Morphological diversity of the velamen radicum in the genus Anthurium (Araceae).

Epiphytes develop anatomical features to improve efficiency of the uptake of water and nutrients, such as absorptive foliar scales or a velamen radicum. Despite substantial studies on the occurrence, morphology, development and phylogeny of the velamen, most of the available literature is focused on Orchidaceae, making current knowledge on velamen clearly biased. A recent publication firmly established that velamina are common in Anthurium species. Thus, this study provides further insights by describing velamen morphological characteristics of Anthurium species and classifying them into different velamen types. Furthermore, we investigate if the different velamen morphological traits are clade-specific and phylogenetically conserved within the genus. Using SEM, we performed a morphological study on 89 Anthurium species, describing six micromorphological traits of velamen and exodermis, following traits used to classify Orchidaceae velamen by Porembski & Barthlott (1988). We distinguished nine velamen types, including two that are unique to Anthurium and not similar to any type found in Orchidaceae. Comparing velamen morphology within the phylogenetic tree of Anthurium revealed clear phylogenetic signals. This study provides detailed morphological descriptions among 89 species of Anthurium from the Araceae, and substantially broadens our knowledge of this tissue. However, velamen function has been even less studied, with hardly anything known about functional significance of having secondary cell wall thickening and perforations on velamen cell walls. Therefore, a logical next step would be to connect these anatomical features to their functions.

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis.

This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

Elastic Net Regularization Paths for All Generalized Linear Models.

The lasso and elastic net are popular regularized regression models for supervised learning. Friedman, Hastie, and Tibshirani (2010) introduced a computationally efficient algorithm for computing the elastic net regularization path for ordinary least squares regression, logistic regression and multinomial logistic regression, while Simon, Friedman, Hastie, and Tibshirani (2011) extended this work to Cox models for right-censored data. We further extend the reach of the elastic net-regularized regression to all generalized linear model families, Cox models with (start, stop] data and strata, and a simplified version of the relaxed lasso. We also discuss convenient utility functions for measuring the performance of these fitted models.

Feature-weighted elastic net: using "features of features" for better prediction.

In some supervised learning settings, the practitioner might have additional information on the features used for prediction. We propose a new method which leverages this additional information for better prediction. The method, which we call the feature-weighted elastic net ("fwelnet"), uses these "features of features" to adapt the relative penalties on the feature coefficients in the elastic net penalty. In our simulations, fwelnet outperforms the lasso in terms of test mean squared error and usually gives an improvement in true positive rate or false positive rate for feature selection. We also apply this method to early prediction of preeclampsia, where fwelnet outperforms the lasso in terms of 10-fold cross-validated area under the curve (0.86 vs. 0.80). We also provide a connection between fwelnet and the group lasso and suggest how fwelnet might be used for multi-task learning.

Reliability and Validity of Personal Stigma Scale Among University Students in Singapore.

OBJECTIVES: The study aims to examine the reliability and validity of the Personal Stigma Scale and the intrapersonal variables that affect the personal stigma level towards depression among university students in Singapore. METHODS: University students aged 18 to 24 years old who were literate in English were recruited using convenience sampling through online platforms. Participants' stigma towards depression was assessed using the Personal Stigma Scale. Each item of the scale was rated from 1 (strongly disagree) to 5 (strongly agree); higher scores indicate higher stigmatising attitudes. Internal consistency, divergent validity, construct validity, and test-retest reliability of the Personal Stigma Scale were evaluated, as were the intrapersonal variables that affect the personal stigma level towards depression. RESULTS: Personal stigma level was significantly lower in female participants, those from healthcare faculties, those with prior contact with healthcare professionals, those without mental health disorders, and those with loved ones with mental health disorders. Exploratory factor analysis revealed a three-factor structure. The Personal Stigma Scale was negatively correlated with the Depression Literacy Scale weakly and the Anxiety Literacy Scale moderately. Cronbach's alpha for internal consistency was 0.71 for the overall scale and 0.18 to 0.67 for subscales. Test-retest reliability coefficient was 0.74 at 2 weeks and 0.70 at 2 months. CONCLUSION: The Personal Stigma Scale is valid and reliable to measure self-stigma towards depression among university students in Singapore. Nonetheless, further studies are required to examine its factor structure, especially among various cultures and populations.

