Computer vision digitization of smartphone images of anesthesia paper health records from low-middle income countries.

BACKGROUND: In low-middle income countries, healthcare providers primarily use paper health records for capturing data. Paper health records are utilized predominately due to the prohibitive cost of acquisition and maintenance of automated data capture devices and electronic medical records. Data recorded on paper health records is not easily accessible in a digital format to healthcare providers. The lack of real time accessible digital data limits healthcare providers, researchers, and quality improvement champions to leverage data to improve patient outcomes. In this project, we demonstrate the novel use of computer vision software to digitize handwritten intraoperative data elements from smartphone photographs of paper anesthesia charts from the University Teaching Hospital of Kigali. We specifically report our approach to digitize checkbox data, symbol-denoted systolic and diastolic blood pressure, and physiological data. METHODS: We implemented approaches for removing perspective distortions from smartphone photographs, removing shadows, and improving image readability through morphological operations. YOLOv8 models were used to deconstruct the anesthesia paper chart into specific data sections. Handwritten blood pressure symbols and physiological data were identified, and values were assigned using deep neural networks. Our work builds upon the contributions of previous research by improving upon their methods, updating the deep learning models to newer architectures, as well as consolidating them into a single piece of software. RESULTS: The model for extracting the sections of the anesthesia paper chart achieved an average box precision of 0.99, an average box recall of 0.99, and an mAP0.5-95 of 0.97. Our software digitizes checkbox data with greater than 99% accuracy and digitizes blood pressure data with a mean average error of 1.0 and 1.36 mmHg for systolic and diastolic blood pressure respectively. Overall accuracy for physiological data which includes oxygen saturation, inspired oxygen concentration and end tidal carbon dioxide concentration was 85.2%. CONCLUSIONS: We demonstrate that under normal photography conditions we can digitize checkbox, blood pressure and physiological data to within human accuracy when provided legible handwriting. Our contributions provide improved access to digital data to healthcare practitioners in low-middle income countries.

Intraoperative short-term blood pressure variability and postoperative acute kidney injury: a single-center retrospective cohort study using sample entropy analysis.

BACKGROUND: To investigate if intraoperative very short-term variability in blood pressure measured by sample entropy improves discrimination of postoperative acute kidney injury after noncardiac surgery. METHODS: Adult surgical patients undergoing general, thoracic, urological, or gynecological surgery between August 2016 to June 2017 at Seoul National University Hospital were included. The primary outcome was acute kidney injury stage 1, defined by the Kidney Disease: Improving Global Outcomes guidelines. Exploratory and explanatory variables included sample entropy of the mean arterial pressure and standard demographic, surgical, anesthesia and hypotension over time indices known to be associated with acute kidney injury respectively. Random forest classification and L1 logistic regression were used to assess four models for discriminating acute kidney injury: (1) Standard risk factors which included demographic, anesthetic, and surgical variables (2) Standard risk factors and cumulative hypotension over time (3) Standard risk factors and sample entropy (4) Standard risk factors, cumulative hypotension over time and sample entropy. RESULTS: Two hundred and thirteen (7.4%) cases developed postoperative acute kidney injury. The median and interquartile range for sample entropy of mean arterial pressure was 0.34 and [0.26, 0.42] respectively. C-statistics were identical between the random forest and L1 logistic regression models. Results demonstrated no improvement in discrimination of postoperative acute kidney injury with the addition of the sample entropy of mean arterial pressure: Standard risk factors: 0.81 [0.76, 0.85], Standard risk factors and hypotension over time indices: 0.80 [0.75, 0.85], Standard risk factors and sample entropy of mean arterial pressure: 0.81 [0.76, 0.85] and Standard risk factors, sample entropy of mean arterial pressure and hypotension over time indices: 0.81 [0.76, 0.86]. CONCLUSION: Assessment of very short-term blood pressure variability does not improve the discrimination of postoperative acute kidney injury in patients undergoing non-cardiac surgery in this sample.

Is the future female for turtles? Climate change and wetland configuration predict sex ratios of a freshwater species.

