Exploring the interplay between the core microbiota, physicochemical factors, agrobiochemical cycles in the soil of the historic tokaj mád wine region.

A Hungarian survey of Tokaj-Mád vineyards was conducted. Shotgun metabarcoding was applied to decipher the microbial-terroir. The results of 60 soil samples showed that there were three dominant fungal phyla, Ascomycota 66.36% ± 15.26%, Basidiomycota 18.78% ± 14.90%, Mucoromycota 11.89% ± 8.99%, representing 97% of operational taxonomic units (OTUs). Mutual interactions between microbiota diversity and soil physicochemical parameters were revealed. Principal component analysis showed descriptive clustering patterns of microbial taxonomy and resistance gene profiles in the case of the four historic vineyards (Szent Tamás, Király, Betsek, Nyúlászó). Linear discriminant analysis effect size was performed, revealing pronounced shifts in community taxonomy based on soil physicochemical properties. Twelve clades exhibited the most significant shifts (LDA > 4.0), including the phyla Verrucomicrobia, Bacteroidetes, Chloroflexi, and Rokubacteria, the classes Acidobacteria, Deltaproteobacteria, Gemmatimonadetes, and Betaproteobacteria, the order Sphingomonadales, Hypomicrobiales, as well as the family Sphingomonadaceae and the genus Sphingomonas. Three out of the four historic vineyards exhibited the highest occurrences of the bacterial genus Bradyrhizobium, known for its positive influence on plant development and physiology through the secretion of steroid phytohormones. During ripening, the taxonomical composition of the soil fungal microbiota clustered into distinct groups depending on altitude, differences that were not reflected in bacteriomes. Network analyses were performed to unravel changes in fungal interactiomes when comparing postveraison and preharvest samples. In addition to the arbuscular mycorrhiza Glomeraceae, the families Mycosphaerellacae and Rhyzopodaceae and the class Agaricomycetes were found to have important roles in maintaining soil microbial community resilience. Functional metagenomics showed that the soil Na content stimulated several of the microbiota-related agrobiogeochemical cycles, such as nitrogen and sulphur metabolism; steroid, bisphenol, toluene, dioxin and atrazine degradation and the synthesis of folate.

Changes in the Composition of Unstimulated and Stimulated Saliva Due to Chewing Sour Cherry Gum and a Toothbrush Change.

BACKGROUND: Our previous studies demonstrated that sour cherry anthocyanins (AC) reduce the salivary count of Streptococcus mutans and inhibit salivary amylase activity within 30 minutes after chewing AC gum. AC gum and changing toothbrushes after scaling reduced the Gram-negative species in the unstimulated salivary microbiota. The present study examined the effect of AC gums on salivary factors, including changes in microbiome. METHODS: The study was conducted over three weeks with two groups; young adults (18-30) and adults (30-45). Ten participants changed their toothbrushes, while the other 10 participants did not change after the control period. After scaling, all participants received three doses of AC gum daily. The salivary mRNA and protein levels of cytokines, mucins, melatonin, and the microbiota of unstimulated and stimulated saliva were determined by polymerase chain reaction, enzyme-linked immunosorbent assay, and 16S rRNA gene sequencing. RESULTS: Significantly higher levels of tumor necrosis factor α (TNFα), interleukin-1ß (IL-1ß), mucin5B (MUC5B), mucin7 (MUC7), and melatonin were detected in stimulated saliva. Correlation analysis of these factors with the microbiota showed positive correlations with the genera Lachnospiraceae, Eikenella, Saccharibacteria_(TM7), Streptococcus, Prevotella, and Haemophilus. CONCLUSIONS: AC chewing gum has a beneficial effect on the composition of the oral microbiome, and toothbrush replacement leads to changes in the levels of salivary pro-inflammatory cytokines.

An updated PREDICT breast cancer prognostic model including the benefits and harms of radiotherapy.

