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Seaweed from macroalgae and cellulose from nonwood materials have gained attention in various fields. This study explores how seaweed and cellulose nanofibers (CNF) interact to form 3D networks in composite bioaerogels. The ratio of CNF and seaweed was varied to see how it affects the aerogel’s inside and its properties. The observations show that the biocomposite aerogel is more rigid and shrinks less than using a single biopolymer. The CNF aerogel has a fine, thin network structure, and the seaweed aerogel has a thin sheet structure. The biocomposite aerogel combines both a fine network and a thin sheet structure. The composite aerogel’s mechanical properties are significantly influenced by seaweed composition. The introduction of CNF increases elasticity, while seaweed enhances firmness. Generated computer modelling revealed that the abundant hydroxyl groups in CNF facilitated the formation of intermolecular bonds with seaweed. The bonding led to increased adhesion and entanglement between biopolymers, consequently enhancing elasticity and establishing a stable intermolecular interaction. The 3D X-ray imaging model shows that the skeletal framework primarily consists of seaweed biopolymer, with CNF serving to reinforce this structure thus enhancing the mechanical properties and robustness of the composite bioaerogels.

ABSTRACT: The pressing issue of the moment affecting public health is antimicrobial resistance. Methicillin-resistant Staphylococcus aureus (MRSA) is an emerging pathogen that is proven to be resistant to a range of antibiotics like penicillins, cephalosporins, other β ββ-lactam antibiotics, and carbapenems. Baicalein, a phytochemical of the traditional Chinese herb, Scutellaria baicalensis has an inhibitory effect on different bacteria. The current study aimed to investigate the synergistic effect of baicalein and β ββlactam antibiotics (amoxicillin and ampicillin) against methicillin-resistant S. aureus (MRSA). The minimum inhibitory concentrations (MICs) of baicalein and β-lactam antibiotics (amoxicillin and ampicillin) against the MRSA were determined through the broth dilution method. The determination of synergism was tested by checkerboard tube dilution and time-kill assay procedure. The combination of baicalein with amoxicillin showed more potent synergy against MRSA strain compared to the combination of baicalein and ampicillin. The fractional inhibitory concentration indices (FICI) of amoxicillin + baicalein varied from 0.19 to 0.50. The fractional inhibitory concentration (FIC) indices of ampicillin + baicalein varied from 0.19 to 0.25. These findings offer significant evidence that the amoxicillin combined with baicalein showed potent synergistic activity against MRSA in vitro and might offer promising therapeutic implications for MRSA infections.

INTRODUCTION: Most people with Alzheimer's disease and related dementia (ADRD) also suffer from two or more chronic conditions, known as multiple chronic conditions (MCC). While many studies have investigated the MCC patterns, few studies have considered the synergistic interactions with other factors (called the syndemic factors) specifically for people with ADRD. METHODS: We included 40,290 visits and identified 18 MCC from the National Alzheimer's Coordinating Center. Then, we utilized a multi‐label XGBoost model to predict developing MCC based on existing MCC patterns and individualized syndemic factors. RESULTS: Our model achieved an overall arithmetic mean of 0.710 AUROC (SD = 0.100) in predicting 18 developing MCC. While existing MCC patterns have enough predictive power, syndemic factors related to dementia, social behaviors, mental and physical health can improve model performance further. DISCUSSION: Our study demonstrated that the MCC patterns among people with ADRD can be learned using a machine‐learning approach with syndemic framework adjustments. Highlights: Machine learning models can learn the MCC patterns for people with ADRD.The learned MCC patterns should be adjusted and individualized by syndemic factors.The model can predict which disease is developing based on existing MCC patterns.As a result, this model enables early specific MCC identification and prevention. [ABSTRACT FROM AUTHOR]

Abstract Phages can successfully be used in vitro and in planta to biocontrol the phytopathogenic Ralstonia solanacearum bacterium—the causal agent of bacterial wilt disease. However, phage biocontrol outcomes are still variable, and it is unclear what causes this. In this study, we assessed the efficiency of four phages in controlled in vitro and in planta experiments in all one‐ and two‐phage combinations. We found that using phages in combination did not improve the phage biocontrol efficiency relative to single phage treatments, while certain phages and their combinations were more effective than the others. High intra‐treatment variability in phage efficiency was observed across all phage treatments, which was associated with clear shifts in microbiome composition, a reduction in R. solanacearum and an increase in phage densities. We further identified the bacterial taxa that were associated with these ‘shifted’ microbiomes and conducted additional plant growth experiments, demonstrating that some of the enriched bacterial species could protect plants from R. solanacearum infections—a pattern which was also observed using partial least squares path modelling (PLS‐PM). Together, these results suggest that phages could open niche space for beneficial bacteria by reducing pathogen densities and that variability in phage biocontrol outcomes is rhizosphere microbiome‐dependent, which can introduce between‐replicate variation, even in controlled greenhouse conditions.

