Retina. 2025 Jan 17. doi: 10.1097/IAE.0000000000004409. Online ahead of print.

ABSTRACT

PURPOSE: Predicting long-term anatomical responses in neovascular age-related macular degeneration (nAMD) patients is critical for patient-specific management. This study validates a generative deep learning (DL) model to predict 12-month posttreatment optical coherence tomography (OCT) images and evaluates the impact of incorporating clinical data on predictive performance.

METHODS: A total of 533 eyes from 513 treatment-naïve nAMD patients were analyzed. A conditional generative adversarial network (cGAN) served as the baseline model, generating 12-month OCT images using pretreatment OCT, fluorescein angiography (FA), and indocyanine green angiography (ICGA). We then sequentially added OCT after three loading doses, baseline visual acuity (VA), treatment regimen (pro re nata or treat-and-extend), drug type, and switching events. The generated and patient OCT images were compared for intraretinal fluid, subretinal fluid, pigment epithelial detachment, and subretinal hyperreflective material, both qualitatively and quantitatively.

RESULTS: The baseline model achieved acceptable accuracy for four macular fluid compartments (range 0.74-0.96). Incorporating OCT after loading doses and other clinical parameters improved accuracy (range 0.91-0.98). With all the clinical inputs, the model achieved 92% accuracy in distinguishing wet macular status from dry macular status. The segmented fluid compartments in the generated images correlated positively with those in the patient images.

CONCLUSION: Integrating clinical and treatment data, particularly OCT data after loading doses, significantly enhanced the 12-month predictive performance of cGANs. This approach can help clinicians anticipate anatomical outcomes and guide personalized, long-term nAMD treatment strategies.

PMID:39841905 | DOI:10.1097/IAE.0000000000004409

J Chem Theory Comput. 2025 Jan 22. doi: 10.1021/acs.jctc.4c01525. Online ahead of print.

ABSTRACT

Metal-organic frameworks (MOFs) hold great potential in gas separation and storage. Graph neural networks (GNNs) have proven effective in exploring structure-property relationships and discovering new MOF structures. Unlike molecular graphs, crystal graphs must consider the periodicity and patterns. MOFs' specific features at different scales, such as covalent bonds, functional groups, and global structures, influenced by interatomic interactions, exert varying degrees of impact on gas adsorption or selectivity. Moreover, redundant interatomic interactions hinder training accuracy, leading to overfitting. This research introduces a construction method for multiscale crystal graphs, which considers specific features at different scales by decomposing the crystal graph into multiple subgraphs based on interatomic interactions within varying distance ranges. Additionally, it takes into account the global structure of the crystal by encoding the periodic patterns of the unit cells. We propose MSAIGNN, a multiscale atomic interaction graph neural network with self-attention-based graph pooling mechanism, which incorporates three-body bond angle information, accounts for structural features at different scales, and minimizes interference from redundant interactions. Compared with traditional methods, MSAIGNN demonstrates higher prediction accuracy in assessing single-component adsorption, gas separation, and structural features. Visualization of attention scores confirms effective learning of structural features at different scales, highlighting MSAIGNN's interpretability. Overall, MSAIGNN offers a novel, efficient, multilayered, and interpretable approach for property prediction of complex porous crystal structures like MOFs using deep learning.

PMID:39841881 | DOI:10.1021/acs.jctc.4c01525

Biomed Phys Eng Express. 2025 Jan 22;11(2). doi: 10.1088/2057-1976/ada9f0.