High resolution front-side visualization of charge stored in EEPROM with scanning nonlinear dielectric microscopy (SNDM).

By exposing floating gates of EEPROM memory cells with frontside sample preparation, scanning nonlinear dielectric microscopy (SNDM) succeeded in reading back the data stored in the memory cells with a 250 nm node size. At an optimized voltage bias of AC = 3 V and DC = 1 V, a clear signal contrast between programmed and erased cells is obtained. The high resolution SNDM signal reveals the details of bowling-pin shape structure of memory cells, providing high confidence in data assignment during forensic applications. Such high resolution also makes SNDM a promising technique for newer generation devices with smaller node size.

Probiotic therapy for periodontal and peri-implant health - silver bullet or sham?

Probiotics are thought to be beneficial microbes that influence health-related outcomes through host immunomodulation and modulation of the bacteriome. Its reported success in the treatment of gastrointestinal disorders has led to further research on its potential applicability within the dental field due to similarities such as a polymicrobial aetiology and disease associated microbial-shifts. Although the literature is replete with studies demonstrating its efficacy, the use of probiotics in dentistry continues to polarise opinion. Here, we explore the evidence for probiotics and its effect on periodontal and peri-implant health. MEDLINE, EMBASE, and CENTRAL were systemically searched from June 2010 to June 2020 based on a formulated search strategy. Of 1,956 potentially relevant articles, we selected 27 double-blinded randomised clinical trials in the areas of gingivitis, periodontitis, residual pockets during supportive periodontal therapy, and peri-implant diseases, and reviewed their efficacy in these clinical situations. We observed substantial variation in treatment results and protocols between studies. Overall, the evidence for probiotic therapy for periodontal and peri-implant health appears unconvincing. The scarcity of trials with adequate power and follow-up precludes any meaningful clinical recommendations. Thus, the routine use of probiotics for these purposes are currently unsubstantiated. Further multi-centre trials encompassing a standardised investigation on the most promising strains and administration methods, with longer observation times are required to confirm the benefits of probiotic therapy for these applications.

Neural network predicts need for red blood cell transfusion for patients with acute gastrointestinal bleeding admitted to the intensive care unit.

Acute gastrointestinal bleeding is the most common gastrointestinal cause for hospitalization. For high-risk patients requiring intensive care unit stay, predicting transfusion needs during the first 24 h using dynamic risk assessment may improve resuscitation with red blood cell transfusion in admitted patients with severe acute gastrointestinal bleeding. A patient cohort admitted for acute gastrointestinal bleeding (N = 2,524) was identified from the Medical Information Mart for Intensive Care III (MIMIC-III) critical care database and separated into training (N = 2,032) and internal validation (N = 492) sets. The external validation patient cohort was identified from the eICU collaborative database of patients admitted for acute gastrointestinal bleeding presenting to large urban hospitals (N = 1,526). 62 demographic, clinical, and laboratory test features were consolidated into 4-h time intervals over the first 24 h from admission. The outcome measure was the transfusion of red blood cells during each 4-h time interval. A long short-term memory (LSTM) model, a type of Recurrent Neural Network, was compared to a regression-based models on time-updated data. The LSTM model performed better than discrete time regression-based models for both internal validation (AUROC 0.81 vs 0.75 vs 0.75; P < 0.001) and external validation (AUROC 0.65 vs 0.56 vs 0.56; P < 0.001). A LSTM model can be used to predict the need for transfusion of packed red blood cells over the first 24 h from admission to help personalize the care of high-risk patients with acute gastrointestinal bleeding.

Bridging the communication gap between radiographers and patients to improve chest radiography image acquisition: A multilingual solution in the COVID-19 pandemic.

