AmyloidPETNet: Classification of Amyloid Positivity in Brain PET Imaging Using End-to-End Deep Learning.

Background Visual assessment of amyloid PET scans relies on the availability of radiologist expertise, whereas quantification of amyloid burden typically involves MRI for processing and analysis, which can be computationally expensive. Purpose To develop a deep learning model to classify minimally processed brain PET scans as amyloid positive or negative, evaluate its performance on independent data sets and different tracers, and compare it with human visual reads. Materials and Methods This retrospective study used 8476 PET scans (6722 patients) obtained from late 2004 to early 2023 that were analyzed across five different data sets. A deep learning model, AmyloidPETNet, was trained on 1538 scans from 766 patients, validated on 205 scans from 95 patients, and internally tested on 184 scans from 95 patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) fluorine 18 (18F) florbetapir (FBP) data set. It was tested on ADNI scans using different tracers and scans from independent data sets. Scan amyloid positivity was based on mean cortical standardized uptake value ratio cutoffs. To compare with model performance, each scan from both the Centiloid Project and a subset of the Anti-Amyloid Treatment in Asymptomatic Alzheimer's Disease (A4) study were visually interpreted with a confidence level (low, intermediate, high) of amyloid positivity/negativity. The area under the receiver operating characteristic curve (AUC) and other performance metrics were calculated, and Cohen κ was used to measure physician-model agreement. Results The model achieved an AUC of 0.97 (95% CI: 0.95, 0.99) on test ADNI 18F-FBP scans, which generalized well to 18F-FBP scans from the Open Access Series of Imaging Studies (AUC, 0.95; 95% CI: 0.93, 0.97) and the A4 study (AUC, 0.98; 95% CI: 0.98, 0.98). Model performance was high when applied to data sets with different tracers (AUC ≥ 0.97). Other performance metrics provided converging evidence. Physician-model agreement ranged from fair (Cohen κ = 0.39; 95% CI: 0.16, 0.60) on a sample of mostly equivocal cases from the A4 study to almost perfect (Cohen κ = 0.93; 95% CI: 0.86, 1.0) on the Centiloid Project. Conclusion The developed model was capable of automatically and accurately classifying brain PET scans as amyloid positive or negative without relying on experienced readers or requiring structural MRI. Clinical trial registration no. NCT00106899 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Bryan and Forghani in this issue.

Data-driven decomposition and staging of flortaucipir uptake in Alzheimer's disease.

INTRODUCTION: Previous approaches pursuing in vivo staging of tau pathology in Alzheimer's disease (AD) have typically relied on neuropathologically defined criteria. In using predefined systems, these studies may miss spatial deposition patterns which are informative of disease progression. METHODS: We selected discovery (n = 418) and replication (n = 132) cohorts with flortaucipir imaging. Non-negative matrix factorization (NMF) was applied to learn tau covariance patterns and develop a tau staging system. Flortaucipir components were also validated by comparison with amyloid burden, gray matter loss, and the expression of AD-related genes. RESULTS: We found eight flortaucipir covariance patterns which were reproducible and overlapped with relevant gene expression maps. Tau stages were associated with AD severity as indexed by dementia status and neuropsychological performance. Comparisons of flortaucipir uptake with amyloid and atrophy also supported our model of tau progression. DISCUSSION: Data-driven decomposition of flortaucipir uptake provides a novel framework for tau staging which complements existing systems. HIGHLIGHTS: NMF reveals patterns of tau deposition in AD. Data-driven staging of flortaucipir tracks AD severity. Learned flortaucipir patterns overlap with AD-related gene expression.

Relationships and Mendelian Randomization of Gut Microbe-Derived Metabolites with Metabolic Syndrome Traits in the METSIM Cohort.

