Causality methods to study the functional connectivity in brain networks: the basal ganglia - thalamus causal interactions.

This study uses methods recently developed to study the complex evolution of atmospheric phenomena which have some similarities with the dynamics of the human brain. In both cases, it is possible to record the activity of particular centers (geographic regions or brain nuclei) but not to make an experimental modification of their state. The study of "causality", which is necessary to understand the dynamics of these complex systems and to develop robust models that can predict their evolution, is hampered by the experimental restrictions imposed by the nature of both systems. The study was performed with data obtained in the thalamus and basal ganglia of awake humans executing different tasks. This work studies the linear, non-linear and more complex relationships of these thalamic centers with the cortex and main BG nuclei, using three complementary techniques: the partial correlation regression method, the Gaussian process regression/distance correlation and a model-free method based on nearest-neighbor that computes the conditional mutual information. These causality methods indicated that the basal ganglia present a different functional relationship with the anterior-ventral (motor), intralaminar and medio-dorsal thalamic centers, and that more than 60% of these thalamus-basal ganglia relationships present a non-linear dynamic (35 of the 57 relationships found). These functional interactions were observed for basal ganglia nuclei with direct structural connections with the thalamus (primary somatosensory and motor cortex, striatum, internal globus pallidum and substantia nigra pars reticulata), but also for basal ganglia without structural connections with the thalamus (external globus pallidum and subthalamic nucleus). The motor tasks induced rapid modifications of the thalamus-basal ganglia interactions. These findings provide new perspectives of the thalamus - BG interactions, many of which may be supported by indirect functional relationships and not by direct excitatory/inhibitory interactions.

Assumptions in theories of change.

This paper argues that assumptions in a theory of change are the causal connections, events, and conditions that need to be realized for the intervention to work. Using an example of an intervention aimed at improving educational outcomes for girls in a conservative region, two kinds of assumptions are discussed: cause-effect assumptions and causal-link assumptions. Implications for the use of theories of change, including their use in setting causality and the utility of evidence in argument for learning about and testing assumptions are also discussed. The need for an explicit description of what is meant by the term 'assumptions' in association with a theory of change is also highlighted.

Vocal Fold Vertical Thickness in Human Voice Production and Control: A Review.

While current voice research often focuses on laryngeal adjustments in a two-dimensional plane from a superior endoscopic view, recent computational simulations showed that vocal control is three-dimensional and the medial surface vertical thickness plays an important role in regulating the glottal closure pattern and the spectral shape of the produced voice. In contrast, while a small glottal gap is required to initiate and sustain phonation, further changes in the glottal gap within this small range have only small effects on glottal closure and spectral shape. Vocal fold stiffness, particularly along the anterior-posterior direction, plays an important role in pitch control but has only a small effect on glottal closure and spectral shape. These results suggest that voice research should pay more attention to medial surface shape in the vertical dimension. Future studies in a large population of both normal speakers and patients are needed to better characterize the three-dimensional medial surface shape, its variability between speakers, changes throughout the life span, and how it is impacted by voice disorders and clinical interventions. The implications for voice pedagogy and clinical intervention are discussed.

Influenza vaccine coverage in 6months-64 years-old patients affected by chronic diseases: A retrospective cohort study in Italy.

At the beginning of each flu season, the Italian Ministry of Health defines the categories at higher risk of influenza complications, for which vaccination is actively and freely offered. The vaccine coverage (VC) of the influenza vaccine in subjects from 6 months to 64 years of age suffering from diseases that increase the risk of complications from influenza during the 2020-2021 season was evaluated. Our study wants to evaluate the VCs of the influenza vaccine in these subjects during the 2020/2021 season in Apulia. The digital archives relative to the Apulian population were used. A retrospective cohort study design was performed. 484,636 Apulian residents aged between 6 months and 64 years suffered from at least one chronic disease; 139,222 of 484,636 subjects received the influenza vaccine (VC: 28.7%) from October 2020 to January 2021. Considering the single comorbidities, the greatest values are found for pathologies for which major surgical interventions are planned and chronic renal failure/adrenal insufficiency patients, while the worst for chronic liver diseases and pathologies for which major surgical interventions are planned. In any case, it would seem that better VC is achieved in subjects with more than one chronic condition. Influenza vaccination must be promoted as a central public health measure, also because by reducing the burden on hospitals, it can greatly benefit the management of COVID-19 patients. Greater efforts by public health institutions must be implemented in order to achieve better VC in the target categories, including chronic patients.

