-
Microbiology Spectrum Jun 2024This study aimed to investigate the presence of antimicrobial resistance determinants (ARDs) in the Neanderthal microbiome through meticulous analysis of metagenomic...
UNLABELLED
This study aimed to investigate the presence of antimicrobial resistance determinants (ARDs) in the Neanderthal microbiome through meticulous analysis of metagenomic data derived directly from dental calculus and fecal sediments across diverse Neanderthal sites in Europe. Employing a targeted locus mapping approach followed by a consensus strategy instead of an assembly-first approach, we aimed to identify and characterize ARDs within these ancient microbial communities. A comprehensive and redundant ARD database was constructed by amalgamating data from various antibiotic resistance gene repositories. Our results highlighted the efficacy of the KMA tool in providing a robust alignment of ancient metagenomic reads to the antibiotic resistance gene database. Notably, the KMA tool identified a limited number of ARDs, with only the 23S ribosomal gene from the dental calculus sample of Neanderthal remains at Goyet Troisieme Caverne exhibiting ancient DNA (aDNA) characteristics. Despite not identifying ARDs with typical ancient DNA damage patterns or negative distance proportions, our findings suggest a nuanced identification of putative antimicrobial resistance determinants in the Neanderthal microbiome's genetic repertoire based on the taxonomy-habitat correlation. Nevertheless, our findings are limited by factors such as environmental DNA contamination, DNA fragmentation, and cytosine deamination of aDNA. The study underscores the necessity for refined methodologies to unlock the genomic assets of prehistoric populations, fostering a comprehensive understanding of the intricate dynamics shaping the microbial landscape across history.
IMPORTANCE
The results of our analysis demonstrate the challenges in identifying determinants of antibiotic resistance within the endogenous microbiome of Neanderthals. Despite the comprehensive investigation of multiple studies and the utilization of advanced analytical techniques, the detection of antibiotic resistance determinants in the ancient microbial communities proved to be particularly difficult. However, our analysis did reveal the presence of some authentic ancient conservative genes, indicating the preservation of certain genetic elements over time. These findings raise intriguing questions about the factors influencing the presence or absence of antibiotic resistance in ancient microbial communities. It could be speculated that the spread of current antibiotic resistance, which has reached alarming levels in modern times, is primarily driven by anthropogenic factors such as the widespread use and misuse of antibiotics in medical and agricultural practices.
PubMed: 38916350
DOI: 10.1128/spectrum.02662-23 -
Malaria in under-five children: prevalence and multi-factor analysis of high-risk African countries.BMC Public Health Jun 2024Malaria remains a significant public health challenge in Sub-Saharan Africa (SSA), particularly affecting under-five (UN5) children. Despite global efforts to control...
BACKGROUND
Malaria remains a significant public health challenge in Sub-Saharan Africa (SSA), particularly affecting under-five (UN5) children. Despite global efforts to control the disease, its prevalence in high-risk African countries continues to be alarming, with records of substantial morbidity and mortality rates. Understanding the association of multiple childhood, maternal, and household factors with malaria prevalence, especially among vulnerable young populations, is crucial for effective intervention strategies.
OBJECTIVE
This study examines the prevalence of malaria among UN5 children in selected high-risk SSA countries and analyzes its association with various childhood, maternal, and household factors.
METHODS
Data from the Malaria Indicator Surveys (MIS) spanning from 2010 to 2023 were analyzed. A weighted sample of 35,624 UN5 children from seven countries in sub-Saharan Africa (SSA) known for high malaria prevalence was considered in the analyses. Descriptive statistics and modified Poisson regression analysis were used to assess the association of multiple factors with malaria prevalence. Stata version 15 software was used in analyzing the data and statistical significance was set at a 5% significance level.
RESULTS
The overall pooled prevalence of malaria among the studied population was 26.2%, with substantial country-specific variations observed. In terms of child factors, a child's age was significantly associated with malaria prevalence (APR = 1.010, 95% CI: 1.007-1.012). Children of mothers with higher education levels (APR for higher education = 0.586, 95% CI: 0.425-0.806) and Fansidar uptake during pregnancy (APR = 0.731, 95% CI: 0.666-0.802) were associated with lower malaria risk. Children from middle-wealth (APR = 0.783, 95% CI: 0.706-0.869) and rich (APR = 0.499, 95% CI: 0.426-0.584) households had considerably lower malaria prevalence compared to those from poor households. Additionally, rural residency was associated with a higher risk of malaria compared to urban residency (APR = 1.545, 95% CI: 1.255-1.903).
