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ObjectiveFibrosis stage is strongly associated with liver-related outcomes and is a key surrogate endpoint in drug trials for non-alcoholic steatohepatitis. Dual-photon microscopy allows automated quantification of fibrosis-related parameters (q-FPs) and may facilitate large-scale histological studies. We aim to validate the performance of q-FPs in a large histological cohort.Design344 patients with non-alcoholic fatty liver disease (NAFLD) underwent 428 liver biopsies (240 had paired transient elastography examination). Fibrosis stage was scored using the NASH Clinical Research Network system, and q-FPs were measured by dual-photon microscopy using unstained slides. Patients were randomly assigned to the training and validation cohorts to test the performance of individual q-FPs and derive optimal cut-offs.ResultsOver 25 q-FPs had area under the receiver-operating characteristics curves >0.90 for different fibrosis stages. Among them, the perimeter of collagen fibres and number of long collagen fibres had the highest accuracy. At the best cut-offs, the two q-FPs had 88.3%–96.2% sensitivity and 78.1%–91.1% specificity for different fibrosis stages in the validation cohort. q-FPs and histological scoring had nearly identical correlations with liver stiffness measurement, suggesting that the accuracy of q-FPs approached that of histological assessment. Among patients with paired liver biopsies, changes in the same q-FPs were associated with changes in fibrosis stage. At a median follow-up of 5.6 years, baseline q-FPs predicted liver-related events.Conclusionq-FP is highly accurate in the assessment of fibrosis in NAFLD patients. This automated platform can be used in future studies as objective and reliable evaluation of histological fibrosis.

Background Psittacosis outbreak investigations require rapid identification of cases in order to trace possible sources and perform public health risk assessments. In recent outbreaks in the Netherlands, such investigations were hampered by the non-specificity of laboratory testing methods to identify human Chlamydia psittaci infections. Method A systematic search of PubMed and Scopus databases of literature published between 01 January, 1986 and 03 July, 2017 was done to find best practices of laboratory-testing methods used in psittacosis outbreaks of two or more human cases. Reference lists of included articles were hand searched to identify additional articles. Results Thirty-seven eligible articles were identified, describing 44 human psittacosis outbreaks in 12 countries. Laboratory tests performed were PCR (with various targets), serologic tests (complement binding reactions, ELISA’s, immunofluorescence tests and immuno-peroxidase tests) and culture, in various combinations. The literature provided no ‘gold standard’ laboratory testing strategy to identify recent human C. psittaci infections. In most psittacosis outbreaks, for a considerable number of cases (or tested individuals in an exposed cohort), C. psittaci infection could not be confirmed, nor excluded as causative pathogen. None of the testing strategies was found to be suitable for (nearly) full case finding. Conclusion PCR enables rapid identification of human psittacosis patients and helps source finding by genotyping but has the disadvantage that sensitivity is high only in the acute phase. In outbreak situations, there is often a time delay and therefore, there is a need for new serologic testing methods next to PCR, with good specificity and sensitivity. Moreover, serum is easier to collect than the preferred diagnostic materials for PCR. A serologic test that can reliably confirm infection status without the necessity of convalescent serum sampling would enhance case finding, source tracing, identification of risk factors and assessment of burden of disease in various settings.

Key Points UTIs are increasingly caused by multidrug-resistant organisms as a result of the overuse of empirical, broad-spectrum antibiotic therapy Antimicrobial susceptibility, determined by the phenotypic response to antibiotic exposure, is key for clinical decision making for treating the wide variety of uropathogens and identifying resistance markers Existing technologies (such as PCR, fluorescence in situ hybridization, and mass spectrometry) and new technologies (such as droplet microfluidic and biosensor platforms) need to focus on direct urine testing to expedite objective diagnoses Integrated biosensor–microfluidic platforms have the most potential for point-of-care testing, as they facilitate direct urine analysis and can encompass all assay steps in a compact device New technologies are a key step towards improved antimicrobial stewardship Timely and accurate identification and determination of the antimicrobial susceptibility of uropathogens is central to the management of UTIs and antimicrobial stewardship. In this Review, Davenport and colleagues discuss emerging technologies including biosensors, microfluidics, and other integrated platforms that could improve UTI diagnosis and treatment choice. Timely and accurate identification and determination of the antimicrobial susceptibility of uropathogens is central to the management of UTIs. Urine dipsticks are fast and amenable to point-of-care testing, but do not have adequate diagnostic accuracy or provide microbiological diagnosis. Urine culture with antimicrobial susceptibility testing takes 2–3 days and requires a clinical laboratory. The common use of empirical antibiotics has contributed to the rise of multidrug-resistant organisms, reducing treatment options and increasing costs. In addition to improved antimicrobial stewardship and the development of new antimicrobials, novel diagnostics are needed for timely microbial identification and determination of antimicrobial susceptibilities. New diagnostic platforms, including nucleic acid tests and mass spectrometry, have been approved for clinical use and have improved the speed and accuracy of pathogen identification from primary cultures. Optimization for direct urine testing would reduce the time to diagnosis, yet these technologies do not provide comprehensive information on antimicrobial susceptibility. Emerging technologies including biosensors, microfluidics, and other integrated platforms could improve UTI diagnosis via direct pathogen detection from urine samples, rapid antimicrobial susceptibility testing, and point-of-care testing. Successful development and implementation of these technologies has the potential to usher in an era of precision medicine to improve patient care and public health.

