Local Ablation for Hepatocellular Carcinoma: 2024 Expert Consensus-Based Practical Recommendations of the Korean Liver Cancer Association.

Local ablation for hepatocellular carcinoma (HCC), a non-surgical option that directly targets and destroys tumor cells, has advanced significantly since the 1990s. Therapies with different energy sources, such as radiofrequency ablation, microwave ablation, and cryoablation, employ different mechanisms to induce tumor necrosis. The precision, safety, and effectiveness of these therapies have increased with advances in guiding technologies and device improvements. Consequently, local ablation has become the first-line treatment for early-stage HCC. The lack of organized evidence and expert opinions regarding patient selection, pre-procedure preparation, procedural methods, swift post-treatment evaluation, and follow-up has resulted in clinicians following varied practices. Therefore, an expert consensus-based practical recommendation for local ablation was developed by a group of experts in radiology and hepatology from the Research Committee of the Korean Liver Cancer Association in collaboration with the Korean Society of Image-guided Tumor Ablation to provide useful information and guidance for performing local ablation and for the pre- and post-treatment management of patients.

AI-enabled ECG index for predicting left ventricular dysfunction in patients with ST-segment elevation myocardial infarction.

Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.

Induction of necrosis symptoms by potato virus X in AGO2-silenced tomato plants associates with reduced transcript accumulation of copper chaperon for superoxide dismutase gene.

RNA silencing is a prominent antiviral defense mechanism in plants. When infected with a virus, RNA silencing-deficient plants tend to show exacerbated symptoms along with increased virus accumulation. However, how symptoms are exacerbated is little understood. Here, we investigated the role of the copper chaperon for superoxide dismutase (CCS) 1, in systemic necrosis observed in Argonaute (AGO)2-silenced tomato plants infected with potato virus X (PVX). While infection with the UK3 strain of PVX induced mosaic symptoms in tomato plants, systemic necrosis occurred when AGO2 was silenced. The CCS1 mRNA level was reduced and micro RNA398 (miR398), which potentially target CCS1, was increased in AGO2-knockdown tomato plants infected with PVX-UK3. Ectopic expression of CCS1 using recombinant PVX attenuated necrosis, suggesting that CCS1 alleviates systemic necrosis by activating superoxide dismutases to scavenge reactive oxygen species. Previous reports have indicated a decrease in the levels of CCS1 and superoxide dismutases along with an increased level of miR398 in plants infected with other viruses and viroids, and thus might represent shared regulatory mechanisms that exacerbate symptoms in these plants.

Replacement of P1 of soybean mosaic virus with P1 of clover yellow vein virus has no impact on virus viability and host specificity.

Potyvirus genomes are expressed as polyproteins that are autocatalytically cleaved to produce 10 to 12 multifunctional proteins, among which P1 is the most variable. It has long been hypothesized that P1 plays role(s) in host adaptation and host specificity. We tested this hypothesis using two phylogenetically distinct potyviruses: soybean mosaic virus (SMV), with a narrow host range, and clover yellow vein virus (ClYVV), with a broader host range. When the full-length P1 cistron of SMV-N was replaced with P1 from ClYVV-No.30, the chimera systemically infected only SMV-N-permissive hosts. Hence, there were no changes in the host range or host specificity of the chimeric viruses. Despite sharing only 20.3% amino acid sequence identity, predicted molecular models of P1 proteins from SMV-N and ClYVV-No.30 showed analogous topologies. These observations suggest that P1 of ClYVV-No.30 can functionally replace P1 of SMV-N. However, the P1 proteins of these two potyviruses are not determinants of host specificity and host range.

Deep learning model integrating radiologic and clinical data to predict mortality after ischemic stroke.

