Outcomes of the Together for Life Program in Community-Dwelling Older Adults Living Alone: A Pilot Study.

PURPOSE: The current study developed and tested selected effects of the Together for Life (TFL) program for community-dwelling older adults using an embedded mixed methods design. METHOD: Nine community volunteers participated in the training program, and 14 people aged ≥65 years, living alone, enrolled in the study. Home visits were conducted by home health nurses every 2 weeks, supplemented by weekly home visits and phone counseling provided by volunteers, for a duration of 20 weeks. This mixed methods study used quantitative and qualitative approaches. Quantitative data were gathered through a questionnaire survey to assess the intervention's effects on health-related quality of life (HRQOL), loneliness, and depression. The qualitative component focused on participants' evaluation of the program. RESULTS: Significant changes were noted in HRQOL scores with an effect size of 0.6 (p = 0.01). Results indicated the positive impact of the TFL program on HRQOL among older adults as their health status was continuously monitored by volunteers who provided care and home health nurses who assisted with health management during the coronavirus disease 2019 (COVID-19) pandemic. CONCLUSION: In a pandemic, personalized health management, such as regular health check-ins provided by home health nurses and home or phone visits provided by volunteers, is crucial for isolated older adults. This pilot program enhanced participants' HRQOL through continuous health monitoring, volunteer care, and nurse support, even during the COVID-19 pandemic. [Journal of Gerontological Nursing, xx(x), xx-xx.].

Clinical value of SUVpeak-to-tumor centroid distance on FDG PET/CT for predicting neoadjuvant chemotherapy response in patients with breast cancer.

BACKGROUND: Since it has been found that the maximum metabolic activity of a cancer lesion shifts toward the lesion edge during cancer progression, normalized distances from the hot spot of radiotracer uptake to tumor centroid (NHOC) and tumor perimeter (NHOP) have been suggested as novel F-18 fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) parameters that can reflect cancer aggressiveness. This study aimed to investigate whether NHOC and NHOP parameters could predict pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. METHODS: This study retrospectively enrolled 135 female patients with breast cancer who underwent pretreatment FDG PET/CT and received NAC and subsequent surgical resection. From PET/CT images, normalized distances of maximum SUV and peak SUV-to-tumor centroid (NHOCmax and NHOCpeak) and -to-tumor perimeter (NHOPmax and NHOPpeak) were measured, in addition to conventional PET/CT parameters. RESULTS: Of 135 patients, 32 (23.7%) achieved pathological complete response (pCR), and 34 (25.2%) had events during follow-up. In the receiver operating characteristic (ROC) curve analysis, NHOCmax showed the highest area under the ROC curve value (0.710) for predicting pCR, followed by NHOCpeak (0.694). In the multivariate logistic regression analysis, NHOCmax, NHOCpeak, and NHOPmax were independent predictors for pCR (p < 0.05). In the multivariate survival analysis, NHOCpeak (p = 0.026) was an independent predictor for PFS along with metabolic tumor volume, with patients having higher NHOCpeak showing worse PFS. CONCLUSION: NHOCpeak on pretreatment FDG PET/CT could be a potential imaging parameter for predicting NAC response and survival in patients with breast cancer.

Coronary artery calcium measurement on attenuation correction computed tomography using artificial intelligence: correlation with coronary flow capacity and prognosis.

PURPOSE: This study aimed to test whether the coronary artery calcium (CAC) burden on attenuation correction computed tomography (CTac), measured using artificial intelligence (AI-CACac), correlates with coronary flow capacity (CFC) and prognosis. MATERIALS AND METHODS: We retrospectively enrolled patients who underwent [13N]ammonia positron emission tomography (PET) between September 2021 and May 2023. CTac data were obtained from all the patients. Patients with (history of) acute coronary syndrome, previous coronary stent insertion or bypass surgery, or left ventricular ejection fraction < 40% were excluded. The total Agatston score measured using a dedicated AI-CAC quantification software on CTac was defined as AI-CACac. The correlations between AI-CACac and PET-measured myocardial blood flow (MBF) and CFC and significant ischaemia (summed difference score ≥ 7) were analysed. Their prognostic values for major cardiovascular events (MACE), including death, nonfatal myocardial infarction, hospitalisation due to angina pectoris or heart failure, and late (> 90 days) revascularisation, were also evaluated. RESULTS: In total, 289 patients were included in this study. Significant negative correlations were found between AI-CACac and stress MBF (ρ = -0.363, p < 0.001) and MFR (ρ = -0.305, p < 0.001). AI-CACac > 10 was associated with a significantly higher prevalence of impaired CFC (31% vs. 7%, p < 0.001) and significant ischaemia (20% vs. 7%), which remained significant after adjusting for other risk factors. MACE occurred in 49 (17%) patients (median follow-up, 284 days), and those who experienced MACE had significantly higher AI-CACac (median, 166 vs. 56; p = 0.039). However, multivariable analysis revealed an independent prognostic association among impaired CFC, diabetes, smoking, but not for AI-CACac. CONCLUSION: AI-measured CACac correlates well with PET-measured MBF and CFC, but its prognostic significance requires further validation.