The role of gut microbe-derived metabolites in the development of metabolic syndrome (MetS) remains unclear. This study aimed to evaluate the associations of gut microbe-derived metabolites and MetS traits in the cross-sectional Metabolic Syndrome In Men (METSIM) study. The sample included 10,194 randomly related men (age 57.65 ± 7.12 years) from Eastern Finland. Levels of 35 metabolites were tested for associations with 13 MetS traits using lasso and stepwise regression. Significant associations were observed between multiple MetS traits and 32 metabolites, three of which exhibited particularly robust associations. N-acetyltryptophan was positively associated with Homeostatic Model Assessment for Insulin Resistant (HOMA-IR) (ß = 0.02, p = 0.033), body mass index (BMI) (ß = 0.025, p = 1.3 × 10-16), low-density lipoprotein cholesterol (LDL-C) (ß = 0.034, p = 5.8 × 10-10), triglyceride (0.087, p = 1.3 × 10-16), systolic (ß = 0.012, p = 2.5 × 10-6) and diastolic blood pressure (ß = 0.011, p = 3.4 × 10-6). In addition, 3-(4-hydroxyphenyl) lactate yielded the strongest positive associations among all metabolites, for example, with HOMA-IR (ß = 0.23, p = 4.4 × 10-33), and BMI (ß = 0.097, p = 5.1 × 10-52). By comparison, 3-aminoisobutyrate was inversely associated with HOMA-IR (ß = -0.19, p = 3.8 × 10-51) and triglycerides (ß = -0.12, p = 5.9 × 10-36). Mendelian randomization analyses did not provide evidence that the observed associations with these three metabolites represented causal relationships. We identified significant associations between several gut microbiota-derived metabolites and MetS traits, consistent with the notion that gut microbes influence metabolic homeostasis, beyond traditional risk factors.

Prostate lineage-specific metabolism governs luminal differentiation and response to antiandrogen treatment.

Lineage transitions are a central feature of prostate development, tumourigenesis and treatment resistance. While epigenetic changes are well known to drive prostate lineage transitions, it remains unclear how upstream metabolic signalling contributes to the regulation of prostate epithelial identity. To fill this gap, we developed an approach to perform metabolomics on primary prostate epithelial cells. Using this approach, we discovered that the basal and luminal cells of the prostate exhibit distinct metabolomes and nutrient utilization patterns. Furthermore, basal-to-luminal differentiation is accompanied by increased pyruvate oxidation. We establish the mitochondrial pyruvate carrier and subsequent lactate accumulation as regulators of prostate luminal identity. Inhibition of the mitochondrial pyruvate carrier or supplementation with exogenous lactate results in large-scale chromatin remodelling, influencing both lineage-specific transcription factors and response to antiandrogen treatment. These results establish reciprocal regulation of metabolism and prostate epithelial lineage identity.

Ketogenic diet therapy for pediatric epilepsy is associated with alterations in the human gut microbiome that confer seizure resistance in mice.

The gut microbiome modulates seizure susceptibility and the anti-seizure effects of the ketogenic diet (KD) in animal models, but whether these relationships translate to KD therapies for human epilepsy is unclear. We find that the clinical KD alters gut microbial function in children with refractory epilepsy. Colonizing mice with KD-associated microbes promotes seizure resistance relative to matched pre-treatment controls. Select metagenomic and metabolomic features, including those related to anaplerosis, fatty acid ß-oxidation, and amino acid metabolism, are seen with human KD therapy and preserved upon microbiome transfer to mice. Mice colonized with KD-associated gut microbes exhibit altered hippocampal transcriptomes, including pathways related to ATP synthesis, glutathione metabolism, and oxidative phosphorylation, and are linked to susceptibility genes identified in human epilepsy. Our findings reveal key microbial functions that are altered by KD therapies for pediatric epilepsy and linked to microbiome-induced alterations in brain gene expression and seizure protection in mice.

Patterns of Glucose Metabolism in [ 18 F]FDG PET Indicate Regional Variability and Neurodegeneration in the Progression of Alzheimer's Dementia.

