Design and Implementation of Intelligent Truck Based on Azure Kinect

In recent years, due to the impact of COVID-19, the market prospect of non-contact handling has improved and the development potential is huge. This paper designs an intelligent truck based on Azure Kinect, which can save manpower and improve efficiency, and greatly reduce the infection risk of medical staff and community workers. The target object is visually recognized by Azure Kinect to obtain the center of mass of the target, and the GPS and Kalman filter are used to achieve accurate positioning. The 4-DOF robot arm is selected to grasp and transport the target object, so as to complete the non-contact handling work. In this paper, different shapes of objects are tested. The experiment shows that the system can accurately complete the positioning function, and the accuracy rate is 95.56%. The target object recognition is combined with the depth information to determine the distance, and the spatial coordinates of the object centroid are obtained in real time. The accuracy rate can reach 94.48%, and the target objects of different shapes can be recognized. When the target object is grasped by the robot arm, it can be grasped accurately according to the depth information, and the grasping rate reaches 92.67%. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Platform Behavior under Market Shocks: A Simulation Framework and Reinforcement-Learning Based Study

We study the behavior of an economic platform (e.g., Amazon, Uber Eats, Instacart) under shocks, such as COVID-19 lockdowns, and the effect of different regulation considerations. To this end, we develop a multi-agent simulation environment of a platform economy in a multi-period setting where shocks may occur and disrupt the economy. Buyers and sellers are heterogeneous and modeled as economically-motivated agents, choosing whether or not to pay fees to access the platform. We use deep reinforcement learning to model the fee-setting and matching behavior of the platform, and consider two major types of regulation frameworks: (1) taxation policies and (2) platform fee restrictions. We offer a number of simulated experiments that cover different market settings and shed light on regulatory tradeoffs. Our results show that while many interventions are ineffective with a sophisticated platform actor, we identify a particular kind of regulation - fixing fees to the optimal, no-shock fees while still allowing a platform to choose how to match buyers and sellers - as holding promise for promoting the efficiency and resilience of the economic system. © 2023 ACM.

Detection of Semantic Duplicates in Temporal Domains Using Directed Acyclic Graphs

We demonstrate the feasibility of a generalized technique for semantic deduplication in temporal data domains using graph-based representations of data records. Structured data records with multiple timestamp attributes per record may be represented as a directed graph where the nodes represent the events and the edges represent event sequences. Edge weights are based on elapsed time between connecting nodes. In comparing two records, we may merge these directed graphs and determine a representative directed acyclic graph (DAG) inclusive of a subset of nodes and edges that maintain the transitive weights of the original graphs. This DAG may then be evaluated by weighting elapsed time equivalences between records at each node and measuring the fraction of nodes represented in the DAG versus the union of nodes between the records being compared. With this information, we establish a duplication score and use a specified threshold requirement to assert duplication. This method is referred to as Temporal Deduplication using Directed Acyclic Graphs (TD:DAG). TD:DAG significantly outperformed established ASNM and ASNM+LCS methods for datasets rep-resenting two disparate domains, COVID-19 government policy data and PlayStation Network (PSN) trophy data. TD:DAG produced highly effective and comparable F1 scores of 0.960 and 0.972 for the two datasets, respectively, versus 0.864/0.938 for ASNM+LCS and 0.817/0.708 for ASNM. © 2023 IEEE.