Prognostic model for predicting Alzheimer's disease conversion using functional connectome manifolds.

BACKGROUND: Early detection of Alzheimer's disease (AD) is essential for timely management and consideration of therapeutic options; therefore, detecting the risk of conversion from mild cognitive impairment (MCI) to AD is crucial during neurodegenerative progression. Existing neuroimaging studies have mostly focused on group differences between individuals with MCI (or AD) and cognitively normal (CN), discarding the temporal information of conversion time. Here, we aimed to develop a prognostic model for AD conversion using functional connectivity (FC) and Cox regression suitable for conversion event modeling. METHODS: We developed a prognostic model using a large-scale Alzheimer's Disease Neuroimaging Initiative dataset, and it was validated using external data obtained from the Open Access Series of Imaging Studies. We considered individuals who were initially CN or had MCI but progressed to AD and those with MCI with no progression to AD during the five-year follow-up period. As the exact conversion time to AD is unknown, we inferred this information using imputation approaches. We generated cortex-wide principal FC gradients using manifold learning techniques and computed subcortical-weighted manifold degrees from baseline functional magnetic resonance imaging data. A penalized Cox regression model with an elastic net penalty was adopted to define a risk score predicting the risk of conversion to AD, using FC gradients and clinical factors as regressors. RESULTS: Our prognostic model predicted the conversion risk and confirmed the role of imaging-derived manifolds in the conversion risk. The brain regions that largely contributed to predicting AD conversion were the heteromodal association and visual cortices, as well as the caudate and hippocampus. Our risk score based on Cox regression was consistent with the expected disease trajectories and correlated with positron emission tomography tracer uptake and symptom severity, reinforcing its clinical usefulness. Our findings were validated using an independent dataset. The cross-sectional application of our model showed a higher risk for AD than that for MCI, which correlated with symptom severity scores in the validation dataset. CONCLUSION: We proposed a prognostic model predicting the risk of conversion to AD. The associated risk score may provide insights for early intervention in individuals at risk of AD conversion.

Diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities.

OBJECTIVE: Distinguishing post-COVID-19 residual abnormalities from interstitial lung abnormalities (ILA) on CT can be challenging if clinical information is limited. This study aimed to evaluate the diagnostic performance of radiologists in distinguishing post-COVID-19 residual abnormalities from ILA. METHODS: This multi-reader, multi-case study included 60 age- and sex-matched subjects with chest CT scans. There were 40 cases of ILA (20 fibrotic and 20 non-fibrotic) and 20 cases of post-COVID-19 residual abnormalities. Fifteen radiologists from multiple nations with varying levels of experience independently rated suspicion scores on a 5-point scale to distinguish post-COVID-19 residual abnormalities from fibrotic ILA or non-fibrotic ILA. Interobserver agreement was assessed using the weighted κ value, and the scores of individual readers were compared with the consensus of all readers. Receiver operating characteristic curve analysis was conducted to evaluate the diagnostic performance of suspicion scores for distinguishing post-COVID-19 residual abnormalities from ILA and for differentiating post-COVID-19 residual abnormalities from both fibrotic and non-fibrotic ILA. RESULTS: Radiologists' diagnostic performance for distinguishing post-COVID-19 residual abnormalities from ILA was good (area under the receiver operating characteristic curve (AUC) range, 0.67-0.92; median AUC, 0.85) with moderate agreement (κ = 0.56). The diagnostic performance for distinguishing post-COVID-19 residual abnormalities from non-fibrotic ILA was lower than that from fibrotic ILA (median AUC = 0.89 vs. AUC = 0.80, p = 0.003). CONCLUSION: Radiologists demonstrated good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA, but careful attention is needed to avoid misdiagnosing them as non-fibrotic ILA. KEY POINTS: Question How good are radiologists at differentiating interstitial lung abnormalities (ILA) from changes related to COVID-19 infection? Findings Radiologists had a median AUC of 0.85 in distinguishing post-COVID-19 abnormalities from ILA with moderate agreement (κ = 0.56). Clinical relevance Radiologists showed good diagnostic performance and moderate agreement in distinguishing post-COVID-19 residual abnormalities from ILA; nonetheless, caution is needed in distinguishing residual abnormalities from non-fibrotic ILA.