INTRODUCTION: A chest X-ray (CXR), taken in full inspiration, is important to ensure pathology in the lungs will not be missed. To achieve this, effective communication on breathing instructions for patients is crucial. During the COVID-19 pandemic, radiographers in Sengkang General Hospital (SKH) were challenged when performing CXR for the patients whose native language is not English. Most of these patients were foreign workers living in the same dormitory which had formed the largest COVID-19 cluster in Singapore. These dormitory residents found it difficult to understand and adhere to breathing instructions, resulting in a suboptimal degree of inspiration when the CXRs were taken. This may ultimately affect the diagnostic value of the radiographs. This paper aims to share and evaluate how radiographers tackled this issue and continued to acquire fully-inspired CXR for the dormitory residents despite the language barrier. METHODS: Using a combination of online survey and retrospective analysis of the rejection rates of CXR done over the period of early April to early June, a team of radiographers evaluated the effectiveness of using audio recordings in managing the issue of not achieving a fully inspired CXR for patients due to language barrier. RESULTS: The rejection rate for CXR due to suboptimal inspiration decreased from 26% to 9% upon implementation of the audio recordings. 92.3% of the CXRs taken within this period also fulfilled the criteria of a fully-inspired CXR, as evidenced by having at least 9 posterior ribs seen above the right hemi-diaphragm. Survey results found a fairly balanced number of radiographers who agreed and disagreed that a fully-inspired CXR was achieved for most of their patients after utilisation of translation manuals and audio recordings. CONCLUSION: After the implementation of audio recordings, the decrease in rejection rate of CXR and an audit which demonstrated that CXR quality was upheld had proven that the radiographers successfully achieved fully-inspired CXR for suspected COVID-19 patients. This confirmed that using pre-recorded audio instructions was an efficient intervention albeit being a one-way communication, leads to more accurate imaging results, aligning with existing literature on communication experiences between radiographers and patients. Moreover, the decreased rejection rate of CXRs had increased department efficiency consequently reducing departmental expenses in the long run. IMPLICATIONS OF PRACTICE: Given that we have an ageing population and the vast majority of the elderly converse in their various dialects, positive feedback from radiographers presented opportunities to expand the translation manual and audio recordings to include local dialects. These can be seamlessly integrated in CXR and other procedures in the hospital setting. To ensure that the translations are culturally sensitive, attention should be paid to the translation process of instructions into other languages and local dialects by enlisting the help of native speakers.

Editor's Choice - Decrease in Mortality from Abdominal Aortic Aneurysms (2001 to 2015): Is it Decreasing Even Faster?

OBJECTIVE: The early twenty first century witnessed a decrease in mortality from abdominal aortic aneurysms (AAA), which was associated with variations in the prevalence of cardiovascular risk factors. This study investigated whether these trends continued into the second decade of the twenty first century. METHODS: Information on AAA mortality (2001 - 2015) using International Classification of Diseases codes was extracted from the World Health Organization (WHO) mortality database. Data on risk factors were extracted from the Institute of Health Metrics and Evaluation and WHO InfoBase, and data on population from the World Development Indicators database. Regression analysis of temporal trends in cardiovascular risk factors was done independently for correlations with AAA mortality trends. RESULTS: Seventeen countries across four continents met the inclusion criteria (Australasia, two; Europe, 11; North America, two; Asia, two). Male AAA mortality decreased in 13 countries (population weighted average: -2.84%), while female AAA mortality decreased in 11 countries (population weighted average: -1.64%). The decrease in AAA mortality was seen in both younger (< 65 years) and older (> 65 years) patients. The decrease in AAA mortality was more marked in the second decade of the twenty first century (2011 - 2015) compared with the first decade (2001 - 2005 and 2006 - 2010). Trends in AAA mortality positively correlated with smoking (males: p = .03X, females: p = .001) and hypertension (males: p = .001, females: p = .01X). Conversely, AAA mortality negatively correlated with obesity (males: p = .001, females: p = .001), while there was no significant correlation with diabetes. CONCLUSION: AAA mortality has continued to decline and seems to have declined at an even faster rate in the second decade of the twenty first century, albeit with heterogeneity among countries. These variations are multifactorial in origin but further efforts targeting smoking cessation and blood pressure control will probably contribute to continued reductions in AAA mortality.

Early identification of patients with acute gastrointestinal bleeding using natural language processing and decision rules.

