You're using an outdated browser. Please upgrade to a modern browser for the best experience.
Conventional and Modern Methods for Management of Sepsis: Comparison
Please note this is a comparison between Version 2 by Jason Zhu and Version 1 by Anupam Jyoti.

Sepsis is one of the deadliest disorders in the new21th century due to specific limitations in early and differential diagnosis. Moreover, antimicrobial resistance (AMR) is becoming the dominant threat to human health globally. The only way to encounter the spread and emergence of AMR is through the active detection and identification of the pathogen along with the quantification of resistance. For better management of such disease, there is an essential requirement to approach many suitable diagnostic techniques for the proper administration of antibiotics and elimination of these infectious diseases. 

  • antimicrobial resistance
  • sepsis
  • early diagnosis
  • conventional methods

1. Introduction

Sepsis is one of the most ambiguous disorders in medicine due to its early onset. Previously, sepsis was thought to be the process through which flesh decomposes, swamps acquire a putrid odour, and wounds deteriorate [1]. Later, it was renamed as systemic infection, which is commonly referred to as “blood poisoning” and was acknowledged as a result of pathogenic organisms growing within the circulation and evading the host’s immune system. As a result, it was established that the pathogen, not the host, is the perpetrator in the pathophysiology of sepsis [2]. Pathogens interact in the host immune system during infection, triggering a downstream inflammatory cascade including cytokines and other mediators, eventually producing immunosuppression, which leads to various types of organ failure and subsequent clinical degeneration [3].
Originally, sepsis was thought to be the result of internal organs rotting or decaying, with sepsis being defined as a result of the host’s systemic inflammatory response syndrome (SIRS) to infection, severe sepsis (sepsis associated with organ dysfunction, hyperfusion, or hypotension), and septic shock (sepsis induced hypotension that persists despite adequate fluid resuscitation) [4]. However, due to a lack of effective antimicrobials and supportive treatment, the definition of sepsis has evolved with time, preventing patients with sepsis from living long enough to be analyzed or they acquire a sequel of organ failure. As a result, the American College of Chest Physicians (ACCP) and the Society of Critical Care Medicine (SCCM) announced SIRS and published new agreements as a definition of sepsis and associated medical criteria (Sepsis-3) with deadly organ dysfunction caused by a dysregulated host response to an infection, which can be accepted as a shift in complete Sequential Organ Failure Assessment (SOFA) score points ≥2 subsequent toward the disease [3,4,5][3][4][5]. The guidelines of the SOFA score were presumed to be zero in the patients having no infection or organ failure whereas a SOFA score ≥2 mirrors a general fatality danger of approx. 10% in a common emergency population with suspected infection [6]. This novel and advanced definition focus on the power of the non-homeostatic response of the host to infection, the potential fatality of the infection that is impressively more than a direct disease, and the requirement for dire acknowledgement. As depicted later, even an unpredictable level of organ dysfunction when it was suspected earlier is related to an in-emergency mortality rate of nearly 10%.
According to the World Health Organization (WHO), in hospitals, sepsis is not just the costliest condition to treat as an additional main source of death, but certain reports have assessed a great many cases at a greater expense and death rates influencing more than 30 million individuals worldwide, thus steadily prompting 6 million deaths with rates of mortality between 20% and 50% [7]. The weight of sepsis is probably most noteworthy in low-income countries [8]. A reported 31.5 million people have are diagnosed with sepsis annually around the world, of which, 19.4 million individuals suffer from severe sepsis while 5.3 million individuals experience death, 3 million cases are estimated in neonates worldwide annually and there are 1.2 million cases in children, with a death rate of 11–19% [9], as well as 75,000 annual deaths in females due to puerperal sepsis globally [5]. Additionally, as per the ongoing safety information of Centers for Disease Control and Prevention (CDC) reports, sepsis inpatient admissions stay high for septic shock, roughly 60%; for severe sepsis, around 36%; for sepsis credited to a particular creature, roughly 31%; and for unknown sepsis, roughly 27% [10]. The latest in-hospital mortality estimates for sepsis patients has decreased from 28% to 18% [11]. Patients with severe sepsis admitted to the ICU increased from 7.2% to 11.1% and hospital mortality in severe sepsis decreased from 35% to 18%, according to a recent study of prospectively collected data from more than 90% of all intensive care unit (ICU) hospitalizations, confirming these trends in both incidence and mortality. Finally, the best epidemiologic evidence shows that severe sepsis is becoming more common while simultaneously becoming less fatal [12].
Because of its narrow window, early and systemic diagnosis of sepsis is a crucial task which may deviate towards shock, organ failure or even death. Delay in every hour in providing the correct treatment results in a decline in the survival rate of sepsis patients by 7.6% [13]. To combat this, non-specific broad-spectrum antibiotics are administered immediately in suspected cases which results not only in poor patient outcomes but also in the development of multi-drug resistance (MDR) [14,15][14][15]. Hence, the diagnostic method should be rapid and applicable to detect pathogens along with drug resistance (preferably within 3–5 h of patient admission) [16,17][16][17]. It should be capable enough to diagnose polymicrobial infections along with unknown and emerging pathogens. Furthermore, the results of the diagnosis should be able to provide appropriate decisions and antibiotic stewardship within a key time window of hours in order to limit morbidity and death [14]. The procedures that are sensitive, specific and quick for identifying the pathogen are therefore the major operational instruments for critical care units [18,19][18][19].

2. Conventional Methods for Management of Sepsis

2.1. Microbiological Methods

The detection of pathogens using these techniques is based on the apparent growth of microorganisms on a suitable culture media (solid agar and broth). There are two different methods and they have been discussed in detail in the section below.

2.1.1. Identification through Blood Culture/Gram Staining

For detection as well as identification of causative pathogens, sampling of blood/urine/lavage from patients, their routine culture followed by the Gram staining test remains the gold standard method [20,21,22][20][21][22]. To enhance the diagnosis, a minimum requirement of blood samples collected aseptically is 0.5–1 mL. Preferably, blood is drawn for blood cultures from two distinct venipunctures sites. One is the central venous catheters, which allows blood to be obtained concurrently from a peripheral and a vascular catheter, allows for faster detection of peripheral bacteremia vs. catheter-related bloodstream infections and appropriate dosing for clinical treatment [23]. Since some microbes can only be identified at the collection site and not in the blood, continuous monitoring of the collection sites is necessary when a positive culture is detected, which facilitates the further processing for pathogen identification in proper sepsis assessment [24]. These are the confirmatory tests to detect the potentiality of microbes in the given sample [25]. Comparatively, gram staining analysis is rapid (<15 min), economical, and provides information about the categorization of the infectious microbe as either Gram-positive, Gram-negative, Gram variable or Gram indeterminate [26,27][26][27].