Prediction of early clinical response to neoadjuvant chemotherapy in Triple-negative breast cancer: Incorporating Radiomics through breast MRI.

This study assessed pretreatment breast MRI coupled with machine learning for predicting early clinical responses to neoadjuvant chemotherapy (NAC) in triple-negative breast cancer (TNBC), focusing on identifying non-responders. A retrospective analysis of 135 TNBC patients (107 responders, 28 non-responders) treated with NAC from January 2015 to October 2022 was conducted. Non-responders were defined according to RECIST guidelines. Data included clinicopathologic factors and clinical MRI findings, with radiomics features from contrast-enhanced T1-weighted images, to train a stacking ensemble of 13 machine learning models. For subgroup analysis, propensity score matching was conducted to adjust for clinical disparities in NAC response. The efficacy of the models was evaluated using the area under the receiver-operating-characteristic curve (AUROC) before and after matching. The model combining clinicopathologic factors and clinical MRI findings achieved an AUROC of 0.752 (95% CI 0.644-0.860) for predicting non-responders, while radiomics-based models showed 0.749 (95% CI 0.614-0.884). An integrated model of radiomics, clinicopathologic factors, and clinical MRI findings reached an AUROC of 0.802 (95% CI 0.699-0.905). After propensity score matching, the hierarchical order of key radiomics features remained consistent. Our study demonstrated the potential of using machine learning models based on pretreatment MRI to non-invasively predict TNBC non-responders to NAC.

Peritumoral Adipose Tissue Features Derived from [18F]fluoro-2-deoxy-2-d-glucose Positron Emission Tomography/Computed Tomography as Predictors for Response to Neoadjuvant Chemotherapy in Breast Cancer Patients.

This study investigated whether the textural features of peritumoral adipose tissue (AT) on [18F]fluoro-2-deoxy-2-d-glucose (FDG) positron emission tomography/computed tomography (PET/CT) can predict the pathological response to neoadjuvant chemotherapy (NAC) and progression-free survival (PFS) in breast cancer patients. We retrospectively enrolled 147 female breast cancer patients who underwent staging FDG PET/CT and completed NAC and underwent curative surgery. We extracted 10 first-order features, 6 gray-level co-occurrence matrix (GLCM) features, and 3 neighborhood gray-level difference matrix (NGLDM) features of peritumoral AT and evaluated the predictive value of those imaging features for pathological complete response (pCR) and PFS. The results of our study demonstrated that GLCM homogeneity showed the highest predictability for pCR among the peritumoral AT imaging features in the receiver operating characteristic curve analysis. In multivariate logistic regression analysis, the mean standardized uptake value (SUV), 50th percentile SUV, 75th percentile SUV, SUV histogram entropy, GLCM entropy, and GLCM homogeneity of the peritumoral AT were independent predictors for pCR. In multivariate survival analysis, SUV histogram entropy and GLCM correlation of peritumoral AT were independent predictors of PFS. Textural features of peritumoral AT on FDG PET/CT could be potential imaging biomarkers for predicting the response to NAC and disease progression in breast cancer patients.

Lung Cancer Staging Using Chest CT and FDG PET/CT Free-Text Reports: Comparison Among Three ChatGPT Large-Language Models and Six Human Readers of Varying Experience.

