Objectives: This study aimed to examine the impact of pertussis on the global, regional, and national levels between 1990 and 2019. Methods: Data on pertussis on a global scale from 1990 to 2019 were collected from the 2019 Global Burden of Disease Study. We performed a secondary analysis to report the global epidemiology and disease burden of pertussis. Results: During the period spanning from 1990 to 2019, pertussis exhibited a steady global decline in the age-standardized incidence rate (ASIR), age-standardized disability-adjusted life years rate (ASYR), and age-standardized death rate (ASDR). Nevertheless, upon delving into an in-depth analysis of various regions, it was apparent that ASIR in southern sub-Saharan Africa, ASYR and ASDR in high-income North America, and ASDR in Western Europe and Australasia, were witnessing an upward trajectory. Moreover, a negative correlation was observed between the Socio‑demographic Index (SDI) and burden inflicted by pertussis. Notably, the incidence of pertussis was comparatively lower in men than in women, with 0–4-year-olds emerging as the most profoundly affected demographic. Conclusion: The global pertussis burden decreased from 1990 to 2019. However, certain regions and countries faced an increasing disease burden. Therefore, urgent measures are required to alleviate the pertussis burden in these areas.

Alzheimer’s disease has become a public health crisis globally due to its increasing incidence. The purpose of this study was to establish an early warning model using artificial neural network (ANN) for early diagnosis of AD and to explore early sensitive markers for AD. A population based nested case-control study design was used. 89 new AD cases with good compliance who were willing to provide urine and blood specimen were selected from the cohort of 2482 community-dwelling elderly aged 60 years and over from 2013 to 2016. For each case, two controls living nearby were identified. Biomarkers for AD in urine and blood, neuropsychological functions and epidemiological parameters were included to analyze potential risk factors of AD. Compared with logistic regression, k-Nearest Neighbor (kNN) and support vector machine (SVM) model, back-propagation neural network of three-layer topology structures was applied to develop the early warning model. The performance of all models were measured by sensitivity, specificity, accuracy, positive prognostic value (PPV), negative prognostic value (NPV), the area under curve (AUC), and were validated using bootstrap resampling. The average age of AD group was about 5 years older than the non-AD controls (P < 0.001). Patients with AD included a significantly larger proportion of subjects with family history of dementia, compared with non-AD group. After adjusting for age and gender, the concentrations of urinary AD7c-NTP and aluminum in blood were significantly higher in AD group than non-AD group (2.01 ± 1.06 vs 1.03 ± 0.43, 1.74 ± 0.62 vs 1.24 ± 0.41 respectively), but the concentration of Selenium in AD group (2.26 ± 0.59) was significantly lower than that in non-AD group (2.61 ± 1.07). All the models were established using 18 variables that were significantly different between AD patients and controls as independent variables. The ANN model outperformed the other classifiers. The AUC for this ANN was 0.897 and the model obtained the accuracy of 92.13%, the sensitivity of 87.28% and the specificity of 94.74% on the average. Increased risk of AD may be associated with higher age among senior citizens in urban communities. Urinary AD7c-NTP is clinically valuable for the early diagnosis. The established ANN model obtained a high accuracy and diagnostic efficiency, which could be a low-cost practicable tool for the screening and diagnosis of AD for citizens.

Monocytes are key effectors in autoimmunity-related diseases in the central nervous system (CNS) due to the critical roles of these cells in the production of proinflammatory cytokines, differentiation of T-helper (Th) cells, and antigen presentation. The JAK–STAT signaling is crucial for initiating monocytes induced immune responses by relaying cytokines signaling. However, the role of this pathway in modulating the communication between monocytes and Th cells in the pathogenesis of multiple sclerosis (MS) is unclear. Here, we show that the JAK1/2/3 and STAT1/3/5/6 subtypes involved in the demyelination mediated by the differentiation of pathological Th1 and Th17 and the CNS-infiltrating inflammatory monocytes in experimental autoimmune encephalomyelitis (EAE), a model for MS. JAK inhibition prevented the CNS-infiltrating CCR2-dependent Ly6C^hi monocytes and monocyte-derived dendritic cells in EAE mice. In parallel, the proportion of GM-CSF⁺CD4⁺ T cells and GM-CSF secretion were decreased in pathological Th17 cells by JAK inhibition, which in turns converted CNS-invading monocytes into antigen-presenting cells to mediate tissue damage. Together, our data highlight the therapeutic potential of JAK inhibition in treating EAE by blocking the GM-CSF-driven inflammatory signature of monocytes.

Objective: Using visual bibliometric analysis, the application and development of artificial intelligence in clinical esophageal cancer are summarized, and the research progress, hotspots, and emerging trends of artificial intelligence are elucidated.Methods: On April 7th, 2022, articles and reviews regarding the application of AI in esophageal cancer, published between 2000 and 2022 were chosen from the Web of Science Core Collection. To conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field, VOSviewer (version 1.6.18), CiteSpace (version 5.8.R3), Microsoft Excel 2019, R 4.2, an online bibliometric platform (http://bibliometric.com/) and an online browser plugin (https://www.altmetric.com/) were used.Results: A total of 918 papers were included, with 23,490 citations. 5,979 authors, 39,962 co-cited authors, and 42,992 co-cited papers were identified in the study. Most publications were from China (317). In terms of the H-index (45) and citations (9925), the United States topped the list. The journal “New England Journal of Medicine” of Medicine, General & Internal (IF = 91.25) published the most studies on this topic. The University of Amsterdam had the largest number of publications among all institutions. The past 22 years of research can be broadly divided into two periods. The 2000 to 2016 research period focused on the classification, identification and comparison of esophageal cancer. Recently (2017-2022), the application of artificial intelligence lies in endoscopy, diagnosis, and precision therapy, which have become the frontiers of this field. It is expected that closely esophageal cancer clinical measures based on big data analysis and related to precision will become the research hotspot in the future.Conclusions: An increasing number of scholars are devoted to artificial intelligence-related esophageal cancer research. The research field of artificial intelligence in esophageal cancer has entered a new stage. In the future, there is a need to continue to strengthen cooperation between countries and institutions. Improving the diagnostic accuracy of esophageal imaging, big data-based treatment and prognosis prediction through deep learning technology will be the continuing focus of research. The application of AI in esophageal cancer still has many challenges to overcome before it can be utilized.