Diagnosis Of Infectious Diseases Research Articles

Application of metagenomic next-generation sequencing in the diagnosis of infectious diseases

Metagenomic next-generation sequencing (mNGS) is a transformative approach in the diagnosis of infectious diseases, utilizing unbiased high-throughput sequencing to directly detect and characterize microbial genomes from clinical samples. This review comprehensively outlines the fundamental principles, sequencing workflow, and platforms utilized in mNGS technology. The methodological backbone involves shotgun sequencing of total nucleic acids extracted from diverse sample types, enabling simultaneous detection of bacteria, viruses, fungi, and parasites without prior knowledge of the infectious agent. Key advantages of mNGS include its capability to identify rare, novel, or unculturable pathogens, providing a more comprehensive view of microbial communities compared to traditional culture-based methods. Despite these strengths, challenges such as data analysis complexity, high cost, and the need for optimized sample preparation protocols remain significant hurdles. The application of mNGS across various systemic infections highlights its clinical utility. Case studies discussed in this review illustrate its efficacy in diagnosing respiratory tract infections, bloodstream infections, central nervous system infections, gastrointestinal infections, and others. By rapidly identifying pathogens and their genomic characteristics, mNGS facilitates timely and targeted therapeutic interventions, thereby improving patient outcomes and infection control measures. Looking ahead, the future of mNGS in infectious disease diagnostics appears promising. Advances in bioinformatics tools and sequencing technologies are anticipated to streamline data analysis, enhance sensitivity and specificity, and reduce turnaround times. Integration with clinical decision support systems promises to further optimize mNGS utilization in routine clinical practice. In conclusion, mNGS represents a paradigm shift in the field of infectious disease diagnostics, offering unparalleled insights into microbial diversity and pathogenesis. While challenges persist, ongoing technological advancements hold immense potential to consolidate mNGS as a pivotal tool in the armamentarium of modern medicine, empowering clinicians with precise, rapid, and comprehensive pathogen detection capabilities.

Editorial: Value of a multidisciplinary approach for modern diagnosis of infectious diseases

Editorial: Value of a multidisciplinary approach for modern diagnosis of infectious diseases

Development of a novel strategy to reduce diagnostic errors in real-time polymerase chain reaction using probe-based techniques

Real-time PCR assays are valuable tools for the rapid and accurate diagnosis of infectious diseases by identifying the nucleic acid sequences of pathogens. Here, we aimed to develop a recombinant plasmid-based standard for validating the sensitivity of different molecular diagnostic methods adopting the Jonstrup assay (J assay). Chimeric plasmid DNA (cpDNA) harboring various pathogen genes and the target site of the J assay was constructed. Our findings revealed that the J assay could detect a single copy of the target gene similar to digital droplet PCR. The detection sensitivity of each established real-time PCR method for various disease diagnoses was evaluated using the cpDNA. Although most methods showed high sensitivity, similar to that of the J assay, the VHS Garver and SARS-CoV-2 diagnostic methods exhibited detection sensitivities tenfold lower than that of the J assay. To ensure accuracy of results and avoid genetic contamination from positive controls, we introduced an additional probe attachment site emitting distinct fluorescent signals within the cpDNA. Target genes and exogenous sequences within the plasmid DNA were simultaneously detected in a single assay using this unique method. Our approach will help improve the sensitivity of diagnostic methods and develop novel diagnostic methods based on molecular techniques.

Diagnostic uncertainties in patients with bacteraemia: impact on antibiotic prescriptions and outcome.

