Faster Turnaround Time Research Articles

Molecular and Clinical Epidemiology of SARS-CoV-2 Infection among Vaccinated and Unvaccinated Individuals in a Large Healthcare Organization from New Jersey.

New Jersey was among the first states impacted by the COVID-19 pandemic, with one of the highest overall death rates in the nation. Nevertheless, relatively few reports have been published focusing specifically on New Jersey. Here we report on molecular, clinical, and epidemiologic observations, from the largest healthcare network in the state, in a cohort of vaccinated and unvaccinated individuals with laboratory-confirmed SARS-CoV-2 infection. We conducted molecular surveillance of SARS-CoV-2-positive nasopharyngeal swabs collected in nine hospitals from December 2020 through June 2022, using both whole genome sequencing (WGS) and a real-time RT-PCR screening assay targeting spike protein mutations found in variants of concern (VOCs) within our region. De-identified clinical data were obtained retrospectively, including demographics, COVID-19 vaccination status, ICU admission, ventilator support, mortality, and medical history. Statistical analyses were performed to identify associations between SARS-CoV-2 variants, vaccination status, clinical outcomes, and medical risk factors. A total of 5007 SARS-CoV-2-positive nasopharyngeal swabs were successfully screened and/or sequenced. Variant screening identified three predominant VOCs, including Alpha (n = 714), Delta (n = 1877), and Omicron (n = 1802). Omicron isolates were further sub-typed as BA.1 (n = 899), BA.2 (n = 853), or BA.4/BA.5 (n = 50); the remaining 614 isolates were classified as "Other". Approximately 31.5% (1577/5007) of the samples were associated with vaccine breakthrough infections, which increased in frequency following the emergence of Delta and Omicron. Severe clinical outcomes included ICU admission (336/5007 = 6.7%), ventilator support (236/5007 = 4.7%), and mortality (430/5007 = 8.6%), with increasing age being the most significant contributor to each (p < 0.001). Unvaccinated individuals accounted for 79.7% (268/336) of ICU admissions, 78.3% (185/236) of ventilator cases, and 74.4% (320/430) of deaths. Highly significant (p < 0.001) increases in mortality were observed in individuals with cardiovascular disease, hypertension, cancer, diabetes, and hyperlipidemia, but not with obesity, thyroid disease, or respiratory disease. Significant differences (p < 0.001) in clinical outcomes were also noted between SARS-CoV-2 variants, including Delta, Omicron BA.1, and Omicron BA.2. Vaccination was associated with significantly improved clinical outcomes in our study, despite an increase in breakthrough infections associated with waning immunity, greater antigenic variability, or both. Underlying comorbidities contributed significantly to mortality in both vaccinated and unvaccinated individuals, with increasing risk based on the total number of comorbidities. Real-time RT-PCR-based screening facilitated timely identification of predominant variants using a minimal number of spike protein mutations, with faster turnaround time and reduced cost compared to WGS. Continued evolution of SARS-CoV-2 variants will likely require ongoing surveillance for new VOCs, with real-time assessment of clinical impact.

Neuroevolution-Based Network Architecture Evolution in Semiconductor Manufacturing.

Promoted model architectures or algorithms are crucial for intelligent manufacturing since developing them takes a lot of trial and error to embed the domain knowledge into the models correctly. Especially in semiconductor manufacturing, the whole processes depend on complicated physical equations and sophisticated fine-tuning. Therefore, we use a neuroevolution-based model to search the optimized architecture automatically. The collector current value at a particular bias of the silicon-germanium (SiGe) heterojunction bipolar transistor, generated by technology computer-aided design (TCAD), is used as the target dataset with six process parameters as the inputs. The processes include oxidation, dry and wet etching, implantation, annealing, diffusion, and chemical-mechanical polishing. Our work can build a suitable model network with a fast turnaround time, and practical physical constraints are fused in it without domain knowledge extraction. Take the case with 3840 data and one output as an instance. The mean square errors of the train set and validation set, as well as the mean absolute percentage error of the test set, are 1.317 × 10-6, 7.215 × 10-7, and 0.216 while using multilayer perceptron (MLP) and they are 3.285 × 10-7, 1.661 × 10-7, and 0.097 while using NE. The consequences show that the work in this vein is promising. According to the trend plot and results, the ability to extract physic is much better than the traditional (MLP) model.

