Single-cell analysis of a progressive Rosai-Dorfman disease affecting the cerebral parenchyma: a case report.

Neurologic Rosai-Dorfman disease (RDD) is a rare type of non-Langerhans cell histiocytosis that affects the central nervous system. Most neurologic RDDs grow like meningiomas, have clear boundaries, and can be completely resected. However, a few RDDs are invasive and aggressive, and no effective treatment options are available because the molecular mechanisms involved remain unknown. Here, we report a case of deadly and glucocorticoid-resistant neurologic RDD and explore its possible pathogenic mechanisms via single-cell RNA sequencing. First, we identified two distinct but evolutionarily related histiocyte subpopulations (the C1Q+ and SPP1+ histiocytes) that accumulated in the biopsy sample. The expression of genes in the KRAS signaling pathway was upregulated, indicating gain-of-function of KRAS mutations. The C1Q+ and SPP1+ histiocytes were highly differentiated and arrested in the G1 phase, excluding the idea that RDD is a lympho-histio-proliferative disorder. Second, although C1Q+ histiocytes were the primary RDD cell type, SPP1+ histiocytes highly expressed several severe inflammation-related and invasive factors, such as WNT5A, IL-6, and MMP12, suggesting that SPP1+ histiocytes plays a central role in driving the progression of this disease. Third, oligodendrocytes were found to be the prominent cell type that initiates RDD via MIF and may resist glucocorticoid treatment via the MDK and PTN signaling pathways. In summary, in this case, we report a rare presentation of neurologic RDD and provided new insight into the pathogenic mechanisms of progressive neurologic RDD. This study will also offer evidence for developing precision therapies targeting this complex disease.

Interstitial fibrosis increases the risk of end-stage kidney disease in patients with lupus nephritis.

OBJECTIVE: To evaluate the risk of end-stage kidney disease (ESKD) in lupus nephritis (LN) patients using tubulointerstitial lesion scores. METHODS: Clinical profiles and histopathological presentations of 151 biopsy-proven LN patients were retrospectively examined. Risk factors of ESKD based on characteristics and scoring of their tubulointerstitial lesions (e.g. interstitial inflammation [II], tubular atrophy [TA], and interstitial fibrosis [IF]) were analyzed. RESULTS: The mean age of 151 LN patients was 36 years old, and 136 (90.1%) were female. The LN cases examined included: class I/II (n = 3, 2%), class III/IV (n = 119, 78.8%), class V (n = 23, 15.2%), and class VI (n = 6, 4.0%). The mean serum creatinine level was 1.4 mg/dl. Tubulointerstitial lesions were recorded in 120 (79.5%) patients. Prior to receiving renal biopsy, 9 (6.0%) patients developed ESKD. During the follow-up period (mean, 58 months), an additional 47 patients (31.1%) progressed to ESKD. Multivariate analyses identified serum creatinine (hazard ratio [HR]: 1.7, 95% confidence interval [CI]: 1.42-2.03, p < 0.001) and IF (HR: 3.2, 95% CI: 1.58-6.49, p = 0.001) as independent risk factors of ESKD. Kaplan-Meier analysis further confirmed a heightened risk of ESKD associated with IF. CONCLUSION: Tubulointerstitial involvement is commonly observed in histopathological presentation of LN. However, IF, rather than II, or TA, was found to increase the risk of ESKD in our cohort. Therefore, to predict renal outcome in LN patients prior to adjusting immunosuppressive treatment, degree of IF should be reviewed.

A novel radiomics-based technique for identifying vulnerable coronary plaques: a follow-up study.

BACKGROUND: Previous reports have suggested that coronary computed tomography angiography (CCTA)-based radiomics analysis is a potentially helpful tool for assessing vulnerable plaques. We aimed to investigate whether coronary radiomic analysis of CCTA images could identify vulnerable plaques in patients with stable angina pectoris. METHODS: This retrospective study included patients initially diagnosed with stable angina pectoris. Patients were randomly divided into either the training or test dataset at an 8 : 2 ratio. Radiomics features were extracted from CCTA images. Radiomics models for predicting vulnerable plaques were developed using the support vector machine (SVM) algorithm. The model performance was assessed using the area under the curve (AUC); the accuracy, sensitivity, and specificity were calculated to compare the diagnostic performance using the two cohorts. RESULTS: A total of 158 patients were included in the analysis. The SVM radiomics model performed well in predicting vulnerable plaques, with AUC values of 0.977 and 0.875 for the training and test cohorts, respectively. With optimal cutoff values, the radiomics model showed accuracies of 0.91 and 0.882 in the training and test cohorts, respectively. CONCLUSION: Although further larger population studies are necessary, this novel CCTA radiomics model may identify vulnerable plaques in patients with stable angina pectoris.

