Goh Ohji

1. Haemophilus influenzae is a gram-negative bacterium that causes sinusitis, otitis media, and lower respiratory tract infections [1]. Although the number of H. influenzae type b (Hib) strains has...

Gram staining is one of the basic tests to identify the organism in microbiological laboratory, especially in blood culture. However, microbiological specialists are not always available, which can lead to treatment delays. Therefore, we developed a computer-aided diagnosis (CAD) system that uses artificial intelligence to predict causative pathogens from Gram-stained blood culture images, focusing on both morphological (Class 1) and detailed (Class 2) classification. In this retrospective observational study, we compared the accuracy of predictions made by microbiology specialists (MS) and by CAD using iPhone-captured images of clinical samples stained with the Bartholomew and Mittwer method collected from two tertiary care hospitals between 1 October 2022 and 31 January 2023. Among 126 samples (378 images) for aerobic bottles (AE) and 90 samples (270 images) for anaerobic bottles (AN), the accuracy of MS and CAD prediction (95% confidence interval) was 91.5% (90.5%–92.3%) and 75.4% (70.7%–79.7%) for AE and 90.1% (88.9%–91.2%) and 76.7% (71.2%–81.6%) for AN, respectively. Although we could not demonstrate non-inferiority, most of the mispredictions occurred with Gram-negative cocci, Haemophilus influenzae , and Campylobacter spp., while relatively good results were obtained for other species. CAD has the potential to provide early diagnostic results and indicate timely treatment during off-hours and in medical facilities without microbiology specialists. On the other hand, it should be noted that certain bacterial species remain challenging for AI-based identification, and improving accuracy in these areas is an important task for future development. IMPORTANCE: Gram staining of a positive blood culture is essential for early diagnosis and appropriate antibiotic selection, but interpretation requires trained specialists, limiting rapid reporting. While artificial intelligence (AI)-based automation has been explored, most previous studies either focused on a limited range of bacterial species or evaluated only the performance of the device itself. Also, most systems rely on bulky equipment. To address this, we developed a mobile device-based computer-aided diagnosis system for Gram stain interpretation and conducted a non-inferiority trial comparing its accuracy with that of microbiology specialists. Although non-inferiority was not demonstrated, we identified bacterial species that the AI system had difficulty distinguishing. With an appropriate understanding of these limitations, AI-assisted Gram stain interpretation could help reduce turnaround time and support timely clinical decision-making.

The B-cell receptor (BCR) repertoire serves as a historical record of immunological events. However, deciphering antigen-specific sequences from this vast dataset remains a challenge, particularly for novel pathogens where prior knowledge is absent. While time-course analysis methods such as QASAS have proven effective for tracking immune responses, they rely on existing antibody databases, limiting their applicability to emerging diseases. To overcome this limitation, we introduce LM-QASAS, a reference-free computational framework that integrates antibody language models with repertoire dynamics. By mapping sequences into a high-dimensional semantic embedding space, LM-QASAS identifies functionally convergent clusters of sequences that are semantically similar and exhibit transient expansion upon immune stimulation. In healthy individuals vaccinated with SARS-CoV-2 mRNA vaccines, our method identified spike-specific sequences with over 90% purity, significantly outperforming methods based on simple sequence identity or abundance. Leave-one-out cross-validation demonstrated that LM-QASAS could accurately reconstruct immune dynamics in unseen individuals without external references. Conversely, the method showed limited sensitivity in an influenza vaccine cohort, revealing that the approach is most effective under conditions of robust clonal expansion (high signal-to-noise ratio), such as those induced by mRNA vaccines. LM-QASAS provides a rapid, high-precision platform for monitoring humoral immunity against emerging threats. Significance statement: Antibodies that recognize a pathogen leave traces in a person’s B-cell receptor (BCR) repertoire, but identifying those antigen-specific sequences is difficult, especially for new diseases lacking reference databases. We present LM-QASAS, a reference-free method that combines antibody language models with time-course repertoire data to detect clusters of sequences that expand transiently after immune stimulation. In SARS-CoV-2 mRNA vaccinees, LM-QASAS enriched spike-related sequences with very high precision and enabled reconstruction of immune-response dynamics in unseen individuals using pseudo-reference databases built from other subjects. Analyses of convalescent, post-transplant, and influenza cohorts clarify when the approach works best: strong clonal expansion and a high signal-to-noise ratio. This framework supports rapid monitoring of humoral immunity against emerging threats.