Mycobacterium tuberculosis complex whole-genome sequencing in New York State: Implementation of a reduced phenotypic drug susceptibility testing algorithm

Whole-genome sequencing (WGS) can predict drug resistance and antimicrobial susceptibility in Mycobacterium tuberculosis complex (MTBC) and has shown promise in partially replacing culture-based phenotypic drug susceptibility testing (pDST).We performed a two-year side by side study comparing the prediction of drug resistance and antimicrobial susceptibility by WGS molecular DST (mDST) to pDST to determine resistance at the critical concentration by Mycobacterial Growth Indicator Tube (MGIT) and agar proportion testing. Negative predictive values of WGS results were consistently high for the first-line drugs: rifampin (99.9%), isoniazid (99.0%), pyrazinamide (98.5%), and ethambutol (99.8%); the rates of resistance to these drugs, among strains in our population, are 2.9%, 10.4%, 46.3%, and 2.3%, respectively. WGS results were available an average 8 days earlier than first-line MGIT pDST.Based on these findings, we implemented a new testing algorithm with an updated WGS workflow in which strains predicted pan-susceptible were no longer tested by pDST. This algorithm was applied to 1177 isolates between October 2018 and September 2020, eliminating pDST for 66.6% of samples and reducing pDST for an additional 22.0%. This algorithm change resulted in faster turnaround times and decreased cost while maintaining comprehensive antimicrobial susceptibility profiles of all culture-positive MTBC cases in New York.

Evaluation of the Illumina iSeq whole genome sequencing system for enteric disease surveillance and outbreak detection

The Illumina iSeq low-capacity sequencing platform was evaluated for use in foodborne disease surveillance and outbreak detection. The platform produced high quality sequence data comparable to that of the Illumina MiSeq and was cost-effective with fast turn-around time in low sample volume environments.

In situ Nanopore sequencing reveals metabolic characteristics of the Qilian glacier meltwater microbiome.

Nanopore metagenomic sequencing enables rapid annotating microbiological ecosystems, and the previous glacier-related sequencing applications (e.g., targeted ice sheets, ice lake, and cryoconite holes) inspire us to explore high-altitude glacier meltwater at Qilian Mountain, China (3000 to 4000 m above sea level, MASL). Our findings suggest that (1) despite only several hundred meters apart, the microbial communities and functionalities are quite different among vertical alpine distributions; (2) the high-altitude Qilian meltwater microbiome serve several main metabolic functions, including sulfur oxidation, selenite decomposing, photosynthesis, energy production, enzymic, and UV tolerant activities. Meanwhile, our Nanopore metagenomic results indicate that the microbial classifications and functionalities (e.g., chaperones, cold-shock, specific tRNA species, oxidative stress, and resistance to toxic compounds) of Qilian meltwater are highly consistent with the other glacial microbiome, emphasizing that only certain microbial species can survive in the cold environment and the molecular adaptions and lifestyles remain stable all over the world. Besides, we have shown Nanopore metagenomic sequencing can provide reliable prokaryotic classifications within or among studies, which therefore can encourage more applications in the field given faster turnaround time. However, we recommend accumulating at least 400 ng nucleic acids (after extraction) and maximizing Nanopore library preparation efficiency before on-site sequencing to obtain better resolutions.

False positive cases in automated blood culture systems due to hyperleukocytosis

Automated blood culture systems are widely used in clinical microbiology laboratories to minimize the workload of laboratory personnel and permit fast turnaround times. However, sometimes false positive signals occur due to leukocytosis, presence of fastidious bacteria, or unexplained causes. We experienced false positive signal in a patient with hyperleukocytosis for the first time since the automated blood culture system was introduced in our hospital over 20 years ago. We present two case reports with literature review and describe the procedure for dealing with false-positive cases in our hospital.

Plasma glycoproteomic biomarkers identify metastatic melanoma patients with reduced clinical benefit from immune checkpoint inhibitor therapy.

