Genome-based taxonomic identification and safety assessment of an Enterococcus strain isolated from a homemade dairy product.

The aim of this study was to explore the taxonomic identification and evaluate the safety of a bacterium, Enterococcus lactis IDCC 2105, isolated from homemade cheese in Korea, using whole genome sequence (WGS) analysis. It sought to identify the species level of this Enterococcus spp., assess its antibiotic resistance, and evaluate its virulence potential. WGS analysis confirmed the bacterial strain IDCC 2105 as E. lactis and identified genes responsible for resistance to erythromycin and clindamycin, specifically msrC, and eatAv, which are chromosomally located, indicating a minimal risk for horizontal gene transfer. The absence of plasmids in E. lactis IDCC 2105 further diminishes the likelihood of resistance gene dissemination. Additionally, our investigation into seven virulence factors, including hemolysis, platelet aggregation, biofilm formation, hyaluronidase, gelatinase, ammonia production, and ß-glucuronidase activity, revealed no detectable virulence traits. Although bioinformatic analysis suggested the presence of collagen adhesion genes acm and scm, these were not corroborated by phenotypic virulence assays. Based on these findings, E. lactis IDCC 2105 presents as a safe strain for potential applications, contributing valuable information on its taxonomy, antibiotic resistance profile, and lack of virulence factors, supporting its use in food products.

The effect of prescription drug insurance on the incidence of potentially inappropriate prescribing: Evidence from Medicare Part D.

The Medicare Part D program has been documented to increase the affordability and accessibility of drugs and improve the quality of prescription drug use; however, less is known about the equity impact of the Part D program on potentially inappropriate prescribing-specifically, incidences of polypharmacy and potentially inappropriate medication (PIM) use based on different racial/ethnic groups. Using a difference in the regression discontinuity design, we found that among Whites, Part D was associated with increases in polypharmacy and "broadly defined" PIM use, while the use of "always avoid" PIM remained unchanged. Conversely, Blacks and Hispanics reported no changes in such drug utilization patterns.

The Current Incidence and Future Projection of Acetabular Fractures in Korea.

BACKGROUND: As one of the most challenging fractures to orthopedic surgeons, acetabular fractures show a wide range of incidence among countries and regions with even more variance in the treatment modalities. In this study, we aimed to investigate the epidemiology of acetabular fractures, and to compare the rate of subsequent total hip arthroplasty (THA) between nonoperative and operative treatments in South Korea using a medical claims database. METHODS: This was a retrospective study using the Korean Health Insurance Review and Assessment database. Patients admitted for acetabular fractures from January 2007 to December 2018 were identified using International Classification of Diseases-10 codes. Kaplan-Meier survival analysis was used to compare the cumulative incidence of THA between two groups. We also evaluated the survivorship of operative group according to the type of institutions. RESULTS: The incidence rate of acetabular fractures increased by 28% between 2007 and 2018. Acetabular fractures were more common in men (62%) than women (38%), and most common in the patients older than 80 years. The number of acetabular fractures was estimated to increase 1.7-fold in 2030 compared to 2018. Operative treatment accounted for 16% of cases, and nonoperative treatment for 84%. The incidence of subsequent THA was higher in the operative treatment group than in the nonoperative group (P < 0.001). The higher rate in the operative treatment group is probably related with the severity of the fracture type. The rate of subsequent THA was higher in patients who initially treated in general hospitals compared with those who were initially treated in tertiary hospitals. CONCLUSION: The incidence of acetabular fractures is increasing in South Korea, in line with global trends. Most acetabular fractures are treated conservatively, and those who receive surgery are more likely to require a subsequent THA. Patients who were operated in general hospitals had highest possibility of subsequent THA after acetabular fractures.

Early Prediction of Mortality for Septic Patients Visiting Emergency Room Based on Explainable Machine Learning: A Real-World Multicenter Study.