In disorders of cognitive impairment, such as Alzheimer's disease, neurodegeneration is the final common pathway of disease progression. Modulating, reversing, or preventing disease progression is a clinical imperative most likely to succeed following accurate and explanatory understanding of neurodegeneration, requiring enhanced consistency with quantitative measurements and expanded interpretability of complex data. The on-going study of neurodegeneration has robustly demonstrated the advantages of accumulating large amounts of clinical data that include neuroimaging, motiving multi-center studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). Demonstrative advantages also arise from highly multivariate analysis methods, and this work reports advances provided by non-negative matrix factorization (NMF). NMF revealed patterns of covariance for glucose metabolism, estimated by positron emission tomography of [ 18 F]fluorodeoxyglucose, in 243 healthy normal participants of ADNI. Patterns for glucose metabolism provided cross-sectional inferences for 860 total participants of ADNI with and without cerebral amyloidosis and clinical dementia ratings (CDR) ranging 0-3. Patterns for glucose metabolism were distinct in number and topography from patterns identified in previous studies of structural MRI. They were also distinct from well-establish topographies of resting-state neuronal networks mapped by functional magnetic resonance imaging. Patterns for glucose metabolism identified significant topographical landmarks relating age, sex, APOE ε4 alleles, amyloidosis, CDR, and neurodegeneration. Patterns involving insular and orbitofrontal cortices, as well as midline regions of frontal and parietal lobes demonstrated the greatest neurodegeneration with progressive Alzheimer's dementia. A single pattern for the lateral parietal and posterior superior temporal cortices demonstrated preserved glucose metabolism for all diagnostic groups, including Alzheimer's dementia. Patterns correlated significantly with topical terms from the Neurosynth platform, thereby providing semantic representations for patterns such as attention, memory, language, fear/reward, movement and motor planning. In summary, NMF is a data-driven, principled, supervised statistical learning method that provides interpretable patterns of neurodegeneration. These patterns can help inform the understanding and treatment of Alzheimer's disease. Highlights: ▪ Data-driven non-negative matrix factorization (NMF) identified 24 canonical patterns of spatial covariance of cerebral glucose metabolism. The training data comprised healthy older participants (CDR = 0 without amyloidosis) cross-sectionally drawn from ADNI. ▪ In healthy participants, mean SUVRs for specific patterns in precuneus, lateral parietal cortex, and subcortical areas including superficial white matter and striatum, demonstrated increasing glucose metabolism with advancing age. ▪ In asymptomatic participants with amyloidosis , glucose metabolism increased compared to those who were asymptomatic without amyloid , particularly in medial prefrontal cortex, frontoparietal cortex, occipital white, and posterior cerebellar regions. ▪ In symptomatic participants with amyloidosis , insular cortex, medial frontal cortex, and prefrontal cortex demonstrated the most severe losses of glucose metabolism with increasing CDR. Lateral parietal and posterior superior temporal cortices retained glucose metabolism even for CDR > 0.5. ▪ NMF models of glucose metabolism are consistent with models arising from principal components, or eigenbrains, while adding additional regional interpretability. ▪ NMF patterns correlated with regions catalogued in Neurosynth. Following corrections for spatial autocorrelations, NMF patterns revealed meta-analytic identifications of patterns with Neurosynth topics of fear/reward, attention, memory, language, and movement with motor planning. Patterns varied with degrees of cognitive impairment.

Scalable Orthonormal Projective NMF via Diversified Stochastic Optimization.

The increasing availability of large-scale neuroimaging initiatives opens exciting opportunities for discovery science of human brain structure and function. Data-driven techniques, such as Orthonormal Projective Non-negative Matrix Factorization (opNMF), are well positioned to explore multivariate relationships in big data towards uncovering brain organization. opNMF enjoys advantageous interpretability and reproducibility compared to commonly used matrix factorization methods like Principal Component Analysis (PCA) and Independent Component Analysis (ICA), which led to its wide adoption in clinical computational neuroscience. However, applying opNMF in large-scale cohort studies is hindered by its limited scalability caused by its accompanying computational complexity. In this work, we address the computational challenges of opNMF using a stochastic optimization approach that learns over mini-batches of the data. Additionally, we diversify the stochastic batches via repulsive point processes, which reduce redundancy in the mini-batches and in turn lead to lower variance in the updates. We validated our framework on gray matter tissue density maps estimated from 1000 subjects part of the Open Access Series of Imaging (OASIS) dataset. We demonstrated that operations over mini-batches of data yield significant reduction in computational cost. Importantly, we showed that our novel optimization does not compromise the accuracy or interpretability of factors when compared to standard opNMF. The proposed model enables new investigations of brain structure using big neuroimaging data that could improve our understanding of brain structure in health and disease.

Scalable NMF via linearly optimized data compression.

Orthonormal projective non-negative matrix factorization (opNMF) has been widely used in neuroimaging and clinical neuroscience applications to derive representations of the brain in health and disease. The non-negativity and orthonormality constraints of opNMF result in intuitive and well-localized factors. However, the advantages of opNMF come at a steep computational cost that prohibits its use in large-scale data. In this work, we propose novel and scalable optimization schemes for orthonormal projective non-negative matrix factorization that enable the use of the method in large-scale neuroimaging settings. We replace the high-dimensional data matrix with its corresponding singular value decomposition (SVD) and QR decompositions and combine the decompositions with opNMF multiplicative update algorithm. Empirical validation of the proposed methods demonstrated significant speed-up in computation time while keeping memory consumption low without compromising the accuracy of the solution.