Climate change and land-use change are leading drivers of biodiversity decline, affecting demographic parameters that are important for population persistence. For example, scientists have speculated for decades that climate change may skew adult sex ratios in taxa that express temperature-dependent sex determination (TSD), but limited evidence exists that this phenomenon is occurring in natural settings. For species that are vulnerable to anthropogenic land-use practices, differential mortality among sexes may also skew sex ratios. We sampled the spotted turtle (Clemmys guttata), a freshwater species with TSD, across a large portion of its geographic range (Florida to Maine), to assess the environmental factors influencing adult sex ratios. We present evidence that suggests recent climate change has potentially skewed the adult sex ratio of spotted turtles, with samples following a pattern of increased proportions of females concomitant with warming trends, but only within the warmer areas sampled. At intermediate temperatures, there was no relationship with climate, while in the cooler areas we found the opposite pattern, with samples becoming more male biased with increasing temperatures. These patterns might be explained in part by variation in relative adaptive capacity via phenotypic plasticity in nest site selection. Our findings also suggest that spotted turtles have a context-dependent and multi-scale relationship with land use. We observed a negative relationship between male proportion and the amount of crop cover (within 300 m) when wetlands were less spatially aggregated. However, when wetlands were aggregated, sex ratios remained consistent. This pattern may reflect sex-specific patterns in movement that render males more vulnerable to mortality from agricultural machinery and other threats. Our findings highlight the complexity of species' responses to both climate change and land use, and emphasize the role that landscape structure can play in shaping wildlife population demographics.

Digitization of Symbol-Denoted Blood Pressure Data From Intraoperative Paper Health Records in a Low-Middle-Income Country Using Deep Image Segmentation and Associated Postoperative Outcomes: A Feasibility Study.

BACKGROUND: In low-middle-income countries (LMICs), perioperative clinical information is almost universally collected on paper health records (PHRs). The lack of accessible digital databases limits LMICs in leveraging data to predict and improve patient outcomes after surgery. In this feasibility study, our aims were to: (1) determine the detection performance and prediction error of the U-Net deep image segmentation approach for digitization of hand-drawn blood pressure symbols from an image of the intraoperative PHRs and (2) evaluate the association between deep image segmentation-derived blood pressure parameters and postoperative mortality and length of stay. METHODS: A smartphone mHealth platform developed by our team was used to capture images of completed intraoperative PHRs. A 2-stage deep image segmentation modeling approach was used to create 2 separate segmentation masks for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Iterative postprocessing was utilized to convert the segmentation mask results into numerical SBP and DBP values. Detection performance and prediction errors were evaluated for the U-Net models by comparison with ground-truth values. Using multivariate regression analysis, we investigated the association of deep image segmentation-derived blood pressure values, total time spent in predefined blood pressure ranges, and postoperative outcomes including in-hospital mortality and length of stay. RESULTS: A total of 350 intraoperative PHRs were imaged following surgery. Overall accuracy was 0.839 and 0.911 for SBP and DBP symbol detections, respectively. The mean error rate and standard deviation for the difference between the actual and predicted blood pressure values were 2.1 ± 4.9 and -0.8 ± 3.9 mm Hg for SBP and DBP, respectively. Using the U-Net model-derived blood pressures, minutes of time where DBP <50 mm Hg (odds ratio [OR], 1.03; CI, 1.01-1.05; P = .003) was associated with an increased in-hospital mortality. In addition, increased cumulative minutes of time with SBP between 80 and 90 mm Hg was significantly associated with a longer length of stay (incidence rate ratio, 1.02 [1.0-1.03]; P < .05), while increased cumulative minutes of time where SBP between 140 and 160 mm Hg was associated with a shorter length of stay (incidence rate ratio, 0.9 [0.96-0.99]; P < .05). CONCLUSIONS: In this study, we report our experience with a deep image segmentation model for digitization of symbol-denoted blood pressure from intraoperative anesthesia PHRs. Our data support further development of this novel approach to digitize PHRs from LMICs, to provide accessible, curated, and reproducible data for both quality improvement- and outcome-based research.

