A comprehensive description of kidney disease progression after acute kidney injury from a prospective, parallel-group cohort study.

Acute kidney injury (AKI) is associated with adverse long-term outcomes, but many studies are retrospective, focused on specific patient groups or lack adequate comparators. The ARID (AKI Risk in Derby) Study was a five-year prospective parallel-group cohort study to examine this. Hospitalized cohorts with and without exposure to AKI were matched 1:1 for age, baseline kidney function, and diabetes. Estimated glomerular filtration rate (eGFR) and the urinary albumin:creatinine ratio (uACR) were measured at three-months, one-, three- and five-years. Outcomes included kidney disease progression, heart failure episodes and mortality. In 866 matched individuals, kidney disease progression at five years was found to be significantly increased in 30% of the exposed group versus 7% of those non-exposed (adjusted odds ratio 2.49 [95% confidence interval 1.43 to 4.36]). In the AKI group, this was largely characterized by incomplete recovery of kidney function by three months. Further episodes of AKI during follow-up were significantly more common in the exposed group (odds ratio 2.71 [1.94 to 3.77]) and had an additive effect on risk of kidney disease progression. Mortality and heart failure episodes were more frequent in the exposed group, but the association with AKI was no longer significant when models were adjusted for three-month eGFR and uACR. In a general hospitalized population, kidney disease progression after five years was common and strongly associated with AKI. Thus, the time course of changes and the attenuation of associations with adverse outcomes after adjustment for three-month eGFR and uACR suggest non-recovery of kidney function is an important assessment in post-AKI care and a potential future target for intervention. STUDY REGISTRATION: ISRCTN25405995.

Influence of setting-dependent contacts and protective behaviours on asymptomatic SARS-CoV-2 infection amongst members of a UK university.

We survey 62 users of a university asymptomatic SARS-CoV-2 testing service on details of their activities, protective behaviours and contacts in the 7 days prior to receiving a positive or negative SARS-CoV-2 PCR test result in the period October 2020-March 2021. The resulting data set is novel in capturing very detailed social contact history linked to asymptomatic disease status during a period of significant restriction on social activities. We use this data to explore 3 questions: (i) Did participation in university activities enhance infection risk? (ii) How do contact definitions rank in their ability to explain test outcome during periods of social restrictions? (iii) Do patterns in the protective behaviours help explain discrepancies between the explanatory performance of different contact measures? We classify activities into settings and use Bayesian logistic regression to model test outcome, computing posterior model probabilities to compare the performance of models adopting different contact definitions. Associations between protective behaviours, participant characteristics and setting are explored at the level of individual activities using multiple correspondence analysis (MCA). We find that participation in air travel or non-university work activities was associated with a positive asymptomatic SARS-CoV-2 PCR test, in contrast to participation in research and teaching settings. Intriguingly, logistic regression models with binary measures of contact in a setting performed better than more traditional contact numbers or person contact hours (PCH). The MCA indicates that patterns of protective behaviours vary between setting, in a manner which may help explain the preference for any participation as a contact measure. We conclude that linked PCR testing and social contact data can in principle be used to test the utility of contact definitions, and the investigation of contact definitions in larger linked studies is warranted to ensure contact data can capture environmental and social factors influencing transmission risk.

Linear Binary Classifier to Predict Bacterial Biofilm Formation on Polyacrylates.

Bacterial infections are increasingly problematic due to the rise of antimicrobial resistance. Consequently, the rational design of materials naturally resistant to biofilm formation is an important strategy for preventing medical device-associated infections. Machine learning (ML) is a powerful method to find useful patterns in complex data from a wide range of fields. Recent reports showed how ML can reveal strong relationships between bacterial adhesion and the physicochemical properties of polyacrylate libraries. These studies used robust and predictive nonlinear regression methods that had better quantitative prediction power than linear models. However, as nonlinear models' feature importance is a local rather than global property, these models were hard to interpret and provided limited insight into the molecular details of material-bacteria interactions. Here, we show that the use of interpretable mass spectral molecular ions and chemoinformatic descriptors and a linear binary classification model of attachment of three common nosocomial pathogens to a library of polyacrylates can provide improved guidance for the design of more effective pathogen-resistant coatings. Relevant features from each model were analyzed and correlated with easily interpretable chemoinformatic descriptors to derive a small set of rules that give model features tangible meaning that elucidate relationships between the structure and function. The results show that the attachment of Pseudomonas aeruginosa and Staphylococcus aureus can be robustly predicted by chemoinformatic descriptors, suggesting that the obtained models can predict the attachment response to polyacrylates to identify anti-attachment materials to synthesize and test in the future.

