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Bartonella quintana Endocarditis: A Systematic Review of Individual Cases

BACKGROUND

Bartonella quintana is a louse-borne bacterium that remains a neglected cause of endocarditis in low-resource settings. Our understanding of risk factors, clinical manifestations, and treatment of B. quintana endocarditis are biased by older studies from high-income countries.

METHODS

We searched Pubmed Central, Medline, Scopus, Embase, EBSCO (CABI) Global Health, Web of Science and international trial registers for articles published before March 2023 with terms related to Bartonella quintana endocarditis. We included articles containing case-level information on B. quintana endocarditis and extracted data related to patient demographics, clinical features, diagnostic testing, treatment, and outcome.

RESULTS

A total of 975 records were identified, of which 569 duplicates were removed prior to screening. In total, 84 articles were eligible for inclusion, describing a total of 167 cases. Infections were acquired in 40 different countries; 62 cases (37.1%) were acquired in low- and middle-income countries (LMICs). Disproportionately more female and pediatric patients were from LMICs. More patients presented with heart failure (n = 70/167 [41.9%]) than fever (n = 65/167 [38.9%]). Mean time from symptom onset to presentation was 5.1 months. Also, 25.7% of cases (n = 43/167) were associated with embolization, most commonly to the spleen and brain; 65.5% of antimicrobial regimens included doxycycline. The vast majority of cases underwent valve replacement surgery (n = 154/167, [98.0%]). Overall case fatality rate was 9.6% (n = 16/167).

CONCLUSIONS

B. quintana endocarditis has a global distribution, and long delays between symptom onset and presentation frequently occur. Improved clinician education and diagnostic capacity are needed to screen at-risk populations and identify infection before endocarditis develops.

Clinicopathological differences between Bartonella and other bacterial endocarditis-related glomerulonephritis - our experience and a pooled analysis

Background

Although Staphylococcus aureus is the leading cause of acute infective endocarditis (IE) in adults, Bartonella spp. has concomitantly emerged as the leading cause of "blood culture-negative IE" (BCNE). Pre-disposing factors, clinical presentation and kidney biopsy findings in Bartonella IE-associated glomerulonephritis (GN) show subtle differences and some unique features relative to other bacterial infection-related GNs. We highlight these features along with key diagnostic clues and management approach in Bartonella IE-associated GN.

Methods

We conducted a pooled analysis of 89 cases of Bartonella IE-associated GN (54 published case reports and case series; 18 published conference abstracts identified using an English literature search of several commonly used literature search modalities); and four unpublished cases from our institution.

Results

Bartonella henselae and Bartonella quintana are the most commonly implicated species causing IE in humans. Subacute presentation, affecting damaged native and/or prosthetic heart valves, high titer anti-neutrophil cytoplasmic antibodies (ANCA), mainly proteinase-3 (PR-3) specificity, fastidious nature and lack of positive blood cultures of these Gram-negative bacilli, a higher frequency of focal glomerular crescents compared to other bacterial infection-related GNs are some of the salient features of Bartonella IE-associated GN. C3-dominant, but frequent C1q and IgM immunofluorescence staining is seen on biopsy. A "full-house" immunofluorescence staining pattern is also described but can be seen in IE -associated GN due to other bacteria as well. Non-specific generalized symptoms, cytopenia, heart failure and other organ damage due to embolic phenomena are the highlights on clinical presentation needing a multi-disciplinary approach for management. Awareness of the updated modified Duke criteria for IE, a high index of suspicion for underlying infection despite negative microbiologic cultures, history of exposure to animals, particularly infected cats, and use of send-out serologic tests for Bartonella spp. early in the course of management can help in early diagnosis and initiation of appropriate treatment.

Conclusion

Diagnosis of IE-associated GN can be challenging particularly with BCNE. The number of Bartonella IE-associated GN cases in a single institution tends to be less than IE due to gram positive cocci, however Bartonella is currently the leading cause of BCNE. We provide a much-needed discussion on this topic.

Studying the mechanism underlying lipid metabolism in osteosarcoma based on transcriptomic RNA sequencing and single-cell data

BACKGROUND

We aimed to provide a new typing method for osteosarcoma (OS) based on single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the perspective of lipid metabolism and examine its potential mechanisms in the onset and progression of OS.

METHODS

Scores for six lipid metabolic pathways were calculated by single-sample gene set enrichment analysis (ssGSEA) based on a scRNA-seq dataset and three microarray expression profiles. Subsequently, cluster typing was conducted using unsupervised consistency clustering. Furthermore, single-cell clustering and dimensionality-reduction analyses identified cell subtypes. Finally, an analysis of cellular receptors was performed using CellphoneDB to identify cellular communication.