ABSTRACT

In fundus images, precisely segmenting retinal blood vessels is important for diagnosing eye-related conditions, such as diabetic retinopathy and hypertensive retinopathy or other eye-related disorders. In this work, we propose an enhanced U-shaped network with dual-attention, named DAU-Net, divided into encoder and decoder parts. Wherein, we replace the traditional convolutional layers with ConvNeXt Block and SnakeConv Block to strengthen its recognition ability for different forms of blood vessels while lightweight the model. Additionally, we designed two efficient attention modules, namely Local-Global Attention (LGA) and Cross-Fusion Attention (CFA). Specifically, LGA conducts attention calculations on the features extracted by the encoder to accentuate vessel-related characteristics while suppressing irrelevant background information; CFA addresses potential information loss during feature extraction by globally modeling pixel interactions between encoder and decoder features. Comprehensive experiments in terms of public datasets DRIVE, CHASE_DB1, and STARE demonstrate that DAU-Net obtains excellent segmentation results on all three datasets. The results show an AUC of 0.9818, ACC of 0.8299, and F1 score of 0.9585 on DRIVE; 0.9894, 0.8499, and 0.9700 on CHASE_DB1; and 0.9908, 0.8620, and 0.9712 on STARE, respectively. These results strongly demonstrate the effectiveness of DAU-Net in retinal vessel segmentation, highlighting its potential for practical clinical use.

PMID:39841872 | DOI:10.1088/2057-1976/ada9f0

PLoS One. 2025 Jan 22;20(1):e0316380. doi: 10.1371/journal.pone.0316380. eCollection 2025.

ABSTRACT

Marine heatwaves are increasing in intensity and frequency however, responses and survival of reef corals vary geographically. Geographical differences in thermal tolerance may be in part a consequence of intraspecific diversity, where high-diversity localities are more likely to support heat-tolerant alleles that promote survival through thermal stress. Here, we assessed geographical patterns of intraspecific genetic diversity in the ubiquitous coral Pocillopora damicornis species complex using 428 sequences of the Internal Transcribed Spacer 2 (ITS2) region across 44 sites in the Pacific and Indian Oceans. We focused on detecting genetic diversity hotspots, wherein some individuals are likely to possess gene variants that tolerate marine heatwaves. A deep-learning, multi-layer neural-network model showed that geographical location played a major role in intraspecific diversity, with mean sea-surface temperature and oceanic regions being the most influential predictor variables differentiating diversity. The highest estimate of intraspecific variation was recorded in French Polynesia and Southeast Asia. The corals on these reefs are more likely than corals elsewhere to harbor alleles with adaptive potential to survive climate change, so managers should prioritize high-diversity regions when forming conservation goals.

PMID:39841675 | DOI:10.1371/journal.pone.0316380

PLoS One. 2025 Jan 22;20(1):e0314823. doi: 10.1371/journal.pone.0314823. eCollection 2025.

ABSTRACT

As education increasingly relies on data-driven methodologies, accurately predicting student performance is essential for implementing timely and effective interventions. The California Student Performance Dataset offers a distinctive basis for analyzing complex elements that affect educational results, such as student demographics, academic behaviours, and emotional health. This study presents the GNN-Transformer-InceptionNet (GNN-TINet) model to overcome the constraints of prior models that fail to effectively capture intricate interactions in multi-label contexts, where students may display numerous performance categories concurrently. The GNN-TINet utilizes InceptionNet, transformer architectures, and graph neural networks (GNN) to improve precision in multi-label student performance forecasting. Advanced preprocessing approaches, such as Contextual Frequency Encoding (CFI) and Contextual Adaptive Imputation (CAI), were used on a dataset of 97,000 occurrences. The model achieved exceptional outcomes, exceeding current standards with a Predictive Consistency Score (PCS) of 0.92 and an accuracy of 98.5%. Exploratory data analysis revealed significant relationships between GPA, homework completion, and parental involvement, emphasizing the complex nature of academic achievement. The results illustrate the GNN-TINet's potential to identify at-risk pupils, providing a robust resource for educators and policymakers to improve learning outcomes. This study enhances educational data mining by enabling focused interventions that promote educational equality, tackling significant challenges in the domain.

PMID:39841673 | DOI:10.1371/journal.pone.0314823

PLoS One. 2025 Jan 22;20(1):e0316081. doi: 10.1371/journal.pone.0316081. eCollection 2025.

