Proportion of Pelvic Inflammatory Disease caused by Chlamydia trachomatis: consistent picture from different methods

Background. Pelvic inflammatory disease (PID) is a leading cause of both tubal factor infertility and ectopic pregnancy. Chlamydia trachomatis is an important risk factor for PID, but the proportion of PID cases caused by C. trachomatis is unclear. Estimates of this are required to evaluate control measures. Methods. We consider 5 separate methods of estimating age-group-specific population excess fractions (PEFs) of PID due to C. trachomatis, using routine data, surveys, case-control studies, and randomized controlled trials, and apply these to data from the United Kingdom before introduction of the National Chlamydia Screening Programme. Results. As they are informed by randomized comparisons and national exposure and outcome estimates, our preferred estimates of the proportion of PID cases caused by C. trachomatis are 35% (95% credible interval [CrI], 11%–69%) in women aged 16–24 years and 20% (95% CrI, 6%–38%) in women aged 16–44 years in the United Kingdom. There is a fair degree of consistency between adjusted estimates of PEF, but all have wide 95% CrIs. The PEF decreases from 53.5% (95% CrI, 15.6%–100%) in women aged 16–19 years to 11.5% (95% CrI, 3.0%–25.7%) in women aged 35–44 years. Conclusions. The PEFs of PID due to C. trachomatis decline steeply with age by a factor of around 5-fold between younger and older women. Further studies of the etiology of PID in different age groups are required

Pelvic Inflammatory Disease and Salpingitis: incidence of primary and repeat episodes in England

Pelvic inflammatory disease (PID) and more specifically salpingitis (visually confirmed inflammation) is the primary cause of tubal factor infertility and is an important risk factor for ectopic pregnancy. The risk of these outcomes increases following repeated episodes of PID. We developed a homogenous discrete-time Markov model for the distribution of PID history in the UK. We used a Bayesian framework to fully propagate parameter uncertainty into the model outputs. We estimated the model parameters from routine data, prospective studies, and other sources. We estimated that for women aged 35–44 years, 33·6% and 16·1% have experienced at least one episode of PID and salpingitis, respectively (diagnosed or not) and 10·7% have experienced one salpingitis and no further PID episodes, 3·7% one salpingitis and one further PID episode, and 1·7% one salpingitis and ⩾2 further PID episodes. Results are consistent with numerous external data sources, but not all. Studies of the proportion of PID that is diagnosed, and the proportion of PIDs that are salpingitis together with the severity distribution in different diagnostic settings and of overlap between routine data sources of PID would be valuable

In situ revascularization with silver-coated polyester grafts to treat aortic infection: early and midterm results

AbstractPurposeIn this prospective study we analyzed the immediate and midterm outcome in patients with abdominal aorta infection (mycotic aneurysm, prosthetic graft infection) managed by excision of the aneurysm or the infected vascular prosthesis and in situ replacement with a silver-coated polyester prosthesis.MethodsFrom January 2000 to December 2001, 27 consecutive patients (25 men, 2 women; mean age, 69 years) with an abdominal aortic infection were entered in the study at seven participating centers. Infection was managed with either total (n = 18) or partial (n = 6) excision of the infected aorta and in situ reconstruction with an InterGard Silver (IGS) collagen and silver acetate–coated polyester graft. Assessment of outcome was based on survival, limb salvage, persistent or recurrent infection, and prosthetic graft patency.ResultsTwenty-four patients had prosthetic graft infections, graft–duodenal fistula in 12 and graft-colonic fistula in 1; and the remaining 3 patients had primary aortic infections. Most organisms cultured were of low virulence. The IGS prosthesis was placed emergently in 11 patients (41%). Mean follow-up was 16.5 months (range, 3-30 months). Perioperative mortality was 15%; all four patients who died had a prosthetic graft infection. Actuarial survival at 24 months was 85%. No major amputations were noted in this series. Recurrent infection developed in only one patient (3.7%). Postoperative antibiotic therapy did not exceed 3 months, except in one patient. No incidence of prosthetic graft thrombosis was noted during follow-up.ConclusionPreliminary results in this small series demonstrate favorable outcome with IGS grafts used to treat infection in abdominal aortic grafts and aneurysms caused by organisms with low virulence. Larger series and longer follow-up will be required to compare the role of IGS grafts with other treatment options in infected fields

Abortion in Northern Ireland: has the Rubicon been crossed?