Turnaround time and productivity of clinical mass spectrometric (MS) testing are hampered by time-consuming manual review of the analytical quality of MS data before release of patient results. To determine whether a classification model created by using standard machine learning algorithms can verify analytically acceptable MS results and thereby reduce manual review requirements. We obtained retrospective data from gas chromatography-MS analyses of 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH) in 1267 urine samples. The data for each sample had been labeled previously as either analytically unacceptable or acceptable by manual review. The dataset was randomly split into training and test sets (848 and 419 samples, respectively), maintaining equal proportions of acceptable (90%) and unacceptable (10%) results in each set. We used stratified 10-fold cross-validation in assessing the abilities of 6 supervised machine learning algorithms to distinguish unacceptable from acceptable assay results in the training dataset. The classifier with the highest recall was used to build a final model, and its performance was evaluated against the test dataset. In comparison testing of the 6 classifiers, a model based on the Support Vector Machines algorithm yielded the highest recall and acceptable precision. After optimization, this model correctly identified all unacceptable results in the test dataset (100% recall) with a precision of 81%. Automated data review identified all analytically unacceptable assays in the test dataset, while reducing the manual review requirement by about 87%. This automation strategy can focus manual review only on assays likely to be problematic, allowing improved throughput and turnaround time without reducing quality.

Background Blood stream infections (BSIs) are a major cause of morbidity and mortality. The time from taking blood cultures to obtain results of antibiotic sensitivity can be up to five days which impacts patient care. The Alfred 60 AST™ can reduce laboratory time from positive culture bottle to susceptibility results from 16 to 25 h to 5–6 h, transforming patient care. To evaluate the diagnostic accuracy of a rapid antimicrobial susceptibility system, the Alfred 60 AST™, in clinical isolates from patients with BSIs and confirm time to results. 301 Gram-negative and 86 Gram-positive isolates were analysed directly from positive blood culture bottles following Gram staining. Antimicrobial susceptibility results and time-to-results obtained by rapid Alfred 60 AST system and BD Phoenix were compared . Results A total of 2196 antimicrobial susceptibility test results (AST) were performed: 1863 Gram-negative and 333 Gram-positive. AST categorical agreement (CA) for Alfred 60 AST™ was 95% (1772/1863) for Gram-negative and 89% (295/333) for Gram-positive isolates. Gram-negative CA: ampicillin 96% (290/301); ciprofloxacin 95% (283/297); ceftriaxone 96% (75/78); meropenem 97% (288/297); piperacillin-tazobactam 95% (280/295); gentamicin 94% (279/297) and amikacin 93% (277/298). The median time to susceptibility results from blood culture flagging positive was 6.3 h vs 20 h ( p < 0.01 ) for Alfred system vs BD Phoenix™. Conclusion Alfred 60 AST system greatly reduced time to antimicrobial susceptibility results in Gram-negative and Gram-positive BSIs with good performance and cost, particularly for Gram-negative bacteraemia.

