E.B. Gueguim Kana

Bio: E.B. Gueguim Kana is an academic researcher from University of KwaZulu-Natal. The author has contributed to research in topics: Biohydrogen & Dark fermentation. The author has an hindex of 21, co-authored 40 publications receiving 1108 citations. Previous affiliations of E.B. Gueguim Kana include Ladoke Akintola University of Technology.

Improving the quality of agro-wastes by solid-state fermentation : enhanced antioxidant activities and nutritional qualities

Abstract: Solid substrate fermentations of some agro- wastes, namely cocoa pod husk (CPH), cassava peel (CP), and palm kernel cake (PKC) were carried out for the pro- duction of fructosyltransferase (FTase) by a newly isolated fungal strain Rhizopus stolonifer LAU 07. The fermented substrate were studied for improved nutritional quality by determining the crude protein, crude fibre, ash and lipid contents, and antioxidant activities. The cyanide content of cassava peels was also determined. Some levels of value- addition occured as a result of the fermentation. The pro- tein contents of the substrates increased by 33.3, 55.4, and 94.8%, while the crude fibre contents decreased by 44.5, 8.6, and 7.2% in PKC, CP, and CPH, respectively. The cyanide content of cassava peel was reduced by 90.6%. Generally, fermentation of the substrates by R. stolonifer LAU 07 increased the antioxidant activity in a DPPH (1,1- diphenyl-2-picrylhydrazyl) assay. The IC50 (mg/ml) values of the methanolic extracts (fermented/unfermented) were obtained as 7.0/14.9, 4.4/10.6, and 5.5/14.7 mg/ml for PKC, CP, and CPH, respectively. Results herein reported showed that the nutritional qualities and antioxidant activities of all the investigated solid substrates were enhanced by fungal fermentation. Thus, scope exists for microbial upgrading of these low-quality agro-wastes and development of healthy animal feed supplements.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

Recent Trends in the Pretreatment of Lignocellulosic Biomass for Value-Added Products

Abstract: Lignocellulosic biomass (LCB) is the most abundantly available bioresource amounting to about a global yield of up to 1.3 billion tons per year. The hydrolysis of LCB results in the release of various reducing sugars which are highly valued in the production of biofuels such as bioethanol and biogas, various organic acids, phenols, and aldehydes. The majority of LCB is composed of biological polymers such as cellulose, hemicellulose and lignin, which are strongly associated with each other by covalent and hydrogen bonds thus forming a highly recalcitrant structure. The presence of lignin renders the bio-polymeric structure highly resistant to solubilization thereby inhibiting the hydrolysis of cellulose and hemicellulose which presents a significant challenge for the isolation of the respective bio-polymeric components. This has led to extensive research in the development of various pretreatment techniques utilizing various physical, chemical, physicochemical and biological approaches which are specifically tailored towards the source biomaterial and its application. The objective of this review is to discuss the various pretreatment strategies currently in use and provide an overview of their utilization for the isolation of high-value bio-polymeric components. The article further discusses the advantages and disadvantages of the various pretreatment methodologies as well as addresses the role of various key factors that are likely to have a significant impact on the pretreatment and digestibility of LCB.

Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans

Abstract: Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts. Many machine learning-based approaches have been developed for the prognosis and diagnosis of COVID-19 from medical images and this Analysis identifies over 2,200 relevant published papers and preprints in this area. After initial screening, 62 studies are analysed and the authors find they all have methodological flaws standing in the way of clinical utility. The authors have several recommendations to address these issues.