Pathogenic Mutations Differentially Regulate Cell-to-Cell Transmission of α-Synuclein

Recent studies suggest that the cell-to-cell spread of pathological α-synuclein (α-syn) plays important roles in the development of Parkinson’s disease (PD). PD patients who carry α-syn gene mutations often have an earlier onset and more severe clinical symptoms and pathology than sporadic PD cases who carry the wild-type (WT) α-syn gene. However, the molecular mechanism by which α-syn gene mutations promote PD remains unclear. Here, we hypothesized that pathogenic mutations facilitate the intercellular transfer and cytotoxicity of α-syn, favoring an early disease onset and faster progression. We investigated the effects of eight known pathogenic mutations in human α-syn (A18T, A29S, A30P, E46K, H50Q, G51D, A53E, and A53T) on its pathological transmission in terms of secretion, aggregation, intracellular level, cytotoxicity, seeding, and induction of neuroinflammation in SH-SY5Y neuroblastoma cells, cultured rat neurons, and microglia, and the rat substantia nigra pars compacta. We found that 2 of the 8 mutations (H50Q and A53T) significantly increased α-syn secretion while 6 mutations (A18T, A29S, A30P, G51D, A53E, and E46K) tended to enhance it. In vitroα-syn aggregation experiments showed that H50Q promoted while G51D delayed aggregation most strongly. Interestingly, 3 mutations (E46K, H50Q, and G51D) greatly increased the intracellular α-syn level when cultured cells were treated with preformed α-syn fibrils (PFFs) compared with the WT, while the other 5 had no effect. We also demonstrated that H50Q, G51D, and A53T PFFs, but not E46K PFFs, efficiently seeded in vivo and acutely induced neuroinflammation in rat substantia nigra pars compacta. Our data indicate that pathogenic mutations augment the prion-like spread of α-syn at different steps and blockade of this pathogenic propagation may serve as a promising therapeutic intervention for PD

Knowledge mapping of anaplastic thyroid cancer treatments: a bibliometric analysis (2000-2023)

ContextAnaplastic thyroid cancer (ATC) is a relatively rare and extensively malignant kind of thyroid carcinoma. The poor prognosis and high mortality rate of ATC can be attributed to its invasive features and undifferentiated phenotype. At present, there is a lack of efficacious therapeutic options. In light of the elevated fatality rate, it is vital to possess a comprehensive comprehension of the scientific terrain pertaining to ATC. To gather the perspectives of different researchers about the topic of ATC treatment, we did a bibliometric network analysis, which offers a comprehensive view of the scholarly literature.MethodologyA systematic search was conducted on the WoSCC database to identify publications pertaining to ATC treatment between the years 2000 and 2023. In this bibliometric investigation, the tools VOSviewers, CiteSpace, and the R package “bibliometrix” were employed to investigate the general attributes, developmental framework, and academic frontiers of the subject matter.Results1223 publications in total, written by 6937 scholars from 53 areas and 1402 institutions and published in 358 scholarly journals, were analyzed. There has been a gradual increase in the quantity of publications pertaining to ATC treatment. The United States and China emerged as the most prominent nations. The University of Texas MD Anderson Cancer Center and Memorial Sloan Kettering Cancer Counseling Center are prominent research institutions in highly productive countries. The journal Thyroid holds a prominent position within its discipline, being widely recognized as both the most popular and highly co-cited publication. According to the available data, Maria Cabanillas has authored the highest number of published articles, while RC Smallridge has received the highest number of co-citations. It turned out that the prevailing keywords encompassed expression, therapy, apoptosis, survival, activation, proliferation, metastasis, and other related terms. Immunotherapy, targeted therapy, and prognostic factors are the emerging research hotspots and trends.ConclusionsThis paper presents a complete overview of research trends and advancements in the treatment of ATC using bibliometric analysis. The acquisition of information will offer vital insights for funding and potential creative strategies in researching the treatment of ATC, which indicates the research frontiers as well as prevalent directions in recent years

Mining Event Logs to Support Workflow Resource Allocation

Workflow technology is widely used to facilitate the business process in enterprise information systems (EIS), and it has the potential to reduce design time, enhance product quality and decrease product cost. However, significant limitations still exist: as an important task in the context of workflow, many present resource allocation operations are still performed manually, which are time-consuming. This paper presents a data mining approach to address the resource allocation problem (RAP) and improve the productivity of workflow resource management. Specifically, an Apriori-like algorithm is used to find the frequent patterns from the event log, and association rules are generated according to predefined resource allocation constraints. Subsequently, a correlation measure named lift is utilized to annotate the negatively correlated resource allocation rules for resource reservation. Finally, the rules are ranked using the confidence measures as resource allocation rules. Comparative experiments are performed using C4.5, SVM, ID3, Na\"ive Bayes and the presented approach, and the results show that the presented approach is effective in both accuracy and candidate resource recommendations.Comment: T. Liu et al., Mining event logs to support workflow resource allocation, Knowl. Based Syst. (2012), http://dx.doi.org/ 10.1016/j.knosys.2012.05.01

