Streamlining Social Media Information Retrieval for Public Health Research with Deep Learning

The utilization of social media in epidemic surveillance has been well established. Nonetheless, bias is often introduced when pre-defined lexicons are used to retrieve relevant corpus. This study introduces a framework aimed at curating extensive dictionaries of medical colloquialisms and Unified Medical Language System (UMLS) concepts. The framework comprises three modules: a BERT-based Named Entity Recognition (NER) model that identifies medical entities from social media content, a deep-learning powered normalization module that standardizes the extracted entities, and a semi-supervised clustering module that assigns the most probable UMLS concept to each standardized entity. We applied this framework to COVID-19-related tweets from February 1, 2020, to April 30, 2022, generating a symptom dictionary (available at https://github.com/ningkko/UMLS_colloquialism/) composed of 9,249 standardized entities mapped to 876 UMLS concepts and 38,175 colloquial expressions. This framework demonstrates encouraging potential in addressing the constraints of keyword matching information retrieval in social media-based public health research.Comment: Accepted to ICHI 2023 (The 11th IEEE International Conference on Healthcare Informatics) as a poster presentatio

Using Twitter Data to Understand Public Perceptions of Approved versus Off-label Use for COVID-19-related Medications

Understanding public discourse on emergency use of unproven therapeutics is crucial for monitoring safe use and combating misinformation. We developed a natural language processing-based pipeline to comprehend public perceptions of and stances on coronavirus disease 2019 (COVID-19)-related drugs on Twitter over time. This retrospective study included 609,189 US-based tweets from January 29, 2020, to November 30, 2021, about four drugs that garnered significant public attention during the COVID-19 pandemic: (1) Hydroxychloroquine and Ivermectin, therapies with anecdotal evidence; and (2) Molnupiravir and Remdesivir, FDA-approved treatments for eligible patients. Time-trend analysis was employed to understand popularity trends and related events. Content and demographic analyses were conducted to explore potential rationales behind people's stances on each drug. Time-trend analysis indicated that Hydroxychloroquine and Ivermectin were discussed more than Molnupiravir and Remdesivir, particularly during COVID-19 surges. Hydroxychloroquine and Ivermectin discussions were highly politicized, related to conspiracy theories, hearsay, and celebrity influences. The distribution of stances between the two major US political parties was significantly different (P < .001); Republicans were more likely to support Hydroxychloroquine (55%) and Ivermectin (30%) than Democrats. People with healthcare backgrounds tended to oppose Hydroxychloroquine (7%) more than the general population, while the general population was more likely to support Ivermectin (14%). Our study found that social media users have varying perceptions and stances on off-label versus FDA-authorized drug use at different stages of COVID-19. This indicates that health systems, regulatory agencies, and policymakers should design tailored strategies to monitor and reduce misinformation to promote safe drug use.Comment: Full paper published in JAMI

Efficacy and safety of whole-lung lavage for pulmonary alveolar proteinosis: A protocol for a systematic review and meta-analysis

Pulmonary alveolar proteinosis (PAP) is an ultra-rare syndrome first described by Rosen et al. in 1958. In a recent study, the prevalence of PAP has been estimated to be 6.87 per million in the general population. PAP is characterized by abnormal surfactant homeostasis and resultant accumulation of surfactant in pulmonary alveoli and alveolar macrophages. The typical physiological consequence of PAP is impaired gas exchange, resulting in progressive dyspnoea, hypoxemia, or even respiratory failure and death. PAP can be classified into three different types based on the pathogenetic mechanism. The most frequent form is primary PAP, which includes autoimmune disease type, associated with elevated levels of granulocyte-macrophage colony-stimulating factor (GM-CSF) autoantibodies. Secondary PAP results from alveolar macrophages dysfunction due to hematopoietic disorders, immune dysregulation, environmental exposures, and pharmaceutical agents, while genetic PAP affects almost exclusively children. Autoimmune PAP comprises the most significant share (90–95%) of adult patients, whereas secondary PAP accounts for 5–10% of adult cases. Notwithstanding the considerably evolved understanding of PAP over the past several decades, limited treatment options are available for this disease. By tradition, whole-lung lavage (WLL) has been the gold standard of care in primary PAP and some causes of secondary PAP (but not congenital PAP). Many improvements have been made since its initial introduction in the 1960. In brief, WLL is an invasive procedure. It requires general anesthesia and isolation of the two lungs using a double-lumen endotracheal tube. One lung is mechanically ventilated while the other is repeatedly filled with saline and drained. Each lung is usually washed with 15–20 liters of saline, but the volume can be up to 50 liters. No randomized controlled trial has been reported on WLL, probably due to the extreme rarity of PAP. However, numerous observational studies have been published, and the cumulative experience may be valuable in assessment. Although widely considered as the first-line management for PAP, the clinical efficacy of WLL has not been evaluated systematically. Besides, new therapeutic strategies for PAP have emerged, such as inhaled or subcutaneous GM-CSF, rituximab, plasmapheresis, and statins. Moreover, WLL is not without morbidity. There is a real need to evaluate the efficacy and safety of WLL in this heterogeneous disease. Therefore, to appropriately apply the available evidence to the clinical practice of WLL in PAP, the systematic and meta-analysis of reported observational studies will be performed strictly following the Cochrane Handbook for Systematic Reviews of Interventions

