Introduction of monkeypox virus in Benin,2022

Dear Editor,Monkeypox is an infectious disease that is endemic in a dozen of African countries.Some imported cases have been also reported outside of Africa in the past[1].Since early May 2022,monkeypox infections including human-to-human transmission,were reported in a multi-country outbreak in non-endemic countries and declared Public Health Emergency of International Concern(PHEIC)by the World Health Organization(WHO)in July 2022[2].As of 20 September 2022,a total of at least 62,798 human cases of monkeypox with 20 deaths have been confirmed in 115 countries in five WHO regions[3].

Influence of Variety and Maturity Level on Natural Convective Heat Drying of Four Onion Varieties Grown in Senegal

The considerable post-harvest losses (5% to 40% depending on storage time) due to the high water content level of the onion varieties in Senegal are the main cause of the problem of access to local onion all year round. Therefore, drying is one of the techniques that can be used to solve the problem of onion perishability. This study deals with the characterization of naturally convective kinetics drying of four onion varieties in relation to their maturity level. The experiment was carried out using the gravimetric method. The Welch and Turkey statistical tests display a significant difference between the effective diffusivity coefficients depending on the maturity level within each variety and across the four varieties. The effective diffusivity coefficients of the Galmi Violet, Safari, Gandiol F1 and Orient F1varieties range from 2.18 × 10−11 ± 2.69 × 10−12 to 1.32 × 10−10 ± 1.17 × 10−11 m2⋅s−1 at a maturity level less than 80%. When the maturity level is greater than 85%, the effective diffusivity coefficients range from 1.30 × 10−11 ± 1.24 × 10−12 to 8.05 × 10−11 ± 8.94 × 10−13 m2⋅s−1. As far as the activation energy is concerned, the study only reveals a significant difference between the varieties whatever the maturity level is. The Galmi Violet variety stands out with an average activation energy of 66.71 ± 0.12 KJ⋅mol−1 K−1 for the maturity level below 80% and 58.74 ± 0.11 KJ⋅mol−1 for the maturity level above 85%. For the three remaining varieties, the average activation energy ranges from 58.15 ± 0.19 to 59.12 ± 0.13 KJ⋅mol−1 for a maturity level less than 80% whereas the rates go from 47.63 ± 0.28 to 49.96 ± 0.77 KJ⋅mol−1 when the maturity level is greater than 85%. In summary, the higher the maturity level is, the lower the effective diffusivity coefficients will be. The same tendency was observed with the activation energy. The Galmi Violet variety represents the limitative one in case of the drying of the four varieties mix together.

Observed versus estimated actual trend of COVID-19 case numbers in Cameroon:A data-driven modelling

Controlling the COVID-19 outbreak remains a challenge for Cameroon,as it is for many other countries worldwide.The number of confirmed cases reported by health authorities in Cameroon is based on observational data,which is not nationally representative.The actual extent of the outbreak from the time when the first case was reported in the country to now remains unclear.This study aimed to estimate and model the actual trend in the number of COVID-19 new infections in Cameroon from March 05,2020 to May 31,2021 based on an observed disaggregated dataset.We used a large disaggregated dataset,and multilevel regression and poststratification model was applied prospectively for COVID-19 cases trend estimation in Cameroon from March 05,2020 to May 31,2021.Subsequently,seasonal autoregressive integrated moving average(SARIMA)modeling was used for forecasting purposes.Based on the prospective MRP modeling findings,a total of about 7450935(30%)of COVID-19 cases was estimated from March 05,2020 to May 31,2021 in Cameroon.Generally,the reported number of COVID-19 infection cases in Cameroon during this period underestimated the estimated actual number by about 94 times.The forecasting indicated a succession of two waves of the outbreak in the next two years following May 31,2021.If no action is taken,there could be many waves of the outbreak in the future.To avoid such situations which could be a threat to global health,public health Abbreviations:ACF,Autocorrelation Function;AIC,Akaike information criterion;COVID-19,Coronavirus Disease 2019;MAE,Mean Absolute Error;MAPE,Mean Absolute Percentage Error;MASE,Mean Absolute Scaled Error;ME,Mean Error;MPE,Mean Percentage Error;MRP,Multilevel Regression and Post-stratification;PACF,Partial Autocorrelation Function;PLACARD,Platform for Collecting,Analyzing and Reporting Data;SARIMA,Seasonal Autoregressive integrated moving average;SARS-CoV-2,Severe Acute Respiratory Syndrome Coronavirus 2.