Machine learning to predict ceftriaxone resistance using single nucleotide polymorphisms within a global database of Neisseria gonorrhoeae genomes.

IMPORTANCE: Antimicrobial resistance in Neisseria gonorrhoeae is an urgent global health issue. The objectives of the study were to use a global collection of 12,936 N. gonorrhoeae genomes from the PathogenWatch database to evaluate different machine learning models to predict ceftriaxone susceptibility/decreased susceptibility using 97 mutations known to be associated with ceftriaxone resistance. We found the random forest classifier model had the highest performance. The analysis also reported the relative contributions of different mutations within the ML model predictions, allowing for the identification of the mutations with the highest importance for ceftriaxone resistance. A machine learning model retrained with the top five mutations performed similarly to the model using all 97 mutations. These results could aid in the development of molecular tests to detect resistance to ceftriaxone in N. gonorrhoeae. Moreover, this approach could be applied to building and evaluating machine learning models for predicting antimicrobial resistance in other pathogens.

Olfactory ensheathing cells are hybrid glial cells that promote neural repair.

Olfactory ensheathing cells (OECs) are unique glial cells found in both the central and peripheral nervous systems where they support the continuous axonal outgrowth of immature olfactory sensory neurons to their targets. Here we show that following severe spinal cord injury, olfactory bulb-derived OECs transplanted near the injury site modify the normally inhibitory glial scar and facilitate axon regeneration past the scar border and into the lesion center. To understand the mechanisms underlying the reparative properties of such transplanted OECs, we used single-cell RNA-sequencing to study their gene expression programs. Our analyses revealed five diverse subtypes of OECs, each expressing novel marker genes and pathways indicative of progenitor, axonal regeneration and repair, secreted molecules, or microglia-like functions. As expected, we found substantial overlap of OEC genes with those of Schwann cells, but also with astrocytes, oligodendrocytes and microglia. We confirmed established markers on cultured OECs, and then localized select top genes of OEC subtypes in rat olfactory bulb tissue. In addition, we present evidence that OECs secrete both Reelin and Connective tissue growth factor, extracellular matrix molecules which are important for neural repair and axonal outgrowth. Our results support that adult OECs are a unique hybrid glia, some with progenitor characteristics, and that their gene expression patterns indicate diverse functions related to wound healing, injury repair and axonal regeneration.

Neurotoxicity of diesel exhaust extracts in zebrafish and its implications for neurodegenerative disease.

Long-term air pollution (AP) exposure, including diesel exhaust exposure, is increasingly being recognized as a major contributor to the development of neurodegenerative diseases such as Parkinson's and Alzheimer's disease. How AP increases the risk of neurodegeneration is not well understood but might include direct neurotoxicity and CNS inflammation. We investigated the impact of diesel exhaust particulate extract (DEPe) exposure on the brain and the mechanisms by which microglia and astroglia might mediate neuronal changes. Zebrafish (ZF) were utilized to determine neuronal toxicity of and microglial response to DEPe and single cell RNA sequencing was employed to study cell type-specific transcriptomic responses within the ZF brain. DEPe exposure induced neuronal injury and microglial activation in vivo. However, preventing the development of microglia did not attenuate DEPe-induced neuron loss, leading us to investigate microglial, astroglial, and neuronal response to DEPe exposure at single-cell resolution. Differentially expressed genes and disease-relevant pathways were identified within glial and neuronal clusters after DEPe exposure. Microglia and astroglia existed in multiple states, some of which appear toxic and others protective to neurons. Neuronal transcriptomic analysis revealed that DEPe exposure reduced expression of autophagy-related genes consistent with direct neurotoxicity. In summary, DEPe exposure was neurotoxic in developing ZF larvae and induced neuroinflammation. The microglial inflammatory response did not contribute to neurotoxicity of DEPe and in fact, some glial clusters upregulated transcriptional pathways that are likely protective. Furthermore, DEPe exposure led to reduced expression of autophagy-related genes in neurons that likely contribute to its toxicity.

Neurodevelopmental patterns of early postnatal white matter maturation represent distinct underlying microstructure and histology.

