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BackgroundArtificial intelligence (AI)–driven symptom checkers are available to millions of users globally and are advocated as a tool to deliver health care more efficiently. To achieve the promoted benefits of a symptom checker, laypeople must trust and subsequently follow its instructions. In AI, explanations are seen as a tool to communicate the rationale behind black-box decisions to encourage trust and adoption. However, the effectiveness of the types of explanations used in AI-driven symptom checkers has not yet been studied. Explanations can follow many forms, including why-explanations and how-explanations. Social theories suggest that why-explanations are better at communicating knowledge and cultivating trust among laypeople.ObjectiveThe aim of this study is to ascertain whether explanations provided by a symptom checker affect explanatory trust among laypeople and whether this trust is impacted by their existing knowledge of disease.MethodsA cross-sectional survey of 750 healthy participants was conducted. The participants were shown a video of a chatbot simulation that resulted in the diagnosis of either a migraine or temporal arteritis, chosen for their differing levels of epidemiological prevalence. These diagnoses were accompanied by one of four types of explanations. Each explanation type was selected either because of its current use in symptom checkers or because it was informed by theories of contrastive explanation. Exploratory factor analysis of participants’ responses followed by comparison-of-means tests were used to evaluate group differences in trust.ResultsDepending on the treatment group, two or three variables were generated, reflecting the prior knowledge and subsequent mental model that the participants held. When varying explanation type by disease, migraine was found to be nonsignificant (P=.65) and temporal arteritis, marginally significant (P=.09). Varying disease by explanation type resulted in statistical significance for input influence (P=.001), social proof (P=.049), and no explanation (P=.006), with counterfactual explanation (P=.053). The results suggest that trust in explanations is significantly affected by the disease being explained. When laypeople have existing knowledge of a disease, explanations have little impact on trust. Where the need for information is greater, different explanation types engender significantly different levels of trust. These results indicate that to be successful, symptom checkers need to tailor explanations to each user’s specific question and discount the diseases that they may also be aware of.ConclusionsSystem builders developing explanations for symptom-checking apps should consider the recipient’s knowledge of a disease and tailor explanations to each user’s specific need. Effort should be placed on generating explanations that are personalized to each user of a symptom checker to fully discount the diseases that they may be aware of and to close their information gap.

The COVIPENDIUM, aliving paper on the new coronavirus disease (COVID-19), provides a structured compilation of scientific data about the virus, the disease and its control.Its objective is to help scientists identify the most relevant publications on COVID-19 in the mass of information that appears every day. It is also expected to foster a global understanding of disease control and stimulate transdisciplinary initiatives. This living paper is updated on a weekly basis. It is an initiative of the journal Transciplinary Insights.

Purpose In many cases, the diagnosis of a periprosthetic joint infection (PJI) consisting of the clinical appearance, laboratory tests, and other diagnostic tools remains a difficult task. Single serum biomarkers are easy to collect, are suitable for periodical assessment, and are a crucial tool in PJI diagnosis, but limited sensitivity or specificity is reported in literature. The aim of this study was to combine the best-performing single serum biomarkers into a multi-biomarker model aiming to improve the diagnostic properties. Methods Within a 27-month period, 124 surgical procedures (aseptic or septic revision total knee arthroplasty (TKA) or total hip arthroplasty (THA)) were prospectively included. The serum leukocyte count, C-reactive protein (CRP), interleukin-6, procalcitonin, interferon alpha, and fibrinogen were assessed 1 day prior to surgery. Logistic regression with lasso-regularization was used for the biomarkers and all their ratios. After randomly splitting the data into a training (75%) and a test set (25%), the multi-biomarker model was calculated and validated in a cross-validation approach. Results CRP (AUC 0.91, specificity 0.67, sensitivity 0.90, p value 0.03) and fibrinogen (AUC 0.93, specificity 0.73, sensitivity 0.94, p value 0.02) had the best single-biomarker performances. The multi-biomarker model including fibrinogen, CRP, the ratio of fibrinogen to CRP, and the ratio of serum thrombocytes to CRP showed a similar performance (AUC 0.95, specificity 0.91, sensitivity 0.72, p value 0.01). Conclusion In this study, multiple biomarkers were tested for their diagnostic performance, with CRP and fibrinogen showing the best results regarding the AUC, accuracy, sensitivity, and specificity. It was not possible to further increase the diagnostic accuracy by combining multiple biomarkers using sophisticated statistical methods.

Background Fungal infections of the skin, hair, and nails are the largest and most widespread group of all mycoses. Nannizzia nana is a relatively rare etiological factor of dermatomycosis in humans, as it usually affects animals, e.g. pigs and boars. In addition to the zoophilic nature, there are also reports of the geophilic reservoir of this dermatophyte species. Objective In this study, we present symptomatic infections with N. nana aetiology in humans reported recently in Poland. Interestingly, these cases had a non-specific clinical picture and occurred as skin lesions on the neck and foot as well as onychomycosis of the toenails. From the medical history, the patients had no contact with pigs. Methods Diagnostics of these infections was performed with a combination of classical phenotypic and molecular genomic methods. The genomic diversity of the isolates was determined using the MP-PCR method. In vitro antifungal susceptibility tests against itraconazole, ketoconazole, terbinafine and naftifine hydrochloride were also performed. Results Nannizzia nana has been identified as an etiological factor of dermatomycosis. Moreover, heterogeneity of the genomes was revealed for the obtained strains. In vitro activities of antifungal agents showed that isolates were susceptible to all tested drugs. The patients were treated with oral terbinafine and topical ketoconazole cream, which led to a complete recovery. Conclusions In conclusion, the cases studied by us may indicate that the infrequency of N. nana infections may not necessarily be related to the low infectivity of this fungal agent, but they are rather associated with misdiagnosis. Furthermore, N. nana reservoirs should also be sought in soil.