Who's minding the children: Gender equity in the first 2 years of the pandemic

The wholesale changes brought about by the COVID‐19 pandemic to men and women's paid work arrangements and work–family balance provide a natural experiment for testing the common elements of two theories, needs exposure (Schafer et al. Canadian Review of Sociology/Revue Canadienne De Sociologie, 57(4);2020:523–549) and parental proximity (Sullivan et al. Family Theory & Review, 2018;10(1):263–279) against a third theory also suggested by Schafer et al. (2020), and labelled in this article, entrenchment/exacerbation of gender inequality. Both needs exposure and parental proximity suggest that by being home because of the pandemic, in proximity to their children, fathers are exposed to new and enduring family needs, which may move them toward more equal sharing in childcare and other domestic responsibilities. By contrast to studies that have tested such theories using retrospective, self‐report survey data over a 2‐year period, we analyse more than a decade of time‐use diary data from the American Time Use Survey (ATUS) that covers the first 2 years of the pandemic. We model the secular and quarterly trends to predict what would have occurred in the absence of the pandemic, contrasting this to what indeed happened. Our analyses consider aggregate and individual impacts, using methods of sequence analysis, clustering, and matching. Among our results, we find that the division of childcare responsibilities did not become more equitable during the pandemic. Suggestions for future research are provided as are suggestions for the implementation of social policies that could influence greater gender equity in unpaid work and childcare. [ FROM AUTHOR] Copyright of International Journal of Social Welfare is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Immunotherapy: OFF-THE-SHELF PARTIAL HLA MATCHING SARS-COV-2 ANTIGEN SPECIFIC T CELL THERAPY FOR SEVERE COVID-19 PATIENTS

Background & Aim: Immunological characteristics of COVID-19 show pathological hyperinflammation associated with lymphopenia and dysfunctional T cell responses. These features provide a rationale for restoring functional T cell immunity in COVID-19 patients by adoptive transfer of SARS-CoV-2 specific T cells. Methods, Results & Conclusion(s): To generate SARS-CoV-2 specific T cells, we isolated peripheral blood mononuclear cells from 7 COVID-19 recovered and 13 unexposed donors. Consequently, we stimulated cells with SARS-CoV-2 peptide mixtures covering spike, membrane and nucleocapsid proteins. Then, we culture expanded cells with IL-2 for 21 days. We assessed immunophenotypes, cytokine profiles, antigen specificity of the final cell products. Our results show that SARSCoV- 2 specific T cells could be expanded in both COVID-19 recovered and unexposed groups. Immunophenotypes were similar in both groups showing CD4+ T cell dominance, but CD8+ and CD3+CD56+ T cells were also present. Antigen specificity was determined by ELISPOT, intracellular cytokine assay, and cytotoxicity assays. One out of 14 individuals who were previously unexposed to SARS-CoV-2 failed to show antigen specificity. Moreover, ex-vivo expanded SARS-CoV-2 specific T cells mainly consisted of central and effector memory subsets with reduced alloreactivity against HLA-unmatched cells suggesting the possibility for the development of third-party partial HLA-matching products. In conclusion, our findings show that SARSCoV- 2 specific T cell can be readily expanded from both COVID-19 and unexposed individuals and can therefore be manufactured as a biopharmaceutical product to treat severe COVID-19 patients.Copyright © 2023 International Society for Cell & Gene Therapy

Pharmacovigilance-Based Drug Repurposing

Pharmacovigilance involves evaluation of adverse effects of drugs in the interest of patient safety. Large-scale application of pharmacovigilance generates big datasets that are mined to identify previously unknown drug–event combinations, and, as an extension, may help in identifying new indications for old drugs. The therapeutic potential of a drug using pharmacovigilance-based drug repurposing can be assessed in one of the four ways—serendipity, mechanistic profiling, signature matching, and inverse signaling. Serendipity is the phenomenon of discovery of some valuable information for an already known drug, by chance, like minoxidil. Mechanistic profiling proposed the use of sulfonylureas for diabetes mellitus, based on the observation of their hypoglycemic effect. Signature matching is puzzling out new indications of drugs based on similarity of characteristics in a network of other drugs which are already approved for any condition. Inverse signaling approach takes cues from data mining approaches, applied to pharmacovigilance databases. Currently, this approach is being tried to evaluate existing compounds for Raynaud's phenomenon, COVID-19, Alzheimer' disease, etc. In this chapter, we discuss these pharmacovigilance-based methods as they have immense translational potential for drug repurposing. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

