143174 research outputs found
Sort by
The association of class II HLA alleles with tuberculosis-associated immune reconstitution inflammatory syndrome
No full textGenetic associations within the human leukocyte antigen (HLA) gene complex and linked genes in TB-IRIS outcomes remains population specific and not well understood. Here, we conducted a study including well characterised HIV-TB coinfected patients with (n = 86) and without (n = 124) TB-IRIS from the randomized, double-blind, prophylactic prednisone trial (PredART study) with HLA, ERAP and KIR genotyping data. We confirmed the association of TB-IRIS with lower CD4 counts pre-ART initiation. We identified nine classical class I and II HLA alleles protective against TB-IRIS, while four alleles were linked to increased risk. Associations ranged from strongly protective (HLA-DQB1*05:01, OR: 0.07, 95%CI: 0.02-0.28, Pc < 0.001) to strongly risk associated (notably DRB1*01:02, OR: 5.92, 95%CI: 1.36-26.7, Pc = 0.028), with conflicting signals at the HLA-DRB1 locus. Conditional regression analysis revealed that residue E71 at the polymorphic position 71 within the HLA-DRB1 peptide-binding groove was critical, and grouping of HLA-DRB1 alleles by the residue at position 71 corresponded with differential TB-IRIS association. In conclusion, this study identifies population-specific genetic factors influencing TB-IRIS susceptibility and highlights a potential mechanistic role for specific HLA-DRB1 residues in modulating immune responses during ART
AmpliconTyper – a tool for analysing ONT multiplex PCR data from environmental and other complex samples
No full textAmplicon sequencing is a popular method for understanding the diversity of bacterial communities in samples containing multiple organisms as exemplified by 16S rRNA sequencing. Another application of amplicon sequencing includes multiplexing both primer sets and samples, allowing sequencing of multiple targets in multiple samples in the same sequencing run. Multiple tools exist to process the amplicon sequencing data produced via the short-read Illumina platform, but there are fewer options for long-read Oxford Nanopore Technologies (ONT) sequencing, or for processing data from environmental surveillance or other sources with many different organisms. We have developed AmpliconTyper (v0.1.28, DOI: 10.5281/zenodo.15045111) for analysing multiplex amplicon sequencing data from environmental (e.g. wastewater) or similarly complex samples, generated using ONT devices. The tool uses machine learning to classify sequencing reads into target and non-target organisms with very high specificity and sensitivity. The user can train models using public and/or user-generated data, which can subsequently be applied to analyse new data. The tool can also generate amplicon consensus sequences, as well as identify SNPs and report their genotype implications, such as association with lineages or antimicrobial resistance (AMR). The tool is freely available via Bioconda and GitHub (https://github.com/AntonS-bio/AmpliconTyper). AmpliconTyper allows robust identification of target organism reads in ONT-sequenced environmental samples and can identify user-specified lineage or AMR markers
The use of human mobility estimates in mathematical models of infectious disease spread
No full textReliable estimates of human mobility are important for modelling the spatial spread of infectious diseases and guiding control efforts. However, human mobility data are often unavailable at the necessary temporal or spatial resolutions, leading to the use of proxies based on data from other countries or past periods. This thesis evaluates the impact of using such proxies in spatial epidemic models.
Subnational mobility data are unavailable for many African countries. As a result, proxies are generated by fitting mobility models (e.g. gravity or radiation models) to data from nearby countries. This thesis first assesses how reliance on such proxies can affect model-based predictions of within-country epidemic spread in the absence of adequate empirical data.
This thesis also examines the use of flight passenger data for modelling the international spread of infectious diseases. In the early stages of outbreaks, analyses using flight passenger data to identify countries at risk of importing the pathogen are common, but are typically based on historical data. I explored the validity of this implicit assumption that travel behaviour is unaffected by epidemic events by exploring trends in flight passenger volumes over time and the effects of previous epidemics. I then conducted an epidemic simulation study to compare the performance of spatial models based on historical flight passenger data with models based on contemporary passenger data.
Finally, I developed a framework for modelling the infectious disease risks posed by the international movements of large numbers of people to attend mass gathering events. Using the Hajj as a case study, I examine the impact of mobility proxies on model predictions of epidemiological risks and the cost-effectiveness of control strategies.
This work provides lessons for the use of human movement estimates in future infectious disease outbreak modelling and helps identify priority areas for improved data collection.Open Acces
Olanzapine for young people with anorexia nervosa: a synopsis of the main results from the OPEN open-label feasibility study
No full textBackground: In clinical practice olanzapine is commonly used in young people with anorexia nervosa (AN), although the underpinning evidence-base is limited. However, its efficacy, tolerability, acceptability and adherence rate, and the patients’, carers’ and clinicians’ views of olanzapine treatment are unclear. This synopsis article summarises the methods and results of the OPEN feasibility study, overarching the findings and drawing comprehensive conclusions from a quantitative feasibility outcome paper and two qualitative research papers.