The clinical success of immune-checkpoint inhibitors (ICI) in both resected and metastatic melanoma has confirmed the validity of therapeutic strategies that boost the immune system to counteract cancer. However, half of patients with metastatic disease treated with even the most aggressive regimen do not derive durable clinical benefit. Thus, there is a critical need for predictive biomarkers that can identify individuals who are unlikely to benefit with high accuracy so that these patients may be spared the toxicity of treatment without the likely benefit of response. Ideally, such an assay would have a fast turnaround time and minimal invasiveness. Here, we utilize a novel platform that combines mass spectrometry with an artificial intelligence-based data processing engine to interrogate the blood glycoproteome in melanoma patients before receiving ICI therapy. We identify 143 biomarkers that demonstrate a difference in expression between the patients who died within six months of starting ICI treatment and those who remained progression-free for three years. We then develop a glycoproteomic classifier that predicts benefit of immunotherapy (HR=2.7; p=0.026) and achieves a significant separation of patients in an independent cohort (HR=5.6; p=0.027). To understand how circulating glycoproteins may affect efficacy of treatment, we analyze the differences in glycosylation structure and discover a fucosylation signature in patients with shorter overall survival (OS). We then develop a fucosylation-based model that effectively stratifies patients (HR=3.5; p=0.0066). Together, our data demonstrate the utility of plasma glycoproteomics for biomarker discovery and prediction of ICI benefit in patients with metastatic melanoma and suggest that protein fucosylation may be a determinant of anti-tumor immunity.

#5842 ULTRASOUND AND 3D IMAGING CHARACTERISATION OF A RAT MODEL OF POLYCYSTIC KIDNEY DISEASE

Abstract Background and Aims Polycystic kidney disease (PKD) is a congenital fibrocystic disorder where cysts are primarily forming within the kidneys causing enlargement, loss of kidney function and resulting in chronic kidney disease for which there is no curative treatment. Consequently, PKD is classified as a medical condition with high unmet therapeutic need. Animal models with improved clinical translatability can optimally inform about potential clinical efficacy of novel drug candidates for PKD. The polycystic kidney (PCK) rat is an established genetic model of PKD with natural history and renal histologic abnormalities that resemble the human disease. Gubra has established a PCK rat breeding program to enable fast turnaround time of preclinical drug discovery studies for PKD. In this study, we have characterised disease progression in the PCK rat to aid in designing future pharmacological intervention studies. Method Male PCK (PCK/CrljCrl-Pkhd1pck/Crl) and control (CRL:CD(SD)) rats (Charles River) were randomised into groups based on body weight at the age of 10 weeks. At the age of 17 and 25 weeks, rats underwent ultrasound assessment of kidney volume, urine collection for quantification of albuminuria, and plasma sampling for analysis of urea and creatinine levels. At termination, whole kidneys were harvested and total kidney volume, cyst number and cyst volume were analysed using quantitative 3D light sheet imaging. Results Compared to age-matched control rats, PCK rats displayed marked albuminuria which was significantly increased at week 25 of age. Whereas plasma urea was progressively increased at both time points, plasma creatinine increased at week 25. Ultrasound measurements revealed that total kidney volume progressive increased compared to control rats. 3D Light sheet imaging enabled whole-kidney counting of cysts and quantification of cyst volume as wells as the total kidney volume that closely correlated to kidney ultrasound results. Conclusion The PCK rats displays histological hallmarks of PKD, characterized by age-dependent progressive increases in biomarkers of kidney injury, kidney hypertrophy and cyst formation. In vivo ultrasound and ex vivo quantitative whole-kidney 3D light sheet imaging is highly instrumental for detailed assessment of progressive disease in the PCK rat. The renal histopathological markers may serve as key histological endpoints for assessment of therapeutic effects of preclinical drug candidates in this translational rat model of PKD.

LADA methodologies to localize embedded memory failure

Bitmap tool is commonly used by semiconductor design house to debug embedded memory failure as it is able to localize down to bit cell resolution with faster turnaround time as compared to hardware fault isolation methods. However, bitmap enablement requires tedious effort and high cost as it requires accurate logical-to-physical address scrambling information from memory IP provider to be successful. In this work, usage of hardware-based LADA fault isolation technique to debug system-on-chip (SOC) memory failure is discussed in term of implementation difficulty and success rate.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning.

Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

A deep learning model for the prediction of microsatellite instability and pathogenic POLE mutations in colorectal cancer using histopathologic images.

3543 Background: Immunotherapy has brought about a landmark change in anti-tumor treatment in the past years. High microsatellite instability (MSI-H) is now the only clinically approved biomarker predicting response to immunotherapy in CRC. Increasing evidence suggests that POLE mutations in the exonuclease domain could drive an ultra-mutational phenotype and improve the treatment outcomes of ICI in solid tumors. In this study, we set out to apply a deep learning model using H&amp;E-stained, formalin-fixed, paraffin-embedded (FFPE) whole slide images (WSIs) of CRC primary tumors. Methods: The deep learning model is developed and validated through five-fold cross-validation using WSI of primary tumors from 506 CRC patients and externally validated using 52 WSIs from a prospective cohort. The microsatellite status, tumor mutation burden (TMB) and POLE genotype were determined by next-generation sequencing (NGS). Patients with MSS status and a low TMB (＜20Mutations/Mb) were admitted to the MSS group, and CRCs with a POLE mutation which was defined as an oncogenic mutation referring to the POLE functional mutation list at OncoKB( POLE (oncokb.org) were admitted to the POLE mutant group. Clustering-constrained-attention multiple-instance learning (CLAM) model is employed as the base model, and we conduct the model ensemble by performing a large-scale hyper-parameter search, selecting five models with the highest value in one of the performance metrics, including the AURoC, accuracy, precision, recall, and f1 score, and finally averaging the predictions of the five models. Results: The internal dataset included 237 MSS, 142 MSI-H, and 127 POLE mutant CRC. The three groups had significant differences in primary location (p ＜ 0.0001), histology (p ＜ 0.0001), tumor differentiation (p = 0.002), tumor stage (p ＜ 0.0001), Crohn's-like reaction (p ＜ 0.0001) and tumor growth pattern (p = 0.001). The cross-validation performance of the ensemble model (M E) in the internal dataset achieves an AURoC of 0.944 for three-way classification task (POLE vs. MSI-H vs. MSS) and 0.940 for two-way classification task (POLE &amp; MSI-H vs. MSS) which were superior to the performance of each single CLAM model. To demonstrate the generalizability of the deep learning model, a domestic perspective cohort consisting of 20 MSS, 17 MSI-H, and 15 POLE mutant CRC H&amp;E images were used to validate the external performance. And the M E retained robust performance on the external dataset, with an AURoC of 0.904 for three-way classification task and 0.836 for two-way classification task. Conclusions: A CLAM-based deep learning model could directly predict the MSI-H and POLE mutation from histological images that could be used to stratify CRC patients for immunotherapy with faster turnaround time and lower costs compared with traditional sequencing methods.

Closing the Loop: The Value of Outcome Letters for Prehospital Pediatric Care.

Providing emergency care to acutely ill or injured children is stressful and requires a high level of training. Paramedics who provide prehospital care are typically not involved in the circle of care and do not receive patient outcome information. The aim of this quality improvement project was to assess paramedics' perceptions of standardized outcome letters pertaining to acute pediatric patients that they had treated and transported to an emergency department. Between December 2019 and December 2020, 888 outcome letters were distributed to paramedics who provided care for 370 acute pediatric patients transported to the Children's Hospital of Eastern Ontario in Ottawa, Canada. All paramedics who received a letter (n = 470) were invited to participate in a survey that collected their perceptions and feedback about the letters, as well as their demographic information. The response rate was 37% (172/470). Approximately half of the respondents were Primary Care Paramedics and half Advanced Care Paramedics. The respondents' median age was 36 years, median years of service was 12 years, and 64% identified as male. Most agreed that the outcome letters contained information pertinent to their practice (91%), allowed them to reflect on care they had provided (87%), and confirmed clinical suspicions (93%). Respondents indicated that they found the letters useful for 3 reasons: 1) increases capacity to link differential diagnoses, prehospital care, or patient outcomes; 2) contributes to a culture of continuous learning and improvement; and 3) gives closure, reduces stress, or provides answers for difficult cases. Suggestions for improvement included providing more information, provision of letters on all patients transported, faster turnaround time between call and receipt of letter and inclusion of recommendations or interventions/assessments. Paramedics appreciated receiving hospital-based patient outcome information after their provision of care and reported that the letters offered opportunities for closure, reflection, and learning.