PREDICT Breast ( www.breast .predict.nhs.uk ) is a prognostication tool for early invasive breast cancer. The current version was based on cases diagnosed in 1999-2003 and did not incorporate the benefits of radiotherapy or the harms associated with therapy. Since then, there has been a substantial improvement in the outcomes for breast cancer cases. The aim of this study was to update PREDICT Breast to ensure that the underlying model is appropriate for contemporary patients. Data from the England National Cancer Registration and Advisory Service for invasive breast cancer cases diagnosed 2000-17 were used for model development and validation. Model development was based on 35,474 cases diagnosed and registered by the Eastern Cancer Registry. A Cox model was used to estimate the prognostic effects of the year of diagnosis, age at diagnosis, tumour size, tumour grade and number of positive nodes. Separate models were developed for ER-positive and ER-negative disease. Data on 32,408 cases from the West Midlands Cancer Registry and 100,551 cases from other cancer registries were used for validation. The new model was well-calibrated; predicted breast cancer deaths at 5-, 10- and 15-year were within 10 per cent of the observed validation data. Discrimination was also good: The AUC for 15-year breast cancer survival was 0.809 in the West Midlands data set and 0.846 in the data set for the other registries. The new PREDICT Breast model outperformed the current model and will be implemented in the online tool which should lead to more accurate absolute treatment benefit predictions for individual patients.

First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects.

Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena. However, extracting structural and dynamical information from vibrational spectra is a challenge that requires first-principles simulations, including non-Condon and quantum nuclear effects. We address this challenge by developing a machine-learning enhanced first-principles framework to speed up predictive modelling of infrared, Raman, and sum-frequency generation spectra. Our approach uses machine learning potentials that encode quantum nuclear effects to generate quantum trajectories using simple molecular dynamics efficiently. In addition, we reformulate bulk and interfacial selection rules to express them unambiguously in terms of the derivatives of polarisation and polarisabilities of the whole system and predict these derivatives efficiently using fully-differentiable machine learning models of dielectric response tensors. We demonstrate our framework's performance by predicting the IR, Raman, and sum-frequency generation spectra of liquid water, ice and the water-air interface by achieving near quantitative agreement with experiments at nearly the same computational efficiency as pure classical methods. Finally, to aid the experimental discovery of new phases of nanoconfined water, we predict the temperature-dependent vibrational spectra of monolayer water across the solid-hexatic-liquid phases transition.

Minutes matter: Time it takes to perform point-of-care ultrasound.

Background: While point-of-care ultrasound (PoCUS) is a safe, versatile tool that can improve patient care, the perceived time investment needed to incorporate PoCUS into clinical care is cited as a barrier to performance. We sought to determine the time it takes to perform a PoCUS examination and whether this time was influenced by training level and prior ultrasound experience. Methods: This was a retrospective study looking at time stamps of all emergency medicine (EM) provider-performed PoCUS examinations during clinical shifts from August 10, 2019, to June 7, 2022, at a suburban academic emergency department that is the site for a 3-year EM residency. Our workflow is order-based; when PoCUS is ordered, that patient's information populates the ultrasound machine worklist. Selecting the patient's name from the worklist generates a time-stamped patient information page (PIP). We defined the PIP time stamp as the start of the PoCUS examination. The duration of one PoCUS examination was defined as the time of the last image acquired minus the time of the PIP. General estimating equations were used to estimate differences between training level and between prior scan status using an exchangeable correlation and Tukey adjusted pairwise comparisons. A two-tailed chi-square analysis was used for comparing accuracy according to training level. Results: Of 4187 PoCUS examinations abstracted, 2144 met study criteria. The median (IQR) time spent per examination was 6.0 (3-9) min. First-year residents took the longest to perform PoCUS among all providers (p < 0.0001). Residents with fewer than 250 prior scans took longer than residents with 501-800 (p = 0.0002) and >800 (p = 0.0013). Resident accuracy was not significantly different according to training level. Conclusions: Overall median time to perform PoCUS was 6.0 min. EM residents became more efficient in performing PoCUS as they advanced from first- to third-year, without compromising accuracy.

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science.

The MACE architecture represents the state of the art in the field of machine learning force fields for a variety of in-domain, extrapolation, and low-data regime tasks. In this paper, we further evaluate MACE by fitting models for published benchmark datasets. We show that MACE generally outperforms alternatives for a wide range of systems, from amorphous carbon, universal materials modeling, and general small molecule organic chemistry to large molecules and liquid water. We demonstrate the capabilities of the model on tasks ranging from constrained geometry optimization to molecular dynamics simulations and find excellent performance across all tested domains. We show that MACE is very data efficient and can reproduce experimental molecular vibrational spectra when trained on as few as 50 randomly selected reference configurations. We further demonstrate that the strictly local atom-centered model is sufficient for such tasks even in the case of large molecules and weakly interacting molecular assemblies.