WHO declared the novel coronavirus (COVID-19) outbreak a global pandemic on 11 March 2020. The establishment of standardized RT-qPCR protocols for respiratory secretions testing, as well as sharing of specimens, data, and information became critical. Here, we investigate the analytical performance of two interim RT-qPCR protocols (Charité and Centers for Disease Control (CDC)) for the qualitative detection of SARS-CoV-2 executed in a fully automated platform. Analytical specificity, PCR amplification efficiency, analytical sensitivity (limit of detection), and cross-reactivity were evaluated using contrived samples. The on-going accuracy was evaluated by retrospective analysis of our test results database (real clinical samples). N1, E, and a modified version of RdRP assays presented adequate analytical specificity, amplification efficiency, and analytical sensitivity using contrived samples. The three assays were applied to all individuals who requested the SARS-CoV-2 molecular test assay in our laboratory and it was observed that N1 gave more positive results than E, and E gave more positive results than RdRP (modified). The RdRP and E were removed from the test and its final version, based on N1 assay only, was applied to 30,699 Brazilian individuals (from 19 February 2020 to 8 May 2020). The aggregated test results available in the database were also presented.

Benefits of skills lab training are widely accepted, but there is sparse research on its long-term effectiveness. We therefore conducted a prospective, randomised controlled-trial to investigate whether in a simulated setting students trained according to a \"best practice\" model (BPSL) perform two skills of different complexity (nasogastral tube insertion, NGT; intravenous cannulation, IVC) better than students trained with a traditional \"see one, do one\" teaching approach (TRAD), at follow-up of 3 or 6 months. 94 first-year medical students were randomly assigned to one of four groups: BPSL training or TRAD teaching with follow-up at 3 (3M) or 6 (6M) months. BPSL included structured feedback, practice on manikins, and Peyton's \"Four-Step-Approach\", while TRAD was only based on the \"see one - do one\" principle. At follow-up, manikins were used to assess students' performance by two independent blinded video-assessors using binary checklists and a single-item global assessment scale. BPSL students scored significantly higher immediately after training (NGT: BPSL3M 94.8%±0.2 and BPSL6M 95.4%±0.3 percentage of maximal score ± SEM; TRAD3M 86.1%±0.5 and TRAD6M 84.7%±0.4. IVC: BPSL3M 86.4%±0.5 and BPSL6M 88.0%±0.5; TRAD3M 73.2%±0.7 and TRAD6M 72.5%±0.7) and lost significantly less of their performance ability at each follow-up (NGT: BPSL3M 86.3%±0.3 and TRAD3M 70.3%±0.6; BPSL6M 89.0%±0.3 and TRAD6M 65.4%±0.6; IVC: BPSL3M 79.5%±0.5 and TRAD3M 56.5%±0.5; BPSL6M 73.2%±0.4 and TRAD6M 51.5%±0.8). In addition, BPSL students were more often rated clinically competent at all assessment times. The superiority at assessment after training was higher for the more complex skill (IVC), whereas NGT with its lower complexity profited more with regard to long-term retention. This study shows that within a simulated setting BPSL is significantly more effective than TRAD for skills of different complexity assessed immediately after training and at follow-up. The advantages of BPSL training are seen especially in long-term retention.

Personalized exoskeleton assistance provides users with the largest improvements in walking speed 1 and energy economy 2 – 4 but requires lengthy tests under unnatural laboratory conditions. Here we show that exoskeleton optimization can be performed rapidly and under real-world conditions. We designed a portable ankle exoskeleton based on insights from tests with a versatile laboratory testbed. We developed a data-driven method for optimizing exoskeleton assistance outdoors using wearable sensors and found that it was equally effective as laboratory methods, but identified optimal parameters four times faster. We performed real-world optimization using data collected during many short bouts of walking at varying speeds. Assistance optimized during one hour of naturalistic walking in a public setting increased self-selected speed by 9 ± 4% and reduced the energy used to travel a given distance by 17 ± 5% compared with normal shoes. This assistance reduced metabolic energy consumption by 23 ± 8% when participants walked on a treadmill at a standard speed of 1.5 m s −1 . Human movements encode information that can be used to personalize assistive devices and enhance performance. A portable ankle exoskeleton uses a data-driven method and wearable sensors to adapt to the user as they walk in a natural setting.

Here, we describe the third major release of RELION. CPU-based vector acceleration has been added in addition to GPU support, which provides flexibility in use of resources and avoids memory limitations. Reference-free autopicking with Laplacian-of-Gaussian filtering and execution of jobs from python allows non-interactive processing during acquisition, including 2D-classification, de novo model generation and 3D-classification. Per-particle refinement of CTF parameters and correction of estimated beam tilt provides higher resolution reconstructions when particles are at different heights in the ice, and/or coma-free alignment has not been optimal. Ewald sphere curvature correction improves resolution for large particles. We illustrate these developments with publicly available data sets: together with a Bayesian approach to beam-induced motion correction it leads to resolution improvements of 0.2–0.7 Å compared to previous RELION versions.