Frontal fibrosing alopecia (FFA) is recognized to represent a generalized process of inflammatory scarring alopecia. Apart from the classic form affecting the frontal hairline, there are a range of disease manifestations involving loss of eyebrows and of eyelashes, loss of peripheral body hair, fibrosing alopecia in a pattern distribution, facial and extrafacial skin, mucous membrane, and nail involvement. Classic linear, diffuse “zigzag”, pseudo “fringe sign”, androgenetic alopecia-like, cockade-like, ophiasis-like and incomplete patterns are distinguished. The aetiology of FFA remains obscure, but a number of pathogenetic hypotheses and treatments to halt disease progression have been proposed.

Access to energy is fundamental to economic and technological advancement. Hence, the more the world develops, the greater the demand for energy becomes. Evidently, the production and consumption of energy alone account for more than 80% of global anthropogenic greenhouse gas (GHG) emissions. There is broad scientific consensus that efficiency improvements in energy production and consumption must come first on the path to reducing global GHG emissions. As the largest producer and consumer of energy, the industrial sector faces tremendous challenges due to stringent environmental regulations, intense price-based global competition, rising operating costs and rapidly changing economic conditions. Therefore, increasing energy and resource efficiency while improving throughput and asset reliability is a matter of utmost importance. Satisfying such demanding objectives requires an optimal operation, control and monitoring of plant assets and processes. This is one of the main driving forces behind developing digital solutions, methods, and frameworks that can be integrated with old and new industrial automation platforms. The main focus of this dissertation is to investigate frameworks, process models, soft sensors, control optimization, and diagnostic techniques to improve the operation, control, and monitoring of industrial plants and processes. In this thesis, a generic architecture for control optimization, diagnostics, and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies, one representing large-scale assets and another representing a fleet of small-scale assets, are considered to demonstrate the genericness of the proposed system architecture. In this thesis, a very energy-intensive chemical pulping process represents the case study of large-scale assets, and a micro gas turbine (MGT) fleet for distributed heat and power generation represent the case study of a fleet of small-scale assets. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity, and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a MGT fleet. The main argument behind the selection of these two case studies is that, if the proposed learning system architecture can be adapted for these significantly different cases, then it can be adapted for many other industrial applications as well. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control, and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation, and diagnostics techniques are explored for the MGT fleet. Due to the naturally varying wood quality, significant residence time, insufficient measurements, and complexity of pulping reactions, modelling and controlling a continuous pulp digester is a challenging task. Moreover, process abnormalities due to non-ideal flow in the digester often occur that considerably affect the pulp quality. Within this dissertation, variation of wood-chip quality is identified as one of the main process disturbances. Thereafter, a feedforward model predictive control (MPC) approach is explored by feedforwarding the lignin content of the wood chips to the controller. The result shows that the disturbance rejection and tracking performance of the feedforward MPC are superior to other alternatives, like Proportional–integral–derivative (PID), MPC, and current industrial control. When it comes to diagnostics, a literature gap is identified in the area of modelling digester faults. Hence, the well-known Purdue model, a widely used dynamic model of the digester, is extended to simulate process faults like screen-clogging, hangups, and channelling. The findings suggest that both hangups and channelling considerably affect the pulp quality at the blowline. The impact of channelling is prominent on reaction temperature compared to hangups, while hangups change the residence time of the wood chips significantly. Subsequently, a hybrid diagnostics scheme for pulp digester, combining a physical model and a Bayesian network (BN), is demonstrated. Overall, the results show that fault type and severity can be estimated with acceptable accuracy even in presence of noise. Enabling remote fleet diagnostics is expected to foster the commercialization of distributed micro-combined heat and power (micro-CHP) generators, i.e., MGTs. Even though the modelling and diagnostics of large-scale gas turbines are well researched, studies targeting MGT are limited. In this thesis, a physical model of a commercial MGT system is developed. Subsequently, a hybrid scheme by combining a physics-based gas path analysis with a data-driven approach is used to enable MGT diagnostics. The proposed scheme was tested by simulating case studies corresponding to single and multiple faults. Furthermore, sensitivity studies are performed for different measurement uncertainties (i.e., sensor noise and bias) to evaluate the robustness of the scheme against measurement uncertainties. The findings show that the proposed diagnostics approach performs satisfactorily even under measurement uncertainties. To sum up, the increased availability of data and higher computing power is fostering the development of accurate process models and algorithms necessary for optimal operation, control, and monitoring of industrial processes. With the emergence of new measurement techniques, it is possible to leverage productivity and quality with tighter control of key process parameters. Additionally, studying the underlying mechanism of process degradation and developing diagnostics methods by incorporating these can lead to significant economic benefits. Having said that, to tap the full potential of these digital solutions, an integrated framework like that presented in this thesis, i.e., a learning system is essential. FUDIPO

