Improving the accuracy of medical image interpretation is critical to improving the diagnosis of many diseases. Using both novices (undergraduates) and experts (medical professionals), we investigated methods for improving the accuracy of a single decision maker and a group of decision makers by aggregating repeated decisions in different ways. Participants made classification decisions (cancerous versus non-cancerous) and confidence judgments on a series of cell images, viewing and classifying each image twice. We first examined whether it is possible to improve individual-level performance by using the maximum confidence slating algorithm (Koriat, 2012b), which leverages metacognitive ability by using the most confident response for an image as the ‘final response’. We find maximum confidence slating improves individual classification accuracy for both novices and experts. Building on these results, we show that aggregation algorithms based on confidence weighting scale to larger groups of participants, dramatically improving diagnostic accuracy, with the performance of groups of novices reaching that of individual experts. In sum, we find that repeated decision making and confidence weighting can be a valuable way to improve accuracy in medical image decision-making and that these techniques can be used in conjunction with each other.

Nearly 40 years have elapsed since the invention of the PCR, with its extremely sensitive and specific ability to detect nucleic acids via in vitro enzyme-mediated amplification. In turn, more than 2 years have passed since the onset of the coronavirus disease 2019 (COVID-19) pandemic, during which time molecular diagnostics for infectious diseases have assumed a larger global role than ever before. In this context, we review broadly the progression of molecular techniques in clinical microbiology, to their current prominence. Notably, these methods now entail both the detection and quantification of microbial nucleic acids, along with their sequence-based characterization. Overall, we seek to provide a combined perspective on the techniques themselves, as well as how they have come to shape health care at the intersection of technologic innovation, pathophysiologic knowledge, clinical/laboratory logistics, and even financial/regulatory factors.

Improving the accuracy of medical image interpretation can improve the diagnosis of numerous diseases. We compared different approaches to aggregating repeated decisions about medical images to improve the accuracy of a single decision maker. We tested our algorithms on data from both novices (undergraduates) and experts (medical professionals). Participants viewed images of white blood cells and made decisions about whether the cells were cancerous or not. Each image was shown twice to the participants and their corresponding confidence judgments were collected. The maximum confidence slating (MCS) algorithm leverages metacognitive abilities to consider the more confident response in the pair of responses as the more accurate "final response" (Koriat, 2012), and it has previously been shown to improve accuracy on our task for both novices and experts (Hasan et al., 2021). We compared MCS to similarity-based aggregation (SBA) algorithms where the responses made by the same participant on similar images are pooled together to generate the "final response." We determined similarity by using two different neural networks where one of the networks had been trained on white blood cells and the other had not. We show that SBA improves performance for novices even when the neural network had no specific training on white blood cell images. Using an informative representation (i.e., network trained on white blood cells) allowed one to aggregate over more neighbors and further boosted the performance of novices. However, SBA failed to improve the performance for experts even with the informative representation. This difference in efficacy of the SBA suggests different decision mechanisms for novices and experts.

This chapter discusses Enterobacteriaceae, which consist of a large, heterogeneous group of aerobic gram-negative bacilli whose natural habitat is the gastrointestinal tract of animals and humans. It highlights three members of the Enterobacteriaceae that are considered pathogens whenever they are isolated from humans: namely Salmonella, Shigella, and Yersinia. It describes the unique clinical features of the pathogens, which cause significant community- and hospital-acquired infections. Escherichia coli is the most frequently isolated pathogen, and it has been noted to have a unique pathogenesis in terms of its ability to cause urinary tract infections. This pathogenesis involves adherent fimbriae and production of glycocalyx, which allow E. coli to adhere to normal bladder epithelium and invade bladder cells, where it is able to survive in biofilm colonies.