Deep Learning with Attention Mechanisms for Road Weather Detection.

There is great interest in automatically detecting road weather and understanding its impacts on the overall safety of the transport network. This can, for example, support road condition-based maintenance or even serve as detection systems that assist safe driving during adverse climate conditions. In computer vision, previous work has demonstrated the effectiveness of deep learning in predicting weather conditions from outdoor images. However, training deep learning models to accurately predict weather conditions using real-world road-facing images is difficult due to: (1) the simultaneous occurrence of multiple weather conditions; (2) imbalanced occurrence of weather conditions throughout the year; and (3) road idiosyncrasies, such as road layouts, illumination, and road objects, etc. In this paper, we explore the use of a focal loss function to force the learning process to focus on weather instances that are hard to learn with the objective of helping address data imbalances. In addition, we explore the attention mechanism for pixel-based dynamic weight adjustment to handle road idiosyncrasies using state-of-the-art vision transformer models. Experiments with a novel multi-label road weather dataset show that focal loss significantly increases the accuracy of computer vision approaches for imbalanced weather conditions. Furthermore, vision transformers outperform current state-of-the-art convolutional neural networks in predicting weather conditions with a validation accuracy of 92% and an F1-score of 81.22%, which is impressive considering the imbalanced nature of the dataset.

Developing an Automated Assessment of In-session Patient Activation for Psychological Therapy: Codevelopment Approach.

BACKGROUND: Patient activation is defined as a patient's confidence and perceived ability to manage their own health. Patient activation has been a consistent predictor of long-term health and care costs, particularly for people with multiple long-term health conditions. However, there is currently no means of measuring patient activation from what is said in health care consultations. This may be particularly important for psychological therapy because most current methods for evaluating therapy content cannot be used routinely due to time and cost restraints. Natural language processing (NLP) has been used increasingly to classify and evaluate the contents of psychological therapy. This aims to make the routine, systematic evaluation of psychological therapy contents more accessible in terms of time and cost restraints. However, comparatively little attention has been paid to algorithmic trust and interpretability, with few studies in the field involving end users or stakeholders in algorithm development. OBJECTIVE: This study applied a responsible design to use NLP in the development of an artificial intelligence model to automate the ratings assigned by a psychological therapy process measure: the consultation interactions coding scheme (CICS). The CICS assesses the level of patient activation observable from turn-by-turn psychological therapy interactions. METHODS: With consent, 128 sessions of remotely delivered cognitive behavioral therapy from 53 participants experiencing multiple physical and mental health problems were anonymously transcribed and rated by trained human CICS coders. Using participatory methodology, a multidisciplinary team proposed candidate language features that they thought would discriminate between high and low patient activation. The team included service-user researchers, psychological therapists, applied linguists, digital research experts, artificial intelligence ethics researchers, and NLP researchers. Identified language features were extracted from the transcripts alongside demographic features, and machine learning was applied using k-nearest neighbors and bagged trees algorithms to assess whether in-session patient activation and interaction types could be accurately classified. RESULTS: The k-nearest neighbors classifier obtained 73% accuracy (82% precision and 80% recall) in a test data set. The bagged trees classifier obtained 81% accuracy for test data (87% precision and 75% recall) in differentiating between interactions rated high in patient activation and those rated low or neutral. CONCLUSIONS: Coproduced language features identified through a multidisciplinary collaboration can be used to discriminate among psychological therapy session contents based on patient activation among patients experiencing multiple long-term physical and mental health conditions.