Mature neurons from iPSCs unveil neurodegeneration-related pathways in mucopolysaccharidosis type II: GSK-3ß inhibition for therapeutic potential.

Mucopolysaccharidosis (MPS) type II is caused by a deficiency of iduronate-2-sulfatase and is characterized by the accumulation of glycosaminoglycans (GAGs). Without effective therapy, the severe form of MPS II causes progressive neurodegeneration and death. This study generated multiple clones of induced pluripotent stem cells (iPSCs) and their isogenic controls (ISO) from four patients with MPS II neurodegeneration. MPS II-iPSCs were successfully differentiated into cortical neurons with characteristic biochemical and cellular phenotypes, including axonal beadings positive for phosphorylated tau, and unique electrophysiological abnormalities, which were mostly rescued in ISO-iPSC-derived neurons. RNA sequencing analysis uncovered dysregulation in three major signaling pathways, including Wnt/ß-catenin, p38 MAP kinase, and calcium pathways, in mature MPS II neurons. Further mechanistic characterization indicated that the dysregulation in calcium signaling led to an elevated intracellular calcium level, which might be linked to compromised survival of neurons. Based on these dysregulated pathways, several related chemicals and drugs were tested using this mature MPS II neuron-based platform and a small-molecule glycogen synthase kinase-3ß inhibitor was found to significantly rescue neuronal survival, neurite morphology, and electrophysiological abnormalities in MPS II neurons. Our results underscore that the MPS II-iPSC-based platform significantly contributes to unraveling the mechanisms underlying the degeneration and death of MPS II neurons and assessing potential drug candidates. Furthermore, the study revealed that targeting the specific dysregulation of signaling pathways downstream of GAG accumulation in MPS II neurons with a well-characterized drug could potentially ameliorate neuronal degeneration.

scmFormer Integrates Large-Scale Single-Cell Proteomics and Transcriptomics Data by Multi-Task Transformer.

Transformer-based models have revolutionized single cell RNA-seq (scRNA-seq) data analysis. However, their applicability is challenged by the complexity and scale of single-cell multi-omics data. Here a novel single-cell multi-modal/multi-task transformer (scmFormer) is proposed to fill up the existing blank of integrating single-cell proteomics with other omics data. Through systematic benchmarking, it is demonstrated that scmFormer excels in integrating large-scale single-cell multimodal data and heterogeneous multi-batch paired multi-omics data, while preserving shared information across batchs and distinct biological information. scmFormer achieves 54.5% higher average F1 score compared to the second method in transferring cell-type labels from single-cell transcriptomics to proteomics data. Using COVID-19 datasets, it is presented that scmFormer successfully integrates over 1.48 million cells on a personal computer. Moreover, it is also proved that scmFormer performs better than existing methods on generating the unmeasured modality and is well-suited for spatial multi-omic data. Thus, scmFormer is a powerful and comprehensive tool for analyzing single-cell multi-omics data.

[Fatal Familial Insomnia With Significant Correlations Between Involuntary Movements and Postural Changes:Report of One Case].

Fatal familial insomnia,an autosomal dominant prion disease,is rare.We reported the clinical symptoms,examination results,diagnosis,treatment,and prognosis of a patient who was diagnosed with fatal familial insomnia.Furthermore,we described the unique clinical manifestations that involuntary movements and laryngeal stridor were significantly correlated with postural changes,aiming to provide reference for the clinical diagnosis,treatment,and research of the disease in the future.

A newly synthesized flavone avoids COMT-catalyzed methylation and mitigates myocardial ischemia/reperfusion injury in H9C2 cells via JNK and P38 pathways.