Background: Although radiology reports are commonly used for lung cancer staging, this task can be challenging given radiologists' variable reporting styles as well as reports' potentially ambiguous and/or incomplete staging-related information. Objective: To compare performance of ChatGPT large-language models (LLMs) and human readers of varying experience in lung cancer staging using chest CT and FDG PET/CT free-text reports. Methods: This retrospective study included 700 patients (mean age, 73.8±29.5 years; 509 male, 191 female) from four institutions in Korea who underwent chest CT or FDG PET/CT for non-small cell lung cancer initial staging from January, 2020 to December, 2023. Examinations' reports used a free-text format, written exclusively in English or in mixed English and Korean. Two thoracic radiologists in consensus determined the overall stage group (IA, IB, IIA, IIB, IIIA, IIIB, IIIC, IVA, IVB) for each report using the AJCC 8th-edition staging system, establishing the reference standard. Three ChatGPT models (GPT-4o, GPT-4, GPT-3.5) determined an overall stage group for each report using a script-based application programming interface, zero-shot learning, and prompt incorporating a staging system summary. Six human readers (two fellowship-trained radiologists with lesser experience than the radiologists who determined the reference standard, two fellows, two residents) also independently determined overall stage groups. GPT-4o's overall accuracy for determining the correct stage among the nine groups was compared with that of the other LLMs and human readers using McNemar tests. Results: GPT-4o had an overall staging accuracy of 74.1%, significantly better than the accuracy of GPT-4 (70.1%, p=.02), GPT-3.5 (57.4%, p<.001), and resident 2 (65.7%, p<.001); significantly worse than the accuracy of fellowship-trained radiologist 1 (82.3%, p<.001) and fellowship-trained radiologist 2 (85.4%, p<.001); and not significantly different from the accuracy of fellow 1 (77.7%, p=.09), fellow 2 (75.6%, p=.53), and resident 1 (72.3%, p=.42). Conclusions: The best-performing model, GPT-4o, showed no significant difference in staging accuracy versus fellows, but significantly worse performance versus fellowship-trained radiologists. The findings do not support use of LLMs for lung cancer staging in place of expert healthcare professionals. Clinical Impact: The findings indicate the importance of domain expertise for performing complex specialized tasks such as cancer staging.

Multimodal analysis of disease onset in Alzheimer's disease using Connectome, Molecular, and genetics data.

Alzheimer's disease (AD) and its related age at onset (AAO) are highly heterogeneous, due to the inherent complexity of the disease. They are affected by multiple factors, such as neuroimaging and genetic predisposition. Multimodal integration of various data types is necessary; however, it has been nontrivial due to the high dimensionality of each modality. We aimed to identify multimodal biomarkers of AAO in AD using an extended version of sparse canonical correlation analysis, in which we integrated two imaging modalities, functional magnetic resonance imaging (fMRI) and positron emission tomography (PET), and genetic data in the form of single-nucleotide polymorphisms (SNPs) obtained from the Alzheimer's disease neuroimaging initiative database. These three modalities cover low-to-high-level complementary information and offer multiscale insights into the AAO. We identified multivariate markers of AAO in AD using fMRI, PET, and SNP. Furthermore, the markers identified were largely consistent with those reported in the existing literature. In particular, our serial mediation analysis suggests that genetic variants influence the AAO in AD by indirectly affecting brain connectivity by mediation of amyloid-beta protein accumulation, supporting a plausible path in existing research. Our approach provides comprehensive biomarkers related to AAO in AD and offers novel multimodal insights into AD.

Reconsidering Neurogenetic Indication in the Human Brain: Broad Expression of Doublecortin Transcript in the Hippocampal and Cortical Cell Populations.

INTRODUCTION: Neurogenesis in the adult brain may play an important role in memory and cognition; however, knowledge of neurogenic markers in the human brain remains limited. We compared the single-nucleus transcriptome of the hippocampus with that of other cortical regions to identify hippocampus-specific neurogenic markers. METHODS: We analyzed 26,189 nuclei from four human brains collected within 16 h of death. Clustering and annotation were performed to examine differential expression, gene ontology, and intercellular communication. DCX expression was validated by ddPCR. RESULTS: Immature markers such as DCX, CALB2, NES, SOX2, PAX6, DPYSL3, and TUBB3 were expressed in both hippocampus and prefrontal cortex, with higher levels in the prefrontal cortex. ddPCR confirmed higher expression of DCX in the prefrontal cortex. DCX was involved in both neurogenesis and neuroprotection pathways. CONCLUSION: Neurogenic markers are not definitive indicators of adult neurogenesis as their roles are more complex than previously understood.

