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Within the UK, child mortality from all causes has declined for all ages over the last three decades. However, distinct inequality remains, as child mortality rates are generally found to be higher in males. A significant proportion of childhood deaths in the UK occur in Paediatric Intensive Care Units (PICU). We studied the association of sex with infant mortality in PICUs. We included all infants (0 to 12 months old) admitted to UK PICUs from 01/01/2005 to 31/12/2015 using the Paediatric Intensive Care Audit Network (PICANet) dataset. We considered first admissions to PICU and fitted a cause-specific-hazard-ratio (CSHR) model, and a logistic model to estimate the adjusted association between sex and mortality in PICU. Pre-defined subgroups were children less than 56-days old, and those with a primary diagnosis of infection. Of 71,243 cases, 1,411/29,520 (4.8%) of females, and 1,809/41,723 (4.3%) of males died. The adjusted male/female CSHR was 0.87 (95%-CI 0.81 to 0.92) representing a 13% higher risk of death for females. The adjusted OR for male to female mortality is 0.86 (95%-CI 0.80 to 0.93). Analyses in subgroups yielded similar findings. In our analysis, female infants have a higher rate of PICU mortality compared to male infants.

Background Drug resistant Mycobacterium tuberculosis is complicating the effective treatment and control of tuberculosis disease (TB). With the adoption of whole genome sequencing as a diagnostic tool, machine learning approaches are being employed to predict M. tuberculosis resistance and identify underlying genetic mutations. However, machine learning approaches can overfit and fail to identify causal mutations if they are applied out of the box and not adapted to the disease-specific context. We introduce a machine learning approach that is customized to the TB setting, which extracts a library of genomic variants re-occurring across individual studies to improve genotypic profiling. Results We developed a customized decision tree approach, called Treesist-TB, that performs TB drug resistance prediction by extracting and evaluating genomic variants across multiple studies. The application of Treesist-TB to rifampicin (RIF), isoniazid (INH) and ethambutol (EMB) drugs, for which resistance mutations are known, demonstrated a level of predictive accuracy similar to the widely used TB-Profiler tool (Treesist-TB vs. TB-Profiler tool: RIF 97.5% vs. 97.6%; INH 96.8% vs. 96.5%; EMB 96.8% vs. 95.8%). Application of Treesist-TB to less understood second-line drugs of interest, ethionamide (ETH), cycloserine (CYS) and para-aminosalisylic acid (PAS), led to the identification of new variants (52, 6 and 11, respectively), with a high number absent from the TB-Profiler library (45, 4, and 6, respectively). Thereby, Treesist-TB had improved predictive sensitivity (Treesist-TB vs. TB-Profiler tool: PAS 64.3% vs. 38.8%; CYS 45.3% vs. 30.7%; ETH 72.1% vs. 71.1%). Conclusion Our work reinforces the utility of machine learning for drug resistance prediction, while highlighting the need to customize approaches to the disease-specific context. Through applying a modified decision learning approach (Treesist-TB) across a range of anti-TB drugs, we identified plausible resistance-encoding genomic variants with high predictive ability, whilst potentially overcoming the overfitting challenges that can affect standard machine learning applications.

Background Malaria, caused by Plasmodium parasites, is a major global public health problem. To assist an understanding of malaria pathogenesis, including drug resistance, there is a need for the timely detection of underlying genetic mutations and their spread. With the increasing use of whole-genome sequencing (WGS) of Plasmodium DNA, the potential of deep learning models to detect loci under recent positive selection, historically signals of drug resistance, was evaluated. Methods A deep learning-based approach (called “ DeepSweep” ) was developed, which can be trained on haplotypic images from genetic regions with known sweeps, to identify loci under positive selection. DeepSweep software is available from https://github.com/WDee/Deepsweep . Results Using simulated genomic data, DeepSweep could detect recent sweeps with high predictive accuracy (areas under ROC curve > 0.95). DeepSweep was applied to Plasmodium falciparum (n = 1125; genome size 23 Mbp) and Plasmodium vivax (n = 368; genome size 29 Mbp) WGS data, and the genes identified overlapped with two established extended haplotype homozygosity methods (within-population iHS, across-population Rsb) (~ 60–75% overlap of hits at P < 0.0001). DeepSweep hits included regions proximal to known drug resistance loci for both P. falciparum (e.g. pfcrt , pfdhps and pfmdr1 ) and P. vivax (e.g. pvmrp1 ). Conclusion The deep learning approach can detect positive selection signatures in malaria parasite WGS data. Further, as the approach is generalizable, it may be trained to detect other types of selection. With the ability to rapidly generate WGS data at low cost, machine learning approaches (e.g. DeepSweep ) have the potential to assist parasite genome-based surveillance and inform malaria control decision-making.