Objectives: Luteolin is a flavone that provides defense against myocardial ischemia/reperfusion (I/R) injury. However, this compound is subjected to methylation mediated by catechol-O-methyltransferase (COMT), thus influencing its pharmacological effect. To synthesize a new flavone from luteolin that avoids COMT-catalyzed methylation and find out the protective mechanism of LUA in myocardial I/R injury. Materials and Methods: Luteolin and 2,2'-azobis (2-amidinopropane) dihydrochloride (AAPH) were used to synthesize the new flavone known as LUAAPH-1 (LUA). Then, the myocardial ischemia/reperfusion injury cell model was established using H9c2 cells to detect the effect in myocardial ischemia/reperfusion regulation and to identify the underlying mechanism. Results: Pretreatment with LUA (20 µmol/l) substantially increased cell viability while reducing cell apoptosis rate and caspase-3 expression induced by I/R, and the protective effect of LUA on cell viability was stronger than diosmetin, which is the major methylated metabolite of luteolin. In addition, intracellular reactive oxygen species (ROS) production and calcium accumulation were both inhibited by LUA. Furthermore, we identified that LUA markedly relieved the promotive effects of I/R stimulation upon JNK and p38 phosphorylation. Conclusion: LUT pretreatment conveys significant cardioprotective effects after myocardial I/R injury, and JNK and p38 MAPK signaling pathway may be involved.

Individualized detection of TMPRSS2-ERG fusion status in prostate cancer: a rank-based qualitative transcriptome signature.

BACKGROUND: TMPRSS2-ERG (T2E) fusion is highly related to aggressive clinical features in prostate cancer (PC), which guides individual therapy. However, current fusion prediction tools lacked enough accuracy and biomarkers were unable to be applied to individuals across different platforms due to their quantitative nature. This study aims to identify a transcriptome signature to detect the T2E fusion status of PC at the individual level. METHODS: Based on 272 high-throughput mRNA expression profiles from the Sboner dataset, we developed a rank-based algorithm to identify a qualitative signature to detect T2E fusion in PC. The signature was validated in 1223 samples from three external datasets (Setlur, Clarissa, and TCGA). RESULTS: A signature, composed of five mRNAs coupled to ERG (five ERG-mRNA pairs, 5-ERG-mRPs), was developed to distinguish T2E fusion status in PC. 5-ERG-mRPs reached 84.56% accuracy in Sboner dataset, which was verified in Setlur dataset (n = 455, accuracy = 82.20%) and Clarissa dataset (n = 118, accuracy = 81.36%). Besides, for 495 samples from TCGA, two subtypes classified by 5-ERG-mRPs showed a higher level of significance in various T2E fusion features than subtypes obtained through current fusion prediction tools, such as STAR-Fusion. CONCLUSIONS: Overall, 5-ERG-mRPs can robustly detect T2E fusion in PC at the individual level, which can be used on any gene measurement platform without specific normalization procedures. Hence, 5-ERG-mRPs may serve as an auxiliary tool for PC patient management.

Breaking NGF-TrkA immunosuppression in melanoma sensitizes immunotherapy for durable memory T cell protection.

Melanoma cells, deriving from neuroectodermal melanocytes, may exploit the nervous system's immune privilege for growth. Here we show that nerve growth factor (NGF) has both melanoma cell intrinsic and extrinsic immunosuppressive functions. Autocrine NGF engages tropomyosin receptor kinase A (TrkA) on melanoma cells to desensitize interferon γ signaling, leading to T and natural killer cell exclusion. In effector T cells that upregulate surface TrkA expression upon T cell receptor activation, paracrine NGF dampens T cell receptor signaling and effector function. Inhibiting NGF, either through genetic modification or with the tropomyosin receptor kinase inhibitor larotrectinib, renders melanomas susceptible to immune checkpoint blockade therapy and fosters long-term immunity by activating memory T cells with low affinity. These results identify the NGF-TrkA axis as an important suppressor of anti-tumor immunity and suggest larotrectinib might be repurposed for immune sensitization. Moreover, by enlisting low-affinity T cells, anti-NGF reduces acquired resistance to immune checkpoint blockade and prevents melanoma recurrence.

Enhancing Ki-67 Prediction in Breast Cancer: Integrating Intratumoral and Peritumoral Radiomics From Automated Breast Ultrasound via Machine Learning.