To establish a formal diagnosis in infectious disease is not an easy task. Our aim was to characterize diagnostic uncertainty (DU) in patients for whom blood cultures were positive (PBC) and to determine its impact on both the antibiotic therapy and the outcome. This was a prospective multicentre study including PBC for 6 months. The laboratory gave the PBC result to the infectious disease (ID) specialists in real time (24/7). The latter analysed all data from electronic patient charts and gave therapeutic advice to the physicians in charge of the patient to either initiate an antibiotic therapy, or to modify or to pursue continuing antimicrobial treatment. A DU was defined as no diagnosis of ID after thorough reading of the patient's chart, or more than two diagnoses despite two medical opinions obtained before PBC. An unfavourable outcome was defined by the occurrence of death during hospitalization. One hundred and nighty-nine PBCs were communicated to ID specialists, including 93 DUs (47%). In multivariate analysis, DU was associated with hospitalization in medical wards: [adjusted odds ratio (AOR) (95% CI): 6.94 (3.41-14.28)], the advice to initiate an antibiotic treatment: [3.89 (1.56-9.70)] and piperacillin-tazobactam use [3.75 (1.56-9.00)]; ICU requirement at initiation of care was a protective factor [0.38 (0.17-0.84)]. An unfavourable outcome was observed in 22 cases, and in a second logistic regression showed that DU was associated with the latter [AOR (95% CI): 5.07 (1.60-16.12)]. DUs were frequent during infections proved by PBC, and were associated with admission in medical wards, broad-spectrum antibiotic use and a high rate of unfavourable outcomes.

Case report: Metagenomic next-generation sequencing for the diagnosis of rare Nocardia aobensis infection in a patient with immune thrombocytopenia

BackgroundNocardiosis poses a diagnostic challenge due to its rarity in clinical practice, non-specific clinical symptoms and imaging features, and the limitations of traditional detection methods. Nocardia aobensis (N. aobensis) is rarely detected in clinical samples. Metagenomic next-generation sequencing (mNGS) offers significant advantages over traditional methods for rapid and accurate diagnosis of infectious diseases, especially for rare pathogens.Case presentationA 52-year-old woman with a history of immune thrombocytopenia for over 2 years was hospitalized for recurrent fever and cough lasting for 10 days. Her initial diagnosis on admission was community-acquired pneumonia, based on chest computed tomography findings of lung inflammation lesion. Empirical treatment with moxifloxacin and trimethoprim-sulfamethoxazole (TMP-SMZ) was initiated. However, her condition failed to improve significantly even after 1 week of treatment. Bronchoalveolar lavage fluid (BALF) subjected to mNGS revealed the presence of N. aobensis, resulting in a diagnosis of pulmonary nocardiosis caused by N. aobensis. This diagnosis was also supported by Sanger sequencing of the BALF. After adjusting the antibiotic regimen to include TMP-SMZ in combination with imipenem, the patient’s condition significantly improved. She was finally discharged with instructions to continue oral treatment with TMP-SMZ and linezolid for 6 months. The patient’s first follow-up 1 month after discharge showed good treatment outcomes but with obvious side effects of the drugs. Consequently, the antibiotic regimen was changed to doxycycline, and the patient continued to improve.ConclusionWe report the first detailed case of pulmonary nocardiosis caused by N. aobensis diagnosed by mNGS. mNGS could be an effective method that facilitates early diagnosis and timely decision-making for the treatment of nocardiosis, especially in cases that involve rare pathogens.

16S rRNA gene sequencing for bacterial identification and infectious disease diagnosis

16S rRNA gene sequencing for bacterial identification and infectious disease diagnosis

Comparison of the Efficacy of Cephalosporins and Non-cephalosporins in Preventing Surgical Site Infections in Colorectal Surgery Patients: An Updated Systematic Review and Meta-Analysis.