Empiric broad‐spectrum antimicrobial treatments of urinary tract infections (UTIs) have contributed to widespread antimicrobial resistance. Clinical adoption of evidence‐based treatments necessitates rapid diagnostic methods for pathogen identification (ID) and antimicrobial susceptibility testing (AST) with minimal sample preparation. In response, a microfluidic droplet‐based platform is developed for achieving both ID and AST from urine samples within 30 min. In this platform, fluorogenic hybridization probes are utilized to detect 16S rRNA from single bacterial cells encapsulated in picoliter droplets, enabling molecular identification of uropathogenic bacteria directly from urine in as little as 16 min. Moreover, in‐droplet single‐bacterial measurements of 16S rRNA provide a surrogate for AST, shortening the exposure time to 10 min for gentamicin and ciprofloxacin. A fully integrated device and screening workflow were developed to test urine specimens for one of seven unique diagnostic outcomes including the presence/absence of Gram‐negative bacteria, molecular ID of the bacteriaas Escherichia coli, an Enterobacterales, or other organism, and assessment of bacterial susceptibility to ciprofloxacin. In a 50‐specimen clinical comparison study, the platform demonstrates excellent performance compared to clinical standard methods (areas‐under‐curves, AUCs >0.95), within a small fraction of the turnaround time, highlighting its clinical utility., Single‐cell measurements of bacterial 16S rRNA are leveraged in picoliter droplets to achieve simultaneous molecular detection of bacteria and phenotypic assessment of antimicrobial susceptibility, unlocking a 30 min sample‐to‐answer diagnostic platform for urinary tract infections.

Background Water, Sanitation, and Hygiene (WASH) systems aim to reduce the spread of enteric pathogens, particularly amongst children under five years old. The most common primary outcome of WASH trials is carer-reported diarrhoea. We evaluate different diarrhoea survey instruments as proxy markers of enteric pathogen presence in stool. Methods We recruited 800 community-based participants from the Cox's Bazar Displaced Person's Camp in Bangladesh, split evenly between the rainy (July/August 2020) and dry (November/December 2020) periods. Participants were randomized evenly into either a standard survey asking carers if their child under five years old has had diarrhoea in the past fortnight, or a pictorial survey asking carers to pick from a pictorial chart which stools their child under five years old has had in the past fortnight. We collected stools from a random sub-sample of 120. Stools were examined visually, and tested for proteins associated with enteric infection and 16 enteric pathogens. We calculated sensitivities and specificities for each survey type, visual examination, and proteins with respect to enteric pathogen presence. Findings The sensitivity of the standard survey for enteric pathogen presence was 0.49[95%CI:0.32,0.66] and the specificity was 0.65[0.41,0.85]. Similar sensitivities and specificities were observed for pictorial survey, visual inspection, and proteins. Interpretation While diarrhoea is an important sign in clinical practice it appears that it is a poor proxy for enteric pathogen presence in stool in epidemiological surveys. When enteric infection is of interest, this should be measured directly. Funding The project was funded by the National Institutes for Health Research Global Health Research Unit on Improving Health in Slums (16/136/87) and by the University of Warwick.

Highlights • In remote Australia, many women with GDM are missed due to test sample instability. • FC tubes stabilise glucose but markedly increase GDM diagnosis in lower-risk women. • Adjustment of FC results lowered GDM and improved risk-assessment for a large baby., Aims Preanalytical glycolysis in oral glucose tolerance tests (OGTT) leads to substantial underestimation of gestational diabetes mellitus (GDM) and hence risk for large-for-gestational-age (LGA) babies. This paper quantified the impact of glycolysis on identification of LGA risk in a prospective rural and remote Australian cohort. Methods For 495 women, OGTT results from room temperature fluoride-oxalate (FLOX) tubes were algorithmically corrected for estimated glycolysis compared to 1) the Hyperglycaemia and Adverse Pregnancy Outcomes (HAPO) study protocol (FLOX tubes in ice-slurry); and 2) room temperature fluoride-citrate (FC) tubes. GDM was defined by International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria. Unadjusted and corrected OGTT were related to LGA outcome. Results Correction for FC tubes increased GDM incidence from 9.7% to 44.6%. After correction for HAPO protocol, GDM incidence was 27.7% and prediction of LGA risk (RR 1.82, [1.11–2.99]) improved compared to unadjusted rates (RR 1.12, [0.51–2.47]). To provide similar results for FC tube correction (29.3% GDM; RR 1.81, [1.11–2.96]) required + 0.2 mmol/L adjustment of IADPSG criteria. Conclusions FC tubes present a practical alternative to the HAPO protocol in remote settings but give + 0.2 mmol/L higher glucose readings. Modification of IADPSG criteria would reduce perceived ‘overdiagnosis’ and improve LGA risk-assessment.

