Microsphere-assisted hyperspectral imaging: super-resolution, non-destructive metrology for semiconductor devices.

As semiconductor devices shrink and their manufacturing processes advance, accurately measuring in-cell critical dimensions (CD) becomes increasingly crucial. Traditional test element group (TEG) measurements are becoming inadequate for representing the fine, repetitive patterns in cell blocks. Conventional non-destructive metrology technologies like optical critical dimension (OCD) are limited due to their large spot diameter of approximately 25 µm, which impedes their efficacy for detailed in-cell structural analysis. Consequently, there is a pressing need for small-spot and non-destructive metrology methods. To address this limitation, we demonstrate a microsphere-assisted hyperspectral imaging (MAHSI) system, specifically designed for small spot optical metrology with super-resolution. Utilizing microsphere-assisted super-resolution imaging, this system achieves an optical resolution of 66 nm within a field of view of 5.6 µm × 5.6 µm. This approach effectively breaks the diffraction limit, significantly enhancing the magnification of the system. The MAHSI system incorporating hyperspectral imaging with a wavelength range of 400-790 nm, enables the capture of the reflection spectrum at each camera pixel. The achieved pixel resolution, which is equivalent to the measuring spot size, is 14.4 nm/pixel and the magnification is 450X. The MAHSI system enables measurement of local uniformity in critical areas like corners and edges of DRAM cell blocks, areas previously challenging to inspect with conventional OCD methods. To our knowledge, this approach represents the first global implementation of microsphere-assisted hyperspectral imaging to address the metrology challenges in complex 3D structures of semiconductor devices.

Interaction between the gut microbiota and colonic enteroendocrine cells regulates host metabolism.

Nutrient handling is an essential function of the gastrointestinal tract. Hormonal responses of small intestinal enteroendocrine cells (EECs) have been extensively studied but much less is known about the role of colonic EECs in metabolic regulation. To address this core question, we investigated a mouse model deficient in colonic EECs. Here we show that colonic EEC deficiency leads to hyperphagia and obesity. Furthermore, colonic EEC deficiency results in altered microbiota composition and metabolism, which we found through antibiotic treatment, germ-free rederivation and transfer to germ-free recipients, to be both necessary and sufficient for the development of obesity. Moreover, studying stool and blood metabolomes, we show that differential glutamate production by intestinal microbiota corresponds to increased appetite and that colonic glutamate administration can directly increase food intake. These observations shed light on an unanticipated host-microbiota axis in the colon, part of a larger gut-brain axis, that regulates host metabolism and body weight.

Tryptophan fuels MYC-dependent liver tumorigenesis through indole 3-pyruvate synthesis.

Cancer cells exhibit distinct metabolic activities and nutritional dependencies compared to normal cells. Thus, characterization of nutrient demands by individual tumor types may identify specific vulnerabilities that can be manipulated to target the destruction of cancer cells. We find that MYC-driven liver tumors rely on augmented tryptophan (Trp) uptake, yet Trp utilization to generate metabolites in the kynurenine (Kyn) pathway is reduced. Depriving MYC-driven tumors of Trp through a No-Trp diet not only prevents tumor growth but also restores the transcriptional profile of normal liver cells. Despite Trp starvation, protein synthesis remains unhindered in liver cancer cells. We define a crucial role for the Trp-derived metabolite indole 3-pyruvate (I3P) in liver tumor growth. I3P supplementation effectively restores the growth of liver cancer cells starved of Trp. These findings suggest that I3P is a potential therapeutic target in MYC-driven cancers. Developing methods to target this metabolite represents a potential avenue for liver cancer treatment.

DROSHA regulates mesenchymal gene expression in Wilms tumor.

