Rapid Detection of Coronavirus (COVID-19) Using Microwave Immunosensor Cavity Resonator

This paper presents a rapid diagnostic device for the detection of the pandemic coronavirus (COVID-19) using a micro-immunosensor cavity resonator. Coronavirus has been declared an international public health crisis, so it is important to design quick diagnostic methods for the detection of infected cases, especially in rural areas, to limit the spread of the virus. Herein, a proof-of-concept is presented for a portable laboratory device for the detection of the SARS-CoV-2 virus using electromagnetic biosensors. This device is a microwave cavity resonator (MCR) composed of a sensor operating at industrial, scientific and medical (ISM) 2.45 GHz inserted in 3D housing. The changes of electrical properties of measured serum samples after passing the sensor surface are presented. The three change parameters of the sensor are resonating frequency value, amplitude and phase of the reflection coefficient |S11|. This immune-sensor offers a portable, rapid and accurate diagnostic method for the SARS-CoV-2 virus, which can enable on-site diagnosis of infection. Medical validation for the device is performed through biostatistical analysis using the ROC (Receiver Operating Characteristic) method. The predictive accuracy of the device is 63.3% and 60.6% for reflection and phase, respectively. The device has advantages of low cost, low size and weight and rapid response. It does need a trained technician to operate it since a software program operates automatically. The device can be used at ports’ quarantine units, hospitals, etc.

Influence of Different Precipitation Periods on Dendrolimus superans Occurrence: A Biostatistical Analysis

Precipitation is one of the most important abiotic factors that affect Dendrolimus superans occurrence. In this study, agrey slope-correlation model was used, and a simplified grey slope-correlation model was constructed to uncover the most crucial periods of precipitation that pest occurrence. Results revealed that thetwo models were similar; however, the simplified grey slope-correlation model required less calculative steps and was easier to operate. The calculation results revealed that the most crucial period occurred during thefirst 10 days of July (γ13 = 0.67, γ`13 = 0.69). The other precipitation periods associated with pest occurrence included thefirst 10 days of August (γ16 = 0.62, γ`16 = 0.61), the third 10 days of May (γ09 = 0.59, γ`09 = 0.62), the sec10 days of May (γ08 = 0.58, γ`08 = 0.60), and the third 10 days of August (γ18 = 0.58, γ`18 = 0.60). The less associated precipitation periods included the first 10 days of March (γ01 = 0.54, γ`01 = 0.47), the sec10 days of March (γ02 = 0.50, γ`02 = 0.49), the third 10 days of April (γ06 = 0.47, γ`06 = 0.48), the sec10 days of June (γ11 = 0.51, γ`11 = 0.48), and the third 10 days of June (γ12 = 0.51, γ`12 = 0.51). Precipitation in May (γ07 + γ08 + γ09 = 1.74, γ`07 + γ`08 + γ`09 = 1.79) and July (γ13 + γ14 + γ15 = 1.74, γ`13 + γ`14 + γ`15 = 1.79) was mostly associated with D.superansoccurrence. The findings of this study provided a simple operative model for determining the most crucial precipitation periods of pest occurrence, and these analytical methods can serve as a theoretical reference for pest forecasting and early warning, which contributes to ecological protection.© 2021Friends Science Publishers

97 An insight into the piglet’s microbial colonization evolution: From birth towards weaning

The aim of the present study was to explore the evolution of piglet gut microbiota from birth to weaning. Moreover, it was hypothesized that different farm environments could condition this process. Two farms, distinct in their use of antibiotics, and 10 litters per farm were selected. A total of 100 fecal samples were obtained from the same pig of each litter on d2, d7, d14 and d21 of lactation and d14 after weaning. The DNA was extracted by using the PSP® Spin Stool DNA Kit and sequencing of the 16S rRNA gene (V3-V4 region) performed by Illumina MiSeq Platform. Bioinformatics and biostatistical analysis were performed with QIIME and the open-source software R v3.5.3. (phyloseq package). Alpha diversity was strongly affected by age (P< 0.001), with an increasing richness of species through time. Beta diversity decreased after weaning (P< 0.001), suggesting a convergent evolution among individuals. Regarding the structure of the microbiota, a clear clustering of the samples according to age was observed (P< 0.001). A progressive decrease was observed as the piglets aged for Clostridiaceae, Enterobacteriaceae, Fusobacteriaceae, Pasteurellaceae and Streptococcaceae (P< 0.001). In contrast, Lachnospiraceae (P=0.003), Lactobacillaceae (P=0.003) and Veillonellaceae (P=0.025) increased along the d7–d14 period, but decreased afterwards. Campylobacteraceae, Erysipelotrichaceae, Ruminococcaceae (P< 0.001) and Prevotellaceae (P=0.005) gradually increased with age reflecting the change from a milk-oriented microbiome towards a butyrate-producing one. Regarding the impact of the farm, differences in species richness were found and also a distinct microbial structure (ANOSIM: P=0.025) associated to changes in some particular taxonomic groups. In conclusion, during the transition from birth to weaning, the pig microbiota showed a relevant succession of microbial groups towards a more stable ecosystem better adapted to the dry feed. In this relevant early-age process differences between farms seems to have a limited impact.

EVALUATION OF HAEMATOLOGICAL PARAMETERS IN PATIENTS WITH COVID-19 INFECTION – A TEACHING HOSPITAL EXPERIENCE

Introduction - The mankind is facing a major pandemic seen in decades known as COVID-19 disease whose etiological agent is Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2).[1] Haematological Parameters play a very important role in the management of the disease.[4] The present study was designed to evaluate the Haematological Parameters and assess any significant findings associated with the severity of COVID-19 disease. Methods – The COVID-19 RT-PCR confirmed cases were admitted in Dr. Bhim Rao Ambedkar Memorial Hospital, Raipur, Chhattisgarh, India. Two groups were formed and admitted according to the severity of the disease and ICMR Guidelines. Asymptomatic and Mildly Symptomatic cases (ILI cases) were admitted in COVID ward while Severe cases presenting with SARI were admitted in ICU Ward. Haematological Parameters of both the groups from 1st June 2020 to 31st July 2020 were assessed and Biostatistical Analysis was done. Results – Total 87 RT-PCR COVID-19 confirmed cases were admitted with 67 admitted in COVID Isolation ward (Non- ICU) & 20 in ICU ward respectively. No gender differentiation was observed regarding COVID19 infection. Median age of admission is 41.2 years (± 15.5 years, n=87) with ICU admission at 52 years (± 13.9 years, n=20) and Non-ICU admission at 38 years (±14.4 years, n=67). Conclusion – Mean age of Hospitalization in COVID19 disease is 41.2 years ((±15.5, n=87) with ICU admission at 52 years (± 13.9, n=20) and Non-ICU admission at 38 years (±14.4 , n=67). Severity of COVID19 disease increases with senility and co-morbidities while high and/or increasing Total Leucocyte Count (TLC), Absolute Neutrophil Count (ANC), Absolute Monocyte Count (AMC) & low and/or decreasing Absolute Lymphocyte Count (ALC) are the most important Haematological Parameters for COVID-19 diagnosis, severity assessment, prognosis and management.

Biostatistical Analysis on COVID-19

Since receiving unexplained pneumonia patients at the Jinyintan Hospital in Wuhan, China in December 2019, the new coronavirus (COVID-19) has rapidly spread in Wuhan, China and spread to the entire China and some neighboring countries. We establish the dynamics model of infectious diseases and time series model to predict the trend and short-term prediction of the transmission of COVID-19, which will be conducive to the intervention and prevention of COVID-19 by departments at all levels in mainland China and buy more time for clinical trials.