A novel injectable radiopaque hydrogel with potent properties for multicolor CT imaging in the context of brain and cartilage regenerative therapy.

Cell therapy is promising to treat many conditions, including neurological and osteoarticular diseases. Encapsulation of cells within hydrogels facilitates cell delivery and can improve therapeutic effects. However, much work remains to be done to align treatment strategies with specific diseases. The development of imaging tools that enable monitoring cells and hydrogel independently is key to achieving this goal. Our objective herein is to longitudinally study an iodine-labeled hydrogel, incorporating gold-labeled stem cells, by bicolor CT imaging after in vivo injection in rodent brains or knees. To this aim, an injectable self-healing hyaluronic acid (HA) hydrogel with long-persistent radiopacity was formed by the covalent grafting of a clinical contrast agent on HA. The labeling conditions were tuned to achieve sufficient X-ray signal and to maintain the mechanical and self-healing properties as well as injectability of the original HA scaffold. The efficient delivery of both cells and hydrogel at the targeted sites was demonstrated by synchrotron K-edge subtraction-CT. The iodine labeling enabled to monitor the hydrogel biodistribution in vivo up to 3 days post-administration, which represents a technological first in the field of molecular CT imaging agents. This tool may foster the translation of combined cell-hydrogel therapies into the clinics.

High-Throughput Sequencing Analysis of the Changes in the Salivary Microbiota of Hungarian Young and Adult Subpopulation by an Anthocyanin Chewing Gum and Toothbrush Change.

The sour cherry contains anthocyanins, which have bactericide action against some oral bacteria (Klebsiella pneumoniae, Pseudomonas aeruginosa). Sour cherry also has antibiofilm action against Streptococcus mutans, Candida albicans, and Fusobacterium nucleatum. Our earlier research proved that chewing sour cherry anthocyanin gum significantly reduces the amount of human salivary alpha-amylase and Streptococcus mutans levels. The microbiota of a toothbrush affects oral health and regular toothbrush change is recommended. A total of 20 healthy participants were selected for the study. We analysed saliva samples with 16S rRNA sequencing to investigate the effect of 2 weeks (daily three times, after main meals) of chewing sour cherry anthocyanin gum-supplemented by toothbrush change in half of our case-control study cohort-after scaling on human oral microbiota. A more stable and diverse microbiome could be observed after scaling by the anthocyanin gum. Significant differences between groups (NBR: not toothbrush changing; BR: toothbrush changing) were evaluated by log2 proportion analysis of the most abundant family and genera. The analysis showed that lower level of some Gram-negative anaerobic (Prevotella melaninogenica, Porphyromonas pasteri, Fusobacterium nucleatum subsp. vincentii) and Gram-positive (Rothia mucilaginosa) bacteria could be observed in the case group (BR), accompanied by build-up of health-associated Streptococcal network connections.

Hyperactive learning for data-driven interatomic potentials.

Data-driven interatomic potentials have emerged as a powerful tool for approximating ab initio potential energy surfaces. The most time-consuming step in creating these interatomic potentials is typically the generation of a suitable training database. To aid this process hyperactive learning (HAL), an accelerated active learning scheme, is presented as a method for rapid automated training database assembly. HAL adds a biasing term to a physically motivated sampler (e.g. molecular dynamics) driving atomic structures towards uncertainty in turn generating unseen or valuable training configurations. The proposed HAL framework is used to develop atomic cluster expansion (ACE) interatomic potentials for the AlSi10 alloy and polyethylene glycol (PEG) polymer starting from roughly a dozen initial configurations. The HAL generated ACE potentials are shown to be able to determine macroscopic properties, such as melting temperature and density, with close to experimental accuracy.

ASEPTIC: primary antibiotic prophylaxis using co-trimoxazole to prevent SpontanEous bacterial PeritoniTIs in Cirrhosis-study protocol for an interventional randomised controlled trial.