Risk factors associated with post-acute sequelae of SARS-CoV-2: an N3C and NIH RECOVER study.

BACKGROUND: More than one-third of individuals experience post-acute sequelae of SARS-CoV-2 infection (PASC, which includes long-COVID). The objective is to identify risk factors associated with PASC/long-COVID diagnosis. METHODS: This was a retrospective case-control study including 31 health systems in the United States from the National COVID Cohort Collaborative (N3C). 8,325 individuals with PASC (defined by the presence of the International Classification of Diseases, version 10 code U09.9 or a long-COVID clinic visit) matched to 41,625 controls within the same health system and COVID index date within ± 45 days of the corresponding case's earliest COVID index date. Measurements of risk factors included demographics, comorbidities, treatment and acute characteristics related to COVID-19. Multivariable logistic regression, random forest, and XGBoost were used to determine the associations between risk factors and PASC. RESULTS: Among 8,325 individuals with PASC, the majority were > 50 years of age (56.6%), female (62.8%), and non-Hispanic White (68.6%). In logistic regression, middle-age categories (40 to 69 years; OR ranging from 2.32 to 2.58), female sex (OR 1.4, 95% CI 1.33-1.48), hospitalization associated with COVID-19 (OR 3.8, 95% CI 3.05-4.73), long (8-30 days, OR 1.69, 95% CI 1.31-2.17) or extended hospital stay (30 + days, OR 3.38, 95% CI 2.45-4.67), receipt of mechanical ventilation (OR 1.44, 95% CI 1.18-1.74), and several comorbidities including depression (OR 1.50, 95% CI 1.40-1.60), chronic lung disease (OR 1.63, 95% CI 1.53-1.74), and obesity (OR 1.23, 95% CI 1.16-1.3) were associated with increased likelihood of PASC diagnosis or care at a long-COVID clinic. Characteristics associated with a lower likelihood of PASC diagnosis or care at a long-COVID clinic included younger age (18 to 29 years), male sex, non-Hispanic Black race, and comorbidities such as substance abuse, cardiomyopathy, psychosis, and dementia. More doctors per capita in the county of residence was associated with an increased likelihood of PASC diagnosis or care at a long-COVID clinic. Our findings were consistent in sensitivity analyses using a variety of analytic techniques and approaches to select controls. CONCLUSIONS: This national study identified important risk factors for PASC diagnosis such as middle age, severe COVID-19 disease, and specific comorbidities. Further clinical and epidemiological research is needed to better understand underlying mechanisms and the potential role of vaccines and therapeutics in altering PASC course.

Increased stroke severity and mortality in patients with SARS-CoV-2 infection: An analysis from the N3C database.

BACKGROUND: Studies from early in the COVID-19 pandemic showed that patients with ischemic stroke and concurrent SARS-CoV-2 infection had increased stroke severity. We aimed to test the hypothesis that this association persisted throughout the first year of the pandemic and that a similar increase in stroke severity was present in patients with hemorrhagic stroke. METHODS: Using the National Institute of Health National COVID Cohort Collaborative (N3C) database, we identified a cohort of patients with stroke hospitalized in the United States between March 1, 2020 and February 28, 2021. We propensity score matched patients with concurrent stroke and SARS-COV-2 infection and available NIH Stroke Scale (NIHSS) scores to all other patients with stroke in a 1:3 ratio. Nearest neighbor matching with a caliper of 0.25 was used for most factors and exact matching was used for race/ethnicity and site. We modeled stroke severity as measured by admission NIHSS and the outcomes of death and length of stay. We also explored the temporal relationship between time of SARS-COV-2 diagnosis and incidence of stroke. RESULTS: Our query identified 43,295 patients hospitalized with ischemic stroke (5765 with SARS-COV-2, 37,530 without) and 18,107 patients hospitalized with hemorrhagic stroke (2114 with SARS-COV-2, 15,993 without). Analysis of our propensity matched cohort revealed that stroke patients with concurrent SARS-COV-2 had increased NIHSS (Ischemic stroke: IRR=1.43, 95% CI:1.33-1.52, p<0.001; hemorrhagic stroke: IRR=1.20, 95% CI:1.08-1.33, p<0.001), length of stay (Ischemic stroke: estimate = 1.48, 95% CI: 1.37, 1.61, p<0.001; hemorrhagic stroke: estimate = 1.25, 95% CI: 1.06, 1.47, p=0.007) and higher odds of death (Ischemic stroke: OR 2.19, 95% CI: 1.79-2.68, p<0.001; hemorrhagic stroke: OR 2.19, 95% CI: 1.79-2.68, p<0.001). We observed the highest incidence of stroke diagnosis on the same day as SARS-COV-2 diagnosis with a logarithmic decline in counts. CONCLUSION: This retrospective observational analysis suggests that stroke severity in patients with concurrent SARS-COV-2 was increased throughout the first year of the pandemic.

