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Green synthesis of nanoparticles (NPs) is considered a highly promising technology for materials. The use of plant-based NPs is the simplest with high reproducibility. Iron nanoparticles (Fe-NPs) were synthesized by pruned tea leaf extract and FeSO4 and were analyzed by XRD, FTIR, XPS, and TEM. The catalytic activity of the Fe-NPs was evaluated for dye degradation under different operating parameters. Kinetc and thermodynamic tests were also conducted to study the reaction behavior. Results showed that the Fe-NPs had an average pore size of 17.9 nm with a surface area of 2.54 m2 g−1. The Fe-NPs contained Fe–O bonds and bioactive substances. The crystalline Fe-NPs were composed of FeOOH, Fe2O3, and α-Fe forms. XPS showed the presence of Fe3+ and Fe2+ in a ratio of 1.78, with Fe2+ accounting for over 64%. TEM revealed the spherical structure with particle size around 34–52 nm. The degradation efficiency of Congo red (CR) surpassed 95% within 30 min for the Fenton-like system, while sole adsorption only achieved 50%. Increasing the solution pH had a significant negative effect. Pseudo-second-order model provided the best fit. Thermodynamic calculations revealed that the catalytic degradation is favorable, spontaneous, and endothermic. Reusability demonstrated that over 95% retention was achieved after five cycles. HIGHLIGHTS Fe-nanoparticles were synthesized using pruned tea leaf extract.; Fe-nanoparticles exhibited superior catalytic activity in a Fenton-like system.; More than 95% degradation efficiency was achieved after five cycles.; The pseudo-second-order kinetic model provided the best fit for the experimental data.; The reaction was spontaneous endothermic and increased disorder.;

Abstract Accurate segmentation of multiple organs in the head, neck, chest, and abdomen from medical images is an essential step in computer-aided diagnosis, surgical navigation, and radiation therapy. In the past few years, with a data-driven feature extraction approach and end-to-end training, automatic deep learning-based multi-organ segmentation methods have far outperformed traditional methods and become a new research topic. This review systematically summarizes the latest research in this field. We searched Google Scholar for papers published from January 1, 2016 to December 31, 2023, using keywords “multi-organ segmentation” and “deep learning”, resulting in 327 papers. We followed the PRISMA guidelines for paper selection, and 195 studies were deemed to be within the scope of this review. We summarized the two main aspects involved in multi-organ segmentation: datasets and methods. Regarding datasets, we provided an overview of existing public datasets and conducted an in-depth analysis. Concerning methods, we categorized existing approaches into three major classes: fully supervised, weakly supervised and semi-supervised, based on whether they require complete label information. We summarized the achievements of these methods in terms of segmentation accuracy. In the discussion and conclusion section, we outlined and summarized the current trends in multi-organ segmentation.

Abstract Background Microsatellite instability-high (MSI-H) is a unique genomic status in many cancers. However, its role in the genomic features and immunotherapy in cholangiocarcinoma (CCA) is unclear. This study aimed to systematically investigate the genomic characterization and immunotherapy efficacy of MSI-H patients with CCA. Methods We enrolled 887 patients with CCA in this study. Tumor samples were collected for next-generation sequencing. Differences in genomic alterations between the MSI-H and microsatellite stability (MSS) groups were analyzed. We also investigated the survival of PD-1 inhibitor-based immunotherapy between two groups of 139 patients with advanced CCA. Results Differential genetic alterations between the MSI-H and MSS groups included mutations in ARID1A, ACVR2A, TGFBR2, KMT2D, RNF43, and PBRM1 which were enriched in MSI-H groups. Patients with an MSI-H status have a significantly higher tumor mutation burden (TMB) (median 41.7 vs. 3.1 muts/Mb, P