Cerebral white matter undergoes a rapid and complex maturation during the early postnatal period. Prior magnetic resonance imaging (MRI) studies of early postnatal development have often been limited by small sample size, single-modality imaging, and univariate analytics. Here, we applied nonnegative matrix factorization, an unsupervised multivariate pattern analysis technique, to T2w/T1w signal ratio maps from the Developing Human Connectome Project (n = 342 newborns) revealing patterns of coordinated white matter maturation. These patterns showed divergent age-related maturational trajectories, which were replicated in another independent cohort (n = 239). Furthermore, we showed that T2w/T1w signal variations in these maturational patterns are explained by differential contributions of white matter microstructural indices derived from diffusion-weighted MRI. Finally, we demonstrated how white matter maturation patterns relate to distinct histological features by comparing our findings with postmortem late fetal/early postnatal brain tissue staining. Together, these results delineate concise and effective representation of early postnatal white matter reorganization.

The University of Pennsylvania glioblastoma (UPenn-GBM) cohort: advanced MRI, clinical, genomics, & radiomics.

Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the "University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics" (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments.

PharmOmics: A species- and tissue-specific drug signature database and gene-network-based drug repositioning tool.

Drug development has been hampered by a high failure rate in clinical trials due to our incomplete understanding of drug functions across organs and species. Therefore, elucidating species- and tissue-specific drug functions can provide insights into therapeutic efficacy, potential adverse effects, and interspecies differences necessary for effective translational medicine. Here, we present PharmOmics, a drug knowledgebase and analytical tool that is hosted on an interactive web server. Using tissue- and species-specific transcriptome data from human, mouse, and rat curated from different databases, we implemented a gene-network-based approach for drug repositioning. We demonstrate the potential of PharmOmics to retrieve known therapeutic drugs and identify drugs with tissue toxicity using in silico performance assessment. We further validated predicted drugs for nonalcoholic fatty liver disease in mice. By combining tissue- and species-specific in vivo drug signatures with gene networks, PharmOmics serves as a complementary tool to support drug characterization and network-based medicine.

Reliability of Genetic Alterations in Predicting Ceftriaxone Resistance in Neisseria gonorrhoeae Globally.

Antimicrobial resistance in N. gonorrhoeae is increasing globally, and ceftriaxone is the recommended treatment for empirical therapy in most settings. Developing molecular assays to detect decreased ceftriaxone susceptibility is critical. Using PathogenWatch, a public database of N. gonorrhoeae genomes, antibiotic susceptibility data and DNA sequences of different genes associated with ceftriaxone resistance were extracted. That information was used to determine the sensitivity and specificity of different molecular markers and algorithms to predict decreased susceptibility to ceftriaxone. A total of 12,943 N. gonorrhoeae genomes were extracted from the PathogenWatch database, of which 9,540 genomes were used in the analysis. The sensitivity and specificity of specific molecular markers and algorithms were largely consistent with prior reports. Small variation (<10%) in either sensitivity or specificity occurred. Certain algorithms using different molecular markers at various prevalence of decreased ceftriaxone susceptibility identified a potentially clinically useful range of positive and negative predictive values. We validated previously described mutations and algorithms in a large public database containing a global collection of N. gonorrhoeae genomes. Certain mutations and algorithms resulted in sensitivity and specificity values consistent with those of prior studies. Further research is needed to integrate these markers and algorithms into the development of molecular assays to predict decreased ceftriaxone susceptibility. IMPORTANCE Antimicrobial resistance in Neisseria gonorrhoeae (N. gonorrhoeae), the causative agent of gonorrhea, is rising globally. Ceftriaxone is the last remaining antibiotic for empirical treatment of gonorrhea. Developing molecular tests to predict ceftriaxone resistance can help to improve detection and surveillance of ceftriaxone resistance. Here, we utilized PathogenWatch, a public global online database of N. gonorrhoeae genomes, to evaluate different genetic markers in predicting decreased susceptibility to ceftriaxone. We compiled MICs for ceftriaxone from the PathogenWatch database and used a computational approach to extract all the genetic markers from the genomic data. We determined the sensitivity and specificity for predicting decreased ceftriaxone susceptibility among several combinations of genetic markers. We identified several combinations of genetic markers with high predictive values for decreased susceptibility to ceftriaxone. These combinations of genetic markers might be promising candidates for future molecular tests to predict ceftriaxone resistance.