BACKGROUND AND AIM: Guidelines recommend risk stratification scores in patients presenting with gastrointestinal bleeding (GIB), but such scores are uncommonly employed in practice. Automation and deployment of risk stratification scores in real time within electronic health records (EHRs) would overcome a major impediment. This requires an automated mechanism to accurately identify ("phenotype") patients with GIB at the time of presentation. The goal is to identify patients with acute GIB by developing and evaluating EHR-based phenotyping algorithms for emergency department (ED) patients. METHODS: We specified criteria using structured data elements to create rules for identifying patients and also developed multiple natural language processing (NLP)-based approaches for automated phenotyping of patients, tested them with tenfold cross-validation for 10 iterations (n = 7144) and external validation (n = 2988) and compared them with a standard method to identify patient conditions, the Systematized Nomenclature of Medicine. The gold standard for GIB diagnosis was the independent dual manual review of medical records. The primary outcome was the positive predictive value. RESULTS: A decision rule using GIB-specific terms from ED triage and ED review-of-systems assessment performed better than the Systematized Nomenclature of Medicine on internal validation and external validation (positive predictive value = 85% confidence interval:83%-87% vs 69% confidence interval:66%-72%; P < 0.001). The syntax-based NLP algorithm and Bidirectional Encoder Representation from Transformers neural network-based NLP algorithm had similar performance to the structured-data fields decision rule. CONCLUSIONS: An automated decision rule employing GIB-specific triage and review-of-systems terms can be used to trigger EHR-based deployment of risk stratification models to guide clinical decision making in real time for patients with acute GIB presenting to the ED.

Pre-Transplant Marital Status and Hematopoietic Cell Transplantation Outcomes

Background: Evidence about the impact of marital status before hematopoietic cell transplantation (hct) on outcomes after hct is conflicting. Methods: We identified patients 40 years of age and older within the Center for International Blood and Marrow Transplant Research registry who underwent hct between January 2008 and December 2015. Marital status before hct was declared as one of: married or living with a partner, single (never married), separated or divorced, and widowed. We performed a multivariable analysis to determine the association of marital status with outcomes after hct. Results: We identified 10,226 allogeneic and 5714 autologous hct cases with, respectively, a median follow-up of 37 months (range: 1-102 months) and 40 months (range: 1-106 months). No association between marital status and overall survival was observed in either the allogeneic (p = 0.58) or autologous (p = 0.17) setting. However, marital status was associated with grades 2-4 acute graft-versus-host disease (gvhd), p < 0.001, and chronic gvhd, p = 0.04. The risk of grades 2-4 acute gvhd was increased in separated compared with married patients [hazard ratio (hr): 1.13; 95% confidence interval (ci): 1.03 to 1.24], and single patients had a reduced risk of grades 2-4 acute gvhd (hr: 0.87; 95% ci: 0.77 to 0.98). The risk of chronic gvhd was lower in widowed compared with married patients (hr: 0.82; 95% ci: 0.67 to 0.99). Conclusions: Overall survival after hct is not influenced by marital status, but associations were evident between marital status and grades 2-4 acute and chronic gvhd. To better appreciate the effects of marital status and social support, future research should consider using validated scales to measure social support and patient and caregiver reports of caregiver commitment, and to assess health-related quality of life together with health care utilization.

Reluctant Generalised Additive Modelling.

Sparse generalised additive models (GAMs) are an extension of sparse generalised linear models that allow a model's prediction to vary non-linearly with an input variable. This enables the data analyst build more accurate models, especially when the linearity assumption is known to be a poor approximation of reality. Motivated by reluctant interaction modelling, we propose a multi-stage algorithm, called reluctant generalised additive modelling (RGAM), that can fit sparse GAMs at scale. It is guided by the principle that, if all else is equal, one should prefer a linear feature over a non-linear feature. Unlike existing methods for sparse GAMs, RGAM can be extended easily to binary, count and survival data. We demonstrate the method's effectiveness on real and simulated examples.

Validation of a Machine Learning Model That Outperforms Clinical Risk Scoring Systems for Upper Gastrointestinal Bleeding.