An Interpretable Fake News Detection Method Based on Commonsense Knowledge Graph

Existing deep learning-based methods for detecting fake news are uninterpretable, and they do not use external knowledge related to the news. As a result, the authors of the paper propose a graph matching-based approach combined with external knowledge to detect fake news. The approach focuses on extracting commonsense knowledge from news texts through knowledge extraction, extracting background knowledge related to news content from a commonsense knowledge graph through entity extraction and entity disambiguation, using external knowledge as evidence for news identification, and interpreting the final identification results through such evidence. To achieve the identification of fake news containing commonsense errors, the algorithm uses random walks graph matching and compares the commonsense knowledge embedded in the news content with the relevant external knowledge in the commonsense knowledge graph. The news is then discriminated as true or false based on the results of the comparative analysis. From the experimental results, the method can achieve 91.07%, 85.00%, and 89.47% accuracy, precision, and recall rates, respectively, in the task of identifying fake news containing commonsense errors.

Shifting the Surgical Residency Match to a 100% Virtual Interview Format During the COVID-19 Pandemic, How has It Affected Placement Into Surgical Training Programs?

INTRODUCTION: The AAMC transitioned to virtual interview formats for the 2020-2021 residency match. This study aims to examine the impact of the 100% virtual interview format for the 2020-2021 residency match on both application and match changes for multiple surgical specialties, including neurosurgery (NS), orthopedic surgery (OS), plastic surgery (PS), general surgery (GS), thoracic surgery (TS), and vascular surgery (VS). METHODS: Cross-sectional study comparing application and match changes between the in-person 2019-2020 and virtual 2020-2021 residency match cycles for different surgical specialties. RESULTS: There was an overall increase in the number of applicants for 5 of the surgery specialties but not VS, and an overall increase in the number of applications per residency program across all specialties. The average number of applications per applicant also increased, except in TS. There were no major match changes except in TS, which saw an increase in number of spots filled by MDs to nearly 100% from 84.2% in the previous cycle. CONCLUSION: The switch to the 100% virtual 2020-2021 residency match interview format was associated with an overall increase in the number of applications per program and number of applications per applicant across multiple surgical specialties. There was a decrease in the number of applicants to VS and an increase in the number of applications per applicant. The switch to a virtual interview format in 2020-2021 was also associated with an increase in TS spots filled by MDs to almost 98%, increasing the already concerning TS match disparity for DO and IMG applicants.

Nasal spray of an IgM-like ACE2 fusion protein HH-120 accelerates SARS-CoV-2 clearance: A single-center propensity score-matched cohort study.

HH-120, a recently developed IgM-like ACE2 fusion protein with broad-spectrum neutralizing activity against all ACE2-utilizing coronaviruses, has been developed as a nasal spray for use as an early treatment agent to reduce disease progression and airborne transmission. The objective of this study was to evaluate the safety and efficacy of the HH-120 nasal spray in SARS-CoV-2-infected subjects. Eligible symptomatic or asymptomatic SARS-CoV-2-infected participants were enrolled in a single-arm trial to receive the HH-120 nasal spray for no longer than 6 days or until viral clearance at a single hospital between August 3 and October 7, 2022. An external control was built from real-world data of SARS-CoV-2-infected subjects contemporaneously hospitalized in the same hospital using a propensity score matching (PSM) method. After PSM, 65 participants in the HH-120 group and 103 subjects with comparable baseline characteristics in the external control group were identified. The viral clearance time was significantly shorter in participants receiving the HH-120 nasal spray than that in subjects of the control group (median 8 days vs. 10 days, p < 0.001); the difference was more prominent in those subgroup subjects with higher baseline viral load (median 7.5 days vs. 10.5 days, p < 0.001). The incidence of treatment-emergent adverse events and treatment-related adverse events of HH-120 group were 35.1% (27/77) and 3.9% (3/77), respectively. All the adverse events observed were mild, being of CTCAE grade 1 or 2, and transient. The HH-120 nasal spray showed a favorable safety profile and promising antiviral efficacy in SARS-CoV-2-infected subjects. The results from this study warrant further assessment of the efficacy and safety of the HH-120 nasal spray in large-scale randomized controlled clinical trials.