Genome insights into the pharmaceutical and plant growth promoting features of the novel species Nocardia alni sp. nov.

BACKGROUND: Recent studies highlighted the biosynthetic potential of nocardiae to produce diverse novel natural products comparable to that of Streptomyces, thereby making them an attractive source of new drug leads. Many of the 119 Nocardia validly named species were isolated from natural habitats but little is known about the diversity and the potential of the endophytic nocardiae of root nodule of actinorhizal plants. RESULTS: The taxonomic status of an actinobacterium strain, designated ncl2T, was established in a genome-based polyphasic study. The strain was Gram-stain-positive, produced substrate and aerial hyphae that fragmented into coccoid and rod-like elements and showed chemotaxonomic properties that were also typical of the genus Nocardia. It formed a distinct branch in the Nocardia 16S rRNA gene tree and was most closely related to the type strains of Nocardia nova (98.6%), Nocardia jiangxiensis (98.4%), Nocardia miyuensis (97.8%) and Nocardia vaccinii (97.7%). A comparison of the draft genome sequence generated for the isolate with the whole genome sequences of its closest phylogenetic neighbours showed that it was most closely related to the N. jiangxiensis, N. miyuensis and N. vaccinii strains, a result underpinned by average nucleotide identity and digital DNA-DNA hybridization data. Corresponding taxogenomic data, including those from a pan-genome sequence analysis showed that strain ncl2T was most closely related to N. vaccinii DSM 43285T. A combination of genomic, genotypic and phenotypic data distinguished these strains from one another. Consequently, it is proposed that strain ncl2T (= DSM 110931T = CECT 30122T) represents a new species within the genus Nocardia, namely Nocardia alni sp. nov. The genomes of the N. alni and N. vaccinii strains contained 36 and 29 natural product-biosynthetic gene clusters, respectively, many of which were predicted to encode for a broad range of novel specialised products, notably antibiotics. Genome mining of the N. alni strain and the type strains of its closest phylogenetic neighbours revealed the presence of genes associated with direct and indirect mechanisms that promote plant growth. The core genomes of these strains mainly consisted of genes involved in amino acid transport and metabolism, energy production and conversion and transcription. CONCLUSIONS: Our genome-based taxonomic study showed that isolate ncl2T formed a new centre of evolutionary variation within the genus Nocardia. This novel endophytic strain contained natural product biosynthetic gene clusters predicted to synthesize novel specialised products, notably antibiotics and genes associated with the expression of plant growth promoting compounds.

Determination of the nanoparticle- and cell-specific toxicological mechanisms in 3D liver spheroids using scRNAseq analysis.

Engineered nanomaterials (ENMs) are commonly used in consumer products, allowing exposure to target organs such as the lung, liver, and skin that could lead to adverse health effects in humans. To better reflect on toxicological effects in liver cells, it is important to consider the contribution of hepatocyte morphology, function, and intercellular interactions in a dynamic 3D microenvironment. Herein, we used a 3D liver spheroid model containing hepatocyte and Kupffer cells (KCs) to study the effects of three different material compositions, namely vanadium pentoxide (V2O5), titanium dioxide (TiO2), or graphene oxide (GO). Additionally, we used single-cell RNA sequencing (scRNAseq) to determine the nanoparticle (NP) and cell-specific toxicological responses. A general finding was that hepatocytes exhibit more variation in gene expression and adaptation of signaling pathways than KCs. TNF-α production tied to the NF-κB pathway was a commonly affected pathway by all NPs while impacts on the metabolic function of hepatocytes were unique to V2O5. V2O5 NPs also showed the largest number of differentially expressed genes in both cell types, many of which are related to pro-inflammatory and apoptotic response pathways. There was also evidence of mitochondrial ROS generation and caspase-1 activation after GO and V2O5 treatment, in association with cytokine production. All considered, this study provides insight into the impact of nanoparticles on gene responses in key liver cell types, providing us with a scRNAseq platform that can be used for high-content screening of nanomaterial impact on the liver, for use in biosafety and biomedical applications.

Effects of abdominal drawing-in maneuver with or without prior iliopsoas stretching on gluteus maximus activity during prone hip extension.