Dynamic early warning scores for predicting clinical deterioration in patients with respiratory disease.

BACKGROUND: The National Early Warning Score-2 (NEWS-2) is used to detect patient deterioration in UK hospitals but fails to take account of the detailed granularity or temporal trends in clinical observations. We used data-driven methods to develop dynamic early warning scores (DEWS) to address these deficiencies, and tested their accuracy in patients with respiratory disease for predicting (1) death or intensive care unit admission, occurring within 24 h (D/ICU), and (2) clinically significant deterioration requiring urgent intervention, occurring within 4 h (CSD). METHODS: Clinical observations data were extracted from electronic records for 31,590 respiratory in-patient episodes from April 2015 to December 2020 at a large acute NHS Trust. The timing of D/ICU was extracted for all episodes. 1100 in-patient episodes were annotated manually to record the timing of CSD, defined as a specific event requiring a change in treatment. Time series features were entered into logistic regression models to derive DEWS for each of the clinical outcomes. Area under the receiver operating characteristic curve (AUROC) was the primary measure of model accuracy. RESULTS: AUROC (95% confidence interval) for predicting D/ICU was 0.857 (0.852-0.862) for NEWS-2 and 0.906 (0.899-0.914) for DEWS in the validation data. AUROC for predicting CSD was 0.829 (0.817-0.842) for NEWS-2 and 0.877 (0.862-0.892) for DEWS. NEWS-2 ≥ 5 had sensitivity of 88.2% and specificity of 54.2% for predicting CSD, while DEWS ≥ 0.021 had higher sensitivity of 93.6% and approximately the same specificity of 54.3% for the same outcome. Using these cut-offs, 315 out of 347 (90.8%) CSD events were detected by both NEWS-2 and DEWS, at the time of the event or within the previous 4 h; 12 (3.5%) were detected by DEWS but not by NEWS-2, while 4 (1.2%) were detected by NEWS-2 but not by DEWS; 16 (4.6%) were not detected by either scoring system. CONCLUSION: We have developed DEWS that display greater accuracy than NEWS-2 for predicting clinical deterioration events in patients with respiratory disease. Prospective validation studies are required to assess whether DEWS can be used to reduce missed deteriorations and false alarms in real-life clinical settings.

Clinical laboratory parameters and comorbidities associated with severity of coronavirus disease 2019 (COVID-19) in Kurdistan Region of Iraq.

Background: The pandemic coronavirus disease (COVID-19) dramatically spread worldwide. Considering several laboratory parameters and comorbidities may facilitate the assessment of disease severity. Early recognition of disease progression associated with severe cases of COVID-19 is essential for timely patient triaging. Our study investigated the characteristics and role of laboratory results and comorbidities in the progression and severity of COVID-19 cases. Methods: The study was conducted from early-June to mid-August 2020. Blood samples and clinical data were taken from 322 patients diagnosed with COVID-19 at Qala Hospital, Kalar, Kurdistan Region of Iraq. Biological markers used in this study include complete blood count (CBC), D-dimer, erythrocyte sedimentation rate (ESR), serum ferritin, blood sugar, C-reactive protein (CRP) and SpO2. Results: The sample included 154 males (47.8%) and 168 females (52.2%). Most females were in the mild and moderate symptom groups, while males developed more severe symptoms. Regarding comorbidities, diabetes mellitus was considered the greatest risk factor for increasing the severity of COVID-19 symptoms. As for biological parameters, WBC, granulocytes, ESR, Ferritin, CRP and D-Dimer were elevated significantly corresponding to the severity of the disease, while lymphocytes and SpO2 showed the opposite pattern. Higher RBC was significantly associated with COVID-19 severity, especially in females. Conclusion: Gender, age and diabetes mellitus are important prognostic risk factors associated with severity and mortality of COVID-19. Relative to non-severe COVID-19, severe cases are characterized by an increase of most biological markers. These markers could be used to recognize severe cases and to monitor the clinical course of COVID-19.