Objective: Most prognostic indexes for ischemic stroke mortality lack radiologic information. We aimed to create and validate a deep learning-based mortality prediction model using brain diffusion weighted imaging (DWI), apparent diffusion coefficient (ADC), and clinical factors. Methods: Data from patients with ischemic stroke who admitted to tertiary hospital during acute periods from 2013 to 2019 were collected and split into training (n = 1109), validation (n = 437), and internal test (n = 654). Data from patients from secondary cardiovascular center was used for external test set (n = 507). The algorithm for predicting mortality, based on DWI and ADC (DLP_DWI), was initially trained. Subsequently, important clinical factors were integrated into this model to create the integrated model (DLP_INTG). The performance of DLP_DWI and DLP_INTG was evaluated by using time-dependent area under the receiver operating characteristic curves (TD AUCs) and Harrell concordance index (C-index) at one-year mortality. Results: The TD AUC of DLP_DWI was 0.643 in internal test set, and 0.785 in the external dataset. DLP_INTG had a higher performance at predicting one-year mortality than premise score in internal dataset (TD- AUC: 0.859 vs. 0.746; p = 0.046), and in external dataset (TD- AUC: 0.876 vs. 0.808; p = 0.007). DLP_DWI and DLP_INTG exhibited strong discrimination for the high-risk group for one-year mortality. Interpretation: A deep learning model using brain DWI, ADC and the clinical factors was capable of predicting mortality in patients with ischemic stroke.

The current state of phage therapy in livestock and companion animals.

In a global context, bacterial diseases caused by pathogenic bacteria have inflicted sustained damage on both humans and animals. Although antibiotics initially appeared to offer an easy treatment for most bacterial infections, the recent rise of multidrug-resistant bacteria, stemming from antibiotic misuse, has prompted regulatory measures to control antibiotic usage. Consequently, various alternatives to antibiotics are being explored, with a particular focus on bacteriophage (phage) therapy for treating bacterial diseases in animals. Animals are broadly categorized into livestock, closely associated with human dietary habits, and companion animals, which have attracted increasing attention. This study highlights phage therapy cases targeting prominent bacterial strains in various animals. In recent years, research on bacteriophages has gained considerable attention, suggesting a promising avenue for developing alternative substances to antibiotics, particularly crucial for addressing challenging bacterial diseases in the future.

Clinical course of patients with hepatocellular carcinoma who experienced radiologic complete response after radioembolization.

Purpose: The purpose of this study is to elucidate the patterns of recurrence of hepatocellular carcinoma and to analyze factors that can predict recurrence after complete response to radioembolization. Materials and methods: A total of 289 consecutive patients who underwent radioembolization for the treatment of hepatocellular carcinoma at a single tertiary center were retrospectively reviewed. Baseline characteristics were collected and compared between the group showing complete response and the group showing noncomplete response. Data on recurrence status, time to recurrence, and the patterns of recurrence among the patients who showed radiologic complete response were collected. The group that maintained complete response and the group that experienced recurrence were compared, and the risk factors affecting recurrence were evaluated by logistic regression analysis. Results: The complete response rate was 24.9% (73/289). Age, sex, tumor markers, maximum tumor diameter, multiplicity, presence of vascular invasion, and target radiation dose were significantly different between the complete response and noncomplete response groups. The recurrence rate after complete response was 38.4% (28/73), and 67.9% (19/28) of recurrences occurred by 8 months after complete response. Eight patients who underwent resection/transplantation after complete response experienced no recurrence. Multiple tumors and a lower target radiation dose were independent risk factors of recurrence after complete response in the multivariate logistic regression. Conclusion: Hepatocellular carcinoma recurrence following complete response after radioembolization is not uncommon and frequently occurs within 1 year after complete response. Multiple tumors and a lower target radiation dose may be risk factors for recurrence.

Thoracic Duct Embolization for Treatment of Chyle Leakage After Thyroidectomy and Neck Dissection.