Evaluating Mechanisms of Short-term Woodland Salamander Response to Forest Management.

Contemporary forest management often requires meeting diverse ecological objectives including maintaining ecosystem function and promoting biodiversity through timber harvesting. Wildlife are essential in this process by providing ecological services that can facilitate forest resiliency in response to timber harvesting. However, the mechanisms driving species' responses remain ambiguous. The goal of this study was to assess mechanisms influencing eastern red-backed salamander (RBS; Plethodon cinereus) response to overstory cover removal. We evaluated two mitigation strategies for the RBS in response to overstory removal. We used a before-after-control-impact design to study how (1) retaining residual trees or (2) eliminating soil compaction affected RBS surface counts and body condition index (BCI) up to two-years post-treatment. Additionally, we assessed how surface counts of RBS were influenced by overstory tree cover. Surface counts of RBS were not strongly influenced by overstory removal when tree residuals were retained. Body condition index increased in treatments where harvest residuals were retained. In treatments where soil compaction was eliminated, surface counts and BCI were inversely related. Finally, surface counts from both mitigation strategies were not strongly influenced by overstory cover. Overall, both mitigation techniques appeared to ameliorate impacts of overstory removal on RBS. These results highlight the importance of understanding mechanisms driving species' responses to forest management. To reduce the perceived negative effects of overstory removal on RBS, incorporating these mitigation measures may contribute to the viability and stability of RBS populations. Incorporating species' life history traits into management strategies could increase continuity of ecological function and integrity through harvesting.

Embeddings of genomic region sets capture rich biological associations in lower dimensions.

MOTIVATION: Genomic region sets summarize functional genomics data and define locations of interest in the genome such as regulatory regions or transcription factor binding sites. The number of publicly available region sets has increased dramatically, leading to challenges in data analysis. RESULTS: We propose a new method to represent genomic region sets as vectors, or embeddings, using an adapted word2vec approach. We compared our approach to two simpler methods based on interval unions or term frequency-inverse document frequency and evaluated the methods in three ways: First, by classifying the cell line, antibody or tissue type of the region set; second, by assessing whether similarity among embeddings can reflect simulated random perturbations of genomic regions; and third, by testing robustness of the proposed representations to different signal thresholds for calling peaks. Our word2vec-based region set embeddings reduce dimensionality from more than a hundred thousand to 100 without significant loss in classification performance. The vector representation could identify cell line, antibody and tissue type with over 90% accuracy. We also found that the vectors could quantitatively summarize simulated random perturbations to region sets and are more robust to subsampling the data derived from different peak calling thresholds. Our evaluations demonstrate that the vectors retain useful biological information in relatively lower-dimensional spaces. We propose that vector representation of region sets is a promising approach for efficient analysis of genomic region data. AVAILABILITY AND IMPLEMENTATION: https://github.com/databio/regionset-embedding. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Evaluating the effect of expert elicitation techniques on population status assessment in the face of large uncertainty.