Abstract Recent advances in next-generation sequencing (NGS) technology have greatly accelerated the need for efficient annotation to accurately interpret clinically relevant genetic variants in human diseases. Therefore, it is crucial to develop appropriate analytical tools to improve the interpretation of disease variants. Given the unique genetic characteristics of mitochondria, including haplogroup, heteroplasmy, and maternal inheritance, we developed a suite of variant analysis toolkits specifically designed for primary mitochondrial diseases: the Mitochondrial Missense Variant Annotation Tool (MmisAT) and the Mitochondrial Missense Variant Pathogenicity Predictor (MmisP). MmisAT can handle protein-coding variants from both nuclear DNA and mtDNA and generate 349 annotation types across six categories. It processes 4.78 million variant data in 76 min, making it a valuable resource for clinical and research applications. Additionally, MmisP provides pathogenicity scores to predict the pathogenicity of genetic variations in mitochondrial disease. It has been validated using cross-validation and external datasets and demonstrated higher overall discriminant accuracy with a receiver operating characteristic (ROC) curve area under the curve (AUC) of 0.94, outperforming existing pathogenicity predictors. In conclusion, the MmisAT is an efficient tool that greatly facilitates the process of variant annotation, expanding the scope of variant annotation information. Furthermore, the development of MmisP provides valuable insights into the creation of disease-specific, phenotype-specific, and even gene-specific predictors of pathogenicity, further advancing our understanding of specific fields.

In the study of the deep learning classification of medical images, deep learning models are applied to analyze images, aiming to achieve the goals of assisting diagnosis and preoperative assessment. Currently, most research classifies and predicts normal and cancer cells by inputting single-parameter images into trained models. However, for ovarian cancer (OC), identifying its different subtypes is crucial for predicting disease prognosis. In particular, the need to distinguish high-grade serous carcinoma from clear cell carcinoma preoperatively through non-invasive means has not been fully addressed. This study proposes a deep learning (DL) method based on the fusion of multi-parametric magnetic resonance imaging (mpMRI) data, aimed at improving the accuracy of preoperative ovarian cancer subtype classification. By constructing a new deep learning network architecture that integrates various sequence features, this architecture achieves the high-precision prediction of the typing of high-grade serous carcinoma and clear cell carcinoma, achieving an AUC of 91.62% and an AP of 95.13% in the classification of ovarian cancer subtypes.

IntroductionPrecise delineation of glioblastoma in multi-parameter magnetic resonance images is pivotal for neurosurgery and subsequent treatment monitoring. Transformer models have shown promise in brain tumor segmentation, but their efficacy heavily depends on a substantial amount of annotated data. To address the scarcity of annotated data and improve model robustness, self-supervised learning methods using masked autoencoders have been devised. Nevertheless, these methods have not incorporated the anatomical priors of brain structures.MethodsThis study proposed an anatomical prior-informed masking strategy to enhance the pre-training of masked autoencoders, which combines data-driven reconstruction with anatomical knowledge. We investigate the likelihood of tumor presence in various brain structures, and this information is then utilized to guide the masking procedure.ResultsCompared with random masking, our method enables the pre-training to concentrate on regions that are more pertinent to downstream segmentation. Experiments conducted on the BraTS21 dataset demonstrate that our proposed method surpasses the performance of state-of-the-art self-supervised learning techniques. It enhances brain tumor segmentation in terms of both accuracy and data efficiency.DiscussionTailored mechanisms designed to extract valuable information from extensive data could enhance computational efficiency and performance, resulting in increased precision. It's still promising to integrate anatomical priors and vision approaches.

BackgroundAcute Respiratory Infections (ARIs) are a major cause of morbidity and mortality worldwide. Human Adenovirus (HAdV), responsible for 5%–10% of children's ARIs, is one of the most prevalent pathogens. Our study aimed to analyze the epidemiology and phylogenesis of HAdV in pediatric patients with ARIs in Hangzhou during the COVID-19 pandemic.MethodBetween November 2020 and March 2021, we collected 1,442 nasopharyngeal swabs from children with ARIs at Children's Hospital, Zhejiang University School of Medicine. Epidemiological statistics, phylogenetic and amino acid (AA) mutation analysis were conducted.ResultsOur findings revealed that 386 (26.77%) samples tested positive for HAdV, with the highest rate in children aged 6–18 years and the lowest in children aged 0–1 year, indicating a different age preference of HAdV compared with pre-pandemic period. Outpatients had a significantly higher positive rate than inpatients. Moreover, patients with HAdV-coinfection exhibited more severe clinical symptoms than those with HAdV-single infection. Our phylogenetic analysis demonstrated that species HAdV-C (type 1, 2, 6) were the predominant circulating strains in Hangzhou during the COVID-19 pandemic. Further AA mutation analysis identified seventeen mutations of particular concern for biological characterization.ConclusionIn conclusion, our study provides valuable epidemiological and molecular data that will aid in epidemiological surveillance, antiviral therapies and the development of specific vaccine types, leading to improve public health.