CONCLUSION
The study highlights a notable malaria prevalence among under-five (UN5) children in high-risk SSA countries, influenced significantly by factors such as maternal education, Fansidar uptake during pregnancy, socioeconomic status, and residency. These findings underscore the importance of targeted malaria prevention strategies that address these key determinants to effectively reduce the malaria burden in this vulnerable population.
Topics: Humans; Prevalence; Female; Child, Preschool; Malaria; Male; Africa South of the Sahara; Infant; Risk Factors; Infant, Newborn; Factor Analysis, Statistical; Socioeconomic Factors
PubMed: 38915034
DOI: 10.1186/s12889-024-19206-1 -
Heliyon Jun 2024The discrepancy between the operating and design capacities of solar plants in eastern Uganda is alarming; about 35 % underperformance in solar power generation is...
The discrepancy between the operating and design capacities of solar plants in eastern Uganda is alarming; about 35 % underperformance in solar power generation is observed. The goal of the current study is to minimize this disparity by improving the design models. Considering only cell temperature in the power generation model is responsible for the observed difference in design and operational solar power generated, the present study used a thermocouple to directly measure cell temperature, an anemometer to measure wind speed, and a solar power meter to measure irradiance. These extrinsic factors were used to modify the power generation model based only on cell temperature through the direct correlation of cell temperature, wind speed, and irradiance with solar power generation. Thus, the absence of extrinsic factors (wind speed and irradiance) in the design models is responsible for the colossal drop in solar power generated. Empirically, the missing extrinsic factors were used to transform the implicit solar power model into an explicit model. The development of a solar power generation model, multiple differential models, simulation and experimentation with a pilot solar rig served as alternate model for the prediction of solar power generation. The second-order differential model validated well with empirical solar power generated in Busitema, Mayuge, Soroti, and Tororo study areas based on RMSEs (0.6437, 0.6692, 0.2008, 0.1804, respectively), thus, narrowing the gap between the designed and operational solar power generated. Mayuge and Soroti recorded the highest solar power generation of 9.028 MW compared to Busitema (8.622 MW) and Tororo (8.345 MW), suggesting that it has a conducive site for installing future solar plants. The above results support the use of empirical explicit (triple) and second-order differential models for the design and operation of power plants.
PubMed: 38912472
DOI: 10.1016/j.heliyon.2024.e32353 -
Ochsner Journal 2024The use of electronic vapor products (EVPs) increases the risks of nicotine addiction, drug-seeking behavior, mood disorders, and avoidable premature morbidities and...
The use of electronic vapor products (EVPs) increases the risks of nicotine addiction, drug-seeking behavior, mood disorders, and avoidable premature morbidities and mortality. We explored temporal trends in EVP use among US adolescents. We used data from the Youth Risk Behavior Survey for school grades 9 through 12 from 2015 (earliest available data) to 2021 (the most recently available data) from the US Centers for Disease Control and Prevention (n=57,006). Daily use of EVPs increased from 2.0% in 2015 to 7.2% in 2019, a greater than 3.5-fold increase. Although the percentage decreased to 5.0% in 2021, it was still a >2.5-fold increase since 2015. In 2015, the percentage of EVP use was significantly higher in boys (2.8%) than girls (1.1%). By 2021, the percentage of EVP use was higher in girls (5.6%) than boys (4.5%), a 1.24-fold increase. In addition, the percentage of EVP use in 2021 was higher in White youth (6.5%) vs Black (3.1%), Asian (1.2%), and Hispanic/Latino (3.4%) youth compared to 2015, but White and Black adolescents had the highest increases of approximately 3.0-fold between 2015 and 2021. Adolescents in grade 12 had the highest percentages of EVP use at all periods. These data show alarming statistically significant and clinically important increases in EVP use in US adolescents in school grades 9 through 12. The magnitude of the increases may have been blunted by coronavirus disease 2019, a hypothesis that requires direct testing in analytic studies. These trends create clinical and public health challenges that require targeted interventions such as mass media campaigns and peer interventions to combat the influences of social norms that promote the adoption of risky health behaviors during adolescence.
PubMed: 38912186
DOI: 10.31486/toj.24.0004 -
Environmental Research Jun 2024Rapid global urbanization and population growth have ignited an alarming surge in emerging contaminants in water bodies, posing health risks, even at trace...