Population projection models are important tools for conservation and management. They are often used for population status assessments, for threat analyses, and to predict the consequences of conservation actions. Although conservation decisions should be informed by science, critical decisions are often made with very little information to support decision-making. Conversely, postponing decisions until better information is available may reduce the benefit of a conservation decision. When empirical data are limited or lacking, expert elicitation can be used to supplement existing data and inform model parameter estimates. The use of rigorous techniques for expert elicitation that account for uncertainty can improve the quality of the expert elicited values and therefore the accuracy of the projection models. One recurring challenge for summarizing expert elicited values is how to aggregate them. Here, we illustrate a process for population status assessment using a combination of expert elicitation and data from the ecological literature. We discuss the importance of considering various aggregation techniques, and illustrate this process using matrix population models for the wood turtle (Glyptemys insculpta) to assist U.S. Fish and Wildlife Service decision-makers with their Species Status Assessment. We compare estimates of population growth using data from the ecological literature and four alternative aggregation techniques for the expert-elicited values. The estimate of population growth rate based on estimates from the literature (λmean = 0.952, 95% CI: 0.87-1.01) could not be used to unequivocally reject the hypotheses of a rapidly declining population nor the hypothesis of a stable, or even slightly growing population, whereas our results for the expert-elicited estimates supported the hypothesis that the wood turtle population will decline over time. Our results showed that the aggregation techniques used had an impact on model estimates, suggesting that the choice of techniques should be carefully considered. We discuss the benefits and limitations associated with each method and their relevance to the population status assessment. We note a difference in the temporal scope or inference between the literature-based estimates that provided insights about historical changes, whereas the expert-based estimates were forward looking. Therefore, conducting an expert-elicitation in addition to using parameter estimates from the literature improved our understanding of our species of interest.

Artificial Intelligence-based Analytics for Diagnosis of Small Bowel Enteropathies and Black Box Feature Detection.

OBJECTIVES: Striking histopathological overlap between distinct but related conditions poses a disease diagnostic challenge. There is a major clinical need to develop computational methods enabling clinicians to translate heterogeneous biomedical images into accurate and quantitative diagnostics. This need is particularly salient with small bowel enteropathies; environmental enteropathy (EE) and celiac disease (CD). We built upon our preliminary analysis by developing an artificial intelligence (AI)-based image analysis platform utilizing deep learning convolutional neural networks (CNNs) for these enteropathies. METHODS: Data for the secondary analysis was obtained from three primary studies at different sites. The image analysis platform for EE and CD was developed using CNNs including one with multizoom architecture. Gradient-weighted class activation mappings (Grad-CAMs) were used to visualize the models' decision-making process for classifying each disease. A team of medical experts simultaneously reviewed the stain color normalized images done for bias reduction and Grad-CAMs to confirm structural preservation and biomedical relevance, respectively. RESULTS: Four hundred and sixty-one high-resolution biopsy images from 150 children were acquired. Median age (interquartile range) was 37.5 (19.0-121.5) months with a roughly equal sex distribution; 77 males (51.3%). ResNet50 and shallow CNN demonstrated 98% and 96% case-detection accuracy, respectively, which increased to 98.3% with an ensemble. Grad-CAMs demonstrated models' ability to learn different microscopic morphological features for EE, CD, and controls. CONCLUSIONS: Our AI-based image analysis platform demonstrated high classification accuracy for small bowel enteropathies which was capable of identifying biologically relevant microscopic features and emulating human pathologist decision-making process. Grad-CAMs illuminated the otherwise "black box" of deep learning in medicine, allowing for increased physician confidence in adopting these new technologies in clinical practice.

Restraint of Human Skin Fibroblast Motility, Migration, and Cell Surface Actin Dynamics, by Pannexin 1 and P2X7 Receptor Signaling.