Mass Spectrometry Imaging of Lumpectomy Specimens Deciphers Diacylglycerols as Potent Biomarkers for the Diagnosis of Breast Cancer.

Detecting breast tumor markers with a fast turnaround time from frozen sections should foster intraoperative histopathology in breast-conserving surgery, reducing the need for a second operation. Hence, rapid label-free discrimination of the spatially resolved molecular makeup between cancer and adjacent normal breast tissue is of growing importance. We performed desorption electrospray ionization mass spectrometry imaging (DESI-MSI) of fresh-frozen excision specimens, including cancer and paired adjacent normal sections, obtained from the lumpectomy of 73 breast cancer patients. The results demonstrate that breast cancer tissue posits sharp metabolic upregulation of diacylglycerol, a lipid second messenger that activates protein kinase C for promoting tumor growth. We identified four specific sn-1,2-diacylglycerols that outperformed all other lipids simultaneously mapped by the positive ion mode DESI-MSI for distinguishing cancers from adjacent normal specimens. This result contrasts with several previous DESI-MSI studies that probed metabolic dysregulation of glycerophospholipids, sphingolipids, and free fatty acids for cancer diagnoses. A random forest-based supervised machine learning considering all detected ion signals also deciphered the highest diagnostic potential of these four diacylglycerols with the top four importance scores. This led us to construct a classifier with 100% overall prediction accuracy of breast cancer by using the parsimonious set of four diacylglycerol biomarkers only. The metabolic pathway analysis suggested that increased catabolism of phosphatidylcholine in breast cancer contributes to diacylglycerol overexpression. These results open up opportunities for mapping diacylglycerol signaling in breast cancer in the context of novel therapeutic and diagnostic developments, including the intraoperative assessment of breast cancer margin status.

Point-of-care laboratory testing in primary care: utilization, limitations and perspectives of general practitioners in Germany

BackgroundDue to their fast turnaround time and user-friendliness, point-of-care tests (POCTs) possess a great potential in primary care. The purpose of the study was to assess general practitioners’ (GPs) perspectives on POCT use in German primary care, including utilization, limitations and requirements.MethodsWe conducted a cross-sectional survey study among GPs in Germany (federal states of Thuringia, Bremen and Bavaria (Lower Franconia), study period: 04/22–06/2022).ResultsFrom 2,014 GPs reached, 292 participated in our study (response rate: 14.5%). The median number of POCTs used per GP was 7.0 (IQR: 5.0–8.0). Six POCTs are used by the majority of surveyed GPs (> 50%): urine dipstick tests (99%), glucose (urine [91%] and plasma [69%]), SARS-CoV-2 (80%), urine microalbumin (77%), troponin I/T (74%) and prothrombin time / international normalized ratio (65%). The number of utilized POCTs did not differ between GP practice type (p = 0.307) and population size of GP practice location (p = 0.099). The great majority of participating German GPs (93%) rated POCTs as useful diagnostic tools in the GP practice. GPs ranked immediate decisions on patient management and the increase in diagnostic certainty as the most important reasons for performing POCTs. The most frequently reported limitations of POCT use in the GP practice were economic aspects (high costs and inadequate reimbursement), concerns regarding diagnostic accuracy, and difficulties to integrate POCT-testing into practice routines (e.g. time and personnel expenses).ConclusionAlthough participating German GPs generally perceive POCTs as useful diagnostic tools and numerous POCTs are available, several test-related and contextual factors contribute to the relatively low utilization of POCTs in primary care.

