Validation of a sonographic checklist for the detection of histologic placenta accreta spectrum

Background: To standardize research terminology and reduce unanticipated placenta accreta spectrum (PAS), the European Working Group for Abnormally Invasive Placenta (EW-AIP) developed a consensus checklist for reporting PAS suspected on antenatal ultrasound. The diagnostic accuracy of the EW-AIP checklist has not been assessed. Objective: To test the performance of the EW-AIP sonographic checklist in predicting histologic PAS. Study Design: This is a multi-site, blinded, retrospective review of transabdominal ultrasound studies performed between 26-32 weeks gestation for subjects with histologic PAS between 2016-2020. We matched a 1:1 control cohort of subjects without histologic PAS. To reduce reader bias, we matched the control cohort for known risk factors including previa, number of prior cesarean deliveries, prior dilation and curettage (D&C), in vitro fertilization (IVF), and clinical factors affecting image quality including multiple gestation, body mass index (BMI) and gestational age at the ultrasound. Nine sonologists from 5 referral centers, blinded to the histologic outcomes, interpreted the randomized ultrasound studies using the EW-AIP checklist. The primary outcome was the sensitivity and specificity of the checklist to predict PAS. Two separate sensitivity analyses were performed: 1) we excluded subjects with mild disease (i.e. only assessed subjects with histologic increta and percreta); 2) we excluded interpretations from the 2 most junior sonologists. Results: 78 subjects were included (39 PAS, 39 matched control). Clinical risk factors and image quality markers were statistically similar between cohorts. The checklist sensitivity (95% Confidence Interval, CI) was 76.6% (63.4%-90.6%) and specificity (95% CI) was 92.0% (63.4%-99.9%), with a positive and negative likelihood ratio of 9.6 and 0.3, respectively. When we excluded subjects with mild PAS disease, the sensitivity (95% CI) increased to 84.7% (73.6%-96.4%) and specificity was unchanged at 92.0% (83.2%-99.9%). Sensitivity and specificity were unchanged when the interpretations from the 2 most junior sonologists were excluded. Conclusion: The 2016 EW-AIP checklist for interpreting PAS has a reasonable performance in detecting and excluding histologic placenta accreta spectrum

Comparison of germinal center markers CD10, BCL6 and human germinal center-associated lymphoma (HGAL) in follicular lymphomas

<p>Abstract</p> <p>Background</p> <p>Recently, human germinal center-associated lymphoma (HGAL) gene protein has been proposed as an adjunctive follicular marker to CD10 and BCL6.</p> <p>Methods</p> <p>Our aim was to evaluate immunoreactivity for HGAL in 82 cases of follicular lymphomas (FLs) - 67 nodal, 5 cutaneous and 10 transformed - which were all analysed histologically, by immunohistochemistry and PCR.</p> <p>Results</p> <p>Immunostaining for HGAL was more frequently positive (97.6%) than that for BCL6 (92.7%) and CD10 (90.2%) in FLs; the cases negative for bcl6 and/or for CD10 were all positive for HGAL, whereas the two cases negative for HGAL were positive with BCL6; no difference in HGAL immunostaining was found among different malignant subtypes or grades.</p> <p>Conclusions</p> <p>Therefore, HGAL can be used in the immunostaining of FLs as the most sensitive germinal center (GC)-marker; when applied alone, it would half the immunostaining costs, reserving the use of the other two markers only to HGAL-negative cases.</p

Opportunities for primary and secondary prevention of excess gestational weight gain: General Practitioners' perspectives

BackgroundThe impact of excess gestational weight gain (GWG) on maternal and child health outcomes is well documented. Understanding how health care providers view and manage GWG may assist with influencing healthy gestational weight outcomes. This study aimed to assess General Practitioner\u27s (GPs) perspectives regarding the management and assessment of GWG and to understand how GPs can be best supported to provide healthy GWG advice to pregnant women.MethodsDescriptive qualitative research methods utilising semi - structured interview questions to assess GPs perspectives and management of GWG. GPs participating in shared antenatal care in Geelong, Victoria and Sydney, New South Wales were invited to participate in semi - structured, individual interviews via telephone or in person. Interviews were digitally recorded and transcribed verbatim. Data was analysed utilising thematic analysis for common emerging themes.ResultsTwenty eight GPs participated, 14 from each state. Common themes emerged relating to awareness of the implications of excess GWG, advice regarding weight gain, regularity of gestational weighing by GPs, options for GPs to seek support to provide healthy lifestyle behaviour advice and barriers to engaging pregnant women about their weight. GPs perspectives concerning excess GWG were varied. They frequently acknowledged maternal and child health complications resulting from excess GWG yet weighing practices and GWG advice appeared to be inconsistent. The preferred support option to promote healthy weight was referral to allied health practitioners yet GPs noted that cost and limited access were barriers to achieving this.ConclusionsGPs were aware of the importance of healthy GWG yet routine weighing was not standard practice for diverse reasons. Management of GWG and perspectives of the issue varied widely. Time efficient and cost effective interventions may assist GPs in ensuring women are supported in achieving healthy GWG to provide optimal maternal and infant health outcomes.<br /

Ankyrin is the major oxidised protein in erythrocyte membranes from end-stage renal disease patients on chronic haemodialysis and oxidation is decreased by dialysis and vitamin C supplementation