BACKGROUND & AIMS: Scoring systems are suboptimal for determining risk in patients with upper gastrointestinal bleeding (UGIB); these might be improved by a machine learning model. We used machine learning to develop a model to calculate the risk of hospital-based intervention or death in patients with UGIB and compared its performance with other scoring systems. METHODS: We analyzed data collected from consecutive unselected patients with UGIB from medical centers in 4 countries (the United States, Scotland, England, and Denmark; n = 1958) from March 2014 through March 2015. We used the data to derive and internally validate a gradient-boosting machine learning model to identify patients who met a composite endpoint of hospital-based intervention (transfusion or hemostatic intervention) or death within 30 days. We compared the performance of the machine learning prediction model with validated pre-endoscopic clinical risk scoring systems (the Glasgow-Blatchford score [GBS], admission Rockall score, and AIMS65). We externally validated the machine learning model using data from 2 Asia-Pacific sites (Singapore and New Zealand; n = 399). Performance was measured by area under receiver operating characteristic curve (AUC) analysis. RESULTS: The machine learning model identified patients who met the composite endpoint with an AUC of 0.91 in the internal validation set; the clinical scoring systems identified patients who met the composite endpoint with AUC values of 0.88 for the GBS (P = .001), 0.73 for Rockall score (P < .001), and 0.78 for AIMS65 score (P < .001). In the external validation cohort, the machine learning model identified patients who met the composite endpoint with an AUC of 0.90, the GBS with an AUC of 0.87 (P = .004), the Rockall score with an AUC of 0.66 (P < .001), and the AIMS65 with an AUC of 0.64 (P < .001). At cutoff scores at which the machine learning model and GBS identified patients who met the composite endpoint with 100% sensitivity, the specificity values were 26% with the machine learning model versus 12% with GBS (P < .001). CONCLUSIONS: We developed a machine learning model that identifies patients with UGIB who met a composite endpoint of hospital-based intervention or death within 30 days with a greater AUC and higher levels of specificity, at 100% sensitivity, than validated clinical risk scoring systems. This model could increase identification of low-risk patients who can be safely discharged from the emergency department for outpatient management.

Physical Function and Strength in Relation to Inflammation in Older Adults with Obesity and Increased Cardiometabolic Risk.

BACKGROUND: Inflammation is implicated in functional decline and the development of disability in aging. This study aimed to investigate the association of inflammation with physical function and muscle strength in older adults with obesity and increased cardiometabolic risk. DESIGN: In baseline assessments from the CROSSROADS randomized controlled trial, serum interleukin-6 (IL-6), tumor necrosis factor-α (TNFα) and C-reactive protein (hs-CRP) were assayed in 163 older adults (37% males, 24% African American, BMI 34±3, age 70±5yrs) with hypertension, dyslipidemia and/or diabetes. Physical function was assessed by six-minute walk test (6MWT), chair sit-and-reach (CSR), hand-grip and knee-extension strength; specific-strength as muscle strength/mass ratio. Analyses included ANCOVA and multiple linear regression adjusted for thigh skeletal muscle (MRI), arm lean mass (DXA) and moderate-to-vigorous intensity physical activity (MVPA; accelerometry). RESULTS: Higher hs-CRP (p<0.01) and IL-6 (p=0.07) were associated with lower 6MWT and CSR, respectively. A composite inflammation score combining all 3 inflammatory markers showed the strongest inverse association with 6MWT (p<0.01). MVPA moderated associations such that amongst participants who engaged in low MVPA, 6MWT distances and CSR scores were significantly lower in those with high IL-6 and TNFα (p<0.05), respectively. In participants with high MVPA, higher hs-CRP (p<0.05) and TNFα (p=0.07) were associated with poorer upper-extremity specific-strength. CONCLUSIONS: Chronic inflammation was associated with poorer physical function and specific strength in older adults with obesity and increased cardiometabolic risk. This association was strongest in participants with multiple elevated inflammatory markers. Physical activity levels below current recommendations mitigated the deleterious effects of inflammation on lower body mobility, underscoring the benefits of exercise for preserving physical function with age.

Artificial neural network modelling for organic and total nitrogen removal of aerobic granulation under steady-state condition.

Aerobic granulation is a recent technology with high level of complexity and sensitivity to environmental and operational conditions. Artificial neural networks (ANNs), computational tools capable of describing complex non-linear systems, are the best fit to simulate aerobic granular bioreactors. In this study, two feedforward backpropagation ANN models were developed to predict chemical oxygen demand (Model I) and total nitrogen removal efficiencies (Model II) of aerobic granulation technology under steady-state condition. Fundamentals of ANN models and the steps to create them were briefly reviewed. The models were respectively fed with 205 and 136 data points collected from laboratory-, pilot-, and full-scale studies on aerobic granulation technology reported in the literature. Initially, 60%, 20%, and 20%, and 80%, 10%, and 10% of the points in the corresponding datasets were randomly chosen and used for training, testing, and validation of Model I, and Model II, respectively. Overall coefficient of determination (R2) value and mean squared error (MSE) of the two models were initially 0.49 and 15.5, and 0.37 and 408, respectively. To improve the model performance, two data division methods were used. While one method is generic and potentially applicable to other fields, the other can only be applied to modelling the performance of aerobic granular reactors. R2 value and MSE were improved to 0.90 and 2.54, and 0.81 and 121.56, respectively, after applying the new data division methods. The results demonstrated that ANN-based models were capable simulation approach to predict a complicated process like aerobic granulation.