Platform headspace gas chromatography method for high-throughput determination of residual solvents in pharmaceutical materials

Static headspace capillary gas chromatography (HSGC) has been employed to monitor the level of residual solvents in the pharmaceutical materials. Most of the HSGC methods, however, consume significant amounts of diluents and require considerable amount of sample preparation time. Accordingly, a HSGC method featured with fast turnaround time, and minimal amount of solvent use has been developed for the quantitative analysis of 27 residual solvents frequently used in the development and manufacturing processes of pharmaceutical industry. This HSGC-FID method employs a commercially available fused silica capillary column, a split injection (40:1), and a programmed temperature ramp. It was qualified for specificity, accuracy, repeatability/precision, linearity, LOQ, solution stability, and robustness using two representative sample matrices. The standards, samples and spiked samples were demonstrated to be stable for at least 10 days at room temperature in sealed headspace vials with a recovery of ≥ 93%. The method was also shown to be robust, and its performance was not affected by small changes of carrier gas flow rate, initial oven temperature or the headspace oven temperature. In this new approach, the analytical sample was prepared by dissolving the sample into 1 mL of the diluent and the standard solution was prepared by diluting 1 mL of the custom-made stock into 9 mL of the diluent whereas the traditional approach requires liters of the diluent, making the new approach environmentally friendly, sustainable, economical, agile, error-proofing and thus appropriate for a variety of pharmaceutical applications.

Lab on a Chip Device for Diagnostic Evaluation and Management in Chronic Renal Disease: A Change Promoting Approach in the Patients' Follow Up.

Lab-on-a-chip (LOC) systems are miniaturized devices aimed to perform one or several analyses, normally carried out in a laboratory setting, on a single chip. LOC systems have a wide application range, including diagnosis and clinical biochemistry. In a clinical setting, LOC systems can be associated with the Point-of-Care Testing (POCT) definition. POCT circumvents several steps in central laboratory testing, including specimen transportation and processing, resulting in a faster turnaround time. Provider access to rapid test results allows for prompt medical decision making, which can lead to improved patient outcomes, operational efficiencies, patient satisfaction, and even cost savings. These features are particularly attractive for healthcare settings dealing with complicated patients, such as those affected by chronic kidney disease (CKD). CKD is a pathological condition characterized by progressive and irreversible structural or functional kidney impairment lasting for more than three months. The disease displays an unavoidable tendency to progress to End Stage Renal Disease (ESRD), thus requiring renal replacement therapy, usually dialysis, and transplant. Cardiovascular disease (CVD) is the major cause of death in CKD, with a cardiovascular risk ten times higher in these patients than the rate observed in healthy subjects. The gradual decline of the kidney leads to the accumulation of uremic solutes, with negative effect on organs, especially on the cardiovascular system. The possibility to monitor CKD patients by using non-invasive and low-cost approaches could give advantages both to the patient outcome and sanitary costs. Despite their numerous advantages, POCT application in CKD management is not very common, even if a number of devices aimed at monitoring the CKD have been demonstrated worldwide at the lab scale by basic studies (low Technology Readiness Level, TRL). The reasons are related to both technological and clinical aspects. In this review, the main technologies for the design of LOCs are reported, as well as the available POCT devices for CKD monitoring, with a special focus on the most recent reliable applications in this field. Moreover, the current challenges in design and applications of LOCs in the clinical setting are briefly discussed.

GEM: A Generalized Memristor Device Modeling Framework Based on Neural Network for Transient Circuit Simulation

Conventional physics-based memristor device modeling methods highly rely on human expertise, which results in a long development period. To address the aforementioned challenges, we propose a new generalized memristor (GEM) device modeling framework based on the artificial neural network (ANN) technique, which has a minimum dependency on the underlying physics, resulting in a fast turn-around development time for customized memristor devices. GEM framework models the switching and conducting behaviors of the memristor devices separately, avoiding the signal-dependence issue in the prior time-series data modeling method. The result of the GEM framework is a compact model that supports general-purpose circuit simulators. Experimental results show that our compact model achieves a ratio of root-mean-square error to peak-to-peak (RMSE/PP) of 3.6% compared to the physics-based device model. Performance analysis of memristor-based logic and memristor crossbar circuits are conducted to demonstrate the effectiveness of our proposed GEM framework for the design and analysis of memristor-based circuits.