Probabilistic linguistic fuzzy cognitive maps: applications to the critical factors affecting the health of rural older adults.

BACKGROUND: Achieving healthy ageing has become the only way for China to alleviate the pressure of ageing, especially in rural areas. However, the factors affecting the health of rural older adults are numerous and complex. It is important to identify the critical factors that affecting the health of older adults in rural areas and provide decision-making support for targeted health interventions. METHODS: To overcome some limitations of existing works, an extended probabilistic linguistic fuzzy cognitive map model is proposed in this paper as a useful tool for modeling the cause-effect relationship between factors. The proposed model integrates the advantages of probabilistic linguistic term sets and fuzzy cognitive maps. In the end, to rank and identify the critical factors affecting the health, a novel similarity measure based on Euclidean distance and Z-mapping function is proposed. RESULTS: The proposed model can effectively deal with the uncertainty of experts and reflect different opinions of groups well. In terms of representing uncertainty and ambiguity, the proposed method outperforms other models in modeling complex systems. In the real-world case analysis, we find that education is the most important factor affecting the health of rural older adults, followed by previous occupational experiences, psychology, and physical exercise, among other things. Intergenerational relationship has become another important factor affecting the health of rural older adults in China as the development of Chinese society. CONCLUSIONS: From a macro perspective, social economic status, living environment, lifestyle, and health management, are the variables that have the greatest impact on the health of rural older adults. As a result, providing more precise health interventions with the characteristics of factors influencing health is a crucial guarantee for preserving and improving the health of rural older adults in China.

Autoimmune myasthenia gravis after COVID-19 in a triple vaccinated patient.

Despite a well characterized mechanism, myasthenia gravis (MG) remains a dilemma in terms of etiology. Several case reports and series of cases suggest a potential cause-effect relation between SARS-CoV-2 infection or vaccination and MG. We present the case of an autoimmune MG occurring post Covid-19 in an elderly male, vaccinated with three doses of the BNT162b2/Pfizer-BioNTech vaccine. The 78-year-old male was admitted in the Neurology Clinic in early November 2021 with double vision, bilateral ptosis, dysphonia and dysphagia, 16 days after receiving a third dose of the BNT162b2/Pfizer-BioNTech SARS-CoV-2 vaccine and 12 days after testing positive for SARS-CoV-2 infection. The symptoms began to emerge at 9 days after COVID-19 diagnosis. Clinical neurological examination included ice-pack test and intramuscular neostigmine, both with positive results. Myasthenia gravis positive diagnosis was confirmed by slow repetitive nerve stimulation and abnormally increased serum levels of antibodies against acetylcholine receptors. Due to patient's refusal of further hospitalization, he was discharged with therapy recommendations. Under treatment with oral pyridostigmine, but no oral corticosteroid due to therapeutic noncompliance, the patient was readmitted two months later with aggravated symptoms. The myasthenic crisis was successfully treated with intravenous immunoglobulins, corticosteroid therapy and oral pyridostigmine. The novelty of the current case resides in the fact that, to the best of our knowledge, appears to be the first case of MG clinically manifested after COVID-19 infection in a fully vaccinated patient.

Causal associations of iron status and back pain risk: A Mendelian randomization study.