BACKGROUND: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. METHODS: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO2/FIO2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine). The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley's additive explanations (SHAP). RESULTS: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756-0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626-0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. CONCLUSION: Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.

HDPose: Post-Hierarchical Diffusion with Conditioning for 3D Human Pose Estimation.

Recently, monocular 3D human pose estimation (HPE) methods were used to accurately predict 3D pose by solving the ill-pose problem caused by 3D-2D projection. However, monocular 3D HPE still remains challenging owing to the inherent depth ambiguity and occlusions. To address this issue, previous studies have proposed diffusion model-based approaches (DDPM) that learn to reconstruct a correct 3D pose from a noisy initial 3D pose. In addition, these approaches use 2D keypoints or context encoders that encode spatial and temporal information to inform the model. However, they often fall short of achieving peak performance, or require an extended period to converge to the target pose. In this paper, we proposed HDPose, which can converge rapidly and predict 3D poses accurately. Our approach aggregated spatial and temporal information from the condition into a denoising model in a hierarchical structure. We observed that the post-hierarchical structure achieved the best performance among various condition structures. Further, we evaluated our model on the widely used Human3.6M and MPI-INF-3DHP datasets. The proposed model demonstrated competitive performance with state-of-the-art models, achieving high accuracy with faster convergence while being considerably more lightweight.

Determination of Sulfites in Dried Fruits by Paper Spray Ionization Tandem Mass Spectrometry.

Sulfite, a widely used food additive, is subject to regulated labeling. The extraction of sulfite as the stable hydroxymethylsulfonate (HMS) form and its quantitative analysis by liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been recognized for their good sensitivity, selectivity, and versatility across various food materials. This study aimed to develop a cost-effective and simpler method for sulfite quantitation, while maintaining the superior sensitivity and selectivity of mass spectrometry (MS). To achieve this, we introduced paper spray ionization (PSI), an ambient desorption ionization technique that could achieve the direct measurement of analytes without employing separation. We also employed a novel internal standard (IS) structurally similar to the analyte, replacing the more expensive isotopically labeled IS. Although the PSI-MS/MS method developed in this study exhibited slightly lower analytical performance compared to the conventional LC-MS/MS, it remained effective for sulfite analysis in dried fruits.

Predictors of Patient-Reported Outcomes After Hyaluronic Acid Injections: Effect of Expectations and Psychological Stress.

INTRODUCTION: Hyaluronic acid (HA) injections are a common nonsurgical treatment of knee osteoarthritis (OA). Patient expectations and psychological stress are believed to affect outcomes after orthopaedic procedures. METHODS: This was a prospective cohort study seeking to identify factors predictive of greater patient-reported outcomes after HA injections, particularly expectations and psychological stress. 250 patients receiving a series of HA injections for knee OA were enrolled, with 196 being included for analysis. Demographics, surgical history, and preoperative Kellgren-Lawrence severity scores were collected, and patients completed the Knee Osteoarthritis Outcome Score (KOOS) questionnaire, a modified KOOS questionnaire assessing their 6-month postinjection expectations, and the Perceived Stress Scale before the first injection. Outcomes were assessed at 3 weeks and 3 and 6 months after the final injection. RESULTS: KOOS scores improved from preinjection to 6-month follow-up but did not meet patients' expectations or minimal clinically important difference. Expectations correlated with 6-month KOOS pain, activities of daily living, sport, and quality of life subscales (ρ = 0.19 to 0.34), but not the symptom subscale (P = 0.10). Expectations (ρ = 0.31 to 0.37), younger age (ρ = -0.17 to -0.18), and greater perceived stress (ρ = 0.23) correlated with greater improvement from baseline KOOSs. Lower body mass index (ρ = -0.19 to -0.22), male sex (ρ = -0.17), and greater preinjection function (ρ = 0.37 to 0.46) correlated with greater 6-month outcomes. Stress measured on the Perceived Stress Scale did not correlate with 6-month KOOSs (P ≥ 0.27). Lower Kellgren-Lawrence severity score was weakly associated with greater 6-month KOOS activities of daily living and sport scores (ρ = -0.15 to -0.16) and greater improvement in the KOOS symptom score (ρ = -0.15). DISCUSSION: This study identified that higher expectations, lower body mass index, younger age, male sex, lower radiographic severity, greater preinjection function, and greater perceived stress are associated with greater patient outcomes after HA injection. Physicians should consider these factors when counseling patients with knee OA about viscosupplementation. STUDY TYPE: Prospective Cohort Study (Level of Evidence II).

