Evaluation of Repeated Web-Based Screening for Predicting Postpartum Depression: Prospective Cohort Study

Background Postpartum depression (PPD) is a severe mental disorder that often results in poor maternal-infant attachment and negatively impacts infant development. Universal screening has recently been recommended to identify women at risk, but the optimal screening time during pregnancy has not been defined so far. Thus, web-based technologies with widespread use among women of childbearing age create new opportunities to detect pregnancies with a high risk for adverse mental health outcomes at an early stage. Objective The aim of this study was to stratify the risk for PPD and to determine the optimal screening time during pregnancy by using a web-based screening tool collecting electronic patient-reported outcomes (ePROs) as the basis for a screening algorithm. Methods In total, 214 women were repeatedly tested for depressive symptoms 5 times during and 3 times after pregnancy by using the Edinburgh Postnatal Depression Scale (EPDS), accessible on a web-based pregnancy platform, developed by the authors of this study. For each prenatal assessment, the area under the curve (AUC), sensitivity, specificity, and predictive values for PPD were calculated. Multivariate logistic regression analyses were applied to identify further potential predictors, such as age, education, parity, relationship quality, and anxiety, to increase predictive accuracy. Results Digitally collected data from 214 pregnant women were analyzed. The predictive accuracy of depressive symptoms 3 and 6 months postpartum was reasonable to good regarding the screening in the second (AUC=0.85) and third (AUC=0.75) trimester. The multivariate logistic regression analyses resulted in an excellent AUC of 0.93 at 3 months and a good AUC of 0.87 at 6 months postpartum. Conclusions The best predictive accuracy for PPD has been shown for screening between the 24th and the 28th gestational week (GW) and seems to be beneficial for identifying women at risk. In combination with the aforementioned predictive factors, the discriminatory power improved, particularly at 3 months postpartum. Screening for depression during pregnancy, combined with the women’s personal risk profile, can be used as a starting point for developing a digital screening algorithm. Thereby, web-based assessment tools constitute feasible, efficient, and cost-effective approaches. Thus, they seem to be beneficial in detecting high-risk pregnancies in order to improve maternal and infant birth outcomes in the long term.

A Practical Approach to Screening for Strongyloides stercoralis

Strongyloides stercoralis, causative agent of a neglected tropical disease, is a soil-transmitted helminth which may cause lifelong persisting infection due to continuous autoinfection. In the case of immunosuppression, life-threatening hyperinfection and disseminated strongyloidiasis can develop. We propose a pragmatic screening algorithm for latent strongyloidiasis based on epidemiologic exposure and immunosuppression status that can be applied for any kind of immunosuppressive therapy. The algorithm allows the diagnosis of latent strongyloidiasis with optimal accuracy in a well-equipped setting, while for endemic settings where the complete testing array is unavailable, an empiric treatment is generally recommended. Accurate diagnosis and extensive empiric treatment will both contribute to decreasing the current neglect of strongyloidiasis.

On the Misclassification of Dust as Cloud at an AERONET site in the Sonoran Desert

Here we present retrievals of aerosol optical depth τ from an Aerosol Robotic Network (AERONET) station in the southeastern corner of California, an area where dust storms are frequent. By combining AERONET data with collocated ceilometer measurements, camera imagery, and satellite data, we show that during significant dust outbreaks the AERONET cloud-screening algorithm oftentimes classifies dusty measurements as cloud contaminated, thus removing them from the aerosol record. During dust storms we estimate that approximately 85% of all dusty retrievals of τ and more than 95% of retrievals when τ > 0.1 are rejected, resulting in a factor 2 reduction in dust-storm averaged τ. We document the specific components in the screening algorithm responsible for the misclassification. We find that a major reason for the loss of these dusty measurements is the high temporal variability in τ during the passage of dust storms over the site, which itself is related to the proximity of the site to the locations of emission. We describe a method to recover these dusty measurements that is based on collocated ceilometer measurements. These results suggest that AERONET sites located close to dust source regions may require ancillary measurements in order to aid in the identification of dust.