Background: Observational studies have previously suggested a link between iron status makers and back pain. We conducted a two-sample Mendelian randomization (MR) study to determine the putative causal relationship between systemic iron status and back pain. Materials and methods: In this MR study, a genome-wide association study (GWAS) involving 48,972 individuals was used to identify genetic instruments highly associated with systemic iron status. The outcome data (back pain) were derived from the Neale Lab consortium's summary data from the UK Biobank (85,221 cases and 336,650 controls). With the inverse variance weighted (IVW) method as the main analysis, conservative analyses (selecting SNPs with concordant change of iron status biomarkers) and liberal analyses (selecting SNPs with genome-wide significant association with each iron status biomarker) were carried out. For sensitivity analyses, the MR-Egger, MR-Egger intercept, weighted median, weighted mode, and MR based on a Bayesian model averaging approaches were used. The Cochran's Q-test was used to detect heterogeneity. Results: Back pain was associated with genetically instrumented serum iron (OR = 1.01; 95% CI = 1.00-1.02, p = 0.01), ferritin (OR = 1.02; 95% CI = 1.00-1.04, p = 0.02), and transferrin saturation (OR = 1.01; 95% CI = 1.00-1.01, p = 0.01). Furthermore, there was no evidence of a link between transferrin and the risk of back pain (OR = 0.99, 95% CI = 0.98-1.00, p = 0.08). The sensitivity analyses and Cochran's Q-test indicated that no pleiotropy or heterogeneity was detected (all p > 0.05). Conclusion: We provided potential genetic evidences for the causal associations of iron status with increased incidence of back pain. However, the evidences were weakened due to the low power. Further larger MR studies or RCTs are needed to investigate small effects.

Genetically Predicted Milk Intake Increased Femoral Neck Bone Mineral Density in Women But Not in Men.

Background: Cow milk contains more calcium, magnesium, potassium, zinc, and phosphorus minerals. For a long time, people have believed that increasing milk intake is beneficial to increasing bone density. Many confounding factors can affect milk consumption, and thus the association described to date may not be causal. We explored the causal relationship between genetically predicted milk consumption and Bone Mineral Density (BMD) of the femoral neck and lumbar spine based on 53,236 individuals from 27 studies of European ancestry using the Mendelian randomization (MR) study. 32,961 individuals of European and East Asian ancestry were used for sensitivity analysis. Methods: A genetic instrument used for evaluating milk consumption is rs4988235, a locus located at 13,910 base pairs upstream of the LCT gene. A Mendelian randomization (MR) analysis was conducted to study the effect of selected single nucleotide polymorphisms (SNPs) and BMD. The summary-level data for BMD of the femoral neck and lumbar spine were obtained from two GWAS meta-analyses ['Data Release 2012' and 'Data Release 2015' in the GEnetic Factors for OSteoporosis Consortium (GEFOS)]. Results: we found that genetically predicted milk consumption was not associated with FN-BMD(OR 1.007; 95% CI 0.991-1.023; P = 0.385), LS-BMD(OR 1.003; 95% CI 0.983-1.024; P = 0.743) by performing a meta-analysis of several different cohort studies. High levels of genetically predicted milk intake were positively associated with increased FN-BMD in Women. The OR for each additional milk intake increasing allele was 1.032 (95%CI 1.005-1.059; P = 0.014). However, no causal relationship was found between milk consumption and FN-BMD in men (OR 0.996; 95% CI 0.964-1.029; P = 0.839). Genetically predicted milk consumption was not significantly associated with LS-BMD in women (OR 1.017; 95% CI 0.991-1.043; P = 0.198) and men (OR 1.011; 95% CI 0.978-1.045; P = 0.523). Conclusion: Our study found that women who consume more milk have a higher FN-BMD. When studying the effect of milk consumption on bone density in further studies, we need to pay more attention to women.

A non-parametric cause-effect testing for environmental variables - method and application.