A Deep Learning-Based Approach for Prediction of Vancomycin Treatment Monitoring: Retrospective Study Among Patients With Critical Illness.

BACKGROUND: Vancomycin pharmacokinetics are highly variable in patients with critical illnesses, and clinicians commonly use population pharmacokinetic (PPK) models based on a Bayesian approach to dose. However, these models are population-dependent, may only sometimes meet the needs of individual patients, and are only used by experienced clinicians as a reference for making treatment decisions. To assist real-world clinicians, we developed a deep learning-based decision-making system that predicts vancomycin therapeutic drug monitoring (TDM) levels in patients in intensive care unit. OBJECTIVE: This study aimed to establish joint multilayer perceptron (JointMLP), a new deep-learning model for predicting vancomycin TDM levels, and compare its performance with the PPK models, extreme gradient boosting (XGBoost), and TabNet. METHODS: We used a 977-case data set split into training and testing groups in a 9:1 ratio. We performed external validation of the model using 1429 cases from Kangwon National University Hospital and 2394 cases from the Medical Information Mart for Intensive Care-IV (MIMIC-IV). In addition, we performed 10-fold cross-validation on the internal training data set and calculated the 95% CIs using the metric. Finally, we evaluated the generalization ability of the JointMLP model using the MIMIC-IV data set. RESULTS: Our JointMLP model outperformed other models in predicting vancomycin TDM levels in internal and external data sets. Compared to PPK, the JointMLP model improved predictive power by up to 31% (mean absolute error [MAE] 6.68 vs 5.11) on the internal data set and 81% (MAE 11.87 vs 6.56) on the external data set. In addition, the JointMLP model significantly outperforms XGBoost and TabNet, with a 13% (MAE 5.75 vs 5.11) and 14% (MAE 5.85 vs 5.11) improvement in predictive accuracy on the inner data set, respectively. On both the internal and external data sets, our JointMLP model performed well compared to XGBoost and TabNet, achieving prediction accuracy improvements of 34% and 14%, respectively. Additionally, our JointMLP model showed higher robustness to outlier data than the other models, as evidenced by its higher root mean squared error performance across all data sets. The mean errors and variances of the JointMLP model were close to zero and smaller than those of the PPK model in internal and external data sets. CONCLUSIONS: Our JointMLP approach can help optimize treatment outcomes in patients with critical illnesses in an intensive care unit setting, reducing side effects associated with suboptimal vancomycin administration. These include increased risk of bacterial resistance, extended hospital stays, and increased health care costs. In addition, the superior performance of our model compared to existing models highlights its potential to help real-world clinicians.

Mapping Changes in Glutamate with Glutamate-Weighted MRI in Forced Swim Test Model of Depression in Rats.

Chemical exchange saturation transfer with glutamate (GluCEST) imaging is a novel technique for the non-invasive detection and quantification of cerebral Glu levels in neuromolecular processes. Here we used GluCEST imaging and 1H magnetic resonance spectroscopy (1H MRS) to assess in vivo changes in Glu signals within the hippocampus in a rat model of depression induced by a forced swim test. The forced swimming test (FST) group exhibited markedly reduced GluCEST-weighted levels and Glu concentrations when examined using 1H MRS in the hippocampal region compared to the control group (GluCEST-weighted levels: 3.67 ± 0.81% vs. 5.02 ± 0.44%, p < 0.001; and Glu concentrations: 6.560 ± 0.292 µmol/g vs. 7.133 ± 0.397 µmol/g, p = 0.001). Our results indicate that GluCEST imaging is a distinctive approach to detecting and monitoring Glu levels in a rat model of depression. Furthermore, the application of GluCEST imaging may provide a deeper insight into the neurochemical involvement of glutamate in various psychiatric disorders.