ABSTRACT

Citrus farming is one of the major agricultural sectors of Pakistan and currently represents almost 30% of total fruit production, with its highest concentration in Punjab. Although economically important, citrus crops like sweet orange, grapefruit, lemon, and mandarins face various diseases like canker, scab, and black spot, which lower fruit quality and yield. Traditional manual disease diagnosis is not only slow, less accurate, and expensive but also relies heavily on expert intervention. To address these issues, this research examines the implementation of an automated disease classification system using deep learning and optimal feature selection. The system incorporates data augmentation and transfer learning with pre-trained models such as DenseNet-201 and AlexNet to improve diagnostic accuracy, efficiency, and cost-effectiveness. Experimental results on a citrus leaves dataset show an impressive 99.6% classification accuracy. The proposed framework outperforms existing methods, offering a robust and scalable solution for disease detection in citrus farming, contributing to more sustainable agricultural practices.

PMID:39841644 | DOI:10.1371/journal.pone.0316081

PLoS One. 2025 Jan 22;20(1):e0317741. doi: 10.1371/journal.pone.0317741. eCollection 2025.

ABSTRACT

AIMS: T1 mapping on cardiac magnetic resonance (CMR) imaging is useful for diagnosis and prognostication in patients with light-chain cardiac amyloidosis (AL-CA). We conducted this study to evaluate the performance of T1 mapping parameters, derived from artificial intelligence (AI)-automated segmentation, for detection of cardiac amyloidosis (CA) in patients with left ventricular hypertrophy (LVH) and their prognostic values in patients with AL-CA.

METHODS AND RESULTS: A total of 300 consecutive patients who underwent CMR for differential diagnosis of LVH were analyzed. CA was confirmed in 50 patients (39 with AL-CA and 11 with transthyretin amyloidosis), hypertrophic cardiomyopathy in 198, hypertensive heart disease in 47, and Fabry disease in 5. A semi-automated deep learning algorithm (Myomics-Q) was used for the analysis of the CMR images. The optimal cutoff extracellular volume fraction (ECV) for the differentiation of CA from other etiologies was 33.6% (diagnostic accuracy 85.6%). The automated ECV measurement showed a significant prognostic value for a composite of cardiovascular death and heart failure hospitalization in patients with AL-CA (revised Mayo stage III or IV) (adjusted hazard ratio 4.247 for ECV ≥40%, 95% confidence interval 1.215-14.851, p-value = 0.024). Incorporation of automated ECV measurement into the revised Mayo staging system resulted in better risk stratification (integrated discrimination index 27.9%, p = 0.013; categorical net reclassification index 13.8%, p = 0.007).

CONCLUSIONS: T1 mapping on CMR imaging, derived from AI-automated segmentation, not only allows for improved diagnosis of CA from other etiologies of LVH, but also provides significant prognostic value in patients with AL-CA.

PMID:39841643 | DOI:10.1371/journal.pone.0317741

Brief Bioinform. 2024 Nov 22;26(1):bbaf019. doi: 10.1093/bib/bbaf019.

ABSTRACT

Identifying therapeutic genes is crucial for developing treatments targeting genetic causes of diseases, but experimental trials are costly and time-consuming. Although many deep learning approaches aim to identify biomarker genes, predicting therapeutic target genes remains challenging due to the limited number of known targets. To address this, we propose HIT (Hypergraph Interaction Transformer), a deep hypergraph representation learning model that identifies a gene's therapeutic potential, biomarker status, or lack of association with diseases. HIT uses hypergraph structures of genes, ontologies, diseases, and phenotypes, employing attention-based learning to capture complex relationships. Experiments demonstrate HIT's state-of-the-art performance, explainability, and ability to identify novel therapeutic targets.

PMID:39841592 | DOI:10.1093/bib/bbaf019

Mol Biol Cell. 2025 Jan 22:mbcE24100438. doi: 10.1091/mbc.E24-10-0438. Online ahead of print.

ABSTRACT

Cell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While significant progress has been made in understanding the molecular determinants of these processes, historically, cell fate transitions were identified through light microscopy that focused on changes in cell morphology and function. Modern techniques have shifted towards probing molecular effectors to quantify these transitions, offering more precise quantification and mechanistic understanding. However, challenges remain in cases where the molecular signals are ambiguous, complicating the assignment of cell fate. During viral infection, programmed cell death (PCD) pathways, including apoptosis, necroptosis, and pyroptosis, exhibit complex signaling and molecular crosstalk. This can lead to simultaneous activation of multiple PCD pathways, which confounds assignment of cell fate based on molecular information alone. To address this challenge, we employed deep learning-based image classification of dying cells to analyze PCD in single Herpes Simplex Virus-1 (HSV-1)-infected cells. Our approach reveals that despite heterogeneous activation of signaling, individual cells adopt predominantly prototypical death morphologies. Nevertheless, PCD is executed heterogeneously within a uniform population of virus-infected cells and varies over time. These findings demonstrate that image-based phenotyping can provide valuable insights into cell fate decisions, complementing molecular assays. [Media: see text] [Media: see text] [Media: see text] [Media: see text].