The Extent of User Involvement in the Design of Self-tracking Technology for Bipolar Disorder: Literature Review.

BACKGROUND: The number of self-monitoring apps for bipolar disorder (BD) is increasing. The involvement of users in human-computer interaction (HCI) research has a long history and is becoming a core concern for designers working in this space. The application of models of involvement, such as user-centered design, is becoming standardized to optimize the reach, adoption, and sustained use of this type of technology. OBJECTIVE: This paper aims to examine the current ways in which users are involved in the design and evaluation of self-monitoring apps for BD by investigating 3 specific questions: are users involved in the design and evaluation of technology? If so, how does this happen? And what are the best practice ingredients regarding the design of mental health technology? METHODS: We reviewed the available literature on self-tracking technology for BD and make an overall assessment of the level of user involvement in design. The findings were reviewed by an expert panel, including an individual with lived experience of BD, to form best practice ingredients for the design of mental health technology. This combines the existing practices of patient and public involvement and HCI to evolve from the generic guidelines of user-centered design and to those that are tailored toward mental health technology. RESULTS: For the first question, it was found that out of the 11 novel smartphone apps included in this review, 4 (36%) self-monitoring apps were classified as having no mention of user involvement in design, 1 (9%) self-monitoring app was classified as having low user involvement, 4 (36%) self-monitoring apps were classified as having medium user involvement, and 2 (18%) self-monitoring apps were classified as having high user involvement. For the second question, it was found that despite the presence of extant approaches for the involvement of the user in the process of design and evaluation, there is large variability in whether the user is involved, how they are involved, and to what extent there is a reported emphasis on the voice of the user, which is the ultimate aim of such design approaches. For the third question, it is recommended that users are involved in all stages of design with the ultimate goal of empowering and creating empathy for the user. CONCLUSIONS: Users should be involved early in the design process, and this should not just be limited to the design itself, but also to associated research ensuring end-to-end involvement. Communities in health care-based design and HCI design need to work together to increase awareness of the different methods available and to encourage the use and mixing of the methods as well as establish better mechanisms to reach the target user group. Future research using systematic literature search methods should explore this further.

Machine learning to determine the main factors affecting creep rates in laser powder bed fusion.

There is an increasing need for the use of additive manufacturing (AM) to produce improved critical application engineering components. However, the materials manufactured using AM perform well below their traditionally manufactured counterparts, particularly for creep and fatigue. Research has shown that this difference in performance is due to the complex relationships between AM process parameters which affect the material microstructure and consequently the mechanical performance as well. Therefore, it is necessary to understand the impact of different AM build parameters on the mechanical performance of parts. Machine learning (ML) models are able to find hidden relationships in data using iterative statistical analyses and have the potential to develop process-structure-property-performance relationships for manufacturing processes, including AM. The aim of this work is to apply ML techniques to materials testing data in order to understand the effect of AM process parameters on the creep rate of additively built nickel-based superalloy and to predict the creep rate of the material from these process parameters. In this work, the predictive capabilities of ML and its ability to develop process-structure-property relationships are applied to the creep properties of laser powder bed fused alloy 718. The input data for the ML model included the Laser Powder Bed Fusion (LPBF) build parameters used-build orientation, scan strategy and number of lasers-and geometrical material descriptors which were extracted from optical microscope porosity images using image analysis techniques. The ML model was used to predict the minimum creep rate of the Laser Powder Bed Fused alloy 718 samples, which had been creep tested at 650 ∘ C and 600 MPa. The ML model was also used to identify the most relevant material descriptors affecting the minimum creep rate of the material (determined by using an ensemble feature importance framework). The creep rate was accurately predicted with a percentage error of 1.40 % in the best case. The most important material descriptors were found to be part density, number of pores, build orientation and scan strategy. These findings show the applicability and potential of using ML to determine and predict the mechanical properties of materials fabricated via different manufacturing processes, and to find process-structure-property relationships in AM. This increases the readiness of AM for use in critical applications.