Malaria, caused by Plasmodium parasites, is a major global health challenge. Whole genome sequencing (WGS) of Plasmodium falciparum and Plasmodium vivax genomes is providing insights into parasite genetic diversity, transmission patterns, and can inform decision making for clinical and surveillance purposes. Advances in sequencing technologies are helping to generate timely and big genomic datasets, with the prospect of applying Artificial Intelligence analytical techniques (e.g., machine learning) to support programmatic malaria control and elimination. Here, we assess the potential of applying deep learning convolutional neural network approaches to predict the geographic origin of infections (continents, countries, GPS locations) using WGS data of P. falciparum (n = 5957; 27 countries) and P. vivax (n = 659; 13 countries) isolates. Using identified high-quality genome-wide single nucleotide polymorphisms (SNPs) ( P. falciparum : 750 k, P. vivax : 588 k), an analysis of population structure and ancestry revealed clustering at the country-level. When predicting locations for both species, classification (compared to regression) methods had the lowest distance errors, and > 90% accuracy at a country level. Our work demonstrates the utility of machine learning approaches for geo-classification of malaria parasites. With timelier WGS data generation across more malaria-affected regions, the performance of machine learning approaches for geo-classification will improve, thereby supporting disease control activities.

BackgroundThere is growing recognition that recent global increases in obesity are the product of a complex interplay between genetic and environmental factors. However, in gene-environment studies of obesity, ‘environment’ usually refers to individual behavioural factors that influence energy balance, whereas more upstream environmental factors are overlooked. We examined gene-environment interactions between genetic risk of obesity and two neighbourhood characteristics likely to be associated with obesity (proximity to takeaway/fast-food outlets and availability of physical activity facilities).MethodsWe used data from 335 046 adults aged 40–70 in the UK Biobank cohort to conduct a population-based cross-sectional study of interactions between neighbourhood characteristics and genetic risk of obesity, in relation to body mass index (BMI). Proximity to a fast-food outlet was defined as distance from home address to nearest takeaway/fast-food outlet, and availability of physical activity facilities as the number of formal physical activity facilities within 1 km of home address. Genetic risk of obesity was operationalised by weighted Genetic Risk Scores of 91 or 69 single nucleotide polymorphisms (SNP), and by six individual SNPs considered separately. Multivariable, mixed-effects models with product terms for the gene-environment interactions were estimated.ResultsAfter accounting for likely confounding, the association between proximity to takeaway/fast-food outlets and BMI was stronger among those at increased genetic risk of obesity, with evidence of an interaction with polygenic risk scores (p=0.018 and p=0.028 for 69-SNP and 91-SNP scores, respectively) and in particular with a SNP linked to MC4R (p=0.009), a gene known to regulate food intake. We found very little evidence of gene-environment interaction for the availability of physical activity facilities.ConclusionsIndividuals at an increased genetic risk of obesity may be more sensitive to exposure to the local fast-food environment. Ensuring that neighbourhood residential environments are designed to promote a healthy weight may be particularly important for those with greater genetic susceptibility to obesity.