PMID:39841552 | DOI:10.1091/mbc.E24-10-0438

Sci Adv. 2025 Jan 24;11(4):eadn6377. doi: 10.1126/sciadv.adn6377. Epub 2025 Jan 22.

ABSTRACT

Birth is one of the most important life events for animals. However, its significance in the developmental process is not fully understood. Here, we found that birth-induced alteration of glutamine metabolism in radial glia (RG), the embryonic neural stem cells (NSCs), is required for the acquisition of quiescence and long-term maintenance of postnatal NSCs. Preterm birth impairs this cellular process, leading to transient hyperactivation of RG. Consequently, in the postnatal brain, the NSC pool is depleted and neurogenesis is decreased. We also found that the maintenance of quiescent RG after preterm birth improves postnatal neurogenesis. This study demonstrates the significance of birth in the maintenance of quiescent NSCs.

PMID:39841848 | DOI:10.1126/sciadv.adn6377

Database (Oxford). 2025 Jan 22;2025:baae133. doi: 10.1093/database/baae133.

ABSTRACT

Ontologies and knowledge graphs (KGs) are general-purpose computable representations of some domain, such as human anatomy, and are frequently a crucial part of modern information systems. Most of these structures change over time, incorporating new knowledge or information that was previously missing. Managing these changes is a challenge, both in terms of communicating changes to users and providing mechanisms to make it easier for multiple stakeholders to contribute. To fill that need, we have created KGCL, the Knowledge Graph Change Language (https://github.com/INCATools/kgcl), a standard data model for describing changes to KGs and ontologies at a high level, and an accompanying human-readable Controlled Natural Language (CNL). This language serves two purposes: a curator can use it to request desired changes, and it can also be used to describe changes that have already happened, corresponding to the concepts of "apply patch" and "diff" commonly used for managing changes in text documents and computer programs. Another key feature of KGCL is that descriptions are at a high enough level to be useful and understood by a variety of stakeholders-e.g. ontology edits can be specified by commands like "add synonym 'arm' to 'forelimb'" or "move 'Parkinson disease' under 'neurodegenerative disease'." We have also built a suite of tools for managing ontology changes. These include an automated agent that integrates with and monitors GitHub ontology repositories and applies any requested changes and a new component in the BioPortal ontology resource that allows users to make change requests directly from within the BioPortal user interface. Overall, the KGCL data model, its CNL, and associated tooling allow for easier management and processing of changes associated with the development of ontologies and KGs. Database URL: https://github.com/INCATools/kgcl.

PMID:39841813 | DOI:10.1093/database/baae133

Sci Transl Med. 2025 Jan 22;17(782):eadr4049. doi: 10.1126/scitranslmed.adr4049. Epub 2025 Jan 22.

ABSTRACT

Oxygen is essential for human life, yet a growing body of preclinical research is demonstrating that chronic continuous hypoxia can be beneficial in models of mitochondrial disease, autoimmunity, ischemia, and aging. This research is revealing exciting new and unexpected facets of oxygen biology, but translating these findings to patients poses major challenges, because hypoxia can be dangerous. Overcoming these barriers will require integrating insights from basic science, high-altitude physiology, clinical medicine, and sports technology. Here, we explore the foundations of this nascent field and outline a path to determine how chronic continuous hypoxia can be safely, effectively, and practically delivered to patients.

PMID:39841808 | DOI:10.1126/scitranslmed.adr4049

New Phytol. 2025 Jan 22. doi: 10.1111/nph.20415. Online ahead of print.