Students' Views towards Sars-Cov-2 Mass Asymptomatic Testing, Social Distancing and Self-Isolation in a University Setting during the COVID-19 Pandemic: A Qualitative Study.

We aimed to explore university students' perceptions and experiences of SARS-CoV-2 mass asymptomatic testing, social distancing and self-isolation, during the COVID-19 pandemic. This qualitative study comprised of four rapid online focus groups conducted at a higher education institution in England, during high alert (tier 2) national COVID-19 restrictions. Participants were purposively sampled university students (n = 25) representing a range of gender, age, living circumstances (on/off campus), and SARS-CoV-2 testing/self-isolation experiences. Data were analysed using an inductive thematic approach. Six themes with 16 sub-themes emerged from the analysis of the qualitative data: 'Term-time Experiences', 'Risk Perception and Worry', 'Engagement in Protective Behaviours', 'Openness to Testing', 'Barriers to Testing' and 'General Wellbeing'. Students described feeling safe on campus, believed most of their peers are adherent to protective behaviours and were positive towards asymptomatic testing in university settings. University communications about COVID-19 testing and social behaviours need to be timely and presented in a more inclusive way to reach groups of students who currently feel marginalised. Barriers to engagement with SARS-CoV-2 testing, social distancing and self-isolation were primarily associated with fear of the mental health impacts of self-isolation, including worry about how they will cope, high anxiety, low mood, guilt relating to impact on others and loneliness. Loneliness in students could be mitigated through increased intra-university communications and a focus on establishment of low COVID-risk social activities to help students build and enhance their social support networks. These findings are particularly pertinent in the context of mass asymptomatic testing programmes being implemented in educational settings and high numbers of students being required to self-isolate. Universities need to determine the support needs of students during self-isolation and prepare for the long-term impacts of the pandemic on student mental health and welfare support services.

Hypoalbuminemia in patients following their recovery from severe coronavirus disease 2019.

Coronavirus disease 2019 (COVID-19) is caused by a contagious virus that has spread to more than 200 countries, territories, and regions. Thousands of studies to date have examined all aspects of this disease, yet little is known about the postrecovery status of patients, especially in the long term. Here, we examined erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), and serum albumin biomarkers in patients with a history of severe and mild-to-moderate COVID-19 following their recovery. In patients with severe COVID-19 serum albumin had a strong negative correlation with both ESR and CRP levels (R2 = - 0.861 and R2 = - 0.711), respectively. Also, there was a positive correlation between ESR and CRP level (R2 = 0.85) in the same group. However, there was no correlation between these biomarkers among mild-to-moderate COVID-19 patients. In addition, no correlation was recorded between the severe and mild-to-moderate COVID-19 groups. This finding highlights the sustained elevation of ESR and CRP level and reduced serum albumin level that may persist postrecovery in patients with a history of severe COVID-19.

Discovery of synergistic material-topography combinations to achieve immunomodulatory osteoinductive biomaterials using a novel in vitro screening method: The ChemoTopoChip.

Human mesenchymal stem cells (hMSCs) are widely represented in regenerative medicine clinical strategies due to their compatibility with autologous implantation. Effective bone regeneration involves crosstalk between macrophages and hMSCs, with macrophages playing a key role in the recruitment and differentiation of hMSCs. However, engineered biomaterials able to simultaneously direct hMSC fate and modulate macrophage phenotype have not yet been identified. A novel combinatorial chemistry-topography screening platform, the ChemoTopoChip, is used here to identify materials suitable for bone regeneration by screening 1008 combinations in each experiment for human immortalized mesenchymal stem cell (hiMSCs) and human macrophage response. The osteoinduction achieved in hiMSCs cultured on the "hit" materials in basal media is comparable to that seen when cells are cultured in osteogenic media, illustrating that these materials offer a materials-induced alternative to osteo-inductive supplements in bone-regeneration. Some of these same chemistry-microtopography combinations also exhibit immunomodulatory stimuli, polarizing macrophages towards a pro-healing phenotype. Maximum control of cell response is achieved when both chemistry and topography are recruited to instruct the required cell phenotype, combining synergistically. The large combinatorial library allows us for the first time to probe the relative cell-instructive roles of microtopography and material chemistry which we find to provide similar ranges of cell modulation for both cues. Machine learning is used to generate structure-activity relationships that identify key chemical and topographical features enhancing the response of both cell types, providing a basis for a better understanding of cell response to micro topographically patterned polymers.