BACKGROUND: Bacterial infection is a major cause of mortality in patients with cirrhosis. Spontaneous bacterial peritonitis (SBP) is a serious and common infection in patients with cirrhosis and ascites. Secondary prophylactic antibiotic therapy has been shown to improve outcomes after an episode of SBP but primary prophylaxis to prevent the first episode of SBP remains contentious. The aim of this trial is to assess whether primary antibiotic prophylaxis with co-trimoxazole improves overall survival compared to placebo in adults with cirrhosis and ascites. METHODS: The ASEPTIC trial is a multicentre, placebo-controlled, double-blinded, randomised controlled trial (RCT) in England, Scotland, and Wales. Patients aged 18 years and older with cirrhosis and ascites requiring diuretic treatment or paracentesis, and no current or previous episodes of SBP, are eligible, subject to exclusion criteria. The trial aims to recruit 432 patients from at least 30 sites. Patients will be randomised in a 1:1 ratio to receive either oral co-trimoxazole 960 mg or an identical placebo once daily for 18 months, with 6 monthly follow-up visits thereafter (with a maximum possible follow-up period of 48 months, and a minimum of 18 months). The primary outcome is overall survival. Secondary outcomes include the time to the first incidence of SBP, hospital admission rates, incidence of other infections (including Clostridium difficile) and antimicrobial resistance, patients' health-related quality of life, health and social care resource use, incidence of cirrhosis-related decompensation events, liver transplantation, and treatment-related serious adverse events. DISCUSSION: This trial will investigate the efficacy, safety, and cost-effectiveness of co-trimoxazole for patients with liver cirrhosis and ascites to determine whether this strategy improves clinical outcomes. Given there are no treatments that improve survival in decompensated cirrhosis outside of liver transplant, if the trial has a positive outcome, we anticipate widespread adoption of primary antibiotic prophylaxis. TRIAL REGISTRATION: ClinicalTrials.gov NCT043955365 . Registered on 18 April 2020. Research ethical approval was granted by the Research Ethics Committee (South Central - Oxford B; REC 19/SC/0311) and the Medicines and Healthcare products Regulatory Agency (MHRA).

Circulating microRNA sequencing revealed miRNome patterns in hematology and oncology patients aiding the prognosis of invasive aspergillosis.

Invasive aspergillosis (IA) may occur as a serious complication of hematological malignancy. Delays in antifungal therapy can lead to an invasive disease resulting in high mortality. Currently, there are no well-established blood circulating microRNA biomarkers or laboratory tests which can be used to diagnose IA. Therefore, we aimed to define dysregulated miRNAs in hematology and oncology (HO) patients to identify biomarkers predisposing disease. We performed an in-depth analysis of high-throughput small transcriptome sequencing data obtained from the whole blood samples of our study cohort of 50 participants including 26 high-risk HO patients and 24 controls. By integrating in silico bioinformatic analyses of small noncoding RNA data, 57 miRNAs exhibiting significant expression differences (P < 0.05) were identified between IA-infected patients and non-IA HO patients. Among these, we found 36 differentially expressed miRNAs (DEMs) irrespective of HO malignancy. Of the top ranked DEMs, we found 14 significantly deregulated miRNAs, whose expression levels were successfully quantified by qRT-PCR. MiRNA target prediction revealed the involvement of IA related miRNAs in the biological pathways of tumorigenesis, the cell cycle, the immune response, cell differentiation and apoptosis.

Determinants of aflatoxin exposures in Kenyan School-aged children.

Aflatoxins are naturally occurring food toxins known to contaminate cereals with a carry-over effect in milk and meat products from farm animals raised on contaminated feed. In children, continuous consumption of aflatoxin-contaminated food is linked to immune suppression, vaccine interference and growth faltering while in adult populations, carcinogenesis in the liver has been established. We evaluate the main determinants of aflatoxin exposures among children recruited from primary schools in Makueni and Siaya Counties. A five-part questionnaire was administered to collect information from randomly selected participants. AflatoxinB1-lysine adducts in children's sera and total aflatoxins in food samples were analyzed by High-Performance Liquid Chromatography with Fluorescence detection. Using Chi-squared tests and Kruskal-Wallis tests, children from low-income households had the highest aflatoxin exposure, p-value = 0.0029. Smaller family size, greater food diversity, and good farming practices were associated with low aflatoxin exposures p < 0.001. Individual households living under severe levels of poverty were evidently exposed to higher levels of aflatoxins.

The disambiguation of people names in biological collections.