Reemployment premium effect of furlough programs: evaluating Spain's scheme during the COVID-19 crisis.

This paper presents an average treatment effect analysis of Spain's furlough program during the onset of the COVID-19 pandemic. Using 2020 labour force quarterly microdata, we construct a counterfactual made of comparable nonfurloughed individuals who lost their jobs and apply propensity score matching based on their pretreatment characteristics. Our findings show that the probability of being re-employed in the next quarter significantly increased for the treated (furlough granted group). These results appear robust across models, after testing a wide range of matching specifications that reveal a reemployment probability premium of near 30 percentage points in the group of workers who had been furloughed for a single quarter. Nevertheless, a different time arrangement affected the magnitude of the effect, suggesting that it may decrease with the furlough duration. Thus, an analogous analysis for a longer (two quarter) scheme estimated a still positive but smaller effect, approximately 12 percentage points. Although this finding might alert against long lasting schemes under persistent recessions, this policy still stands as a useful strategy to face essentially transitory adverse shocks.

Reducedhumoral response against variants of concern in childhood solid cancer patients compared to adult patients and healthy children after SARS-CoV-2 vaccination.

Introduction: Although there is extended research on the response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccines in adult cancer patients (ACP), the immunogenicity to the variants of concern (VOCs) in childhood cancer patients (CCP) and safety profiles are now little known. Methods: A prospective, multi-center cohort study was performed by recruiting children with a solid cancer diagnosis and childhood healthy control (CHC) to receive standard two-dose SARS-CoV-2 vaccines. An independent ACP group was included to match CCP in treatment history. Humoral response to six variants was performed and adverse events were followed up 3 months after vaccination. Responses to variants were compared with ACP and CHC by means of propensity score-matched (PSM) analysis. Results: The analysis included 111 CCP (27.2%, median age of 8, quartile 5.5-15 years), 134 CHC (32.8%), and 163 ACP (40.0%), for a total 408 patients. Pathology included carcinoma, neural tumors, sarcoma, and germ cell tumors. Median chemotherapy time was 7 (quartile, 5-11) months. In PSM sample pairs, the humoral response of CCP against variants was significantly decreased, and serology titers (281.8 ± 315.5 U/ml) were reduced, as compared to ACP (p< 0.01 for the rate of neutralization rate against each variant) and CHC (p< 0.01 for the rate of neutralization against each variant) groups. Chemotherapy time and age (Pearson r ≥ 0.8 for all variants) were associated with the humoral response against VOCs of the CHC group. In the CCP group, less than grade II adverse events were observed, including 32 patients with local reactions, and 29 patients had systemic adverse events, including fever (n = 9), rash (n = 20), headache (n = 3), fatigue (n = 11), and myalgia (n = 15). All reactions were well-managed medically. Conclusions: The humoral response against VOCs after the CoronaVac vaccination in CCP was moderately impaired although the vaccine was safe. Age and chemotherapy time seem to be the primary reason for poor response and low serology levels.

Impact of economic shocks on mental health during COVID-19: A longitudinal study of adult residents of Indian metropolitan cities.