Evaluating the impact of Heavy Goods Vehicle driver monitoring and coaching to reduce risky behaviour.

Determining the impact of driver-monitoring technologies to improve risky driving behaviours allows stakeholders to understand which aspects of onboard sensors and feedback need enhancement to promote road safety and education. This study investigates the influence of camera monitoring on Heavy Goods Vehicle (HGV) drivers' risky behaviours. We also assess whether monitoring affects individual driving events further when coupled with safe driving practices coaching. We evaluate the outcome of those practices on three telematics incidents heavily reliant on driving errors and violations, i.e., the number of vehicle harsh braking, harsh cornering and over speeding incidents. The objective is to understand how frequently individual incidents caused by risky driving behaviour occur (a) without camera monitoring and without any coaching; (b) after camera installation; and (c) after camera installation and coaching. We investigate two commercial HGV companies (Company 1 and Company 2) with 263 and 269 vehicles, respectively, over a 16 months period, from which the first 8 months contain data collected before the installation of cameras (baseline) and the rest of the dataset contains incident counts after the installation of cameras (intervention). Company 1 provides coaching during the intervention phase while Company 2 does not offer coaching. Our analysis considers the baseline and the intervention phases during the same seasons to eliminate any possible bias due to the influence of weather on driving behaviour. Results show an overall significant reduction in the mean frequency of harsh braking incidents from baseline to intervention by 16.82% in Company 1 and 4.62% in Company 2, and a significant reduction in the mean frequency of over speeding incidents from baseline to intervention by 34.29% in Company 1 and 28.13% in Company 2. Furthermore, the effect of coaching has a significant difference in reducing the frequency of harsh braking (p = .011) and harsh cornering (p < .001) compared to just camera monitoring. These results suggest that coaching interventions are more effective in reducing driving errors while monitoring reduces both driving errors and violations.

Immune Modulation by Design: Using Topography to Control Human Monocyte Attachment and Macrophage Differentiation.

Macrophages play a central role in orchestrating immune responses to foreign materials, which are often responsible for the failure of implanted medical devices. Material topography is known to influence macrophage attachment and phenotype, providing opportunities for the rational design of "immune-instructive" topographies to modulate macrophage function and thus foreign body responses to biomaterials. However, no generalizable understanding of the inter-relationship between topography and cell response exists. A high throughput screening approach is therefore utilized to investigate the relationship between topography and human monocyte-derived macrophage attachment and phenotype, using a diverse library of 2176 micropatterns generated by an algorithm. This reveals that micropillars 5-10 µm in diameter play a dominant role in driving macrophage attachment compared to the many other topographies screened, an observation that aligns with studies of the interaction of macrophages with particles. Combining the pillar size with the micropillar density is found to be key in modulation of cell phenotype from pro to anti-inflammatory states. Machine learning is used to successfully build a model that correlates cell attachment and phenotype with a selection of descriptors, illustrating that materials can potentially be designed to modulate inflammatory responses for future applications in the fight against foreign body rejection of medical devices.

Discovery of (meth)acrylate polymers that resist colonization by fungi associated with pathogenesis and biodeterioration.

Fungi have major, negative socioeconomic impacts, but control with bioactive agents is increasingly restricted, while resistance is growing. Here, we describe an alternative fungal control strategy via materials operating passively (i.e., no killing effect). We screened hundreds of (meth)acrylate polymers in high throughput, identifying several that reduce attachment of the human pathogen Candida albicans, the crop pathogen Botrytis cinerea, and other fungi. Specific polymer functional groups were associated with weak attachment. Low fungal colonization materials were not toxic, supporting their passive, anti-attachment utility. We developed a candidate monomer formulation for inkjet-based 3D printing. Printed voice prosthesis components showed up to 100% reduction in C. albicans biofilm versus commercial materials. Furthermore, spray-coated leaf surfaces resisted fungal infection, with no plant toxicity. This is the first high-throughput study of polymer chemistries resisting fungal attachment. These materials are ready for incorporation in products to counteract fungal deterioration of goods, food security, and health.

