Charles W. Stratton

The 2019 novel coronavirus disease (COVID‐19) now is considered a global public health emergency. One of the unprecedented challenges is defining optimal therapy for those patients with severe pneumonia and systemic manifestations of COVID‐19. 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. In order 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 6 major therapeutic goals for COVID‐19 therapy based on 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. It can be argued that whether a symptomatic respiratory illness is COVID‐19, influenza or another respiratory pathogen, an oxygen saturation less that 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 reducing 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. Patient 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 world‐wide 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.

Advanced techniques in the field of diagnostic microbiology have made amazing progress over the past 25 years due largely to a technological revolution in the molecular aspects of microbiology [1, 2]. In particular, rapid molecular methods for nucleic acid amplification and characterization combined with automation in the clinical microbiology laboratory as well as user-friendly software and robust laboratory informatics systems have significantly broadened the diagnostic capabilities of modern clinical microbiology laboratories. Molecular methods such as nucleic acid amplification tests (NAATs) rapidly are being developed and introduced in the clinical laboratory setting [3, 4]. Indeed, every section of the clinical microbiology laboratory, including bacteriology, mycology, mycobacteriology, parasitology, and virology, has benefited from these advanced techniques. Because of the rapid development and adaptation of these molecular techniques, the interpretation and relevance of the results produced by such molecular methods continues to lag behind. The purpose of this chapter is to review, update, and discuss the interpretation and relevance of results produced by these advanced molecular techniques.

MicroRNAs (miRNAs) encoded by viral genome or host have been found participating in host-microbe interactions. Differential expression profiles of miRNAs were shown linking to specific disease pathologies which indicated its potency as diagnostic/prognostic biomarkers of infectious disease. This was emphasized by the discovery of circulating miRNAs which were found to be remarkably stable in mammalian biofluids. Standardized methods of miRNA quantification including RNA isolation should be established before they will be ready for use in clinical practice.

Training individuals to make accurate decisions from medical images is a critical component of education in diagnostic pathology. We describe a joint experimental and computational modeling approach to examine the similarities and differences in the cognitive processes of novice participants and experienced participants (pathology residents and pathology faculty) in cancer cell image identification. For this study we collected a bank of hundreds of digital images that were identified by cell type and classified by difficulty by a panel of expert hematopathologists. The key manipulations in our study included examining the speed-accuracy tradeoff as well as the impact of prior expectations on decisions. In addition, our study examined individual differences in decision-making by comparing task performance to domain general visual ability (as measured using the Novel Object Memory Test (NOMT) (Richler et al. Cognition 166:42–55, 2017). Using signal detection theory and the diffusion decision model (DDM), we found many similarities between experts and novices in our task. While experts tended to have better discriminability, the two groups responded similarly to time pressure (i.e., reduced caution under speed instructions in the DDM) and to the introduction of a probabilistic cue (i.e., increased response bias in the DDM). These results have important implications for training in this area as well as using novice participants in research on medical image perception and decision-making.

Training individuals to make accurate decisions from medical images is a critical component of education in diagnostic pathology. We describe a joint experimental and computational modeling approach to examine the similarities and differences in the cognitive processes of novice participants and experienced participants (pathology residents and pathology faculty) in cancer cell image identification. For this study we collected a bank of hundreds of digital images that were identified by cell type and classified by difficulty by a panel of expert hematopathologists. The key manipulations in our study included examining the speed-accuracy tradeoff as well as the impact of prior expectations on decisions. In addition, our study examined individual differences in decision-making by comparing task performance to domain general visual ability (as measured using the Novel Object Memory Test (NOMT) (Richler et al., 2017). Using Signal Detection Theory (SDT) and the Diffusion Decision Model (DDM), we found many similarities between expert and novices in our task. While experts tended to have better discriminability, the two groups responded similarly to time pressure (i.e., reduced caution under speed instructions in the DDM) and to the introduction of a probabilistic cue (i.e., increased response bias in the DDM). These results have important implications for training in this area as well as using novice participants in research on medical image perception and decision-making.

One of the most common urologic problems afflicting millions of people worldwide is urinary tract infection (UTI). The severity of UTIs ranges from asymptomatic bacteriuria to acute cystitis, and in severe cases, pyelonephritis and urosepsis. The primary cause of UTIs is uropathogenic Escherichia coli (UPEC), for which current antibiotic therapies often fail. UPEC forms multicellular communities known as biofilms on urinary catheters, as well as on and within bladder epithelial cells. Biofilm formation protects UPEC from environmental conditions, antimicrobial therapy, and the host immune system. Previous studies have investigated UPEC biofilm formation in aerobic conditions (21% oxygen); however, urine oxygen tension is reduced (4–6%), and urine contains molecules that can be used by UPEC as alternative terminal electron acceptors (ATEAs) for respiration. This study was designed to determine whether these different terminal electron acceptors utilized by E. coli influence biofilm formation. A panel of 50 urine-associated E. coli isolates was tested for the ability to form biofilm under anaerobic conditions and in the presence of ATEAs. Biofilm production was reduced under all tested sub-atmospheric levels of oxygen, with the notable exception of 4% oxygen, the reported concentration of oxygen within the bladder.

Advanced techniques in the field of diagnostic microbiology have made amazing progress over the past two decades due largely to a technological revolution in the molecular aspects of microbiology [1, 2]. In particular, rapid molecular methods for nucleic acid amplification and characterization combined with automation and user-friendly software have significantly broadened the diagnostic capabilities of modern clinical microbiology laboratories. Molecular methods such as nucleic acid amplification tests (NAATs) rapidly are being developed and introduced in the clinical laboratory setting. Indeed, every section of the clinical microbiology laboratory, including bacteriology, mycology, mycobacteriology, parasitology, and virology, have benefited from these advanced techniques. Because of the rapid development and adaptation of these molecular techniques, the interpretation and relevance of the results produced by such molecular methods has lagged somewhat behind. The purpose of this chapter is to review and discuss the interpretation and relevance of results produced by these advanced molecular techniques. Moreover, this chapter will address the “myths” of NAATs, as these myths can markedly influence the interpretation and relevance of these results.

Biomarkers are continuously being sought in the field of diagnostic microbiology for the laboratory diagnosis and assessment of microbial infections. A set of clinical and laboratory criteria necessary for an ideal diagnostic marker of infection have previously been proposed by Ng and his colleagues [1]. According these criteria, an ideal biomarker should possess at a minimum the following characteristics: (a) biochemically, a biomarker should be stable and remain significantly deregulated in the body fluid compartment for at least 12–24 h even after commencement of appropriate treatment that may allow an adequate time window for specimen collection or storage without significant decomposition of the active compound until laboratory processing; (b) its concentration should be determined quantitatively and the method of measurement should be automatic, rapid, easy, and inexpensive; (c) the collection of a specimen should be minimally invasive and require a small volume (e.g., <0.5 mL blood). Numerous biomarkers have been found and tested in clinical practice. Currently, microRNA (miRNA) molecules are without a doubt the biomarkers with the greatest potential capacities in the diagnostic microbiology field.