Robot-assisted minimally invasive surgery (RAMIS) has gained significant traction in clinical practice in recent years. However, most surgical robots rely on touch-based human-robot interaction (HRI), which increases the risk of bacterial diffusion. This risk is particularly concerning when surgeons must operate various equipment with their bare hands, necessitating repeated sterilization. Thus, achieving touch-free and precise manipulation with a surgical robot is challenging. To address this challenge, we propose a novel HRI interface based on gesture recognition, leveraging hand-keypoint regression and hand-shape reconstruction methods. By encoding the 21 keypoints from the recognized hand gesture, the robot can successfully perform the corresponding action according to predefined rules, which enables the robot to perform fine-tuning of surgical instruments without the need for physical contact with the surgeon. We evaluated the surgical applicability of the proposed system through both phantom and cadaver studies. In the phantom experiment, the average needle tip location error was 0.51 mm, and the mean angle error was 0.34 degrees. In the simulated nasopharyngeal carcinoma biopsy experiment, the needle insertion error was 0.16 mm, and the angle error was 0.10 degrees. These results indicate that the proposed system achieves clinically acceptable accuracy and can assist surgeons in performing contactless surgery with hand gesture interaction.

Abstract Background Bladder cancer is one of the most lethal malignancy in urological system, and 20–25% of bladder cancer patients are muscle invasive with unfavorable prognosis. However, the role of alternative splicing (AS) in muscle-invasive bladder cancer (MIBC) remains to be elucidated. Methods Percent spliced in (PSI) data obtained from the Cancer Genome Atlas (TCGA) SpliceSeq database (n = 394) were utilized to evaluate the AS events in MIBC. Prognosis-associated AS events were screened out by univariate Cox regression. LASSO Cox regression was used to identify reliable prognostic patterns in a training set and further validated in a test set. Splicing regulatory networks were constructed by correlations between PSI of AS events and RNA expression of splicing factors. Results As a result, a total of 2589 prognosis-related AS events in MIBC were identified. Pathways of spliceosomal complex (FDR = 0.017), DNA-directed RNA polymerase II, core complex (FDR = 0.032), and base excision repair (FDR = 0.038) were observed to be significantly enriched. Additionally, we noticed that most of the prognosis-related AS events were favorable factors. According to the LASSO and multivariate Cox regression analyses, 15-AS-based signature was established with the area under curve (AUC) of 0.709, 0.823, and 0.857 at 1-, 3-, and 5- years, respectively. The MIBC patients were further divided into high- and low-risk groups based on median risk sores. Interestingly, we observed that the prevalence of FGFR3 with mutations and focal amplification was significantly higher in low-risk group. Functional and immune infiltration analysis suggested potential signaling pathways and distinct immune states between these two groups. Moreover, splicing correlation network displayed a regulatory mode of prognostic splicing factors (SF) in MIBC patients. Conclusions This study not only provided novel insights into deciphering the possible mechanism of tumorgenesis and pathogenesis but also help refine risk stratification systems and potential treatment of decision-making for MIBC.

In this study, we produced zirconia nanoparticles with a pure tetragonal phase, good dispersion, and an average particle size of approximately 7.3 nm using the modified hydrothermal method. Zirconium oxychloride (ZrOCl2-8H2O) was used as zirconium source, while propanetriol was used as an additive. The influence of propanetriol content, sonication time, hydrothermal temperature, and type of dispersant on the physical phase and dispersibility of zirconia nanoparticles was investigated. Monoclinic zirconia was found to completely transform into a tetragonal structure when the mass fraction of glycerol was increased to 5 wt%. With the increase in the mechanical stirring time under ultrasonic conditions, the size distribution range of the prepared particles became narrower and then wider, and the particle size became first smaller and then larger. Ultrasonic and mechanical stirring for 5 min had the best effect. When comparing the effects of different dispersants (PEG8000, PVP, and CTAB), it was found that the average particle size of zirconia nanoparticles prepared with 0.5 wt% PVP was the smallest. Furthermore, by adding different concentrations of pure tetragonal phase nanozirconia to 3Y-ZrO2 as reinforcement additives, the bending strength of the prepared ceramics increased first and then decreased with increasing addition amounts. When the amount of addition was 1 wt% and the ceramic was calcined at 1600 °C, the flexural strength of the ceramic increased significantly, which was about 1.6 times that of the unadded ceramic. The results are expected to provide a reference for the reinforcement of high-purity zirconia ceramics.