Minimal Access vs Conventional Nipple-Sparing Mastectomy.

Importance: While nipple-sparing mastectomy (NSM) for breast cancer was only performed using the open method in the past, its frequency using endoscopic and robotic surgical instruments has been increasing rapidly. However, there are limited studies regarding postoperative complications and the benefits and drawbacks of minimal access NSM (M-NSM) compared with conventional NSM (C-NSM). Objective: To examine the differences in postoperative complications between C-NSM and M-NSM. Design, Setting, Participants: This was a retrospective multicenter cohort study enrolling 1583 female patients aged 19 years and older with breast cancer who underwent NSM at 21 university hospitals in Korea between January 2018 and December 2020. Those with mastectomy without preserving the nipple-areolar complex (NAC), clinical or pathological malignancy in the NAC, inflammatory breast cancer, breast cancer infiltrating the chest wall or skin, metastatic breast cancer, or insufficient medical records were excluded. Data were analyzed from November 2021 to March 2024. Exposures: M-NSM or C-NSM. Main Outcomes and Measures: Clinicopathological factors and postoperative complications within 3 months of surgery were assessed. Statistical analyses, including logistic regression, were used to identify the factors associated with complications. Results: There were 1356 individuals (mean [SD] age, 45.47 [8.56] years) undergoing C-NSM and 227 (mean [SD] age, 45.41 [7.99] years) undergoing M-NSM (35 endoscopy assisted and 192 robot assisted). There was no significant difference between the 2 groups regarding short- and long-term postoperative complications (<30 days: C-NSM, 465 of 1356 [34.29%] vs M-NSM, 73 of 227 [32.16%]; P = .53; <90 days: C-NSM, 525 of 1356 [38.72%] vs M-NSM, 73 of 227 [32.16%]; P = .06). Nipple-areolar complex necrosis was more common in the long term after C-NSM than M-NSM (C-NSM, 91 of 1356 [6.71%] vs M-NSM, 5 of 227 [2.20%]; P = .04). Wound infection occurred more frequently after M-NSM (C-NSM, 58 of 1356 [4.28%] vs M-NSM, 18 of 227 [7.93%]; P = .03). Postoperative seroma occurred more frequently after C-NSM (C-NSM, 193 of 1356 [14.23%] vs M-NSM, 21 of 227 [9.25%]; P = .04). Mild or severe breast ptosis was a significant risk factor for nipple or areolar necrosis (odds ratio [OR], 4.75; 95% CI, 1.66-13.60; P = .004 and OR, 8.78; 95% CI, 1.88-41.02; P = .006, respectively). Conversely, use of a midaxillary, anterior axillary, or axillary incision was associated with a lower risk of necrosis (OR for other incisions, 32.72; 95% CI, 2.11-508.36; P = .01). Necrosis occurred significantly less often in direct-to-implant breast reconstruction compared to other breast reconstructions (OR, 2.85; 95% CI, 1.11-7.34; P = .03). Conclusions and Relevance: The similar complication rates between C-NSM and M-NSM demonstrates that both methods were equally safe, allowing the choice to be guided by patient preferences and specific needs.

Transcriptomic Expression of T2-Inflammation Genes in Peripheral Blood Mononuclear Cells and Longitudinal Clinical Outcomes in Asthma: Insights from the COREA Study.

BACKGROUND: Gene expression can provide distinct information compared to clinical biomarkers in the context of longitudinal clinical outcomes in asthma patients. OBJECTIVE: This study examined the association between the gene expression levels of upstream (IL-25, IL-33, and TSLP) and downstream cytokines (IL-5, IL-4, and IL-13) in the T2 inflammatory pathway with a 12-month follow-up of exacerbation, lung function, and steroid use. METHODS: Transcriptomic sequencing analysis was performed on peripheral blood mononuclear cells from 279 adult asthmatics. Survival analysis and linear mixed-effect models were used to investigate potential differences between the high-level and low-level gene expression groups and the clinical outcomes. Analysis was performed separately for the upstream, downstream, and all 6 cytokines. RESULTS: In general, T2 inflammatory cytokine gene expression showed a weak correlation with blood eosinophil counts (all r < 0.1) and clinical outcomes. Among moderate-to-severe eosinophilic asthma (MSEA) patients, individuals with elevated levels of downstream cytokines were at increased risk of time-to-first exacerbation (p = 0.044) and a greater increase of inhaled corticosteroid use over time (p = 0.002) compared to those with lower gene expression. There was no association between baseline T2 inflammatory cytokine gene expression and the longitudinal changes in lung function over time among MSEA patients. CONCLUSION: These findings suggest that, among MSEA patients, the gene expression levels of downstream cytokines in the T2 inflammatory pathway may serve as indicators for endotyping asthma.