2.1.2. Identification through Bactec Fx/VITEK 2

This automated system is built on sensors that detect any change in pressure within the blood culture bottle or track the CO2 emitted by actively metabolizing species. Gram-negative microbes take 14 to 24 h in blood culture bottles to detect microbial growth, while Gram-positive bacteria take 24 to 48 h. It is now feasible to eliminate enough pathogenic load for direct identification in Bactec FX without first growing on an agar plate until a blood culture bottle has been marked positive and is suitable for traditional diagnosis [28,29][28][29]. Blood culture bottles are prepared, checked and incubated for 5 days at 35 °C with shaking agitation every 10 min in the instrument. By spreading 0.1 mL of consecutive 10-fold dilutions on a blood agar plate, the quantitative plate count technique is used to determine microbial fixation. They are then tested after overnight incubation at 350 °C on plates containing the colonies [30]. The following are the three main issues with using this culture-based method: Firstly, the conventional microbiological tests require 5 days to detect and identify the pathogens involved in sepsis. As a result, the procedure does not provide results promptly, which may be a significant source of anxiety for patients not having the infection. Secondly, the blood culture/Gram stains analysis has a lower sensitivity. Only 30% to 60% of the overall positive result of sensitivity has been estimated despite using it in the proper analytical manner, standardized procedures and accurate collection of amounts of blood sample [28,29,30,31][28][29][30][31]. These recommended false-negative results which range from 40% to 70% might be owing to a scarcity of particular microbial species that flourish in laboratory culture medium, or distantly similar microbial strains [32,33][32][33]. Another reason for the false-negative result is self-medication and administration of antibiotics to the patients before the sampling for diagnosis [34]. Thirdly, there is the possibility of false positives due to non-adherence with the sterile condition during sample processing. As a consequence, patients receive antibiotics for those bacteria of which they are not infected. This misuse has led to the prolonged exposure of antibiotics, resulting in allergic reactions, toxicity, development of MDR, a long stay of patients in hospitals and hence increased medical costs [34,35,36][34][35][36]. As a result, certain points should be proposed for laboratory experts, concerned bodies and other stakeholders to improve the laboratory service by applying and using refined and emerging technology to classify and assess sensitivity to drugs for various microorganisms. However, there is a need for more intensive interventional trials to assess the effect of these technologies on sepsis treatment in clinical practice.

2.2. Biochemical Test

Biochemical tests including the mannitol test, citrate tests, triple sugar iron (TSI) test, indole test, methyl red test and enzymatic tests such as oxidase tests, urease tests and coagulase tests are used to distinguish pathogenic organisms that depend on the various diverging biochemical processes of different bacteria. For the identification of extracellular and intracellular bacterial enzymes, biochemical tests are used. Hence, they are distinguished based on biochemical activities [37].

3. Modern Methods for Management of Sepsis

There is a paradigm shift from conventional culture and biochemical-based detection of pathogens to modern techniques including molecular as well as emerging methods which rely on detection at the strain level in much less time (20 min to 3 h). System biology has been thrust into the spotlight in molecular research due to technological advances and the knowledge provided by the human genome project, which has accelerated multiple methods that may not only produce an expanded understanding of complicated sepsis pathophysiology but can also articulate undetermined methodologies [38,39][38][39]. Reviewed here are the modern advancements in early detection, including inventive high-throughput approaches.

3.1. Implementing Molecular Detection for the Identification of Pathogens

Molecular detection using PCR depends upon the amplification of the pathogen’s target nucleic acid region using gene-specific primers or probes. For the identification of different pathogens, a variety of molecular targets are used [40,41,42,43][40][41][42][43].

3.1.1. PCR

In 1983, Kary Mullis [44] devised the standard PCR, which allows the detection of a single bacterial pathogen by identifying a specific target DNA sequence [45]. A sensitive examination is portrayed here to classify the microorganisms in the entire bloodstream by the PCR. A particular primer–probe set is intended to reproducibly detect bacteria of purified DNA from whole blood. This assay framework was demonstrated to be comprehensive for all strains, equally from all bacteria. This unique PCR-based test was created to assist amplification from a range of human, bacterial and yeast genomic DNAs due to its inefficiency. A broad sample preparation methodology was designed that was applicable for the DNA purification from various bacteria in whole blood. With the help of this method, it was feasible to distinguish every particular bacterial DNA from whole blood samples inoculated with a minimum of 4 CFU/mL. Co-purified human blood DNA did not influence the sensitivity of detection by PCR [46]. PCR can identify even a single copy of a target DNA sequence under ideal conditions in a given sample. Therefore, prior multiplication enrichment of the microbe is not needed, all things considered with basic DNA probe tests. Thus, PCR-based diagnostic tests have made a significant advanced improvement for infectious agents subsequently [47]. The technology has been updated to expand the usage of traditional PCR. The use of the primers pair in parallel reactions with simultaneous amplification for different target DNA sequences, known as multiplex-PCR, is the first absolute shift. As a result, several DNA sequences replicated in the same processor might be amplified [48]. Another change is nested PCR, which uses two sets of primers with a preferred target for amplification of an internal DNA sequence. The first reaction is carried out using the first set of primers, and the results are subsequently subjected to a second amplification with various primer sets [49].

3.1.2. Real-Time PCR

Despite several advances, high-throughput science research laboratories can redirect standard PCR methods, including nested PCR techniques, due to the immense exposure of excess contamination with amplified products [50]. Traditional PCR procedures rely on automated detection of fluorescence from PCR amplicons, while real-time PCR systems are faster, less sensitive to contamination and require less labour [51,52][51][52]. Real-time PCR techniques also allow for infinite and comparable calibration of the desired sequence, which may help to increase the gap in critical microbial potential [53,54][53][54]. In addition, compared to traditional PCR, real-time PCR has several novel advantages, including simplicity, quantitative capacity and speed [55]. Fluorescence-based real-time PCR is based on the detection of the fluorescent signal generated during DNA amplification. After a specific number of cycles, the real-time assays synthesize a quantity of target DNA. When the amplification of a PCR product is controlled, it is initially observed during cycling by determining the cycle number at which the reporter dye’s discharge energy dominates the background noise. As a result, the threshold cycle is named after this cycle number (Ct). This Ct value is determined during the PCR exponential phase and is inversely proportional to the target’s copy number. As a result, the difference in fluorescence signal is observed before the incident when the beginning of the copy number of the target DNA is higher, and the Ct value is lower [56]. SYBR Green, TaqMan, molecular beacons and scorpions are the four types of fluorescent DNA probes currently available for real-time PCR product detection. All of these probes emit a fluorescent signal that can be used to detect the PCR products [57]. The ability to quantify the process is a key function of RT-PCR [58]. An internal calibrant is put to each well of the assay plates as a reference in each experiment, and PCR is used to examine each well for quantification. The peak heights in the mass spectrum for amplicons determine the proportionate ratios of calibrant to microorganisms [59,60][59][60]. The concentration of the pathogen can be thoroughly evaluated using the known initial concentration of calibrant. Having a significant effect on pathogen detection in diagnostic microbiology, real-time PCR is primarily focused on the pathogen genotype [61]. The role of microbes in various human diseases is examined in these essays, which is the most critical use of real-time technology in microbiology. Due to the strength of complexity the technology has positively evaluated the various microbes and their nucleic acid targets present in a single sample, also it has allowed the differentiation of various forms of microbial genotypes in a single reaction tube [62]. The significance of RT-PCR in microbial load detection is that these techniques are extremely useful since they actively reveal the spectrum of increasing infection, host–pathogen interaction and antimicrobial medication effectiveness. It also enables the administration of antibiotics promptly. Real-time assays are useful for distinguishing serotypes within a particular microbial population [73][63], diagnosing pathogens in clinical samples [74,75][64][65] and bacteria (viruses, bacteria, fungi, protozoa or toxins produced by them that cause diseases) used as biological warfare agents [76][66]. Despite these, molecular techniques including PCR do not provide any information about AMR among pathogens [77][67].