This study aims to compare the efficacy of cephalosporin and non-cephalosporin antibiotics in preventing surgical site infections (SSIs) in patients undergoing colorectal surgery, providing a scientific basis for the selection of perioperative antibiotics through systematic review and meta-analysis. Adhering to the PRISMA guidelines, this research employed an extensive literature search strategy across multiple databases to identify relevant studies, including randomized controlled trials (RCTs) and observational studies. Inclusion criteria were studies on adults undergoing colorectal surgery who received preoperative prophylaxis with either cephalosporin or non-cephalosporin antibiotics. Exclusion criteria included preoperative diagnosis of infectious diseases and preoperative use of antimicrobial agents. Meta-analysis was conducted using RevMan 5.3 software to assess the effectiveness of both antibiotic classes in preventing SSIs, including subgroup analyses. Eight studies met the inclusion criteria, comprising 872 patients. Meta-analysis revealed that the incidence of SSI in the cephalosporin group was lower than in the non-cephalosporin group (14.8% vs 25.1%), with statistical significance (OR = 1.94, 95% CI: 1.38-2.74, PP = =0.0001). However, no significant difference in SSI risk was observed between the groups within 30days (OR = =1.92, 95% CI: 1.08-3.42). Subgroup analysis indicated that higher-quality studies were associated with larger effect sizes. This study suggests that cephalosporin antibiotics may be superior to non-cephalosporins in preventing SSIs following colorectal surgery, especially in the short term. However, their long-term efficacy in SSI prevention may be similar. The selection of perioperative antibiotics should consider factors such as the antimicrobial spectrum, pharmacokinetic properties, and bacterial resistance.

Risks of high temperature exposure on infectious diseases in Portuguese hospitals

Abstract Background The convergence of climate change and infectious diseases presents a growing public health challenge. As global temperatures continue to climb, the geographical spread of climate-sensitive diseases is shifting, underscoring the necessity for comprehensive early warning systems grounded in a One Health framework. Methods This retrospective observational study analyzed daily maximum temperature from May to September, between 2000 and 2018, in mainland Portugal, at a county level, obtained from a European-level gridded observations dataset. Hospital admissions with a primary diagnosis of infectious and parasitic diseases were sourced from the Hospital Morbidity Database. A distributed lag non-linear model was developed to estimate the immediate and lagged relative risks (RR) of temperature exposure on admissions, focused on a lag of 10 days. Results Our research reveals a 16% (95% CI: 9% - 23%) rise in the RR of hospital admissions due to infectious and parasitic diseases after exposure to extremely high temperatures (39 °C). This elevated risk persists for up to 2 days following exposure, with the second day exhibiting the largest increase at 53% (31% - 80%). Furthermore, our findings indicate a subsequent resurgence in RR, reaching 6% (2% - 11%) 9 days after exposure. Conclusions Our results highlight a short-term effect of extreme temperature exposure on infectious and parasitic disease admissions. This data provides valuable insights to guide policymakers in identifying critical periods of vulnerability and implementing this knowledge into heat early warning systems, contributing to developing effective public health strategies against climate change. Acknowledgments The authors thank Fundação para a Ciência e a Tecnologia (FCT, Portugal) for research grant UIDP/04923/2020 and NOVA National School of Public Health for access to the Hospital Morbidity Database made available for research purposes by Administração Central do Sistema de Saúde, IP (ACSS). Key messages • Exposure to extremely high temperatures increases the risk of admission due to infectious and parasitic diseases by 16%, with a peak occurring two days after exposure. • This data is essential for policymakers to identify and integrate vulnerable periods into early heat warning systems, enhancing effective public health strategies against climate change.

Chest x-ray images: transfer learning model in COVID-19 detection.