On 7 June 2018, the Supreme Court delivered their long anticipated ruling on whether the abortion laws in Northern Ireland are compatible with the European Convention on Human Rights. Although the case was dismissed on procedural grounds, a majority of the court held that, obiter, the current Northern Irish law was incompatible with the right to respect for private and family life, protected by Article 8 ECHR, “insofar as it prohibits abortion in cases of rape, incest and fatal foetal abnormality”. This Supreme Court decision, seen alongside the May 2018 Irish referendum liberalising abortion, and the 5 June 2018 Parliamentary debate seeking to liberalise abortion laws in Northern Ireland and the rest of the UK, places renewed focus upon the abortion laws of Northern Ireland and Great Britain, which suggests that the ‘halfway house’ of the Abortion Act 1967 Act finally be close to being reformed to hand the decision of abortion to women themselves

Precipitation instruments at Rothera Station, Antarctic Peninsula: a comparative study

Direct measurement of precipitation in the Antarctic using ground-based instruments is important to validate the results from climate models, reanalyses and satellite observations. Quantifying precipitation in Antarctica faces many unique challenges such as wind and other technical difficulties due to the harsh environment. This study compares a variety of precipitation measurements in Antarctica, including satellite data and reanalysis fields atRothera Station, Antarctica Peninsula. The tipping bucket gauges (TBGs) were less sensitive than laser-based sensors (LBSs). The most sensitive LBS (Visibility and Present Weather Sensor, VPF-730) registered 276 precipitation days, while the most sensitive TBG (Universal Precipitation Gauge, UPG-1000) detected 152 precipitation days. Case studies of the precipitation and seasonal accumulation results show the VPF-730 to be the most reliable precipitation sensor of the evaluated instruments. The precipitation amounts given by the reanalyses were positively correlated with wind speed. The precipitation from the Japanese 55-year Reanalysis was most affected by wind speed. Case studies also show that during low wind periods, precipitation measurements from the instruments were very close to the precipitation measurement given by the Global Precipitation Climatology Project (GPCP) 1-degree-daily (1DD) data. During strong wind events, the GPCP 1DD did not fully capture the effect of wind, accounting for the relatively small precipitation amount. The Laser Precipitation Monitor (LPM) and Campbell Scientific-700 (CS700H) experienced instrumental errors during the study, which caused the precipitation readings to become exceedingly high and low, respectively. Installing multiple LBSs in different locations (in close proximity) can help identify inconsistency in the readings

The optimal second-line therapy for older adults with type 2 diabetes mellitus: protocol for a systematic review and network meta-analysis using individual participant data (IPD)

Background: Due to increasing life expectancy, almost half of people with type 2 diabetes are aged 65 years or over worldwide. When metformin alone does not control blood sugar, the choice of which second-line therapy to prescribe next is not clear from currently available evidence. The existence of frailty and comorbidities in older adults further increases the complexity of medical decision-making. As only a relatively small proportion of trials report results separately for older adults, the relative efficacy and safety of second-line therapies in older adults with type 2 diabetes mellitus are unknown and require further investigation. This individual participant data (IPD) network meta-analysis evaluates the relative efficacy and safety of second-line therapies on their own or in combination in older adults with type 2 diabetes mellitus. Methods: All relevant published and unpublished trials will be identified. Studies published prior to 2015 will be identified from two previous comprehensive aggregate data network meta-analyses. Searches will be conducted in CENTRAL, MEDLINE, and EMBASE from 1st January 2015 onwards, and in clinicaltrials.gov from inception. Randomised controlled trials with at least 100 estimated older adults (≥ 65 years) receiving at least 24 weeks of intervention that assess the effects of glucose-lowering drugs on mortality, glycemia, vascular and other comorbidities outcomes, and quality of life will be eligible. The screening and data extraction process will be conducted independently by two researchers. The quality of studies will be assessed using the Cochrane risk of bias tool 2. Anonymised IPD of all eligible trials will be requested via clinical trial portals or by contacting the principal investigators or sponsors. Received data will be reanalysed where necessary to standardise outcome metrics. Network meta-analyses will be performed to determine the relative effectiveness of therapies. Discussion: With the increasing number of older adults with type 2 diabetes worldwide, an IPD network meta-analysis using data from all eligible trials will provide new insights into the optimal choices of second-line antidiabetic drugs to improve patient management and reduce unnecessary adverse events and the subsequent risk of comorbidities in older adults. Systematic review registration: PROSPERO CRD42021272686

What is the overlap between HIV and shigellosis epidemics in England: further evidence of MSM transmission?

BACKGROUND: Evidence suggests that sexual transmission between men has replaced foreign travel as the predominant mode of Shigella transmission in England. However, sexuality and HIV status are not routinely recorded for laboratory-reported Shigella, and the role of HIV in the Shigella epidemic is not well understood. METHODS: The Modular Open Laboratory Information System containing all Shigella cases reported to Public Health England (PHE) and the PHE HIV and AIDS Reporting System holding all adults living with diagnosed HIV in England were matched using a combination of Soundex code, date of birth and gender. RESULTS: From 2004 to 2015, 88 664 patients were living with HIV, and 10 269 Shigella cases were reported in England; 9% (873/10 269) of Shigella cases were diagnosed with HIV, of which 93% (815/873) were in men. Shigella cases without reported travel history were more likely to be living with HIV than those who had travelled (14% (751/5427) vs 3% (134/4854); p<0.01). From 2004 to 2015, the incidence of Shigella in men with HIV rose from 47/100 000 to 226/100 000 (p<0.01) peaking in 2014 at 265/100 000, but remained low in women throughout the study period (0-24/100 000). Among Shigella cases without travel and with HIV, 91% (657/720) were men who have sex with men (MSM). HIV preceded Shigella diagnosis in 86% (610/720), and 65% (237/362) had an undetectable viral load (<50 copies/mL). DISCUSSION: We observed a sustained increase in the national rate of shigellosis in MSM with HIV, who may experience more serious clinical disease. Sexual history, HIV status and STI risk might require sensitive investigation in men presenting with gastroenteritis

