Reckitt Benckiser (United States)

<b>Conclusions:</b> LAIs, especially SG formulations, are associated with reduced rehospitalization in BD-I compared to OAs, supporting their broader use to improve adherence and reduce readmissions.

Household washing machines host diverse microbial communities that may include opportunistic pathogens, potentially impacting laundry hygiene and human health. However, our understanding of these communities and their transfer abilities remains limited. We examined microbial communities from 10 household washing machines (five front-load and five top-load) using surface swabs from specific hotspots and sterile sentinel washcloths. Samples were analyzed using culture-based methods and 16S rRNA/ITS metabarcoding. We tested microbial transfer during washing cycles with and without clothing and evaluated the effects of machine drying on this transfer. Front-load machines had significantly higher microbial loads than top-load machines (average bacterial counts: 6.50 ± 2.46 Log10/swab vs. 3.79 ± 1.73 Log10/swab). The microbial community composition was mainly shaped by the machine user rather than the machine type or sampling location. The dominant bacterial genera included Pseudomonas, Micrococcus, and Sphingomonas, while Aspergillus, Cladosporium, and Penicillium dominated the fungal communities. Opportunistic microorganisms were identified, but no highly pathogenic species (pathogenicity score 3) were found. Machine drying did not significantly decrease microbial loads, whereas the presence of soiled clothing impacted community composition. Household washing machines host user-specific microbial communities, including potential opportunistic pathogens. Current laundry practices may be inadequate for the complete elimination of pathogens, especially in immunocompromised individuals. These results support the need for additional household laundry sanitization strategies.

Nanobubbles (NBs) in water have shown promise in promoting plant growth and seed germination, potentially reducing the need for water and fertilizer inputs. To elucidate the mechanisms underlying these benefits, this study investigated the effects of oxygen nanobubbles (ONBs) and nitrogen nanobubbles (NNBs) on the tomato rhizosphere microbiome over a 34-day irrigation period. Treatments included varying NB types, dissolved oxygen (DO) levels (9.30 and 26.73 mg L −1 for ONBs; 2.69 and 8.84 mg L −1 for NNBs), and irrigation frequencies. Multivariate analysis of 16S rRNA gene sequencing revealed that both ONBs and NNBs significantly altered rhizosphere microbial community composition compared to controls. NB concentration and irrigation frequency further influenced microbiome structure. High-concentration NB treatments increased the relative abundance of multiple beneficial rhizobacteria involved in nutrient cycling, including Bacteroidota, Chitinophagales, Gemmatimonadota, and Ureibacillus , while low-concentration treatments had lower impacts on plant growth-promoting rhizobacteria. Network analysis indicated enhanced microbial connectivity and reduced niche specialization in NB-treated rhizosphere soils. Moreover, significant positive correlations were observed between tomato agronomic traits and several rhizobacteria, including known plant growth-promoting taxa like Comamonadaceae and other potentially beneficial microbes. These findings demonstrate that NB type, concentration, and irrigation regime can modulate rhizosphere microbial dynamics, offering insights for optimizing nanobubble applications in sustainable precision agriculture. • O 2 and N 2 nanobubbles (NBs) altered the tomato rhizosphere microbiome structure. • High-concentration NBs had more pronounced impacts on the rhizosphere microbiome than low-concentration NBs. • NBs enriched beneficial microbial taxa that are involved in soil nutrient turnover. • NBs promoted interactions between diverse microbial groups in the rhizosphere.

ABSTRACT Background Urban public transport systems, particularly metro networks, serve as key hubs for microbial transmission, yet the urban microbiome in densely populated regions like India remains poorly characterized. These environments harbour diverse microbial communities, including beneficial and pathogenic species, which can influence public health. The COVID-19 pandemic has further underscored the need to monitor microbial ecosystems, particularly with respect to antimicrobial resistance (AMR) genes, which may have escalated due to the increased use of antibiotics during health crises. In a first-of-its-kind study in India, we comprehensively characterized microbial communities and the prevalence of AMR genes in the Chennai Metro system. We collected 96 surface swab samples from 12 stations across two metro lines, targeting four surfaces: bannisters, kiosks, rods, and ticket counters. Forty-seven samples passed quality control and were analyzed using whole-genome metagenomic sequencing. We employed taxonomic classification and conducted comparative and diversity analyses, as well as differential taxa profile analyses, across the sample collection objects. We studied the prevalence and abundance of AMR genes using AMR annotations from the CARD database. We performed pangenome analysis by constructing Metagenome Assembled Genomes (MAGs) from the collected samples and comparing them with the NCBI reference genome. Results Comparative analysis with global urban microbiome datasets revealed distinct microbial profiles, including nine species that are differentially prevalent in Chennai samples. Surface type significantly influenced microbial diversity, with kiosks exhibiting the highest diversity. We successfully reconstructed several high-quality MAGs, providing insights into the genomic potential and adaptability of dominant taxa in this environment. While the overall prevalence of AMR genes was minimal, genes associated with Sulfonamide and Rifamycin resistance were detected. Conclusion These findings highlight unique microbial signatures and emphasize the need for ongoing surveillance and targeted interventions to mitigate microbial transmission risks in densely populated urban areas.