Abstract Introduction/Objective The COVID-19 pandemic exacerbated deficiencies of testing personnel, reagents, supplies and disposables, instruments, and automation in many clinical laboratories. Upon entering respiratory season, a strategy was warranted to optimize laboratory resources when supplies were already limited and expected respiratory season test volume was unknown. An algorithm was devised to prioritize test ordering and TAT based on patient clinical scenario. Methods/Case Report The institutional respiratory season SARS-CoV-2 algorithm was constructed by a multidisciplinary team including infectious disease, infection prevention, laboratory, and IT/LIS leadership. CDC guidance on influenza testing was incorporated. Antigen-based testing was discontinued; only molecular amplification- based platforms with FDA EUA were utilized. Platforms had a range of TAT (20 minutes to 8 hours) and included fully- automated high throughput, rapid random access, point-of-care, and CDC SARS-CoV-2 assays. Test bundles included SARS-CoV-2 (monoplex), or SARS-CoV-2 + fluA&B (triplex), or SARS-CoV-2 + respiratory pathogen panel (multiplex RPP; includes 22 targets, including flu A&B). Results (if a Case Study enter NA) Key factors in the algorithm included whether the patient was outpatient or inpatient, hospital employee or not, symptomatic or not, immunocompetent or immunocompromised, and whether a concurrent order for other respiratory pathogens was included or not. Clinician responses for these factors determined the type of swab collected (wet swab in VTM or dry swab) and how quickly the TAT was indicated for a given patient using a colored-dot sticker system. Priority TAT in decreasing order was symptomatic inpatients, asymptomatic pre- procedure patients, asymptomatic admissions, symptomatic employees, and symptomatic outpatients. Conclusion An algorithm for respiratory pathogen testing during an unprecedented respiratory season prioritizes result TAT to an individual patient’s clinical situation while maximizing laboratory stewardship by eliminating redundant influenza testing and requiring ‘all upfront’ orders to avoid add-on orders that require ‘dumpster diving’ for samples. Limitations include inherent differences in sensitivity, LOD, and specificity when multiple different platforms are utilized to detect the same analytes.

OBJECTIVESWe developed and participated in a 1-week laboratory medicine training presented from June 3, 2019, to June 7, 2019.METHODSThe training was a combination of daily morning lectures and case presentations as well as afternoon practical sessions in the clinical laboratory. The content was selected over months by local organizers and the visiting faculty and further modified on site to reflect local needs.RESULTSParticipants identified practice changes that could be realized in the short term but most faced significant barriers to implementation in the absence of structured and long-term follow-up.CONCLUSIONSIn this report, we review insights learned from our experience and reflect on strategies for realistic, meaningful, and relevant contributions in the setting of laboratory medicine-oriented short-term programs.

Many important real-world decision tasks involve the detection of rarely occurring targets (e.g., weapons in luggage, potentially cancerous abnormalities in radiographs). Over the past decade, it has been repeatedly demonstrated that extreme prevalence (both high and low) leads to an increase in errors. While this "prevalence effect" is well established, the cognitive and/or perceptual mechanisms responsible for it are not. One reason for this is that the most common tool for analyzing prevalence effects, Signal Detection Theory, cannot distinguish between different biases that might be present. Through an application to pathology image-based decision-making, we illustrate that an evidence accumulation modeling framework can be used to disentangle different types of biases. Importantly, our results show that prevalence influences both response expectancy and stimulus evaluation biases, with novices (students, N = 96) showing a more pronounced response expectancy bias and experts (medical laboratory professionals, N = 19) showing a more pronounced stimulus evaluation bias.