OBJECTIVE: This study aimed to evaluate the safety and efficacy of intranodal lymphangiography and thoracic duct embolization (TDE) for chyle leakage (CL) after thyroid surgery. MATERIALS AND METHODS: Fourteen patients who underwent intranodal lymphangiography and TDE for CL after thyroid surgery were included in this retrospective study. Among the 14 patients, 13 underwent bilateral total thyroidectomy with neck dissection (central compartment neck dissection [CCND], n = 13; left modified radical neck dissection (MRND), n = 11; bilateral MRND, n = 2), and one patient underwent left hemithyroidectomy with CCND. Ten patients (76.9%) had high-output CL (> 500 mL/d). Before the procedure, surgical intervention was attempted in three patients (thoracic duct ligation, n = 1; lymphatic leakage site ligation, n = 2). Lymphangiographic findings, technical and clinical successes, and complications were analyzed. Technical success was defined as the successful embolization of the thoracic duct after access to the lymphatic duct via the transabdominal route. Clinical success was defined as the resolution of CL or surgical drain removal. RESULTS: On lymphangiography, ethiodized oil leakage near the surgical bed was identified in 12 of 14 patients (85.7%). The technical success rate of TDE was 78.6% (11/14). Transabdominal antegrade access was not feasible due to the inability to visualize the identifiable cisterna chyli or a prominent lumbar lymphatic duct. Among patients who underwent a technically successful TDE, the clinical success rate was 90.1% (10/11). The median time from the procedure to drain removal was 3 days (with a range of 1-13 days) for the 13 patients who underwent surgical drainage. No CL recurrence was observed during the follow-up period (ranging from 2-44 months; median, 8 months). There were no complications, except for one case of chylothorax that developed after TDE. CONCLUSION: TDE appears to be a safe and effective minimally invasive treatment option for CL after thyroid surgery, with acceptable technical and clinical success rates.

Efficacy of intra-arterial lidocaine administration on pain and inflammatory response after uterine artery embolization for symptomatic fibroids.

BACKGROUND: There have been conflicting outcomes regarding the use of lidocaine to reduce pain after uterine artery embolization (UAE). PURPOSE: To investigate the efficacy of intra-arterial lidocaine injection for pain and inflammatory response control within 24 h of UAE for symptomatic uterine fibroids. MATERIAL AND METHODS: Of 1530 patients who underwent UAE for uterine fibroids in 2007-2021, 5 mL of 1% lidocaine was injected into each uterine artery immediately after UAE in 23 patients. A disease-matched control group (n = 23) who did not receive intra-arterial lidocaine was generated from the same registry. The pain score, white blood cell (WBC) count, C-reactive protein (CRP), neutrophil/lymphocyte ratio (NLR), and fentanyl consumption were compared before and after UAE. Complete infarction of the dominant fibroid was assessed using magnetic resonance imaging. RESULTS: Significantly lower WBC count, CRP level, and NLR were noted 24 h after UAE in the lidocaine group. No statistically significant difference was noted in the pain score between groups at 0-24 h. The cumulative fentanyl dose administered during the first 24 h after UAE was not significantly different. After embolization, fibroid-related symptoms resolved in all patients. No significant difference was observed in the rate of complete infarction of the dominant fibroid. CONCLUSION: Lidocaine administration immediately after UAE resulted in a significant reduction in the inflammatory response. However, such a difference in the inflammatory reaction did not contribute to significant reductions in pain scores or fentanyl consumption.

Applicable Machine Learning Model for Predicting Contrast-induced Nephropathy Based on Pre-catheterization Variables.

Objective Contrast agents used for radiological examinations are an important cause of acute kidney injury (AKI). We developed and validated a machine learning and clinical scoring prediction model to stratify the risk of contrast-induced nephropathy, considering the limitations of current classical and machine learning models. Methods This retrospective study included 38,481 percutaneous coronary intervention cases from 23,703 patients in a tertiary hospital. We divided the cases into development and internal test sets (8:2). Using the development set, we trained a gradient boosting machine prediction model (complex model). We then developed a simple model using seven variables based on variable importance. We validated the performance of the models using an internal test set and tested them externally in two other hospitals. Results The complex model had the best area under the receiver operating characteristic (AUROC) curve at 0.885 [95% confidence interval (CI) 0.876-0.894] in the internal test set and 0.837 (95% CI 0.819-0.854) and 0.850 (95% CI 0.781-0.918) in two different external validation sets. The simple model showed an AUROC of 0.795 (95% CI 0.781-0.808) in the internal test set and 0.766 (95% CI 0.744-0.789) and 0.782 (95% CI 0.687-0.877) in the two different external validation sets. This was higher than the value in the well-known scoring system (Mehran criteria, AUROC=0.67). The seven precatheterization variables selected for the simple model were age, known chronic kidney disease, hematocrit, troponin I, blood urea nitrogen, base excess, and N-terminal pro-brain natriuretic peptide. The simple model is available at http://52.78.230.235:8081/Conclusions We developed an AKI prediction machine learning model with reliable performance. This can aid in bedside clinical decision making.