Background Recently, machine learning (ML) has been transforming our daily lives by enabling intelligent voice assistants, personalized support for purchase decisions, and efficient credit card fraud detection. In addition to its everyday applications, ML holds the potential to improve medicine as well, especially with regard to diagnostics in clinics. In a world characterized by population growth, demographic change, and the global COVID-19 pandemic, ML systems offer the opportunity to make diagnostics more effective and efficient, leading to a high interest of clinics in such systems. However, despite the high potential of ML, only a few ML systems have been deployed in clinics yet, as their adoption process differs significantly from the integration of prior health information technologies given the specific characteristics of ML. Objective This study aims to explore the factors that influence the adoption process of ML systems for medical diagnostics in clinics to foster the adoption of these systems in clinics. Furthermore, this study provides insight into how these factors can be used to determine the ML maturity score of clinics, which can be applied by practitioners to measure the clinic status quo in the adoption process of ML systems. Methods To gain more insight into the adoption process of ML systems for medical diagnostics in clinics, we conducted a qualitative study by interviewing 22 selected medical experts from clinics and their suppliers with profound knowledge in the field of ML. We used a semistructured interview guideline, asked open-ended questions, and transcribed the interviews verbatim. To analyze the transcripts, we first used a content analysis approach based on the health care–specific framework of nonadoption, abandonment, scale-up, spread, and sustainability. Then, we drew on the results of the content analysis to create a maturity model for ML adoption in clinics according to an established development process. Results With the help of the interviews, we were able to identify 13 ML-specific factors that influence the adoption process of ML systems in clinics. We categorized these factors according to 7 domains that form a holistic ML adoption framework for clinics. In addition, we created an applicable maturity model that could help practitioners assess their current state in the ML adoption process. Conclusions Many clinics still face major problems in adopting ML systems for medical diagnostics; thus, they do not benefit from the potential of these systems. Therefore, both the ML adoption framework and the maturity model for ML systems in clinics can not only guide future research that seeks to explore the promises and challenges associated with ML systems in a medical setting but also be a practical reference point for clinicians.

Background Artificial intelligence (AI) has shown potential to improve diagnostics of various diseases, especially for early detection of skin cancer. Studies have yet to investigate the clear application of AI technology in clinical practice or determine the added value for younger user groups. Translation of AI-based diagnostic tools can only be successful if they are accepted by potential users. Young adults as digital natives may offer the greatest potential for successful implementation of AI into clinical practice, while at the same time, representing the future generation of skin cancer screening participants. Objective We conducted an anonymous online survey to examine how and to what extent individuals are willing to accept AI-based mobile apps for skin cancer diagnostics. We evaluated preferences and relative influences of concerns, with a focus on younger age groups. Methods We recruited participants below 35 years of age using three social media channels—Facebook, LinkedIn, and Xing. Descriptive analysis and statistical tests were performed to evaluate participants’ attitudes toward mobile apps for skin examination. We integrated an adaptive choice-based conjoint to assess participants’ preferences. We evaluated potential concerns using maximum difference scaling. Results We included 728 participants in the analysis. The majority of participants (66.5%, 484/728; 95% CI 0.631-0.699) expressed a positive attitude toward the use of AI-based apps. In particular, participants residing in big cities or small towns (P=.02) and individuals that were familiar with the use of health or fitness apps (P=.02) were significantly more open to mobile diagnostic systems. Hierarchical Bayes estimation of the preferences of participants with a positive attitude (n=484) revealed that the use of mobile apps as an assistance system was preferred. Participants ruled out app versions with an accuracy of ≤65%, apps using data storage without encryption, and systems that did not provide background information about the decision-making process. However, participants did not mind their data being used anonymously for research purposes, nor did they object to the inclusion of clinical patient information in the decision-making process. Maximum difference scaling analysis for the negative-minded participant group (n=244) showed that data security, insufficient trust in the app, and lack of personal interaction represented the dominant concerns with respect to app use. Conclusions The majority of potential future users below 35 years of age were ready to accept AI-based diagnostic solutions for early detection of skin cancer. However, for translation into clinical practice, the participants’ demands for increased transparency and explainability of AI-based tools seem to be critical. Altogether, digital natives between 18 and 24 years and between 25 and 34 years of age expressed similar preferences and concerns when compared both to each other and to results obtained by previous studies that included other age groups.

We introduce a novel diagnostic approach for the uncovering of hidden content in layered documents, based on a photoacoustic imaging setup with optimized acoustic detection parameters. By exploiting the laser-induced ultrasound following the intense absorption of pulsed optical radiation by printed ink, this traditional biomedical imaging method is proven capable of revealing text characters buried under several layers of stacked paper sheets at high spatial resolution and excellent contrast levels. Such a remarkable performance benefits from the effective combination of strong light scattering by paper fibrils and the virtually unobstructed propagation of the generated ultrasonic waves through multiple layers, enabling thus the accurate recording of text, even in double-sided prints, with minimum shadowing artifacts. The imaging effectiveness, the implementation simplicity, the robustness and the relatively low cost features offered by the proposed photoacoustic modality, are anticipated to contribute in its wide adoption by the preservation of cultural heritage community, as a powerful diagnostic tool for the in-depth investigation of complex multi-layered objects.

Highlights • Review of freeze-dried cell-free (FD-CF) systems. • FD-CF applications in sensing and portable biomanufacturing. • Challenges and opportunities ahead for the field., Cell-free synthetic biology is an exciting and new branch in the field of synthetic biology. Based on in vitro transcription and translation systems, this application-focused domain builds on decades of cell-free biochemistry and protein expression to operate synthetic gene networks outside of cellular environments. This has brought new and perhaps even unexpected advantages. Chief among these is the ability to operate genetically encoded tools in a sterile and abiotic format. Recent work has extended this advantage by freeze-drying these cell-free systems into dried pellets or embedded paper-based reactions. Taken together, these new ideas have solved the longstanding challenge of how to deploy poised synthetic gene networks in a biosafe mode outside of the laboratory. There is significant excitement in the potential of this newfound venue and the community has begun to extend proof-of-concept demonstrations in important and creative ways. Here I explore these new efforts and provide my thoughts on the challenges and opportunities ahead for freeze-dried, cell-free synthetic biology.