3.1.3. Surface-Enhanced Raman Spectroscopy (SERS)

Surface-enhanced Raman spectroscopy (SERS) is emerging as an important technology for the identification which amplifies the Raman dispersing of the objective particles on a superficial layer of metal made of graphene or other different materials [78,79,80,81,82][68][69][70][71][72]. This technique has the ability to facilitate the label-free nucleic acid identification [83][73]. Surface plasmons are generated by applying an excitation frequency that is in phase with the particle’s plasmon assimilation profile, resulting in a solid electromagnetic field on the metal surface. The emission of the Raman dispersed light is radiated in every direction of the particle is gathered through a microscope and consequently identified. In addition, the Raman dispersed light is then coordinated against a reference profile of microorganisms to be recognized [84,85,86,87][74][75][76][77]. Consequently, SERS can efficiently recognize the occupancy of microbial cells on the outer surface, thus yielding a data-rich spectrum that can be used for microorganism identification [88,89,90,91,92,93,94][78][79][80][81][82][83][84]. In addition to the pathogen identification, SERS has also been utilized for antibiotic susceptibilities in urosepsis [91][81]. The primary reason the SERS procedure has not been set up as a routine scientific strategy is that it does not withstand its high sensitivity and specificity, which are the significant disadvantage of SERS, and restricted ability in investigating polymicrobial tests which are because of the lower reproducibility of the SERS signal [95][85]. Thus, SERS combined with different methods should be incorporated which outlines the wide uses of this incredible method.

3.1.4. MALDI-TOF

Matrix-assisted laser desorption ionization-time of flight mass spectroscopy (MALDI-TOF-MS) is another newly discovered procedure that is now being used in clinical research to detect bacterial species. This technique guarantees fast detection of causative bacterial microorganisms demonstrated to be viable in the positive blood cultures that can rapidly recognize bacterial development, hence accelerating the general process of the antimicrobial resistance report [96][86]. MALDI is a protein identification ionization technique in which the analyte crystallizes in a strong lattice matrix crystal that absorbs laser light, allowing it to ionize and desorb from the matrix. The ionized atoms are isolated and dependent on their mass to charge (m/z) ratio from the entire microscopic organism’s test when it flies through a vacuum tube produces an m/z profiles of the apparent multitude of proteins in the sample [97][87]. Mass spectroscopy has been utilized for the inoculation of different species, along with the microbial suspension. Because of the low microbial concentration, MS cannot conduct direct examinations on human blood samples, which is a major disadvantage. Because of the low reproducibility and changeability in preliminary methodology and the matrix composition, blood culture is typically needed to improve the microscopic organisms to a detachable level [98][88]. Another drawback of MS is its restricted capacity in arranging various microbes from the polymicrobial samples [99,100,101][89][90][91] as the spectral profiles formed by this technique are more complicated, making it difficult to deconvolute the composite spectra gathered at the same time from different microbial species in the poly-microbial samples.

3.2. Broad-Spectrum Genomic Detection of AMR

3.2.1. High Resolution Melting Analysis Technology

High resolution melt (HRM) analysis depends on the detection of differences in melting temperature (Tm) due to the presence of a mutation in a previously amplified target that produces melt curve profiles specific to pathogens. The size and the sequence of the PCR amplicon is the major reason on which the melting curve profile depends [102][92]. It is so sensitive that even a single point mutation resulting in a Tm shift can be detected [103][93]. Therefore, it allows molecular detection of resistant genes and hereditary mutations rapidly with a higher output of post-PCR examination which allows the researchers to identify and classify the new hereditary mutations and variations along with single nucleotide polymorphisms without sequencing (gene scanning) or before sequencing in a population [104][94]. Several antibiotic resistance marker genes conferring to a bacterium have been usually detected. A study reported real-time PCR-based rapid identification of Escherichia coli, Staphylococcus aureus, Enterococcus faecalis and Proteus mirabilis using 16S rRNA gene-specific primers. Furthermore, HRM and machine learning algorithm approaches were used to determine the antimicrobial susceptibility test within 6.5 h [105][95].

3.2.2. Sequencing

In the diagnostic field, the aim of the utilization of genomic rather than gene-based techniques for both bacterial species detection and AMR detection is growing. There is a need for more effective and rapid AMR preventive measures driving the shift to whole-genome sequencing (WGS). Bacterial and AMR gene identification using automated bioinformatics examination methods are easy to perform after an organism has been isolated by culture [114][96]. WGS allows all genes involved in resistance to be tracked, allowing all genomic data of resistant factors to be present in a bacterial cell to be analyzed. Next-generation sequencing (NGS), which has revolutionized the biological sciences, is another emerging tool. NGS makes large-scale whole-genome sequencing (WGS) affordable and realistic for the average researcher with its super high throughput, versatility and speed. It also allows scientists to sequence the entire human genome in a single experiment, allowing them to research biological processes at a level never before possible. Furthermore, in the age of complex genomic science, which necessitates a deeper understanding of details outside the boundaries of conventional DNA technology, it has filled the gap and become a routine research method to resolve those issues [115][97]. NGS combined with meta-genomic approaches that essentially include genome sequencing of infectious biological samples such as blood, urine and lavage without culturing them and provide the diverse profile of all species including those that are targeted and untargeted present in the sample. This approach has revolutionized the identification of all including new resistance genes in a single specimen [116,117][98][99]. The sequencing-based metagenomic approach has analyzed the kinetics of gut microbiota before, during and after antibiotic treatment [118][100].