Radiofrequency ablation of subcapsular versus nonsubcapsular hepatocellular carcinomas ≤ 3 cm: analysis of long-term outcomes from two large-volume liver centers.

OBJECTIVES: To compare the safety and efficacy of RFA for single HCCs ≤ 3 cm in subcapsular versus nonsubcapsular locations using a propensity score matched analysis. MATERIALS AND METHODS: This retrospective study included patients with solitary HCCs ≤ 3 cm in size who underwent percutaneous RFA from 2005 to 2015 as initial treatment at two large-volume liver centers. Patients were divided into two groups, consisting of those with subcapsular and nonsubcapsular tumor locations. Complications, local tumor progression (LTP), and overall survival (OS) were compared in these two groups before and after propensity score matching (PSM). RESULTS: The study population consisted of 964 patients (712 men [74%]) of mean age 58.3 years. Of these 964 patients, 561 (58%) had nonsubcapsular and 403 (42%) had subcapsular HCCs. PSM generated 402 pairs of patients. Major complication rate was low, but significantly higher in the subcapscular group (p = 0.047). Rates of technical effectiveness in these two groups were 99% and 98%, respectively (p = 0.315). However, during follow-up, cumulative 1-, 3-, 5-, and 10-year LTP and OS rates did significantly differ in both entire and PSM cohorts, resulting in the latter 8%, 15%, 20%, and 26% in the nonsubcapsular group vs. 13%, 24%, 30%, and 31% in the subcapsular group (p = 0.015), and 99%, 91%, 80%, and 59% vs. 98%, 85%, 73%, and 50% in the two groups (p = 0.004), respectively. CONCLUSION: Rates of major complications, LTP, and OS differed significantly following first-line RFA treatment of single HCCs ≤ 3 cm in favor of the nonsubcapsular locations. CLINICAL RELEVANCE STATEMENT: This large-scale study provides evidence that radiofrequency ablation for small (≤ 3 cm) hepatocellular carcinomas is safer and more effective in nonsubcapsular location than in subcapsular location. KEY POINTS: • There exist conflicting outcomes on the effectiveness of RFA for early HCC depending on tumor location. • Rate of local tumor progression was significantly higher in the subcapsular hepatocellular carcinomas. • Overall survival rate was significantly poorer in the subcapsular hepatocellular carcinomas.

ROMIAE (Rule-Out Acute Myocardial Infarction Using Artificial Intelligence Electrocardiogram Analysis) trial study protocol: a prospective multicenter observational study for validation of a deep learning-based 12-lead electrocardiogram analysis model for detecting acute myocardial infarction in patients visiting the emergency department.

OBJECTIVE: Based on the development of artificial intelligence (AI), an emerging number of methods have achieved outstanding performances in the diagnosis of acute myocardial infarction (AMI) using an electrocardiogram (ECG). However, AI-ECG analysis using a multicenter prospective design for detecting AMI has yet to be conducted. This prospective multicenter observational study aims to validate an AI-ECG model for detecting AMI in patients visiting the emergency department. METHODS: Approximately 9,000 adult patients with chest pain and/or equivalent symptoms of AMI will be enrolled in 18 emergency medical centers in Korea. The AI-ECG analysis algorithm we developed and validated will be used in this study. The primary endpoint is the diagnosis of AMI on the day of visiting the emergency center, and the secondary endpoint is a 30-day major adverse cardiac event. From March 2022, patient registration has begun at centers approved by the institutional review board. DISCUSSION: This is the first prospective study designed to identify the efficacy of an AI-based 12-lead ECG analysis algorithm for diagnosing AMI in emergency departments across multiple centers. This study may provide insights into the utility of deep learning in detecting AMI on electrocardiograms in emergency departments. Trial registration ClinicalTrials.gov identifier: NCT05435391. Registered on June 28, 2022.

