Impact of Social Media Challenges on Pediatric Single-Use Detergent Sacs and Diphenhydramine Ingestions Reported to United States Poison Control Centers

Background: Social media platforms are popular among children and often feature challenges that become viral. Notably, the Tide Pod® and Benadryl® challenges encouraged viewers to ingest these substances for their visual appeal and hallucinogenic effects, respectively. This study aimed to assess the clinical impact and outcomes of single-use detergent sacs (SUDS) and diphenhydramine challenges on pediatric ingestions reported to United States (U.S.) Poison Control Centers (PCCs). Methods: We conducted a retrospective review of pediatric exposures reported to U.S. PCCs using data from the National Poison Data System (NPDS). The study included intentional single-substance ingestions of both brand-name and generic forms of SUDS and diphenhydramine among children ≤ 19 years. We compared the number of calls, clinical effects, disposition, and management strategies for SUDS (pre: 01/01/17 to 12/31/17 vs. post: 01/01/18 to 12/31/18) and diphenhydramine (pre: 08/01/19 to 07/31/20 vs. post: 08/01/20 to 07/31/21) ingestions 12 months before and after the introduction of the respective social media challenges. Differences in proportions were compared using the Chi-square test. Results: During the study period, 469 ingestions of SUDS and 5,702 ingestions of diphenhydramine were reported. Post-challenge periods saw an increase in both SUDS (pre: 82 vs. post: 387;372% increase) and diphenhydramine ingestions (pre: 2,672 vs. post: 3,030;13% increase). While there were no significant changes in moderate or major clinical outcomes, hospitalizations increased post-challenge for both SUDS [pre: 4 (4.9%) vs. post: 33 (8.5%);p = 0.25] and diphenhydramine [pre: n = 904 (33.8%) vs. post: 1,190 (39.3%);p Conclusion: Pediatric ingestions reported to U.S. PCCs and hospitalizations increased coinciding with the introduction of Tide Pod® and Benadryl® challenges. While causality cannot be definitively established, it is essential for pediatricians and parents to be aware of these challenges and educate vulnerable children about the harmful effects of participation in such challenges.

Letters and Visits System Optimization and Implementation

The letters and visits system plays a vital role in government work, serving as a crucial tool for supervising law enforcement and administrative conduct, ensuring public officials’ integrity, and promoting governance by law. As Chinese citizens’ political awareness grows, the volume of letters and visits has increased steadily. This paper reviews the current state of letters and visits information construction, identifies challenges and problems in system integration, presents integration ideas for existing systems, and proposes an innovative approach to letters and visits system integration. This research aims to provide valuable insights and guidance for other units undertaking similar system integration efforts.

WORLD POLITICAL PARTIES HIGHLY APPRAISE THE KEYNOTE SPEECH BY XI JINPING,THE GENERAL SECRETARY OF THE COMMUNIST PARTY OF CHINA CENTRAL COMMITTEE:VIEWS OF INTERNATIONAL COMMUNITY TOWARDS THE CPC IN DIALOGUE WITH WORLD POLITICAL PARTIES HIGH-LEVEL MEETING

During the CPC in Dialogue with World Political Parties High- Level Meeting held in Beijing from Nov. 3o to Dec＂. 3, 2ox7, leaders from nearly 300 parties and political or- ganizations in more than 12o countries and regions gathered together to share the views of the responsibilities of politi- cal parties in building a community of shared future and a better world. The dialogue is not only the first multilat- eral diplomatic event held after the 19th CPC National Congress, but also the first high-level dialogue held by the CPC with the world＇s parties and is of great significance for both the CPC and other parties, with the largest number of attendances ever seen in such an event.