3.2.3. DNA Microarray

Microarrays have been updated as useful methods for bacterial detection and identification due to their strong parallelism in screening for the expression of a wide variety of genes after specific gene amplification by either a broad-range or a multiplex-PCR before microarray analysis [121][101] Microarrays employ surface-immobilized DNA and RNA probes to collect and categorize DNA/RNA of microorganisms via sequence-specific complementary hybridization, decreasing sample and reagent consumption and costs while permitting precise segregation down to the species or strain level. A study conducted by Ballarini et al. utilized an oligonucleotide-based microarray (BactoChip) for culture-independent detection, quantification as well as differentiation from 21 different bacterial genera among clinical isolates [122][102]. Additionally, the Verigene stage from Luminex Corporation can distinguish the Gram-positive board of nine species of bacteria and three genes of AMR against methicillin and vancomycin and five species and six AMR genes for carbapenemase and expanded range beta-lactamases [123,124,125][103][104][105]. Being independent of culture, microarray-based detection is rapid, hence gaining importance in clinics in combination with antimicrobial stewardship [126,127][106][107]. Despite these, not a single microarray platform has been commercialized so far to effectively recognize all microorganisms in polymicrobial diseases [128][108].

References

  1. Kumar, S.; Payal, N.; Srivastava, V.K.; Kaushik, S.; Saxena, J.; Jyoti, A. Neutrophil extracellular traps and organ dysfunction in sepsis. Clin. Chim. Acta 2021, 523, 152–162.
  2. Guirgis, F.; Black, L.P.; DeVos, E.L. Updates and controversies in the early management of sepsis and septic shock. Emerg. Med. Pract. 2018, 20, 1–28.
  3. Kumar, S.; Gupta, E.; Srivastava, V.K.; Kaushik, S.; Saxena, J.; Goyal, L.K.; Mehta, S.; Jyoti, A. Nitrosative stress and cytokines are linked with the severity of sepsis and organ dysfunction. Br. J. Biomed. Sci. 2019, 76, 29–34.
  4. Gyawali, B.; Ramakrishna, K.; Dhamoon, A.S. Sepsis: The evolution in definition, pathophysiology, and management. SAGE Open Med. 2019, 7, 2050312119835043.
  5. Rudd, K.E.; Johnson, S.C.; Agesa, K.M.; Shackelford, K.A.; Tsoi, D.; Kievlan, D.R.; Colombara, D.V.; Ikuta, K.S.; Kissoon, N.; Finfer, S.; et al. Global, regional, and national sepsis incidence and mortality, 1990–2017: Analysis for the Global Burden of Disease Study. Lancet 2020, 395, 200–211.
  6. Rudnov, V.A.; Kulabukhov, V.V. Sepsis-3: Updated main definitions, potential problems and next practical steps. Messenger Anesthesiol. Resusc. 2018, 13, 4–11.
  7. Masoudifar, M.; Gouya, M.M.; Pezeshki, Z.; Eshrati, B.; Afhami, S.; Farzami, M.R.; Seifi, A. Health care-associated infections, including device-associated infections, and antimicrobial resistance in Iran: The national update for 2018. J. Prev. Med. Hyg. 2021, 62, E943.
  8. Fleischmann-Struzek, C.; Mellhammar, L.; Rose, N.; Cassini, A.; Rudd, K.E.; Schlattmann, P.; Allegranzi, B.; Reinhart, K. Incidence and mortality of hospital-and ICU-treated sepsis: Results from an updated and expanded systematic review and meta-analysis. Crit. Care Med. 2020, 46, 1552–1562.
  9. Makic, M.B.F.; Bridges, E. CE: Managing sepsis and septic shock: Current guidelines and definitions. Am. J. Nurs. 2018, 118, 34–39.
  10. Buchman, T.G.; Simpson, S.Q.; Sciarretta, K.L.; Finne, K.P.; Sowers, N.; Collier, M.; Chavan, S.; Oke, I.; Pennini, M.E.; Santhosh, A.; et al. Sepsis among medicare beneficiaries: 1. The burdens of sepsis, 2012–2018. Crit. Care Med. 2020, 48, 276.
  11. Chávez-Vivas, M.; Cristo-Martínez, D.; Tascón, A.J. Epidemiological characteristics of patients diagnosed with sepsis and septic shock in a hospital in Cali, Colombia. Acta Med. Costarric. 2018, 60, 150–156.
  12. Fleischmann-Struzek, C.; Mikolajetz, A.; Schwarzkopf, D.; Cohen, J.; Hartog, C.S.; Pletz, M.; Gastmeier, P.; Reinhart, K. Challenges in assessing the burden of sepsis and understanding the inequalities of sepsis outcomes between National Health Systems: Secular trends in sepsis and infection incidence and mortality in Germany. Intensive Care Med. 2018, 44, 1826–1835.
  13. Abe, T.; Ogura, H.; Kushimoto, S.; Shiraishi, A.; Sugiyama, T.; Deshpande, G.A.; Uchida, M.; Nagata, I.; Saitoh, D.; Fujishima, S.; et al. Variations in infection sites and mortality rates among patients in intensive care units with severe sepsis and septic shock in Japan. J. Intensive Care 2019, 7, 28.
  14. Gajdács, M. The concept of an ideal antibiotic: Implications for drug design. Molecules 2019, 24, 892.
  15. van Belkum, A.; Burnham, C.A.D.; Rossen, J.W.; Mallard, F.; Rochas, O.; Dunne, W.M. Innovative and rapid antimicrobial susceptibility testing systems. Nat. Rev. Microbiol. 2020, 18, 299–311.
  16. Kethireddy, S.; Bilgili, B.; Sees, A.; Kirchner, H.L.; Ofoma, U.R.; Light, R.B.; Mirzanejad, Y.; Maki, D.; Kumar, A.; Layon, A.J.; et al. Culture-negative septic shock compared with culture-positive septic shock: A retrospective cohort study. Crit. Care Med. 2018, 46, 506–512.
  17. Falcone, M.; Bassetti, M.; Tiseo, G.; Giordano, C.; Nencini, E.; Russo, A.; Graziano, E.; Tagliaferri, E.; Leonildi, A.; Barnini, S.; et al. Time to appropriate antibiotic therapy is a predictor of outcome in patients with bloodstream infection caused by KPC-producing Klebsiella pneumoniae. Crit. Care 2020, 24, 24.