RATIONALE AND OBJECTIVES: Traditional Ki-67 evaluation in breast cancer (BC) via core needle biopsy is limited by repeatability and heterogeneity. The automated breast ultrasound system (ABUS) offers reproducibility but is constrained to morphological and echoic assessments. Radiomics and machine learning (ML) offer solutions, but their integration for improving Ki-67 predictive accuracy in BC remains unexplored. This study aims to enhance ABUS by integrating ML-assisted radiomics for Ki-67 prediction in BC, with a focus on both intratumoral and peritumoral regions. MATERIALS AND METHODS: A retrospective analysis was conducted on 936 BC patients, split into training (n = 655) and testing (n = 281) cohorts. Radiomics features were extracted from intra- and peritumoral regions via ABUS. Feature selection involved Z-score normalization, intraclass correlation, Wilcoxon rank sum tests, minimum redundancy maximum relevance, and least absolute shrinkage and selection operator logistic regression. ML classifiers were trained and optimized for enhanced predictive accuracy. The interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP). RESULTS: Of the 2632 radiomics features in each patient, 15 were significantly associated with Ki-67 levels. The support vector machine (SVM) was identified as the optimal classifier, with area under the receiver operating characteristic curve values of 0.868 (training) and 0.822 (testing). SHAP analysis indicated that five peritumoral and two intratumoral features, along with age and lymph node status, were key determinants in the predictive model. CONCLUSION: Integrating ML with ABUS-based radiomics effectively enhances Ki-67 prediction in BC, demonstrating the SVM model's strong performance with both radiomics and clinical factors.

Prognostic significance and relationship of SMAD3 phospho-isoforms and VEGFR-1 in gastric cancer: A clinicopathological study.

BACKGROUND: The TGF-ß/SMAD3 and VEGFR-1 signaling pathways play important roles in gastric cancer metastasis. SMAD3 phosphorylation is a crucial prognostic marker in gastric cancer. AIM: To determine the prognostic value and relationship of SMAD3 phospho-isoforms and VEGFR-1 in gastric cancer. METHODS: This was a single-center observational study which enrolled 98 gastric cancer patients and 82 adjacent normal gastric tissues from patients aged 32-84 years (median age 65) between July 2006 and April 2007. Patients were followed up until death or the study ended (median follow-up duration of 28.5 mo). The samples were used to generate tissue microarrays (TMAs) for immunohistochemical (IHC) staining. The expressions of TGF-ß1, pSMAD3C(S423/425), pSMAD3L(S204), and VEGFR-1 in gastric cancer (GC) tumor tissue and normal tissue were measured by IHC staining using TMAs obtained from 98 GC patients. Prognosis and survival information of the patients was recorded by Outdo Biotech from May 2007 to July 2015. The relationship between TGF-ß1, pSMAD3C(S423/425), pSMAD3L(S204), and VEGFR-1 protein expression levels was analyzed using Pearson's correlation coefficient. The relationship between protein expression levels and clinicopathological parameters was analyzed using the Chi-squared test. A survival curve was generated using the Kaplan-Meier survival analysis. RESULTS: TGFß-1 and VEGFR-1 expression was significantly upregulated in gastric cancer tissue compared to adjacent non-cancerous tissue. The positive expression of phosphorylated isoforms of Smad3 varied depending on the phosphorylation site [pSMAD3C(S423/425): 51.0% and pSMAD3L(S204): 31.6%]. High expression of pSMAD3L(S204) was significantly correlated with larger tumors (P = 0.038) and later N stages (P = 0.035). Additionally, high expression of VEGFR-1 was closely correlated with tumor size (P = 0.015) and pathological grading (P = 0.013). High expression of both pSMAD3L(S204) and VEGFR-1 was associated with unfavorable outcomes in terms of overall survival (OS). Multivariate analysis indicated that high expression of pSMAD3L(S204) and VEGFR-1 were independent risk factors for prognosis in GC patients. VEGFR-1 protein expression was correlated with TGF-ß1 (r = 0.220, P = 0.029), pSMAD3C(S423/425) (r = 0.302, P = 0.002), and pSMAD3L(S204) (r = 0.201, P = 0.047), respectively. Simultaneous overexpression of pSMAD3L(S204) and VEGFR-1 was associated with poor OS in gastric cancer patients. CONCLUSION: Co-upregulation of pSMAD3L(S204) and VEGFR-1 can serve as a predictive marker for poor gastric cancer prognosis, and pSMAD3L(204) may be involved in enhanced gastric cancer metastasis in a VEGFR-1-dependent manner.

GMean-a semi-supervised GRU and K-mean model for predicting the TF binding site.

