Trends in the detection of viruses causing gastroenteritis over a 10-year period and impact of nonpharmaceutical interventions.

BACKGROUND: Viral gastroenteritis continues to be a leading cause of death in low-income countries. The impact of nonpharmaceutical interventions (NPIs) on the transmission of gastroenteritis-causing viruses during the COVID-19 pandemic is understudied. OBJECTIVES: To investigate the 10-year trends of enteric viruses and estimate the impact of implementing and mitigating NPIs. STUDY DESIGN: Data regarding norovirus, rotavirus, adenovirus, astrovirus, and sapovirus detection were collected from five Korean hospitals between January 2013 and April 2023. We compared positivity between the pre-pandemic, pandemic, and post-pandemic periods. The causal effects of implementing and mitigating NPIs were quantified using the Bayesian Structural Time Series (BSTS) model. RESULTS: Norovirus was most frequently detected (9.9 %), followed by rotavirus (6.7 %), adenovirus (3.3 %), astrovirus (1.4 %), and sapovirus (0.6 %). During the pandemic, the positivity of all five viruses decreased, ranging from -1.0 % to -8.1 %, with rotavirus showing the greatest decrease. In the post-pandemic period, positivity rebounded for all viruses except for rotavirus. The BSTS model revealed that NPI implementation negatively affected the detection of all five viruses, resulting in reductions ranging from -73.0 % to -91.0 % compared to the prediction, with rotavirus being the least affected. Conversely, NPI mitigation positively affected the detection of all viruses, ranging from 79.0 % to 200.0 %, except for rotavirus. CONCLUSIONS: Trends observed over 10 years show that NPIs have had a major impact on changes in enteric virus detection. The effect of vaccines, in addition to NPIs, on rotavirus detection requires further investigation. Our findings emphasize the importance of NPIs in infection control and prevention.

Potentiation of Antibiotic Activity of Aztreonam against Metallo-ß-Lactamase-Producing Multidrug-Resistant Pseudomonas aeruginosa by 3-O-Substituted Difluoroquercetin Derivatives.

The combination of aztreonam (ATM) and ceftazidime-avibactam (CAZ-AVI; CZA) has shown therapeutic potential against serine-ß-lactamase (SBL)- and metallo-ß-lactamase (MBL)-producing Enterobacterales. However, the ability of CZA to restore the antibiotic activity of ATM is severely limited in MBL-producing multidrug-resistant (MDR) Pseudomonas aeruginosa strains because of the myriad of intrinsic and acquired resistance mechanisms associated with this pathogen. We reasoned that the simultaneous inhibition of multiple targets associated with multidrug resistance mechanisms may potentiate the antibiotic activity of ATM against MBL-producing P. aeruginosa. During a search for the multitarget inhibitors through a molecular docking study, we discovered that di-F-Q, the previously reported efflux pump inhibitor of MDR P. aeruginosa, binds to the active sites of the efflux pump (MexB), as well as various ß-lactamases, and these sites are open to the 3-O-position of di-F-Q. The 3-O-substituted di-F-Q derivatives were thus synthesized and showed hereto unknown multitarget MDR inhibitory activity against various ATM-hydrolyzing ß-lactamases (AmpC, KPC, and New Delhi metallo-ß-lactamase (NDM)) and the efflux pump of P. aeruginosa, presumably by forming additional hydrophobic contacts with the targets. The multitarget MDR inhibitor 27 effectively potentiated the antimicrobial activity of ATM and reduced the MIC of ATM more than four-fold in 19 out of 21 MBL-producing P. aeruginosa clinical strains, including the NDM-producing strains which were highly resistant to various combinations of ATM with ß-lactamase inhibitors and/or efflux pump inhibitors. Our findings suggest that the simultaneous inhibition of multiple MDR targets might provide new avenues for the discovery of safe and efficient MDR reversal agents which can be used in combination with ATM against MBL-producing MDR P. aeruginosa.

Performance Evaluation of the Roche Cobas 5800 HBV and HCV Tests: Comparison of the 200 and 500 µL Protocols.