Deep Learning with Dynamically Weighted Loss Function for Sensor-Based Prognostics and Health Management.

Deep learning has been employed to prognostic and health management of automotive and aerospace with promising results. Literature in this area has revealed that most contributions regarding deep learning is largely focused on the model's architecture. However, contributions regarding improvement of different aspects in deep learning, such as custom loss function for prognostic and health management are scarce. There is therefore an opportunity to improve upon the effectiveness of deep learning for the system's prognostics and diagnostics without modifying the models' architecture. To address this gap, the use of two different dynamically weighted loss functions, a newly proposed weighting mechanism and a focal loss function for prognostics and diagnostics task are investigated. A dynamically weighted loss function is expected to modify the learning process by augmenting the loss function with a weight value corresponding to the learning error of each data instance. The objective is to force deep learning models to focus on those instances where larger learning errors occur in order to improve their performance. The two loss functions used are evaluated using four popular deep learning architectures, namely, deep feedforward neural network, one-dimensional convolutional neural network, bidirectional gated recurrent unit and bidirectional long short-term memory on the commercial modular aero-propulsion system simulation data from NASA and air pressure system failure data for Scania trucks. Experimental results show that dynamically-weighted loss functions helps us achieve significant improvement for remaining useful life prediction and fault detection rate over non-weighted loss function predictions.

Single-Cell Tracking on Polymer Microarrays Reveals the Impact of Surface Chemistry on Pseudomonas aeruginosa Twitching Speed and Biofilm Development.

Bacterial biofilms exhibit up to 1000 times greater resistance to antibiotic or host immune clearance than planktonic cells. Pseudomonas aeruginosa produces retractable type IV pili (T4P) that facilitate twitching motility on surfaces. The deployment of pili is one of the first responses of bacteria to surface interactions and because of their ability to contribute to cell surface adhesion and biofilm formation, this has relevance to medical device-associated infections. While polymer chemistry is known to influence biofilm development, its impact on twitching motility is not understood. Here, we combine a polymer microarray format with time-lapse automated microscopy to simultaneously assess P. aeruginosa twitching motility on 30 different methacrylate/acrylate polymers over 60 min post inoculation using a high-throughput system. During this critical initial period where the decision to form a biofilm is thought to occur, similar numbers of bacterial cells accumulate on each polymer. Twitching motility is observed on all polymers irrespective of their chemistry and physical surface properties, in contrast to the differential biofilm formation noted after 24 h of incubation. However, on the microarray polymers, P. aeruginosa cells twitch at significantly different speeds, ranging from 5 to ∼13 nm/s, associated with crawling or walking and are distinguishable from the different cell surface tilt angles observed. Chemometric analysis using partial least-squares (PLS) regression identifies correlations between surface chemistry, as measured by time-of-flight secondary ion mass spectrometry (ToF-SIMS), and both biofilm formation and single-cell twitching speed. The relationships between surface chemistry and these two responses are different for each process. There is no correlation between polymer surface stiffness and roughness as determined by atomic force measurement (AFM), or water contact angle (WCA), and twitching speed or biofilm formation. This reinforces the dominant and distinct contributions of material surface chemistry to twitching speed and biofilm formation.

Short and Long term predictions of Hospital emergency department attendances.