Validation of the Utility of the Genetically Shared Regions of Chromosomes (GD-ICS) Measuring Method in Identifying Complicated Genetic Relatedness.

BACKGROUND: Relatives share more genomic regions than unrelated individuals, with closer relatives sharing more regions. This concept, paired with the increased availability of high-throughput single nucleotide polymorphism (SNP) genotyping technologies, has made it feasible to measure the shared chromosomal regions between individuals to assess their level of relation to each other. However, such techniques have remained in the conceptual rather than practical stages in terms of applying measures or indices. Recently, we developed an index called "genetic distance-based index of chromosomal sharing (GD-ICS)" utilizing large-scale SNP data from Korean family samples and demonstrated its potential for practical applications in kinship determination. In the current study, we present validation results from various real cases demonstrating the utility of this method in resolving complex familial relationships where information obtained from traditional short tandem repeats (STRs) or lineage markers is inconclusive. METHODS: We obtained large-scale SNP data through microarray analysis from Korean individuals involving 13 kinship cases and calculated GD-ICS values using the method described in our previous study. Based on the GD-ICS reference constructed for Korean families, each disputed kinship was evaluated and validated using a combination of traditional STRs and lineage markers. RESULTS: The cases comprised those A) that were found to be inconclusive using the traditional approach, B) for which it was difficult to apply traditional testing methods, and C) that were more conclusively resolved using the GD-ICS method. This method has overcome the limitations faced by traditional STRs in kinship testing, particularly in a paternity case with STR mutational events and in confirming distant kinship where the individual of interest is unavailable for testing. It has also been demonstrated to be effective in identifying various relationships without specific presumptions and in confirming a lack of genetic relatedness between individuals. CONCLUSION: This method has been proven effective in identifying familial relationships across diverse complex and practical scenarios. It is not only useful when traditional testing methods fail to provide conclusive results, but it also enhances the resolution of challenging kinship cases, which suggests its applicability in various types of practical casework.

Ischemic stroke outcome after promoting CD4+CD25+ Treg cell migration through CCR4 overexpression in a tMCAO animal model.

The importance of neuroinflammation during the ischemic stroke has been extensively studied. The role of CD4+CD25+ regulatory T (Treg) cells during the recovery phase have shown infarct size reduction and functional improvement, possibly through the mitigation of inflammatory immune responses. We aimed to investigate the molecular factors involved in microglia-Treg cell communication that result in Treg trafficking. First, we observed the migration patterns of CD8+ (cytotoxic) T cells and Treg cells and then searched for chemokines released by activated microglia in an oxygen-glucose deprivation (OGD) model. The transwell migration assay showed increased migration into OGD media for both cell types, in agreement with the increase in chemokines involved in immune cell trafficking from the mouse chemokine profiling array. MSCV retrovirus was transduced to overexpress CCR4 in Treg cells. CCR4-overexpressed Treg cells were injected into the mouse transient middle cerebral artery occlusion (tMCAO) model to evaluate the therapeutic potential via the tetrazolium chloride (TTC) assay and behavioral tests. A general improvement in the prognosis of animals after tMCAO was observed. Our results suggest the increased mobility of CCR4-overexpressed Treg cells in response to microglia-derived chemokines in vitro and the therapeutic potential of Treg cells with increased mobility in cellular therapy.

Selecting serum-free hepatocyte cryopreservation stage and storage temperature for the application of an "off-the-shelf" bioartificial liver system.

