ABSTRACT
All the countries of the world are experiencing a process of business transformation and innovation through the use of digital technologies. COVID-19 has accelerated the digital transformation, but to fully grasp the possibility offered by this crisis, concrete actions are needed to achieve the digital transformation. The effect of new technologies, in the digital first perspective, will lead not only to a more efficient system but above all to relaunch the economy, in particular of some strategic production sectors. The intent of this chapter is to outline global trends and future developments of digitalization in the perspective of Industry 4.0. Thus, a survey and a literature analysis based on structural and conceptual frameworks are developed. The result is the definition of understanding current state of knowledge and to propose future research opportunities in the field of manufacturing digitalization. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
Background: We illustrate a clinical application of Covid-Death Mean-Imputation (CoDMI) algorithm in survival analysis. CoDMI algorithm is a new statistical tool that allows to adjust, through mean imputation based on the Kaplan-Meier model, Covid-19 death events in oncologic clinical trials, providing a complete sample of observations to which any statistical method in survival analysis can be applied. Methods: We analyzed a group of patients who received trimodal treatment - neoadjuvant chemoradiotherapy, followed by surgery and adjuvant chemotherapy - for primary locally advanced rectal cancer (LARC). Overall survival (OS) was calculated in months from the date of diagnosis to the first event, including date of last follow-up or death. Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using CoDMI algorithm. In a traditional Kaplan-Meier approach, patient died of Covid-19 (DoC) can be: i) excluded to the cohort;ii) counted as censored (Cen);iii) considered as died of disease (DoD). CoDMI algorithm offers an additional, more satisfactory option: iv) DoC events are mean-imputed as no-DoC cases at later follow-up times. With this approach, the observed lifetime of each DoC patient is considered as an “incomplete data” and is extended by an additional expected lifetime computed using the classical Kaplan-Meyer model. Results: In total 94 patient records were collected. At the time of the analysis, there were 16 DoD cases, 1 DoC patient and 77 Cen cases. The DoC patient died due to Covid-19 52 months after diagnosis. CoDMI algorithm computed the expected future lifetime (beyond the DoC time of occurrence) provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months, CoDMI algorithm (applied in its standard form: DoC as virtual DoD) estimated that this patient would be died after 79.5 months of follow-up. Table summarizes the 2-year OS and the 5-year OS rates for the different treatment of DoC event. Since our sample contains only one DoC case, the effects on the estimates of the options considered differ very little. In this situation, however, one can better understand how CoDMI algorithm works. Conclusions: CoDMI algorithm leads to the “unbiased” (appropriately adjusted) OS probability in LARC patients with Covid-19 infection, compared with that provided by a naïve application of the Kaplan-Meier approach. This allows a proper interpretation of Covid-19 events in survival analysis. A user-friendly version of CoDMI is available at https://github.com/alef-innovation/codmi.