Abstract Background The goal of this study is to disclose the clinically significant genomic alterations in the Chinese and Western patients with intrahepatic cholangiocarcinoma. Methods A total of 86 Chinese patients were enrolled in this study. A panel of 579 pan-cancer genes was sequenced for the qualified samples from these patients. Driver genes, actionability, and tumor mutational burden were inferred and compared to a cohort of Western patients. Results Totally, 36 and 12 driver genes were identified in the Chinese and Western cohorts, respectively. Of them, seven driver genes (IDH1, KRAS, TP53, BAP1, PBRM1, ARID1A, and NRAS) were shared by the two cohorts. Four driver genes (SPTA1, ARID2, TP53, and GATA1) were found significantly correlated with the tumor mutational burden. For both cohorts, half of the patients had actionable mutations. The two cohorts shared the most actionable genes but differed much in their frequency. Though KRAS mutations were at the first and second actionable rank respectively for the Chinese and Western populations, they were still at a relatively low level of actionable evidence. Conclusions The study on the clinical significance of genomic alterations directs the future development of precision medicine for intrahepatic cholangiocarcinoma treatment.

Background: Metaplastic or sarcomatoid carcinomas (MSCs) are rare epithelial malignancies with heterologous histological differentiation that can occur in different organs. The objective of the current study was to identify novel somatically mutated genes in MSCs from different organs.Methods: Whole-exome sequencing was performed in 16 paired MSCs originating from the breast (n = 10), esophagus (n = 3), lung (n = 2), and kidney (n = 1). In addition, we collected data on KMT2D mutations from eight independent cohorts (n = 195) diagnosed with MSCs derived from the breast (n = 83), liver (n = 8), esophagus (n = 15), lung (n = 10), and uterus or ovary (n = 79). The expression of KMT2D and its clinical significance were evaluated in our cohort.Results: The most frequently mutated genes were TP53 (13/16, 81%) and KMT2D (5/16,31%). We identified seven somatic KMT2D mutations in the exploratory cohort (n = 16 tumors), including three nonsense mutations, two frameshift indels, one missense mutation, and one splice site mutation. Interestingly, two patients showed double hits on KMT2D with nonsense mutations and frameshift indels. In the eight validation cohorts (n = 195), the average mutation rates for TP53 and KMT2D were 78% (152/195) and 13% (25/195), respectively. Two or more hits on KMT2D were also present in three validation cohorts. Furthermore, KMT2D mutations were associated with low expression of KMT2D, large tumor size and unfavorable prognosis.Conclusions: These findings provide clues for understanding the genetic basis of MSCs originating from different organs and implicate KMT2D alteration as a frequent pathogenic mutation, allowing provision of appropriate treatment for this rare malignant disease in the future.

Abstract Background Schizophrenia is a severe mental disease with high morbidity and heritability. The SLC39A8 gene is located in 4q24 and encodes a protein that transports many metal ions. Multiple previous studies found that one of the most pleiotropic single nucleotide polymorphisms (SNPs) in SLC39A8, rs13107325, is associated with schizophrenia in the European population. However, the polymorphism of this locus is rare in other populations. In China, the Han Chinese and the Uygur Chinese are two ethnic populations that originate from different races. Methods A case-control study was conducted with 983 schizophrenia cases and 1230 healthy controls of the Chinese Uygur population. To validate the most promising SNP, meta-analyses were conducted with the Han Chinese and the European PGC2 data sets reported previously. Results A susceptible locus, rs10014145 (p allele = 0.014, p allele = 0.098 after correction; p genotype = 0.004, p genotype = 0.032 after correction) was identified in case-control study of the Chinese Uygur population. Further, the association between rs10014145 and schizophrenia was supported by a meta-analysis of Han and Uygur Chinese samples (pooled OR [95% CI] =1.10 [1.03–1.17], Z = 2.73, p = 0.006). The association between rs10014145 and schizophrenia was not significant in a meta-analysis of combined Chinese and European samples (pooled OR [95% CI] =1.07 [1.00–1.14], Z = 1.88, and p = 0.06). In addition, the “CCAC” haplotype of rs4698844-rs233814-rs13114343-rs151394 was significantly associated with schizophrenia in Uygur Chinese (P = 0.003, corrected p = 0.012). Conclusions The results of this study support that SLC39A8 is a susceptible gene for schizophrenia in the populations of Han Chinese and Uygur Chinese in China, further studies are suggested to validate the association.