Wound healing is a dynamic process required to maintain skin integrity and which relies on the precise migration of different cell types. A key molecule that regulates this process is ATP. However, the mechanisms involved in extracellular ATP management are poorly understood, particularly in the human dermis. Here, we explore the role, in human fibroblast migration during wound healing, of Pannexin 1 channels and their relationship with purinergic signals and in vivo cell surface filamentous actin dynamics. Using siRNA against Panx isoforms and different Panx1 channel inhibitors, we demonstrate in cultured human dermal fibroblasts that the absence or inhibition of Panx1 channels accelerates cell migration, increases single-cell motility, and promotes actin redistribution. These changes occur through a mechanism that involves the release of ATP to the extracellular space through a Panx1-dependent mechanism and the activation of the purinergic receptor P2X7. Together, these findings point to a pivotal role of Panx1 channels in skin fibroblast migration and suggest that these channels could be a useful pharmacological target to promote damaged skin healing.

Lack of effect of short-term DHEA supplementation on the perimenopausal ovary†.

Dehydroepiandrosterone (DHEA) hormonal supplementation can improve oocyte quality in women with diminished ovarian function. However, it is unclear whether DHEA supplementation can also enhance ovarian function during the perimenopause (i.e., when the number of follicles in the ovary has undergone a marked reduction). To address this question, we examined the impact of 2.5-months of daily 5-mg oral DHEA supplementation on the number of ovarian follicles and the concentration of anti-Müllerian hormone (AMH) in perimenopausal rhesus macaques. Like women, these long-lived nonhuman primates have ~ 28-day menstrual cycles and eventually undergo menopause. They also show similar age-related neuroendocrine changes, including a marked decrease in circulating concentrations of DHEA and DHEA sulfate (DHEAS). Our experimental design involved the following three groups of animals (N = 6 per group): Young adult (mean age = 11.6 years), Old control (mean age = 23.1 years), and Old DHEA-treated (mean age = 23.5 years). Histological examination of the ovaries revealed a significant age-related decrease in the mean number of primordial follicles despite DHEA supplementation. Moreover, AMH concentrations within the ovaries and circulation, assessed by Western analysis and ELISA, respectively, showed significant age-related decreases that were not attenuated by DHEA supplementation. Taken together, these results fail to show a clear effect of short-term physiological DHEA supplementation on the perimenopausal ovary. However, they do not exclude the possibility that alternative DHEA supplementation paradigms (e.g., involving an earlier start date, longer duration and using pharmacological doses) may extend reproductive potential during aging.

Nonlinear optical crosslinking (NLO CXL) for correcting refractive errors.

Ultraviolet A (UVA) light-based photoactivation of riboflavin (Rf) to induce corneal crosslinking (CXL) and mechanical stiffening is now a well-known treatment for corneal ectasia and Keratoconus that is being used in a topographically guided photorefractive intrastromal CXL (PiXL) procedure to treat low degrees of refractive errors. Alternative approaches for non-invasive treatment of refractive errors have also been proposed that use femtosecond lasers (FS) that provide much faster, more precise, and safer results than UVA CXL. One such treatment, nonlinear optical crosslinking (NLO CXL), has been able to replicate the effects of UVA CXL, while producing a smaller area of cellular damage and requiring a shorter procedure time. Unlike UVA CXL, the treatment volume of NLO CXL only occurs within the focal volume of the laser, which can be placed at any depth and scanned into any pattern for true topographically guided refractive correction. This review presents our experience with using FS lasers to photoactivate Rf and perform highly controlled corneal CXL that leads to mechanical stiffening and changes in corneal shape.

Artificial Intelligence Applied to Gastrointestinal Diagnostics: A Review.

Artificial intelligence (AI), a discipline encompassed by data science, has seen recent rapid growth in its application to healthcare and beyond, and is now an integral part of daily life. Uses of AI in gastroenterology include the automated detection of disease and differentiation of pathology subtypes and disease severity. Although a majority of AI research in gastroenterology focuses on adult applications, there are a number of pediatric pathologies that could benefit from more research. As new and improved diagnostic tools become available and more information is retrieved from them, AI could provide physicians a method to distill enormous amounts of data into enhanced decision-making and cost saving for children with digestive disorders. This review provides a broad overview of AI and examples of its possible applications in pediatric gastroenterology.

Revealing and Responding to Multiple Health Risks in Informal Settlements in Sub-Saharan African Cities.