This research aims to develop an effective algorithm for diagnosing COVID-19 in chest X-rays using the transfer learning method and support vector machines. In total, data was collected from 10 clinics, including both large city hospitals and smaller medical institutions. This ensured a diverse range of geographical and demographic information in the sample. An extensive data set was collected, including 10,000 chest X-ray images. 5000 images represent normal cases, 3993 images represent pneumonia cases, and 1007 images represent COVID-19 cases. Machine learning methods were applied to develop a classification model, and the results were compared with seven state-of-the-art models and a lightweight CNN architecture. The results showed that the proposed method achieves high accuracy values (Accuracy): 0.95 for COVID-19, 0.89 for pneumonia, and 0.92 for normal images (p < 0.05). Comparison with other models demonstrates statistically significant superiority of our method in accuracy across all three classes. The EfficientNet-B0 model surpasses our method only in accuracy for normal images with p < 0.01, confirming the advantages of our method. Our method demonstrates high sensitivity values (Sensitivity): 0.96 for COVID-19, 0.88 for pneumonia, and 0.93 for normal images (p < 0.05), outperforming most of the compared models. Correlation analysis showed Pearson coefficients of 0.92, 0.89, and 0.94 for COVID-19, pneumonia, and normal images, respectively, confirming a high degree of consistency between predicted and true class labels. In addition, the model was validated on external datasets to assess its generalizability. This validation confirmed its high level of effectiveness in a variety of clinical settings. This study confirms the importance of applying machine learning methods in medical applications and opens new perspectives for early diagnosis of infectious diseases. The practical application of the obtained results can enhance the efficiency of diagnosis and control the spread of COVID-19, as well as contribute to the development of innovative methods in medical practice.

ADVANCED PREDICTIVE MODELING FOR THE BIOFIRE FILMARRAY SYSTEM IN INFECTIOUS DISEASE DIAGNOSTICS

The Bio Fire Film Array System has emerged as a powerful tool in clinical diagnostics, offering rapid and multiplexed detection of infectious pathogens. This study presents the development of a comprehensive mathematical model aimed at predicting the accuracy and sensitivity of the BioFire FilmArray System under diverse conditions. Key factors such as sample type, pathogen load, specimen characteristics, and environmental variables were systematically analyzed to elucidate their impact on the system's performance. The model incorporates a range of parameters including sample volume, specimen complexity, microbial diversity, and system-specific variables to generate probabilistic estimates of diagnostic outcomes. By leveraging large datasets encompassing diverse clinical scenarios and pathogen profiles, the model achieves robustness and generalizability, thereby facilitating its applicability across different healthcare settings and populations. Validation of the model against independent datasets demonstrates its efficacy in accurately predicting the performance of the BioFire FilmArray System across a spectrum of conditions. Overall, this mathematical model represents a significant advancement in optimizing the utility of the BioFire FilmArray System for infectious disease diagnosis. Its integration into clinical practice holds promise for improving patient care, guiding sample processing protocols, and informing decision-making processes in infectious disease management.

A case of severe psittacosis pneumonia complicated by splenic infarction

Clinical dataChlamydia psittaci pneumonia is a community-acquired pneumonia caused by Chlamydia psittaci. While severe cases may lead to critical conditions such as respiratory failure, splenic infarction is relatively uncommon. A severe patient with Chlamydia psittaci pneumonia admitted to our hospital experienced a splenic infarction during treatment. Fortunately, the patient’s situation was improved after careful treatment. Now, the patient has been discharged. Further exploration of the mechanism of concurrent splenic infarction is required.BackgroudPsittacosis pneumonia, a zoonotic infectious disease transmitted from birds to humans, is caused by Chlamydia psittaci and represents a type of chlamydial pneumonia [1]. Insome instances, the disease may progress to severe pneumonia and respiratory failure, necessitating intensive support measures, including mechanical ventilation. The advent of technologies such as Metagenomic Next-Generation Sequencing (mNGS) for the etiological diagnosis of infectious diseases [2] has improved the diagnostic and treatment success rates for Psittacosis. Instances of severe chlamydial pneumonia with complications such as splenic infarction are uncommon. A patient with severe Psittacosis pneumonia complicated by splenic infarction was admitted to the Emergency Intensive Care Unit (EICU) of Haining People’s Hospital and subsequently improved following effective anti-infective and anticoagulant therapy. This report is provided herein.

Approaches to developing and implementing a molecular diagnostics stewardship program for infectious diseases: an ASM Laboratory Practices Subcommittee report.