Point set registration under affine transformation is an important problem in computer vision because not only it has many direct applications, but it is also often used as an initial step for non-rigid registration. This problem is challenging when no correspondences between the two point sets are known, and most existing methods start from an initial pose and find a local optimal transformation. This paper presents a deterministic global optimization method for affine point set registration, which is called GO-APSR. We model the two sets to be registered with Gaussian Mixture Models (GMMs) and minimize the L2 distance between the two GMMs under affine transformation. Branch-and-Bound (BnB) is employed to search the transformation parameter space, and we propose a convex quadratic function as the under-estimator of the objective function in each branch. Therefore, calculation of the lower bound in each branch is casted into a bound-constrained convex quadratic programming problem, which can be solved globally and efficiently. Experiment results verify the global optimality of the proposed method and its robustness to noise and outliers. Furthermore, it works very well in the challenging partially-overlap scenarios.

At present, deep learning has been widely adopted in medical image processing. However, the current deep neural networks depend on a large number of labeled training data, but medical images segmentation tasks often suffer from the problem of small quantity of labeled data because labeling medical images is a very expensive and time-consuming task. In order to overcome this difficulty, this paper proposes a new image augmentation strategy based on statistical shape model and three-dimensional thin plate spline, which can generate many simulated images from a small number of real images. Firstly, the shape information of the real labeled images is modeled with the statistical shape model, and a series of simulated shapes are generated by sampling from this model. Secondly, the simulated shapes are filled with texture using three-dimensional thin plate spline to generate the simulated images. Finally, the simulated images and the real images are used together for training deep neural networks. The proposed framework is a general data augmentation method that can be used in any anatomical structure segmentation tasks with any deep neural network architecture. We used two different datasets, including prostate MRI dataset and liver CT dataset, and used two different deep network structures, including multi-scale 3D Convolutional Neural Networks (multi-scale 3D CNN) and U-net. The experimental results showed that the proposed data augmentation strategy can improve the accuracy of existing segmentation algorithms based on deep neural networks.

Accurate segmentation of organs at risk (OARs) plays a critical role in the treatment planning of image-guided radiotherapy of head and neck cancer. This segmentation task is challenging for both humans and automated algorithms because of the relatively large number of OARs to be segmented, the large variability in size and morphology across different OARs, and the low contrast between some OARs and the background. In this study, we propose a two-stage segmentation framework based on 3D U-Net. In this framework, the segmentation of each OAR is decomposed into two subtasks: locating a bounding box of the OAR and segmenting the OAR from a small volume within the bounding box, and each subtask is fulfilled by a dedicated 3D U-Net. The decomposition makes each subtask much easier so that it can be better completed. We evaluated the proposed method and compared it to state-of-the-art methods using the Medical Image Computing and Computer-Assisted Intervention 2015 Challenge dataset. In terms of the boundary-based metric 95% Hausdorff distance, the proposed method ranked first for seven of nine OARs and ranked second for the other OARs. In terms of the area-based metric dice similarity coefficient, the proposed method ranked first for five of nine OARs and ranked second for the other three OARs with a small difference from the method that ranked first.

Background Lung adenocarcinoma (LUAD) is the most commonhistological lung cancer subtype, with an overall five-year survivalrate of only 17%. In this study, we aimed to identify autophagy-related genes (ARGs) and develop an LUAD prognostic signature. Methods In this study, we obtained ARGs from three databases and downloaded gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We used TCGA-LUAD (n = 490) for a training and testing dataset, and GSE50081 (n = 127) as the external validation dataset.The least absolute shrinkage and selection operator (LASSO) Cox and multivariate Cox regression models were used to generate an autophagy-related signature. We performed gene set enrichment analysis (GSEA) and immune cell analysis between the high- and low-risk groups. A nomogram was built to guide the individual treatment for LUAD patients. Results We identified a total of 83 differentially expressed ARGs (DEARGs) from the TCGA-LUAD dataset, including 33 upregulated DEARGs and 50 downregulated DEARGs, both with thresholds of adjusted P 1.5. Using LASSO and multivariate Cox regression analyses, we identified 10 ARGs that we used to build a prognostic signature with areas under the curve (AUCs) of 0.705, 0.715, and 0.778 at 1, 3, and 5 years, respectively. Using the risk score formula, the LUAD patients were divided into low- or high-risk groups. Our GSEA results suggested that the low-risk group were enriched in metabolism and immune-related pathways, while the high-risk group was involved in tumorigenesis and tumor progression pathways. Immune cell analysis revealed that, when compared to the high-risk group, the low-risk group had a lower cell fraction of M0- and M1- macrophages, and higher CD4 and PD-L1 expression levels. Conclusion Our identified robust signature may provide novel insight into underlying autophagy mechanisms as well as therapeutic strategies for LUAD treatment.

