Screening for Autism Spectrum Disorder in Young Children: Still Not Enough Evidence.

BACKGROUND: Early detection of autism spectrum disorder (ASD) has the potential to significantly reduce the impact of the condition, however previous reviews have found little evidence to support screening programs for ASD in young children. METHODS: We conducted a review with the aim of updating evidence on 3 aspects: (a) diagnostic stability of ASD in young children; (b) accuracy of ASD screening tools in young children; and (c) the benefits of early interventions in screen-detected young children with ASD. RESULTS: A total of 33 studies were included in our review. Five studies looking at diagnostic stability reported estimates ranging from 71.9% to 100%, however the majority only included a follow-up of 24 months and all studies raised concerns regarding the risk of bias due particularly to lack of blinding, sample size, and patient flow. A total of 25 studies, reported in 26 articles, were identified that reported accuracy data on 11 screening tools. Most of the reports were concerned with versions of M-CHAT, reporting sensitivity estimates from 0.67 to 1.0; however, many of these were deemed to be of high risk of bias due to lack of blinding and follow-up. Four studies reported on early interventions in screen-detected children; however, the majority did not find significant improvements on the relevant outcomes. CONCLUSIONS: Overall, the evidence on screening for ASD in young children captured by this review is not conclusive regarding the 3 aspects of screening in this population. Future studies should attempt to ensure blinded diagnostic assessments, include longer follow-up periods and limit attrition.

Accuracy of an artificial intelligence as a medical device as part of a UK-based skin cancer teledermatology service.

Introduction: An artificial intelligence as a medical device (AIaMD), built on convolutional neural networks, has demonstrated high sensitivity for melanoma. To be of clinical value, it needs to safely reduce referral rates. The primary objective of this study was to demonstrate that the AIaMD had a higher rate of correctly classifying lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatology standard of care (SoC), while achieving the same sensitivity to detect malignancy. Secondary endpoints included the sensitivity, specificity, positive and negative predictive values, and number needed to biopsy to identify one case of melanoma or squamous cell carcinoma (SCC) by both the AIaMD and SoC. Methods: This prospective, single-centre, single-arm, masked, non-inferiority, adaptive, group sequential design trial recruited patients referred to a teledermatology cancer pathway (clinicaltrials.gov NCT04123678). Additional dermoscopic images of each suspicious lesion were taken using a smartphone with a dermoscopic lens attachment. The images were assessed independently by a consultant dermatologist and the AIaMD. The outputs were compared with the final histological or clinical diagnosis. Results: A total of 700 patients with 867 lesions were recruited, of which 622 participants with 789 lesions were included in the per-protocol (PP) population. In total, 63.3% of PP participants were female; 89.0% identified as white, and the median age was 51 (range 18-95); and all Fitzpatrick skin types were represented including 25/622 (4.0%) type IV-VI skin. A total of 67 malignant lesions were identified, including 8 diagnosed as melanoma. The AIaMD sensitivity was set at 91 and 92.5%, to match the literature-defined clinician sensitivity (91.46%) as closely as possible. In both settings, the AIaMD identified had a significantly higher rate of identifying lesions that did not need a biopsy or urgent referral compared to SoC (p-value = 0.001) with comparable sensitivity for skin cancer. Discussion: The AIaMD identified significantly more lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatologists. This has the potential to reduce the burden of unnecessary referrals when used as part of a teledermatology service.

Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review.

OBJECTIVES: To systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening. METHODS: We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively. RESULTS: Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was <80% for all ML systems and in 6/31 studies evaluating DL systems. Studies reported high accuracy for detection of ungradable images, but the latter were analysed and reported inconsistently. Seven studies reported that AI was more sensitive but less specific than human graders. CONCLUSIONS: AI-based systems are more sensitive than human graders and could be safe to use in clinical practice but have variable specificity. However, for many systems evidence is limited, at high risk of bias and may not generalise across settings. Therefore, pre-implementation assessment in the target clinical pathway is essential to obtain reliable and applicable accuracy estimates.

The impact of ultrasound-based antenatal screening strategies to detect vasa praevia in the United Kingdom: An exploratory study using decision analytic modelling methods.