Scientific collections have been built by people. For hundreds of years, people have collected, studied, identified, preserved, documented and curated collection specimens. Understanding who those people are is of interest to historians, but much more can be made of these data by other stakeholders once they have been linked to the people's identities and their biographies. Knowing who people are helps us attribute work correctly, validate data and understand the scientific contribution of people and institutions. We can evaluate the work they have done, the interests they have, the places they have worked and what they have created from the specimens they have collected. The problem is that all we know about most of the people associated with collections are their names written on specimens. Disambiguating these people is the challenge that this paper addresses. Disambiguation of people often proves difficult in isolation and can result in staff or researchers independently trying to determine the identity of specific individuals over and over again. By sharing biographical data and building an open, collectively maintained dataset with shared knowledge, expertise and resources, it is possible to collectively deduce the identities of individuals, aggregate biographical information for each person, reduce duplication of effort and share the information locally and globally. The authors of this paper aspire to disambiguate all person names efficiently and fully in all their variations across the entirety of the biological sciences, starting with collections. Towards that vision, this paper has three key aims: to improve the linking, validation, enhancement and valorisation of person-related information within and between collections, databases and publications; to suggest good practice for identifying people involved in biological collections; and to promote coordination amongst all stakeholders, including individuals, natural history collections, institutions, learned societies, government agencies and data aggregators.

A botanical demonstration of the potential of linking data using unique identifiers for people.

Natural history collection data available digitally on the web have so far only made limited use of the potential of semantic links among themselves and with cross-disciplinary resources. In a pilot study, botanical collections of the Consortium of European Taxonomic Facilities (CETAF) have therefore begun to semantically annotate their collection data, starting with data on people, and to link them via a central index system. As a result, it is now possible to query data on collectors across different collections and automatically link them to a variety of external resources. The system is being continuously developed and is already in production use in an international collection portal.

Mining of Cyanobacterial Genomes Indicates Natural Product Biosynthetic Gene Clusters Located in Conjugative Plasmids.

Microbial natural products are compounds with unique chemical structures and diverse biological activities. Cyanobacteria commonly possess a wide range of biosynthetic gene clusters (BGCs) to produce natural products. Although natural product BGCs have been found in almost all cyanobacterial genomes, little attention has been given in cyanobacterial research to the partitioning of these biosynthetic pathways in chromosomes and plasmids. Cyanobacterial plasmids are believed to disperse several natural product BGCs, such as toxins, by plasmids through horizontal gene transfer. Therefore, plasmids may confer the ability to produce toxins and may play a role in the evolution of diverse natural product BGCs from cyanobacteria. Here, we performed an analysis of the distribution of natural product BGCs in 185 genomes and mapped the presence of genes involved in the conjugation in plasmids. The 185 analyzed genomes revealed 1817 natural products BGCs. Individual genomes contained 1-42 biosynthetic pathways (mean 8), 95% of which were present in chromosomes and the remaining 5% in plasmids. Of the 424 analyzed cyanobacterial plasmids, 12% contained homologs of genes involved in conjugation and natural product biosynthetic pathways. Among the biosynthetic pathways in plasmids, manual curation identified those to produce aeruginosin, anabaenopeptin, ambiguine, cryptophycin, hassallidin, geosmin, and microcystin. These compounds are known toxins, protease inhibitors, odorous compounds, antimicrobials, and antitumorals. The present study provides in silico evidence using genome mining that plasmids may be involved in the distribution of natural product BGCs in cyanobacteria. Consequently, cyanobacterial plasmids have importance in the context of biotechnology, water management, and public health risk assessment. Future research should explore in vivo conjugation and the end products of natural product BGCs in plasmids via chemical analyses.

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE.

We demonstrate that fast and accurate linear force fields can be built for molecules using the atomic cluster expansion (ACE) framework. The ACE models parametrize the potential energy surface in terms of body-ordered symmetric polynomials making the functional form reminiscent of traditional molecular mechanics force fields. We show that the four- or five-body ACE force fields improve on the accuracy of the empirical force fields by up to a factor of 10, reaching the accuracy typical of recently proposed machine-learning-based approaches. We not only show state of the art accuracy and speed on the widely used MD17 and ISO17 benchmark data sets, but we also go beyond RMSE by comparing a number of ML and empirical force fields to ACE on more important tasks such as normal-mode prediction, high-temperature molecular dynamics, dihedral torsional profile prediction, and even bond breaking. We also demonstrate the smoothness, transferability, and extrapolation capabilities of ACE on a new challenging benchmark data set comprised of a potential energy surface of a flexible druglike molecule.