Identifying Atrial Fibrillation With Sinus Rhythm Electrocardiogram in Embolic Stroke of Undetermined Source: A Validation Study With Insertable Cardiac Monitors.

BACKGROUND AND OBJECTIVES: Paroxysmal atrial fibrillation (AF) is a major potential cause of embolic stroke of undetermined source (ESUS). However, identifying AF remains challenging because it occurs sporadically. Deep learning could be used to identify hidden AF based on the sinus rhythm (SR) electrocardiogram (ECG). We combined known AF risk factors and developed a deep learning algorithm (DLA) for predicting AF to optimize diagnostic performance in ESUS patients. METHODS: A DLA was developed to identify AF using SR 12-lead ECG with the database consisting of AF patients and non-AF patients. The accuracy of the DLA was validated in 221 ESUS patients who underwent insertable cardiac monitor (ICM) insertion to identify AF. RESULTS: A total of 44,085 ECGs from 12,666 patient were used for developing the DLA. The internal validation of the DLA revealed 0.862 (95% confidence interval, 0.850-0.873) area under the curve (AUC) in the receiver operating curve analysis. In external validation data from 221 ESUS patients, the diagnostic accuracy of DLA and AUC were 0.811 and 0.827, respectively, and DLA outperformed conventional predictive models, including CHARGE-AF, C2HEST, and HATCH. The combined model, comprising atrial ectopic burden, left atrial diameter and the DLA, showed excellent performance in AF prediction with AUC of 0.906. CONCLUSIONS: The DLA accurately identified paroxysmal AF using 12-lead SR ECG in patients with ESUS and outperformed the conventional models. The DLA model along with the traditional AF risk factors could be a useful tool to identify paroxysmal AF in ESUS patients.

Feasibility of Percutaneous Pancreatic Stent Placement in Postoperative Pancreaticojejunostomy Stenosis.

OBJECTIVE: To evaluate the role of percutaneous pancreatic stent placement in postoperative pancreaticojejunostomy stenosis (PJS). MATERIALS AND METHODS: This retrospective single-center study included seven procedures in five patients (four males and one female; median age, 63 years) who underwent percutaneous pancreatic stent placement for postoperative PJS between January 2005 and December 2021. The patients were referred to interventional radiology because of unfavorable anatomy or bowel abnormalities. The pancreatic duct was accessed under ultrasound and/or computed tomography guidance. A stent was placed after balloon dilatation of the PJS. Moreover, plastic stents were placed for the first two procedures, whereas bare-metal stents were used for the remaining five procedures. Technical success was defined as the successful placement of stents for the PJS, meanwhile, clinical success was defined as the normalization of pancreatic enzymes without recurrence of pancreatitis. RESULTS: Pancreatic duct access and stent placement were successfully performed in all patients (technical success rate: 100%). All the procedures initially yielded clinical success. However, recurrence of pancreatitis was observed after two procedures that used plastic stents because of stent migration at 0.3 and 3 months after the procedure. In contrast, no instances of recurrent pancreatitis were noted after metal stent placement for a follow-up duration of 1-36 months. No serious procedure-related adverse events were observed. CONCLUSION: Percutaneous pancreatic stent placement may be a viable option for patients with postoperative PJS in whom an endoscopic approach is not feasible. Metal stents may be considered over plastic stents for the management of PJS, considering the possible lower stent migration and infeasibility of frequent endoscopic stent exchange due to the altered anatomy.

Electrocardiogram-based deep learning model to screen peripartum cardiomyopathy.