Journal of Medical VirologyVolume 92, Issue 6 p. 546-547 COMMENTARY The Wuhan SARS-CoV-2—What's next for China Hongzhou Lu, Corresponding Author Hongzhou Lu [email protected] Department of Infectious Diseases, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China Correspondence Hongzhou Lu, Department of Infectious Diseases, Shanghai Public Health Clinical Center, Fudan University, 2901 Caolang Hwy, Shanghai 201508, China. Email: [email protected] Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, 518 Fuquan N Rd, Shanghai 200325, China. Email: [email protected]Search for more papers by this authorCharles W. Stratton, Charles W. Stratton Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TennesseeSearch for more papers by this authorYi-Wei Tang, Corresponding Author Yi-Wei Tang [email protected] orcid.org/0000-0003-4888-6771 Cepheid, Danaher Diagnostic Platform, Shanghai, China Correspondence Hongzhou Lu, Department of Infectious Diseases, Shanghai Public Health Clinical Center, Fudan University, 2901 Caolang Hwy, Shanghai 201508, China. Email: [email protected] Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, 518 Fuquan N Rd, Shanghai 200325, China. Email: [email protected]Search for more papers by this author Hongzhou Lu, Corresponding Author Hongzhou Lu [email protected] Department of Infectious Diseases, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China Correspondence Hongzhou Lu, Department of Infectious Diseases, Shanghai Public Health Clinical Center, Fudan University, 2901 Caolang Hwy, Shanghai 201508, China. Email: [email protected] Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, 518 Fuquan N Rd, Shanghai 200325, China. Email: [email protected]Search for more papers by this authorCharles W. Stratton, Charles W. Stratton Department of Pathology, Microbiology and Immunology, Vanderbilt University Medical Center, Nashville, TennesseeSearch for more papers by this authorYi-Wei Tang, Corresponding Author Yi-Wei Tang [email protected] orcid.org/0000-0003-4888-6771 Cepheid, Danaher Diagnostic Platform, Shanghai, China Correspondence Hongzhou Lu, Department of Infectious Diseases, Shanghai Public Health Clinical Center, Fudan University, 2901 Caolang Hwy, Shanghai 201508, China. Email: [email protected] Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, 518 Fuquan N Rd, Shanghai 200325, China. Email: [email protected]Search for more papers by this author First published: 01 March 2020 https://doi.org/10.1002/jmv.25738Citations: 33Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. REFERENCES 1Lu H, Stratton CW, Tang YW. Outbreak of pneumonia of unknown etiology in Wuhan China: the mystery and the miracle. J Med Virol. 2020; 92(4): 401-402. 10.1002/jmv.25678 CASPubMedWeb of Science®Google Scholar 2de Wit E, van Doremalen N, Falzarano D, Munster VJ. SARS and MERS: recent insights into emerging coronaviruses. Nat Rev Microbiol. 2016; 14(8): 523-534. https://doi.org/10.1038/nrmicro.2016.1081 10.1038/nrmicro.2016.81 PubMedWeb of Science®Google Scholar 3Cui J, Li F, Shi ZL. Origin and evolution of pathogenic coronaviruses. Nat Rev Microbiol. 2019; 17(3): 181-192. https://doi.org/110.1038/s41579-41018-40118-41579 10.1038/s41579-018-0118-9 CASPubMedWeb of Science®Google Scholar 4Zhu N, Zhang D, Wang W, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. 2020; 382(8): 727-733. 10.1056/NEJMoa2001017 CASPubMedWeb of Science®Google Scholar 5Li Q, Guan X, Wu P, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus-infected pneumonia. N Engl J Med. 2020. 10.1056/NEJMoa2001316 Web of Science®Google Scholar 6Chen L, Liu W, Zhang Q, et al. RNA based mNGS approach identifies a novel human coronavirus from two individual pneumonia cases in 2019 Wuhan outbreak. Emerg Microbes Infect. 2020; 9(1): 313-319. https://doi.org/10.1080/22221751.22222020.21725399 10.1080/22221751.2020.1725399 CASPubMedWeb of Science®Google Scholar 7Chan JF, Kok KH, Zhu Z, et al. Genomic characterization of the 2019 novel human-pathogenic coronavirus isolated from a patient with atypical pneumonia after visiting Wuhan. Emerg Microbes Infect. 2020; 9(1): 221-236. https://doi.org/10.1080/22221751.22222020.21719902 10.1080/22221751.2020.1719902 CASPubMedWeb of Science®Google Scholar 8Holshue ML, DeBolt C, Lindquist S, et al. First case of 2019 novel coronavirus in the United States. N Engl J Med. 2020. 