OBJECTIVE: Emergency departments in the United Kingdom (UK) experience significant difficulties in achieving the 95% NHS access standard due to unforeseen variations in patient flow. In order to maximize efficiency and minimize clinical risk, better forecasting of patient demand is necessary. The objective is therefore to create a tool that accurately predicts attendance at emergency departments to support optimal planning of human and physical resources. METHODS: Historical attendance data between Jan-2011 - December-2015 from four hospitals were used as a training set to develop and validate a forecasting model. To handle weekday variations, the data was first segmented into each weekday time series and a separate model for each weekday was performed. Seasonality testing was performed, followed by Box-Cox transformations. A modified heuristics based on a fuzzy time series model was then developed and compared with autoregressive integrated moving average and neural networks models using Harvey, Leybourne and Newbold (HLN) test. The time series models were tested in four emergency department sites to assess forecasting accuracy using the root mean square error and mean absolute percentage error. The models were tested for (i) short term prediction (four weeks ahead), using weekday time series; and (ii) long term predictions (four months ahead) using monthly time series. RESULTS: Data analysis revealed that presentations to emergency department and subsequent admissions to hospital were not a purely random process and therefore could be predicted with acceptable accuracy. Prediction accuracy improved as the forecast time intervals became wider (from daily to monthly). For each weekday time series modelling using fuzzy time series, for forecasting daily admissions, the mean absolute percentage error ranged from 2.63% to 4.72% while for monthly time series mean absolute percentage error varied from 2.01%-2.81%. For weekday time series, the mean absolute percentage error for autoregressive integrated moving average and neural network forecasting models ranged from 6.25% to 7.47% and 6.04%-7.42% respectively. The proposed fuzzy time series model proved to have statistically significant performance using Harvey, Leybourne and Newbold (HLN) test. This was explained by variations in attendances in different sites and weekdays. CONCLUSIONS: This paper described a heuristic-based fuzzy logic model for predicting emergency department attendances which could help resource allocation and reduce pressure on busy hospitals. Valid and reproducible prediction tools could be generated from these hospital data. The methodology had an acceptable accuracy over a relatively short time period, and could be used to assist better bed management, staffing and elective surgery scheduling. When compared to other prediction models usually applied for emergency department attendances prediction, the proposed heuristic model had better accuracy.

A signalome screening approach in the autoinflammatory disease TNF receptor associated periodic syndrome (TRAPS) highlights the anti-inflammatory properties of drugs for repurposing.

TNF receptor associated periodic syndrome (TRAPS) is an autoinflammatory disease caused by mutations in TNF Receptor 1 (TNFR1). Current therapies for TRAPS are limited and do not target the pro-inflammatory signalling pathways that are central to the disease mechanism. Our aim was to identify drugs for repurposing as anti-inflammatories based on their ability to down-regulate molecules associated with inflammatory signalling pathways that are activated in TRAPS. This was achieved using rigorously optimized, high through-put cell culture and reverse phase protein microarray systems to screen compounds for their effects on the TRAPS-associated inflammatory signalome. 1360 approved, publically available, pharmacologically active substances were investigated for their effects on 40 signalling molecules associated with pro-inflammatory signalling pathways that are constitutively upregulated in TRAPS. The drugs were screened at four 10-fold concentrations on cell lines expressing both wild-type (WT) TNFR1 and TRAPS-associated C33Y mutant TNFR1, or WT TNFR1 alone; signalling molecule levels were then determined in cell lysates by the reverse-phase protein microarray. A novel mathematical methodology was developed to rank the compounds for their ability to reduce the expression of signalling molecules in the C33Y-TNFR1 transfectants towards the level seen in the WT-TNFR1 transfectants. Seven high-ranking drugs were selected and tested by RPPA for effects on the same 40 signalling molecules in lysates of peripheral blood mononuclear cells (PBMCs) from C33Y-TRAPS patients compared to PBMCs from normal controls. The fluoroquinolone antibiotic lomefloxacin, as well as others from this class of compounds, showed the most significant effects on multiple pro-inflammatory signalling pathways that are constitutively activated in TRAPS; lomefloxacin dose-dependently significantly reduced expression of 7/40 signalling molecules across the Jak/Stat, MAPK, NF-κB and PI3K/AKT pathways. This study demonstrates the power of signalome screening for identifying candidates for drug repurposing.