Beyond memory impairment: the complex phenotypic landscape of Alzheimer's disease.

Neuropsychiatric symptoms (NPSs) in Alzheimer's disease (AD) constitute multifaceted behavioral manifestations that reflect processes of emotional regulation, thinking, and social behavior. They are as prevalent in AD as cognitive impairment and develop independently during the progression of neurodegeneration. As studying NPSs in AD is clinically challenging, most AD research to date has focused on cognitive decline. In this opinion article we summarize emerging literature on the prevalence, time course, and the underlying genetic, molecular, and pathological mechanisms related to NPSs in AD. Overall, we propose that NPSs constitute a cluster of core symptoms in AD, and understanding their neurobiology can lead to a more holistic approach to AD research, paving the way for more accurate diagnostic tests and personalized treatments embracing the goals of precision medicine.

Alzheimer's disease transcriptional landscape in ex-vivo human microglia.

Microglia are resident immune cells of the brain and are implicated in the etiology of Alzheimer's Disease (AD) and other diseases. Yet the cellular and molecular processes regulating their function throughout the course of the disease are poorly understood. Here, we present the transcriptional landscape of primary microglia from 189 human postmortem brains, including 58 healthy aging individuals and 131 with a range of disease phenotypes, including 63 patients representing the full spectrum of clinical and pathological severity of AD. We identified transcriptional changes associated with multiple AD phenotypes, capturing the severity of dementia and neuropathological lesions. Transcript-level analyses identified additional genes with heterogeneous isoform usage and AD phenotypes. We identified changes in gene-gene coordination in AD, dysregulation of co-expression modules, and disease subtypes with distinct gene expression. Taken together, these data further our understanding of the key role of microglia in AD biology and nominate candidates for therapeutic intervention.

Genetic regulation of cell type-specific chromatin accessibility shapes brain disease etiology.

Nucleotide variants in cell type-specific gene regulatory elements in the human brain are risk factors for human disease. We measured chromatin accessibility in 1932 aliquots of sorted neurons and non-neurons from 616 human postmortem brains and identified 34,539 open chromatin regions with chromatin accessibility quantitative trait loci (caQTLs). Only 10.4% of caQTLs are shared between neurons and non-neurons, which supports cell type-specific genetic regulation of the brain regulome. Incorporating allele-specific chromatin accessibility improves statistical fine-mapping and refines molecular mechanisms that underlie disease risk. Using massively parallel reporter assays in induced excitatory neurons, we screened 19,893 brain QTLs and identified the functional impact of 476 regulatory variants. Combined, this comprehensive resource captures variation in the human brain regulome and provides insights into disease etiology.

Projective light-sheet microscopy with flexible parameter selection.

Projection imaging accelerates volumetric interrogation in fluorescence microscopy, but for multi-cellular samples, the resulting images may lack contrast, as many structures and haze are summed up. Here, we demonstrate rapid projective light-sheet imaging with parameter selection (props) of imaging depth, position and viewing angle. This allows us to selectively image different sub-volumes of a sample, rapidly switch between them and exclude background fluorescence. Here we demonstrate the power of props by functional imaging within distinct regions of the zebrafish brain, monitoring calcium firing inside muscle cells of moving Drosophila larvae, super-resolution imaging of selected cell layers, and by optically unwrapping the curved surface of a Drosophila embryo. We anticipate that props will accelerate volumetric interrogation, ranging from subcellular to mesoscopic scales.