Сlostridium difficile Infection in a COVID-19 Patient

Aim. The clinical observation highlights plausible compound origins of diarrhoea, fever and neutrophilic leucocytosis in COVID-19 and the rationale to exclude Clostridium difficile infection in such patients.Key points. A 57-yo female patient was admitted in May 2020 with the complaints of 39 °C fever, general weakness, polymyalgia, diarrhoea to 3–4 times a day (mushy stool, no morbid inclusions). Initial diarrhoea was non-severe and likely triggered by the coronavirus infection. A background antibiotic and putative-immunosuppressive therapy proceeded with watery diarrhoea to 7–8 times a day and C. difficile toxins A and B detected in stool. The C. difficile infection relapsed on day 10 of vancomycin withdrawal and associated with elevated body temperature, diarrhoea and neutrophil leucocytosis; signs of colitis determined in ultrasound and CT. Exacerbation was successfully treated in a repeated metronidazole-combined vancomycin course.Conclusion. Patients with COVID-19 are at risk of clostridial colitis due to massive antibiotic, systemic glucocorticoid and biologics-based therapy they receive. The opportunistic bacterial infection of C. difficile often proceeds undetected due to its potential mirroring of COVID-19 presentation. A screening algorithm in COVID-19 patients with diarrhoea should imply steps for C. difficile detection.

Sexing white 2D footprints using convolutional neural networks

Footprints are left, or obtained, in a variety of scenarios from crime scenes to anthropological investigations. Determining the sex of a footprint can be useful in screening such impressions and attempts have been made to do so using single or multi landmark distances, shape analyses and via the density of friction ridges. Here we explore the relative importance of different components in sexing two-dimensional foot impressions namely, size, shape and texture. We use a machine learning approach and compare this to more traditional methods of discrimination. Two datasets are used, a pilot data set collected from students at Bournemouth University (N = 196) and a larger data set collected by podiatrists at Sheffield NHS Teaching Hospital (N = 2677). Our convolutional neural network can sex a footprint with accuracy of around 90% on a test set of N = 267 footprint images using all image components, which is better than an expert can achieve. However, the quality of the impressions impacts on this success rate, but the results are promising and in time it may be possible to create an automated screening algorithm in which practitioners of whatever sort (medical or forensic) can obtain a first order sexing of a two-dimensional footprint.

The Use of Screening Algorithm to Defer Blood Donors with Subclinical Malaria

Plasmodium falciparum infection in blood donors is common in malaria endemic countries, including Ghana. To date, there are no established exclusion criteria to defer a donor carrying malaria parasites. Therefore, based on significant independent variables identified in this study, donor malaria screening algorithm was developed to be used by blood banks to screen blood donors for subclinical malaria. Each significant variable was weighted one (1) point and its alternative response was weighted negative one (−1) point. Accumulation of the points determines the risk level of the donor. These weighted points were used to categorize infected donors as having negligible (<2 points), tolerable (3-4 points), undesirable (5–8 points), or intolerable (>9 points) risk. Based on accumulated weight of ≥5 points, the algorithm was 94.7% (54/57) sensitive but 82% (298/364) specific. With this level of specificity, 18% of the blood donors without malaria would have been deferred. Therefore, it is imperative that all donors with accumulated risk ≥5 be screened for malaria using either malaria rapid test kit or microscopy.

Second Tier Testing to Reduce the Number of Non-actionable Secondary Findings and False-Positive Referrals in Newborn Screening for Severe Combined Immunodeficiency

Purpose Newborn screening (NBS) for severe combined immunodeficiency (SCID) is based on the detection of T-cell receptor excision circles (TRECs). TRECs are a sensitive biomarker for T-cell lymphopenia, but not specific for SCID. This creates a palette of secondary findings associated with low T-cells that require follow-up and treatment or are non-actionable. The high rate of (non-actionable) secondary findings and false-positive referrals raises questions about the harm-benefit-ratio of SCID screening, as referrals are associated with high emotional impact and anxiety for parents. Methods An alternative quantitative TREC PCR with different primers was performed on NBS cards of referred newborns (N = 56) and epigenetic immune cell counting was used as for relative quantification of CD3 + T-cells (N = 59). Retrospective data was used to determine the reduction in referrals with a lower TREC cutoff value or an adjusted screening algorithm. Results When analyzed with a second PCR with different primers, 45% of the referrals (25/56) had TREC levels above cutoff, including four false-positive cases in which two SNPs were identified. With epigenetic qPCR, 41% (24/59) of the referrals were within the range of the relative CD3 + T-cell counts of the healthy controls. Lowering the TREC cutoff value or adjusting the screening algorithm led to lower referral rates but did not prevent all false-positive referrals. Conclusions Second tier tests and adjustments of cutoff values or screening algorithms all have the potential to reduce the number of non-actionable secondary findings in NBS for SCID, although second tier tests are more effective in preventing false-positive referrals.