Rapid global urbanization and population growth have ignited an alarming surge in emerging contaminants in water bodies, posing health risks, even at trace concentrations. To address this challenge, novel water treatment and reuse technologies are required as current treatment systems are associated with high costs and energy requirements. These drawbacks provide additional incentives for the application of cost-effective and sustainable biomass-derived activated carbon, which possesses high surface area and low toxicity. Herein, we synthesized microporous activated carbon (MAC) and its magnetic derivative (m-MAC) from tannic acid to decaffeinate contaminated aqueous solutions. Detailed characterization using SEM, BET, and PXRD revealed a very high surface area (>1800 m/g) and a highly porous, amorphous, heterogeneous sponge-like structure. Physicochemical and thermal analyses using XPS, TGA, and EDS confirmed thermal stability, unique surface moieties, and homogeneous elemental distribution. High absorption performance (>96 %) and adsorption capacity (287 and 394 mg/g) were recorded for m-MAC and MAC, respectively. Mechanistic studies showed that the sorption of caffeine is in tandem with multilayer and chemisorptive mechanisms, considering the models' correlation and error coefficients. π-π stacking and hydrogen bonding were among the interactions that could facilitate MAC-Caffeine and m-MAC-Caffeine bonding interactions. Regeneration and reusability experiments revealed adsorption efficiency ranging from 90.5-98.4 % for MAC and 88.6-93.7 % for m-MAC for five cycles. Our findings suggest that MAC and its magnetic derivative are effective for caffeine removal, and potentially other organic contaminants with the possibility of developing commercially viable and cost-effective water polishing tools.
PubMed: 38909946
DOI: 10.1016/j.envres.2024.119446 -
Malaria Journal Jun 2024Elimination of malaria has become a United Nations member states target: Target 3.3 of the sustainable development goal no. 3 (SDG3). Despite the measures taken, the... (Review)
Review
Elimination of malaria has become a United Nations member states target: Target 3.3 of the sustainable development goal no. 3 (SDG3). Despite the measures taken, the attainment of this goal is jeopardized by an alarming trend of increasing malaria case incidence. Globally, there were an estimated 241 million malaria cases in 2020 in 85 malaria-endemic countries, increasing from 227 million in 2019. Malaria case incidence was 59, which means effectively no changes in the numbers occurred, compared with the baseline 2015. Jennifer Doudna-co-inventor of CRISPR/Cas9 technology-claims that CRISPR holds the potential to lessen or even eradicate problems lying in the centre of SDGs. On the same note, CRISPR/Cas9-mediated mosquito-targeting gene drives (MGD) are perceived as a potential means to turn this trend back and put momentum into the malaria elimination effort. This paper assessed two of the critical elements of the World Health Organization Genetically modified mosquitoes (WHO GMM) Critical Pathway framework: the community and stakeholders' engagement (inability to employ widely used frameworks, segmentation of the public, 'bystander' status, and guidelines operationalization) and the regulatory landscape (lex generali, 'goldilocks dilemma', and mode of regulation) concerning mosquito-oriented gene drives (MGD) advances. Based on the assessment findings, the author believes that CRISPR/Cas-9-mediated MGD will not contribute to the attainment of SDG3 (Target 3.3), despite the undisputable technology's potential. This research pertains to the state of knowledge, legal frameworks, and legislature, as of November 2022.
Topics: CRISPR-Cas Systems; Malaria; Animals; Disease Eradication; Humans; Sustainable Development; Community Participation; Mosquito Vectors; Gene Drive Technology; Mosquito Control; Gene Editing
PubMed: 38898518
DOI: 10.1186/s12936-024-04996-x -
Scientific Reports Jun 2024According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning...
According to the literature, seizure prediction models should be developed following a patient-specific approach. However, seizures are usually very rare events, meaning the number of events that may be used to optimise seizure prediction approaches is limited. To overcome such constraint, we analysed the possibility of using data from patients from an external database to improve patient-specific seizure prediction models. We present seizure prediction models trained using a transfer learning procedure. We trained a deep convolutional autoencoder using electroencephalogram data from 41 patients collected from the EPILEPSIAE database. Then, a bidirectional long short-term memory and a classifier layers were added on the top of the encoder part and were optimised for 24 patients from the Universitätsklinikum Freiburg individually. The encoder was used as a feature extraction module. Therefore, its weights were not changed during the patient-specific training. Experimental results showed that seizure prediction models optimised using pretrained weights present about four times fewer false alarms while maintaining the same ability to predict seizures and achieved more 13% validated patients. Therefore, results evidenced that the optimisation using transfer learning was more stable and faster, saving computational resources. In summary, adopting transfer learning for seizure prediction models represents a significant advancement. It addresses the data limitation seen in the seizure prediction field and offers more efficient and stable training, conserving computational resources. Additionally, despite the compact size, transfer learning allows to easily share data knowledge due to fewer ethical restrictions and lower storage requirements. The convolutional autoencoder developed in this study will be shared with the scientific community, promoting further research.