Diagnostic stewardship (DxS) for infectious disease testing requires a multi-disciplinary approach to optimize test selection, performance, interpretation and patient treatment. Nucleic acid amplification-based tests for the diagnosis of infectious diseases, or "molecular microbiology tests," have rapidly expanded over the past two decades. With the increased availability and complexity of these tests, there is also an increased need for collaborative approaches to optimize test use to promote positive impacts on patient care, while mitigating potential negative impact or resource waste. In this review, we provide recommendations on building collaborative DxS teams, including microbiologists and the diverse stakeholders that use and interpret molecular microbiology tests. We then detail approaches to identify high-priority molecular microbiology tests that may need utilization assessment, select appropriate diagnostic stewardship interventions, and monitor the impact of implemented interventions. This strategic process may be employed by laboratories to realize optimal testing for selected tests at their institution.

Harnessing the host response for precision infectious disease diagnosis.

SUMMARYDetection of the presence of infection and its etiology must be accurate and timely to facilitate appropriate antimicrobial use. Diagnostic strategies that rely solely on pathogen detection often are insufficient due to poor test characteristics, inability to differentiate colonization from infection, or protracted delay to result. Understanding the human response across different pathogens on a clinical and molecular level can provide more accurate, timely, and useful answers, especially in critical illness and diagnostic uncertainty. Improvements in understanding the human immune response including genomics, protein analysis, gene expression, and cellular morphology have led to rapid innovation of new host response-based diagnostic tests. This review describes the limitations of pathogen-focused technology and the benefits of examining the breadth of immune response to diagnose infection. It then explores biomarkers that have been studied for this purpose and scrutinizes the performance of host-based multianalyte testing. Currently cleared diagnostics and those in late-stage development are described in depth, with a focus on the purpose of testing and its utility for clinicians. Finally, it concludes by examining opportunities for further host response-derived diagnostic innovation.

Assisted peritoneal dialysis compared to in-centre hemodialysis – an observational study of outcomes from the Swedish Renal Registry

BackgroundIn-center hemodialysis (IHD) is the most common dialysis modality. Assisted peritoneal dialysis (assPD) is an option for frail and/or incapacitated patients. Both modalities can be used to alleviate uremic symptoms towards the end of life. There are few studies comparing these modalities. The primary aim is to compare hospital admissions between assPD and IHD. The secondary aim is to compare continuation of the dialysis modality and patient survival.MethodsPatients > 65 years, registered in the Swedish Renal Registry (SRR) and starting dialysis 2010–2017 were eligible for inclusion. Patients starting on assPD were matched with patients starting on IHD according to sex, Charlson Index, age and date for start of dialysis. Data were collected from SRR and other registries.ResultsDuring the first year, patients on assPD and IHD had in median one (IQR 0–5.0; 0–4.0) hospitalization (p = 0.412). There was no significant difference after two years, in the annual number of days admitted to hospital, in hospitalizations with cardiovascular or infectious disease diagnoses or continuation of the dialysis modality, respectively. However, patients on assPD had a worse median survival (1.1 years IQR 0.6–2.1; IHD 3.1 years IQR 0.2–5.8; p < 0.001).ConclusionIn this study patients starting assPD, often as a palliative treatment, showed no difference compared to IHD concerning the number of hospitalizations, number of days in hospital/year or continuation of the dialysis modality. Patients on assPD had a worse survival, which is likely due to residual confounding. Without that, patients on assPD would probably have lower number of hospitalizations. Despite limitations due to the retrospective observational design of the study, the results indicate that assPD is a feasible alternative to IHD when self-care dialysis is not possible and/or IHD too arduous.

Antibacterial Capabilities of Metallic Nanoparticles and Influencing Factors

ABSTRACTThe increase of antibiotic resistance in bacteria has become a major concern for successful diagnosis and treatment of infectious diseases. Over the past few decades, significant progress has been achieved on the development of nanotechnology‐based medicines for combating multidrug resistance in microorganisms. Among these, metallic nanoparticles (MNPs) hold great promise in addressing this challenge due to their broad‐spectrum and robust antimicrobial properties. This review illustrates the antibacterial activities of MNPs and further elucidates how different factors including synthesis method, size, shape, surface charge, pH, dose, type of capping or stabilizing agents of MNPs, and Gram‐type of the bacteria, impact their antibacterial activities, which are expected to promote the future development of more potent MNP‐based antibacterial agents.