10.1056/NEJMoa2001191 PubMedWeb of Science®Google Scholar 9Huang C, Wang Y, Li X, et al. Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet. 2020; 395: 497-506. 10.1016/S0140-6736(20)30183-5 CASPubMedWeb of Science®Google Scholar 10Lu R, Zhao X, Li J, et al. Genomic characterisation and epidemiology of 2019 novel coronavirus: implications for virus origins and receptor binding. Lancet. 2020; 395: 565-574. 10.1016/S0140-6736(20)30251-8 CASPubMedWeb of Science®Google Scholar 11Rothe C, Schunk M, Sothmann P, et al. Transmission of 2019-nCoV infection from an asymptomatic contact in Germany. N Engl J Med. 2020. 10.1056/NEJMc2001468 PubMedWeb of Science®Google Scholar 12Wang D, Hu B, Hu C, et al. Clinical characteristics of 138 hospitalized patients with 2019 novel coronavirus-infected pneumonia in Wuhan, China. JAMA. 2020. 10.1001/jama.2020.1585 Web of Science®Google Scholar 13Zhou P, Yang XL, Wang XG, et al. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature. 2020. 10.1038/s41586-020-2012-7 Web of Science®Google Scholar 14Kofi Ayittey F, Dzuvor C, Kormla Ayittey M, Bennita Chiwero N, Habib A. Updates on Wuhan 2019 novel coronavirus epidemic. J Med Virol. 2020; 92(4): 403-407. 10.1002/jmv.25695 CASPubMedWeb of Science®Google Scholar 15Heffernan JM, Smith RJ, Wahl LM. Perspectives on the basic reproductive ratio. J R Soc Interface. 2005; 2(4): 281-293. https://doi.org/210.1098/rsif.2005.0042 10.1098/rsif.2005.0042 CASPubMedWeb of Science®Google Scholar 16Xu Z, Li S, Tian S, Li H, Kong L. Full spectrum of COVID-19 severity still being depicted. Lancet. 2020. https://doi.org/10.1016/s0140-6736(20)30308-1 10.1016/S0140-6736(20)30308-1 Web of Science®Google Scholar 17Park SW, Bolker BM, Champredon D, et al. Reconciling early-outbreak estimates of the basic reproduction number and its uncertainty: a new framework and applications to the novel coronavirus (2019-NCoV) outbreak. medRxiv. 2020. https://doi.org/10.1101/2020.01.30.20019877 Google Scholar 18Liu P, Chen W, Chen JP. Viral metagenomics revealed sendai virus and coronavirus infection of Malayan pangolins (Manis javanica). Viruses. 2019; 11(11): 979. https://doi.org/11110910.11113390/v11110979 10.3390/v11110979 CASGoogle Scholar 19Wang LF, Crameri G. Emerging zoonotic viral diseases. Rev Sci Tech. 2014; 33(2): 569-581. https://doi.org/10.20506/rst.20533.20502.22311 10.20506/rst.33.2.2311 PubMedWeb of Science®Google Scholar 20Barnard DL, Day CW, Bailey K, et al. Evaluation of immunomodulators, interferons and known in vitro SARS-coV inhibitors for inhibition of SARS-coV replication in BALB/c mice. Antivir Chem Chemother. 2006; 17(5): 275-284. https://doi.org/10.1177/095632020601700505 10.1177/095632020601700505 CASPubMedGoogle Scholar 21Sheahan TP, Sims AC, Graham RL, et al. Broad-spectrum antiviral GS-5734 inhibits both epidemic and zoonotic coronaviruses. Sci Transl Med. 2017; 9:eaa13653. 10.1126/scitranslmed.aal3653 Web of Science®Google Scholar 22Assiri A, McGeer A, Perl TM, et al. Hospital outbreak of Middle East respiratory syndrome coronavirus. N Engl J Med. 2013; 369(5): 407-416. https://doi.org/10.1056/NEJMoa1306742 10.1056/NEJMoa1306742 CASPubMedWeb of Science®Google Scholar 23McDonald LC, Simor AE, Su IJ, et al. SARS in healthcare facilities, Toronto and Taiwan. Emerg Infect Dis. 2004; 10(5): 777-781. https://doi.org/10.3201/eid1005.030791 10.3201/eid1005.030791 PubMedWeb of Science®Google Scholar 24Munster VJ, Koopmans M, van Doremalen N, van Riel D, de Wit E. A novel coronavirus emerging in China—key questions for impact assessment. N Engl J Med. 2020; 382: 692-694. 10.1056/NEJMp2000929 CASPubMedWeb of Science®Google Scholar 25Smith PW, Anderson AO, Christopher GW, et al. Designimg a biocontainment unit to care for patients with serious communicable deseases: a consensus statement. Biosec Bioterr. 2006; 4: 351-365. 10.1089/bsp.2006.4.351 PubMedWeb of Science®Google Scholar Citing Literature Volume92, Issue6Special Issue on New coronavirus (2019‐nCoV or SARS‐CoV‐2) and the outbreak of the respiratory illness (COVID‐19): Part‐IIIJune 2020Pages 546-547 ReferencesRelatedInformation

