Statistical heart rate variability analysis for healthy person: Influence of gender and body posture.

Short-term ECG-derived heart rate variability can assess autonomic function non-invasively. The purpose of this study is to investigate the influence of body posture and gender on parasympathetic-sympathetic balance by utilising electrocardiogram (ECG). A total of sixty participants including thirty males (95% CI: 23.34-26.32 years old) and thirty females (95% CI: 23.33-26.07 years old) voluntarily executed three sets of 5-min ECG recordings in supine, sitting and standing posture. A nonparametric Friedman test followed by Bonferroni post-hoc test was carried out to find the statistical differences between the group. A significant difference was observed for RR mean, low frequency (LF), high frequency (HF), ratio LF/HF and the ratio long term variability to short term variability (SD2/SD1) for p < 0.01 while respiration rate (Resp Rate), standard deviation of heart rate (STD_HR), long term variability (SD2), approximate entropy (ApEn), correlation dimension (CD) are non-significant (p > 0.01) for supine, sitting and standing. HRV indices such as standard deviation of NN (SDNN), HRV triangular index (HRVi), and triangular interpolation of NN interval (TINN) are statistically not significant for males but there are significant differences for females at a significance level 1%. Relative reliability and relatedness were evaluated through the interclass coefficient (ICC) and spearman correlation coefficient. The experimental results advocate that there is a posture-specific difference in HRV indices while the correlational studies suggest no such significant differences.

Cost-effectiveness of results-based financing, Zambia: a cluster randomized trial.

OBJECTIVE: To evaluate the cost-effectiveness of results-based financing and input-based financing to increase use and quality of maternal and child health services in rural areas of Zambia. METHODS: In a cluster-randomized trial from April 2012 to June 2014, 30 districts were allocated to three groups: results-based financing (increased funding tied to performance on pre-agreed indicators), input-based financing (increased funding not tied to performance) or control (no additional funding), serving populations of 1.33, 1.26 and 1.40 million people, respectively. We assessed incremental financial costs for programme implementation and verification, consumables and supervision. We evaluated coverage and quality effectiveness of maternal and child health services before and after the trial, using data from household and facility surveys, and converted these to quality-adjusted life years (QALYs) gained. FINDINGS: Coverage and quality of care increased significantly more in results-based financing than control districts: difference in differences for coverage were 12.8% for institutional deliveries, 8.2% postnatal care, 19.5% injectable contraceptives, 3.0% intermittent preventive treatment in pregnancy and 6.1% to 29.4% vaccinations. In input-based financing districts, coverage increased significantly more versus the control for institutional deliveries (17.5%) and postnatal care (13.2%). Compared with control districts, 641 more lives were saved (lower-upper bounds: 580-700) in results-based financing districts and 362 lives (lower-upper bounds: 293-430) in input-based financing districts. The corresponding incremental cost-effectiveness ratios were 809 United States dollars (US$) and US$ 413 per QALY gained, respectively. CONCLUSION: Compared with the control, both results-based financing and input-based financing were cost-effective in Zambia.

Microfluidic paper device with on-site heating to produce reactive peroxide species for enhanced smartphone enabled chemiluminescence signal.

Chemiluminescence signal amplification (CLSA) is of huge interest because of its sensitive detection in various applications such as food analysis, biomedical diagnosis and environmental monitoring. Due to this, there is a manifold attention to develop rapidly prototyped and miniaturized devices for CLSA. In this context, herein, a novel CLSA approach is demonstrated on a 3D printed microfluidic paper-based analytical device (µPADs), fabricated using Fused deposition modeling (FDM) printing technology. Influence of working temperature, ranging 30 °C-110 °C, on CL signal generation from well-established Luminol/Co+2 - H2O2 reaction was analyzed using a screen-printed flexible heater onto the 3D printed reaction platform. A smartphone-based capturing/detection system provided the amenability for a point-of-care testing system. For the first time, strong and stable CLSA was found with about 255% ± 5% increase in its signal intensity without using any additional external enhancers. The on-site working temperature was directly in proportional to the intensity of CL signal generated from Luminol/Co+2 - H2O2 reaction under optimum conditions, wherein the device had a wide linear range from 50 nM to 1 µM with a detection limit of 35 nM for H2O2 detection. The reliability of the developed amplification method was tested for practicability to detect the concentration of H2O2 in milk as real sample analysis. Overall, such CLSA mechanism in miniaturized µPADs will have strong potential for multiple CL based detection and monitoring application.

Bioprospecting Microbiome for Soil and Plant Health Management Amidst Huanglongbing Threat in Citrus: A Review.

Microorganisms have dynamic and complex interactions with their hosts. Diverse microbial communities residing near, on, and within the plants, called phytobiome, are an essential part of plant health and productivity. Exploiting citrus-associated microbiomes represents a scientific approach toward sustained and environment-friendly module of citrus production, though periodically exposed to several threats, with Huanglongbing (HLB) predominantly being most influential. Exploring the composition and function of the citrus microbiome, and possible microbial redesigning under HLB disease pressure has sparked renewed interest in recent times. A concise account of various achievements in understanding the citrus-associated microbiome, in various niche environments viz., rhizosphere, phyllosphere, endosphere, and core microbiota alongside their functional attributes has been thoroughly reviewed and presented. Efforts were also made to analyze the actual role of the citrus microbiome in soil fertility and resilience, interaction with and suppression of invading pathogens along with native microbial communities and their consequences thereupon. Despite the desired potential of the citrus microbiota to counter different pathogenic diseases, utilizing the citrus microbiome for beneficial applications at the field level is yet to be translated as a commercial product. We anticipate that advancement in multiomics technologies, high-throughput sequencing and culturing, genome editing tools, artificial intelligence, and microbial consortia will provide some exciting avenues for citrus microbiome research and microbial manipulation to improve the health and productivity of citrus plants.