  18. Nath, P.; Kabir, A.; Khoubafarin Doust, S.; Kreais, Z.J.; Ray, A. Detection of bacterial and viral pathogens using photonic point-of-care devices. Diagnostics 2020, 10, 841.
  19. Choi, J.A.; Bae, S.M.; Kim, J.W.; Lee, K.J. Development of a Two Triplex Real-Time Polymerase Chain Reaction for Rapid Detection of Six Carbapenemase Genes in Enterobacteriaceae. Osong Public Health Res. Perspect. 2020, 11, 53.
  20. Mejia-Chew, C.; O’Halloran, J.A.; Olsen, M.A.; Stwalley, D.; Kronen, R.; Lin, C.; Salazar, A.S.; Larson, L.; Hsueh, K.; Powderly, W.G.; et al. Effect of infectious disease consultation on mortality and treatment of patients with candida bloodstream infections: A retrospective, cohort study. Lancet Infect. Dis. 2019, 19, 1336–1344.
  21. Edmiston, C.E.; Garcia, R.; Barnden, M.; DeBaun, B.; Johnson, H.B. Rapid diagnostics for bloodstream infections: A primer for infection preventionists. Am. J. Infect. Control 2018, 46, 1060–1068.
  22. Sato, H.; Nakao, A.; Sato, K.; Otomo, Y.; Niijima, S.; Shimizu, T. Comparison of time to positivity of pediatric blood cultures obtained within the first year of life and in later years. J. Infect. Chemother. 2020, 26, 813–817.
  23. Salinas, M.; López-Garrigós, M.; Flores, E.; Leiva-Salinas, C. Current Practice and Regional Variability in Recommendations for Patient Preparation for Laboratory Testing in Primary Care. Lab. Med. 2020, 51, e32–e37.
  24. Zelellw, D.A.; Dessie, G.; Worku Mengesha, E.; Balew Shiferaw, M.; Mela Merhaba, M.; Emishaw, S. A Systemic Review and Meta-analysis of the Leading Pathogens Causing Neonatal Sepsis in Developing Countries. BioMed Res. Int. 2021, 2021, 6626983.
  25. Özenci, V.; Strålin, K. Clinical implementation of molecular methods in detection of microorganisms from blood with a special focus on PCR electrospray ionization mass spectrometry. Expert Rev. Mol. Diagn. 2019, 19, 389–395.
  26. Quirino, A.; Marascio, N.; Peronace, C.; Gallo, L.; Barreca, G.S.; Giancotti, A.; Lamberti, A.G.; Colosimo, M.; Minchella, P.; Trecarichi, E.M.; et al. Direct antimicrobial susceptibility testing (AST) from positive blood cultures using Microscan system for early detection of bacterial resistance phenotypes. Diagn. Microbiol. Infect. Dis. 2021, 101, 115485.
  27. Butler-Laporte, G.; Yansouni, C.P.; Paquette, K.; Lawandi, A.; Stabler, S.N.; Akhter, M.; Davidson, A.C.; Gavric, M.; Jinah, R.; Saeed, Z.; et al. September. Real-word time-to-positivity of two widely used commercial blood culture systems in patients with severe manifestations of sepsis: An analysis of the FABLED study. Open Forum Infect. Dis. 2020, 7, ofaa371.
  28. Rule, R.; Paruk, F.; Becker, P.; Neuhoff, M.; Chausse, J.; Said, M. Diagnostic accuracy of the BioFire FilmArray blood culture identification panel when used in critically ill patients with sepsis. J. Microbiol. Methods 2021, 189, 106303.
  29. Rodrigues, C.; Siciliano, R.F.; Charbel, C.E.; de Carvalho Sarahyba da Silva, L.; Baiardo Redaelli, M.; de Paula Rosa Passetti, A.P.; Franco, M.R.G.; Rossi, F.; Zeigler, R.; De Backer, D.; et al. The effect of a rapid molecular blood test on the use of antibiotics for nosocomial sepsis: A randomized clinical trial. J. Intensive Care 2019, 7, 37.
  30. Lin, J.F.; Ge, M.C.; Liu, T.P.; Chang, S.C.; Lu, J.J. A simple method for rapid microbial identification from positive monomicrobial blood culture bottles through matrix-assisted laser desorption ionization time-of-flight mass spectrometry. J. Microbiol. Immunol. Infect. 2018, 51, 659–665.
  31. Wu, Y.; Yao, Y.M.; Ke, H.L.; Ying, L.; Wu, Y.; Zhao, G.J.; Lu, Z.Q. Mdivi-1 protects CD4+ T cells against apoptosis via balancing mitochondrial fusion-fission and preventing the induction of endoplasmic reticulum stress in sepsis. Mediators Inflamm. 2019, 2019, 7329131.
  32. Ransom, E.M.; Alipour, Z.; Wallace, M.A.; Burnham, C.A.D. Evaluation of optimal blood culture incubation time to maximize clinically relevant results from a contemporary blood culture instrument and media system. J. Clin. Microbiol. 2021, 59, e02459-20.
  33. Du Plessis, A. Influence of Blood Culture Results on Antimicrobial Prescribing in a Private Hospital in North West, South Africa. Ph.D. Thesis, North-West University, Potchefstroom, South Africa, 2020.
  34. Chou, W.K.; Vaikunthan, M.; Schröder, H.V.; Link, A.J.; Kim, H.; Brynildsen, M.P. Synergy screening identifies a compound that selectively enhances the antibacterial activity of nitric oxide. Front. Bioeng. Biotechnol. 2020, 8, 1001.
  35. Kaczor, A.; Witek, K.; Podlewska, S.; Sinou, V.; Czekajewska, J.; Żesławska, E.; Doroz-Płonka, A.; Lubelska, A.; Latacz, G.; Nitek, W.; et al. Molecular insights into an antibiotic enhancer action of new morpholine-containing 5-arylideneimidazolones in the fight against MDR bacteria. Int. J. Mol. Sci. 2021, 22, 2062.
  36. Bakhit, M. Antibiotic Resistance: Patient-Clinician Communication and Decision-Making about Antibiotic Use in Primary Care. Ph. D. Thesis, Bond University, Gold Coast, Australia, 2018.
  37. Mehta, Y.; Paul, R.; Rabbani, R.; Acharya, S.P.; Withanaarachchi, U.K. Sepsis Management in Southeast Asia: A Review and Clinical Experience. J. Clin. Med. 2022, 11, 3635.