BACKGROUND: Peripartum cardiomyopathy, one of the most fatal conditions during delivery, results in heart failure secondary to left ventricular systolic dysfunction. Left ventricular dysfunction can result in abnormalities in electrocardiography. However, the usefulness of electrocardiography in the identification of peripartum cardiomyopathy in pregnant women remains unclear. OBJECTIVE: This study aimed to evaluate the effectiveness of a 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device for screening peripartum cardiomyopathy. STUDY DESIGN: This retrospective cohort study included pregnant women who underwent transthoracic echocardiography between a month before and 5 months after delivery and underwent 12-lead electrocardiography within 30 days of echocardiography between December 2011 and May 2022 at Seoul National University Hospital. The performance of 12-lead electrocardiography-based artificial intelligence/machine learning analysis (AiTiALVSD software; version 1.00.00, which was developed to screen for left ventricular systolic dysfunction in the general population) was evaluated for the identification of peripartum cardiomyopathy. In addition, the performance of another artificial intelligence/machine learning algorithm using only 1-lead electrocardiography to detect left ventricular systolic dysfunction was evaluated in identifying peripartum cardiomyopathy. The results were obtained under a 95% confidence interval and considered significant when P<.05. RESULTS: Among the 14,557 women who delivered during the study period, 204 (1.4%) underwent transthoracic echocardiography a month before and 5 months after delivery. Among them, 12 (5.8%) were diagnosed with peripartum cardiomyopathy. The results showed that AiTiALVSD for 12-lead electrocardiography was highly effective in detecting peripartum cardiomyopathy, with an area under the receiver operating characteristic of 0.979 (95% confidence interval, 0.953-1.000), an area under the precision-recall curve of 0.715 (95% confidence interval, 0.499-0.951), a sensitivity of 0.917 (95% confidence interval, 0.760-1.000), a specificity of 0.927 (95% confidence interval, 0.890-0.964), a positive predictive value of 0.440 (95% confidence interval, 0.245-0.635), and a negative predictive value of 0.994 (95% confidence interval, 0.983-1.000). In addition, a 1-lead (lead I) artificial intelligence/machine learning algorithm showed excellent performance; the area under the receiver operating characteristic, area under the precision-recall curve, sensitivity, specificity, positive predictive value, and negative predictive value were 0.944 (95% confidence interval, 0.895-0.993), 0.520 (95% confidence interval, 0.319-0.801), 0.833 (95% confidence interval, 0.622-1.000), 0.880 (95% confidence interval, 0.834-0.926), 0.303 (95% confidence interval, 0.146-0.460), and 0.988 (95% confidence interval, 0.972-1.000), respectively. CONCLUSION: The 12-lead electrocardiography-based artificial intelligence/machine learning-based software as a medical device (AiTiALVSD) and 1-lead algorithm are noninvasive and effective ways of identifying cardiomyopathies occurring during the peripartum period, and they could potentially be used as highly sensitive screening tools for peripartum cardiomyopathy.

Tricuspid regurgitation: a hidden risk factor for atrial fibrillation related stroke?

Background and purpose: Tricuspid regurgitation (TR) is a common but overlooked valvular disease, and its association with the etiologic subtypes of ischemic stroke is unclear. We explored the relationship between TR and atrial fibrillation (AF) in patients with acute ischemic stroke. Methods: This retrospective analysis of ongoing stroke registry assessed 6,886 consecutive acute ischemic stroke patients who underwent transthoracic echocardiography during their in-hospital care. Multivariable logistic regression models adjusted for age, sex, stroke characteristics, and echocardiographic indices were used to investigate the association between TR and total AF, and newly diagnosed AF during hospitalization and a 1-year follow-up period, respectively. Results: TR was present in 877 (12.7%) patients (mild, 9.9%; moderate, 2.4%; severe, 0.5%). AF was identified in 24.1% (medical history, 11.1%; first detected in the emergency room, 6.6%; newly diagnosed after admission, 6.4%). TR was associated with AF [adjusted odds ratio (aOR) 4.87 (95% confidence interval (CI), 2.63-9.03)], compared with no/trivial TR. The association between TR and AF was consistent regardless of severity (aOR [95% CI], 4.57 [2.63-7.94] for mild and 7.05 [2.57-19.31] for moderate-to-severe TR) or subtype of TR (5.44 [2.91-10.14] for isolated and 3.81 [2.00-7.28] for non-isolated TR). Among the AF-naïve patients at admission, TR was associated with newly diagnosed AF during hospitalization and a 1-year follow-up period (aOR [95% CI], 2.68 [1.81-3.97]). Conclusions: TR is associated with AF in acute ischemic stroke patients regardless of severity and subtypes of TR. TR is also associated with newly diagnosed AF after stroke.

Hypothalamic GABRA5-positive neurons control obesity via astrocytic GABA.