Echinococcus granulosus sensu lato is a globally prevalent zoonotic parasitic cestode leading to cystic echinococcosis (CE) in both humans and sheep with both medical and financial impacts, whose reduction requires the application of a One Health approach to its control. Regarding the animal health component of this approach, lack of accurate and practical diagnostics in livestock impedes the assessment of disease burden and the implementation and evaluation of control strategies. We use of a Bayesian Latent Class Analysis (LCA) model to estimate ovine CE prevalence in sheep samples from the Río Negro province of Argentina accounting for uncertainty in the diagnostics. We use model outputs to evaluate the performance of a novel recombinant B8/2 antigen B subunit (rEgAgB8/2) indirect enzyme-linked immunosorbent assay (ELISA) for detecting E. granulosus in sheep. Necropsy (as a partial gold standard), western blot (WB) and ELISA diagnostic data were collected from 79 sheep within two Río Negro slaughterhouses, and used to estimate individual infection status (assigned as a latent variable within the model). Using the model outputs, the performance of the novel ELISA at both individual and flock levels was evaluated, respectively, using a receiver operating characteristic (ROC) curve, and simulating a range of sample sizes and prevalence levels within hypothetical flocks. The estimated (mean) prevalence of ovine CE was 27.5% (95%Bayesian credible interval (95%BCI): 13.8%–58.9%) within the sample population. At the individual level, the ELISA had a mean sensitivity and specificity of 55% (95%BCI: 46%–68%) and 68% (95%BCI: 63%–92%), respectively, at an optimal optical density (OD) threshold of 0.378. At the flock level, the ELISA had an 80% probability of correctly classifying infection at an optimal cut-off threshold of 0.496. These results suggest that the novel ELISA could play a useful role as a flock-level diagnostic for CE surveillance in the region, supplementing surveillance activities in the human population and thus strengthening a One Health approach. Importantly, selection of ELISA cut-off threshold values must be tailored according to the epidemiological situation., Highlights • Development of a Bayesian LCA model for ovine cystic echinococcosis (CE) in Argentina. • Evaluation of diagnostic performance for a novel rEgAgB8/2 indirect ELISA. • Identification of optimal ELISA thresholds for use as a flock-level surveillance tool. • Improvement of CE surveillance in sheep will help strengthen the One Health approach.

Background Chlamydia trachomatis (CT) is one of the most prevalent bacterial sexually transmitted infections (STIs) globally but has been inadequately detected for intervention. Introduction of point-of-care tests (POCTs) for CT is critical for filling the intervention gaps. We conducted a systematical review and meta-analysis on diagnostic performance of POCTs for CT to assist in guiding the application of these assays in CT screening and detection. Methods We searched PubMed/Medline and Embase databases, from January 2004 to May 2021, for studies reporting the performance of POCTs for identifying CT using specimens collected from urethral, vaginal, cervical, anorectal, or pharyngeal site or of urine. Two investigators independently screened and extracted data for controlling the quality of data extraction. Any discrepancies in study selection and data extraction were resolved through consensus. We only included studies with sufficient data to estimate sensitivity and specificity, and used laboratory-based nucleic acid amplification test (NAAT) as the reference standard. The main outcomes were pooled sensitivity, specificity, and diagnostic odds ratio (DOR) and their corresponding 95% confidence intervals (CIs). Summary estimates were calculated using a random-effects model and summary receiver operator curves (SROCs) were generated using the Moses-Littenberg method. STATA 14.0 and Meta-DiSc 1.4 were used for statistical analysis. The study protocol is registered with PROSPERO, number CRD42019140544. Findings Of 3,038 records identified, 39 studies (42,336 specimens) were included in the study, including 14 studies on evaluation of antigen detection (AD)-based and 25 on NAAT-based POCTs. The overall pooled sensitivity, specificity and DOR were 56% (95% CI 45%–67%), 99% (95% CI 98%–99%) and 86 (95% CI 46–163), respectively, for AD-based POCTs and corresponding values for NAAT-based POCTs were 94% (95% CI 91%–96%), 99% (95% CI 99%–99%) and 1,933(95% CI 1,018–3,669), respectively. The pooled sensitivity of AD-based POCTs varied across the types of specimens, indicating 46% for cervical swabs (95% CI 37%–56%; range 22.7%–71.4%), 52% for vaginal swabs (95% CI 34%–70%; range 17.1%–86.8%) and 57% for male urine (95% CI 36%–75%; range 20.0%–82.6%). For NAAT-based POCTs, the pooled sensitivity was 94% (95% CI 90%–96%) for cervical swabs, 94% (95% CI 86%–98%) for vaginal swabs, 95% (95% CI 91%–97%) for urine specimens and 93% (95% CI 87%–96%) for anorectal swabs. Interpretation NAAT-based POCTs for CT have a significantly better performance particularly in sensitivity for diagnosing the infection with CT than the AD-based POCTs. Screening strategy with AD-based POCTs may potentially result in a substantial under-detection of the infections.