Experimental and Analytical Study on Creep Characteristics of Box Section Bamboo-Steel Composite Columns under Long-Term Loading

To expand the application of bamboo as a building material, a new type of box section composite column that combined bamboo and steel was considered in this paper. The creep characteristics of eight bamboo-steel composite columns with different parameters were tested to evaluate the effects of load level, section size and interface type under long-term loading. Then, the deformation development of the composite column under long-term loading was observed and analyzed. In addition, the creep-time relationship curve and the creep coefficient were created. Furthermore, the creep model of the composite column was proposed based on the relationship between the creep of the composite column and the creep of bamboo, and the calculated value of creep was compared with the experimental value. The experimental results showed that the creep development of the composite column was fast at first, and then became stable after about 90 days. The creep characteristics were mainly affected by long-term load level and section size. The creep coefficient was between 0.160 and 0.190. Moreover, the creep model proposed in this paper was applicable to predict the creep development of bamboo-steel composite columns. The calculation results were in good agreement with the experimental results

Effect of Pressure Relief Hole Spacing on Energy Dissipation in Coal Seam at Various Mining Depths

The large diameter pressure relief borehole is one of the most effective technical means to prevent and control rockburst during deep mining. Based on the engineering background of rockburst mines, the mechanical model of coal energy dissipation of large diameter pressure relief holes is established by theoretical analysis, and the approximate formula for calculating energy dissipation of coal is obtained. Combined with numerical simulation methods, the energy accumulation and dissipation laws of coal under various mining depths and the various spacings of pressure relief holes is studied. The results show that the upper and lower ends of the pressure relief holes have the highest degree of energy dissipation and the largest range of energy dissipation. While the energy dissipation effect on the left and right sides of the pressure relief holes is poor, a high accumulation of elastic strain energy occurs at a certain distance on the left and right sides of the relief holes. The dissipated energy of the coal seam increases continuously with the increase in mining depth and the decrease in spacing of pressure relief holes. The dissipated energy rises especially suddenly when the hole spacing changes from 1.0 m to 0.5 m. For coal seams with high rockburst risk, the spacing of pressure relief holes can be set to be less than or equal to 0.5 m, which can greatly improve the energy dissipation effect of coal seams. The studies can provide a theoretical basis for the optimization parameters of pressure relief holes for rockburst prevention

Application of artificial intelligence in ophthalmic plastic surgery

The advancement of computers and data explosion have ushered in the third wave of artificial intelligence(AI). AI is an interdisciplinary field that encompasses new ideas, new theories, and new technologies, etc. AI has brought convenience to ophthalmology application and promoted its intelligent, precise, and minimally invasive development. At present, AI has been widely applied in various fields of ophthalmology, especially in oculoplastic surgery. AI has made rapid progress in image detection, facial recognition, etc., and its performance and accuracy have even surpassed humans in some aspects. This article reviews the relevant research and applications of AI in oculoplastic surgery, including ptosis, single eyelid, pouch, eyelid mass, and exophthalmos, and discusses the challenges and opportunities faced by AI in oculoplastic surgery, and provides prospects for its future development, aiming to provide new ideas for the development of AI in oculoplastic surgery