The lateral hypothalamic area (LHA) regulates food intake and energy balance. Although LHA neurons innervate adipose tissues, the identity of neurons that regulate fat is undefined. Here we show that GABRA5-positive neurons in LHA (GABRA5LHA) polysynaptically project to brown and white adipose tissues in the periphery. GABRA5LHA are a distinct subpopulation of GABAergic neurons and show decreased pacemaker firing in diet-induced obesity mouse models in males. Chemogenetic inhibition of GABRA5LHA suppresses fat thermogenesis and increases weight gain, whereas gene silencing of GABRA5 in LHA decreases weight gain. In the diet-induced obesity mouse model, GABRA5LHA are tonically inhibited by nearby reactive astrocytes releasing GABA, which is synthesized by monoamine oxidase B (Maob). Gene silencing of astrocytic Maob in LHA facilitates fat thermogenesis and reduces weight gain significantly without affecting food intake, which is recapitulated by administration of a Maob inhibitor, KDS2010. We propose that firing of GABRA5LHA suppresses fat accumulation and selective inhibition of astrocytic GABA is a molecular target for treating obesity.

Astrocytic scar restricting glioblastoma via glutamate-MAO-B activity in glioblastoma-microglia assembloid.

BACKGROUND: Glial scar formation is a reactive glial response confining injured regions in a central nervous system. However, it remains challenging to identify key factors formulating glial scar in response to glioblastoma (GBM) due to complex glia-GBM crosstalk. METHODS: Here, we constructed an astrocytic scar enclosing GBM in a human assembloid and a mouse xenograft model. GBM spheroids were preformed and then co-cultured with microglia and astrocytes in 3D Matrigel. For the xenograft model, U87-MG cells were subcutaneously injected to the Balb/C nude female mice. RESULTS: Additional glutamate was released from GBM-microglia assembloid by 3.2-folds compared to GBM alone. The glutamate upregulated astrocytic monoamine oxidase-B (MAO-B) activity and chondroitin sulfate proteoglycans (CSPGs) deposition, forming the astrocytic scar and restricting GBM growth. Attenuating scar formation by the glutamate-MAO-B inhibition increased drug penetration into GBM assembloid, while reducing GBM confinement. CONCLUSIONS: Taken together, our study suggests that astrocytic scar could be a critical modulator in GBM therapeutics.

Novel next generation sequencing panel method for the multiple detection and identification of foodborne pathogens in agricultural wastewater.

Detecting and identifying the origins of foodborne pathogen outbreaks is a challenging. The Next-Generation Sequencing (NGS) panel method offers a potential solution by enabling efficient screening and identification of various bacteria in one reaction. In this study, new NGS panel primer sets that target 18 specific virulence factor genes from six target pathogens (Bacillus cereus, Yersinia enterocolitica, Staphylococcus aureus, Vibrio cholerae, Vibrio parahaemolyticus, and Vibrio vulnificus) were developed and optimized. The primer sets were validated for specificity and selectivity through singleplex PCR, confirming the expected amplicon size. Crosscheck and multiplex PCR showed no interference in the primer set or pathogenic DNA mixture. The NGS panel analysis of spiked water samples detected all 18 target genes in a single reaction, with pathogen concentrations ranging from 108 to 105 colony-forming units (CFUs) per target pathogen. Notably, the total sequence read counts from the virulence factor genes showed a positive association with the CFUs per target pathogen. However, the method exhibited relatively low sensitivity and occasional false positive results at low pathogen concentrations of 105 CFUs. To validate the detection and identification results, two sets of quantitative real-time PCR (qPCR) analyses were independently performed on the same spiked water samples, yielding almost the same efficiency and specificity compared to the NGS panel analysis. Comparative statistical analysis and Spearman correlation analysis further supported the similarity of the results by showing a negative association between the NGS panel sequence read counts and qPCR cycle threshold (Ct) values. To enhance NGS panel analysis for better detection, optimization of primer sets and real-time NGS sequencing technology are essential. Nonetheless, this study provides valuable insights into applying NGS panel analysis for multiple foodborne pathogen detection, emphasizing its potential in ensuring food safety.