The objective of this exploratory modelling study was to estimate the effects of second-trimester, ultrasound-based antenatal detection strategies for vasa praevia (VP) in a hypothetical cohort of pregnant women. For this, a decision-analytic tree model was developed covering four discrete detection pathways/strategies: no screening; screening targeted at women undergoing in-vitro fertilisation (IVF); screening targeted at women with low-lying placentas (LLP); screening targeted at women with velamentous cord insertion (VCI) or a bilobed or succenturiate (BL/S) placenta. Main outcome measures were the number of referrals to transvaginal sonography (TVS), diagnosed and undiagnosed cases of VP, overdetected cases of VCI, and VP-associated perinatal mortality. The greatest number of referrals to TVS occurred in the LLP-based (2,083) and VCI-based screening (1,319) pathways. These two pathways also led to the highest proportions of pregnancies diagnosed with VP (VCI-based screening: 552 [78.9% of all pregnancies]; LLP-based: 371 [53.5%]) and the lowest proportions of VP leading to perinatal death (VCI-based screening: 100 [14.2%]; LLP-based: 196 [28.0%]). In contrast, the IVF-based pathway resulted in 66 TVS referrals, 50 VP diagnoses (7.1% of all VP pregnancies), and 368 (52.6%) VP-associated perinatal deaths which was comparable to the no screening pathway (380 [54.3%]). The VCI-based pathway resulted in the greatest detection of VCI (14,238 [99.1%]), followed by the IVF-based pathway (443 [3.1%]); no VCI detection occurred in the LLP-based or no screening pathways. In conclusion, the model results suggest that a targeted LLP-based approach could detect a substantial proportion of VP cases, while avoiding VCI overdetection and requiring minimal changes to current clinical practice. High-quality data is required to explore the clinical and cost-effectiveness of this and other detection strategies further. This is necessary to provide a robust basis for future discussion about routine screening for VP.

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.

QUADAS-C: A Tool for Assessing Risk of Bias in Comparative Diagnostic Accuracy Studies.

Comparative diagnostic test accuracy studies assess and compare the accuracy of 2 or more tests in the same study. Although these studies have the potential to yield reliable evidence regarding comparative accuracy, shortcomings in the design, conduct, and analysis may bias their results. The currently recommended quality assessment tool for diagnostic test accuracy studies, QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies-2), is not designed for the assessment of test comparisons. The QUADAS-C (Quality Assessment of Diagnostic Accuracy Studies-Comparative) tool was developed as an extension of QUADAS-2 to assess the risk of bias in comparative diagnostic test accuracy studies. Through a 4-round Delphi study involving 24 international experts in test evaluation and a face-to-face consensus meeting, an initial version of the tool was developed that was revised and finalized following a pilot study among potential users. The QUADAS-C tool retains the same 4-domain structure of QUADAS-2 (Patient Selection, Index Test, Reference Standard, and Flow and Timing) and comprises additional questions to each QUADAS-2 domain. A risk-of-bias judgment for comparative accuracy requires a risk-of-bias judgment for the accuracy of each test (resulting from QUADAS-2) and additional criteria specific to test comparisons. Examples of such additional criteria include whether participants either received all index tests or were randomly assigned to index tests, and whether index tests were interpreted with blinding to the results of other index tests. The QUADAS-C tool will be useful for systematic reviews of diagnostic test accuracy addressing comparative questions. Furthermore, researchers may use this tool to identify and avoid risk of bias when designing a comparative diagnostic test accuracy study.

Symptom scores in the diagnosis of pediatric cow's milk protein allergy: A systematic review.

BACKGROUND: Cow's milk protein allergy (CMPA) is an immune-mediated allergic response to proteins in milk that is common in infants. Broad CMPA symptoms make diagnosis a challenge, particularly in primary care. Symptom scores may improve a clinician's awareness of symptoms, indicating a need for further testing. This systematic review examined the development and evaluation of such symptom scores for use in infants. METHODS: CENTRAL, MEDLINE, EMBASE and CINAHL databases were searched from inception to 3 December 2019 (Updated 14 November 2020) for diagnostic accuracy studies, randomised controlled trials, observational studies, economic evaluations, qualitative studies and studies reporting development of the tools. Data were not suitable for meta-analysis due to clinical and methodological heterogeneity, so were narratively synthesised. RESULTS: We found two symptom scores evaluated in one and fourteen studies, respectively. Estimated sensitivity and specificity ranged from 37% to 98% and 38% to 93%. The evaluations of each tool were at high risk of bias or failed to address issues such as clinical and cost-effectiveness. CONCLUSIONS: Estimates of accuracy of symptom scores for CMPA offered so far should be interpreted cautiously. Rigorous, conflict-free research based on well-defined roles for the tools is urgently required.