Wilms tumor, the most common pediatric kidney cancer, resembles embryonic renal progenitors. Currently, there are no ways to therapeutically target Wilms tumor driver mutations, such as in the microRNA processing gene DROSHA. Here we used a "multi-omics" approach to define the effects of DROSHA mutation in Wilms tumor. We categorized Wilms tumor mutations into four mutational subclasses with unique transcriptional effects: microRNA processing, MYCN activation, chromatin remodeling, and kidney developmental factors. In particular, we find that DROSHA mutations are correlated with de-repressing microRNA target genes that regulate differentiation and proliferation and a self-renewing, mesenchymal state. We model these findings by inhibiting DROSHA expression in a Wilms tumor cell line, which led to upregulation of the cell cycle regulator cyclin D2 (CCND2). Furthermore, we observed that DROSHA mutations in Wilms tumor and DROSHA silencing in vitro were associated with a mesenchymal state with aberrations in redox metabolism. Accordingly, we demonstrate that Wilms tumor cells lacking microRNAs are sensitized to ferroptotic cell death through inhibition of glutathione peroxidase 4 (GPX4), the enzyme that detoxifies lipid peroxides. Implications: This study reveals genotype-transcriptome relationships in Wilms tumor and points to ferroptosis as a potentially therapeutic vulnerability in one subset of Wilms tumor.

X-ray free-electron laser induced acoustic microscopy (XFELAM).

The X-ray free-electron laser (XFEL) has remarkably advanced X-ray imaging technology and enabled important scientific achievements. The XFEL's extremely high power, short pulse width, low emittance, and high coherence make possible such diverse imaging techniques as absorption/emission spectroscopy, diffraction imaging, and scattering imaging. Here, we demonstrate a novel XFEL-based imaging modality that uses the X-ray induced acoustic (XA) effect, which we call X-ray free-electron laser induced acoustic microscopy (XFELAM). Initially, we verified the XA effect by detecting XA signals from various materials, then we validated the experimental results with simulation outcomes. Next, in resolution experiments, we successfully imaged a patterned tungsten target with drilled various-sized circles at a spatial resolution of 7.8 ± 5.1 µm, which is the first micron-scale resolution achieved by XA imaging. Our results suggest that the novel XFELAM can expand the usability of XFEL in various areas of fundamental scientific research.

Development and Validation of Deep Learning-Based Automated Detection of Cervical Lymphadenopathy in Patients with Lymphoma for Treatment Response Assessment: A Bi-institutional Feasibility Study.

The purpose is to train and evaluate a deep learning (DL) model for the accurate detection and segmentation of abnormal cervical lymph nodes (LN) on head and neck contrast-enhanced CT scans in patients diagnosed with lymphoma and evaluate the clinical utility of the DL model in response assessment. This retrospective study included patients who underwent CT for abnormal cervical LN and lymphoma assessment between January 2021 and July 2022. Patients were grouped into the development (n = 76), internal test 1 (n = 27), internal test 2 (n = 87), and external test (n = 26) cohorts. A 3D SegResNet model was used to train the CT images. The volume change rates of cervical LN across longitudinal CT scans were compared among patients with different treatment outcomes (stable, response, and progression). Dice similarity coefficient (DSC) and the Bland-Altman plot were used to assess the model's segmentation performance and reliability, respectively. No significant differences in baseline clinical characteristics were found across cohorts (age, P = 0.55; sex, P = 0.13; diagnoses, P = 0.06). The mean DSC was 0.39 ± 0.2 with a precision and recall of 60.9% and 57.0%, respectively. Most LN volumes were within the limits of agreement on the Bland-Altman plot. The volume change rates among the three groups differed significantly (progression (n = 74), 342.2%; response (n = 8), - 79.2%; stable (n = 5), - 8.1%; all P < 0.01). Our proposed DL segmentation model showed modest performance in quantifying the cervical LN burden on CT in patients with lymphoma. Longitudinal changes in cervical LN volume, as predicted by the DL model, were useful for treatment response assessment.

Susceptible bacteria can survive antibiotic treatment in the mammalian gastrointestinal tract without evolving resistance.