Background: Clinical management of patients infected with hepatitis B virus (HBV) or hepatitis C virus (HCV) relies on the viral load (VL). The Cobas 5800 system (Roche Diagnostics) can determine VLs in 200 and 500 µL samples, but the performance of each protocol has not been compared. We evaluated the performance of both protocols for the HBV and HCV tests. Methods: Precision and linearity were verified using commercial panels. Probit analyses were used to determine limits of detection (LoDs). The results obtained with 336 samples were compared using the 200 and 500 µL protocols. Data from 6,737 retrospective HBV and 768 HCV samples were compared to estimate the effects of the different LoDs on the diagnostic results of the protocols. Correlations between protocols were tested with Spearman's rank correlation coefficients (rho). Results: The precision and linearity of both protocols were verified. The LoDs for the 200 and 500 µL protocols were 6.5 and 2.7 IU/mL for HBV and 29.7 and 8.2 IU/mL for HCV, respectively. The agreement between the protocols ranged from 0.8 to 1.0. The results obtained with the HBV and HCV tests showed a strong correlation (rho=0.994). Only 0.4% of HBV and 0.4% of HCV test results were affected by the LoDs of the 200 µL protocol. Conclusions: The Cobas 5800 200 and 500 µL protocols for the HBV DNA and HCV RNA tests demonstrated excellent performance. These findings establish the 200 µL protocol as a new option for low-volume samples, especially for pediatric and difficult-to-bleed patients.

Immune-checkpoint proteins, cytokines, and microbiome impact on patients with cervical insufficiency and preterm birth.

Background: Microenvironmental factors, including microbe-induced inflammation and immune-checkpoint proteins that modulate immune cells have been associated with both cervical insufficiency and preterm delivery. These factors are incompletely understood. This study aimed to explore and compare interactions among microbiome and inflammatory factors, such as cytokines and immune-checkpoint proteins, in patients with cervical insufficiency and preterm birth. In particular, factors related to predicting preterm birth were identified and the performance of the combination of these factors was evaluated. Methods: A total of 220 swab samples from 110 pregnant women, prospectively recruited at the High-Risk Maternal Neonatal Intensive Care Center, were collected between February 2020 and March 2021. This study included 63 patients with cervical insufficiency receiving cerclage and 47 control participants. Endo- and exocervical swabs and fluids were collected simultaneously. Shotgun metagenomic sequencing for the microbiome and the measurement of 34 immune-checkpoint proteins and inflammatory cytokines were performed. Results: First, we demonstrated that immune-checkpoint proteins, the key immune-regulatory molecules, could be measured in endocervical and exocervical samples. Secondly, we identified significantly different microenvironments in cervical insufficiency and preterm birth, with precise cervical locations, to provide information about practically useful cervical locations in clinical settings. Finally, the presence of Moraxella osloensis (odds ratio = 14.785; P = 0.037) and chemokine CC motif ligand 2 levels higher than 73 pg/mL (odds ratio = 40.049; P = 0.005) in endocervical samples were associated with preterm birth. Combining M. osloensis and chemokine CC motif ligand 2 yielded excellent performance for predicting preterm birth (area under the receiver operating characteristic curve = 0.846, 95% confidence interval = 0.733-0.925). Conclusion: Multiple relationships between microbiomes, immune-checkpoint proteins, and inflammatory cytokines in the cervical microenvironment were identified. We focus on these factors to aid in the comprehensive understanding and therapeutic modulation of local microbial and immunologic compositions for the management of cervical insufficiency and preterm birth.

Evaluation of the BD Phoenix CPO Detect Panel for Detection and Classification of Carbapenemase Producing Enterobacterales.

Carbapenem-resistant Enterobacterales (CRE) pose a serious public health threat due to their resistance to most antibiotics. Rapid and correct detection of carbapenemase producing organisms (CPOs) can help inform clinician decision making on antibiotic therapy. The BD Phoenix™ CPO detect panel, as part of antimicrobial susceptibility testing (AST), detects carbapenemase activity (P/N) and categorizes CPOs according to Ambler classes. We evaluated a CPO detect panel against 109 carbapenemase producing Enterobacterales (CPE) clinical isolates from Korea. The panel correctly detected carbapenemases production in 98.2% (n = 107/109) isolates and identified 78.8% (n = 26/33) class A, 65.9% (n = 29/44) class B, and 56.3% (n = 18/32) class D carbapenemase producers as harboring their corresponding Ambler classes. Specifically, the panel correctly classified 81.3% (n = 13/16) of K. pneumoniae KPC isolates to class A. However, the panel failed to classify 40.0% (n = 4/10) IMP and 63.6% (n = 7/11) VIM isolates to class B. Despite 27.5% (n = 30/109) CPE not being assigned Ambler classes, all of them tested carbapenemase positive. Our results demonstrate that the CPO detect panel is a sensitive test for detecting CPE and classifying KPC as class A, helping with antibiotics selection, but one-third of CPE remained unclassified for Ambler classes.