Methods: The OPEN study assessed the feasibility of a future randomized controlled trial (RCT) on olanzapine in young people with AN in an open-label, one-armed feasibility study. In this study, we aimed to include 55 patients with AN or atypical AN aged 12-24 who gained <2 kg within at least one month of treatment as usual (TAU). Time points for assessments were at baseline, 8 weeks, 16 weeks, and 6 or 12 months. We estimated recruitment, adherence, and attrition rates; and mean changes in body weight, body mass index (BMI) and ED psychopathology. In addition, we explored the views on and experiences with olanzapine treatment within a clinical trial setting using qualitative interviews with young people with AN, their families and clinicians.
Results: Fifty-two people were pre-screened, 35 were eligible, and 20 participants were recruited and started olanzapine. Of these, 15 continued olanzapine for ≥16 weeks. Participants experienced, on average, a decrease in their ED psychopathology, and body weight and BMI increased during treatment with olanzapine as an adjunct to TAU. Important themes derived from semi-structured qualitative interviews with young people and their parents were: moving away from the illness towards recovery, evaluating information on olanzapine, consent and trust in shared decision-making and the ambivalence around recovery. The main themes expressed by clinicians included: acknowledging the concerns of young people with AN and their families, prioritising person-centred care, the limited service capacity and strict study eligibility criteria.
Limitations: The study failed to meet the recruitment target and showed low adherence rates for treatment with olanzapine. Possible reasons for the recruitment difficulties and the low adherence rate include the high clinical workload of ED services during and after the COVID-19 pandemic, the heterogeneity of ED service setup across the country, and the reluctance of patients to agree to take olanzapine under the relatively restricted conditions of a clinical study.
Conclusions: A realistic time schedule for site-preparation, recruitment, treatment and follow up, realistic recruitment targets and easy access to medical and laboratory examinations may improve the success of future feasibility studies and RCTs in this patient group.
Future work: The difficulties in conducting the OPEN study will inform the planning for future pharmacological and non-pharmacological studies in AN. Therefore, we developed a checklist with action points for the planning of pharmacological trials in eating disorders. Furthermore, novel pharmacological options such as typical and atypical psychedelic drugs (e.g., psilocybin and ketamine) might be more acceptable for people with AN as they do not have the side effect of immediate weight gain
UHPLC-MS/MS analysis of PFAS in the serum of patients with Alzheimer's disease, mild cognitive impairment and controls: a preliminary study
No full textComplimentary UHPLC-MS methods for 6 ultra short chain (polar) and 30 long chain (and relatively non-polar) PFAS compounds have been developed and used to profile these compounds in human serum. The methods were developed to be rapid, with both having analysis times of ca. 5 min, to enable the rapid screening of samples. Both methods were sensitive enough to detect the target PFAS over the range 0.5–50 ng/mL. These methods were then applied to the screening of the serum of 137 subjects comprising healthy controls (HC), subjects with mild cognitive impairment (MCI) and those with Alzheimer's disease (AD). These methods demonstrated the presence of the 33 of targeted PFAS in these serum samples, presenting over a wide concentrations range with PFPrA found at the highest maximum concentration of 707 ng/mL. Interestingly the PFAS profiles found for the HC and MCI subjects differed from those of the AD patients. The reason(s) for differences in the PFAS profiles obtained for serum from AD patients compared to HC/MCI subjects is unclear and may be unrelated to disease but clearly warrants further investigation to clarify the underlying reasons for this observation
Adverse health outcomes associated with drinking highly saline water: a systematic review
No full textIn climate change-affected coastal areas, sea level rise, storm surges, droughts and altered rainfalls are significantly increasing salinity levels in drinking water sources. This is a major public health problem that affects many millions of people. We systematically reviewed and assessed the strength and quality of the evidence on the relationship between drinking water with high sodium levels (> 200 mgNa/l) and adverse cardiovascular, renal, and pregnancy-related health outcomes, following the PRISMA guidelines, the ROBINS-E Cochrane tool and the Navigation Guide. From five bibliographic databases, we identified 22 relevant studies, some of which assessed more than one health domain. The evidence was of moderate quality and strength. 14 analyses from eight studies at low risk of bias and four studies at moderate risk of bias, linked drinking high-salinity water to adverse health outcomes including hypertension and cardiovascular disease, impaired renal function, gestational hypertension and preeclampsia, and higher infant mortality. Eight studies were inconclusive. Three analyses, of which two at low risk of bias, associated drinking high-salinity water to improved health outcomes. Overall, our findings suggest that salinisation of drinking water sources is likely to increase adverse cardiovascular, renal, and pregnancy-related health outcomes. This conclusion highlights the importance of effective and timely adaptation at scale, and calls for a revision of the WHO guidelines for the intake of salt from water. The latest WHO guidelines (2022) do not set any health-based standard for sodium levels in drinking water, a problem that affects millions of people and will worsen with climate change
The dynamics of the gut microbiota in prediabetes during a four-year follow-up among European patients-an IMI-DIRECT prospective study
No full textBackground
Previous case–control studies have reported aberrations of the gut microbiota in individuals with prediabetes. The primary objective of the present study was to explore the dynamics of the gut microbiota of individuals with prediabetes over 4 years with a secondary aim of relating microbiota dynamics to temporal changes of metabolic phenotypes.