  38. Nguyen, M.; Brettin, T.; Long, S.; Musser, J.M.; Olsen, R.J.; Olson, R.; Shukla, M.; Stevens, R.L.; Xia, F.; Yoo, H.; et al. Developing an in silico minimum inhibitory concentration panel test for Klebsiella pneumoniae. Sci. Rep. 2018, 8, 421.
  39. Kharb, S. Biochemical Tests in Clinical Medicine. In Mind Maps in Clinical Chemistry (Part I); Bentham Science Publishers: Sharjah, United Arab Emirates, 2021; p. 15.
  40. Tavassoly, I.; Goldfarb, J.; Iyengar, R. Systems biology primer: The basic methods and approaches. Essays Biochem. 2018, 62, 487–500.
  41. Pilecky, M.; Schildberger, A.; Orth-Höller, D.; Weber, V. Pathogen enrichment from human whole blood for the diagnosis of bloodstream infection: Prospects and limitations. Diagn. Microbiol. Infect. Dis. 2019, 94, 7–14.
  42. Cheung, S.W.; Bi, W. Novel applications of array comparative genomic hybridization in molecular diagnostics. Expert Rev. Mol. Diagn. 2018, 18, 531–542.
  43. Iregbu, K.; Dramowski, A.; Milton, R.; Nsutebu, E.; Howie, S.R.; Chakraborty, M.; Lavoie, P.M.; Costelloe, C.E.; Ghazal, P. Global health systems’ data science approach for precision diagnosis of sepsis in early life. Lancet Infect. Dis. 2021, 22, e143–e152.
  44. Philips, C.A.; Ahamed, R.; Rajesh, S.; George, T.; Mohanan, M.; Augustine, P. Update on diagnosis and management of sepsis in cirrhosis: Current advances. World J. Hepatol. 2020, 2, 451.
  45. Sune, D.; Rydberg, H.; Augustinsson, Å.N.; Serrander, L.; Jungeström, M.B. Optimization of 16S rRNA gene analysis for use in the diagnostic clinical microbiology service. J. Microbiol. Methods 2020, 170, 105854.
  46. Llerena, J.P.; Araujo, P.; Mazzafera, P. Optimization of RT-PCR reactions in studies with genes of lignin biosynthetic route in Saccharum spontaneum. An. Acad. Bras. Cienc. 2018, 90, 509–519.
  47. Sreejith, K.R.; Ooi, C.H.; Jin, J.; Dao, D.V.; Nguyen, N.T. Digital polymerase chain reaction technology–recent advances and future perspectives. Lab. Chip. 2018, 18, 3717–3732.
  48. Manzano, M. Labelled and unlabelled probes for pathogen detection with molecular biology methods and biosensors. Methods Microbiol. 2021, 48, 79–225.
  49. Ferguson, J.; Duran, J.; Killinen, W.; Wagner, J.; Kulesza, C.; Chatterley, C.; Li, Y. A Field-Deployable and Low-Cost PCR (FLC-PCR) Thermocycler for the Rapid Detection of Environmental E. coli. In Proceedings of the 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 20–24 July 2020; IEEE: Piscataway, NJ, USA, 2020; pp. 2209–2212.
  50. Dayarathne, M.C.; Mridha, A.U.; Wang, Y. Diagnosis of Fungal Plant Pathogens Using Conventional and Molecular Approaches. In Diagnostics of Plant Diseases; IntechOpen: London, UK, 2020.
  51. Paul, R.; Ostermann, E.; Wei, Q. Advances in point-of-care nucleic acid extraction technologies for rapid diagnosis of human and plant diseases. Biosens. Bioelectron. 2020, 169, 112592.
  52. Kumar, S.S.; Ghosh, A.R. Assessment of bacterial viability: A comprehensive review on recent advances and challenges. Microbiology 2019, 165, 593–610.
  53. Jiang, X.W.; Huang, T.S.; Xie, L.; Chen, S.Z.; Wang, S.D.; Huang, Z.W.; Li, X.Y.; Ling, W.P. Development of a diagnostic assay by three-tube multiplex real-time PCR for simultaneous detection of nine microorganisms causing acute respiratory infections. Sci. Rep. 2022, 12, 13306.
  54. Mota, F.A.; Pereira, S.A.; Araújo, A.R.; Passos, M.L.; Saraiva, M.L.M. Biomarkers in the diagnosis of wounds infection: An analytical perspective. Trends Anal. Chem. 2021, 143, 116405.
  55. Saha, O.; Islam, M.R.; Rahman, M.S.; Hoque, M.N.; Hossain, M.A.; Sultana, M. Genome-wide diversity and differentiation of two novel multidrug-resistant populations of Pasteurella multocida type B: 2 from fowl cholera. bioRxiv 2020.
  56. Gunsolus, I.L.; Sweeney, T.E.; Liesenfeld, O.; Ledeboer, N.A. Diagnosing and managing sepsis by probing the host response to infection: Advances, opportunities, and challenges. J. Clin. Microbiol. 2019, 57, e00425-19.
  57. Mishra, D.; Satpathy, G.; Chawla, R.; Venkatesh, P.; Ahmed, N.H.; Panda, S.K. Utility of broad-range 16S rRNA PCR assay versus conventional methods for laboratory diagnosis of bacterial endophthalmitis in a tertiary care hospital. Br. J. Ophthalmol. 2019, 103, 152–156.
  58. Verbakel, J.Y.; Matheeussen, V.; Loens, K.; Kuijstermans, M.; Goossens, H.; Ieven, M.; Butler, C.C. Performance and ease of use of a molecular point-of-care test for influenza A/B and RSV in patients presenting to primary care. Eur. J. Clin. Microbiol. Infect. Dis. 2020, 39, 1453–1460.
  59. Reta, D.H.; Tessema, T.S.; Ashenef, A.S.; Desta, A.F.; Labisso, W.L.; Gizaw, S.T.; Abay, S.M.; Melka, D.S.; Reta, F.A. Molecular and immunological diagnostic techniques of medical viruses. Int. J. Microbiol. 2020, 2020, 8832728.
  60. Nik Zuraina, N.M.N.; Mohamad, S.; Hasan, H.; Goni, M.D.; Suraiya, S. Diagnostic performance of an in-house multiplex PCR assay and the retrospective surveillance of bacterial respiratory pathogens at a teaching hospital, Kelantan, Malaysia. Pathog. Glob. Health 2022, 1–13.
  61. Davidson, K.R.; Ha, D.M.; Schwarz, M.I.; Chan, E.D. Bronchoalveolar lavage as a diagnostic procedure: A review of known cellular and molecular findings in various lung diseases. J. Thorac. Dis. 2020, 12, 4991.
  62. Cao, X.; Zhao, L.; Zhang, J.; Chen, X.; Shi, L.; Fang, X.; Xie, H.; Chang, Y.; Wang, L. Detection of viable but nonculturable Vibrio parahaemolyticus in shrimp samples using improved real-time PCR and real-time LAMP methods. Food Control 2019, 103, 145–152.
