimus through time- and pH-dependent layer coating: preparation, in vitro and in vivo studies

The intent of the present investigation is to develop and evaluate colon-specific coated tacrolimus solid dispersion pellet (SDP) that retards drug release in the stomach and small intestine but progressively releases in the colon. Tacrolimus-SDP was prepared by extrusion-spheronization technology and optimized by the micromeritic properties including flowability, friability, yields and dissolution rate. Subsequently, the pH-dependent layer (Eudragit L30D55) and time-dependent layer (Eudragit NE30D and L30D55) were coated on the SDP to form tacrolimus colon-specific pellets (CSP) using a fluidized bed coater. Under in vitro gradient pH environment, tacrolimus only released from CSP after changing pH to 6.8 and then quickly released in the phosphate buffer solution of pH 7.2. The Cmax of CSP was 195.68 ± 3.14 ng/mL at Tmax 4.5 ± 0.24 h where as in case of SDP, the Cmax was 646.16 ± 8.15 ng/mL at Tmax 0.5 ± 0.03 h, indicating the ability of CSP targeted to colon. The highest area under the curve was achieved 2479.58 ± 183.33 ng·h/mL for SDP, which was 2.27-fold higher than tacrolimus suspension. However, the best biodistribution performance was achieved from CSP. In conclusion, SDP combining of pH- and time-dependent approaches was suitable for targeted delivery of tacrolimus to colon.

IL-17A Is Critical for CD8+ T Effector Response in Airway Epithelial Injury After Transplantation.

BACKGROUND: Airway epithelium is the primary target of trachea and lung transplant rejection, the degree of epithelial injury is closely correlated with obliterative bronchiolitis development. In this study, we investigated the cellular and molecular mechanisms of IL-17A-mediated airway epithelial injury after transplantation. METHODS: Murine orthotopic allogeneic trachea or lung transplants were implemented in wild type or RORγt mice. Recipients received anti-IL-17A or anti-IFNγ for cytokine neutralization, anti-CD8 for CD8 T-cell depletion, or STAT3 inhibitor to suppress type 17 CD4+/CD8+ T cell development. Airway injury and graft inflammatory cell infiltration were examined by histopathology and immunohistochemistry. Gene expression of IL-17A, IFNγ, perforin, granzyme B, and chemokines in grafts was quantitated by real-time RT-PCR. RESULTS: IL-17A and IFNγ were rapidly expressed and associated with epithelial injury and CD8 T-cell accumulation after allotransplantation. Depletion of CD8 T cells prevented airway epithelial injury. Neutralization of IL-17A or devoid of IL-17A production by RORγt deficiency improved airway epithelial integrity of the trachea allografts. Anti-IL-17A reduced the expression of CXCL9, CXCL10, CXCL11, and CCL20, and abolished CD8 T-cell accumulation in the trachea allografts. Inhibition of STAT3 activation significantly reduced IL-17A expression in both trachea and lung allografts; however, it increased IFNγ expression and cytotoxic activities, which resulted in the failure of airway protection. CONCLUSIONS: Our data reveal the critical role of IL-17A in mediating CD8 T effector response that causes airway epithelial injury and lung allograft rejection, and indicate that inhibition of STAT3 signals could drive CD8 T cells from Tc17 toward Tc1 development.