Antibiotic resistance and evasion are incompletely understood and complicated by the fact that murine interval dosing models do not fully recapitulate antibiotic pharmacokinetics in humans. To better understand how gastrointestinal bacteria respond to antibiotics, we colonized germ-free mice with a pan-susceptible genetically barcoded Escherichia coli clinical isolate and administered the antibiotic cefepime via programmable subcutaneous pumps, allowing closer emulation of human parenteral antibiotic dynamics. E. coli was only recovered from intestinal tissue, where cefepime concentrations were still inhibitory. Strikingly, "some" E. coli isolates were not cefepime resistant but acquired mutations in genes involved in polysaccharide capsular synthesis increasing their invasion and survival within human intestinal cells. Deleting wbaP involved in capsular polysaccharide synthesis mimicked this phenotype, allowing increased invasion of colonocytes where cefepime concentrations were reduced. Additionally, "some" mutant strains exhibited a persister phenotype upon further cefepime exposure. This work uncovers a mechanism allowing "select" gastrointestinal bacteria to evade antibiotic treatment.

Identification of Atrial Fibrillation With Single-Lead Mobile ECG During Normal Sinus Rhythm Using Deep Learning.

BACKGROUND: The acquisition of single-lead electrocardiogram (ECG) from mobile devices offers a more practical approach to arrhythmia detection. Using artificial intelligence for atrial fibrillation (AF) identification enhances screening efficiency. However, the potential of single-lead ECG for AF identification during normal sinus rhythm (NSR) remains under-explored. This study introduces a method to identify AF using single-lead mobile ECG during NSR. METHODS: We employed three deep learning models: recurrent neural network (RNN), long short-term memory (LSTM), and residual neural networks (ResNet50). From a dataset comprising 13,509 ECGs from 6,719 patients, 10,287 NSR ECGs from 5,170 patients were selected. Single-lead mobile ECGs underwent noise filtering and segmentation into 10-second intervals. A random under-sampling was applied to reduce bias from data imbalance. The final analysis involved 31,767 ECG segments, including 15,157 labeled as masked AF and 16,610 as Healthy. RESULTS: ResNet50 outperformed the other models, achieving a recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and an area under the receiver operating characteristic curve (AUC) of 0.79 in identifying AF from NSR ECGs. Comparative performance scores for RNN and LSTM were 0.75 and 0.74, respectively. In an external validation set, ResNet50 attained an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68. CONCLUSION: The deep learning model using single-lead mobile ECG during NSR effectively identified AF at risk in future. However, further research is needed to enhance the performance of deep learning models for clinical application.

Induced degradation of lineage-specific oncoproteins drives the therapeutic vulnerability of small cell lung cancer to PARP inhibitors.

Although BRCA1/2 mutations are not commonly found in small cell lung cancer (SCLC), a substantial fraction of SCLC shows clinically relevant response to PARP inhibitors (PARPis). However, the underlying mechanism(s) of PARPi sensitivity in SCLC is poorly understood. We performed quantitative proteomic analyses and identified proteomic changes that signify PARPi responses in SCLC cells. We found that the vulnerability of SCLC to PARPi could be explained by the degradation of lineage-specific oncoproteins (e.g., ASCL1). PARPi-induced activation of the E3 ligase HUWE1 mediated the ubiquitin-proteasome system (UPS)-dependent ASCL1 degradation. Although PARPi induced a general DNA damage response in SCLC cells, this signal generated a cell-specific response in ASCL1 degradation, leading to the identification of HUWE1 expression as a predictive biomarker for PARPi. Combining PARPi with agents targeting these pathways markedly improved therapeutic response in SCLC. The degradation of lineage-specific oncoproteins therefore represents a previously unidentified mechanism for PARPi efficacy in SCLC.

Anticancer benzoxaboroles block pre-mRNA processing by directly inhibiting CPSF3.