Background/Objectives: Gram staining is an essential diagnostic technique used for the rapid identification of bacterial and fungal infections, playing a pivotal role in clinical decision-making, especially in point-of-care (POC) settings. Manual staining, while effective, is labor-intensive and prone to variability, relying heavily on the skill of laboratory personnel. Current automated Gram-staining systems are primarily designed for high-throughput laboratory environments, limiting their feasibility in decentralized healthcare settings such as emergency departments and rural clinics. This study aims to introduce and evaluate the Point-of-Care Gram Stainer (PoCGS^®), a compact, automated device engineered for single-slide processing, addressing challenges related to portability, standardization, and efficiency in POC applications. Methods: The PoCGS^® device was developed to emulate expert manual staining techniques through features such as methanol fixation and programmable reagent application. A comparative evaluation was performed using 40 urine samples, which included both clinical and artificial specimens. These samples were processed using PoCGS^®, manual staining by skilled experts, and manual staining by unskilled personnel. The outcomes were assessed based on microbial identification concordance, the staining uniformity, presence of artifacts, and agreement with the culture results. Statistical analyses, including agreement rates and quality scoring, were conducted to compare the performance of PoCGS^® against manual staining methods. Results: PoCGS^® achieved a 100% concordance rate with expert manual staining in terms of microbial identification, confirming its diagnostic accuracy. However, staining quality parameters such as the uniformity and presence of artifacts showed statistically significant differences when compared to skilled and unskilled personnel. Despite these limitations, PoCGS^® demonstrated a comparable performance regarding artifact reduction and agreement with the culture results, indicating its potential utility in POC environments. Challenges such as fixed processing times and limited adaptability to varying specimen characteristics were identified as areas for further improvement. Conclusions: The study findings suggest that PoCGS^® is a reliable and valuable tool for microbial identification in POC settings, with a performance comparable to skilled manual staining. Its compact design, automation, and ease of use make it particularly beneficial for resource-limited environments. Although improvements in staining uniformity and background clarity are required, PoCGS^® has the potential to standardize Gram staining protocols and improve diagnostic turnaround times. Future developments will focus on optimizing staining parameters and expanding its application to other clinical sample types, ensuring robustness and broader usability in diverse healthcare settings.

A monovalent Omicron XBB.1.5 mRNA RBD analogue vaccine, MAFB-7256a (DS-5670d), was newly developed and approved in Japan in the Spring of 2024 for preventing COVID-19. However, clinical efficacy data for this vaccine are currently lacking. We previously established the Quantification of Antigen-specific Antibody Sequence (QASAS) method to assess the response to SARS-CoV-2 vaccination at the mRNA level using B-cell receptor (BCR) repertoire assays and the Coronavirus Antibody Database (CoV-AbDab). Here, we used this method to evaluate the immunogenicity of MAFB-7256a. We analyzed repeated blood samples using the QASAS method from three healthy volunteers before and after MAFB-7256a vaccination. BCR response increased rapidly one week post-vaccination and then decreased, as with conventional vaccine. Notably, the matched sequences after MAFB-7256a vaccination specifically bound to the receptor-binding domain (RBD), with no sequences binding to other epitopes. These results validate that MAFB-7256a is an effective vaccine that exclusively induces antibodies specific for the RBD, demonstrating its targeted immunogenic effect.

Monovalent Omicron XBB.1.5 mRNA vaccines (BNT162b2 XBB.1.5 and mRNA- 1273.815) were newly developed and approved by the FDA in Autumn 2023 for preventing COVID-19. However, clinical efficacy for these vaccines is currently lacking. We previously established the Quantification of Antigen-specific Antibody Sequence (QASAS) method to assess the response to SARS-CoV-2 vaccination at the mRNA level using B-cell receptor (BCR) repertoire assay and the Coronavirus Antibody Database (CoV-AbDab). Here, we used this method to evaluate the immunogenicity of monovalent XBB.1.5 vaccines in healthy volunteers. We analyzed repeated blood samples before and after vaccination for the BCR repertoire to assess BCR/antibody sequences that matched SARS-CoV-2-specific sequences in the database. The number of matched unique sequences and their total reads quickly increased 1 week after vaccination. Matched sequences included those bound to the Omicron strain and Omicron XBB sublineage. The antibody sequences that can bind to the Omicron strain and XBB sublineage revealed that the monovalent XBB.1.5 vaccines showed a stronger response than previous vaccines or SARS-CoV-2 infection before the emergence of XBB sublineage. The QASAS method was able to demonstrate the immunogenic effect of monovalent XBB.1.5 vaccines for the 2023-2024 COVID-19 vaccination campaign.