Analytical validation and implementation of a pan cancer next-generation sequencing panel, CANSeqTMKids for molecular profiling of childhood malignancies.

Next-Generation Sequencing (NGS) allows rapid analysis of multiple genes for the detection of clinically actionable variants. This study reports the analytical validation of a targeted pan cancer NGS panel CANSeqTMKids for molecular profiling of childhood malignancies. Analytical validation included DNA and RNA extracted from de-identified clinical specimens including formalin fixed paraffin embedded (FFPE) tissue, bone marrow and whole blood as well as commercially available reference materials. The DNA component of the panel evaluates 130 genes for the detection of single nucleotide variants (SNVs), Insertion and Deletions (INDELs), and 91 genes for fusion variants associated with childhood malignancies. Conditions were optimized to use as low as 20% neoplastic content with 5ng of nucleic acid input. Evaluation of the data determined greater than 99% accuracy, sensitivity, repeatability, and reproducibility. The limit of detection was established to be 5% allele fraction for SNVs and INDELs, 5 copies for gene amplifications and 1,100 reads for gene fusions. Assay efficiency was improved by automation of library preparation. In conclusion, the CANSeqTMKids allows for the comprehensive molecular profiling of childhood malignancies from different specimen sources with high quality and fast turnaround time.

Integrity and efficiency: AbbVie's journey of building an integrated nonregulated bioanalytical laboratory.

While bioanalytical outsourcing is widely adopted in the pharmaceutical industry, AbbVie is one of the few large biopharmaceutical companies having an internal bioanalytical unit to support nearly all its drug metabolism and pharmacokinetic studies. This article highlights our experience and perspective in building an integrated and centralized laboratory to provide early discovery and preclinical-stage bioanalytical support with high operational efficiency, cost-effectiveness and data integrity. The advantages of in-house nonregulated bioanalytical support include better control of data quality, faster turnaround times, real-time knowledge sharing and troubleshooting, and lower near- and long-term costs. The success of an in-house model depends upon a comprehensively optimized and streamlined workflow, fueled by continuous improvements and implementation of innovative technologies.

Evaluation of five commercial DNA extraction kits using Salmonella as a model for implementation of rapid Nanopore sequencing in routine diagnostic laboratories.

Oxford Nanopore long-read sequencing offers advantages over Illumina short reads for the identification and characterization of bacterial pathogens for outbreak detection and surveillance activities within a diagnostic public health laboratory context. Compared to Illumina, Nanopore is more cost-effective for small batches, has a lower capital cost and has a faster turnaround time, in addition to the ability to assemble complete bacterial genomes. The quantity and quality of DNA required for Nanopore sequencing are greater than for Illumina, and the DNA extraction methods recommended for obtaining high-molecular-weight DNA are different from those typically used in diagnostic laboratories. Using a Salmonella isolate with a previously closed PacBio genome as a model Enterobacteriaceae organism, we evaluated the quantity, quality and fragmentation of five commercial DNA extraction kits. Nanopore sequencing performance was evaluated for the top three methods: Qiagen EZ1 DNA Tissue, Qiagen DNeasy Blood and Tissue, and a modified, in-house version of the MasterPure Complete DNA and RNA purification. To evaluate the effect of post-extraction DNA purification methods, we subjected extracted DNA from the three selected extraction methods to purification by AMPure beads or ethanol precipitation and compared these outputs with untreated DNA as a control. All methods are suitable for routine whole-genome sequencing (WGS), since all 60 replicates had very high genome recovery rates, with ≥98 % of the reference genome covered by mapped Nanopore reads. For 85 % of the replicates, assembly was able to produce a complete, circular chromosome using either Flye or Canu. In most cases, it is recommended to move directly from extraction to sequencing, as untreated DNA had the highest rates of genome closure regardless of extraction method. Using our evaluation criteria, the Qiagen DNeasy Blood and Tissue kit was found to be the best overall method due to its low cost, ability to scale from single tubes to 96-well plates, and high consistency in yield and sequencing performance.