The transcription factor binding site is a deoxyribonucleic acid sequence that binds to transcription factors. Transcription factors are proteins that regulate the transcription gene. Abnormal turnover of transcription factors can lead to uncontrolled cell growth. Therefore, discovering the relationships between transcription factors and deoxyribonucleic acid sequences is an important component of bioinformatics research. Numerous deep learning and machine learning language models have been developed to accomplish these tasks. Our goal in this work is to propose a GMean model for predicting unlabelled deoxyribonucleic acid sequences. The GMean model is a hybrid model with a combination of gated recurrent unit and K-mean clustering. The GMean model is developed in three phases. The labelled and unlabelled data are processed based on k-mers and tokenization. The labelled data is used for training. The unlabelled data are used for testing and prediction. The experimental data consists of deoxyribonucleic acid experimental of GM12878, K562 and HepG2. The experimental results show that GMean is feasible and effective in predicting deoxyribonucleic acid sequences, as the highest accuracy is 91.85% in predicting K562 and HepG2. This is followed by the prediction of the sequence between GM12878 and K562 with an accuracy of 89.13%. The lowest accuracy is the prediction of the sequence between HepG2 and GM12828, which is 88.80%.

CT radiomics based on different machine learning models for classifying gross tumor volume and normal liver tissue in hepatocellular carcinoma.

BACKGROUND & AIMS: The present study utilized extracted computed tomography radiomics features to classify the gross tumor volume and normal liver tissue in hepatocellular carcinoma by mainstream machine learning methods, aiming to establish an automatic classification model. METHODS: We recruited 104 pathologically confirmed hepatocellular carcinoma patients for this study. GTV and normal liver tissue samples were manually segmented into regions of interest and randomly divided into five-fold cross-validation groups. Dimensionality reduction using LASSO regression. Radiomics models were constructed via logistic regression, support vector machine (SVM), random forest, Xgboost, and Adaboost algorithms. The diagnostic efficacy, discrimination, and calibration of algorithms were verified using area under the receiver operating characteristic curve (AUC) analyses and calibration plot comparison. RESULTS: Seven screened radiomics features excelled at distinguishing the gross tumor area. The Xgboost machine learning algorithm had the best discrimination and comprehensive diagnostic performance with an AUC of 0.9975 [95% confidence interval (CI): 0.9973-0.9978] and mean MCC of 0.9369. SVM had the second best discrimination and diagnostic performance with an AUC of 0.9846 (95% CI: 0.9835- 0.9857), mean Matthews correlation coefficient (MCC)of 0.9105, and a better calibration. All other algorithms showed an excellent ability to distinguish between gross tumor area and normal liver tissue (mean AUC 0.9825, 0.9861,0.9727,0.9644 for Adaboost, random forest, logistic regression, naivem Bayes algorithm respectively). CONCLUSION: CT radiomics based on machine learning algorithms can accurately classify GTV and normal liver tissue, while the Xgboost and SVM algorithms served as the best complementary algorithms.

scCorrector: a robust method for integrating multi-study single-cell data.

The advent of single-cell sequencing technologies has revolutionized cell biology studies. However, integrative analyses of diverse single-cell data face serious challenges, including technological noise, sample heterogeneity, and different modalities and species. To address these problems, we propose scCorrector, a variational autoencoder-based model that can integrate single-cell data from different studies and map them into a common space. Specifically, we designed a Study Specific Adaptive Normalization for each study in decoder to implement these features. scCorrector substantially achieves competitive and robust performance compared with state-of-the-art methods and brings novel insights under various circumstances (e.g. various batches, multi-omics, cross-species, and development stages). In addition, the integration of single-cell data and spatial data makes it possible to transfer information between different studies, which greatly expand the narrow range of genes covered by MERFISH technology. In summary, scCorrector can efficiently integrate multi-study single-cell datasets, thereby providing broad opportunities to tackle challenges emerging from noisy resources.

Branched-Chain Amino Acid Accumulation Fuels the Senescence-Associated Secretory Phenotype.

The essential branched-chain amino acids (BCAAs) leucine, isoleucine, and valine play critical roles in protein synthesis and energy metabolism. Despite their widespread use as nutritional supplements, BCAAs' full effects on mammalian physiology remain uncertain due to the complexities of BCAA metabolic regulation. Here a novel mechanism linking intrinsic alterations in BCAA metabolism is identified to cellular senescence and the senescence-associated secretory phenotype (SASP), both of which contribute to organismal aging and inflammation-related diseases. Altered BCAA metabolism driving the SASP is mediated by robust activation of the BCAA transporters Solute Carrier Family 6 Members 14 and 15 as well as downregulation of the catabolic enzyme BCAA transaminase 1 during onset of cellular senescence, leading to highly elevated intracellular BCAA levels in senescent cells. This, in turn, activates the mammalian target of rapamycin complex 1 (mTORC1) to establish the full SASP program. Transgenic Drosophila models further indicate that orthologous BCAA regulators are involved in the induction of cellular senescence and age-related phenotypes in flies, suggesting evolutionary conservation of this metabolic pathway during aging. Finally, experimentally blocking BCAA accumulation attenuates the inflammatory response in a mouse senescence model, highlighting the therapeutic potential of modulating BCAA metabolism for the treatment of age-related and inflammatory diseases.