ABSTRACT

Precise gene-editing methods are valuable tools to enhance genetic traits. Gene editing is commonly achieved via stable integration of a gene-editing cassette in the plant's genome. However, this technique is unfavorable for field applications, especially in vegetatively propagated plants, such as many commercial tree species, where the gene-editing cassette cannot be segregated away without breaking the genetic constitution of the elite variety. Here, we describe an efficient method for generating gene-edited Populus tremula × P. alba (poplar) trees without incorporating foreign DNA into its genome. Using Agrobacterium tumefaciens, we expressed a base-editing construct targeting CCoAOMT1 along with the ALS genes for positive selection on a chlorsulfuron-containing medium. About 50% of the regenerated shoots were derived from transient transformation and were free of T-DNA. Overall, 7% of the chlorsulfuron-resistant shoots were T-DNA free, edited in the CCoAOMT1 gene and nonchimeric. Long-read whole-genome sequencing confirmed the absence of any foreign DNA in the tested gene-edited lines. Additionally, we evaluated the CodA gene as a negative selection marker to eliminate lines that stably incorporated the T-DNA into their genome. Although the latter negative selection is not essential for selecting transgene-free, gene-edited Populus tremula × P. alba shoots, it may prove valuable for other genotypes or varieties.

PMID:39841625 | DOI:10.1111/nph.20415

Mol Biol Cell. 2025 Jan 22:mbcE24100438. doi: 10.1091/mbc.E24-10-0438. Online ahead of print.

ABSTRACT

Cell fate decisions, such as proliferation, differentiation, and death, are driven by complex molecular interactions and signaling cascades. While significant progress has been made in understanding the molecular determinants of these processes, historically, cell fate transitions were identified through light microscopy that focused on changes in cell morphology and function. Modern techniques have shifted towards probing molecular effectors to quantify these transitions, offering more precise quantification and mechanistic understanding. However, challenges remain in cases where the molecular signals are ambiguous, complicating the assignment of cell fate. During viral infection, programmed cell death (PCD) pathways, including apoptosis, necroptosis, and pyroptosis, exhibit complex signaling and molecular crosstalk. This can lead to simultaneous activation of multiple PCD pathways, which confounds assignment of cell fate based on molecular information alone. To address this challenge, we employed deep learning-based image classification of dying cells to analyze PCD in single Herpes Simplex Virus-1 (HSV-1)-infected cells. Our approach reveals that despite heterogeneous activation of signaling, individual cells adopt predominantly prototypical death morphologies. Nevertheless, PCD is executed heterogeneously within a uniform population of virus-infected cells and varies over time. These findings demonstrate that image-based phenotyping can provide valuable insights into cell fate decisions, complementing molecular assays. [Media: see text] [Media: see text] [Media: see text] [Media: see text].

PMID:39841552 | DOI:10.1091/mbc.E24-10-0438

J Bone Joint Surg Am. 2025 Jan 22. doi: 10.2106/JBJS.24.00261. Online ahead of print.

ABSTRACT

BACKGROUND: Limb-salvage surgery for malignant bone tumors can be associated with considerable perioperative blood loss. The aim of this randomized controlled trial was to assess the safety and efficacy of the intraoperative infusion of tranexamic acid (TXA) in children and adolescents undergoing limb-salvage surgery.

METHODS: All participants were <18 years of age at the time of surgery and diagnosed with a malignant bone tumor of the femur that was treated with resection and reconstruction with a megaprosthesis. Exclusion criteria included anatomic locations other than the femur, reconstruction with a vascularized fibular graft, and a previous history of deep venous thrombosis, coagulopathy, or renal dysfunction. Participants were randomly allocated to either the TXA group (a preoperative loading dose infusion of 10 mg/kg of TXA followed by a continuous infusion of 5 mg/kg/hr until the end of surgery) or the placebo group (the same dosage but with TXA substituted with an infusion of normal saline solution). Intraoperative and perioperative blood loss were calculated with use of the hemoglobin balance method. Perioperative blood loss at postoperative day 1 and at discharge from the hospital were calculated. The total volumes of blood transfused intraoperatively and postoperatively were recorded. A statistical comparison between the groups was performed for blood loss and blood transfusion as well as for possible independent variables other than TXA, including age, body mass index, histopathologic diagnosis, tumor volume, preoperative hemoglobin level, type of resection, and the duration of surgery.