An Extensively Drug Resistant Acinetobacter baumannii from Soft Tissue Isolated in a Hospital in Senegal

Emerging and rapidly spreading multidrug resistant bacteria constitute a rising public health concern worldwide. Acinetobacter baumannii is one of these bacteria that cause different infections including pneumonia, bacteremia, meningitis, soft-tissue, and urinary tract infections, and are associated with high mortality and economic burden. We present a case of a 43-year-old woman, admitted at the department of orthopedics, regional hospital of Ourossogui, North-East of Senegal for soft-tissue injuries. Initially diagnosed with Yersinia pestis infection, the patient was well managed before being released. Supplementary sampling for confirmatory tests allowed the detection of an extensively drug-resistant Acinetobacter baumannii clone.

High-dimensional supervised classification in a context of non-independence of observations to identify the determining SNPs in a phenotype

This work addresses the problem of supervised classification for highly correlated highdimensional data describing non-independent observations to identify SNPs related to a phenotype.We use a general penalized linear mixed model with a single random effect that performs simultaneous SNP selection and population structure adjustment in highdimensional prediction models.Specifically,the model simultaneously selects variables and estimates their effects,taking into account correlations between individuals.Single nucleotide polymorphisms(SNPs)are a type of genetic variation and each SNP represents a difference in a single DNA building block,namely a nucleotide.Previous research has shown that SNPs can be used to identify the correct source population of an individual and can act in isolation or simultaneously to impact a phenotype.In this regard,the study of the contribution of genetics in infectious disease phenotypes is of great importance.In this study,we used uncorrelated variables from the construction of blocks of correlated variables done in a previous work to describe the most related observations of the dataset.The model was trained with 90%of the observations and tested with the remaining 10%.The best model obtained with the generalized information criterion(GIC)identified the SNP named rs2493311 located on the first chromosome of the gene called PRDM16((PR/SET domain 16))as the most decisive factor in malaria attacks.

Concurrent PIEZO1 activation and ATP2B4 blockade effectively reduce the risk of cerebral malaria and inhibit in vitro Plasmodium falciparum multiplication in red blood cells

Malaria caused by the Plasmodium falciparum parasite is responsible for more than 240 million cases per year and killed 627,000 people in 2020,mostly African children.The malaria parasite is transmitted by mosquitos belonging to the genus Anopheles.After an asymptomatic liver stage,the parasite is released into the bloodstream to invade red blood cells(RBCs)and replicate asexually.This erythrocytic phase is associated with a variety of clinical manifestations,including mild and severe malaria.Cerebral malaria(CM)is one of the most severe forms,characterized by the sequestration of parasitized RBCs in the small capillaries of the brain and the local development of cytokine-mediated inflammation.Genetic variants in genes encoding proteins involved in red blood cell physiology are protective factors against severe malaria,as clearly demonstrated for the sickle cell variant of hemoglobin(HbS).

GISAID’s Role in Pandemic Response

GISAID is a global data science initiative and the primary source of genomic and associated metadata of all influenza viruses,Respiratory Syncytial Virus(RSV)and severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),the pandemic coronavirus causing coronavirus disease 2019(COVID-19).GISAID’s publicly accessible data sharing platform enables collaboration of over 42,000 participating researchers from 198 nations and data generators from over 3,500 institutions across the globe.Since the first wholegenome sequences were made available by China CDC through GISAID on January 10.