Risk Factors and Incidence of Serious Infections in Patients With Systemic Lupus Erythematosus Undergoing Rituximab Therapy.

OBJECTIVE: To evaluate the risk and protective factors of serious infection (SI) in patients with systemic lupus erythematosus (SLE) within 180 days of rituximab (RTX) treatment. METHODS: Patients with SLE treated with RTX were analyzed. SI was defined as any infectious disease requiring hospitalization. The clinical characteristics, laboratory profiles, medications, and incidence rate (IR) are presented. Multivariate Cox proportional hazards models and Kaplan-Meier analysis for risk factors of SI were performed. RESULTS: A total of 174 patients with SLE receiving RTX treatment were enrolled. The overall IR of SIs was 51.0/100 patient-years (PYs). Pneumonia (30.4/100 PYs), followed by soft tissue infections, intra-abdominal infections, and Pneumocystis jiroveci pneumonia (all 6.1/100 PYs) were the leading types of SIs. Twelve patients died during the 180-day follow-up (crude mortality rate: 14.6/100 PYs). Chronic kidney disease (CKD), defined as an estimated glomerular filtration rate < 60 mL/min/1.73 m2 (hazard ratio [HR] 2.88, 95% CI 1.30-6.38), and a background prednisolone (PSL) equivalent dosage ≥ 15 mg/day (HR 3.50, 95% CI 1.57-7.78) were risk factors for SIs among all patients with SLE. Kaplan-Meier analysis confirmed the risk of SI for patients with SLE with CKD and a background PSL equivalent dosage ≥ 15 mg/day (log-rank P = 0.001 and 0.02, respectively). Hydroxychloroquine (HCQ) reduced the risk of SIs in patients with SLE (HR 0.35, 95% CI 0.15-0.82; log-rank P = 0.003). CONCLUSION: SI was prevalent in patients with SLE after RTX treatment. Patients with SLE with CKD and high-dose glucocorticoid use required constant vigilance. HCQ may reduce the risk of SI among patients with SLE administered RTX.

GSLCDA: An Unsupervised Deep Graph Structure Learning Method for Predicting CircRNA-Disease Association.

Growing studies reveal that Circular RNAs (circRNAs) are broadly engaged in physiological processes of cell proliferation, differentiation, aging, apoptosis, and are closely associated with the pathogenesis of numerous diseases. Clarification of the correlation among diseases and circRNAs is of great clinical importance to provide new therapeutic strategies for complex diseases. However, previous circRNA-disease association prediction methods rely excessively on the graph network, and the model performance is dramatically reduced when noisy connections occur in the graph structure. To address this problem, this paper proposes an unsupervised deep graph structure learning method GSLCDA to predict potential CDAs. Concretely, we first integrate circRNA and disease multi-source data to constitute the CDA heterogeneous network. Then the network topology is learned using the graph structure, and the original graph is enhanced in an unsupervised manner by maximize the inter information of the learned and original graphs to uncover their essential features. Finally, graph space sensitive k-nearest neighbor (KNN) algorithm is employed to search for latent CDAs. In the benchmark dataset, GSLCDA obtained 92.67% accuracy with 0.9279 AUC. GSLCDA also exhibits exceptional performance on independent datasets. Furthermore, 14, 12 and 14 of the top 16 circRNAs with the most points GSLCDA prediction scores were confirmed in the relevant literature in the breast cancer, colorectal cancer and lung cancer case studies, respectively. Such results demonstrated that GSLCDA can validly reveal underlying CDA and offer new perspectives for the diagnosis and therapy of complex human diseases.

MAGCDA: A Multi-Hop Attention Graph Neural Networks Method for CircRNA-Disease Association Prediction.