Deep point cloud neural networks have achieved promising performance in remote sensing applications, and the prevalence of Transformer in natural language processing and computer vision is in stark contrast to underexplored point-based methods. In this paper, we propose an effective transformer-based network for point cloud learning. To better learn global and local information, we propose a group-in-group relation-based transformer architecture to learn the relationships between point groups to model global information and between points within each group to model local semantic information. To further enhance the local feature representation, we propose a Radius Feature Abstraction (RFA) module to extract radius-based density features characterizing the sparsity of local point clouds. Extensive evaluation on public benchmark datasets demonstrate the effectiveness and competitive performance of our proposed method on point cloud classification and part segmentation.

BackgroundAlternative splicing (AS) is important in the regulation of gene expression and aberrant AS is emerging as a major factor in the pathogenesis of human conditions, including cancer. Triple-negative breast cancer (TNBC) is the most challenging subtype of breast cancer with strong invasion, high rate of metastasis, and poor prognosis. Here we report a systematic profiling of aberrant AS in TNBC.MethodsThe percent spliced in (PSI) values for AS events in 151 TNBC patients were obtained from The Cancer Genome Atlas (TCGA) SpliceSeq database. Univariate Cox and stepwise Multivariate Cox regression analyses were conducted to find the best prognostic AS model. Splicing regulatory networks were constructed by prognosis-related spliceosome and aberrant AS events. Additionally, pathway enrichment and gene set enrichment analysis (GSEA) were further employed to reveal the significant pathways for prognosis-related AS genes. Finally, splicing regulatory networks were constructed via Spearman’s rank correlation coefficients between prognosis-related AS events and splicing factor expressions.ResultsA total of 1,397 prognosis-associated AS events were identified in TNBC. The majority of the parent genes of prognostic AS events exhibited direct interactions to each other in the STRING gene network. Pathways of focal adhesion (p < 0.001), RNA splicing (p = 0.007), homologous recombination (p = 0.042) and ECM-receptor interaction (p = 0.046) were found to be significantly enriched for prognosis-related AS. Additionally, the area under curve (AUC) of the best AS prognostic predictor model reached 0.949, showing a powerful capability to predict outcomes. The Exon Skip (ES) type of AS events displayed more robust and efficient capacity in predicting performance than any other specific AS events type in terms of prognosis. The ES AS signature might confer a strong oncogenic phenotype in the high-risk group with elevated activities in cell cycle and SUMOylating pathways of tumorigenesis, while programmed cell death and metabolism pathways were found to be enriched in the low-risk group of TNBC. The splicing correlation network also revealed a regulatory mode of prognostic splicing factors (SFs) in TNBC.ConclusionOur analysis of AS events in TNBC could not only contribute to elucidating the tumorigenesis mechanism of AS but also provide clues to uncovering underlying prognostic biomarkers and therapeutic targets for further study.