An Efficient Transfer Learning-Based Framework for Health Care Application

Deep learning has revolutionized healthcare applications, particularly in the diagnosis, treatment, and management of infectious diseases. The main objectives of this investigation are to propose several methods for assessing high-resolution X-ray images with the purpose of identifying the occurrence or not of symptoms associated with pneumonia. The objective of this exam was to identify fixes for these existing problems. Our offering entails a deep learning (DL) technique for detecting chest anomalies using the X-ray modality using the EfficientNet B0 model. In order to make accurate diagnoses of pneumonia, both the EfficientNet B0 and the upgraded CNN model undergo extensive data-driven training. The CNN model that underwent upgrades was determined to be the most effective in this analysis because to its high level of accuracy. The results of our research conclusion are that DL models are capable of monitoring pneumonia's development, increasing diagnostic precision overall and giving patients new optimism for immediate relief.

Nonprescribed Substance Use in the General Hospital: A Retrospective Study

Non-prescribed substance use (NPSU) is a recognized phenomena exhibited by patients with substance use disorders while admitted to inpatient hospitals. What factors distinguish patients who engage in NPSU, or how their hospitalizations and outcomes differ, remains to be understood in full. Our study describes a cohort of medically admitted patients with substance use disorders with behaviors concerning for non-prescribed substance use (NPSU) during their hospitalization. We extracted electronic health record data for all hospital encounters when an addiction consult was documented (n=3100). We defined NPSU cases during a clinical, interdisciplinary case review in which patients were deemed high risk based on team members' observations of one or more behaviors described in the NPSU Checklist. These individuals were placed on a "NPSU Protocol," which was implemented for optimization of care, destigmatization, and risk mitigation (n=61). We compared clinical characteristics, resource utilization, and treatment outcomes among the NPSU cohort to addiction consult patients without suspicion of NPSU but with stimulant or opioid use disorder diagnoses. Patients on the NPSU protocol were younger and had higher rates of infectious disease diagnoses reported during hospitalization than patients without concern for NPSU. Hospitalizations for individuals suspected of NPSU were longer, had higher rates of before medically advised discharge, as well as discharges without medications for opioid use disorder (MOUD). These outcome differences were also observed when analysis was restricted to hospitalizations in which an infectious disease was diagnosed. Our study characterizes a population of people who exhibited behaviors concerning for NPSU and highlights key outcome disparities. To our knowledge, this study is the first to show a direct correlation between infectious disease diagnosis and NPSU, as well as a direct correlation between suspected NPSU and outcomes such as before medically advised discharge and discharge without MOUD, irrespective of infectious disease diagnosis. Further study is necessary to determine interventions to reduce poor outcomes among hospitalized patients with NPSU.

IgG neutralization potential of COVISHIELD™ vaccinated individual’s sera after booster vaccination: Longitudinal and prospective cohort study