This paper underscores the need for detailed data on health and disaster risks for sub-Saharan African cities, particularly for their informal settlements. Systems that should contribute to the information base on health and health risks in each locality are rarely functional. In most cities, there is a lack of data on health risks, health outcomes, and health determinants; where data are available, they are usually too aggregated to be useful to urban governments. Such data shortfalls likely hide the scale of premature death, serious illness, and injury in informal settlements; limited data can also curtail the identification of particularly vulnerable urban residents. After outlining data shortfalls, this paper considers two sources of data that can help fill data gaps on health and health determinants. The first is from city case studies undertaken within a research programme called Urban Africa: Risk Knowledge (Urban-ARK). Urban-ARK's findings reveal the large spectrum of health risks in informal settlements, ranging from 'everyday' risks (e.g. infectious and parasitic diseases) to small- and larger-scale disasters. The second is from data collected by slum/shack dweller federations, which offer qualitative and quantitative findings on health, disasters, and other health determinants in informal settlements. Our conclusion reflects upon the need for additional data on multiple risks to advance urban health and well-being and support the 2030 Agenda for Sustainable Development. It also highlights the need to strengthen accountable urban governance in sub-Saharan Africa.

Study of Environmental Enteropathy and Malnutrition (SEEM) in Pakistan: protocols for biopsy based biomarker discovery and validation.

BACKGROUND: Environmental Enteropathy (EE), characterized by alterations in intestinal structure, function, and immune activation, is believed to be an important contributor to childhood undernutrition and its associated morbidities, including stunting. Half of all global deaths in children < 5 years are attributable to under-nutrition, making the study of EE an area of critical priority. METHODS: Community based intervention study, divided into two sub-studies, 1) Longitudinal analyses and 2) Biopsy studies for identification of EE features via omics analyses. Birth cohorts in Matiari, Pakistan established: moderately or severely malnourished (weight for height Z score (WHZ) < - 2) children, and well-nourished (WHZ > 0) children. Blood, urine, and fecal samples, for evaluation of potential biomarkers, will be collected at various time points from all participants (longitudinal analyses). Participants will receive appropriate educational and nutritional interventions; non-responders will undergo further evaluation to determine eligibility for further workup, including upper gastrointestinal endoscopy. Histopathological changes in duodenal biopsies will be compared with duodenal biopsies obtained from USA controls who have celiac disease, Crohn's disease, or who were found to have normal histopathology. RNA-Seq will be employed to characterize mucosal gene expression across groups. Duodenal biopsies, luminal aspirates from the duodenum, and fecal samples will be analyzed to define microbial community composition (omic analyses). The relationship between histopathology, mucosal gene expression, and community configuration will be assessed using a variety of bioinformatic tools to gain better understanding of disease pathogenesis and to identify mechanism-based biomarkers. Ethical review committees at all collaborating institutions have approved this study. All results will be made available to the scientific community. DISCUSSION: Operational and ethical constraints for safely obtaining intestinal biopsies from children in resource-poor settings have led to a paucity of human tissue-based investigations to understand and reverse EE in vulnerable populations. Furthermore, EE biomarkers have rarely been correlated with gold standard histopathological confirmation. The Study of Environmental Enteropathy and Malnutrition (SEEM) is designed to better understand the pathophysiology, predictors, biomarkers, and potential management strategies of EE to inform strategies to eradicate this debilitating pathology and accelerate progress towards the 2030 Sustainable Development Goals. TRIAL REGISTRATION: Retrospectively registered; clinicaltrials.gov ID NCT03588013 .

A spectrum of methods for a spectrum of risk: Generating evidence to understand and reduce urban risk in sub-Saharan Africa.