Purpose: Many effective vaccines against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have been developed, but a weaker response in individuals undergoing anticancer treatment has been reported. This study evaluates the immunogenic status and safety of SARS-CoV-2 vaccines for patients with non-small-cell lung cancer (NSCLC), receiving tegafur–uracil (UFT) as postoperative adjuvant chemotherapy. Methods: The subjects of this prospective study were 40 patients who underwent surgery for NSCLC and received SARS-CoV-2 vaccines postoperatively. We compared the antibody titers of SARS-CoV-2 vaccines and the adverse events between patients who received adjuvant UFT and patients who did not. Results: The mean anti-S1 IgG titers were not significantly different between the UFT and without-UFT groups (mean optimal density, 0.194 vs. 0.205; P = 0.76). Multivariate analysis identified the period after the second vaccination as an independent predictor of anti-S1 IgG titer (P = 0.049), but not the UFT status (with or without-UFT treatment; P = 0.47). The prevalence of adverse events did not differ significantly between the groups, and no severe adverse events occurred. Conclusions: The efficacy and safety of the SARS-CoV-2 vaccines for NSCLC patients who received postoperative adjuvant UFT chemotherapy were comparable to those for NSCLC patients who did not receive postoperative adjuvant UFT chemotherapy. Clinical trial registration: This study was registered with the University Hospital Medical Information Network (UMIN) in Japan (UMIN000047380).

We previously reported that a second dose of COVID-19 mRNA vaccine was safe and effective for allogeneic hematopoietic stem cell transplantation (HSCT) patients. However, some of these patients did not achieve seroconversion. Here, we investigated the safety and efficacy of a third dose of COVID-19 mRNA vaccine in Japanese allogeneic HSCT patients. Antibody titers against the S1 spike protein were measured using the QuaResearch COVID-19 Human IgM IgG ELISA kit. The previous study included 25 allogeneic HSCT patients who received two doses of COVID-19 mRNA vaccine. Following the exclusion of three patients because of the development of COVID-19 (n = 2) and loss to follow-up (n = 1), the study evaluated 22 allogeneic HSCT patients who received a third dose of COVID-19 mRNA vaccine (BNT162b2 [n = 15] and mRNA-1273 [n = 7]). Median age at the time of the first vaccination was 56 (range, 23–71) years. Median time from HSCT to the third vaccination and from the second to the third vaccination was 1842 (range, 378–4279) days and 219 (range, 194–258) days, respectively. Five patients were receiving immunosuppressants at the third vaccination, namely calcineurin inhibitors (CI) alone (n = 1), steroids alone (n = 2), or CI combined with steroids (n = 2). Median optical density of S1 IgG titers before and after the third dose was 0.099 (range, 0.001–0.713) and 1.315 (range, 0.006–1.730), respectively. Among 22 evaluable patients, 21 (95%) seroconverted after the third dose. Four of the five patients treated with steroids or CI seroconverted after the third vaccination. One patient with a serum IgG level of 173 mg/dL who received steroids did not achieve seroconversion. On one-week follow-up, none of our patients had > grade 3 or serious adverse events, new-onset graft-versus-host disease (GVHD), or GVHD exacerbation after vaccination. The most frequent adverse event was mild pain at the injection site. A third dose of the BNT162b2 and mRNA-1273 COVID-19 vaccines was safe and effective for allogeneic HSCT patients.

Background: Cutibacterium modestumwas named in 2020.C. modestumwas previously calledPropionibacterium humerusii. Several implant-associated infections caused by Cutibacterium species have been previously reported, but native vertebral osteomyelitis due to these bacteria has rarely been reported.Case presentation: A 72-year-old man, who had previously received several nerve block injections for low back pain, was referred to our hospital for deterioration in back pain in the last 1 month. MRI findings were suggestive of L5-S1 vertebral osteomyelitis. Blood cultures and bone biopsy culture revealed the presence of Gram-positive bacilli. The isolate was identified asC. modestumby 16SrRNA gene sequencing. A diagnosis of vertebral osteomyelitis caused byC. modestumwas made. Minocycline followed by oral amoxicillin was administered for 3 months. His symptom improved and did not recur after treatment completion.Conclusion: A case of vertebral osteomyelitis caused byC. modestumwas encountered. AlthoughC. modestumis very similar toC. acnes, it could be accurately identified by 16SrRNA gene sequencing. This case represents the first documentedC. modestuminfection in humans.

We investigated the efficacy of BNT162b2 mRNA COVID-19 vaccine in patients with B-cell malignancies treated with anti-CD20 antibody. Although T-cell-mediated immune responses were detected even in patients receiving R-CHOP treatment, the S1 antibody titer following BNT162b2 vaccination remained only marginally increased for more than 3 years after the final dose of anti-CD20 antibody. We found no relationship between the percent of B-cells and S1 antibody titer. The duration of this suppression was much longer than we anticipated. Further protection and treatment strategies against COVID-19 for these patients are warranted.