RESULTS: A total of 48 participants, with a mean age of 12.5 ± 3.44 years (range, 5 to 18 years) and a male-to-female ratio of 1.18, were included. All participants were Egyptians by race and ethnicity. There were no minor or major drug-related adverse events. There was no significant difference between the groups with respect to intraoperative blood loss (p = 0.0616) or transfusion requirements (p = 0.812), but there was a significant difference in perioperative blood loss at postoperative day 1 (p = 0.0144) and at discharge from the hospital (p = 0.0106) and in perioperative blood transfusion (p = 0.023).

CONCLUSIONS: TXA can be safely infused intraoperatively in children and adolescents undergoing limb-salvage surgery, and it contributes significantly to the reduction of perioperative blood loss and transfusion requirements.

LEVEL OF EVIDENCE: Therapeutic Level I. See Instructions for Authors for a complete description of levels of evidence.

PMID:39841811 | DOI:10.2106/JBJS.24.00261

ANZ J Surg. 2025 Jan 22. doi: 10.1111/ans.19405. Online ahead of print.

ABSTRACT

BACKGROUND: Metformin is a diabetes medication with anti-mitotic properties. A narrative review was performed to investigate people using metformin and the risk of developing pancreatic ductal adenocarcinoma (PDAC) as well as survival outcomes in established PDAC.

METHODS: Relevant studies on metformin use and PDAC were retrieved from PubMed including observational studies on metformin and the risk of developing PDAC and survival outcomes in PDAC, and randomized controlled trials of metformin as a treatment in PDAC.

RESULTS: Of the 367 studies searched, 26 studies fulfilled the criteria for this review. Metformin was not consistently associated with a reduced risk of developing PDAC. However, metformin use, especially higher cumulative doses, in some studies was associated with longer survival in patients with established PDAC, especially in the subgroup with resectable PDAC. Metformin use was not associated with longer survival in more advanced (non-resectable metastatic) PDAC.

CONCLUSION: Metformin was not consistently associated with a reduced risk of developing PDAC. Metformin may be associated with overall survival benefits in patients with PDAC including the resectable PDAC subgroup but not in the metastatic PDAC subgroup. The evidence to date does not support the routine use of metformin as an adjuvant therapy for advanced PDAC.

PMID:39840695 | DOI:10.1111/ans.19405

Calcif Tissue Int. 2025 Jan 22;116(1):32. doi: 10.1007/s00223-024-01323-z.

ABSTRACT

Rare bone diseases are clinically and genetically heterogenous. Despite those differences, the underlying pathophysiology is not infrequently different. Several of these diseases are characterized by abnormal bone metabolism and turnover with subsequent abnormalities in markers of bone turnover, rendering them useful adjuncts in the diagnostic process. As most rare bone diseases are inherited, genetic testing for implicated pathogenic variants, where known, is another relevant tool that can aid in diagnosis. While some skeletal disorders can be localized or monostotic, others can involve multiple skeletal sites and warrant imaging tools to localize them and determine the severity of disease and/or presence of complications as well as to assess bone quality and the potential risk of fractures. Rare bone disorders pose a great challenge in their diagnosis, ultimately resulting in delayed diagnosis, higher risk of complications and a poor quality of life in affected individuals. In this review we discuss the biochemical and radiological tools that can be utilized to diagnose selected orphan bone disorders, the clinical utility and limitations of these diagnostic tools, and areas where future research is warranted.

PMID:39841287 | DOI:10.1007/s00223-024-01323-z

Dtsch Arztebl Int. 2024 Oct 18;121(21):712-713. doi: 10.3238/arztebl.m2024.0126.

NO ABSTRACT

PMID:39841500 | DOI:10.3238/arztebl.m2024.0126

J Clin Microbiol. 2025 Jan 22:e0148024. doi: 10.1128/jcm.01480-24. Online ahead of print.

