Malignancy risk analysis in patients with inadequate fine needle aspiration cytology (FNAC) of the thyroid

Background Thyroid fine needle aspiration cytology (FNAC) is the standard diagnostic modality for thyroid nodules. However, it has limitations among which is the incidence of non-diagnostic results (Thy1). Management of cases with repeatedly non-diagnostic FNAC ranges from simple observation to surgical intervention. We aim to evaluate the incidence of malignancy in non-diagnostic FNAC, and the success rate of repeated FNAC. We also aim to evaluate risk factors for malignancy in patients with non-diagnostic FNAC. Materials and Methods Retrospective analyses of consecutive cases with thyroid non diagnostic FNAC results were included. Results Out of total 1657 thyroid FNAC done during the study period, there were 264 (15.9%) non-diagnostic FNAC on the first attempt. On repeating those, the rate of a non-diagnostic result on second FNAC was 61.8% and on third FNAC was 47.2%. The overall malignancy rate in Thy1 FNAC was 4.5% (42% papillary, 42% follicular and 8% anaplastic), and the yield of malignancy decreased considerably with successive non-diagnostic FNAC. Ultrasound guidance by an experienced head neck radiologist produced the lowest non-diagnostic rate (38%) on repetition compared to US guidance by a generalist radiologist (65%) and by non US guidance (90%). Conclusions There is a low risk of malignancy in patients with a non-diagnostic FNAC result, commensurate to the risk of any nodule. The yield of malignancy decreased considerably with successive non-diagnostic FNAC

Use of the WHO Access, Watch, and Reserve classification to define patterns of hospital antibiotic use (AWaRe): an analysis of paediatric survey data from 56 countries

BACKGROUND: Improving the quality of hospital antibiotic use is a major goal of WHO's global action plan to combat antimicrobial resistance. The WHO Essential Medicines List Access, Watch, and Reserve (AWaRe) classification could facilitate simple stewardship interventions that are widely applicable globally. We aimed to present data on patterns of paediatric AWaRe antibiotic use that could be used for local and national stewardship interventions. METHODS: 1-day point prevalence survey antibiotic prescription data were combined from two independent global networks: the Global Antimicrobial Resistance, Prescribing, and Efficacy in Neonates and Children and the Global Point Prevalence Survey on Antimicrobial Consumption and Resistance networks. We included hospital inpatients aged younger than 19 years receiving at least one antibiotic on the day of the survey. The WHO AWaRe classification was used to describe overall antibiotic use as assessed by the variation between use of Access, Watch, and Reserve antibiotics, for neonates and children and for the commonest clinical indications. FINDINGS: Of the 23 572 patients included from 56 countries, 18 305 were children (77·7%) and 5267 were neonates (22·3%). Access antibiotic use in children ranged from 7·8% (China) to 61·2% (Slovenia) of all antibiotic prescriptions. The use of Watch antibiotics in children was highest in Iran (77·3%) and lowest in Finland (23·0%). In neonates, Access antibiotic use was highest in Singapore (100·0%) and lowest in China (24·2%). Reserve antibiotic use was low in all countries. Major differences in clinical syndrome-specific patterns of AWaRe antibiotic use in lower respiratory tract infection and neonatal sepsis were observed between WHO regions and countries. INTERPRETATION: There is substantial global variation in the proportion of AWaRe antibiotics used in hospitalised neonates and children. The AWaRe classification could potentially be used as a simple traffic light metric of appropriate antibiotic use. Future efforts should focus on developing and evaluating paediatric antibiotic stewardship programmes on the basis of the AWaRe index. FUNDING: GARPEC was funded by the PENTA Foundation. GARPEC-China data collection was funded by the Sanming Project of Medicine in Shenzhen (SZSM2015120330). bioMérieux provided unrestricted funding support for the Global-PPS

Scalable data structure detection and classification for C/C++ binaries

Many existing techniques for reversing data structures in C/C ++ binaries are limited to low-level programming constructs, such as individual variables or structs. Unfortunately, without detailed information about a program's pointer structures, forensics and reverse engineering are exceedingly hard. To fill this gap, we propose MemPick, a tool that detects and classifies high-level data structures used in stripped binaries. By analyzing how links between memory objects evolve throughout the program execution, it distinguishes between many commonly used data structures, such as singly- or doubly-linked lists, many types of trees (e.g., AVL, red-black trees, B-trees), and graphs. We evaluate the technique on 10 real world applications, 4 file system implementations and 16 popular libraries. The results show that MemPick can identify the data structures with high accuracy