Topics: Humans; Seizures; Electroencephalography; Databases, Factual; Machine Learning; Female; Male; Neural Networks, Computer; Adult
PubMed: 38898066
DOI: 10.1038/s41598-024-64802-1 -
F1000Research 2024Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world's rapid technological...
BACKGROUND
Researchers are focusing their emphasis on quick and real-time healthcare and monitoring systems because of the contemporary modern world's rapid technological improvements. One of the best options is smart healthcare, which uses a variety of on-body and off-body sensors and gadgets to monitor patients' health and exchange data with hospitals and healthcare professionals in real time. Utilizing the primary user (PU) spectrum, cognitive radio (CR) can be highly useful for efficient and intelligent healthcare systems to send and receive patient health data.
METHODS
In this work, we propose a method that combines energy detection (ED) and cyclostationary (CS) spectrum sensing (SS) algorithms. This method was used to test spectrum sensing in CR-based smart healthcare systems. The proposed ED-CS in cognitive radio systems improves the precision of the spectrum sensing. Owing to its straightforward implementation, ED is initially used to identify the idle spectrum. If the ED cannot find the idle spectrum, the signals are found using CS-SS, which uses the cyclic statistical properties of the signals to separate the main users from the interference.
RESULTS
In the simulation analysis, the probability of detection (Pd), probability of a false alarm (Pfa), power spectral density (PSD), and bit error rate (BER) of the proposed ED-CS is compared to those of the traditional Matched Filter (MF), ED, and CS.
CONCLUSIONS
The results indicate that the suggested strategy improves the performance of the framework, making it more appropriate for smart healthcare applications.
Topics: Humans; Algorithms; Delivery of Health Care; Spectrum Analysis
PubMed: 38895702
DOI: 10.12688/f1000research.144624.2 -
BioRxiv : the Preprint Server For... Jun 2024Resident memory T cells (T ) have been described in barrier tissues as having a 'sensing and alarm' function where, upon sensing cognate antigen, they alarm the...
Resident memory T cells (T ) have been described in barrier tissues as having a 'sensing and alarm' function where, upon sensing cognate antigen, they alarm the surrounding tissue and orchestrate local recruitment and activation of immune cells. In the immunologically unique and tightly restricted CNS, it remains unclear if and how brain T , which express the inhibitory receptor PD-1, alarm the surrounding tissue during antigen re-encounter. Here, we reveal that T are sufficient to drive the rapid remodeling of the brain immune landscape through activation of microglia, DCs, NK cells, and B cells, expansion of Tregs, and recruitment of macrophages and monocytic dendritic cells. Moreover, we report that while PD-1 restrains granzyme B expression by reactivated brain T , it has no effect on cytotoxicity or downstream alarm responses. We conclude that T are sufficient to trigger rapid immune activation and recruitment in the CNS and may have an unappreciated role in driving neuroinflammation.
PubMed: 38895249
DOI: 10.1101/2024.06.06.597370 -
Heliyon May 2024Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When...
Most accidents in a chemical process are caused by abnormal or deviations of the process parameters, and the existing research is focused on short-term prediction. When the early warning time is advanced, many false and missing alarms will occur in the system, which will cause certain problems for on-site personnel; how to ensure the accuracy of early warning as much as possible while the early warning time is a technical problem requiring an urgent solution. In the present work, a bidirectional long short-term memory network (BiLSTM) model was established according to the temporal variation characteristics of process parameters, and the Whale optimization algorithm (WOA) was used to optimize the model's hyperparameters automatically. The predicted value was further constructed as a Modified Inverted Normal Loss Function (MINLF), and the probability of abnormal fluctuations of process parameters was calculated using the residual time theory. Finally, the WOA-BiLSTM-MINLF process parameter prediction model with inherent risk and trend risk was established, and the fluctuation process of the process parameters was transformed into dynamic risk values. The results show that the prediction model alarms 16 min ahead of distributed control systems (DCS), which can reserve enough time for operators to take safety protection measures in advance and prevent accidents.
PubMed: 38894726
DOI: 10.1016/j.heliyon.2024.e30821