Background: The causal impact of income shocks on mental well-being in developing countries is an under-researched area. The COVID-19 pandemic, along with the economic recession caused by the lockdowns to reduce transmission, provides a natural experimental setting to examine the causal impact of a decline in monthly per capita expenditure (MPCE) on mental health of the general population in India during the pandemic. Aim: To evaluate the impact of income shocks on mental health of adults in metro cities during the COVID-19 pandemic. Materials and Methods: The data were collected using the abridged version of the Depression Anxiety Stress Schedule administered through a telephonic survey on adult residents of six metropolitan cities in September-August 2020 and July-August 2021. Results: In the present study, 994 adults participated from six metropolitan cities. Average treatment effects were estimated using Propensity Score Matching. The mean normalized scores are significantly higher for respondents whose MPCE had fallen (treated) vis-à-vis respondents whose MPCE had remained same or increased (control): anxiety (0.21 for treated vs -0.19 for control), stress (0.16 vs -0.14), and depression (0.04 vs -0.19). Propensity score matching reveals that the normalized scores for anxiety, stress, and depression were 33 (95% confidence intervals, CI: 20.0-46.7), 25 (95% CI: 12.9-36.9), and 36 (95% CI: 18.6-53.1) higher among the treated group vis-a-vis control group. The ATET was 34 (95% CI: 18.9-48.9), 26 (95% CI: 10.1-42.9), and 32 (955 CI: 12.3-50.7) for these three outcomes, respectively. The post-estimation tests indicated that the results are valid. Conclusions: The study advocates that policies to ensure income security should be made an integral part of the response packages to tackle pandemics like COVID-19.

Impact of HIV on COVID-19 Outcomes: A Propensity Score Matching Analysis with Varying Age Differences.

To exploratorily test (1) the impact of HIV and aging process among PLWH on COVID-19 outcomes; and (2) whether the effects of HIV on COVID-19 outcomes differed by immunity level. The data used in this study was retrieved from the COVID-19 positive cohort in National COVID Cohort Collaborative (N3C). Multivariable logistic regression models were conducted on populations that were matched using either exact matching or propensity score matching (PSM) with varying age difference between PLWH and non-PLWH to examine the impact of HIV and aging process on all-cause mortality and hospitalization among COVID-19 patients. Subgroup analyses by CD4 counts and viral load (VL) levels were conducted using similar approaches. Among the 2,422,864 adults with a COVID-19 diagnosis, 15,188 were PLWH. PLWH had a significantly higher odds of death compared to non-PLWH until age difference reached 6 years or more, while PLWH were still at an elevated risk of hospitalization across all matched cohorts. The odds of both severe outcomes were persistently higher among PLWH with CD4 < 200 cells/mm3. VL ≥ 200 copies/ml was only associated with higher hospitalization, regardless of the predefined age differences. Age advancement in HIV might significantly contribute to the higher risk of COVID-19 mortality and HIV infection may still impact COVID-19 hospitalization independent of the age advancement in HIV.

Determination of mRNA copy number in degradable lipid nanoparticles via density contrast analytical ultracentrifugation.

Lipid nanoparticles as delivery system for mRNA have recently attracted attention to a broader audience as COVID-19 mRNA vaccines. Their low immunogenicity and capability to deliver a variety of nucleic acids renders them an interesting and complementary alternative to gene therapy vectors like AAVs. An important quality attribute of LNPs is the copy number of the encapsulated cargo molecule. This work describes how density and molecular weight distributions obtained by density contrast sedimentation velocity can be used to calculate the mRNA copy number of a degradable lipid nanoparticle formulation. The determined average copy number of 5 mRNA molecules per LNP is consistent with the previous studies using other biophysical techniques, such as single particle imaging microscopy and multi-laser cylindrical illumination confocal spectroscopy (CICS).

Adapting to the new normal: The impact of remote work on firm performance in Jordan and Morocco

Businesses have been impacted particularly hard by the COVID-19 pandemic, resulting in a decline in productivity. Whether a remote work policy boosts the firm's productivity is still debatable. We use the COVID-19 World Bank Enterprise Survey, a cross-sectional dataset that covers Jordan and Morocco, to empirically examine this question. We use the propensity score matching technique to estimate the causal effect between remote work and firm performance. Results suggest the existence of a positive impact, suggesting that remote work policies cause an increase in productivity. In a further investigation, we perform our regression by country and firm size. Coefficients are found to remain positive in both countries but statistically significant only in Morocco. Regarding firm size, coefficients are found to be positive and statistically significant across all models. The paper offers some recommendations for policymakers in both countries to mitigate the ongoing crisis on firm performance.