Traditional similarity or resemblance indexes are insufficient to directly reveal the cause-effect relations between environmental variables. Even the typical regression methods are not persuasive enough, since they rely on the assumptions about the data distribution and thus they are not really suitable for small amount of data. In this research, we devise a method to measure the strength of cause and effect (SCE), which is then turned into a non-parametric statistic. By analysing the empirical environmental data from the European Union, we calculate the SCE of these related variables. In addition, by constructing the ranking space and calculating the statistic distribution, we further specify the critical levels and values to conduct the cause-effect testing of these variables. The results show some sectoral activities do, to some degree, directly affect the quality of water and air. Moreover, there is a very clear-cut cause-effect relation between water quality and biodiversity. These results shall provide the policy makers with some ideas regarding the relations between environmental variables.

Informative Causality Extraction from Medical Literature via Dependency-Tree-Based Patterns.

Extracting cause-effect entities from medical literature is an important task in medical information retrieval. A solution for solving this task can be used for compilation of various causality relations, such as causality between disease and symptoms, between medications and side effects, and between genes and diseases. Existing solutions for extracting cause-effect entities work well for sentences where the cause and the effect phrases are name entities, single-word nouns, or noun phrases consisting of two to three words. Unfortunately, in medical literature, cause and effect phrases in a sentence are not simply nouns or noun phrases, rather they are complex phrases consisting of several words, and existing methods fail to correctly extract the cause and effect entities in such sentences. Partial extraction of cause and effect entities conveys poor quality, non-informative, and often, contradictory facts, comparing to the one intended in the given sentence. In this work, we solve this problem by designing an unsupervised method for cause and effect phrase extraction, PatternCausality, which is specifically suitable for the medical literature. Our proposed approach first uses a collection of cause-effect dependency patterns as template to extract head words of cause and effect phrases and then it uses a novel phrase extraction method to obtain complete and meaningful cause and effect phrases from a sentence. Experiments on a cause-effect dataset built from sentences from PubMed articles show that for extracting cause and effect entities, PatternCausality is substantially better than the existing methods-with an order of magnitude improvement in the F-score metric over the best of the existing methods. We also build different variants of PatternCausality, which use different phrase extraction methods; all variants are better than the existing methods. PatternCausality and its variants also show modest performance improvement over the existing methods for extracting cause and effect entities in a domain-neutral benchmark dataset, in which cause and effect entities are nouns or noun phrases consisting of one to two words.

Circulating Vitamin D Levels and the Risk of Atrial Fibrillation: A Two-Sample Mendelian Randomization Study.

Aim: We performed a two-sample Mendelian randomization (MR) analysis to evaluate the association between serum vitamin D levels and atrial fibrillation (AF) risks. Methods: Data on the single-nucleotide polymorphisms (SNPs) related to vitamin D, 25-hydroxyvitamin D, and AF outcome were obtained from a UK Biobank study, SUNLIGHT consortium, and the latest meta-analysis of genome-wide association studies GWASs with six independent cohorts, respectively. MR analysis was performed to obtain the estimates, followed by the use of inverse variance weighted (IVW) method, weighted median method, maximum likelihood, MR-egger method, and MR-PRESSO methods. Results: The IVW estimate showed that genetically predicted vitamin D and 25-hydroxyvitamin D levels were not causally associated with the risk of AF with two models. The association was consistent in complementary analyses. Conclusions: Our MR finding suggested that no genetic evidence of serum vitamin D levels was significantly associated with AF risk. Further researches are necessary to explore the potential role and mechanisms of circulating serum vitamin D levels on AF.

Humanized Germ-Free Mice for Investigating the Intervention Effect of Commensal Microbiome on Cancer Immunotherapy.