The measurement of biological tissues’ dielectric properties plays a crucial role in determining the state of health, and recent studies have reported microwave biosensing to be an innovative method with great potential in this field. Research has been conducted from the tissue level to the cellular level but, to date, cellular adhesion has never been considered. In addition, conventional systems for diagnosing tumor aggressiveness, such as a biopsy, are rather expensive and invasive. Here, we propose a novel microwave approach for biosensing adherent cancer cells with different malignancy degrees. A circular patch resonator was designed adjusting its structure to a standard Petri dish and a network analyzer was employed. Then, the resonator was realized and used to test two groups of different cancer cell lines, based on various tumor types and aggressiveness: low- and high-aggressive osteosarcoma cell lines (SaOS-2 and 143B, respectively), and low- and high-aggressive breast cancer cell lines (MCF-7 and MDA-MB-231, respectively). The experimental results showed that the sensitivity of the sensor was high, in particular when measuring the resonant frequency. Finally, the sensor showed a good ability to distinguish low-metastatic and high-metastatic cells, paving the way to the development of more complex measurement systems for noninvasive tissue diagnosis.

Independent studies reported metabolic alterations in connective tissues of hernia patients, especially involving collagen fibers, compared to healthy controls. In the present work, we evaluated plasma concentrations of metalloproteinases (MMPs) and lysyl oxidase (LOX), enzymes involved in collagen metabolism, and peptides produced during collagen biosynthesis (PINP, PIIINP, and PIVNP) as potential biomarkers for the estimation of hernia risk. Zymography and ELISA assays were performed with plasma samples of 51 patients with primary or recurrent inguinal hernia and 42 healthy controls. A reduction in PINP (p = 0.007) and a concomitant increase in PIIINP (p < 0.001) were observed in patients. In controls, PINP levels were inversely related to age, whereas in patients PIIINP levels increased with age. Body mass index (BMI) showed a strong positive correlation with PIIINP plasma levels in controls but not in patients (p < 0.001). Moreover, patients with larger lesions had the lowest PINP/PIIINP ratio (p = 0.003). PIVNP collagen did not differ between controls and hernia patients. Plasma MMP-9 was reduced in patients (p = 0.015), while MMP-2 and LOX were unchanged. However, MMP-2 concentrations appeared lower in patients with familial history of hernia compared to those without. In regression analysis, the PINP/PIIINP ratio was inversely related to hernia risk, and a cut-off value of 0.948 was found by ROC analysis which classified hernia patients with a sensitivity of 82.9% and a specificity of 77.1%. In conclusion, our findings identified the PINP/PIIINP ratio as the most relevant molecular predictor of inguinal hernia risk.

Background Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex disease, whose exact cause remains unclear. A wide range of risk factors has been proposed that helps understanding potential disease pathogenesis. However, there is little consistency for many risk factor associations, thus we undertook an exploratory study of risk factors using data from the UK ME/CFS Biobank participants. We report on risk factor associations in ME/CFS compared with multiple sclerosis participants and healthy controls. Methods This was a cross-sectional study of 269 people with ME/CFS, including 214 with mild/moderate and 55 with severe symptoms, 74 people with multiple sclerosis (MS), and 134 healthy controls, who were recruited from primary and secondary health services. Data were collected from participants using a standardised written questionnaire. Data analyses consisted of univariate and multivariable regression analysis (by levels of proximity to disease onset). Results A history of frequent colds (OR = 8.26, P  <= 0.001) and infections (OR = 25.5, P  = 0.015) before onset were the strongest factors associated with a higher risk of ME/CFS compared to healthy controls. Being single (OR = 4.41, P  <= 0.001), having lower income (OR = 3.71, P  <= 0.001), and a family history of anxiety is associated with a higher risk of ME/CFS compared to healthy controls only (OR = 3.77, P < 0.001). History of frequent colds (OR = 6.31, P  < 0.001) and infections before disease onset (OR = 5.12, P  = 0.005), being single (OR = 3.66, P  = 0.003) and having lower income (OR = 3.48, P  = 0.001), are associated with a higher risk of ME/CFS than MS. Severe ME/CFS cases were associated with lower age of ME/CFS onset (OR = 0.63, P  = 0.022) and a family history of neurological illness (OR = 6.1, P  = 0.001). Conclusions Notable differences in risk profiles were found between ME/CFS and healthy controls, ME/CFS and MS, and mild-moderate and severe ME/CFS. However, we found some commensurate overlap in risk associations between all cohorts. The most notable difference between ME/CFS and MS in our study is a history of recent infection prior to disease onset. Even recognising that our results are limited by the choice of factors we selected to investigate, our findings are consistent with the increasing body of evidence that has been published about the potential role of infections in the pathogenesis of ME/CFS, including common colds/flu.

