Jae Kwon Kim

Background/Objectives: We evaluated the diagnostic performance of the Alifax Molecular Mouse (MM) sepsis panel for rapidly directly detecting pathogens and resistance genes in positive blood culture broths. Methods: In this prospective single-center study, 40 positive blood culture specimens were analyzed using four MM cartridges targeting Gram-positive and -negative bacteria and major antimicrobial resistance genes. The results were compared with those obtained using the routine microbiological protocol, including organism identification using the MALDI Biotyper system and antimicrobial susceptibility testing using the VITEK 2 and MicroScan WalkAway systems. Additionally, the MM panel results were compared with those obtained using the FilmArray BCID2. Results: Compared with the routine protocol, the MM panel achieved a positive percent agreement (PPA) of 89.5% (34/38; 95% confidence interval [CI], 75.9–95.8%) for bacterial identification, indicating moderate-to-good diagnostic performance. For antimicrobial resistance gene detection, the PPA was 92.1% (35/38; 95% CI, 79.2–97.3%). Compared with the FilmArray BCID2, the overall agreement of the MM panel was 89.4% (34/38) for bacterial identification and 84.2% (32/38) for antimicrobial resistance gene detection. The MM panel generated clinically actionable results within 1 h (mean turnaround time, 55.5 min; 95% CI, 55.1–55.9 min), substantially reducing the turnaround time compared with the conventional microbiological workflow, which requires 1.5–2 days for subculture and automated antimicrobial susceptibility testing. Conclusions: The MM sepsis panel enabled rapid, direct identification of bloodstream pathogens and key antimicrobial resistance markers from positive blood cultures, substantially reducing turnaround time and exhibiting its potential as an adjunct to routine microbiological workflows.

Microbial contamination of platelet concentrates (PC) remains a persistent challenge in transfusion medicine, necessitating robust preventive measures prior to product release. Differentially expressed gene (DEG) analysis of microbe inoculated PC offers a promising approach to identifying potential biomarkers for contamination detection. Within PC, each S. aureus (ATCC 29213) and S. epidermidis (ATCC 12228) was inoculated in a 10³ CFU/mL concentration. Total RNA was extracted from the samples at predetermined time points (0-, 1-, 3-, and 6-hours post-inoculation), followed by high-throughput RNA sequencing. DEG, gene enrichment, and pathway analysis were conducted. Diagnostic potential was evaluated through the calculation of area under the curve (AUC) values and the assessment of additional performance metrics. DEG identified 5884 and 974 DEGs in S. aureus and S. epidermidis samples, respectively. Pathway analysis revealed distinct biological responses: S. aureus-inoculated samples showed prominent enrichment in ribosomal and spliceosome pathways, while S. epidermidis-inoculated samples demonstrated significant activation of mitogen-activated protein kinase (MAPK) signaling pathways and natural killer (NK) cell-mediated cytotoxicity pathways. ROC analysis of the commonly differentially expressed genes in both S. aureus and S. epidermidis-inoculated samples demonstrated significant diagnostic potential. The genes H19, CAVIN1, A2M, and EPAS1 exhibited statistically significant adjusted p-values and AUC values exceeding 0.8, with the exception of the H19 gene in S. epidermidis, suggesting their utility as potential biomarkers for staphylococcal contamination detection. Interaction between PC and microbial contaminants resulted in DEG and genes could be analyzed for microbial contamination of PC. However, to establish the robustness and broader applicability of these findings, further studies encompassing a more diverse range of microbial species are necessary.

Acute myeloid leukemia (AML) is a clinical emergency requiring treatment and results in high 30-day (D30) mortality. In this study, the prediction of D30 survival was studied using a machine learning (ML) method. The total cohort consisted of 1700 survivors and 130 non-survivors at D30. Eight clinical and 42 laboratory variables were collected at the time of diagnosis by pathology. Among them, six variables were selected by a feature selection method: induction chemotherapy (CTx), hemorrhage, infection, C-reactive protein, blood urea nitrogen, and lactate dehydrogenase. Clinical and laboratory data were entered into the training model for D30 survival prediction, followed by testing. Among the tested ML algorithms, the decision tree (DT) algorithm showed higher accuracy, the highest sensitivity, and specificity values (95% CI) of 90.6% (0.918–0.951), 70.4% (0.885–0.924), and 92.1% (0.885–0.924), respectively. DT classified patients into eight specific groups with distinct features. Group 1 with CTx showed a favorable outcome with a survival rate of 97.8% (1469/1502). Group 6, with hemorrhage and the lowest fibrinogen level at diagnosis, showed the worst survival rate of 45.5% (25/55) and 20.5 days. Prediction of D30 survival among AML patients by classification of patients with DT showed distinct features that might support clinical decision-making.

The effects of COVID‐19 vaccination on alloimmunization and clinical impact in transplant candidates remain largely unknown. In a 61‐year‐old man who had no donor‐specific antibodies (DSA) and was planned to undergo ABO‐incompatible kidney transplantation (ABOi KT), DSAs (anti‐A24, anti‐B51, and anti‐Cw14) developed after COVID‐19 vaccination. After desensitization therapy, antibody level was further increased, leading to flow cytometric crossmatch‐positive status. Donor‐specific T cell immunity using interferon‐gamma ELISPOT was continuously negative, whereas SARS‐CoV‐2 specific T cell immunity was intact. After confirming the C1q‐negative status of DSA, the patient received ABOi KT. The patient had stable graft function and suppressed alloimmunity up to 2 months after KT. COVID‐19 vaccination might relate to alloimmunization in transplant candidates, and desensitization through immune monitoring can help guide transplantation.