Striking a match between FHIR-based patient data and FHIR-based eligibility criteria

Inputs and Outputs: The Strike-a-Match Function, written in JavaScript version ES6+, accepts the input of two datasets (one dataset defining eligibility criteria for research studies or clinical decision support, and one dataset defining characteristics for an individual patient). It returns an output signaling whether the patient characteristics are a match for the eligibility criteria.Purpose: Ultimately, such a system will play a "matchmaker" role in facilitating point of-care recognition of patient-specific clinical decision support.Specifications: The eligibility criteria are defined in HL7 FHIR (version R5) Evidence Variable Resource JSON structure. The patient characteristics are provided in an FHIR Bundle Resource JSON including one Patient Resource and one or more Observation and Condition Resources which could be obtained from the patient's electronic health record.Application: The Strike-a-Match Function determines whether or not the patient is a match to the eligibility criteria and an Eligibility Criteria Matching Software Demonstration interface provides a human-readable display of matching results by criteria for the clinician or patient to consider. This is the first software application, serving as proof of principle, that compares patient characteristics and eligibility criteria with all data exchanged using HL7 FHIR JSON. An Eligibility Criteria Matching Software Library at https://fevir.net/110192 provides a method for sharing functions using the same information model.

Protective effect of ursodeoxycholic acid on COVID-19 in patients with chronic liver disease.

Objective: Ursodeoxycholic acid (UDCA) may reduce susceptibility to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection by downregulating angiotensin-converting enzyme 2 (ACE2), based on recent experimental investigation. This study aimed to determine the potential protective effect of UDCA against SARS-CoV-2 infection in patients with chronic liver disease. Methods: Patients with chronic liver disease receiving UDCA (taking UDCA ≥1 month) at Beijing Ditan Hospital between January 2022 and December 2022 were consecutively enrolled. These patients were matched in a 1:1 ratio to those with liver disease not receiving UDCA during the same period by using a propensity score matching analysis with nearest neighbor matching algorithm. We conducted a phone survey of coronavirus disease 2019 (COVID-19) infection during the early phase of the pandemic liberation (from 15 December 2022 to 15 January 2023). The risk of COVID-19 was compared in two matched cohorts of 225 UDCA users and 225 non-UDCA users based on patient self-report. Results: In the adjusted analysis, the control group was superior to the UDCA group in COVID-19 vaccination rates and liver function indicators, including γ-glutamyl transpeptidase and alkaline phosphatase (p < 0.05). UDCA was associated with a lower incidence of SARS-CoV-2 infection (UDCA 85.3% vs. control 94.2%, p = 0.002), more mild cases (80.0% vs. 72.0%, p = 0.047), and shorter median time from infection to recovery (5 vs. 7 days, p < 0.001). Logistic regression analysis showed that UDCA was a significant protective factor against COVID-19 infection (OR: 0.32, 95%CI: 0.16-0.64, p = 0.001). Furthermore, diabetes mellitus (OR: 2.48, 95%CI: 1.11-5.54, p = 0.027) and moderate/severe infection (OR: 8.94, 95%CI: 1.07-74.61, p = 0.043) were more likely to prolong the time from infection to recovery. Conclusion: UDCA therapy may be beneficial in reducing COVID-19 infection risk, alleviating symptoms, and shortening the recovery time in patients with chronic liver disease. However, it should be emphasized that the conclusions were based on patient self-report rather than classical COVID-19 detection by experimental investigations. Further large clinical and experimental studies are needed to validate these findings.

Diabetes as a risk factor of death in hospitalized COVID-19 patients - an analysis of a National Hospitalization Database from Poland, 2020.