A novel class of benzoxaboroles was reported to induce cancer cell death but the mechanism was unknown. Using a forward genetics platform, we discovered mutations in cleavage and polyadenylation specific factor 3 (CPSF3) that reduce benzoxaborole binding and confer resistance. CPSF3 is the endonuclease responsible for pre-mRNA 3'-end processing, which is also important for RNA polymerase II transcription termination. Benzoxaboroles inhibit this endonuclease activity of CPSF3 in vitro and also curb transcriptional termination in cells, which results in the downregulation of numerous constitutively expressed genes. Furthermore, we used X-ray crystallography to demonstrate that benzoxaboroles bind to the active site of CPSF3 in a manner distinct from the other known inhibitors of CPSF3. The benzoxaborole compound impeded the growth of cancer cell lines derived from different lineages. Our results suggest benzoxaboroles may represent a promising lead as CPSF3 inhibitors for clinical development.

Optimizing coagulant dosage using deep learning models with large-scale data.

Water treatment plants are facing challenges that necessitate transition to automated processes using advanced technologies. This study introduces a novel approach to optimize coagulant dosage in water treatment processes by employing a deep learning model. The study utilized minute-by-minute data monitored in real time over a span of five years, marking the first attempt in drinking water process modeling to leverage such a comprehensive dataset. The deep learning model integrates a one-dimensional convolutional neural network (Conv1D) and gated recurrent unit (GRU) to effectively extract features and model complex time-series data. Initially, the model predicted coagulant dosage and sedimentation basin turbidity, validated against a physicochemical model. Subsequently, the model optimized coagulant dosage in two ways: 1) maintaining sedimentation basin turbidity below the 1.0 NTU guideline, and 2) analyzing changes in sedimentation basin turbidity resulting from reduced coagulant dosage (5-20%). The findings of the study highlight the effectiveness of the deep learning model in optimizing coagulant dosage with substantial reductions in coagulant dosage (approximately 22% reduction and 21 million KRW/year). The results demonstrate the potential of deep learning models in enhancing the efficiency and cost-effectiveness of water treatment processes, ultimately facilitating process automation.

Comprehensive characterization of patient-derived xenograft models of pediatric leukemia.

Patient-derived xenografts (PDX) remain valuable models for understanding the biology and for developing novel therapeutics. To expand current PDX models of childhood leukemia, we have developed new PDX models from Hispanic patients, a subgroup with a poorer overall outcome. Of 117 primary leukemia samples obtained, successful engraftment and serial passage in mice were achieved in 82 samples (70%). Hispanic patient samples engrafted at a rate (51/73, 70%) that was similar to non-Hispanic patient samples (31/45, 70%). With a new algorithm to remove mouse contamination in multi-omics datasets including methylation data, we found PDX models faithfully reflected somatic mutations, copy-number alterations, RNA expression, gene fusions, whole-genome methylation patterns, and immunophenotypes found in primary tumor (PT) samples in the first 50 reported here. This cohort of characterized PDX childhood leukemias represents a valuable resource in that germline DNA sequencing has allowed the unambiguous determination of somatic mutations in both PT and PDX.

Evaluation of optimal methods and ancestries for calculating polygenic risk scores in East Asian population.