The novel SARS-CoV-2 (COVID-19) has caused widespread human turmoil by posing challenges concerning infection prevention, disease diagnosis, and treatment. Several approved vaccines including Sinovac (CoronaVac), COVISHIELD™ (Oxford/AstraZeneca formulation), Janssen (Johnson &amp; Johnson), Sputnik V (Gamaleya), Covaxin (Bharat Biotech), Pfizer (BNT162b2), and others are being used to combat COVID-19. It is crucial to evaluate the kinetics of SARS-CoV-2 antibodies to predict the possibility of reinfection and the longevity of vaccination protection. There is a lack of data on longitudinal humoral antibody dynamics following two and three doses of the SARS-CoV-2 vaccination ChAdOx1-nCOV (COVISHIELDTM) in Indians. Thus, concerns about the efficacy of current vaccines have been raised by a sharp rise in coronavirus disease 2019 (Covid-19) cases caused by sub-variants of SARS-CoV-2 (Severe acute respiratory syndrome corona virus 2) in communities that have received massive vaccinations. The relative immunogenicity and safety of various COVID-19 immunizations administered as a third (booster) dose are not well known. We examined the reactogenicity and immunogenicity of the COVID-19 vaccine as a third dose after two doses of COVISHIELDTM to produce data to optimize the selection of booster vaccinations. After three doses of the COVID-19 vaccine, we evaluated the sera of COVISHIELDTM vaccine recipients for their ability to neutralize the virus. Primary immunization with two doses of COVISHIELDTM vaccine recipients provided significant protection against symptomatic disease caused by the SARS-CoV-2 variants. A COVISHIELDTM vaccine booster vaccine recipient substantially increased protection. The immunization findings showed a significant difference (p ≥ 0.001) between the COVID-19 naive vaccine (n = 438) and the sera of COVID-19-positive recovered subjects (n = 371) who received three doses of COVISHIELDTM. Our findings reveal that anti-RBD antibodies persist over time, which may reduce the probability of reinfection. A three-dose vaccination (n = 53) increases defense against variations by noticeably increasing cross-neutralizing antibody titers. Particularly against variants with antibody escape mutations.

Pentraxin-3 (PTX-3) as a potential biomarker for predicting death in hospitalized patients with COVID-19

BackgroundPentraxin 3 (PTX3) is an acute-phase protein that belongs to the pentraxin family, which plays an important role in the body’s defense against pathogens. PTX3 levels have been associated with inflammatory processes, and it is a possible biomarker for the diagnosis and prognosis of different infectious diseases, including COVID-19. The objective of this study was to analyze the potential of PTX3 as a plasma biomarker for predicting death in patients hospitalized with COVID-19.MethodsThe study included a total of 312 patients with COVID-19, admitted from July 2020 to August 2021 to hospital ward and intensive care unit beds at two hospitals in the Northeast Region of Brazil. PTX3 was measured using ELISA in samples collected within 24 h after hospital admission. Maximally selected rank statistics were used to determine the PTX3 cutoff point that best distinguished patients who died from those who survived. A receiver operating characteristic (ROC) curve was used to determine the performance of the biomarker. Survival analysis was performed using a Kaplan-Meier curve, and a Cox regression model was used to determine predictors associated with death.ResultsOf the 312 patients included in the study, 233 recovered and 79 died. Patients who died had higher PTX3 levels at the time of admission, when compared to those who recovered (median: 52.84 versus 10.79 ng/mL; p < 0.001). PTX3 showed area under the ROC (AUC) = 0.834, higher than other markers used in clinical practice, such as C-reactive protein (AUC = 0.72) and D-dimer (AUC = 0.77). Furthermore, according to the Kaplan-Meier survival curve, patients with PTX3 concentrations above the cutoff point (27.3 ng/mL) had a lower survival rate (p = 0.014). In multivariate Cox regression, PTX3 > 27.3 ng/mL was an important predictor of death, regardless of other confounding factors (hazard ratio = 1.79; p = 0.027).ConclusionPTX3 can be considered as a potential biomarker for predicting death in patients hospitalized with COVID-19.

Lymph Node Histology and Metagenomic Sequencing in a child with Cat Scratch Disease.

A 4-year-old boy presented with fever and lymphadenopathy. Despite antibiotic treatment for suspected lymphadenitis, there was minimal improvement. A lymph node biopsy revealed granulomas, raising suspicion of cat scratch disease (CSD). The boy had been scratched by a kitten a month prior. Metagenomic testing of the tissue confirmed the presence of Bartonella henselae, the causative agent of CSD. It is important to incorporate molecular diagnostic tools into clinical practice for precise and timely diagnosis of infectious diseases.