  63. Han, H.; Sohn, B.; Choi, J.; Jeon, S. Recent advances in magnetic nanoparticle-based microfluidic devices for the pretreatment of pathogenic bacteria. Biomed. Eng. Lett. 2021, 11, 297–307.
  64. Schmitz, J.E.; Stratton, C.W.; Persing, D.H.; Tang, Y.W. Forty Years of Molecular Diagnostics for Infectious Diseases. J. Clin. Microbiol. 2022, 60, e02446-21.
  65. Bronder, T.S.; Jessing, M.P.; Poghossian, A.; Keusgen, M.; Schöning, M.J. Detection of PCR-amplified tuberculosis DNA fragments with polyelectrolyte-modified field-effect sensors. Anal. Chem. 2018, 90, 7747–7753.
  66. Wang, J.; Yang, J.; Gao, S.; Liu, A.; Rashid, M.; Li, Y.; Liu, Z.; Liu, J.; Liu, G.; Luo, J.; et al. Rapid detection and differentiation of Theileria annulata, T. orientalis and T. sinensis using high-resolution melting analysis. Ticks Tick Borne Dis. 2020, 11, 101312.
  67. Kurbakov, K.A.; Konorov, E.A.; Minaev, M.Y.; Kuznetsova, O.A. Multiplex real-time PCR with HRM for detection of Lactobacillus sakei and Lactobacillus curvatus in Food Samples. Food Technol. Biotechnol. 2019, 57, 97–104.
  68. Pohanka, M. Current trends in the biosensors for biological warfare agents assay. Materials 2019, 12, 2303.
  69. Zheng, W.; Jiang, L.; Lei, Q.; Yang, J.; Gao, X.; Wang, W.; Zhang, Y.; Kong, T.; Chen, Q.; Li, G. Development and validation of quantitative real-time pcr for the detection of residual CHO host cell DNA and optimization of sample pretreatment method in biopharmaceutical products. Biol. Proced. Online 2019, 21, 17.
  70. Chen, X.; Tang, M.; Liu, Y.; Huang, J.; Liu, Z.; Tian, H.; Zheng, Y.; de la Chapelle, M.L.; Zhang, Y.; Fu, W. Surface-enhanced Raman scattering method for the identification of methicillin-resistant Staphylococcus aureus using positively charged silver nanoparticles. Mikrochim. Acta 2019, 186, 1–8.
  71. Jeong, K.; Stanwix, P.L.; May, E.F.; Aman, Z.M. Surface-Enhanced Raman Scattering Imaging of Cetylpyridinium Chloride Adsorption to a Solid Surface. Anal. Chem. 2022, 94, 14169–14176.
  72. Xu, G.; Guo, N.; Zhang, Q.; Wang, T.; Song, P.; Xia, L. An ultrasensitive surface-enhanced Raman scattering sensor for the detection of hydrazine via the Schiff base reaction. J. Hazard. Mater. 2022, 424, 127303.
  73. Ge, M.; Li, P.; Zhou, G.; Chen, S.; Han, W.; Qin, F.; Nie, Y.; Wang, Y.; Qin, M.; Huang, G.; et al. General surface-enhanced Raman spectroscopy method for actively capturing target molecules in small gaps. J. Am. Chem. Soc. 2021, 143, 7769–7776.
  74. Sun, Y.; Chen, X.; Zheng, Y.; Song, Y.; Zhang, H.; Zhang, S. Surface-enhanced Raman scattering trace-detection platform based on continuous-rolling-assisted evaporation on superhydrophobic surfaces. ACS Appl. Nano Mater. 2020, 3, 4767–4776.
  75. Pérez-Jiménez, A.I.; Lyu, D.; Lu, Z.; Liu, G.; Ren, B. Surface-enhanced Raman spectroscopy: Benefits, trade-offs and future developments. Chem. Sci. 2020, 11, 4563–4577.
  76. Shvalya, V.; Filipič, G.; Zavašnik, J.; Abdulhalim, I.; Cvelbar, U. Surface-enhanced Raman spectroscopy for chemical and biological sensing using nanoplasmonics: The relevance of interparticle spacing and surface morphology. Appl. Phys. Rev. 2020, 7, 031307.
  77. Zong, C.; Xu, M.; Xu, L.J.; Wei, T.; Ma, X.; Zheng, X.S.; Hu, R.; Ren, B. Surface-enhanced Raman spectroscopy for bioanalysis: Reliability and challenges. Chem. Rev. 2018, 118, 4946–4980.
  78. Li, P.; Long, F.; Chen, W.; Chen, J.; Chu, P.K.; Wang, H. Fundamentals and applications of surface-enhanced Raman spectroscopy–based biosensors. Curr. Opin. Biomed. Eng. 2020, 13, 51–59.
  79. Sun, J.; Gong, L.; Wang, W.; Gong, Z.; Wang, D.; Fan, M. Surface-enhanced Raman spectroscopy for on-site analysis: A review of recent developments. Luminescence 2020, 35, 808–820.
  80. Liu, S.; Hu, Q.; Li, C.; Zhang, F.; Gu, H.; Wang, X.; Li, S.; Xue, L.; Madl, T.; Zhang, Y.; et al. Wide-range, rapid, and specific identification of pathogenic bacteria by Surface-Enhanced Raman Spectroscopy. ACS Sens. 2021, 6, 2911–2919.
  81. Pyrak, E.; Krajczewski, J.; Kowalik, A.; Kudelski, A.; Jaworska, A. Surface enhanced Raman spectroscopy for DNA biosensors—How far are we? Molecules 2019, 24, 4423.
  82. Kim, J.; Jang, Y.; Kim, N.J.; Kim, H.; Yi, G.C.; Shin, Y.; Kim, M.H.; Yoon, S. Study of chemical enhancement mechanism in non-plasmonic surface enhanced Raman spectroscopy (SERS). Front. Chem. 2019, 7, 582.
  83. Han, Y.Y.; Lin, Y.C.; Cheng, W.C.; Lin, Y.T.; Teng, L.J.; Wang, J.K.; Wang, Y.L. Rapid antibiotic susceptibility testing of bacteria from patients’ blood via assaying bacterial metabolic response with surface-enhanced Raman spectroscopy. Sci. Rep. 2020, 10, 12538.
  84. Wang, K.; Li, S.; Petersen, M.; Wang, S.; Lu, X. Detection and characterization of antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy. Nanomaterials 2018, 8, 762.
  85. Tahir, M.A.; Dina, N.E.; Cheng, H.; Valev, V.K.; Zhang, L. Surface-enhanced Raman spectroscopy for bioanalysis and diagnosis. Nanoscale 2021, 13, 11593–11634.
  86. Dizaji, A.N.; Ozek, N.S.; Aysin, F.; Calis, A.; Yilmaz, A.; Yilmaz, M. Combining vancomycin-modified gold nanorod arrays and colloidal nanoparticles as a sandwich model for the discrimination of Gram-positive bacteria and their detection via surface-enhanced Raman spectroscopy (SERS). Analyst 2021, 146, 3642–3653.