Polygenic risk scores (PRSs) have been studied for predicting human diseases, and various methods for PRS calculation have been developed. Most PRS studies to date have focused on European ancestry, and the performance of PRS has not been sufficiently assessed in East Asia. Herein, we evaluated the predictive performance of PRSs for East Asian populations under various conditions. Simulation studies using data from the Korean cohort, Health Examinees (HEXA), demonstrated that SBayesRC and PRS-CS outperformed other PRS methods (lassosum, LDpred-funct, and PRSice) in high fixed heritability (0.3 and 0.7). In addition, we generated PRSs using real-world data from HEXA for ten diseases: asthma, breast cancer, cataract, coronary artery disease, gastric cancer, glaucoma, hyperthyroidism, hypothyroidism, osteoporosis, and type 2 diabetes (T2D). We utilized the five previous PRS methods and genome-wide association study (GWAS) data from two biobank-scale datasets [European (UK Biobank) and East Asian (BioBank Japan) ancestry]. Additionally, we employed PRS-CSx, a PRS method that combines GWAS data from both ancestries, to generate a total of 110 PRS for ten diseases. Similar to the simulation results, SBayesRC showed better predictive performance for disease risk than the other methods. Furthermore, the East Asian GWAS data outperformed those from European ancestry for breast cancer, cataract, gastric cancer, and T2D, but neither of the two GWAS ancestries showed a significant advantage on PRS performance for the remaining six diseases. Based on simulation data and real data studies, it is expected that SBayesRC will offer superior performance for East Asian populations, and PRS generated using GWAS from non-East Asian may also yield good results.

Genetic determinants underlying the progressive phenotype of ß-lactam/ß-lactamase inhibitor resistance in Escherichia coli.

IMPORTANCE: The increased feasibility of whole-genome sequencing has generated significant interest in using such molecular diagnostic approaches to characterize difficult-to-treat, antimicrobial-resistant (AMR) infections. Nevertheless, there are current limitations in the accurate prediction of AMR phenotypes based on existing AMR gene database approaches, which primarily correlate a phenotype with the presence/absence of a single AMR gene. Our study utilized a large cohort of cephalosporin-susceptible Escherichia coli bacteremia samples to determine how increasing the dosage of narrow-spectrum ß-lactamase-encoding genes in conjunction with other diverse ß-lactam/ß-lactamase inhibitor (BL/BLI) genetic determinants contributes to progressively more severe BL/BLI phenotypes. We were able to characterize the complexity of the genetic mechanisms underlying progressive BL/BLI resistance including the critical role of ß-lactamase encoding gene amplification. For the diverse array of AMR phenotypes with complex mechanisms involving multiple genomic factors, our study provides an example of how composite risk scores may improve understanding of AMR genotype/phenotype correlations.

Loss of p53 and mutational heterogeneity drives immune resistance in an autochthonous mouse lung cancer model with high tumor mutational burden.

The role of tumor mutational burden (TMB) in shaping tumor immunity is a key question that has not been addressable using genetically engineered mouse models (GEMMs) of lung cancer. To induce TMB in lung GEMMs, we expressed an ultra-mutator variant of DNA polymerase-E (POLE)P286R in lung epithelial cells. Introduction of PoleP286R allele into KrasG12D and KrasG12D; p53L/L (KP) models significantly increase their TMB. Immunogenicity and sensitivity to immune checkpoint blockade (ICB) induced by Pole is partially dependent on p53. Corroborating these observations, survival of NSCLC patients whose tumors have TP53truncating mutations is shorter than those with TP53WT with immunotherapy. Immune resistance is in part through reduced antigen presentation and in part due to mutational heterogeneity. Total STING protein levels are elevated in Pole mutated KP tumors creating a vulnerability. A stable polyvalent STING agonist or p53 induction increases sensitivity to immunotherapy offering therapeutic options in these polyclonal tumors.

Highly Water-Dispersed and Stable Deinoxanthin Nanocapsule for Effective Antioxidant and Anti-Inflammatory Activity.