Background: Leprosy, a chronic infectious disease caused by Mycobacterium leprae, is often late- or misdiagnosed leading to irreversible disabilities. Blood transcriptomic biomarkers that prospectively predict those who progress to leprosy (progressors) would allow early diagnosis, better treatment outcomes and facilitate interventions aimed at stopping bacterial transmission. To identify potential risk signatures of leprosy, we collected whole blood of household contacts (HC, n=5,352) of leprosy patients, including individuals who were diagnosed with leprosy 4-61 months after sample collection. Methods: We investigated differential gene expression (DGE) by RNA-Seq between progressors before presence of symptoms (n=40) and HC (n=40), as well as longitudinal DGE within each progressor. A prospective leprosy signature was identified using a machine learning approach (Random Forest) and validated using reverse transcription quantitative PCR (RT-qPCR). Findings: Although no significant intra-individual longitudinal variation within leprosy progressors was identified, 1,613 genes were differentially expressed in progressors before diagnosis compared to HC. We identified a 13-gene prospective risk signature with an Area Under the Curve (AUC) of 95.2%. Validation of this RNA-Seq signature in an additional set of progressors (n=43) and HC (n=43) by RT-qPCR, resulted in a final 4-gene signature, designated RISK4LEP (MT-ND2, REX1BD, TPGS1, UBC) (AUC=86.4%). Interpretation: This study identifies for the first time a prospective transcriptional risk signature in blood predicting development of leprosy 4 to 61 months before clinical diagnosis. Assessment of this signature in contacts of leprosy patients can function as an adjunct diagnostic tool to target implementation of interventions to restrain leprosy development. Funding: This study was supported by R2STOP Research grant, the Order of Malta-Grants-for-Leprosy-Research, the Q.M. Gastmann-Wichers Foundation and the Leprosy Research Initiative (LRI) together with the Turing Foundation (ILEP# 702.02.73 and # 703.15.07).

Significance: Optical scattering signals obtained from tissue constituents contain a wealth of structural information. Conventional intensity features, however, are mostly dictated by the overall morphology and mean refractive index of these constituents, making it very difficult to exclusively sense internal refractive index fluctuations. Aim: We perform a systematic analysis to elucidate how changes in internal refractive index profile of cell nuclei can best be detected via optical scattering. Approach: We construct stochastically inhomogeneous nuclear models and numerically simulate their azimuth-resolved scattering patterns. We then process these two-dimensional patterns with the goal of identifying features that directly point to subnuclear structure. Results: Azimuth-dependent intensity variations over the side scattering range provide significant insights into subnuclear refractive index profile. A particular feature we refer to as contrast ratio is observed to be highly sensitive to the length scale and extent of refractive index fluctuations; further, this feature is not susceptible to changes in the overall size and mean refractive index of nuclei, thereby allowing for selective tracking of subnuclear structure that can be linked to chromatin distribution. Conclusions: Our analysis will potentially pave the way for scattering-based assessment of chromatin reorganization that is considered to be a key hallmark of precancer progression.

Background Andorra is a small country located in the Pyrenees attracting millions of visitors for tourism, mostly associated with skiing, and nature-related activities. As its neighbouring countries, Spain and France, it has been heavily affected by the COVID-19 pandemic. We estimated SARS-CoV-2 seroprevalence in the entire country by universal serological testing under a lockdown environment. Methods A total of 77,543 inhabitants of Andorra were invited to participate in the study. From 4-28 May, 2020, two cross sectional serological surveys were conducted using a rapid serological test (nCOV IgG/IgM) on a finger prick blood sample in 59 drive-through or walk-through checkpoints, all over Andorra. We calculated seroprevalence of antibodies against SARS-CoV-2 and analysed the main sociodemographic factors associated with being seropositive. Findings 70,494 inhabitants (90.9% of the population) participated in at least one survey. Overall seroprevalence was 11.0%. The most affected age groups were those over 90 years old (15.2%) and 80-89 (13.8%), followed by adults 50-59 (13.6%) and adolescents 10-19 (13.7%). Most seropositive participants, 6,061 (95.1%), were asymptomatic before the surveys. The multivariable analysis showed that the odds of being seropositive was higher among seasonal workers (OR 2.41; 95% CI 1.07-5.45) or in the population living in La Massana region, a popular ski-related area (OR 2.66; 95% CI 2.44-2.89). A higher seroprevalence was observed in those familiar nuclei with greater numbers of cohabitants: 18% in families with 6 household members or more; 13% in medium size families (3/4/5 people) and 12% in small size (1 to 2 people) nuclei. Interpretation The prevalence of antibodies against SARS-CoV-2 in the population of Andorra was high during the first wave of the pandemic. Seasonal workers and inhabitants based in La Massana presented a higher seroprevalence. Mass antibody screening allows to identify infection hotspots and should contribute to the design of tailored interventions to prevent SARS-CoV-2 transmission in Andorra. Funding Andorran Ministry of Health, Andorran Health Services.