ABSTRACT

The Burkholderia cepacia complex (BCC) is a group of Gram-negative bacteria that cause opportunistic infections, most notably in people with cystic fibrosis (CF), and have been associated with outbreaks caused by contaminated medical products. Antimicrobial susceptibility testing (AST) is often used to guide treatment for BCC infections, perhaps most importantly in people with CF who are being considered for lung transplant. However, recent studies have highlighted problems with AST methods. Here, we address limitations from previous studies to further evaluate BCC AST methods. We assessed the performance of reference broth microdilution (BMD), disk diffusion (DD) using Mueller-Hinton agar (MHA) from three manufacturers, agar dilution (AD), and gradient diffusion (ETEST) for ceftazidime (CAZ), levofloxacin (LVX), meropenem (MEM), minocycline (MIN), and trimethoprim-sulfamethoxazole (TMP-SMX) on a set of 205 BCC isolates. The isolate set included 100 isolates from people with CF and 105 isolates from people without CF from a variety of sources, which enabled us to systematically evaluate whether specimen source impacts AST performance. For all BCC isolates, BMD reproducibility was 93%, 98%, 99%, 98%, and 96% for CAZ, LVX, MEM, MIN, and TMP-SMX, respectively. Using BMD as the comparator method, we show that DD, AD, and ETEST perform poorly, with neither MHA manufacturer nor specimen source significantly impacting method performance. Based on our data, we recommend that routine AST should not be performed for BCC isolates. If a provider requests AST, clinical microbiology laboratories should perform Clinical and Laboratory Standards Institute reference methodology for BMD (stored frozen) and report MIC only.IMPORTANCEAntimicrobial susceptibility testing for the Burkholderia cepacia complex (BCC) is often used to determine eligibility for lung transplant in people with cystic fibrosis. However, problems with method performance have been reported. Here, we systematically evaluate the performance of reference broth microdilution, disk diffusion, agar dilution, and gradient diffusion (ETEST) for BCC organisms isolated from people with and without cystic fibrosis. We show that broth microdilution reproducibility is acceptable for levofloxacin, meropenem, minocycline, and trimethoprim-sulfamethoxazole, while ceftazidime was just below the acceptability cut-off. Regardless of specimen source, the results from disk diffusion, agar dilution, and ETEST do not correlate with broth microdilution. Based on these findings, we recommend that antimicrobial susceptibility testing should not be routinely performed for BCC, and if requested by the provider, only broth microdilution following Clinical and Laboratory Standards Institute guidelines should be used. Providers should be aware of the significant limitations of antimicrobial susceptibility testing methods for BCC.

PMID:39840992 | DOI:10.1128/jcm.01480-24

Antimicrob Agents Chemother. 2025 Jan 22:e0116224. doi: 10.1128/aac.01162-24. Online ahead of print.

ABSTRACT

Stenotrophomonas maltophilia is an understudied, gram-negative, aerobic bacterium that is widespread in the environment and increasingly a cause of opportunistic infections. Treating S. maltophilia remains difficult, leading to an increase in disease severity and higher hospitalization rates in people with cystic fibrosis, cancer, and other immunocompromised health conditions. The lack of effective antibiotics has led to renewed interest in phage therapy; however, there remains a great need for well-characterized phages, especially against S. maltophilia. In response to an oncology patient with a sepsis infection, we collected 18 phages from Southern California wastewater influent that exhibit different plaque morphology against S. maltophilia host strain B28B. We hypothesized that, when combined into a cocktail, genetically diverse phages would give rise to distinct lytic infection kinetics that would enhance bacterial killing when compared to the individual phages alone. We identified three genetically distinct clusters of phages, and a representative from each group was further investigated and screened for potential therapeutic use. The results demonstrated that the three-phage cocktail significantly suppressed bacterial growth compared with individual phages when observed for 48 h. We also assessed the lytic impacts of our three-phage cocktail against a collection of 46 S. maltophilia strains to determine if a multi-phage cocktail has an expanded host range. Our phages remained strain-specific and infected >50% of tested strains. In six clinically relevant S. maltophilia strains, the multi-phage cocktail has enhanced suppression of bacterial growth. These findings suggest that specialized phage cocktails may be an effective avenue of treatment for recalcitrant S. maltophilia infections resistant to current antibiotics.

PMID:39840957 | DOI:10.1128/aac.01162-24