Validation of Enhancer Regions in Primary Human Neural Progenitor Cells using Capture STARR-seq.

Genome-wide association studies (GWAS) and expression analyses implicate noncoding regulatory regions as harboring risk factors for psychiatric disease, but functional characterization of these regions remains limited. We performed capture STARR-sequencing of over 78,000 candidate regions to identify active enhancers in primary human neural progenitor cells (phNPCs). We selected candidate regions by integrating data from NPCs, prefrontal cortex, developmental timepoints, and GWAS. Over 8,000 regions demonstrated enhancer activity in the phNPCs, and we linked these regions to over 2,200 predicted target genes. These genes are involved in neuronal and psychiatric disease-associated pathways, including dopaminergic synapse, axon guidance, and schizophrenia. We functionally validated a subset of these enhancers using mutation STARR-sequencing and CRISPR deletions, demonstrating the effects of genetic variation on enhancer activity and enhancer deletion on gene expression. Overall, we identified thousands of highly active enhancers and functionally validated a subset of these enhancers, improving our understanding of regulatory networks underlying brain function and disease.

Androgen deprivation induces double-null prostate cancer via aberrant nuclear export and ribosomal biogenesis through HGF and Wnt activation.

Androgen deprivation therapy (ADT) targeting androgen/androgen receptor (AR)- signaling pathways is the main therapy for advanced prostate cancer (PCa). However, ADT eventually fails in most patients who consequently develop castration-resistant prostate cancer (CRPC). While more potent AR antagonists and blockers for androgen synthesis were developed to improve clinical outcomes, they also show to induce more diverse CRPC phenotypes. Specifically, the AR- and neuroendocrine-null PCa, DNPC, occurs in abiraterone and enzalutamide-treated patients. Here, we uncover that current ADT induces aberrant HGF/MET signaling activation that further elevates Wnt/ß-catenin signaling in human DNPC samples. Co-activation of HGF/MET and Wnt/ß-catenin axes in mouse prostates induces DNPC-like lesions. Single-cell RNA sequencing analyses identify increased expression and activity of XPO1 and ribosomal proteins in mouse DNPC-like cells. Elevated expression of XPO1 and ribosomal proteins is also identified in clinical DNPC specimens. Inhibition of XPO1 and ribosomal pathways represses DNPC growth in both in vivo and ex vivo conditions, evidencing future therapeutic targets.

Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data.

Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.

In-Advance Prediction of Pressure Ulcers via Deep-Learning-Based Robust Missing Value Imputation on Real-Time Intensive Care Variables.

Pressure ulcers (PUs) are a prevalent skin disease affecting patients with impaired mobility and in high-risk groups. These ulcers increase patients' suffering, medical expenses, and burden on medical staff. This study introduces a clinical decision support system and verifies it for predicting real-time PU occurrences within the intensive care unit (ICU) by using MIMIC-IV and in-house ICU data. We develop various machine learning (ML) and deep learning (DL) models for predicting PU occurrences in real time using the MIMIC-IV and validate using the MIMIC-IV and Kangwon National University Hospital (KNUH) dataset. To address the challenge of missing values in time series, we propose a novel recurrent neural network model, GRU-D++. This model outperformed other experimental models by achieving the area under the receiver operating characteristic curve (AUROC) of 0.945 for the on-time prediction and AUROC of 0.912 for 48h in-advance prediction. Furthermore, in the external validation with the KNUH dataset, the fine-tuned GRU-D++ model demonstrated superior performances, achieving an AUROC of 0.898 for on-time prediction and an AUROC of 0.897 for 48h in-advance prediction. The proposed GRU-D++, designed to consider temporal information and missing values, stands out for its predictive accuracy. Our findings suggest that this model can significantly alleviate the workload of medical staff and prevent the worsening of patient conditions by enabling timely interventions for PUs in the ICU.