The bioartificial liver (BAL) system can potentially rescue acute liver failure (ALF) patients by providing partial liver function until a suitable donor liver can be found or the native liver has self-regenerated. In this study, we established a suitable cryopreservation process for the development of an off-the-shelf BAL system. The viability of hepatocyte spheroids cryopreserved in liquid nitrogen was comparable to that of fresh primary hepatocyte spheroids. When hepatocyte spheroids were subjected to cryopreservation in a deep freezer, no statistically significant differences were observed in ammonia removal rate or urea secretion rate based on the cryopreservation period. However, the functional activity of the liver post-cryopreservation in a deep freezer was significantly lower than that observed following liquid nitrogen cryopreservation. Moreover, cryopreserving spheroid hydrogel beads in a deep freezer resulted in a significant decrease (approximately 30%) in both ammonia removal and urea secretion rates compared to the group cryopreserved in liquid nitrogen. The viabilities of spheroid hydrogel beads filled into the bioreactor of a BAL system were similar across all four groups. However, upon operating the BAL system for 24 h, the liver function activity was significantly higher in the group comprising hydrogel beads generated after thawing hepatocyte spheroids cryopreserved in liquid nitrogen. Consequently, the manufacturing of beads after the cryopreservation of hepatocyte spheroids is deemed the most suitable method, considering efficiency, economic feasibility, and liver function activity, for producing a BAL system.

Modulating monocyte-derived macrophage polarization in cerebral ischemic injury with hyperglycemia.

Ischemic stroke (IS), characterized by high mortality rate, occurs owing to diminished or blocked blood flow to the brain. Hyperglycemia (HG) is a major contributor to the risk of IS. HG induces augmented oxidative stress and Blood-Brain Barrier breakdown, which increases the influx of blood-derived myeloid cells into the brain parenchyma. In cerebral ischemia, infiltrating monocytes undergo differentiation into pro-inflammatory or anti-inflammatory macrophages, having a large effect on outcomes of ischemic stroke. In addition, interleukin-4 (IL-4) and interleukin-13 (IL-13) engage in post-ischemia repair by polarizing the infiltrating monocytes into an anti-inflammatory phenotype. In this study, we aimed to determine the effect of phenotypic polarization of monocyte-derived macrophages on the prognosis of IS with HG (HG-IS). We first established a hyperglycemic mouse model using streptozotocin (150 mg/kg) and induced transient middle cerebral artery occlusion. We observed that blood-brain barrier permeability increased in HG-IS mice, as per two-photon live imaging and Evans blue staining. We also confirmed the increased infiltration of monocyte-derived macrophages and the downregulation of anti-inflammatory macrophages related to tissue remodeling after inflammation in HG-IS mice through immunohistochemistry, western blotting, and flow cytometry. We observed phenotypic changes in monocyte-derived macrophages, alleviated infarct volume, and improved motor function in HG-IS mice treated with IL-4 and IL-13. These findings suggest that the modulation of phenotypic changes in monocyte-derived macrophages following IS in hyperglycemic mice may influence ischemic recovery.

Clinical and Imaging Characteristics of SARS-CoV-2 Breakthrough Infection in Hospitalized Immunocompromised Patients.

OBJECTIVE: To evaluate the clinical and imaging characteristics of SARS-CoV-2 breakthrough infection in hospitalized immunocompromised patients in comparison with immunocompetent patients. MATERIALS AND METHODS: This retrospective study analyzed consecutive adult patients hospitalized for COVID-19 who received at least one dose of the SARS-CoV-2 vaccine at two academic medical centers between June 2021 and December 2022. Immunocompromised patients (with active solid organ cancer, active hematologic cancer, active immune-mediated inflammatory disease, status post solid organ transplantation, or acquired immune deficiency syndrome) were compared with immunocompetent patients. Multivariable logistic regression analysis was performed to evaluate the effect of immune status on severe clinical outcomes (in-hospital death, mechanical ventilation, or intensive care unit admission), severe radiologic pneumonia (≥ 25% of lung involvement), and typical CT pneumonia. RESULTS: Of 2218 patients (mean age, 69.5 ± 16.1 years), 274 (12.4%), and 1944 (87.6%) were immunocompromised an immunocompetent, respectively. Patients with active solid organ cancer and patients status post solid organ transplantation had significantly higher risks for severe clinical outcomes (adjusted odds ratio = 1.58 [95% confidence interval {CI}, 1.01-2.47], P = 0.042; and 3.12 [95% CI, 1.47-6.60], P = 0.003, respectively). Patient status post solid organ transplantation and patients with active hematologic cancer were associated with increased risks for severe pneumonia based on chest radiographs (2.96 [95% CI, 1.54-5.67], P = 0.001; and 2.87 [95% CI, 1.50-5.49], P = 0.001, respectively) and for typical CT pneumonia (9.03 [95% CI, 2.49-32.66], P < 0.001; and 4.18 [95% CI, 1.70-10.25], P = 0.002, respectively). CONCLUSION: Immunocompromised patients with COVID-19 breakthrough infection showed an increased risk of severe clinical outcome, severe pneumonia based on chest radiographs, and typical CT pneumonia. In particular, patients status post solid organ transplantation was specifically found to be associated with a higher risk of all three outcomes than hospitalized immunocompetent patients.