Journal of Medical VirologyVolume 92, Issue 4 p. 401-402 COMMENTARY Outbreak of pneumonia of unknown etiology in Wuhan, China: The mystery and the miracle Hongzhou Lu, Corresponding Author Hongzhou Lu luhongzhou@fudan.edu.cn Shanghai Public Health Clinical Center, Fudan University, Shanghai, China Correspondence Hongzhou Lu, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China. Email: luhongzhou@fudan.edu.cn (HL) Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, Shanghai, China. Email: yi-wei.tang@cepheid.com (Y-WT)Search for more papers by this authorCharles W. Stratton, Charles W. Stratton Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TennesseeSearch for more papers by this authorYi-Wei Tang, Corresponding Author Yi-Wei Tang yi-wei.tang@cepheid.com orcid.org/0000-0003-4888-6771 Cepheid, Danaher Diagnostic Platform, Shanghai, China Correspondence Hongzhou Lu, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China. Email: luhongzhou@fudan.edu.cn (HL) Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, Shanghai, China. Email: yi-wei.tang@cepheid.com (Y-WT)Search for more papers by this author Hongzhou Lu, Corresponding Author Hongzhou Lu luhongzhou@fudan.edu.cn Shanghai Public Health Clinical Center, Fudan University, Shanghai, China Correspondence Hongzhou Lu, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China. Email: luhongzhou@fudan.edu.cn (HL) Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, Shanghai, China. Email: yi-wei.tang@cepheid.com (Y-WT)Search for more papers by this authorCharles W. Stratton, Charles W. Stratton Department of Pathology, Microbiology, and Immunology, Vanderbilt University Medical Center, Nashville, TennesseeSearch for more papers by this authorYi-Wei Tang, Corresponding Author Yi-Wei Tang yi-wei.tang@cepheid.com orcid.org/0000-0003-4888-6771 Cepheid, Danaher Diagnostic Platform, Shanghai, China Correspondence Hongzhou Lu, Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China. Email: luhongzhou@fudan.edu.cn (HL) Yi-Wei Tang, Cepheid, Danaher Diagnostic Platform, Shanghai, China. Email: yi-wei.tang@cepheid.com (Y-WT)Search for more papers by this author First published: 16 January 2020 https://doi.org/10.1002/jmv.25678Citations: 434Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article.Citing Literature Volume92, Issue4Special Issue: 2019 Novel Coronavirus Origin, Evolution, Disease, Biology and Epidemiology: Part-IApril 2020Pages 401-402 This article also appears in:New coronavirus 2019-nCoV and the outbreak of the respiratory illness RelatedInformation

