Machine learning approaches classify clinical malaria outcomes based on haematological parameters

Background Malaria is still a major global health burden, with more than 3.2 billion people in 91 countries remaining at risk of the disease. Accurately distinguishing malaria from other diseases, especially uncomplicated malaria (UM) from non-malarial infections (nMI), remains a challenge. Furthermore, the success of rapid diagnostic tests (RDTs) is threatened by Pfhrp2/3 deletions and decreased sensitivity at low parasitaemia. Analysis of haematological indices can be used to support the identification of possible malaria cases for further diagnosis, especially in travellers returning from endemic areas. As a new application for precision medicine, we aimed to evaluate machine learning (ML) approaches that can accurately classify nMI, UM, and severe malaria (SM) using haematological parameters. Methods We obtained haematological data from 2,207 participants collected in Ghana: nMI (n = 978), SM (n = 526), and UM (n = 703). Six different ML approaches were tested, to select the best approach. An artificial neural network (ANN) with three hidden layers was used for multi-classification of UM, SM, and uMI. Binary classifiers were developed to further identify the parameters that can distinguish UM or SM from nMI. Local interpretable model-agnostic explanations (LIME) were used to explain the binary classifiers. Results The multi-classification model had greater than 85% training and testing accuracy to distinguish clinical malaria from nMI. To distinguish UM from nMI, our approach identified platelet counts, red blood cell (RBC) counts, lymphocyte counts, and percentages as the top classifiers of UM with 0.801 test accuracy (AUC = 0.866 and F1 score = 0.747). To distinguish SM from nMI, the classifier had a test accuracy of 0.96 (AUC = 0.983 and F1 score = 0.944) with mean platelet volume and mean cell volume being the unique classifiers of SM. Random forest was used to confirm the classifications, and it showed that platelet and RBC counts were the major classifiers of UM, regardless of possible confounders such as patient age and sampling location. Conclusion The study provides proof of concept methods that classify UM and SM from nMI, showing that the ML approach is a feasible tool for clinical decision support. In the future, ML approaches could be incorporated into clinical decision-support algorithms for the diagnosis of acute febrile illness and monitoring response to acute SM treatment particularly in endemic settings