Tracking the Genomic Evolution of SARS-CoV-2 for 29 Months in South Korea.

The pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued, with the persistent emergence of variants of concern (VOCs). Therefore, this study aimed to track the genomic evolution of SARS-CoV-2 strains by sequencing the spike protein for 29 months, which accounted for the majority of the COVID-19 pandemic period. A total of 109 swabs from patients with confirmed coronavirus disease 2019 (COVID-19) infection were randomly collected between March 2020 and July 2022. After genomic sequencing, we analyzed the naming systems and phylogenetic trees. Five surge peaks of COVID-19 cases have been reported in South Korea, resulting in 14,000,000 cumulative confirmed cases and 17,000 deaths. Among the sequenced samples, 34 wild-type strains and 75 VOCs, including 4 Alpha, 33 Delta, 2 Epsilon, and 36 Omicron VOCs, were identified. Omicron strains were comprised of 8 BA.1.1 (21 K), 27 BA.2 (21 L), and 1 BA.2.12.1 (22C). Phylogenetic analysis of the identified isolates and representative sequences of SARS-CoV-2 strains revealed clusters that presented the WHO VOCs. Specific or unique mutations for each VOC waxed and waned according to the variant waves. Our findings allowed recognition of the overall trends of SARS-CoV-2 isolates, which implicated replication advantage, immune evasion, and disease management.

Incidence and risk factors of opportunistic infections after autologous stem cell transplantation: a nationwide, population-based cohort study in Korea.

Several guidelines classify autologous stem cell transplantation (ASCT) as a low to intermediate risk group for infection. In a nationwide population-based study, using the Korean Health Insurance Review and Assessment Service database, patients with lymphoma and multiple myeloma (MM) who underwent ASCT from 2002 to 2016 were retrospectively analyzed. Cumulative incidence rates (CIRs) and risk factors of opportunistic infections were investigated. CIRs of fungal, Varicella zoster virus (VZV), cytomegalovirus (CMV), and Pneumocystis jirovecii infections in lymphoma were 7.9%, 16.0%, 7.4%, and 5.1%, respectively, and CIRs in MM were 6.3%, 19.1%, 4.2%, and 5.6%, respectively. Fungal infection was significantly higher in patients with previous infection (Hazard ratio (HR) 2.003, p = 0.005) in lymphoma. Incidence of CMV infection was significantly higher in patients with prior CMV infection: HR 4.920, p < 0.001 (lymphoma); HR 3.022, p = 0.030 (MM). VZV infection was significantly lower in patients receiving prophylaxis: HR 0.082, p < 0.001 (lymphoma); HR 0.096, p < 0.001 (MM). For P. jirovecii infection, busulfex and melphalan conditioning (HR 1.875, p = 0.032) and previous P. jirovecii infection (HR 4.810, p < 0.001) had a higher incidence in MM. Patients who underwent ASCT should receive VZV prophylaxis and prophylaxis for fungal and P. jirovecii may be considered in patients with previous same infection.

Comparison of the Diagnostic Performance of Deep Learning Algorithms for Reducing the Time Required for COVID-19 RT-PCR Testing.

(1) Background: Rapid and accurate negative discrimination enables efficient management of scarce isolated bed resources and adequate patient accommodation in the majority of areas experiencing an explosion of confirmed cases due to Omicron mutations. Until now, methods for artificial intelligence or deep learning to replace time-consuming RT-PCR have relied on CXR, chest CT, blood test results, or clinical information. (2) Methods: We proposed and compared five different types of deep learning algorithms (RNN, LSTM, Bi-LSTM, GRU, and transformer) for reducing the time required for RT-PCR diagnosis by learning the change in fluorescence value derived over time during the RT-PCR process. (3) Results: Among the five deep learning algorithms capable of training time series data, Bi-LSTM and GRU were shown to be able to decrease the time required for RT-PCR diagnosis by half or by 25% without significantly impairing the diagnostic performance of the COVID-19 RT-PCR test. (4) Conclusions: The diagnostic performance of the model developed in this study when 40 cycles of RT-PCR are used for diagnosis shows the possibility of nearly halving the time required for RT-PCR diagnosis.