This study aims at combining time and quantity of carbohydrate (CH) intake in the definition of eating patterns in UK adults and investigating the association of the derived patterns with type 2 diabetes (T2D). The National Diet and Nutrition Survey (NDNS) Rolling Program included 6155 adults in the UK. Time of the day was categorized into 7 pre-defined time slots: 6–9 am, 9–12 noon, 12–2 pm, 2–5 pm, 5–8 pm, 8–10 pm, and 10 pm–6 am. Responses for CH intake were categorized into: no energy intake, CH <50% or ≥50% of total energy. Non-parametric multilevel latent class analysis (MLCA) was applied to identify eating patterns of CH consumption across day-time, as a novel method accounting for the repeated measurements of intake over 3–4 days nested within individuals. Survey-designed multivariable regression was used to assess the associations of CH eating patterns with T2D. Three CH eating day patterns (low, high CH percentage and regular meal CH intake day) emerged from 24,483 observation days; based on which three classes of CH eaters were identified and characterized as: low (28.1%), moderate (28.8%) and high (43.1%) CH eaters. On average, low-CH eaters consumed the highest amount of total energy intake (7985.8 kJ) and had higher percentages of energy contributed by fat and alcohol, especially after 8 pm. Moderate-CH eaters consumed the lowest amount of total energy (7341.8 kJ) while they tended to have their meals later in the day. High-CH eaters consumed most of their carbohydrates and energy earlier in the day and within the time slots of 6–9 am, 12–2 p.m. and 5–8 pm, which correspond to traditional mealtimes. The high-CH eaters profile had the highest daily intake of CH and fiber and the lowest intake of protein and fat. Low-CH eaters had greater odds than high-CH eaters of having T2D in self-reported but not in previously undiagnosed diabetics. Further research using prospective longitudinal studies is warranted to ascertain the direction of causality in the association of CH patterns with type 2 diabetes.

Background Fever with jaundice is a common symptom of some infectious diseases. In public health surveillance within the Democratic Republic of the Congo (DRC), yellow fever is the only recognized cause of fever with jaundice. However, only 5% of the surveillance cases are positive for yellow fever and thus indicate the involvement of other pathogens. Leptospira spp. are the causative agents of leptospirosis, a widespread bacterial zoonosis, a known cause of fever with jaundice. This study aimed to determine the seropositivity of anti-Leptospira antibodies among suspected yellow fever cases and map the geographical distribution of possible leptospirosis in the DRC. Methods We conducted a retrospective study using 1,300 samples from yellow fever surveillance in the DRC from January 2017 to December 2018. Serum samples were screened for the presence of IgM against Leptospira spp. by a whole cell-based IgM ELISA (Patoc-IgM ELISA) at the Institut National de Recherche Biomedicale in Kinshasa (INRB) according to World Health Organization (WHO) guidance. Exploratory univariable and multivariable logistic regression analyses were undertaken to assess associations between socio-demographic factors and the presence of Leptospira IgM. Results Of the 1,300 serum samples screened, 88 (7%) showed evidence of IgM against Leptospira spp. Most positive cases (34%) were young adult males in the 20-29-year group. There were statistically significant associations between having Leptospira IgM antibodies, age, sex, and living area. Observed positive cases were mostly located in urban settings, and the majority lived in the province of Kinshasa. There was a statistically significant association between seasonality and IgM Leptospira spp. positivity amongst those living in Kinshasa, where most of the positive cases occurred during the rainy season. Conclusions This study showed that leptospirosis is likely an overlooked cause of unexplained cases of fever with jaundice in the DRC and highlights the need to consider leptospirosis in the differential diagnosis of fever with jaundice, particularly in young adult males. Further studies are needed to identify animal reservoirs, associated risk factors, and the burden of human leptospirosis in the DRC.