BACKGROUND: Prone hip extension (PHE) has been investigated to strengthen the hip joint and back extensor muscles. However, it has not been compared with various PHE exercises in individuals with iliopsoas shortness. OBJECTIVE: This study compared pelvic compensation and hip and back extensor muscle activities in individuals with iliopsoas shortness during prone hip extension (PHE) using the abdominal drawing-in maneuver alone (PHEA) and after iliopsoas stretching (PHEAS). METHODS: Twenty-five individuals with iliopsoas shortness were included in the study. Electromyography was used to investigate bilateral erector spinae (ES) and ipsilateral gluteus maximus (GM), biceps femoris (BF), and semitendinosus (ST) muscles during PHE, PHEA, and PHEAS. Pelvic anterior tilting and rotation angles were measured during each PHE exercise via electromagnetic motion tracking. A modified Thomas test was used to examine the hip extension angle before and after iliopsoas stretching. One-way repeated-measures analysis of variance was used to investigate differences in pelvic anterior tilting and rotation angle and in hip and back extensor muscle activities among PHE, PHEA, and PHEAS. The level of statistical significance was set at α= 0.01. RESULTS: GM muscle activity was significantly greater with PHEAS, compared to PHE and PHEA (p< 0.01). Bilateral ES and ipsilateral BF and ST muscle activities were significantly reduced with PHEAS, compared to PHE and PHEA (p< 0.01). Anterior pelvic tilting and rotation angles were significantly reduced with PHEAS, compared to PHE and PHEA (p< 0.01). CONCLUSIONS: PHEAS is recommended to selectively strengthen GM muscles with minimal BF and ST muscle activities and pelvic compensation in individuals with iliopsoas shortness. The abdominal drawing-in maneuver (ADIM) after iliopsoas stretching is more efficient than ADIM alone during PHE, especially in individuals with iliopsoas shortness.

VicPred: A Vibrio cholerae Genotype Prediction Tool.

Genomic information can be used to predict major pathogenic traits of pathogens without the need for laboratory experimentation. However, no Vibrio cholerae genome-based trait identification tools currently exist. The aim of this study was to develop a web-based prediction tool to identify Vibrio pathogenic traits using publicly available 796 whole-genome sequences of V. cholerae. Using this application, 68 structural O-antigen gene clusters belonging to 49 serogroups of V. cholerae were classified, and the composition of the genes within the O-antigen cluster of each serogroup was identified. The arrangement and location of the CTX prophage and related elements of the seventh cholera pandemic strains were also revealed. With the versatile tool, named VicPred, we analyzed the assemblage of various SXTs (sulfamethoxazole/trimethoprim resistance element) and major genomic islands (GIs) of V. cholerae, and the increasing trend in drug-resistance revealing high resistance of the V. cholerae strains to certain antibiotics. The pathogenic traits of newly sequenced V. cholerae strains could be analyzed based on these characteristics. The accumulation of further genome data will expedite the establishment of a more precise genome-based pathogenic traits analysis tool.

Using a public database of Neisseria gonorrhoeae genomes to detect mutations associated with zoliflodacin resistance.

BACKGROUND: Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is an urgent global health threat. Zoliflodacin is a novel antibiotic undergoing clinical trials for the treatment of gonorrhoea. While there are limited data regarding zoliflodacin resistance in N. gonorrhoeae, three amino acid mutations have been associated with increased MICs of zoliflodacin. OBJECTIVES: To determine the prevalence of three amino acid mutations associated with zoliflodacin resistance within a large, public database of nearly 13 000 N. gonorrhoeae genomes. METHODS: PathogenWatch is an online genomic epidemiology platform with a public database of N. gonorrhoeae genomes. That database was used to extract gyrB sequence data and a Basic Local Alignment Search Tool (BLAST) search was performed to identify any of the three amino acid mutations in GyrB that are associated with increased zoliflodacin MICs: D429N, K450N or K450T. As a control for the search methodology, all GyrA sequences were also extracted and S91F mutations were identified and compared with the PathogenWatch database. RESULTS: In total, 12 493 N. gonorrhoeae genomes from the PathogenWatch database were included. Among those genomes, none was identified that harboured any of the three mutations associated with increased zoliflodacin MICs. One genome was identified to have a mutation at position 429 in GyrB (D429V). CONCLUSIONS: The findings suggest that the prevalence of the three mutations associated with zoliflodacin resistance in N. gonorrhoeae is very low. However, further research into the mechanisms of zoliflodacin resistance in N. gonorrhoeae is needed. Genomic epidemiology platforms like PathogenWatch can be used to enhance the global surveillance of AMR.