Purpose The aim of this prospective study was to identify the tumour characteristics that are associated with invasiveness and those that are relevant for disease-specific survival (DSS) in upper tract urothelial carcinoma, UTUC. Methods From a prospective consecutive cohort of patients with suspicion of UTUC, those who were diagnosed with UTUC using URS prior to rNU between 2005 and 2012 were included. Tumour characteristics were analysed for prediction of invasiveness and association with DSS. Stages were categorised as superficial (pTa-1 and CIS only) or invasive (≥ pT2). Tumours were graded according to WHO 1999 classification. DSS was analysed regarding possible association with stage, grade, size, multifocality, location, ploidy and rate of proliferation. Associations were tested using Fisher’s exact test, Pearson Chi-square or Cox’s regression. Kaplan–Meier survival curves were constructed. Results Forty-five consecutive patients were included, and 43 of them were included in the final analyses because their rNU specimens were available for reassessment. The only tumour characteristics that were significantly associated with stage were tumour grade (P

Background The causes of delayed post-traumatic stress disorder (PTSD) are debated, and the validity of late-onset PTSD has been questioned. Aims We aimed to examine predictors of delayed PTSD in a community sample of survivors of a natural disaster. Method Norwegian survivors of the 2004 Indian Ocean tsunami (n = 532) responded to a questionnaire at 6 and 24 months post-disaster. The questionnaire measured PTSD symptoms, recalled exposure and immediate stress responses to the disaster, recalled perceived life threat, personality dimensions, social support and other subsequent adverse life events. Results When dichotomising PTSD symptom scores, 331 participants had low and 194 had high PTSD scores (early-onset PTSD) at 6 months. Of those with initially low scores, 43 (13.0%) had high symptom scores (delayed PTSD) at 24 months. The delayed PTSD group had a lower degree of initially assessed threat and witness experiences of death or suffering, lower immediate stress response and higher degree of memory inflation of perceived threat than the early-onset PTSD group. Among those with low PTSD scores at 6 months, onset of delayed PTSD was associated with neuroticism and memory inflation of life threat, but not with the degree of initially assessed disaster exposure or reports of subsequent adverse life events. Conclusions Lack of association between trauma exposure and delayed onset of PTSD symptoms casts doubt on whether the traumatic event is actually the primary causative factor for delayed PTSD. Our findings suggest that delayed PTSD may be a manifestation of personality factors and memory inflation of the severity of an event.

Driven by intense competition, increasing operational cost and strict environmental regulations, the modern process and energy industry needs to find the best possible way to adapt to maintain profitability. Optimization of control and operation of the industrial systems is essential to satisfy the contradicting objectives of improving product quality and process efficiency while reducing production cost and plant downtime. Use of optimization not only improves the control and monitoring of assets but also offers better coordination among different assets. Thus, it can lead to considerable savings in energy and resource consumption, and consequently offer a reduction in operational costs, by offering better control, diagnostics and decision support. This is one of the main driving forces behind developing new methods, tools and frameworks that can be integrated with the existing industrial automation platforms to benefit from optimal control and operation. The main focus of this dissertation is the use of different process models, soft sensors and optimization techniques to improve the control, diagnostics and decision support for the process and energy industry. A generic architecture for an optimal control, diagnostics and decision support system, referred to here as a learning system, is proposed. The research is centred around an investigation of different components of the proposed learning system. Two very different case studies within the energy-intensive pulp and paper industry and the promising micro-combined heat and power (CHP) industry are selected to demonstrate the learning system. One of the main challenges in this research arises from the marked differences between the case studies in terms of size, functions, quantity and structure of the existing automation systems. Typically, only a few pulp digesters are found in a Kraft pulping mill, but there may be hundreds of units in a micro-CHP fleet. The main argument behind the selection of these two case studies is that if the proposed learning system architecture can be adapted for these significantly different cases, it can be adapted for many other energy and process industrial cases. Within the scope of this thesis, mathematical modelling, model adaptation, model predictive control and diagnostics methods are studied for continuous pulp digesters, whereas mathematical modelling, model adaptation and diagnostics techniques are explored for the micro-CHP fleet. FUDIPO – FUture DIrections for Process industry Optimization

Determination of antibodies against ToRCH antigens at the beginning of pregnancy allows assessment of both the maternal immune status and the risks to an adverse pregnancy outcome. Age-standardised seroprevalences were determined in sera from 1009 women of childbearing age residing in Mexico, Brazil, Germany, Poland, Turkey or China using a multiparametric immunoblot containing antigen substrates for antibodies against Toxoplasma gondii, rubella virus, cytomegalovirus (CMV), herpes simplex viruses (HSV-1, HSV-2), Bordetella pertussis, Chlamydia trachomatis, parvovirus B19, Treponema pallidum and varicella zoster virus (VZV). Seroprevalences for antibodies against HSV-1 were >90% in samples from Brazil and Turkey, whereas the other four countries showed lower mean age-adjusted seroprevalences (range: 62.5–87.9%). Samples from Brazilian women showed elevated seroprevalences of antibodies against HSV-2 (40.1%), C. trachomatis (46.8%) and B. pertussis (56.6%) compared to the other five countries. Seroprevalences of anti-T. gondii antibodies (0.5%) and anti-parvovirus B19 antibodies (7.5%) were low in samples from Chinese women, compared to the other five countries. Samples from German women revealed a low age-standardised seroprevalence of anti-CMV antibodies (28.8%) compared to the other five countries. These global differences in immune status of women in childbearing age advocate country-specific prophylaxis strategies to avoid infection with ToRCH pathogens.