Abstract Background Renal hypouricemia (RHUC) is a heterogeneous genetic disorder that is characterized by decreased serum uric acid concentration and increased fractional excretion of uric acid. Previous reports have revealed many functional mutations in two urate transporter genes, SLC22A12 and/or SLC2A9, to be the causative genetic factors of this disorder. However, there are still unresolved patients, suggesting the existence of other causal genes or new mutations. Here, we report an RHUC patient with novel compound heterozygous mutations in the SLC22A12 gene. Case presentation A 27-year-old female presenting with recurrent hypouricemia during routine checkups was referred to our hospital. After obtaining the patient’s consent, both the patient and her healthy parents were analyzed using whole-exome sequencing (WES) and Sanger sequencing to discover and validate causal mutations, respectively. The prioritization protocol of WES screened out two mutations of c.269G > A/p.R90H and c.1289_1290insGG/p.M430fsX466, which are both located in the SLC22A12 gene, in the patient. Sanger sequencing further confirmed that the patient’s heterozygous c.269G > A/p.R90H mutation, which has been reported previously, derived from her mother, and the heterozygous c.1289_1290insGG/p.M430fsX466 mutation, which was found for the first time, derived from her father. p.R90H, which is highly conserved among different species, may decrease the stability of this domain and was considered to be almost damaging in silicon analysis. p.M430fsX466 lacks the last three transmembrane domains, including the tripeptide motif (S/T)XΦ (X = any amino acid and Φ = hydrophobic residue), at the C-terminal, which interact with scaffolding protein PDZK1 and thus will possibly lead to weak functioning of urate transport through the disruption of the “transporter complex” that is formed by URAT1 and PDZK1. Conclusions We report a Chinese patient with RHUC, which was caused by compound heterozygous mutations of the SLC22A12 gene, using WES and Sanger sequencing for the first time. Mutation-induced structural instability or malfunction of the urate transporter complex may be the main mechanisms for this hereditary disorder.

Abstract Cushing’s disease results from corticotroph adenomas of the pituitary that hypersecrete adrenocorticotropin (ACTH), leading to excess glucocorticoid and hypercortisolism. Mutations of the deubiquitinase gene USP8 occur in 35–62% of corticotroph adenomas. However, the major driver mutations in USP8 wild-type tumors remain elusive. Here, we report recurrent mutations in the deubiquitinase gene USP48 (predominantly encoding p.M415I or p.M415V; 21/91 subjects) and BRAF (encoding p.V600E; 15/91 subjects) in corticotroph adenomas with wild-type USP8. Similar to USP8 mutants, both USP48 and BRAF mutants enhance the promoter activity and transcription of the gene encoding proopiomelanocortin (POMC), which is the precursor of ACTH, providing a potential mechanism for ACTH overproduction in corticotroph adenomas. Moreover, primary corticotroph tumor cells harboring BRAF V600E are sensitive to the BRAF inhibitor vemurafenib. Our study thus contributes to the understanding of the molecular mechanism of the pathogenesis of corticotroph adenoma and informs therapeutic targets for this disease.

Abstract As the most common examination tool in medical practice, chest radiography has important clinical value in the diagnosis of disease. Thus, the automatic detection of chest disease based on chest radiography has become one of the hot topics in medical imaging research. Based on the clinical applications, the study conducts a comprehensive survey on computer-aided detection (CAD) systems, and especially focuses on the artificial intelligence technology applied in chest radiography. The paper presents several common chest X-ray datasets and briefly introduces general image preprocessing procedures, such as contrast enhancement and segmentation, and bone suppression techniques that are applied to chest radiography. Then, the CAD system in the detection of specific disease (pulmonary nodules, tuberculosis, and interstitial lung diseases) and multiple diseases is described, focusing on the basic principles of the algorithm, the data used in the study, the evaluation measures, and the results. Finally, the paper summarizes the CAD system in chest radiography based on artificial intelligence and discusses the existing problems and trends.

Abstract Background Schizophrenia (SCZ) is a common mental disorder with high heritability, and genetic factors play a major role in the pathogenesis. Recent researches indicated that the CACNA1I involved in calcium channels probably affect the potential pathogenesis of SCZ. Results In this study, we attempted to investigate whether the CACNA1I gene contributes the risk to SCZ in the Uighur Chinese population, and performed a case-control study involving 985 patient samples and 1218 normal controls to analyze nine SNPs within the CACNA1I gene. Among these sites, six SNPs were significantly associated with SCZ in the allele distribution: rs132575 (adjusted P allele = 0.039, OR = 1.159), rs713860 (adjusted P allele = 0.039, OR = 0.792), rs738168 (adjusted P allele = 0.039, OR = 0.785), rs136805 (adjusted P allele = 0.014, OR = 1.212), rs5757760 (adjusted P allele = 0.042, OR = 0.873) and rs5750871 (adjusted P allele = 0.039, OR = 0.859). In addition, two SNPs turned to be risk factors for SCZ not only in the allele distribution, but also in the genotype distribution: rs132575 (adjusted P genotype = 0.037) and rs136805 (adjusted P genotype = 0.037). Conclusions Overall, the present study provided evidence that significant association exists between the CACNA1I gene and SCZ in the Uighur Chinese population, subsequent validation of functional analysis and genetic association studies are needed to further extend this study.