RESULTS

OS was classified into three subtypes based on lipid metabolic pathways. Among them, patients in clust3 showed poor prognoses, whereas those in clust1 and clust2 exhibited good prognoses. In addition, ssGSEA analysis showed that patients in clust3 had lower immune cell scores. Moreover, the Th17 cell differentiation pathway was significantly differentially enriched between clust2 and clust3, with lower enrichment scores for metabolic pathways in the former relative to clust1 and clust2. In total, 24 genes were upregulated between clust1 and clust2, whereas 20 were downregulated in clust3. These observations were validated by single-cell data analysis. Finally, through scRNA-seq data analysis, we identified nine ligand-receptor pairs particularly critical for communication between normal and malignant cells.

CONCLUSIONS

Three clusters were identified and the single-cell analysis revealed that malignant cells dominated lipid metabolism patterns in tumors, thereby influencing the tumor microenvironment.

Design, implementation, and evaluation of the computer-aided clinical decision support system based on learning-to-rank: collaboration between physicians and machine learning in the differential diagnosis process

BACKGROUND

We are researching, developing, and publishing the clinical decision support system based on learning-to-rank. The main objectives are (1) To support for differential diagnoses performed by internists and general practitioners and (2) To prevent diagnostic errors made by physicians. The main features are that "A physician inputs a patient's symptoms, findings, and test results to the system, and the system outputs a ranking list of possible diseases".

METHOD

The software libraries for machine learning and artificial intelligence are TensorFlow and TensorFlow Ranking. The prediction algorithm is Learning-to-Rank with the listwise approach. The ranking metric is normalized discounted cumulative gain (NDCG). The loss functions are Approximate NDCG (A-NDCG). We evaluated the machine learning performance on k-fold cross-validation. We evaluated the differential diagnosis performance with validated cases.

RESULTS

The machine learning performance of our system was much higher than that of the conventional system. The differential diagnosis performance of our system was much higher than that of the conventional system. We have shown that the clinical decision support system prevents physicians' diagnostic errors due to confirmation bias.

CONCLUSIONS

We have demonstrated that the clinical decision support system is useful for supporting differential diagnoses and preventing diagnostic errors. We propose that differential diagnosis by physicians and learning-to-rank by machine has a high affinity. We found that information retrieval and clinical decision support systems have much in common (Target data, learning-to-rank, etc.). We propose that Clinical Decision Support Systems have the potential to support: (1) recall of rare diseases, (2) differential diagnoses for difficult-to-diagnoses cases, and (3) prevention of diagnostic errors. Our system can potentially evolve into an explainable clinical decision support system.

Combining bulk RNA-sequencing and single-cell RNA-sequencing data to reveal the immune microenvironment and metabolic pattern of osteosarcoma

Background: Osteosarcoma (OS) is a kind of solid tumor with high heterogeneity at tumor microenvironment (TME), genome and transcriptome level. In view of the regulatory effect of metabolism on TME, this study was based on four metabolic models to explore the intertumoral heterogeneity of OS at the RNA sequencing (RNA-seq) level and the intratumoral heterogeneity of OS at the bulk RNA-seq and single cell RNA-seq (scRNA-seq) level.

Methods: The GSVA package was used for single-sample gene set enrichment analysis (ssGSEA) analysis to obtain a glycolysis, pentose phosphate pathway (PPP), fatty acid oxidation (FAO) and glutaminolysis gene sets score. ConsensusClusterPlus was employed to cluster OS samples downloaded from the Target database. The scRNA-seq and bulk RNA-seq data of immune cells from GSE162454 dataset were analyzed to identify the subsets and types of immune cells in OS. Malignant cells and non-malignant cells were distinguished by large-scale chromosomal copy number variation. The correlations of metabolic molecular subtypes and immune cell types with four metabolic patterns, hypoxia and angiogenesis were determined by Pearson correlation analysis.

Results: Two metabolism-related molecular subtypes of OS, cluster 1 and cluster 2, were identified. Cluster 2 was associated with poor prognosis of OS, active glycolysis, FAO, glutaminolysis, and bad TME. The identified 28608 immune cells were divided into 15 separate clusters covering 6 types of immune cells. The enrichment scores of 5 kinds of immune cells in cluster-1 and cluster-2 were significantly different. And five kinds of immune cells were significantly correlated with four metabolic modes, hypoxia and angiogenesis. Of the 28,608 immune cells, 7617 were malignant cells. The four metabolic patterns of malignant cells were significantly positively correlated with hypoxia and negatively correlated with angiogenesis.

Conclusion: We used RNA-seq to reveal two molecular subtypes of OS with prognosis, metabolic pattern and TME, and determined the composition and metabolic heterogeneity of immune cells in OS tumor by bulk RNA-seq and single-cell RNA-seq.