Many African towns and cities face a range of hazards, which can best be described as representing a "spectrum of risk" of events that can cause death, illness or injury, and impoverishment. Yet despite the growing numbers of people living in African urban centres, the extent and relative severity of these different risks is poorly understood. This paper provides a rationale for using a spectrum of methods to address this spectrum of risk, and demonstrates the utility of mixed-methods approaches in planning for resilience. It describes activities undertaken in a wide-ranging multi-country programme of research, which use multiple approaches to gather empirical data on risk, in order to build a stronger evidence base and provide a more solid base for planning and investment. It concludes that methods need to be chosen in regard to social, political economic, biophysical and hydrogeological context, while also recognising the different levels of complexity and institutional capacity in different urban centres. The paper concludes that as well as the importance of taking individual contexts into account, there are underlying methodological principles - based on multidisciplinary expertise and multi-faceted and collaborative research endeavours - that can inform a range of related approaches to understanding urban risk in sub-Saharan Africa and break the cycle of risk accumulation.

Collagen fiber crimping following in vivo UVA-induced corneal crosslinking.

The purpose of this study was to measure collagen fiber crimping (CFC) using nonlinear optical imaging of second harmonic generated (SHG) signals to determine the effects of UVA-riboflavin induced corneal collagen crosslinking (UVA CXL) on collagen structure. Two groups, four rabbits each, were treated in the right eye with standard UVA CXL. In vivo confocal microscopy was performed at 1, 2, and 4 weeks after treatment for the first group and up to three months for the second group to measure epithelial/stromal thickness and corneal haze during recovery. Rabbits were sacrificed at one and three months, respectively, and their corneas fixed under pressure. Regions of crosslinking were identified by the presence of collagen autofluorescence (CAF) and then collagen structure was imaged using SHG microscopy. The degree of CFC was determined by measuring the percentage difference between the length of the collagen fiber and the linear distance traveled. CFC was measured in the central anterior and posterior CXL region, the peripheral non-crosslinked region in the same cornea, and the central cornea of the non-crosslinked contralateral eye. No change in corneal thickness was detected after one month, however the stromal thickness surpassed its original baseline thickness at three months by 25.9 µm. Corneal haze peaked at one month and then began to clear. Increased CAF was detected in all CXL corneas, localized to the anterior stroma and extending to 42.4 ±â€¯3.4% and 47.7 ±â€¯7.6% of the corneal thickness at one and three months. There was a significant (P < 0.05) reduction in CFC in the CAF region in all eyes averaging 1.007 ±â€¯0.006 and 1.009 ±â€¯0.005 in one and three month samples compared to 1.017 ±â€¯0.04 and 1.016 ±â€¯0.06 for controls. These results indicate that there is a significant reduction in collagen crimping following UVA CXL of approximately 1%. One possible explanation for this loss of crimping could be shortening of the collagen fibers over the CXL region.

Axial mechanical and structural characterization of keratoconus corneas.

PURPOSE: Previous studies indicate that there is an axial gradient of collagen lamellar branching and anastomosing leading to regional differences in corneal tissue stiffness that may control corneal shape. To further test this hypothesis we have measured the axial material stiffness and quantified the collagen lamellar complexity in ectatic and mechanically weakened keratoconus corneas (KC). METHODS: Acoustic radiation force elastic microscopy (ARFEM) was used to probe the axial mechanical properties of the cone region of three donor KC buttons. 3 Dimensional second harmonic generation microscopy (3D-SHG) was used to qualitatively evaluate lamellar organization in 3 kC buttons and quantitatively measure lamellar branching point density (BPD) in a separate KC button that had been treated with epikeratophakia (Epi-KP). RESULTS: The mean elastic modulus for the KC corneas was 1.67 ±â€¯0.44 kPa anteriorly and 0.970 ±â€¯0.30 kPa posteriorly, substantially below that previously measured for normal human cornea. 3D-SHG of KC buttons showed a simplified collagen lamellar structure lacking noticeable angled lamellae in the region of the cone. BPD in the anterior, posterior, central and paracentral regions of the KC cornea were significantly lower than in the overlying Epi-KP lenticule. Additionally, BPD in the cone region was significantly lower than the adjacent paracentral region in the KC button. CONCLUSIONS: The KC cornea exhibits an axial gradient of mechanical stiffness and a BPD that appears substantially lower in the cone region compared to normal cornea. The findings reinforce the hypothesis that collagen architecture may control corneal mechanical stiffness and hence corneal shape.