Study designs for comparative diagnostic test accuracy: A methodological review and classification scheme.

OBJECTIVES: (1) To identify and classify comparative diagnostic test accuracy (DTA) study designs; (2) to describe study design labels used by authors of comparative DTA studies. METHODS: We performed a methodological review of 100 comparative DTA studies published between 2015 and 2017, randomly sampled from studies included in 238 comparative DTA systematic reviews indexed in MEDLINE in 2017. From each study report, we extracted six design elements characterizing participant flow and the labels used by authors. RESULTS: We identified a total of 46 unique combinations of study design features in our sample, based on six design elements characterizing participant flow. We classified the studies into five study design categories based on how participants were allocated to receive each index test: 'fully paired' (n=79), 'partially paired, random subset' (n=0), 'partially paired, nonrandom subset' (n=2), 'unpaired randomized' (n=1) and 'unpaired nonrandomized' (n=3). The allocation method used in 15 studies was unclear. Sixty-one studies reported, in total, 29 unique study design labels but only four labels referred to specific design features of comparative studies. CONCLUSION: Our classification scheme can help systematic review authors define study eligibility criteria, assess risk of bias, and communicate the strength of the evidence. A standardized labelling scheme could be developed to facilitate communication of specific design features.

On the Origin of Sensitivity and Specificity.

Although it is commonly said that the notions of sensitivity and specificity were first defined by Jacob Yerushalmy in 1947, the sensitivity and specificity of diagnostic tests have been assessed as far back as the early 1900s. These notions share a common origin with the development of serology. They were originally immunologic concepts, closely associated with the development of complement fixation reactions for syphilis. Here, the authors trace how immunologic sensitivity and specificity were transformed into diagnostic sensitivity and specificity. By relocating the origins of these concepts to the early 20th century, they highlight how these origins were bound to then-commonplace assumptions about specific infectious disease entities.

Quality of life in restorative versus non-restorative resections for rectal cancer: systematic review.

BACKGROUND: Low rectal cancers could be treated using restorative (anterior resection, AR) or non-restorative procedures with an end/permanent stoma (Hartmann's, HE; or abdominoperineal excision, APE). Although the surgical choice is determined by tumour and patient factors, quality of life (QoL) will also influence the patient's future beyond cancer. This systematic review of the literature compared postoperative QoL between the restorative and non-restorative techniques using validated measurement tools. METHODS: The review was registered on PROSPERO (CRD42020131492). Embase and MEDLINE, along with grey literature and trials websites, were searched comprehensively for papers published since 2012. Inclusion criteria were original research in an adult population with rectal cancer that reported QoL using a validated tool, including the European Organization for Research and Treatment of Cancer QLQ-CR30, QLQ-CR29, and QLQ-CR38. Studies were included if they compared AR with APE (or HE), independent of study design. Risk of bias was assessed using the Risk Of Bias In Non-Randomized Studies of Interventions (ROBINS-I) tool. Outcomes of interest were: QoL, pain, gastrointestinal (GI) symptoms (stool frequency, flatulence, diarrhoea and constipation), and body image. RESULTS: Nineteen studies met the inclusion criteria with a total of 6453 patients; all papers were observational and just four included preoperative evaluations. There was no identifiable difference in global QoL and pain between the two surgical techniques. Reported results regarding GI symptoms and body image documented similar findings. The ROBINS-I tool highlighted a significant risk of bias across the studies. CONCLUSION: Currently, it is not possible to draw a firm conclusion on postoperative QoL, pain, GI symptoms, and body image following restorative or non-restorative surgery. The included studies were generally of poor quality, lacked preoperative evaluations, and showed considerable bias in the data.

Development, validation and effectiveness of diagnostic prediction tools for colorectal cancer in primary care: a systematic review.