With a growing body of evidence establishing circular RNAs (circRNAs) are widely exploited in eukaryotic cells and have a significant contribution in the occurrence and development of many complex human diseases. Disease-associated circRNAs can serve as clinical diagnostic biomarkers and therapeutic targets, providing novel ideas for biopharmaceutical research. However, available computation methods for predicting circRNA-disease associations (CDAs) do not sufficiently consider the contextual information of biological network nodes, making their performance limited. In this work, we propose a multi-hop attention graph neural network-based approach MAGCDA to infer potential CDAs. Specifically, we first construct a multi-source attribute heterogeneous network of circRNAs and diseases, then use a multi-hop strategy of graph nodes to deeply aggregate node context information through attention diffusion, thus enhancing topological structure information and mining data hidden features, and finally use random forest to accurately infer potential CDAs. In the four gold standard data sets, MAGCDA achieved prediction accuracy of 92.58%, 91.42%, 83.46% and 91.12%, respectively. MAGCDA has also presented prominent achievements in ablation experiments and in comparisons with other models. Additionally, 18 and 17 potential circRNAs in top 20 predicted scores for MAGCDA prediction scores were confirmed in case studies of the complex diseases breast cancer and Almozheimer's disease, respectively. These results suggest that MAGCDA can be a practical tool to explore potential disease-associated circRNAs and provide a theoretical basis for disease diagnosis and treatment.

Socioeconomic, meteorological factors and spatiotemporal distribution of human brucellosis in China between 2004 and 2019-A study based on spatial panel model.

BACKGROUND: Human brucellosis continues to be a great threat to human health in China. The present study aimed to investigate the spatiotemporal distribution of human brucellosis in China from 2004 to 2019, to analyze the socioeconomic factors, meteorological factors and seasonal effect affecting human brucellosis incidence in different geographical regions with the help of spatial panel model, and to provide a scientific basis for local health authorities to improve the prevention of human brucellosis. METHODS: The monthly reported number and incidence of human brucellosis in China from January 2004 to December 2019 were obtained from the Data Center for China Public Health Science. Monthly average air temperature and monthly average relative humidity of 31 provincial-level administrative units (22 provinces, 5 autonomous regions and 4 municipalities directly under the central government) in China from October 2003 to December 2019 were obtained from the National Meteorological Science Data Centre. The inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield and gross pastoral product of 31 provincial-level administrative units in China from 2004 to 2019 were obtained from the National Bureau of Statistics of China. The temporal and geographical distribution of human brucellosis was displayed with Microsoft Excel and ArcMap software. The spatial autocorrelation and hotspot analysis was used to describe the association among different areas. Spatial panel model was constructed to explore the combined effects on the incidence of human brucellosis in China. RESULTS: A total of 569,016 cases of human brucellosis were reported in the 31 provincial-level administrative units in China from January 2004 to December 2019. Human brucellosis cases were concentrated between March and July, with a peak in May, showing a clear seasonal increase. The incidence of human brucellosis in China from 2004 to 2019 showed significant spatial correlations, and hotspot analysis indicated that the high incidence of human brucellosis was mainly in the northern China, particularly in Inner Mongolia, Shanxi, and Heilongjiang. The results from spatial panel model suggested that the inventory of cattle, the inventory of sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature (the same month, 2-month lagged and 3-month lagged), average relative humidity (the same month) and season variability were significantly associated with human brucellosis incidence in China. CONCLUSIONS: The epidemic area of human brucellosis in China has been expanding and the spatial clustering has been observed. Inner Mongolia and adjacent provinces or autonomous regions are the high-risk areas of human brucellosis. The inventory of cattle and sheep, beef yield, mutton yield, wool yield, milk yield, gross pastoral product, average air temperature, average relative humidity and season variability played a significant role in the progression of human brucellosis. The present study strengthens the understanding of the relationship between socioeconomic, meteorological factors and the spatial heterogeneity of human brucellosis in China, through which 'One Health'-based strategies and countermeasures can be provided for the government to tackle the brucellosis menace.

[Sporadic Creutzfeldt-Jakob Disease With Slow Progression:Report of One Case].

Sporadic Creutzfeldt-Jakob disease(sCJD)is a prion-caused degenerative disease of the central nervous system,with the typical clinical manifestation of rapidly progressive dementia.The course of disease is less than 1 year in most patients and more than 2 years in only 2% to 3% patients.We reported a case of sCJD with expressive language disorder and slow progression in this paper.By summarizing the clinical manifestations and the electroencephalograhpy,MRI,and pathological features,we aimed to enrich the knowledge about the sCJD with slow progression.