Significance: A growing body of evidence has demonstrated that the commensal microbiome is deeply involved in the host immune response, accounting for significantly divergent clinical outcomes among cancer patients receiving immunotherapy. Therefore, precise screening and evaluating of functional bacterial strains as novel targets for cancer immunotherapy have attracted great enthusiasm from both academia and industry, which calls for the construction and application of advanced animal models to support translational research in this field. Recent Advances: Significant progress has been made to elucidate the intervention effect of commensal microbiome on immunotherapy based on animal experiments. Especially, correlation between gut microbiota and host response to immunotherapy has been continuously discovered in a variety of cancer types, laying the foundation for causality establishment and mechanism research. Critical Issues: In oncology research, it is particularly not uncommon to see that a promising preclinical result fails to translate into clinical success. The use of conventional murine models in immunotherapy-associated microbiome research is very likely to bring discredit on the preclinical findings. We emphasize the value of germ-free (GF) mice and humanized mice as advanced models in this field. Future Directions: Integrating rederivation and humanization to generate humanized GF mice as preclinical models would make it possible to clarify the role of specific bacterial strains in immunotherapy as well as obtain preclinical findings that are more predictive for humans, leading to novel microbiome-based strategies for cancer immunotherapy. Antioxid. Redox Signal. 37, 1291-1302.

Relationship Between Plasma Vitamin C and COVID-19 Susceptibility and Severity: A Two-Sample Mendelian Randomization Study.

Background: Considering the antioxidant function of Vitamin C, also called ascorbic acid, it is widely used against viral infections such as coronavirus disease (COVID-19) based on in vitro, observational, and ecological studies. Many confounding factors that can affect Vitamin C levels. Thus, the association described to date may not be causal. To determine the causal relationship between genetically predicted plasma Vitamin C and COVID-19 susceptibility and severity, we performed two-sample Mendelian randomization (MR) based on large samples. Methods: The summary-level data for Vitamin C was obtained from a GWAS meta-analysis, which included 52,018 individuals from four studies of European ancestry. Data for COVID-19 HGI results were obtained from the meta-analysis of 35 GWASs with more than 1,000,000 subjects of European ancestry, including 32,494 cases with COVID-19 susceptibility and 1,316,207 controls, 9,986 cases with COVID-19 hospitalization and 1,877,672 controls, and 5,101 cases with COVID-19 severe disease and 1,383,241 controls. Mendelian randomization (MR) analysis was conducted to examine the effect of selected single nucleotide polymorphisms and COVID-19 susceptibility, hospitalization, disease severity. Several sensitivity analyses were performed with inverse-variance weighted (random-effect model), inverse variance weighted (fixed-effect model), weighted median, and maximum likelihood methods for estimating the causal effects. Results: In this MR study, genetic predisposition to the levels of plasma Vitamin C was not associated with COVID-19 susceptibility (OR: 0.99, 95% CI: 0.84-1.17, P = 0.91), hospitalization (OR: 1.10, 95% CI: 0.71-1.71, P = 0.67) and severity (OR: 0.83, 95% CI: 0.43-1.59, P = 0.58). The association was consistent in complementary analyses. No potential heterogeneities and directional pleiotropies were observed for the analysis results. Conclusion: According to our study, no correlation was observed between plasma Vitamin C levels and COVID-19 susceptibility and severity. Further studies in different ethnics are necessary to explore the potential role and mechanisms of circulating serum Vitamin C levels on COVID-19.

Combining failure modes and effects analysis and cause-effect analysis: a novel method of risk analysis to reduce anaphylaxis due to contrast media.

BACKGROUND: Contrast media agents are essential for computed tomography (CT)-based diagnoses. However, they can cause fatal adverse effects such as anaphylaxis in patients. Although it is rare, the chances of anaphylaxis increase with the number of examinations. OBJECTIVE: We aimed to design a quality improvement initiative to reduce patient risk to contrast media agents. METHODS: We analysed CT processes using contrast iodine in a tertiary-care academic hospital that performs approximately 14 000 CT scans per year in Japan. We applied a combination of failure modes and effects analysis (FMEA) and cause-effect analysis to reduce the risk of patients developing allergic reactions to iodine-based contrast agents during CT imaging. RESULTS: Our multidisciplinary team comprising seven professionals analysed the data and designed a 56-process flowchart of CT imaging with iodine. We obtained 177 failure modes, of which 15 had a risk-probability number higher than 100. We identified the two riskiest processes and developed cause-and-effect diagrams for both: one was related to the exchange of information between the radiation and hospital information system regarding the patient's allergy, the other was due to education and structural deficiencies in observation following the exam. CONCLUSION: The combined method of FMEA and cause-and-effect analysis reveals high-risk processes and suggests measures to reduce these risks. FMEA is not well-known in healthcare but has significant potential for improving patient safety. Our findings emphasise the importance of adopting new techniques to reduce patient risk and carry out best practices in radiology.