BACKGROUND: Tools based on diagnostic prediction models are available to help general practitioners (GP) diagnose colorectal cancer. It is unclear how well they perform and whether they lead to increased or quicker diagnoses and ultimately impact on patient quality of life and/or survival. The aim of this systematic review is to evaluate the development, validation, effectiveness, and cost-effectiveness, of cancer diagnostic tools for colorectal cancer in primary care. METHODS: Electronic databases including Medline and Web of Science were searched in May 2017 (updated October 2019). Two reviewers independently screened titles, abstracts and full-texts. Studies were included if they reported the development, validation or accuracy of a prediction model, or assessed the effectiveness or cost-effectiveness of diagnostic tools based on prediction models to aid GP decision-making for symptomatic patients presenting with features potentially indicative of colorectal cancer. Data extraction and risk of bias were completed by one reviewer and checked by a second. A narrative synthesis was conducted. RESULTS: Eleven thousand one hundred thirteen records were screened and 23 studies met the inclusion criteria. Twenty-studies reported on the development, validation and/or accuracy of 13 prediction models: eight for colorectal cancer, five for cancer areas/types that include colorectal cancer. The Qcancer models were generally the best performing. Three impact studies met the inclusion criteria. Two (an RCT and a pre-post study) assessed tools based on the RAT prediction model. The third study looked at the impact of GP practices having access to RAT or Qcancer. Although the pre-post study reported a positive impact of the tools on outcomes, the results of the RCT and cross-sectional survey found no evidence that use of, or access to, the tools was associated with better outcomes. No study evaluated cost effectiveness. CONCLUSIONS: Many prediction models have been developed but none have been fully validated. Evidence demonstrating improved patient outcome of introducing the tools is the main deficiency and is essential given the imperfect classification achieved by all tools. This need is emphasised by the equivocal results of the small number of impact studies done so far.

Risk of bias assessment of test comparisons was uncommon in comparative accuracy systematic reviews: an overview of reviews.

OBJECTIVES: Comparative diagnostic test accuracy systematic reviews (DTA reviews) assess the accuracy of two or more tests and compare their diagnostic performance. We investigated how comparative DTA reviews assessed the risk of bias (RoB) in primary studies that compared multiple index tests. STUDY DESIGN AND SETTING: This is an overview of comparative DTA reviews indexed in MEDLINE from January 1st to December 31st, 2017. Two assessors independently identified DTA reviews including at least two index tests and containing at least one statement in which the accuracy of the index tests was compared. Two assessors independently extracted data on the methods used to assess RoB in studies that directly compared the accuracy of multiple index tests. RESULTS: We included 238 comparative DTA reviews. Only two reviews (0.8%, 95% confidence interval 0.1 to 3.0%) conducted RoB assessment of test comparisons undertaken in primary studies; neither used an RoB tool specifically designed to assess bias in test comparisons. CONCLUSION: Assessment of RoB in test comparisons undertaken in primary studies was uncommon in comparative DTA reviews, possibly due to lack of existing guidance on and awareness of potential sources of bias. Based on our findings, guidance on how to assess and incorporate RoB in comparative DTA reviews is needed.

Real-world evaluation of a novel technology for quantitative simultaneous antibody detection against multiple SARS-CoV-2 antigens in a cohort of patients presenting with COVID-19 syndrome.

An evaluation of a rapid portable gold-nanotechnology measuring SARS-CoV-2 IgM, IgA and IgG antibody concentrations against spike 1 (S1), spike 2 (S) and nucleocapsid (N) was conducted using serum samples from 74 patients tested for SARS-CoV-2 RNA on admission to hospital, and 47 historical control patients from March 2019. 59 patients were RNA(+) and 15 were RNA(-). A serum (±) classification was derived for all three antigens and a quantitative serological profile was obtained. Serum(+) was identified in 30% (95% CI 11-48) of initially RNA(-) patients, in 36% (95% CI 17-54) of RNA(+) patients before 10 days, 77% (95% CI 67-87) between 10 and 20 days and 95% (95% CI 86-100) after 21 days. The patient-level diagnostic accuracy relative to RNA(±) after 10 days displayed 88% sensitivity (95% CI 75-95) and 75% specificity (95% CI 22-99), although specificity compared with historical controls was 100% (95%CI 91-100). This study provides robust support for further evaluation and validation of this novel technology in a clinical setting and highlights challenges inherent in assessment of serological tests for an emerging disease such as COVID-19.

Accuracy and precision analysis for a biophotonic assay of C-reactive protein.