ABSTRACT
Purpose or Objective To illustrate a clinical application of Covid-Death Mean-Imputation (CoDMI) algorithm in survival analysis. CoDMI algorithm is a new statistical tool that allows to adjust, through mean imputation based on the Kaplan-Meier model, Covid-19 death events in oncologic clinical trials, providing a complete sample of observations to which any statistical method in survival analysis can be applied. Materials and Methods We analyzed a group of patients who received trimodal treatment – neoadjuvant chemoradiotherapy, followed by surgery and adjuvant chemotherapy – for primary locally advanced rectal cancer. Overall survival was calculated in months from the date of diagnosis to the first event, including date of the last follow-up or death. Because Covid-19 death events potentially bias survival estimation, to eliminate skewed data due to Covid-19 death events the observed lifetime of Covid-19 cases was replaced by its corresponding expected lifetime in absence of the Covid-19 event using CoDMI algorithm. In a traditional Kaplan-Meier approach, patient died of Covid-19 (DoC) can be: i) excluded to the cohort (but this would represent a loss of data), or ii) counted as censored (Cen) (but actually, due to its informative nature, Covid-19 death in a cancer patient cannot be censored as death from other causes), or iii) considered as died of disease (DoD) (but this provides an inappropriate exit cause). CoDMI algorithm offers an additional, more satisfactory option: iv) DoC events are mean-imputed as no-DoC cases at later follow-up times. With this approach, the observed lifetime of each DoC patient is considered as an “incomplete data” and is extended by an additional expected lifetime computed using the classical Kaplan-Meyer model. Results A total of 94 patient records were collected. At the time of the analysis, 16 patients died of disease (DoD), 1 patient died of Covid-19 (DoC) and 77 cases were censored (Cen). The DoC patient died due to Covid-19 52 months after diagnosis. CoDMI algorithm computed the expected future lifetime (beyond the DoC time of occurrence) provided by the Kaplan-Meier estimator applied to the no-DoC observations as well as to the DoC data itself. Given the DoC event at 52 months (red triangle in Figure 1), CoDMI algorithm (applied in its standard form) estimated that this patient would be died after 79.5 months of follow-up. The blue line in Figure 1 represents the newly estimated survival curve, where the additional DoD event is denoted by a circle. (Figure Presented) Conclusion CoDMI algorithm leads to the “unbiased” (appropriately adjusted) probability of overall survival in locally advanced rectal cancer patients with Covid-19 infection, compared with that provided by a naïve application of the Kaplan-Meier approach. This allows a proper interpretation/use of Covid-19 events in survival analysis. A user-friendly version of CoDMI is freely available at https://github.com/alef-innovation/codmi.
ABSTRACT
Background: There are no blood screening tests to assess brain molecular alterations linked to neurological alterations in human coronavirus disease 2019 (COVID-19). Evidence indicates long-term brain abnormalities associated with SARS-CoV-2 infection, including cognitive impairment, which may develop into an emerging health problem as many patients are emerging with cognitive abnormalities that may be associated to an increased risk of AD. Promising results from the field of blood-based biomarkers are emerging with the use of extracellular vesicles (EVs). Neuronal-derived EVs (NDEVs) can be isolated from the total pool of EVs in the blood to investigate biomarkers of brain diseases. Methods: Isolation of NDEVs was performed using the ExoQuick ULTRA EV Isolation System, followed by immunoprecipitation with L1CAM antibody. EVs were characterized by nanoparticle tracking analysis, electron microscopy, Exo-Check Array, and ELISA/immunoblotting to detect exosome proteins. Biomarker measurements in the plasma, CSF and EVs from plasma was done by ELISA. A broader analysis of isolated EVs was done by Mass spectrometry. Results: Levels of cytokines were increased in the blood samples from a cohort of 100 COVID-19 patients compared to controls. We had the opportunity to investigate biomarkers in the CSF of 38 patients and observed that the levels of cytokines and biomarkers of neurodegeneration in CSF samples were increased. Conclusions: COVID-19 was associated with increases in CSF and blood cytokines and markers of neurodegeneration. A close follow up in patients that developed COVID-19 symptoms is important to determine the long-term consequences of infection Funding Source: CNPq, FAPERJ, CIHR Keywords: Extracellular Vesicles, Biomarkers, COVID-19
ABSTRACT
The impact of the pandemic and the lockdown has been more devastating than expected on the world economy. It is essential to formulate strategies in real-time. In this research, a multicriteria decision-making model for increasing the preparedness level of sales departments when facing COVID-19 waves and future pandemics is proposed. The model is comprised of 8 criteria, 29 sub-criteria, and 7 alternatives. The study is based on the integration of the AHP and TOPSIS techniques. AHP is used for calculating the criteria and sub-criteria weights. While, TOPSIS is used for calculating the preparedness level, ranking the companies, and identifying the weaknesses that should be addressed for increasing their effectiveness in the current market scenario. The model is developed with the aid of an experts’ group from the electrical appliance sector and studies from the reported literature. This application is completely novel in the literature and has been applied in the wild with remarkable companies in Colombia. A case study in the electrical appliance sector is presented as a pilot study but it should be noted that the methodology is flexible and scalable in any scenario. © 2021 Elsevier Ltd