Methods
The study included 486 European patients with prediabetes. Gut microbiota profiling was conducted using shotgun metagenomic sequencing and the same bioinformatics pipelines at study baseline and after 4 years. The same phenotyping protocols and core laboratory analyses were applied at the two timepoints. Phenotyping included anthropometrics and measurement of fasting plasma glucose and insulin levels, mean plasma glucose and insulin under an oral glucose tolerance test (OGTT), 2-h plasma glucose after an OGTT, oral glucose insulin sensitivity index, Matsuda insulin sensitivity index, body mass index, waist circumference, and systolic and diastolic blood pressure. Measures of the dynamics of bacterial microbiota were related to concomitant changes in markers of host metabolism.
Results
Over 4 years, significant declines in richness were observed in gut bacterial and viral species and microbial pathways accompanied by significant changes in the relative abundance and the genetic composition of multiple bacterial species. Additionally, bacterial-viral interactions diminished over time. Despite the overall reduction in bacterial richness and microbial pathway richness, 80 dominant core bacterial species and 78 core microbial pathways were identified at both timepoints in 99% of the individuals, representing a resilient component of the gut microbiota. Over the same period, individuals with prediabetes exhibited a significant increase in glycemia and insulinemia alongside a significant decline in insulin sensitivity. Estimates of the gut bacterial microbiota dynamics were significantly correlated with temporal impairments in host metabolic health.
Conclusions
In this 4-year prospective study of European patients with prediabetes, the gut microbiota exhibited major changes in taxonomic composition, bacterial species genetics, and microbial functional potentials, many of which paralleled an aggravation of host metabolism. Whether the temporal gut microbiota changes represent an adaptation to the progression of metabolic abnormalities or actively contribute to these in prediabetes cases remains unsettled
Conformal prediction of molecule-induced cancer cell growth inhibition challenged by strong distribution shifts
No full textThe drug discovery process often employs phenotypic and target-based virtual screening to identify potential drug candidates. Despite the longstanding dominance of target-based approaches, phenotypic virtual screening is undergoing a resurgence due to its potential being now better understood. In the context of cancer cell lines, a well-established experimental system for phenotypic screens, molecules are tested to identify their whole-cell activity, as summarized by their half-maximal inhibitory concentrations. Machine learning has emerged as a potent tool for computationally guiding such screens, yet important research gaps persist, including generalization and uncertainty quantification. To address this, we leverage a clustering-based validation approach, called Leave Dissimilar Molecules Out (LDMO). This strategy enables a more rigorous assessment of model generalization to structurally novel compounds. This study focuses on applying Conformal Prediction (CP), a model-agnostic framework, to predict the activities of novel molecules on specific cancer cell lines. A total of 4320 independent models were evaluated across 60 cell lines, 5 CP variants, 2 set features, and training-test splits, providing strong and consistent results. From this comprehensive evaluation, we concluded that, regardless of the cell line or model, novel molecules with smaller CP-calculated confidence intervals tend to have smaller predicted errors once measured activities are revealed. It was also possible to anticipate the activities of dissimilar test molecules across 50 or more cell lines. These outcomes demonstrate the robust efficacy that LDMO-based models can achieve in realistic and challenging scenarios, thereby providing valuable insights for enhancing decision-making processes in drug discovery
Argtumour: integrating large language models and computational argumentation to discuss treatment options for high-grade glioma
No full textAIMS
High-grade gliomas are aggressive brain tumours with poor prognosis, and patients diagnosed with such tumours often face difficult treatment decisions. While medical guidelines can provide information on the available treatment options, they cannot address individual concerns or offer personalised recommendations. Large language models can provide AI-based language interpretation; Argumentation is a logic-based technique that provides explainability. In this study, we introduce ArgTumour, an interactive, explainable AI system that combines large language models (LLMs) and argumentation to support patient decision-making through summarising treatment options for glioblastoma (GBM) and their justifications.
METHODS
We extracted information on GBM management from NICE guidelines, evidence reviews, and a patient information document, using LLMs to identify treatment options and generate structured arguments for and against them. These arguments were evaluated for faithfulness using the IBM Granite Guardian 8B model, which assesses alignment with the original sources. A clinical expert reviewed the argument structures, and system performance was assessed by comparing confidence scores for NICE-supported vs. non-supported treatments. We have also started some early qualitative evaluation of the system through preliminary patient and public engagement sessions.
RESULTS
Our system identified 14 main treatment options for GBM, extracting 159 supporting and opposing arguments. The Granite Guardian model found 77% of arguments to be well-supported by the used sources, indicating good overall faithfulness. Among the 14 options, 6 aligned with NICE guidelines, with an average system confidence in these options of 73%, while non-NICE-supported options all received a confidence score of 0%.
CONCLUSION
Our findings highlight the potential for using LLM-based argumentation systems in medical decision support, providing more personalized and explainable recommendations. This approach allows us to mine medical guidelines to produce explainable arguments for and against different treatment options, where NICE recommended options are scored much more highly than those that are not