Deep learning application of the discrimination of bone marrow aspiration cells in patients with myelodysplastic syndromes.

Myelodysplastic syndromes (MDS) are a group of hematologic neoplasms accompanied by dysplasia of the bone marrow hematopoietic cells with cytopenia. Detecting dysplasia is important in the diagnosis of MDS, but it takes considerable time and effort. Also, since the assessment of dysplasia is subjective and difficult to quantify, a more efficient tool is needed for quality control and standardization of bone marrow aspiration smear interpretation. In this study, we developed and evaluated an algorithm to automatically discriminate hematopoietic cell lineages and detect dysplastic cells in bone marrow aspiration smears using deep learning technology. Bone marrow aspiration images were acquired from 34 patients diagnosed with MDS and from 24 normal bone marrow slides. In total, 8065 cells were classified into eight categories: normal erythrocytes, normal granulocytes, normal megakaryocytes, dysplastic erythrocytes, dysplastic granulocytes, dysplastic megakaryocytes, blasts, and others. The algorithm demonstrated acceptable performance in classifying dysplastic cells, with an AUC of 0.945-0.996 and accuracy of 0.912-0.993. The algorithm developed in this study could be used as an auxiliary tool for diagnosing patients with MDS and is expected to contribute to shortening the time required for MDS bone marrow aspiration diagnosis and standardizing visual reading.

Prognostic impacts of soluble immune checkpoint regulators and cytokines in patients with SARS-CoV-2 infection.

Coronavirus disease 2019 (COVID-19) has been a pandemic for the past two years. Predicting patient prognosis is critical. Although immune checkpoints (ICs) were shown to be involved in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, quantitative studies of ICs in clinical practice are limited. In this study, various soluble ICs (sICs) and cytokine levels in patients with SARS-CoV-2 infection at different time points were compared between survivors and deaths; we also examined whether sICs are useful for predicting prognosis. sICs and cytokines were measured in serum samples from 38 patients diagnosed with COVID-19 in the first and second week post-diagnosis. All assays were performed by bead-based multiplexed immunoassay system using Luminex Bio-Plex 200 system. The correlation of sICs and cytokines with laboratory markers was evaluated, and the levels of sICs in survivors were compared with those in deaths. Among the sICs, the second-week levels of soluble cluster of differentiation (sCD27, p = 0.012), sCD40 (p< 0.001), cytotoxic T-lymphocyte-associated protein 4 (sCTLA-4, p< 0.001), herpes virus entry mediator (sHVEM, p = 0.026), and T-cell immunoglobulin and mucin-domain containing-3 (sTIM-3, p = 0.002) were significantly higher in deaths than in survivors. The levels of nine cytokines assessed in the second week of deaths were significantly higher than those in survivors. The sICs sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines chemokine CC motif ligand 2 (CCL2), GM-CSF, IL-10, and IL-8 showed significant positive correlations with the levels of C-reactive protein (CRP) and procalcitonin and were negatively correlated with the absolute lymphocyte count and platelet values. Increased levels of sICs including sCD27, sCD40, sCTLA-4, and sTIM-3 and cytokines were significant factors for poor prognosis. sICs, together with cytokines and inflammatory markers, may be useful as prognostic stratification markers in SARS-CoV-2-infected patients.

STANDARD M10 SARS-CoV-2 Assay for Rapid Detection of SARS-CoV-2: Comparison of Four Real-Time PCR Assays.

The demand for assays that can rapidly and accurately detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) remains high. We evaluated the performance of two rapid real-time reverse transcription polymerase chain reaction (RT-qPCR) assays (STANDARD M10 SARS-CoV-2 and Xpert Xpress SARS-CoV-2) against conventional RT-qPCR assays (STANDARD M nCoV and Allplex SARS-CoV-2) for detecting SARS-CoV-2. A total of 225 swab samples were collected and tested using the four assays. The STANDARD M10 SARS-CoV-2 assay showed 97.4% positive percent agreement (PPA) and 100.0% negative percent agreement (NPA) compared to the STANDARD M nCoV assay and Allplex SARS-CoV-2 assay. STANDARD M10 exhibited high performance except in samples with low viral loads (cycle threshold (Ct) > 30). Xpert Xpress showed PPA and NPA of 100.0% compared to the two conventional RT-qPCR assays. The kappa coefficient (Κ) showed nearly almost perfect agreement between each assay and conventional RT-qPCR assays. The correlations of Ct values between the two rapid RT-qPCR and conventional RT-qPCR assays were >0.8, indicating strong correlations. All included assays could detect SARS-CoV-2 variants, such as the Alpha, Beta, and Gamma variants. The recently developed STANDARD M10 has a shorter turnaround time and random-access detection on automated devices, thereby facilitating efficient testing in emergency settings.