A multiplexed biophotonic assay platform has been developed using the localised particle plasmon in gold nanoparticles assembled in an array and functionalised for two assays: total IgG and C-reactive protein (CRP). A protein A/G (PAG) assay, calibrated with a NIST reference material, shows a maximum surface coverage of θmax = 7.13 ± 0.19 mRIU, equivalent to 1.5 ng mm-2 of F(ab)-presenting antibody. The CRP capture antibody has an equivalent surface binding density of θmax = 2.95 ± 0.41 mRIU indicating a 41% capture antibody availability. Free PAG binding to the functionalised anti-CRP surface shows that only 47 ± 3% of CRP capture antibodies are correctly presenting Fab regions for antigen capture. The accuracy and precision of the CRP sensor assay was assessed with 54 blood samples containing spiked CRP in the range 2-160 mg L-1. The mean accuracy was 0.42 mg L-1 with Confidence Interval (CI) at 95% from -14.7 to 13.8 mg L-1 and the precision had a Coefficient of Variation (CV) of 10.6% with 95% CI 0.9%-20.2%. These biophotonic platform performance metrics indicate a CRP assay with 2-160 mg L-1 dynamic range, performed in 8 minutes from 5 µL of whole blood without sample preparation.

A rapid and quantitative technique for assessing IgG monomeric purity, calibrated with the NISTmAb reference material.

The fraction of intact monomer in a sample (moles/moles), the monomeric purity, is measured as a quality control in therapeutic monoclonal antibodies but is often unknown in research samples and remains a major source of variation in quantitative antibody-based techniques such as immunoassay development. Here, we describe a novel multiplex technique for estimating the monomeric purity and antigen affinity of research grade antibody samples. Light scattering was used to simultaneously observe the mass of antibody binding to biosensor surfaces functionalised with antigen (revealing Fab binding kinetics) or protein A/G (PAG). Initial estimates of monomeric purity in 7 antibody samples including a therapeutic infliximab biosimilar were estimated by observing a mass deficit on the PAG surface compared to the NISTmAb standard of high monomeric purity. Monomeric purity estimates were improved in a second step by observing the mass of antigen binding to the mass of antibody on the PAG surface. The NISTmAb and infliximab biosimilar displayed tightly controlled stoichiometries for antigen binding of 1.31 ± 0.57 and 1.71 ± 0.16 (95% confidence interval)-within the theoretical limit of 1-2 antigens per antibody depending on avidity. The other antibodies in the panel displayed antigen binding stoichiometries in the range 0.06-1.15, attributed to lower monomeric purity. The monomeric purity estimates were verified by electrospray ionization mass spectrometry (ESI), the gold standard technique for structural characterization of antibodies. ESI data indicated that the NISTmAb and infliximab biosimilar samples had monomeric purity values of 93.5% and 94.7%, respectively, whilst the research grade samples were significantly lower (54-89%). Our results demonstrate rapid quality control testing for monomeric purity of antibody samples (< 15 min) which could improve the reproducibility of antibody-based experiments.

Evaluating Natural History and Follow Up Strategies for Non-obstructive Urolithiasis in Pediatric Population.

Objective: While small non-obstructive stones in the adult population are usually observed with minimal follow-up, the same guidelines for management in the pediatric population have not been well-studied. We evaluate the clinical outcomes of small non-obstructing kidney stones in the pediatric population to better define the natural history of the disease. Methods: In this IRB-approved retrospective study, patients with a diagnosis of kidney stones from January 2011 to March 2017 were identified using ICD9 and ICD10 codes. Patients with ureteral stones, obstruction, or stones >5 mm in size were excluded. Patients with no follow-up after initial imaging were also excluded. Patients with a history of stones or prior stone interventions were included in our population. Frequency of follow-up ultrasounds while on observation were noted and any ER visits, stone passage episodes, infections, and surgical interventions were documented. Results: Over the 6-year study period, 106 patients with non-obstructing kidney stones were identified. The average age at diagnosis was 12.5 years and the average stone size was 3.6 mm. Average follow-up was 17 months. About half of the patients had spontaneous passage of stones (54/106) at an average time of 13 months after diagnosis. Stone location did not correlate with spontaneous passage rates. Only 6/106 (5.7%) patients required stone surgery with ureteroscopy and/or PCNL at an average time of 12 months after initial diagnosis. The indication for surgery in all 6 cases was pain. 17/106 (16%) patients developed febrile UTIs and a total of 43 ER visits for stone-related issues were noted, but no patients required urgent intervention for an infected obstructing stone. Median interval for follow-up was every 6 months with renal ultrasounds, which then was prolonged to annual follow up in most cases. Conclusions: The observation of pediatric patients with small non-obstructing stones is safe with no episodes of acute obstructive pyelonephritis occurring in these patients. The sole indication for intervention in our patient population was pain, which suggests that routine follow-up ultrasounds may not be necessary for the follow-up of pediatric non-obstructive renal stones ≤5 mm in size.

A UK nationwide prospective study of treatment change in MODY: genetic subtype and clinical characteristics predict optimal glycaemic control after discontinuing insulin and metformin.