Predicting CoVID-19 community mortality risk using machine learning and development of an online prognostic tool.

BACKGROUND: The recent pandemic of CoVID-19 has emerged as a threat to global health security. There are very few prognostic models on CoVID-19 using machine learning. OBJECTIVES: To predict mortality among confirmed CoVID-19 patients in South Korea using machine learning and deploy the best performing algorithm as an open-source online prediction tool for decision-making. MATERIALS AND METHODS: Mortality for confirmed CoVID-19 patients (n = 3,524) between January 20, 2020 and May 30, 2020 was predicted using five machine learning algorithms (logistic regression, support vector machine, K nearest neighbor, random forest and gradient boosting). The performance of the algorithms was compared, and the best performing algorithm was deployed as an online prediction tool. RESULTS: The logistic regression algorithm was the best performer in terms of discrimination (area under ROC curve = 0.830), calibration (Matthews Correlation Coefficient = 0.433; Brier Score = 0.036) and. The best performing algorithm (logistic regression) was deployed as the online CoVID-19 Community Mortality Risk Prediction tool named CoCoMoRP (https://ashis-das.shinyapps.io/CoCoMoRP/). CONCLUSIONS: We describe the development and deployment of an open-source machine learning tool to predict mortality risk among CoVID-19 confirmed patients using publicly available surveillance data. This tool can be utilized by potential stakeholders such as health providers and policymakers to triage patients at the community level in addition to other approaches.

Epidemiology of COVID-19 and Predictors of Recovery in the Republic of Korea.

BACKGROUND: The recent COVID-19 pandemic has emerged as a threat to global health. Though current evidence on the epidemiology of the disease is emerging, very little is known about the predictors of recovery. OBJECTIVES: To describe the epidemiology of confirmed COVID-19 patients in the Republic of Korea and identify predictors of recovery. MATERIALS AND METHODS: Using publicly available data for confirmed COVID-19 cases from the Korea Centers for Disease Control and Prevention from January 20, 2020, to April 30, 2020, we undertook descriptive analyses of cases stratified by sex, age group, place of exposure, date of confirmation, and province. Correlation was tested among all predictors (sex, age group, place of exposure, and province) with Pearson's correlation coefficient. Associations between recovery from COVID-19 and predictors were estimated using a multivariable logistic regression model. RESULTS: Majority of the confirmed cases were females (56%), 20-29 age group (24.3%), and primarily from three provinces-Gyeongsangbuk-do (36.9%), Gyeonggi-do (20.5%), and Seoul (17.1%). The case fatality ratio was 2.1%, and 41.6% cases recovered. Older patients, patients from provinces such as Daegu, Gyeonggi-do, Gyeongsangbuk-do, Jeju-do, Jeollabuk-do, and Jeollanam-do, and those contracting the disease from healthcare settings had lower recovery. CONCLUSIONS: Our study adds to the very limited evidence base on potential predictors of recovery among confirmed COVID-19 cases. We call additional research to explore the predictors of recovery and support development of policies to protect the vulnerable patient groups.

Genetic Variability Based on Tandem Repeat Numbers in a Genomic Locus of 'Candidatus Liberibacter asiaticus' Prevalent in North East India.

The genetic variability of 'Candidatus Liberibacter asiaticus' (CLas) population associated with huanglongbing (HLB) disease of citrus in North Eastern (NE) region of India, a geographically locked region, and home for the diversity of many citrus species was analyzed on the basis of tandem repeat numbers (TRN) in variable CLIBASIA_01645 genomic loci. Fifty-five CLas strains sampled from different groves of NE Hill (NEH) region of India were in single amplicon group, but there was remarkable genetic variability in TRNs. The TRN in HLB-associated CLas strains varied from 0-21 and two novel repeat motifs were also identified. Among the NE population of CLas, TRN5 and TRN9 were most frequent (total frequency of 36.36%) followed by TRN4 (14.55%) and TRN6, TNR7 with a frequency of 12.73% each. Class II type CLas genotypes (5 < TRN ≤ 10) had highest prevalence (frequency of 60.00%) in the samples characterized in present study. Class I (TRN ≤ 5) genotypes were second highest prevalent (29.09%) in the NEH region. Further analysis of genetic diversity parameters using Nei's measure (H value) indicated wide genetic diversity in the CLas strains of NE India (H value of 0.58-0.86). Manipur CLas strains had highest genetic variability (0.86) as compared to Eastern, Southern and Central India. The R10 values (TRN ≤ 10/TRN > 10) of NE CLas population was 10.43 (73/7), higher from other regions of India. Present study conclusively reported the occurrence of high genetic variability in TRN of CLas population in North East Indian citrus groves which have evolved to adapt to the specific ecological niche.