Introduction: Deinoxanthin (DX), a carotenoid, has excellent antioxidant and anti-inflammatory properties. However, owing to its lipophilicity, it is unfavorably dispersed in water and has low stability, limiting its application in cosmetics, food, and pharmaceuticals. Therefore, it is necessary to study nanoparticles to increase the loading capacity and stability of DX. Methods: In this study, DX-loaded nanocapsules (DX@NCs) were prepared by nanoprecipitation by loading DX into nanocapsules. The size, polydispersity index, surface charge, and morphology of DX@NCs were confirmed through dynamic light scattering and transmission electron microscopy. The loading content and loading efficiency of DX in DX@NCs were analyzed using high-performance liquid chromatography. The antioxidant activity of DX@NCs was evaluated by DPPH assay and in vitro ROS. The biocompatibility of DX@NCs was evaluated using an in vitro MTT assay. In vitro NO analysis was performed to determine the effective anti-inflammatory efficacy of DX@NCs. Results: DX@NCs exhibited increased stability and antioxidant efficacy owing to the improved water solubility of DX. The in situ and in vitro antioxidant activity of DX@NCs was higher than that of unloaded DX. In addition, it showed a strong anti-inflammatory effect by regulating the NO level in an in vitro cell model. Conclusion: This study presents a nanocarrier to improve the water-soluble dispersion and stability of DX. These results demonstrate that DX@NC is a carrier with excellent stability and has a high potential for use in cosmetic and pharmaceutical applications owing to its antioxidant and anti-inflammatory effects.

Inducible mismatch repair streamlines forward genetic approaches to target identification of cytotoxic small molecules.

Orphan cytotoxins are small molecules for which the mechanism of action (MoA) is either unknown or ambiguous. Unveiling the mechanism of these compounds may lead to useful tools for biological investigation and new therapeutic leads. In selected cases, the DNA mismatch repair-deficient colorectal cancer cell line, HCT116, has been used as a tool in forward genetic screens to identify compound-resistant mutations, which have ultimately led to target identification. To expand the utility of this approach, we engineered cancer cell lines with inducible mismatch repair deficits, thus providing temporal control over mutagenesis. By screening for compound resistance phenotypes in cells with low or high rates of mutagenesis, we increased both the specificity and sensitivity of identifying resistance mutations. Using this inducible mutagenesis system, we implicate targets for multiple orphan cytotoxins, including a natural product and compounds emerging from a high-throughput screen, thus providing a robust tool for future MoA studies.

Ceftolozane/tazobactam heteroresistance in cystic fibrosis-related Pseudomonas aeruginosa infections.

Objectives: Cystic fibrosis (CF) patients are often colonized with Pseudomonas aeruginosa. During treatment, P. aeruginosa can develop subpopulations exhibiting variable in vitro antimicrobial (ABX) susceptibility patterns. Heteroresistance (HR) may underlie reported discrepancies between in vitro susceptibility results and clinical responses to various ABXs. Here, we sought to examine the presence and nature of P. aeruginosa polyclonal HR (PHR) and monoclonal HR (MHR) to ceftolozane/tazobactam in isolates originating from CF pulmonary exacerbations. Methods: This was a single-centre, non-controlled study. Two hundred and forty-six P. aeruginosa isolates from 26 adult CF patients were included. PHR was defined as the presence of different ceftolozane/tazobactam minimum inhibitory concentration (MIC) values among P. aeruginosa isolates originating from a single patient specimen. Population analysis profiles (PAPs) were performed to assess the presence of MHR, defined as ≥4-fold change in the ceftolozane/tazobactam MIC from a single P. aeruginosa colony. Results: Sixteen of 26 patient specimens (62%) contained PHR P. aeruginosa populations. Of these 16 patients, 6 (23%) had specimens in which PHR P. aeruginosa isolates exhibited ceftolozane/tazobactam MICs with categorical differences (i.e. susceptible versus resistant) compared to results reported as part of routine care. One isolate, PSA 1311, demonstrated MHR. Canonical ceftolozane/tazobactam resistance genes were not found in the MHR isolates (MHR PSA 1311 or PHR PSA 6130). Conclusions: Ceftolozane/tazobactam PHR exists among P. aeruginosa isolates in this work, and approximately a quarter of these populations contained isolates with ceftolozane/tazobactam susceptibiilty interpretations different from what was reported clinically, supporting concerns surrounding the utility of traditional susceptibility testing methodology in the setting of CF specimens. Genome sequencing of isolates with acquired MHR to ceftolozane/tazobactam revealed variants of unknown significance. Future work will be centred on determining the significance of these mutations to better understand these data in clinical context.