Implicit Memory and Anesthesia: A Systematic Review and Meta-Analysis.

General anesthesia should induce unconsciousness and provide amnesia. Amnesia refers to the absence of explicit and implicit memories. Unlike explicit memory, implicit memory is not consciously recalled, and it can affect behavior/performance at a later time. The impact of general anesthesia in preventing implicit memory formation is not well-established. We performed a systematic review with meta-analysis of studies reporting implicit memory occurrence in adult patients after deep sedation (Observer's Assessment of Alertness/Sedation of 0-1 with spontaneous breathing) or general anesthesia. We also evaluated the impact of different anesthetic/analgesic regimens and the time point of auditory task delivery on implicit memory formation. The meta-analysis included the estimation of odds ratios (ORs) and 95% confidence intervals (CIs). We included a total of 61 studies with 3906 patients and 119 different cohorts. For 43 cohorts (36.1%), implicit memory events were reported. The American Society of Anesthesiologists (ASA) physical status III-IV was associated with a higher likelihood of implicit memory formation (OR:3.48; 95%CI:1.18-10.25, p < 0.05) than ASA physical status I-II. Further, there was a lower likelihood of implicit memory formation for deep sedation cases, compared to general anesthesia (OR:0.10; 95%CI:0.01-0.76, p < 0.05) and for patients receiving premedication with benzodiazepines compared to not premedicated patients before general anesthesia (OR:0.35; 95%CI:0.13-0.93, p = 0.05).

The Functions and Legitimization of Suffering in Calais, France.

The instrumentalisation of disaster - long considered a feature of wars and famines in Africa, for example - has now been brought right into the heart of Europe. Suffering in Calais has been manipulated for the purpose of deterrence and for domestic political purposes, and forms part of a wider system of outsourcing violence and suffering that has been legitimised through Arendt's "action as propaganda" and through perverse distributions of shame.

The structure and biosynthesis of heinamides A1-A3 and B1-B5, antifungal members of the laxaphycin lipopeptide family.

Laxaphycins are a family of cyclic lipopeptides with synergistic antifungal and antiproliferative activities. They are produced by multiple cyanobacterial genera and comprise two sets of structurally unrelated 11- and 12-residue macrocyclic lipopeptides. Here, we report the discovery of new antifungal laxaphycins from Nostoc sp. UHCC 0702, which we name heinamides, through antimicrobial bioactivity screening. We characterized the chemical structures of eight heinamide structural variants A1-A3 and B1-B5. These variants contain the rare non-proteinogenic amino acids 3-hydroxy-4-methylproline, 4-hydroxyproline, 3-hydroxy-d-leucine, dehydrobutyrine, 5-hydroxyl ß-amino octanoic acid, and O-carbamoyl-homoserine. We obtained an 8.6-Mb complete genome sequence from Nostoc sp. UHCC 0702 and identified the 93 kb heinamide biosynthetic gene cluster. The structurally distinct heinamides A1-A3 and B1-B5 variants are synthesized using an unusual branching biosynthetic pathway. The heinamide biosynthetic pathway also encodes several enzymes that supply non-proteinogenic amino acids to the heinamide synthetase. Through heterologous expression, we showed that (2S,4R)-4-hydroxy-l-proline is supplied through the action of a novel enzyme LxaN, which hydroxylates l-proline. 11- and 12-residue heinamides have the characteristic synergistic activity of laxaphycins against Aspergillus flavus FBCC 2467. Structural and genetic information of heinamides may prove useful in future discovery of natural products and drug development.

Understanding nurses' responsibilities in promoting equality and diversity.

Nurses have a duty to promote the values of equality and diversity during their interactions with patients and their families and carers, as well as peers and colleagues. This article defines the terms equality, diversity and inclusion, and explains the importance of the Equality Act 2010 and the Human Rights Act 1998 in protecting people from various types of discrimination. It also outlines nurses' responsibilities in promoting equality and diversity by treating all patients and colleagues with respect and dignity, providing compassionate leadership, and practising in accordance with the ethical principle of justice. The article encourages and empowers nurses to recognise and challenge discrimination wherever they see it, thereby delivering high-quality care to all patients.