Development and validation of a deep learning-based protein electrophoresis classification algorithm.

BACKGROUND: Protein electrophoresis (PEP) is an important tool in supporting the analytical characterization of protein status in diseases related to monoclonal components, inflammation, and antibody deficiency. Here, we developed a deep learning-based PEP classification algorithm to supplement the labor-intensive PEP interpretation and enhance inter-observer reliability. METHODS: A total of 2,578 gel images and densitogram PEP images from January 2018 to July 2019 were split into training (80%), validation (10%), and test (10.0%) sets. The PEP images were assessed based on six major findings (acute-phase protein, monoclonal gammopathy, polyclonal gammopathy, hypoproteinemia, nephrotic syndrome, and normal). The images underwent processing, including color-to-grayscale and histogram equalization, and were input into neural networks. RESULTS: Using densitogram PEP images, the area under the receiver operating characteristic curve (AUROC) for each diagnosis ranged from 0.873 to 0.989, and the accuracy for classifying all the findings ranged from 85.2% to 96.9%. For gel images, the AUROC ranged from 0.763 to 0.965, and the accuracy ranged from 82.0% to 94.5%. CONCLUSIONS: The deep learning algorithm demonstrated good performance in classifying PEP images. It is expected to be useful as an auxiliary tool for screening the results and helpful in environments where specialists are scarce.

Quantitative Analysis of Anti-N and Anti-S Antibody Titers of SARS-CoV-2 Infection after the Third Dose of COVID-19 Vaccination.

We quantitatively analyzed SARS-CoV-2 antibody levels in patients after two doses of the ChAdOx1 nCoV-19 vaccine and the third BNT162b2 booster. We obtained 255 serum samples from 149 healthcare workers 1 and 4 months after the third dose. Of the 149 participants, 58 (38.9%) experienced COVID-19 infection during the 4-month study period, with infection occurring 7−62 days before the second blood draw. Total antibody titers against the anti-spike (anti-S) and anti-nucleocapsid (anti-N) proteins of SARS-CoV-2 were measured using Elecsys Anti-SARS-CoV-2 S and Elecsys Anti-SARS-CoV-2 assays (Roche), respectively. The median anti-S antibody titer in the non-infected groups at 4 months after the third dose was significantly decreased compared to that at 1 month after the third dose (from 17,777 to 3673 U/mL, p < 0.001). The infected group showed higher median anti-S antibody titers at 4 months (19,539 U/mL) than the non-infected group (3673 U/mL). The median anti-N antibody titer in the infected group at 4 months after the third dose was a 5.07 cut-off index (79.3% positivity). Anti-N antibody titers in the infected group were correlated with the number of days after SARS-CoV-2 infection. These data provide useful information for determining quarantine strategies and fourth vaccination requirements.

Clinical Performance of Rapid and Point-of-Care Antigen Tests for SARS-CoV-2 Variants of Concern: A Living Systematic Review and Meta-Analysis.

Rapid antigen tests (RATs) for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are widely used in the Coronavirus disease 2019 (COVID-19) pandemic caused by diverse variants. Information on the real-world performance of RATs for variants is urgently needed for decision makers. Systematic searches of the available literature and updates were conducted in PubMed, Ovid-MEDLINE, Ovid-EMBASE, CENTRAL, and KMBASE for articles evaluating the accuracy of instrument-free RATs for variants up until 14 March 2022. A bivariate random effects model was utilized to calculate pooled diagnostic values in comparison with real-time reverse transcription-polymerase chain reaction as the reference test. A total of 7562 samples from six studies were available for the meta-analysis. The overall pooled sensitivity and specificity of RATs for variants were 69.7% (95% confidence interval [CI] = 62.5% to 76.1%) and 100.0% (95% CI = 98.8% to 100.0%), respectively. When an additional 2179 samples from seven studies reporting sensitivities only were assessed, the pooled sensitivity dropped to 50.0% (95% CI = 44.0% to 55.0%). These findings suggest reassessment and monitoring of the diagnostic utility of RATs for variants, especially for the sensitivity aspect, to facilitate appropriate diagnosis and management of COVID-19 patients.