  87. Ahmad, W.; Wang, J.; Li, H.; Jiao, T.; Chen, Q. Trends in the bacterial recognition patterns used in surface enhanced Raman spectroscopy. Trends Anal. Chem. 2021, 142, 116310.
  88. Kumar, M.; Shergill, S.P.S.; Tandel, K.; Sahai, K.; Gupta, R.M. Direct antimicrobial susceptibility testing from positive blood culture bottles in laboratories lacking automated antimicrobial susceptibility testing systems. Med. J. Armed Forces India 2019, 75, 450–457.
  89. Wang, Y.; Jin, Y.; Bai, Y.; Song, Z.; Chu, W.; Zhao, M.; Hao, Y.; Lu, Z. Rapid method for direct identification of positive blood cultures by MALDI-TOF MS. Exp. Ther. Med. 2020, 20, 235.
  90. Dai, Y.; Xu, X.; Yan, X.; Li, D.; Cao, W.; Tang, L.; Hu, M.; Jiang, C. Evaluation of a rapid and simplified protocol for direct identification of microorganisms from positive blood cultures by using Matrix Assisted Laser Desorption Ionization Time-Of-Flight Mass Spectrometry (MALDI-TOF MS). Front. Cell. Infect. Microbiol. 2021, 11, 632679.
  91. Kayin, M.; Mert, B.; Aydemir, S.; Özenci, V. Comparison of rapid BACpro® II, Sepsityper® kit and in-house preparation methods for direct identification of bacteria from blood cultures by MALDI-TOF MS with and without Sepsityper® module analysis. Eur. J. Clin. Microbiol. Infect. Dis. 2019, 38, 2133–2143.
  92. Homolová, R.; Bogdanová, K.; Bardoň, J.; Kolář, M. Direct identification of bacteria in blood cultures by MALDI-TOF MS. Clin. Microbiol. Infect. 2020, 26, 45–50.
  93. Tsuchida, S.; Nakayama, T. MALDI-Based Mass Spectrometry in Clinical Testing: Focus on Bacterial Identification. Appl. Sci. 2022, 12, 2814.
  94. Perini, M.; Batisti Biffignandi, G.; Di Carlo, D.; Pasala, A.R.; Piazza, A.; Panelli, S.; Zuccotti, G.V.; Comandatore, F. MeltingPlot, a user-friendly online tool for epidemiological investigation using High Resolution Melting data. BMC Bioinform. 2021, 22, 76.
  95. Ahmed, M.O.; Baptiste, K.E. Vancomycin-resistant enterococci: A review of antimicrobial resistance mechanisms and perspectives of human and animal health. Microb. Drug Resist. 2018, 24, 590–606.
  96. Leonard, H.; Colodner, R.; Halachmi, S.; Segal, E. Recent advances in the race to design a rapid diagnostic test for antimicrobial resistance. ACS Sens. 2018, 3, 2202–2217.
  97. Schürch, A.C.; Arredondo-Alonso, S.; Willems, R.J.L.; Goering, R.V. Whole genome sequencing options for bacterial strain typing and epidemiologic analysis based on single nucleotide polymorphism versus gene-by-gene–based approaches. Clin. Microbiol. Infect. 2018, 24, 350–354.
  98. Fujii, H.; Kakiuchi, S.; Tsuji, M.; Nishimura, H.; Yoshikawa, T.; Yamada, S.; Omura, N.; Inagaki, T.; Shibamura, M.; Harada, S.; et al. Application of next-generation sequencing to detect acyclovir-resistant herpes simplex virus type 1 variants at low frequency in thymidine kinase gene of the isolates recovered from patients with hematopoietic stem cell transplantation. J. Virol. Methods 2018, 251, 123–128.
  99. Yan, Q.; Wi, Y.M.; Thoendel, M.J.; Raval, Y.S.; Greenwood-Quaintance, K.E.; Abdel, M.P.; Jeraldo, P.R.; Chia, N.; Patel, R. Evaluation of the CosmosID bioinformatics platform for prosthetic joint-associated sonicate fluid shotgun metagenomic data analysis. J. Clin. Microbiol. 2019, 57, e01182-18.
  100. Friães, A.; Mamede, R.; Ferreira, M.; Melo-Cristino, J.; Ramirez, M. Annotated Whole-Genome Multilocus Sequence Typing Schema for Scalable High-Resolution Typing of Streptococcus pyogenes. J. Clin. Microbiol. 2022, 60, e00315-22.
  101. Sturaro, L.L.; Gonoi, T.; Busso-Lopes, A.F.; Tararam, C.A.; Levy, C.E.; Lyra, L.; Trabasso, P.; Schreiber, A.Z.; Kamei, K.; Moretti, M.L. Visible DNA microarray system as an adjunctive molecular test in identification of pathogenic fungi directly from a blood culture bottle. J. Clin. Microbiol. 2018, 56, e01908-17.
  102. Dhanjal, D.S.; Chopra, C.; Chopra, R.S. Metagenomic DNA sequencing: Technological advances and applications. In Metagenomics: Techniques, Applications, Challenges and Opportunities; Springer: Berlin/Heidelberg, Germany, 2020; pp. 37–53.
  103. Schaack, D.; Siegler, B.H.; Tamulyte, S.; Weigand, M.A.; Uhle, F. The immunosuppressive face of sepsis early on intensive care unit—A large-scale microarray meta-analysis. PloS ONE 2018, 13, 0198555.
  104. Carroll, K.C.; Reid, J.L.; Thornberg, A.; Whitfield, N.N.; Trainor, D.; Lewis, S.; Wakefield, T.; Davis, T.E.; Church, K.G.; Samuel, L.; et al. Clinical performance of the novel GenMark Dx ePlex blood culture ID Gram-positive panel. J. Clin. Microbiol. 2020, 58, e01730-19.
  105. Li, Y.; Zhang, F.; Cong, Y.; Zhao, Y. Identification of potential genes and miRNAs associated with sepsis based on microarray analysis. Mol. Med. Rep. 2018, 17, 6227–6234.
  106. Kuchibiro, T.; Hirano, A.; Ogasawara, S.; Nakamura, T. The microcolony detection method (MCD), a simple and rapid screening test for antimicrobial resistance bacteria on positive blood cultures. Heliyon 2020, 6, 05494.
  107. She, R.C.; Bender, J.M. Advances in rapid molecular blood culture diagnostics: Healthcare impact, laboratory implications, and multiplex technologies. J. Appl. Lab. Med. 2019, 3, 617–630.
  108. Huang, T.D.; Melnik, E.; Bogaerts, P.; Evrard, S.; Glupczynski, Y. Evaluation of the ePlex blood culture identification panels for detection of pathogens in bloodstream infections. J. Clin. Microbiol. 2019, 57, e01597-18.
More
Academic Video Service