A 40-year-old woman with a history of recurrent medulloblastoma presented for outpatient evaluation of a postoperative wound of the posterior scalp. The patient was diagnosed with medulloblastoma 9 years prior and was initially managed with surgical resection and craniospinal radiotherapy. She had since experienced two cerebellar recurrences, each of which was treated by tumor resection, radiation, and/or chemotherapy, with the most recent recurrence being 8 months prior. Surgical management of the last recurrence included the placement of a posterior titanium mesh; the craniotomy site subsequently experienced poor wound closure, requiring multiple revision procedures. She had been prescribed several months of oral amoxicillin-clavulanate to prevent infection of the surgical site.

The 2019 novel coronavirus disease (COVID-19) now is considered a global public health emergency. One of the unprecedented challenges is defining the optimal therapy for those patients with severe pneumonia and systemic manifestations of COVID-19. The optimal therapy should be largely based on the pathogenesis of infections caused by this novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Since the onset of COVID-19, there have been many prepublications and publications reviewing the therapy of COVID-19 as well as many prepublications and publications reviewing the pathogenesis of SARS-CoV-2. However, there have been no comprehensive reviews that link COVID-19 therapies to the pathogenic mechanisms of SARS-CoV-2. To link COVID-19 therapies to pathogenic mechanisms of SARS-CoV-2, we performed a comprehensive search through MEDLINE, PubMed, medRxiv, EMBASE, Scopus, Google Scholar, and Web of Science using the following keywords: COVID-19, SARS-CoV-2, novel 2019 coronavirus, pathology, pathologic, pathogenesis, pathophysiology, coronavirus pneumonia, coronavirus infection, coronavirus pulmonary infection, coronavirus cardiovascular infection, coronavirus gastroenteritis, coronavirus autopsy findings, viral sepsis, endotheliitis, thrombosis, coagulation abnormalities, immunology, humeral immunity, cellular immunity, inflammation, cytokine storm, superantigen, therapy, treatment, therapeutics, immune-based therapeutics, antiviral agents, respiratory therapy, oxygen therapy, anticoagulation therapy, adjuvant therapy, and preventative therapy. Opinions expressed in this review also are based on personal experience as clinicians, authors, peer reviewers, and editors. This narrative review linking COVID-19 therapies with pathogenic mechanisms of SARS-CoV-2 has resulted in six major therapeutic goals for COVID-19 therapy based on the pathogenic mechanisms of SARS-CoV-2. These goals are listed below: 1. The first goal is identifying COVID-19 patients that require both testing and therapy. This is best accomplished with a COVID-19 molecular test from symptomatic patients as well as determining the oxygen saturation in such patients with a pulse oximeter. Whether a symptomatic respiratory illness is COVID-19, influenza, or another respiratory pathogen, an oxygen saturation less than 90% means that the patient requires medical assistance. 2. The second goal is to correct the hypoxia. This goal generally requires hospitalization for oxygen therapy; other respiratory-directed therapies such as prone positioning or mechanical ventilation are often used in the attempt to correct hypoxemia due to COVID-19. 3. The third goal is to reduce the viral load of SARS-CoV-2. Ideally, there would be an oral antiviral agent available such as seen with the use of oseltamivir phosphate for influenza. This oral antiviral agent should be taken early in the course of SARS-CoV-2 infection. Such an oral agent is not available yet. Currently, two options are available for reducing the viral load of SARS-CoV-2. These are post-Covid-19 plasma with a high neutralizing antibody titer against SARS-CoV-2 or intravenous remdesivir; both options require hospitalization. 4. The fourth goal is to identify and address the hyperinflammation phase often seen in hospitalized COVID-19 patients. Currently, fever with an elevated C-reactive protein is useful for diagnosing this hyperinflammation syndrome. Low-dose dexamethasone therapy currently is the best therapeutic approach. 5. The fifth goal is to identify and address the hypercoagulability phase seen in many hospitalized COVID-19 patients. Patients who would benefit from anticoagulation therapy can be identified by a marked increase in d-dimer and prothrombin time with a decrease in fibrinogen. To correct this disseminated intravascular coagulation-like phase, anticoagulation therapy with low molecular weight heparin is preferred. Anticoagulation therapy with unfractionated heparin is preferred in COVID-19 patients with acute kidney injuries. 6. The last goal is prophylaxis for persons who are not yet infected. Potential supplements include vitamin D and zinc. Although the data for such supplements is not extremely strong, it can be argued that almost 50% of the population worldwide has a vitamin D deficiency. Correcting this deficiency would be beneficial regardless of any impact of COVID-19. Similarly, zinc is an important supplement that is important in one's diet regardless of any effect on SARS-CoV-2. As emerging therapies are found to be more effective against the SARS-CoV-2 pathogenic mechanisms identified, they can be substituted for those therapies presented in this review.

This commentary highlights the article by Grumaz et al that describes the use of molecular sequencing for fast detection of pathogens directly from blood samples from septic patients.

Abstract Introduction/Objective Hand hygiene (HH) decreases healthcare-associated infections (HAI). Available products include alcohol-based gels, foams, wipes, and “gold-standard” hand-washing with soap and water. We tested an investigational device (HyLuxO3; GMI, LLC, patent pending) for antimicrobial effect (AME). HyLuxO3 was engineered to deliver UV-C light energy and high velocity O3 airflow to safely achieve human skin antisepsis within OSHA and EPA regulatory limits. Combined UV and O3 has yet to be evaluated for HH and may demonstrate synergistic AME. Methods HyLuxO3 was tested on LB agar to titrate device variables to ascertain intensities for optimal AME; later testing was performed on VITRO-SKIN (Florida Suncare Testing, Bunnell, FL), a human skin surrogate. ATCC strains of MRSA, Klebsiella pneumoniae, Pseudomonas aeruginosa, and Candida albicans were used to test AME vs. vegetative microbes; Bacillus atrophaeus spores were used as a surrogate for C. difficile. Tested variables included time under device, [O3], airflow velocity, 222 and/or 254 nm UV light, sample distance from UV lamp, and UV beam width. Positive controls were used to calculate log-kill curves for AME. Results Similar results were seen on LB agar and VITRO-SKIN. >7 log-kill and >5 log-kill were acheived vs. vegetative microbes (<30 sec) and spores (60 sec), respectively, under optimized variables. Presence of UV light and sample distance from and time under the device were the most important variables. 254 nm UV had a significantly better AME than 222 nm; combining both UV lamps had a significant synergistic AME. The narrowest UV beam (2 mm) yielded the greatest AME (total energy input kept constant). Adding O3 to UV had a modest but significant synergistic effect; optimal [O3] was 0.3-0.8 ppm. Changing airflow velocity had no significant effect on AME. Conclusion HyLuxO3 is a novel device that achieves >7 log-kill vs. common pathogenic vegetative microbes and >5 log-kill vs. spores using combined UV light and [O3] safe for human skin antisepsis (and surface/fomite decontamination)- and- yields such impressive AME on faster timescales than those required by bleach/other chemical products unsuitable for human skin. Future studies on human hands (using many other microbes) will determine if HyLuxO3 meets regulatory and efficacy requirements for use in and beyond healthcare settings, especially with the specter of emerging respiratory viruses.