Humoral and Cellular Responses to BNT162b2 as a Booster Following Two Doses of ChAdOx1 nCov-19 Determined Using Three SARS-CoV-2 Antibody Assays and an Interferon-Gamma Release Assay: A Prospective Longitudinal Study in Healthcare Workers.

Data on humoral and cellular responses to BNT162b2 as a booster dose, following two doses of ChAdOx1 nCov-19 vaccine, have seldom been reported. The aim of this study was to assess the positivity rates of three representative antibody assays targeting total, IgG, and neutralizing antibodies, and an interferon-γ release assay (IGRA), and to determine the longitudinal changes in quantitative antibody titers after each vaccination. A total of 1027 samples were collected from healthcare workers. The number of participants after the booster dose was 153, and they all completed a questionnaire on adverse reactions. All antibody assays showed 100.0% positivity at 1 month after booster vaccination. The median antibody titers of the assays were significantly increased compared with those after the second dose (22.1-fold increase for Roche total antibody, 14.0-fold increase for Abbott IgG, and 1.1-fold increase (97.5% inhibition) for GenScript neutralizing antibody). Cellular responses determined using the IGRA were positive in 92.8% of the participants. Most participants (72.5%) reported mild adverse reactions. Correlations between the three antibody assays and IGRA were weak or negligible, indicating a difference between humoral and cellular responses. Overall, our study provides information about booster vaccine strategies and laboratory settings, which could subsequently contribute to the control of the spread of coronavirus disease 2019.

Impact of COVID-19 on Antimicrobial Consumption and Spread of Multidrug-Resistance in Bacterial Infections.

The spread of COVID-19 pandemic may have affected antibiotic consumption patterns and the prevalence of colonized or infected by multidrug-resistant (MDR) bacteria. We investigated the differences in the consumption of antibiotics easily prone to resistance and the prevalence of MDR bacteria during the COVID-19 pandemic (March 2020 to September 2021) compared to in the pre-pandemic period (March 2018 to September 2019). Data on usage of antibiotics and infections caused by methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), carbapenem-resistant Enterobacteriaceae (CRE), carbapenem-resistant Acinetobacter baumannii (CRAB), and carbapenem-resistant Pseudomonas aeruginosa (CRPA) were obtained from hospitalized patients in four university hospitals. The consumption of penicillin with ß-lactamase inhibitors (3.4% in ward, 5.8% in intensive care unit (ICU)), and carbapenems (25.9% in ward, 12.1% in ICU) increased during the pandemic period. The prevalence of MRSA (4.7%), VRE (49.0%), CRE (22.4%), and CRPA (20.1%) isolated in clinical samples from the ward and VRE (26.7%) and CRE (36.4%) isolated in clinical samples from the ICU were significantly increased, respectively. Meanwhile, only the prevalence of CRE (38.7%) isolated in surveillance samples from the ward increased. The COVID-19 pandemic is associated with increased consumption of antibiotics and has influenced the prevalence of infections caused by MDR isolates.

Procalcitonin to C-reactive protein ratio is associated with short-term mortality in ischemic stroke patients: preliminary report.

Introduction: Inflammation is associated with the development and progression of ischemic stroke. In this study, we tested the diagnostic ability of procalcitonin (PCT) to C-reactive protein (CRP) ratio (PC ratio; ×10-6) to predict 90-day mortality in ischemic stroke patients. Material and methods: We retrospectively collected the medical records of patients with a diagnosis of ischemic stroke from February 2008 to January 2018. We analyzed the data of study patients with both PCT and CRP results, and evaluated the relationship between PC ratio and 90-day mortality. Logistic regression was adjusted for confounders and survival analysis was conducted using the Kaplan-Meier estimation. Results: A total of 333 patients were analyzed in this study. As compared with the lowest PC ratio quartile (0-2.1), the odds ratios for 90-day mortality were; 1.47 (95% CI: 0.62-4.20) for the 2nd quartile (2.2-6.3, p = 0.440), 2.54 (95% CI: 0.95-5.91) for the 3rd quartile (6.4-19.6, p = 0.048), and 4.10 (95% CI: 1.73-9.80) for the 4th quartile (≥ 19.7, p = 0.002), after adjusting for age, sex, medical history, and laboratory results. A higher PC ratio (≥ 2.2) was associated with higher mortality (p < 0.05) in ischemic stroke patients in Kaplan-Meier estimation, and this was confirmed by the log-rank test (p < 0.001). Conclusions: Procalcitonin to C-reactive protein ratio was found to be positively associated with 90-day mortality in ischemic stroke patients. Our findings indicate that PC ratio may be a useful marker for predicting mortality after ischemic stroke. Further prospective studies are required to investigate and validate the use of PC ratio in clinical practice.