New methods for recovering rare earth elements (REE) from traditional and nontraditional sources are needed to meet growing global demand. Approaches based on microbiological processes or biologically produced reagents to recover REEs are among the most promising. Biological approaches can avoid many of the hazards and costs associated with corrosive or toxic chemicals used in hydrometallurgy, as well as the high energy, reagent, and capital costs and air emissions associated with pyrometallurgy. Microbial processes are considered environmentally friendly because they are “natural,” although opportunities exist to improve on native capabilities through application of synthetic biology. In this chapter, we focus on some important factors that have not been as widely discussed but which should be considered in development of biological approaches for recovery and purification of REEs. In particular, we focus on geochemical and biogeochemical constraints posed by REE-containing feedstocks, for bioleaching and biosorption, and emphasize the utility of aqueous equilibrium modeling for interpreting results and supporting design of recovery methods. We also discuss some important cost factors for REE recovery specific to biological processes.

Vaginal mucosa undergoes physiological changes across the lifespan, such as increased pH and reduced natural lubrication which are known to impact vaginal commensal microorganisms, hence increasing the chances of vaginal infections. An improved understanding of vaginal microbiome composition in different age groups and the effect of social behaviors, such as the use of personal lubricants, could facilitate the development of new strategies to maintain good vaginal health. The objective of this study was to assess the effect of water-based lubricants on the human vaginal microbiome. Fifty females from three age groups (18-29, 30-44, and 45-65 years) with mild-to-moderate vaginal dryness were randomized to one of five lubricants (four of which were formulated to meet expert guidance on osmolality and pH). Subjects entered the study at tolerance or treatment phase (vaginal intercourse minimum once a week using assigned lubricant). Four vaginal swabs per participant were sampled during pre-("baseline"), post-first ("2 h", "24 h") and post-last ("4 weeks") lubricant application to assess bacterial and fungal diversity via amplicon sequencing. Vaginal pH and relative humidity were measured at baseline, 2 h, and 24 h post-lubricant application. Relative bacteriome abundance was statistically compared between timepoints for each lubricant group. Vaginal moisture, age, BMI, and pH were correlated with bacteriome relative abundance. Lactobacilli and Gardnerella sp. Were predominant across participants. Repeated lubricant application did not significantly alter the vaginal bacteriome during 4 weeks of product use (p > 0.05) when measured by relative abundance and alpha-diversity index. Bacteriome diversity and abundance differed significantly between age groups at baseline whereas lactobacilli relative abundance was negatively associated with age and vaginal pH.

This thorough overview examines AI's disruptive influence on marketing, including its history, trends, and future directions. The paper explains how AI can analyze large databases, automate procedures, and improve decision-making using machine learning algorithms in marketing. Starting with data analytics, AI in marketing has evolved to include big data and marketing automation solutions. The literature study covers three main topics: marketing landscape transition, AI marketing applications, and its pros and cons. AI may improve marketing techniques, but the assessment emphasizes a balanced approach to ethics, data protection, and adverse risks. A literature review shows a move towards customer-centric, data-driven marketing methods using chatbots, predictive analytics, and picture recognition. The theoretical approach uses the Technology Acceptance Model and Personalization-Privacy Paradox to explain AI marketing uptake and effect. According to empirical research, AI-driven solutions boost marketing, customer interaction, and consumer behavior prediction. Consumer behavior insights show how AI improves customization and emphasizes the relevance of ethics. Organizational dynamics and innovation, including multifunctional cooperation and media management, are examined in the research. AI integration into organizational structures presents challenges and possibilities, emphasizing technological and personnel investments. The section on smaller businesses' innovative AI use despite financial limits discusses this. Real-world case studies demonstrate AIpowered marketing strategies' adaptability and effect across sectors. The closing parts summarise significant findings, give practical insights, and advise marketers on AI-driven marketing. The paper finishes with a forward-looking summary of trends and ethical guidelines for responsible AI marketing.

When worn, clothing acquires a microbiome of bacteria and fungi derived from the wearer’s skin and from the environment. The types of bacteria and fungi that may be recovered from clothing in healthcare settings have been well characterized, but less is known regarding the microbiome of clothing worn in non-healthcare settings and the possible roles that such clothing may play in microbial exchange. Culture-based methods and culture-independent genomic sequencing were used to enumerate and identify bacteria and fungi recovered from T-shirts, baby onesies, socks, and underwear worn for a single day after having been purchased new, washed, and dried. The highest bacterial loads were recovered from socks, underwear, and onesies (&gt;106 colony-forming units [cfu]/sample) and the highest fungal loads were obtained from socks and underwear (&gt;5 × 102 cfu/sample). The sequencing method identified opportunistic pathogens present in the samples, including members of genus Staphylococcus and Corynebacterium, as well as anaerobic members of the family Clostridiales. The opportunistic fungal pathogen Candida parapsilosis was identified in a high proportion of worn clothing samples. These results suggest that clothing may represent a pathogen reservoir and a vector for microbial exchange between household occupants or the community outside of the home.