Classifying the surrounding rock of tunnel face using machine learning

Accurately classifying the surrounding rock of tunnel face is essential. In this paper, we propose a machine learning-based automatic classification and dynamic prediction method of the surrounding rocks of tunnel face using the data monitored by a computerized rock drilling trolley based on the intelligent mechanized construction process for drilling and blasting tunnels. This method provides auxiliary support for the intelligent decision of dynamic support at the construction site. First, this method solves the imbalance in the classification of the surrounding rock samples by constructing the Synthetic Minority Oversampling Technique (SMOTE) algorithm using 500 samples of drilling parameters covering different levels and lithologies of a tunnel. Second, it filters the importance of the characteristic samples based on the random forest method. Third, it uses the XGBoost algorithm to model the processed data and compare it with AdaBoost and BP neural network models. The results show that the XGBoost model achieves a higher accuracy of 87.5% when the sample size is small. Finally, we validate the application scenarios of the above algorithm/model regarding the key aspects of the tunnel construction process, such as surrounding rock identification, design interaction, construction supervision, and quality evaluation, which facilitates the upgrading of intelligent tunnel construction

Risk factors for BK virus infection in DCD donor kidney transplant recipients

BackgroundBK virus infection after kidney transplantation can negatively impact the prognosis of patients. However, current risk factor analyses primarily focus on BK virus nephropathy, while BK viruria and BK viruria progressing to BK viremia receive less attention. This study aims to analyze the risk factors associated with BK viruria and BK viruria progressing to BK viremia in recipients of donation after cardiac death (DCD), with the goal of facilitating early intervention.MethodsDonor characteristics and clinical data of recipients before and after transplantation were evaluated, and logistic univariate and multivariate analyses were performed to determine the risk factors associated with BK viruria and the progression of BK viruria to BK viremia. Additionally, machine learning techniques were employed to identify the top five features associated with BK viruria evolving into BK viremia.ResultsDuring a median follow-up time of 1,072 days (range 739–1,418), 69 transplant recipients (15.6% incidence rate) developed BK viruria after transplantation, with 49.3% of cases occurring within 6 months post-transplantation. Moreover, 19 patients progressed to BK viremia. Donor age [OR: 1.022 (1.000, 1.045), p = 0.047] and donor procalcitonin (PCT) levels [0.5–10 ng/ml; OR: 0.482 (0.280, 0.828), p = 0.008] were identified as independent risk factors for BK viruria. High BK viruria [OR: 11.641 (1.745, 77.678), p = 0.011], recipient age [OR: 1.106 (1.017, 1.202), p = 0.018], and immunoinduction regimen [ATG; OR: 0.063 (0.006, 0.683), p = 0.023] were independent risk factors for BK viruria progressing to BK viremia. Machine learning analysis confirmed the importance of high BK viruria, recipient age, and immunoinduction regimen (ATG) in predicting the progression of BK viruria to BK viremia.ConclusionThe development and progression of BK virus in DCD kidney transplant recipients is influenced by multiple factors. Early intervention and treatment could potentially extend the lifespan of the transplanted organ

Vulnerability assessment of the fishery system in China’s coastal provinces since 2000

Coastal fishery systems are vital for both the environment and the economy, and at present, they face heightened vulnerability due to global climate change and natural disasters. A clearer understanding of the challenges that the system presents can be obtained by examining the vulnerabilities of fishery systems. This study employed a vulnerability scoping diagram framework and a multi-indicator approach combined with the entropy weight method for assigning weights to systematically evaluate the vulnerabilities of fishery systems in China’s coastal provinces. The spatiotemporal variation characteristics of vulnerability were analyzed and characterized, and the primary obstacles that affect vulnerability are discussed. The findings suggest that China’s coastal provinces’ fishery systems display vulnerability in terms of both time and space. From a temporal perspective, Liaoning, Hebei, and Shandong provinces exhibited an increasing trend in vulnerability, while Tianjin, Jiangsu, Zhejiang, Shanghai, Fujian, Guangdong, Guangxi, and Hainan showed decreasing trends. From a spatial perspective, Hainan and Liaoning’s fishery systems exhibited extremely high vulnerability in most years. In contrast, Tianjin consistently experienced extremely low vulnerability in most years. From the perspective of obstacles, the main factor was the funding for the extension of aquaculture technology, and this remained the primary obstacle factor across all years. The findings are significant for enhancing our understanding of vulnerability in fishery systems and for strengthening disaster prevention and mitigation measures. The results provide robust support for the improvement of management and the protection of fishery systems