De Novo Assembly and Annotation of the Vaginal Metatranscriptome Associated with Bacterial Vaginosis.

The vaginal microbiome plays an important role in women's health and disease. Here we reanalyzed 40 vaginal transcriptomes from a previous study of de novo assembly (metaT-Assembly) followed by functional annotation. We identified 286,293 contigs and further assigned them to 25 phyla, 209 genera, and 339 species. Lactobacillus iners and Lactobacillus crispatus dominated the microbiome of non-bacterial vaginosis (BV) samples, while a complex of microbiota was identified from BV-associated samples. The metaT-Assembly identified a higher number of bacterial species than the 16S rRNA amplicon and metaT-Kraken methods. However, metaT-Assembly and metaT-Kraken exhibited similar major bacterial composition at the species level. Binning of metatranscriptome data resulted in 176 bins from major known bacteria and several unidentified bacteria in the vagina. Functional analyses based on Clusters of Orthologous Genes (COGs) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways suggested that a higher number of transcripts were expressed by the microbiome complex in the BV-associated samples than in non-BV-associated samples. The KEGG pathway analysis with an individual bacterial genome identified specific functions of the identified bacterial genome. Taken together, we demonstrated that the metaT-Assembly approach is an efficient tool to understand the dynamic microbial communities and their functional roles associated with the human vagina.

Prevalence and Clinical Impact of Coinfection in Patients with Coronavirus Disease 2019 in Korea.

Coinfection rates with other pathogens in coronavirus disease 2019 (COVID-19) varied during the pandemic. We assessed the latest prevalence of coinfection with viruses, bacteria, and fungi in COVID-19 patients for more than one year and its impact on mortality. A total of 436 samples were collected between August 2020 and October 2021. Multiplex real-time PCR, culture, and antimicrobial susceptibility testing were performed to detect pathogens. The coinfection rate of respiratory viruses in COVID-19 patients was 1.4%. Meanwhile, the rates of bacteria and fungi were 52.6% and 10.5% in hospitalized COVID-19 patients, respectively. Respiratory syncytial virus, rhinovirus, Acinetobacter baumannii, Escherichia coli, Pseudomonas aeruginosa, and Candida albicans were the most commonly detected pathogens. Ninety percent of isolated A. baumannii was non-susceptible to carbapenem. Based on a multivariate analysis, coinfection (odds ratio [OR] = 6.095), older age (OR = 1.089), and elevated lactate dehydrogenase (OR = 1.006) were risk factors for mortality as a critical outcome. In particular, coinfection with bacteria (OR = 11.250), resistant pathogens (OR = 11.667), and infection with multiple pathogens (OR = 10.667) were significantly related to death. Screening and monitoring of coinfection in COVID-19 patients, especially for hospitalized patients during the pandemic, are beneficial for better management and survival.

The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection.

Reducing the time to diagnose COVID-19 helps to manage insufficient isolation-bed resources and adequately accommodate critically ill patients. There is currently no alternative method to real-time reverse transcriptase polymerase chain reaction (RT-PCR), which requires 40 cycles to diagnose COVID-19. We propose a deep learning (DL) model to improve the speed of COVID-19 RT-PCR diagnosis. We developed and tested a DL model using the long short-term memory method with a dataset of fluorescence values measured in each cycle of 5810 RT-PCR tests. Among the DL models developed here, the diagnostic performance of the 21st model showed an area under the receiver operating characteristic (AUROC), sensitivity, and specificity of 84.55%, 93.33%, and 75.72%, respectively. The diagnostic performance of the 24th model showed an AUROC, sensitivity, and specificity of 91.27%, 90.00%, and 92.54%, respectively.