Chronically haemodialysed end-stage renal disease patients are at high risk of morbidity arising from complications of dialysis, the underlying pathology that has led to renal disease and the complex pathology of chronic kidney disease. Anaemia is commonplace and its origins are multifactorial, involving reduced renal erythropoietin production, accumulation of uremic toxins and an increase in erythrocyte fragility. Oxidative damage is a common risk factor in renal disease and its co-morbidities and is known to cause erythrocyte fragility. Therefore, we have investigated the hypothesis that specific erythrocyte membrane proteins are more oxidised in end-stage renal disease patients and that vitamin C supplementation can ameliorate membrane protein oxidation. Eleven patients and 15 control subjects were recruited to the study. Patients were supplemented with 2 × 500 mg vitamin C per day for 4 weeks. Erythrocyte membrane proteins were prepared pre- and post-vitamin C supplementation for determination of protein oxidation. Total protein carbonyls were reduced by vitamin C supplementation but not by dialysis when investigated by enzyme linked immunosorbent assay. Using a western blot to detect oxidised proteins, one protein band, later identified as containing ankyrin, was found to be oxidised in patients but not controls and was reduced significantly by 60% in all patients after dialysis and by 20% after vitamin C treatment pre-dialysis. Ankyrin oxidation analysis may be useful in a stratified medicines approach as a possible marker to identify requirements for intervention in dialysis patients

A Model for Heat and Mass Input Control in GMAW Steady-state model is developed to help control weld reinforcement and heat input in gas metal arc welding

ABSTRACT. This work describes derivation of a control model for electrode melting and heat and mass transfer from the electrode to the workpiece in gas metal arc welding (GMAW). Specifically, a model is developed which allows electrode speed and welding speed to be calculated for given values of voltage and torch-to-base metal distance, as a function of the desired heat and mass input to the weldment. Heat input is given on a per unit weld length basis, and mass input is given in terms of transverse cross-sectional area added to the weld bead (termed reinforcement). The relationship to prior work is discussed. The model was demonstrated using a computer-controlled welding machine and a proportional-integral (PI) controller receiving input from a digital filter. The difference between model-calculated welding current and measured current is used as controller feedback. The model is calibrated for use with carbon steel welding wire and base plate with Ar-C0 2 shielding gas. Although the system is intended for application during spray transfer of molten metal from the electrode to the weld pool, satisfactory performance is also achieved during globular and streaming transfer. Data are presented showing steady-state and transient performance, as well as resistance to external disturbances. H. B. SMARTT and C. J. EINERSON are wit

A model-based approach to intelligent control of gas metal arc welding

This paper discusses work on a model-based intelligent process controller for gas metal arc welding. Four sensors input to a neural network, which communicates to a reference model-based adaptive controller that controls process parameters. Reference model derivation and validation are discussed. The state of an arch weld is determined by the composition of the weld and base metal and the weld's thermomechanical history. The composition of the deposited weld metal depends primarily on the amount of filler metal dilution; heat input to the weld, comprising pre-heat and process heat, is the controlling factor in the thermal cycle. Thus, control of the arc welding process should focus on rational specification and in-process control of the heat and mass input to the weld. A control model has been developed in which the governing equations are solved for the process parameters as functions of the desired heat input (in terms of heat input unit weld length) and mass input (in terms of transverse reinforcement area) to the weld. The model includes resistive and arc heating of the electrode wire, characteristics of the welding power supply, and a volumetric heat balance on the electrode material, as well as latent and superheat of the electrode material. Extension of the model to include dynamics of individual droplet transfer events, based on incorporating a nonlinear, lumped parameter droplet analysis, is discussed. A major emphasis has been placed on computational simplicity; model solutions are required at the rate of about 10 Hz during welding. Finally, a process control scheme has been developed for the gas metal arc welding process using the above nonlinear model with a proportional-integral controller with adaptive coefficients to control the weld heat input and reinforcement area independently. Performance of the resulting control method is discussed. 10 refs., 5 figs

Development of an intelligent system for cooling rate and fill control in GMAW. [Gas Metal Arc Welding (GMAW)]

A control strategy for gas metal arc welding (GMAW) is developed in which the welding system detects certain existing conditions and adjusts the process in accordance to pre-specified rules. This strategy is used to control the reinforcement and weld bead centerline cooling rate during welding. Relationships between heat and mass transfer rates to the base metal and the required electrode speed and welding speed for specific open circuit voltages are taught to a artificial neural network. Control rules are programmed into a fuzzy logic system. TRADITOINAL CONTROL OF THE GMAW PROCESS is based on the use of explicit welding procedures detailing allowable parameter ranges on a pass by pass basis for a given weld. The present work is an exploration of a completely different approach to welding control. In this work the objectives are to produce welds having desired weld bead reinforcements while maintaining the weld bead centerline cooling rate at preselected values. The need for this specific control is related to fabrication requirements for specific types of pressure vessels. The control strategy involves measuring weld joint transverse cross-sectional area ahead of the welding torch and the weld bead centerline cooling rate behind the weld pool, both by means of video (2), calculating the required process parameters necessary to obtain the needed heat and mass transfer rates (in appropriate dimensions) by means of an artificial neural network, and controlling the heat transfer rate by means of a fuzzy logic controller (3). The result is a welding machine that senses the welding conditions and responds to those conditions on the basis of logical rules, as opposed to producing a weld based on a specific procedure