AIMS/HYPOTHESIS: Treatment change following a genetic diagnosis of MODY is frequently indicated, but little is known about the factors predicting future treatment success. We therefore conducted the first prospective study to determine the impact of a genetic diagnosis on individuals with GCK-, HNF1A- or HNF4A-MODY in the UK, and to identify clinical characteristics predicting treatment success (i.e. HbA1c ≤58 mmol/mol [≤7.5%]) with the recommended treatment at 2 years. METHODS: This was an observational, prospective, non-selective study of individuals referred to the Exeter Molecular Genetic Laboratory for genetic testing from December 2010 to December 2012. Individuals from the UK with GCK- or HNF1A/HNF4A-MODY who were not on recommended treatment at the time of genetic diagnosis, and who were diagnosed below the age of 30 years and were currently aged less than 50 years, were eligible to participate. RESULTS: A total of 44 of 58 individuals (75.9%) changed treatment following their genetic diagnosis. Eight individuals diagnosed with GCK-MODY stopped all diabetes medication without experiencing any change in HbA1c (49.5 mmol/mol [6.6%] both before the genetic diagnosis and at a median of 1.25 years' follow-up without treatment, p = 0.88). A total of 36 of 49 individuals (73.5%) diagnosed with HNF1A/HNF4A-MODY changed treatment; however, of the 21 of these individuals who were being managed with diet or sulfonylurea alone at 2 years, only 13 (36.1% of the population that changed treatment) had an HbA1c ≤58 mmol/mol (≤7.5%). These individuals had a shorter diabetes duration (median 4.6 vs 18.1 years), lower HbA1c (58 vs 73 mmol/mol [7.5% vs 8.8%]) and lower BMI (median 24.2 vs 26.0 kg/m2) at the time of genetic diagnosis, compared with individuals (n = 23/36) with an HbA1c >58 mmol/mol (>7.5%) (or <58 mmol/mol [<7.5%] on additional treatment) at the 2 year follow-up. Overall, 64% (7/11) individuals with a diabetes duration of ≤11 years and an HbA1c of ≤69 mmol/mol (≤8.5%) at time of the genetic test achieved good glycaemic control (HbA1c ≤58 mmol/mol [≤7.5%]) with diet or sulfonylurea alone at 2 years, compared with no participants with a diabetes duration of >11 years and an HbA1c of >69 mmol/mol (>8.5%) at the time of genetic diagnosis. CONCLUSIONS/INTERPRETATION: In participants with GCK-MODY, treatment cessation was universally successful, with no change in HbA1c at follow-up. In those with HNF1A/HNF4A-MODY, a shorter diabetes duration, lower HbA1c and lower BMI at genetic diagnosis predicted successful treatment with sulfonylurea/diet alone, supporting the need for early genetic diagnosis and treatment change. Our study suggests that, in individuals with HNF1A/HNF4A-MODY with a longer duration of diabetes (>11 years) at time of genetic test, rather than ceasing current treatment, a sulfonylurea should be added to existing therapy, particularly in those who are overweight or obese and have a high HbA1c.

The simultaneous assessment of and relations between children's sympathetic and parasympathetic psychophysiology and their reactive and proactive aggression.

The goal of the current study was to examine the link between children's psychophysiology and aggression when both constructs were assessed simultaneously in scenarios designed to provide the opportunity to aggress for either a reactive reason or a proactive reason. Both sympathetic nervous system (SNS) activity (skin conductance) and parasympathetic nervous system (PNS) activity (respiratory sinus arrhythmia or RSA), as well as their interaction, were included as physiological measures. Participants were 35 5th-grade children who were placed in two virtual-peer scenarios; one scenario provided the opportunity to aggress in response to peer provocation (i.e., reactive aggression) and the other scenario provided the opportunity to aggress for instrumental gain (i.e., proactive aggression). Both skin conductance and RSA were assessed at the time that children were given the opportunity to aggress; this simultaneous assessment of psychophysiology and aggression allowed for an examination of in-the-moment relations between the two constructs. For the reactive scenario, RSA moderated the in-the-moment relation between skin conductance and aggression such that the association was positive at low RSA but negative at high RSA. For the proactive scenario, skin conductance negatively predicted aggression in-the-moment, and RSA positively predicted aggression in-the-moment, but their interaction was not a significant predictor of aggression. Theoretical implications for reactive and proactive aggression and underlying physiological processes are discussed.