ABSTRACT Metabolic exchange plays a crucial role in shaping microbial community interactions and functions, including the exchange of small molecules such as cofactors. Cofactors are fundamental to enzyme catalytic activities; however, the role of cofactors in microbial stress tolerance is unclear. Here, we constructed a synergistic consortium containing two strains that could efficiently mineralize di-(2-ethylhexyl) phthalate under hyperosmotic stress. Integration of transcriptomic analysis, metabolic profiling, and a genome-scale metabolic model (GEM) facilitated the discovery of the potential mechanism of microbial interactions. Multi-omics analysis revealed that the vitamin B12-dependent methionine-folate cycle could be a key pathway for enhancing the hyperosmotic stress tolerance of synergistic consortium. Further GEM simulations revealed interspecies exchange of S-adenosyl-L-methionine and riboflavin, cofactors needed for vitamin B12 biosynthesis, which was confirmed by in vitro experiments. Overall, we proposed a new mechanism of bacterial hyperosmotic stress tolerance: bacteria might promote the production of vitamin B12 to enhance biofilm formation, and the species collaborate with each other by exchanging cofactors to improve consortium hyperosmotic stress tolerance. These findings offer new insights into the role of cofactors in microbial interactions and stress tolerance and are potentially exploitable for environmental remediation.IMPORTANCEMetabolic interactions (also known as cross-feeding) are thought to be ubiquitous in microbial communities. Cross-feeding is the basis for many positive interactions (e.g., mutualism) and is a primary driver of microbial community assembly. In this study, a combination of multi-omics analysis and metabolic modeling simulation was used to reveal the metabolic interactions of a synthetic consortium under hyperosmotic stress. Interspecies cofactor exchange was found to promote biofilm formation under hyperosmotic stress. This provides a new perspective for understanding the role of metabolic interactions in microbial communities to enhance environmental adaptation, which is significant for improving the efficiency of production activities and environmental bioremediation.

Anaerobic ammonium oxidizing (anammox) bacteria are utilized for high efficiency nitrogen removal from nitrogen-laden sidestreams in wastewater treatment plants. The anammox bacteria form a variety of competitive and mutualistic interactions with heterotrophic bacteria that often employ denitrification or dissimilatory nitrate reduction to ammonium (DNRA) for energy generation. These interactions can be heavily influenced by the influent ratio of ammonium to nitrite, NH4+:NO2-, where deviations from the widely acknowledged stoichiometric ratio (1:1.32) have been demonstrated to have deleterious effects on anammox efficiency. Thus, it is important to understand how variable NH4+:NO2- ratios impact the microbial ecology of anammox reactors. We observed the response of the microbial community in a lab scale anammox membrane bioreactor (MBR) to changes in the influent NH4+:NO2- ratio using both 16S rRNA gene and shotgun metagenomic sequencing. Ammonium removal efficiency decreased from 99.77 ± 0.04% when the ratio was 1:1.32 (prior to day 89) to 90.85 ± 0.29% when the ratio was decreased to 1:1.1 (day 89-202) and 90.14 ± 0.09% when the ratio was changed to 1:1.13 (day 169-200). Over this same timespan, the overall nitrogen removal efficiency (NRE) remained relatively unchanged (85.26 ± 0.01% from day 0-89, compared to 85.49 ± 0.01% from day 89-169, and 83.04 ± 0.01% from day 169-200). When the ratio was slightly increased to 1:1.17-1:1.2 (day 202-253), the ammonium removal efficiency increased to 97.28 ± 0.45% and the NRE increased to 88.21 ± 0.01%. Analysis of 16 S rRNA gene sequences demonstrated increased relative abundance of taxa belonging to Bacteroidetes, Chloroflexi, and Ignavibacteriae over the course of the experiment. The relative abundance of Planctomycetes, the phylum to which anammox bacteria belong, decreased from 77.19% at the beginning of the experiment to 12.24% by the end of the experiment. Analysis of metagenome assembled genomes (MAGs) indicated increased abundance of bacteria with nrfAH genes used for DNRA after the introduction of lower influent NH4+:NO2- ratios. The high relative abundance of DNRA bacteria coinciding with sustained bioreactor performance indicates a mutualistic relationship between the anammox and DNRA bacteria. Understanding these interactions could support more robust bioreactor operation at variable nitrogen loading ratios.

The interactions between pathogens during infection and the impact of these interactions on drug effectiveness are poorly understood, making polymicrobial infections challenging to treat. During an infection, cross-interactions between bacteria and fungi can strengthen virulence mechanisms and affect how the disease develops. The purpose of this study is to determine how Pseudomonas aeruginosa interacts with Candida glabrata, Candida albicans, Candida krusei, Candida parapsilosis, and Candida tropicalis in the development of polymicrobial biofilms. Pseudomonas aeruginosa, Candida albicans, Candida krusei, Candida parapsilosis, Candida glabrata, and Candida tropicalis isolates were used in this experimental investigation. After preparing a 0.5 Mc Farland suspension of each isolate, the gold standard for measuring biofilm was applied: the Tissue Plate Culture (TCP) method. After that, an ELISA reader with a wavelength of 595 nm was used to measure the optical density (OD) of the biofilm. SPSS 26.0 was then used for statistical analysis to compare the OD values between Pseudomonas aeruginosa that had not been exposed to Candida and those that had. Pseudomonas aeruginosa and Candida are found to interact synergistically if there is an increase in OD, and antagonistic interaction is discovered if there is a decrease in OD. In comparison to the group that was not exposed to Candida, Pseudomonas aeruginosa exposed to Candida albicans, Candida krusei, Candida parapsilosis, Candida glabrata, and Candida tropicalis showed an increase in the OD value of biofilm. Pseudomonas aeruginosa and Candida albicans, Candida glabrata, Candida krusei, Candida parapsilosis, and Candida tropicalis interact synergistically.