Blood transcriptome differentiates clinical clusters for asthma.

Background: In previous studies, several asthma phenotypes were identified using clinical and demographic parameters. Transcriptional phenotypes were mainly identified using sputum and bronchial cells. Objective: We aimed to investigate asthma phenotypes via clustering analysis using clinical variables and compare the transcription levels among clusters using gene expression profiling of the blood. Methods: Clustering analysis was performed using 6 parameters: age of asthma onset, body mass index, pack-years of smoking, forced expiratory volume in 1 s (FEV1), FEV1/forced vital capacity, and blood eosinophil counts. Peripheral blood mononuclear cells (PBMCs) were isolated from whole blood samples and RNA was extracted from selected PBMCs. Transcriptional profiles were generated (Illumina NovaSeq 6000) and analyzed using the reference genome and gene annotation files (hg19.refGene.gft). Pathway enrichment analysis was conducted using GO, KEGG, and REACTOME databases. Results: In total, 355 patients with asthma were included in the analysis, of whom 72 (20.3%) had severe asthma. Clustering of the 6 parameters revealed 4 distinct subtypes. Cluster 1 (n = 63) had lower predicted FEV1 % and higher pack-years of smoking and neutrophils in sputum. Cluster 2 (n = 43) had a higher proportion and number of eosinophils in sputum and blood, and severe airflow limitation. Cluster 3 (n = 110) consisted of younger subjects with atopic features. Cluster 4 (n = 139) included features of late-onset mild asthma. Differentially expressed genes between clusters 1 and 2 were related to inflammatory responses and cell activation. Th17 cell differentiation and interferon gamma-mediated signaling pathways were related to neutrophilic inflammation in asthma. Conclusion: Four clinical clusters were differentiated based on clinical parameters and blood eosinophils in adult patients with asthma form the Cohort for Reality and Evolution of Adult Asthma in Korea (COREA) cohort. Gene expression profiling and molecular pathways are novel means of classifying asthma phenotypes.

A novel approach of kinship determination based on the physical length of genetically shared regions of chromosomes.

BACKGROUND: Determination of genetic relatedness between individuals plays a crucial role in resolving numerous civil cases involving familial relationships and in forensic investigation concerning missing persons. Short tandem repeats (STRs), known for their high degree of DNA polymorphism, have traditionally been the primary choice of DNA markers in genetic testing, but their application for kinships testing is limited to cases involving close kinship. SNPs have emerged as promising supplementary markers for kinship determination. Nevertheless, the challenging remains in discriminating between third-degree or more distant relatives, such as first cousins, using SNPs. OBJECTIVE: To investigate a kinship analysis method for distant degree of familial relationships using high-density SNP data. METHODS: A high-density SNP data from 337 individuals of Korean families using Affymetrix Axiom KORV1.0-96 Array was obtained for this study. SNPs were aligned by chromosomal positions, and identity-by-state (IBS) was determined, and then shared regions as consecutive SNPs with IBS of 1 or 2 were investigated. The physical lengths of these IBS segments were measured and summed them to create an Index, as a measure of kinship. RESULTS: The kinship was determined by the physical length of shared chromosomal regions that are distinguished by each kinship. Using this method, the relationship was able be distinguished up to the fourth degree of kinship, and non-relatives were clearly distinguished from true relatives. We also found a potential for this approach to be used universally, regardless of microarray platforms for SNP genotyping and populations. CONCLUSION: This method has a potential to determine the different degree of kinship between individuals and to distinguish non-relatives from true relatives, which can be of great help for practical applications in kinship determination.