Mitochondrial-Encoded Complex I Impairment Induces a Targetable Dependency on Aerobic Fermentation in Hürthle Cell Carcinoma of the Thyroid.

A metabolic hallmark of cancer identified by Warburg is the increased consumption of glucose and secretion of lactate, even in the presence of oxygen. Although many tumors exhibit increased glycolytic activity, most forms of cancer rely on mitochondrial respiration for tumor growth. We report here that Hürthle cell carcinoma of the thyroid (HTC) models harboring mitochondrial DNA-encoded defects in complex I of the mitochondrial electron transport chain exhibit impaired respiration and alterations in glucose metabolism. CRISPR-Cas9 pooled screening identified glycolytic enzymes as selectively essential in complex I-mutant HTC cells. We demonstrate in cultured cells and a patient-derived xenograft model that small-molecule inhibitors of lactate dehydrogenase selectively induce an ATP crisis and cell death in HTC. This work demonstrates that complex I loss exposes fermentation as a therapeutic target in HTC and has implications for other tumors bearing mutations that irreversibly damage mitochondrial respiration. SIGNIFICANCE: HTC is enriched in somatic mtDNA mutations predicted to affect complex I of the electron transport chain (ETC). We demonstrate that these mutations impair respiration and induce a therapeutically tractable reliance on aerobic fermentation for cell survival. This work provides a rationale for targeting fermentation in cancers harboring irreversible genetically encoded ETC defects. See related article by Gopal et al., p. 1904. This article is highlighted in the In This Issue feature, p. 1749.

Contribution of the Oral and Gastrointestinal Microbiomes to Bloodstream Infections in Leukemia Patients.

Bloodstream infections (BSIs) pose a significant mortality risk for acute myeloid leukemia (AML) patients. It has been previously reported that intestinal domination (>30% relative abundance [RA] attributed to a single taxon) with the infecting taxa often precedes BSI in stem cell transplant patients. Using 16S rRNA amplicon sequencing, we analyzed oral and stool samples from 63 AML patients with BSIs to determine the correlation between the infectious agent and microbiome composition. Whole-genome sequencing and antimicrobial susceptibilities were performed on all BSI isolates. Species-level detection of the infectious agent and presence of antibiotic resistance determinants in the stool (blaCTX-M-15, blaCTX-M-14, cfrA, and vanA) were confirmed via digital droplet PCR (ddPCR). Individuals with Escherichia coli (stool P < 0.001), Pseudomonas aeruginosa (oral P = 0.004, stool P < 0.001), and viridans group streptococci (VGS) (oral P = 0.001) bacteremia had a significantly higher relative abundance of those respective genera than other BSI patients, which appeared to be site specific. Although 78% of patients showed presence of the infectious genera in the stool and/or saliva, only 7 exhibited microbiome domination. ddPCR confirmed species specificity of the 16S data and detected the antibiotic resistance determinants found in the BSI isolates within concurrent stools. Although gastrointestinal (GI) domination by an infecting organism was not present at the time of most BSIs in AML, the pathogens, along with AMR elements, were detectable in the majority of patients. Thus, rapid genetic assessment of oral and stool samples for the presence of potential pathogens and AMR determinants might inform personalized therapeutic approaches in immunocompromised patients with suspected infection. IMPORTANCE A major cause of mortality in hematologic malignancy patients is BSI. Previous studies have demonstrated that bacterial translocation from the GI microbiome is a major source of BSIs and is often preceded by increased levels of the infectious taxa in the GI (>30% abundance by 16S rRNA sequencing). In this study, we sought to better understand how domination and abundance levels of the oral and gut microbiome relate to bacteremia occurrence in acute myeloid leukemia patients. We conclude that analyses of both oral and stool samples can help identify BSI and antimicrobial resistance determinants, thus potentially improving the timing and tailoring of antibiotic treatment strategies for high-risk patients.