A Framework for Identification, Assessment and Prioritization of Climate Change Adaptation Measures for Roads and Railways.

Severe accidents and high costs associated with weather-related events already occur in today's climate. Unless preventive measures are taken, the costs are expected to increase in future due to ongoing climate change. However, the risk reduction measures are costly as well and may result in unwanted impacts. Therefore, it is important to identify, assess and prioritize which measures are necessary to undertake, as well as where and when these are to be undertaken. To be able to make such evaluations, robust (scientifically based), transparent and systematic assessments and valuations are required. This article describes a framework to assess the cause-and-effect relationships and how to estimate the costs and benefits as a basis to assess and prioritize measures for climate adaptation of roads and railways. The framework includes hazard identification, risk analysis and risk assessment, identification, monetary and non-monetary evaluation of possible risk reduction measures and a step regarding distribution-, goal- and sensitivity analyses. The results from applying the framework shall be used to prioritize among potential risk reduction measures as well as when to undertake them.

Analysis of fitness among Korean adults by the cause-effect relation in lump mean scheme.

BACKGROUND: The aim of this study was to examine whether the fitness of Korean adults can be analyzed by the cause-effect relation using the linearity or Gaussianity in the lump mean scheme (LMS). METHODS: This study analyzed previous results for the sit-up test obtained in the LMS by regression analysis in Sigmaplot 14. The effects of the body mass index (BMI) and new waist-to-height ratio (WHT2R) introduced by the present author on fitness were investigated. RESULTS: The distribution of the sit-up test score with respect to the BMI and WHT2R were interpreted by their Gaussianity and linearity, respectively. This means that the muscular endurance of males is determined by two causes (fat and muscle) when the BMI is a variable and one cause (abdominal fat) when the WHT2R is a variable. CONCLUSIONS: Personal exercise aims were simpler to establish using WHT2R than using BMI. On the other hand, it was recommended for people with a low BMI to increase their fitness using exercises that increase their muscle mass.

Experiment Selection in Meta-Analytic Piecemeal Causal Discovery.

Scientists try to design experiments that will yield maximal information. For instance, given the available evidence and a limitation on the number of variables that can be observed simultaneously, it may be more informative to intervene on variable X and observe the response of variable Y than to intervene on X and observe Z; in other situations, the opposite may be true. Scientists must often make these decisions without primary data. To address this problem, in previous work, we created software for annotating aggregate statistics in the literature and deriving consistent causal explanations, expressed as causal graphs. This meta-analytic pipeline is useful not only for synthesizing evidence but also for planning experiments: one can use it strategically to select experiments that could further eliminate causal graphs from consideration. In this paper, we introduce interpretable policies for selecting experiments in the context of piecemeal causal discovery, a common setting in biological sciences in which each experiment can measure not an entire system but rather a strict subset of its variables. The limits of this piecemeal approach are only beginning to be fully characterized, with crucial theoretical work published recently. With simulations, we show that our experiment-selection policies identify causal structures more efficiently than random experiment selection. Unlike methods that require primary data, our meta-analytic approach offers a flexible alternative for those seeking to incorporate qualitative domain knowledge into their search for causal mechanisms. We also present a method that categorizes hypotheses with respect to their utility for identifying a system's causal structure. Although this categorization is usually infeasible to perform manually, it is critical for conducting research efficiently.

The fungal community outperforms the bacterial community in predicting plant health status.