Väitekirja elektrooniline versioon ei sisalda publikatsioone, Esmased peavalud on heterogeenne grupp haigusi, mille hulka kuuluvad migreen, pingetüüpi peavalu, trigeminaalautonoomsed tsefalalgiad ning muud esmased peavalud. Esmased peavalud põhjustavad olulist elukvaliteedi langust ja mõjutavad rahvatervist ning ühiskonda. Kõikide esmaste peavalude keskmine aastane levimus Euroopas on 53%, migreenil 14,7% ja pingetüüpi peavalul 62,6%. Eesti kohta ei ole esmaste peavalude levimuse andmeid varem avaldatud. Rahvastikupõhised epidemioloogilised uuringud on ressursi- ning ajakulukad. Samas loob praegune kiire digiühiskonna areng eeldused veebipõhistele meetoditele, mis võimaldaksid nende uuringute ressursimahtu vähendada. Antud uurimustöö eesmärgiks oli esmaste peavalude diagnostilise küsimustiku koostamine ning testimine, esmaste peavalude aastase levimuse määramine 20-64 aastaste täiskasvanute seas Eestis ning veebipõhise lahenduse kohaldatavuse hindamine peavalude epidemioloogilises uurimistöös. Tulemused näitavad, et esmaste peavalude aastased levimused Eesti täiskasvanute seas on sarnased teiste Euroopa riikidega, välja arvatud episoodilise pingetüüpi peavalu osas, mille madal levimus antud uuringus on suure tõenäosusega alahinnatud. Seega on esmased peavalud Eestiski oluliseks haigestumise ja elukvaliteedi languse põhjuseks. Veebipõhine peavalualane epidemioloogiline teadustöö tuleb arvesse kui potentsiaalne aja- ja ressursisäästlik meetod riikides, kus internetirakenduste kasutamine on rahvastikus laialt levinud. Selline lähenemine võimaldab koguda suuri uuringuvalimeid suhteliselt lühikese aja jooksul. Lisaks pakuvad IT-lahendused uuritavate tuvastusviise, mis väldivad andmete kontaminatsiooni, ning tööriistu digitaalsete küsimustike näol, mis suudavad erinevaid peavaludiagnoose rahuldavalt eristada. Edasine uurimistöö on vajalik, et leida usaldusväärseid veebipõhise valimi moodustamise ning uuritavate värbamise viise, et saavutada esinduslikke valimeid ja vältida tulemuste kallutatust., Primary headaches are a group of heterogenous disorders that consist of migraine, tension-type headache, trigeminal autonomic cephalagias and other primary headaches. These disorders cause considerable loss of quality of life and have a remarkable socioeconomic impact. The mean 1-year prevalence of all headaches in Europe is 53%, of migraine 14.7% and of tension-type headache 62.6%. The prevalence of primary headaches in Estonia has not been studied before. Traditional epidemiological studies are resource- and time-consuming. However, wide usage of internet in the population creates a possibility for applying web-based methods in headache epidemiological research that can be more resource-saving. The aim of this study was to compile and test a headache diagnostic questionnaire, to determine the 1-year prevalence of primary headaches among 20-64 year olds in Estonia and to evaluate the applicability of web-based surveys in epidemiological studies of primary headaches. The results show that the 1-year prevalences of primary headaches in Estonia are similar to other European countries, except for tension-type headache. The low prevalence of tension-type headache in this study is most probably an underestimation. This implies that primary headaches in Estonia are an important cause of morbidity as well as a prominent socioeconomic burden. It can be concluded, that online headache research may be a time- and resource efficient alternative in IT-developed countries. In addition to obtaining larger study samples in relatively short time periods, the IT solutions can provide participant identification methods that avoid data contamination and are able to distinguish most primary headache disorders sufficiently. However, further research is needed to find more reliable methods of online access and engagement to gain representative samples and overcome the pitfalls of bias and most probably underestimation of headache prevalence in web-based epidemiological research., https://www.ester.ee/record=b5370288

BackgroundIn multiple sclerosis (MS), clinical assessment, MRI and cerebrospinal fluid are important in the diagnostic process. However, no blood biomarker has been confirmed as a useful tool in the diagnostic work-up.ObjectivesBlood platelets contain a rich spliced mRNA repertoire that can alter during megakaryocyte development but also during platelet formation and platelet circulation. In this proof of concept study, we evaluate the diagnostic potential of spliced blood platelet RNA for the detection of MS.MethodsWe isolated and sequenced platelet RNA of blood samples obtained from 57 MS patients and 66 age- and gender-matched healthy controls (HCs). 60% was used to develop a particle swarm-optimized (PSO) support vector machine classification algorithm. The remaining 40% served as an independent validation series.ResultsIn total, 1249 RNAs with differential spliced junction expression levels were identified between platelets of MS patients as compared to HCs, including EPSTI1, IFI6, and RPS6KA3, in line with reported inflammatory signatures in the blood of MS patients. The RNAs were subsequently used as input for a MS classifier, capable of detecting MS with 80% accuracy in the independent validation series.ConclusionsSpliced platelet RNA may enable the blood-based diagnosis of MS, warranting large-scale validation.

No abstract available.