Fungal Planet description sheets : 1182–1283

Novel species of fungi described in this study include those from various countries as follows: Algeria, Phaeoacremonium adelophialidum from Vitis vinifera. Antarctica, Comoclathris antarctica from soil. Australia, Coniochaeta salicifolia as endophyte from healthy leaves of Geijera salicifolia, Eremothecium peggii in fruit of Citrus australis, Microdochium ratticaudae from stem of Sporobolus natalensis, Neocelosporium corymbiae on stems of Corymbia variegata, Phytophthora kelmanii from rhizosphere soil of Ptilotus pyramidatus, Pseudosydowia backhousiae on living leaves of Backhousia citriodora, Pseudosydowia indooroopillyensis, Pseudosydowia louisecottisiae and Pseudosydowia queenslandica on living leaves of Eucalyptus sp. Brazil, Absidia montepascoalis from soil. Chile, Ilyonectria zarorii from soil under Maytenus boaria. Costa Rica, Colletotrichum filicis from an unidentified fern. Croatia, Mollisia endogranulata on deteriorated hardwood. Czech Republic, Arcopilus navicularis from tea bag with fruit tea, Neosetophoma buxi as endophyte from Buxus sempervirens, Xerochrysium bohemicum on surface of biscuits with chocolate glaze and filled with jam. France, Entoloma cyaneobasale on basic to calcareous soil, Fusarium aconidiale from Triticum aestivum, Fusarium juglandicola from buds of Juglans regia. Germany, Tetraploa endophytica as endophyte from Microthlaspi perfoliatum roots. India, Castanediella ambae on leaves of Mangifera indica, Lactifluus kanadii on soil under Castanopsis sp., Penicillium uttarakhandense from soil. Italy, Penicillium ferraniaense from compost. Namibia, Bezerromyces gobabebensis on leaves of unidentified succulent, Cladosporium stipagrostidicola on leaves of Stipagrostis sp., Cymostachys euphorbiae on leaves of Euphorbia sp., Deniquelata hypolithi from hypolith under a rock, Hysterobrevium walvisbayicola on leaves of unidentified tree, Knufia hypolithi and Knufia walvisbayicola from hypolith under a rock, Lapidomyces stipagrostidicola on leaves of Stipagrostis sp., Nothophaeotheca mirabibensis (incl. Nothophaeotheca gen. nov.) on persistent inflorescence remains of Blepharis obmitrata, Paramyrothecium salvadorae on twigs of Salvadora persica, Preussia procaviicola on dung of Procavia sp., Sordaria equicola on zebra dung, Volutella salvadorae on stems of Salvadora persica. Netherlands, Entoloma ammophilum on sandy soil, Entoloma pseudocruentatum on nutrient poor (acid) soil, Entoloma pudens on plant debris, amongst grasses. New Zealand, Amorocoelophoma neoregeliae from leaf spots of Neoregelia sp., Aquilomyces metrosideri and Septoriella callistemonis from stem discolouration and leaf spots of Metrosideros sp., Cadophora neoregeliae from leaf spots of Neoregelia sp., Flexuomyces asteliae (incl. Flexuomyces gen. nov.) and Mollisia asteliae from leaf spots of Astelia chathamica, Ophioceras freycinetiae from leaf spots of Freycinetia banksii, Phaeosphaeria caricis-sectae from leaf spots of Carex secta. Norway, Cuphophyllus flavipesoides on soil in semi-natural grassland, Entoloma coracis on soil in calcareous Pinus and Tilia forests, Entoloma cyaneolilacinum on soil semi-natural grasslands, Inocybe norvegica on gravelly soil. Pakistan, Butyriboletus parachinarensis on soil in association with Quercus baloot. Poland, Hyalodendriella bialowiezensis on debris beneath fallen bark of Norway spruce Picea abies. Russia, Bolbitius sibiricus on а moss covered rotting trunk of Populus tremula, Crepidotus wasseri on debris of Populus tremula, Entoloma isborscanum on soil on calcareous grasslands, Entoloma subcoracis on soil in subalpine grasslands, Hydropus lecythiocystis on rotted wood of Betula pendula, Meruliopsis faginea on fallen dead branches of Fagus orientalis, Metschnikowia taurica from fruits of Ziziphus jujube, Suillus praetermissus on soil, Teunia lichenophila as endophyte from Cladonia rangiferina. Slovakia, Hygrocybe fulgens on mowed grassland, Pleuroflammula pannonica from corticated branches of Quercus sp. South Africa, Acrodontium burrowsianum on leaves of unidentified Poaceae, Castanediella senegaliae on dead pods of Senegalia ataxacantha, Cladophialophora behniae on leaves of Behnia sp., Colletotrichum cliviigenum on leaves of Clivia sp., Diatrype dalbergiae on bark of Dalbergia armata, Falcocladium heteropyxidicola on leaves of Heteropyxis canescens, Lapidomyces aloidendricola as epiphyte on brown stem of Aloidendron dichotomum, Lasionectria sansevieriae and Phaeosphaeriopsis sansevieriae on leaves of Sansevieria hyacinthoides, Lylea dalbergiae on Diatrype dalbergiae on bark of Dalbergia armata, Neochaetothyrina syzygii (incl. Neochaetothyrina gen. nov.) on leaves of Syzygium chordatum, Nothophaeomoniella ekebergiae (incl. Nothophaeomoniella gen. nov.) on leaves of Ekebergia pterophylla, Paracymostachys euphorbiae (incl. Paracymostachys gen. nov.) on leaf litter of Euphorbia ingens, Paramycosphaerella pterocarpi on leaves of Pterocarpus angolensis, Paramycosphaerella syzygii on leaf litter of Syzygium chordatum, Parateichospora phoenicicola (incl. Parateichospora gen. nov.) on leaves of Phoenix reclinata, Seiridium syzygii on twigs of Syzygium chordatum, Setophoma syzygii on leaves of Syzygium sp., Starmerella xylocopis from larval feed of an Afrotropical bee Xylocopa caffra, Teratosphaeria combreti on leaf litter of Combretum kraussii, Teratosphaericola leucadendri on leaves of Leucadendron sp., Toxicocladosporium pterocarpi on pods of Pterocarpus angolensis. Spain, Cortinarius bonachei with Quercus ilex in calcareus soils, Cortinarius brunneovolvatus under Quercus ilex subsp. ballota in calcareous soil, Extremopsis radicicola (incl. Extremopsis gen. nov.) from root-associated soil in a wet heathland, Russula quintanensis on acidic soils, Tubaria vulcanica on volcanic lapilii material, Tuber zambonelliae in calcareus soil. Sweden, Elaphomyces borealis on soil under Pinus sylvestris and Betula pubescens. Tanzania, Curvularia tanzanica on inflorescence of Cyperus aromaticus. Thailand, Simplicillium niveum on Ophiocordyceps camponoti-leonardi on underside of unidentified dicotyledonous leaf. USA, Calonectria californiensis on leaves of Umbellularia californica, Exophiala spartinae from surface sterilised roots of Spartina alterniflora, Neophaeococcomyces oklahomaensis from outside wall of alcohol distillery. Vietnam, Fistulinella aurantioflava on soil. Morphological and culture characteristics are supported by DNA barcodes.http://www.ingentaconnect.com/content/nhn/pimjBiochemistryForestry and Agricultural Biotechnology Institute (FABI)GeneticsMicrobiology and Plant PathologyPlant Production and Soil Scienc

Hypoxic adaptation during development: relation to pattern of neurological presentation and cognitive disability

Children with acute hypoxic-ischaemic events (e.g. stroke) and chronic neurological conditions associated with hypoxia frequently present to paediatric neurologists. Failure to adapt to hypoxia may be a common pathophysiological pathway linking a number of other conditions of childhood with cognitive deficit. There is evidence that congenital cardiac disease, asthma and sleep disordered breathing, for example, are associated with cognitive deficit, but little is known about the mechanism and whether there is any structural change. This review describes what is known about how the brain reacts and adapts to hypoxia, focusing on epilepsy and sickle cell disease (SCD). We prospectively recorded overnight oxyhaemoglobin saturation (SpO 2) in 18 children with intractable epilepsy, six of whom were currently or recently in minor status (MS). Children with MS were more likely to have an abnormal sleep study defined as either mean baseline SpO2 &lt;94 or &gt;4 dips of &gt;4 in SpO2/hour (p = .04). In our series of prospectively followed patients with SCD who subsequently developed acute neurological symptoms and signs, mean overnight SpO2 was lower in those with cerebrovascular disease on magnetic resonance angiography (Mann-Whitney, p = .01). Acute, intermittent and chronic hypoxia may have detrimental effects on the brain, the clinical manifestations perhaps depending on rapidity of presentation and prior exposure.</p

What's so bad about teenage pregnancy?

A systematic literature review identified the most frequently cited medical consequences of teenage pregnancy as anaemia, pregnancy-induced hypertension, low birth weight, prematurity, intra-uterine growth retardation and neonatal mortality. Critical appraisal suggested that increased risks of these outcomes were predominantly caused by the social, economic, and behavioural factors that predispose some young women to pregnancy. Maternal age less than 16 years was associated with a modest (1.2-2.7 fold) increase in prematurity, low birth weight and neonatal death