Patient eligibility for hematopoietic stem cell transplantation: a review of patient-associated variables.

Assessing patient eligibility for hematopoietic stem cell transplantation (HSCT) remains a complex, multifaceted challenge. Among these challenges, the paucity of comprehensive clinical data to guide decision making remains problematic coupled with unclear trade-offs between patient, disease and local HSCT center factors. Moreover, it is unclear that the modification of poor patient characteristics will improve post-HSCT outcomes. However, the use of Comorbidity Indices and Comprehensive Geriatric Assessments helps meet this challenge, but may be limited by overlapping patient characteristics. The increasing consideration for pre-HSCT psychosocial assessments and interventions remains to be studied. Ultimately, the decision to proceed with a HSCT remains interdisciplinary while considering the available evidence discussed in this review.

Maternal Serum B Cell activating factor in hypertensive and normotensive pregnancies.

OBJECTIVES: The objective of this study was the analysis of B-Cell Activating Factor (BAFF) levels in pregnancies affected by PE, and in pregnancies affected by fetal growth restriction without Hypertensive disorders and its possible correlation with pulse wave velocity and cardiac output. STUDY DESIGN: Prospective study of 69 women at 24-40 weeks gestation. Haemodynamic function was assessed in those with Pre-eclampsia (PE, n = 19), fetal growth restriction (FGR, n = 10) and healthy pregnancies (n = 40). Maternal venous BAFF levels at recruitment were measured using ELISA. We analysed the relationship between BAFF and cardiac output (CO), and BAFF and PWV (pulse wave velocity); the gold standard for assessing arterial stiffness. PWV was measured with an oscillometric device and CO using inert gas rebreathing technique. PWV and CO were converted to gestation adjusted indices (z scores). MAIN OUTCOME MEASURES: The association between BAFF levels in PE and FGR, and the relationship of BAFF with PWV and CO. RESULTS: BAFF was higher in PE (p = 0.03) but not in FGR (p = 0.83) when compared to healthy pregnancies. There was a positive correlation between BAFF levels and z score PWV (r = 0.25, p = 0.04), but not CO (r = -0.01, p = 0.91). BAFF levels did not change with gestational age. (r = 0.012, p = 0.925). CONCLUSIONS: These findings provide evidence of a possible contribution of BAFF to both maternal inflammation and arterial dysfunction associated with PE. Though no relationship was found with another disorder of placentation: normotensive FGR, this condition is not thought to be associated with maternal inflammation.

Network geometry of evidence from randomised controlled trials addressing donor selection and source of haematopoietic progenitor cells used in allogeneic transplantation: a systematic scoping review.

BACKGROUND AND METHODS: A scoping review of randomised controlled trials (RCTs) addressing source of cells and choice of donor for allogeneic haematopoietic cell transplantation (HCT) was performed to create a network of best evidence that allows us to identify new potential indirect comparisons for the strategic development of future studies that connect to the existing evidence network. RESULTS: A total of 19 eligible RCTs (2589 total patients) were identified. Nine studies (1566 patients) compared clinical outcomes following the use of peripheral blood progenitor cells (PBPCs) with bone marrow (BM) from matched related donors (eight studies) or matched unrelated donors (one study). The remaining studies compared BM or PBPCs with various methods of BM stimulation or manipulation (six studies), compared different methods of surface molecule-based selection and/or depletion of grafts (two studies) or compared the optimal number of units for paediatric cord blood transplantation (two studies). No published RCTs compared different types of donors. The geometry of the evidence network was analysed to identify opportunities for potential novel indirect comparisons and to identify opportunities to expand the network. Few indirect comparisons are currently feasible due to small sample size and heterogeneity in patient diagnoses and demographics between treatment nodes in the network. CONCLUSION: More RCTs that enrol greater numbers of similar patients are needed to leverage the current evidence network concerning donor choice and source of cells used in allogeneic HCT.