Introduction: Diabetes is one of the comorbidities associated with poor prognosis in hospitalized COVID-19 patients. In this nationwide retrospective study, we evaluated the risk of in-hospital death attributed to diabetes. Methods: We analyzed data from discharge reports of patients hospitalized with COVID-19 in 2020 as submitted to the Polish National Health Fund. Several multivariate logistic regression models were used. In each model, in-hospital death was estimated with explanatory variables. Models were built either on the whole cohorts or cohorts matched with propensity score matching (PSM). The models examined either the main effects of diabetes itself or the interaction of diabetes with other variables. Results: We included 174,621 patients with COVID-19 who were hospitalized in the year 2020. Among them, there were 40,168 diabetic patients (DPs), and the proportion of DPs in this group was higher than in the general population (23.0% vs. 9.5%, p<0.001). In this group of COVID-19 hospitalizations, 17,438 in-hospital deaths were recorded, and the mortality was higher among DPs than non-diabetics (16.3% vs. 8.1%, p<0.001). Multivariate logistic regressions showed that diabetes was a risk factor of death, regardless of sex and age. In the main effect analysis, odds of in-hospital death were higher by 28.3% for DPs than for non-diabetic patients. Similarly, PSM analysis including 101,578 patients, of whom 19,050 had diabetes, showed that the risk of death was higher in DPs regardless of sex with odds higher by 34.9%. The impact of diabetes differed among age groups and was the highest for patients aged 60-69. Conclusions: This nationwide study confirmed that diabetes was an independent risk factor of in-hospital death in the course of COVID-19 infection. However, the relative risk differed across the age groups.

Impact of thromboprophylaxis on hospital acquired thrombosis following discharge in patients admitted with COVID-19: Multicentre observational study in the UK.

Post-discharge thromboprophylaxis in patients admitted with COVID-19 remains controversial. We aimed to determine the impact of thromboprophylaxis on hospital acquired thrombosis (HAT) in patients (≥18 years) discharged following admission for COVID-19 in an observational study across 26 NHS Trusts in the UK (01.04.2020-31.12.2021). Overall, 8895 patients were included to the study: 971 patients were discharged with thromboprophylaxis and propensity score matched (PSM) with a desired ratio of 1:1, from patients discharged without thromboprophylaxis. Patients with heparin induced thrombocytopenia, major bleeding during admission and pregnant women were excluded. As expected from 1:1 PSM, no difference was observed in parameters between the two groups, including duration of hospital stay, except the thromboprophylaxis group had a significantly higher proportion who had received therapeutic dose anticoagulation during admission. There were no differences in the laboratory parameters especially D-dimers between the two groups at admission or discharge. Median duration of thromboprophylaxis following discharge from hospital was 4 weeks (1-8 weeks). No difference was found in HAT in patients discharged with TP versus no TP (1.3% vs. 0.92%, p = 0.52). Increasing age and smoking significantly increased the risk of HAT. Many patients in both cohorts had raised D-dimer at discharge but D-dimer was not associated with increased risk of HAT.

Unsupervised Numerical Information Extraction via Exploiting Syntactic Structures

Numerical information plays an important role in various fields such as scientific, financial, social, statistics, and news. Most prior studies adopt unsupervised methods by designing complex handcrafted pattern-matching rules to extract numerical information, which can be difficult to scale to the open domain. Other supervised methods require extra time, cost, and knowledge to design, understand, and annotate the training data. To address these limitations, we propose QuantityIE, a novel approach to extracting numerical information as structured representations by exploiting syntactic features of both constituency parsing (CP) and dependency parsing (DP). The extraction results may also serve as distant supervision for zero-shot model training. Our approach outperforms existing methods from two perspectives: (1) the rules are simple yet effective, and (2) the results are more self-contained. We further propose a numerical information retrieval approach based on QuantityIE to answer analytical queries. Experimental results on information extraction and retrieval demonstrate the effectiveness of QuantityIE in extracting numerical information with high fidelity.