As the 2019 novel coronavirus disease (COVID-19) outbreak has evolved in each country, the approach to the laboratory assessment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has had to evolve as well. This review addresses the evolving approach to the laboratory assessment of COVID-19 and discusses how algorithms for testing have been driven, in part, by the demand for testing overwhelming the capacity to accomplish such testing. This review focused on testing in the USA, as this testing is evolving, whereas in China and other countries such as South Korea testing is widely available and includes both molecular testing for SARS-CoV-2 as well as serological testing using both enzyme-linked immunosorbent assay methodology and lateral flow immunoassay methodology. Although commercial testing systems are becoming available, there will likely be insufficient numbers of such tests due to high demand. Serological testing will be the next testing issue as the COVID-19 begins to subside. This will allow immunity testing as well as will allow the parameters of the COVID-19 outbreak to be defined.

This article provides a reusable dataset describing detailed phenotypic and associated clinical parameters in n=303 clinical isolates of urinary Escherichia coli collected at Vanderbilt University Medical Center. De-identified clinical data collected with each isolate are detailed here and correlated to biofilm abundance and metabolomics data. Biofilm-abundance data were collected for each isolate under different in vitro conditions along with datasets quantifying biofilm abundance of each isolate under different conditions. Metabolomics data were collected from a subset of bacterial strains isolated from uncomplicated cases of cystitis or cases with no apparent symptoms accompanying colonization. For more insight, please see "Defining a Molecular Signature for Uropathogenic versus Urocolonizing Escherichia coli: The Status of the Field and New Clinical Opportunities" [1].

Pseudomonas aeruginosa is an important bacterial cause of a variety of infections and is associated with high morbidity and mortality. Infections caused by this bacterium are becoming more difficult to treat due to increasing resistance to many of the available antibiotics. Ceftolozane–tazobactam and ceftazidime–avibactam are two new cephalosporin/β-lactamase inhibitor combination antimicrobials that have demonstrated excellent in vitro activity against several multi-drug-resistant pathogens, including multi-drug-resistant P. aeruginosa . Cases of infections with isolates of multi-drug-resistant P. aeruginosa that are resistant to both of these antimicrobials have rarely been reported. We report a case of mastoiditis caused by P. aeruginosa that was resistant to both ceftolozane–tazobactam and ceftazidime–avibactam.

The COVID-19 outbreak has had a major impact on clinical microbiology laboratories in the past several months. This commentary covers current issues and challenges for the laboratory diagnosis of infections caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In the preanalytical stage, collecting the proper respiratory tract specimen at the right time from the right anatomic site is essential for a prompt and accurate molecular diagnosis of COVID-19. Appropriate measures are required to keep laboratory staff safe while producing reliable test results. In the analytic stage, real-time reverse transcription-PCR (RT-PCR) assays remain the molecular test of choice for the etiologic diagnosis of SARS-CoV-2 infection while antibody-based techniques are being introduced as supplemental tools. In the postanalytical stage, testing results should be carefully interpreted using both molecular and serological findings. Finally, random-access, integrated devices available at the point of care with scalable capacities will facilitate the rapid and accurate diagnosis and monitoring of SARS-CoV-2 infections and greatly assist in the control of this outbreak.

In December 2019, a nationwide coronavirus 2019 (COVID-19) epidemic began in China and was finally controlled in early March 2020 [1, 2]. Few new domestic cases have been reported since late March ...