Characterizing the relationship between soil biotic and abiotic properties and plant health status is crucial to understanding the pathogenesis of soil-borne diseases. Here, we compared these properties in the soils of lisianthus with different disease incidence plots and report the cause-effect relationship between soil properties and plant health status using heat treatment coupled with microbiota self/across re-inoculations. The relative importance of soil bacterial and fungal communities in predicting plant health was also analyzed. Results showed that the soils with low and high disease incidences (LDS and HDS) harbored differential microbial communities and physicochemical properties. The LDS soil had relatively low Fusarium oxysporum abundance, electrical conductivity (EC), and NO3--N content. Soil microbial community was the direct determinant of plant health. The disease-suppressive activity of the microbiome in the LDS soil could be transferred to the HDS soil. Also, the relative importance of the fungal community in predicting plant health status was greater than that of the bacterial community, as reflected by (1) the fungal community could drive more complex networks related to healthy plants and (2) the diversity and core taxa of the fungal community had higher mean predictor importance values for plant health. The relative abundances of core genera Acremonium, Mycothermus, and Chryseolinea were significantly and negatively correlated with the disease incidence and the abundances of pathogens, identifying these genera as potential disease-suppressive agents. Taken together, our results reveal a direct relationship between soil properties and plant health status, in which the fungal community composition is most important for predicting plant health status. KEY POINTS: • Soil with differing pathological groups harbors distinct microbial communities. • Soil microbial communities directly determine the plant's health status. • Fungal community is a better predictor of plant health than the bacterial community.

Causal relationship extraction from biomedical text using deep neural models: A comprehensive survey.

The identification of causal relationships between events or entities within biomedical texts is of great importance for creating scientific knowledge bases and is also a fundamental natural language processing (NLP) task. A causal (cause-effect) relation is defined as an association between two events in which the first must occur before the second. Although this task is an open problem in artificial intelligence, and despite its important role in information extraction from the biomedical literature, very few works have considered this problem. However, with the advent of new techniques in machine learning, especially deep neural networks, research increasingly addresses this problem. This paper summarizes state-of-the-art research, its applications, existing datasets, and remaining challenges. For this survey we have implemented and evaluated various techniques including a Multiview CNN (MVC), attention-based BiLSTM models and state-of-the-art word embedding models, such as those obtained with bidirectional encoder representations (ELMo) and transformer architectures (BioBERT). In addition, we have evaluated a graph LSTM as well as a baseline rule based system. We have investigated the class imbalance problem as an innate property of annotated data in this type of task. The results show that a considerable improvement of the results of state-of-the-art systems can be achieved when a simple random oversampling technique for data augmentation is used in order to reduce class imbalance.

Influenza Vaccination and Health Outcomes in COVID-19 Patients: A Retrospective Cohort Study.

COVID-19 is an infectious disease caused by the novel coronavirus SARS-CoV-2. Several measures aimed at containing the spread of this virus have been recommended by international and nation public health institutions, but whether the influenza vaccine, while not protective against COVID-19, nonetheless reduces disease severity is unclear. This study evaluated the potential role of influenza vaccine in reducing the rate of hospitalization and death in COVID-19 patients. COVID-19 cases recorded in the province of Brindisi (Apulia, Southern Italy) during the first pandemic wave (February-May 2020) and occurring in patients vaccinated with the influenza vaccine during the 2019-2020 influenza season were considered. From February 2020 to May 2020, 3872 inhabitants of the province of Brindisi underwent SARS-CoV-2 PCR testing and 664 (8.7%) tested positive. A multivariate analysis showed that among COVID-19 patients neither hospitalization nor death was significantly associated with influenza vaccination (p > 0.05), whereas within this group male sex, older age, and chronic diseases were identified as risk factors for morbidity and mortality. Our study did not show an association between the influenza vaccine and complications of COVID-19. Nonetheless, influenza vaccination must be promoted as a central public health measure, because by reducing the burden on hospitals it can greatly benefit the management of COVID-19 patients.