Modeling the spatial-spectral characteristics of plants for nutrient status identification using hyperspectral data and deep learning methods

Sustainable fertilizer management in precision agriculture is essential for both economic and environmental reasons. To effectively manage fertilizer input, various methods are employed to monitor and track plant nutrient status. One such method is hyperspectral imaging, which has been on the rise in recent times. It is a remote sensing tool used to monitor plant physiological changes in response to environmental conditions and nutrient availability. However, conventional hyperspectral processing mainly focuses on either the spectral or spatial information of plants. This study aims to develop a hybrid convolution neural network (CNN) capable of simultaneously extracting spatial and spectral information from quinoa and cowpea plants to identify their nutrient status at different growth stages. To achieve this, a nutrient experiment with four treatments (high and low levels of nitrogen and phosphorus) was conducted in a glasshouse. A hybrid CNN model comprising a 3D CNN (extracts joint spectral-spatial information) and a 2D CNN (for abstract spatial information extraction) was proposed. Three pre-processing techniques, including second-order derivative, standard normal variate, and linear discriminant analysis, were applied to selected regions of interest within the plant spectral hypercube. Together with the raw data, these datasets were used as inputs to train the proposed model. This was done to assess the impact of different pre-processing techniques on hyperspectral-based nutrient phenotyping. The performance of the proposed model was compared with a 3D CNN, a 2D CNN, and a Hybrid Spectral Network (HybridSN) model. Effective wavebands were selected from the best-performing dataset using a greedy stepwise-based correlation feature selection (CFS) technique. The selected wavebands were then used to retrain the models to identify the nutrient status at five selected plant growth stages. From the results, the proposed hybrid model achieved a classification accuracy of over 94% on the test dataset, demonstrating its potential for identifying nitrogen and phosphorus status in cowpea and quinoa at different growth stages

Machine learning methods for automatic segmentation of images of field-and glasshouse-based plants for high-throughput phenotyping

Image segmentation is a fundamental but critical step for achieving automated high- throughput phenotyping. While conventional segmentation methods perform well in homogenous environments, the performance decreases when used in more complex environments. This study aimed to develop a fast and robust neural-network-based segmentation tool to phenotype plants in both field and glasshouse environments in a high-throughput manner. Digital images of cowpea (from glasshouse) and wheat (from field) with different nutrient supplies across their full growth cycle were acquired. Image patches from 20 randomly selected images from the acquired dataset were transformed from their original RGB format to multiple color spaces. The pixels in the patches were annotated as foreground and background with a pixel having a feature vector of 24 color properties. A feature selection technique was applied to choose the sensitive features, which were used to train a multilayer perceptron network (MLP) and two other traditional machine learning models: support vector machines (SVMs) and random forest (RF). The performance of these models, together with two standard color-index segmentation techniques (excess green (ExG) and excess green–red (ExGR)), was compared. The proposed method outperformed the other methods in producing quality segmented images with over 98%-pixel classification accuracy. Regression models developed from the different segmentation methods to predict Soil Plant Analysis Development (SPAD) values of cowpea and wheat showed that images from the proposed MLP method produced models with high predictive power and accuracy comparably. This method will be an essential tool for the development of a data analysis pipeline for high-throughput plant phenotyping. The proposed technique is capable of learning from different environmental conditions, with a high level of robustness

Symptoms and risk factors for long COVID in non-hospitalized adults

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection is associated with a range of persistent symptoms impacting everyday functioning, known as post-COVID-19 condition or long COVID. We undertook a retrospective matched cohort study using a UK-based primary care database, Clinical Practice Research Datalink Aurum, to determine symptoms that are associated with confirmed SARS-CoV-2 infection beyond 12 weeks in non-hospitalized adults and the risk factors associated with developing persistent symptoms. We selected 486,149 adults with confirmed SARS-CoV-2 infection and 1,944,580 propensity score-matched adults with no recorded evidence of SARS-CoV-2 infection. Outcomes included 115 individual symptoms, as well as long COVID, defined as a composite outcome of 33 symptoms by the World Health Organization clinical case definition. Cox proportional hazards models were used to estimate adjusted hazard ratios (aHRs) for the outcomes. A total of 62 symptoms were significantly associated with SARS-CoV-2 infection after 12 weeks. The largest aHRs were for anosmia (aHR 6.49, 95% CI 5.02–8.39), hair loss (3.99, 3.63–4.39), sneezing (2.77, 1.40–5.50), ejaculation difficulty (2.63, 1.61–4.28) and reduced libido (2.36, 1.61–3.47). Among the cohort of patients infected with SARS-CoV-2, risk factors for long COVID included female sex, belonging to an ethnic minority, socioeconomic deprivation, smoking, obesity and a wide range of comorbidities. The risk of developing long COVID was also found to be increased along a gradient of decreasing age. SARS-CoV-2 infection is associated with a plethora of symptoms that are associated with a range of sociodemographic and clinical risk factors