Association between ICU quality and in-hospital mortality of V-V ECMO-supported patients—the ECMO quality improvement action(EQIA)study:a national cohort study in China from 2017 to 2019

This cohort study was performed to explore the influence of intensive care unit(ICU)quality on in-hospital mortality of veno-venous(V-V)extracorporeal membrane oxygenation(ECMO)-supported patients in China.The study involved all V-V ECMO-supported patients in 318 of 1700 tertiary hospitals from 2017 to 2019,using data from the National Clinical Improvement System and China National Critical Care Quality Control Center.ICU quality was assessed by quality control indicators and capacity parameters.Among the 2563 V-V ECMO-supported patients in 318 hospitals,a significant correlation was found between ECMO-related complications and prognosis.The reintubation rate within 48 hours after extubation and the total ICU mortality rate were independent risk factors for higher in-hospital mortality of V-V ECMO-supported patients(cutoff:1.5%and 7.0%;95%confidence interval:1.05–1.48 and 1.04–1.45;odds ratios:1.25 and 1.23;P=0.012 and P=0.015,respectively).Meanwhile,the V-V ECMO center volume was a protective factor(cutoff of≥50 cases within the 3-year study period;95%confidence interval:0.57–0.83,odds ratio:0.69,P=0.0001).The subgroup analysis of 864 patients in 11 high-volume centers further strengthened these findings.Thus,ICU quality may play an important role in improving the prognosis of V-V ECMO-supported patients.

Multidimensional dynamic prediction model for hospitalized patients with the omicron variant in China

Purpose:To establish dynamic prediction models by machine learning using daily multidimensional data for coronavirus disease 2019(COVID-19)patients.Methods:Hospitalized COVID-19 patients at Peking Union Medical College Hospital from Nov 2nd,2022,to Jan 13th,2023,were enrolled in this study.The outcome was defined as deterioration or recovery of the patient's condition.Demographics,comorbidities,laboratory test results,vital signs,and treatments were used to train the model.To predict the following days,a separate XGBoost model was trained and validated.The Shapley additive explanations method was used to analyze feature importance.Results:A total of 995 patients were enrolled,generating 7228 and 3170 observations for each prediction model.In the deterioration prediction model,the minimum area under the receiver operating characteristic curve(AUROC)for the following 7 days was 0.786(95%CI 0.721-0.851),while the AUROC on the next day was 0.872(0.831-0.913).In the recovery prediction model,the minimum AUROC for the following 3 days was 0.675(0.583-0.767),while the AUROC on the next day was 0.823(0.770-0.876).The top 5 features for deterioration prediction on the 7th day were disease course,length of hospital stay,hypertension,and diastolic blood pressure.Those for recovery prediction on the 3rd day were age,D-dimer levels,disease course,creatinine levels and corticosteroid therapy.Conclusion:The models could accurately predict the dynamics of Omicron patients’conditions using daily multidimensional variables,revealing important features including comorbidities(e.g.,hyperlipidemia),age,disease course,vital signs,D-dimer levels,corticosteroid therapy and oxygen therapy.

Integrated data-model-knowledge representation for natural resource entities

The unified management and planning of national or provincial natural resources distributed both aboveground and underground have become increasingly important.Accurate depictions of natural resource elements and their interactions are key to achieving integrated and systematic management of natural resources.However,current spatiotemporal data models are based only on data descriptions,attribute records,and other model knowledge of a more general basis,without intuitively describing relationships between these elements and natural resources.This paper,therefore,proposes an integrated data-model-knowledge representation model to explicitly describe the time,space,and interaction of natural resource entities through an integrated knowledge graph.First,this study constructs a conceptual model using the aspects of semantics,scale,and data-model-knowledge,thereby explicitly describing the relationships of natural resources.Second,a logical model of natural resource representation is proposed,that is integrated with time,space,attributes,and relationships.Finally,taking the management of water resources as an example,this paper realizes the meticulous presentation of the levels of detail and rich semantic relations of natural resource entities.The findings of this study lay the foundation for a more efficient,precise,and lucid perception of the distribution laws and complicated interactional relationships of natural resources,both aboveground and underground.

Face anti-spoofing based on multi-modal and multi-scale features fusion

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.