Neurological manifestations associated with SARS-CoV-2 infection: an updated review

SARS-CoV-2 is a single-stranded RNA virus that belongs to the group of seven coronaviruses that affect humans, and its infection causes the COVID-19 disease. The association between the COVID-19 condition and risk factors of neurological manifestations is unclear to date. This review aims to update the main neurological manifestations associated with SARS-CoV-2 disease. First, we present the hypothesis of the neuroinvasion mechanisms of SARS-CoV-2. Then, we discuss the possible symptoms related to patients with COVID-19 infection in the central and peripheral nervous systems, followed by the perspectives of diagnosis and treatment of possible neurological manifesta-tions. The hypothesis of the neuroinvasion mechanism includes direct routes, as the virus crosses the blood-brain barrier or the ACE2 receptor pathway role, and indirect pathways, such as malfunctions of the immune system and vascular system dysregulation. Various studies report COVID-19 consequences, such as neuroanatomic alterations and cognitive impairment, besides peripheral condi-tions, such as anosmia, ageusia, and Guillain Barre Syndrome. However, the het-erogeneity of the studies about neurologic damage in patients after COVID-19 infection precludes any generalization of current findings. Finally, new studies are necessary to understand the adequate diagnosis, therapeutic method of early treatment, and risk group of patients for neurological manifestations of COVID-19 post-infection.Copyright © 2023, Instituto de Investigaciones Clinicas. All rights reserved.

C-Reactive protein and SOFA scale: A simple score as early predictor of critical care requirement in patients with COVID-19 pneumonia in Spain.

OBJECTIVE: To identify potential markers at admission predicting the need for critical care in patients with COVID-19 pneumonia. MATERIAL AND METHODS: An approved, observational, retrospective study was conducted between March 15 to April 15, 2020. 150 adult patients aged less than 75 with Charlson comorbidity index ≤6 diagnosed with COVID-19 pneumonia were included. Seventy-five patients were randomly selected from those admitted to the critical care units (critical care group [CG]) and seventy-five hospitalized patients who did not require critical care (non-critical care group [nCG]) represent the control group. One additional cohort of hospitalized patients with COVID-19 were used to validate the score. MEASUREMENTS AND MAIN RESULTS: Multivariable regression showed increasing odds of in-hospital critical care associated with increased C-reactive protein (CRP) (odds ratio 1.052 [1.009-1.101]; P = 0.0043) and higher Sequential Organ Failure Assessment (SOFA) score (1.968 [1.389-2.590]; P < 0.0001), both at the time of hospital admission. The AUC-ROC for the combined model was 0.83 (0.76-0.90) (vs AUC-ROC SOFA P < 0.05). The AUC-ROC for the validation cohort was 0.89 (0.82-0.95) (P > 0.05 vs AUC-ROC development). CONCLUSION: Patients COVID-19 presenting at admission SOFA score ≥ 2 combined with CRP ≥ 9.1 mg/mL could be at high risk to require critical care.

Quality 4.0 — Green, Black and Master Black Belt curricula

Industrial Big Data (IBD) and Artificial Intelligence (AI) are propelling the new era of manufacturing - smart manufacturing. Manufacturing companies can competitively position themselves amongst the most advanced and influential companies by successfully implementing Quality 4.0 practices. Despite the global impact of COVID-19 and the low deployment success rate, industrialization of the AI mega-trend has dominated the business landscape in 2020. Although these technologies have the potential to advance quality standards, it is not a trivial task. A significant portion of quality leaders do not yet have a clear deployment strategy and universally cite difficulty in harnessing such technologies. The lack of people power is one of the biggest challenges. From a career development standpoint, the higher-educated employees (such as engineers) are the most exposed to, and thus affected by, these new technologies. 79% of young professionals have reported receiving training outside of formal schooling to acquire the necessary skills for Industry 4.0. Strategically investing in training is thus important for manufacturing companies to generate value from IBD and AI. Following the path traced by Six Sigma, this article presents a certification curricula for Green, Black, and Master Black Belts. The proposed curriculum combines six areas of knowledge: statistics, quality, manufacturing, programming, learning, and optimization. These areas, along with an ad hoc 7-step problem solving strategy, must be mastered to obtain a certification. Certified professionals will be well positioned to deploy Quality 4.0 technologies and strategies. They will have the capacity to identify engineering intractable problems that can be formulated as machine learning problems and successfully solve them. These certifications are an efficient and effective way for professionals to advance in their career and thrive in Industry 4.0. © 2021 The Authors. Published by Elsevier B.V.

[C-Reactive protein and SOFA scale: A simple score as early predictor of critical care requirement in patients with COVID-19 pneumonia in Spain]. / Proteína C reactiva y escala SOFA: una simple escala como factor predictivo temprano de la necesidad de cuidados críticos en los pacientes con neumonía causada por COVID-19 en España.

OBJECTIVE: To identify potential markers at admission predicting the need for critical care in patients with COVID-19 pneumonia. MATERIAL AND METHODS: An approved, observational, retrospective study was conducted between March 15 to April 15, 2020. 150 adult patients aged less than 75 with Charlson comorbidity index ≤ 6 diagnosed with COVID-19 pneumonia were included. Seventy-five patients were randomly selected from those admitted to the critical care units (critical care group [CG]) and seventy-five hospitalized patients who did not require critical care (non-critical care group [nCG]) represent the control group. One additional cohort of hospitalized patients with COVID-19 were used to validate the score. MEASUREMENTS AND MAIN RESULTS: Multivariable regression showed increasing odds of in-hospital critical care associated with increased C-reactive protein (CRP) (odds ratio 1.052 [1.009-1.101]; P = .0043) and higher Sequential Organ Failure Assessment (SOFA) score (1.968 [1.389-2.590]; P < .0001), both at the time of hospital admission. The AUC-ROC for the combined model was 0.83 (0.76-0.90) (vs AUC-ROC SOFA P < .05). The AUC-ROC for the validation cohort was 0.89 (0.82-0.95) (P > 0.05 vs AUC-ROC development). CONCLUSION: Patients COVID-19 presenting at admission SOFA score ≥ 2 combined with CRP ≥ 9,1 mg/mL could be at high risk to require critical care.

Learning with Missing Data

Many real-world data sets contain missing values, therefore, learning with incomplete data sets is a common challenge faced by data scientists. Handling them in an intelligent way is important to develop robust data models, since there is no perfect approach to compensate for the missing values. Deleting the rows with empty cells is a commonly used approach, this naive method may lead to estimates with larger standard errors due to reduced sample size. On the other hand, imputing the missing records is a better approach, but it should be used with great caution, as it relies on often unrealistic specific assumptions which can potentially bias results. In this paper, a new greedy-like algorithm is proposed to maximize the number of records. The algorithm can be used to generate various maximized sub-sets by varying the number of columns (features) that can be used for learning. It salvages more records than the naive method, and it avoids the bias induced by imputation. The learning algorithms would be able to learn from real sub-sets without the bias induced by artificial data. Finally, the proposed algorithm is applied to a case study, the COVID-19 Open Research data set (CORD-19) that was prepared and posted by The White House and a coalition of leading research groups as a call to action to the world's artificial intelligence experts to answer high priority scientific questions. This data set contains missing records, therefore, resulting maximized sub-sets from this analysis can be further investigated by the research community. © 2020 IEEE.