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High-efficiency photon color conversion is an approach of great potential for implementing color display. Inspired by the observation of emission enhancement in a nanoscale cavity, a novel technique to fabricate an array of color converter by mixing colloidal quantum dots (QDs) with the electrolyte of an electrochemical etching (ECE) process is demonstrated. In this process, QDs flow with the electrolyte into the etched subsurface nanoscale porous structure (PS) and settle inside. Since the PS formation and hence QD insertion are controlled by the flow path of the applied electric current in the ECE process, this technique can be used for fabricating any graphic pattern. The nanostructure of such a QD-inserted mesa is examined to confirm QD insertion. Although only single-color mesa arrays are demonstrated in this paper, this technique can be used for fabricating a multiple-color mesa array if a QD or a light-emitting nanoparticle of higher thermal stability is available., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Background: Cirrhosis is a major global cause of mortality, and upper gastrointestinal (GI) bleeding significantly increases the mortality risk in these patients. Although scoring systems such as the Child-Pugh score and the Model for End-stage Liver Disease evaluate the severity of cirrhosis, none of these systems specifically target the risk of mortality in patients with upper GI bleeding. In this study, we constructed machine learning (ML) models for predicting mortality in patients with cirrhosis and upper GI bleeding, particularly in emergency settings, to achieve early intervention and improve outcomes., Methods: In this retrospective study, we analyzed the electronic health records of adult patients with cirrhosis who presented at an emergency department (ED) with GI bleeding between 2001 and 2019. Data were divided into training and testing sets at a ratio of 90:10. The ability of three ML models-a linear regression model, an XGBoost (XGB) model, and a three-layer neural network model-to predict mortality in the patients was evaluated., Results: A total of 16,025 patients with cirrhosis and 32,826 ED visits for upper GI bleeding were included in the study. The in-hospital and ED mortality rates were 11.2% and 2.2%, respectively. The XGB model exhibited the highest performance in predicting both in-hospital and ED mortality (area under the receiver operating characteristic curve: 0.866 and 0.861, respectively). International normalized ratio, renal function, red blood cell distribution width, age, and white blood cell count were the strongest predictors in all the ML models. The median ED length of stay for the ED mortality group was 17.54 h (7.16-40.01 h)., Conclusions: ML models can be used to predict mortality in patients with cirrhosis and upper GI bleeding. Of the three models, the XGB model exhibits the highest performance. Further research is required to determine the actual efficacy of our ML models in clinical settings.

Epilepsy is a major neurological disorder characterized by recurrent, spontaneous seizures. For patients with drug-resistant epilepsy, treatments include neurostimulation or surgical removal of the epileptogenic zone (EZ), the brain region responsible for seizure generation. Precise targeting of the EZ requires reliable biomarkers. Spike ripples - high-frequency oscillations that co-occur with large amplitude epileptic discharges - have gained prominence as a candidate biomarker. However, spike ripple detection remains a challenge. The gold-standard approach requires an expert manually visualize and interpret brain voltage recordings, which limits reproducibility and high-throughput analysis. Addressing these limitations requires more objective, efficient, and automated methods for spike ripple detection, including approaches that utilize deep neural networks. Despite advancements, dataset heterogeneity and scarcity severely limit machine learning performance. Our study explores long-short term memory (LSTM) neural network architectures for spike ripple detection, leveraging data augmentation to improve classifier performance. We highlight the potential of combining training on augmented and in vivo data for enhanced spike ripple detection and ultimately improving diagnostic accuracy in epilepsy treatment., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

In severe epileptic encephalopathies, epileptic activity contributes to progressive cognitive dysfunction. Epileptic encephalopathies share the trait of spike-wave activation during non-REM sleep (EE-SWAS), a sleep stage dominated by sleep spindles, which are brain oscillations known to coordinate offline memory consolidation. Epileptic activity has been proposed to hijack the circuits driving these thalamocortical oscillations, thereby contributing to cognitive impairment. Using a unique dataset of simultaneous human thalamic and cortical recordings in subjects with and without EE-SWAS, we provide evidence for epileptic spike interference of thalamic sleep spindle production in patients with EE-SWAS. First, we show that epileptic spikes and sleep spindles are both predicted by slow oscillations during stage two sleep (N2), but at different phases of the slow oscillation. Next, we demonstrate that sleep-activated cortical epileptic spikes propagate to the thalamus (thalamic spike rate increases after a cortical spike, P ≈ 0). We then show that epileptic spikes in the thalamus increase the thalamic spindle refractory period (P ≈ 0). Finally, we show that in three patients with EE-SWAS, there is a downregulation of sleep spindles for 30 s after each thalamic spike (P < 0.01). These direct human thalamocortical observations support a proposed mechanism for epileptiform activity to impact cognitive function, wherein epileptic spikes inhibit thalamic sleep spindles in epileptic encephalopathy with spike and wave activation during sleep., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Objective: Although >30% of epilepsy patients have drug-resistant epilepsy (DRE), typically those with generalized or multifocal disease have not traditionally been considered surgical candidates. Responsive neurostimulation (RNS) of the centromedian (CM) region of the thalamus now appears to be a promising therapeutic option for this patient population. We present outcomes following CM RNS for 13 patients with idiopathic generalized epilepsy (IGE) and eight with multifocal onsets that rapidly generalize to bilateral tonic-clonic (focal to bilateral tonic-clonic [FBTC]) seizures., Methods: A retrospective review of all patients undergoing bilateral CM RNS by the senior author through July 2022 were reviewed. Electrodes were localized and volumes of tissue activation were modeled in Lead-DBS. Changes in patient seizure frequency were extracted from electronic medical records., Results: Twenty-one patients with DRE underwent bilateral CM RNS implantation. For 17 patients with at least 1 year of postimplantation follow-up, average seizure reduction from preoperative baseline was 82.6% (SD = 19.0%, median = 91.7%), with 18% of patients Engel class 1, 29% Engel class 2, 53% Engel class 3, and 0% Engel class 4. There was a trend for average seizure reduction to be greater for patients with nonlesional FBTC seizures than for other patients. For patients achieving at least Engel class 3 outcome, median time to worthwhile seizure reduction was 203.5 days (interquartile range = 110.5-343.75 days). Patients with IGE with myoclonic seizures had a significantly shorter time to worthwhile seizure reduction than other patients. The surgical targeting strategy evolved after the first four subjects to achieve greater anatomic accuracy., Significance: Patients with both primary and rapidly generalized epilepsy who underwent CM RNS experienced substantial seizure relief. Subsets of these patient populations may particularly benefit from CM RNS. The refinement of lead targeting, tuning of RNS system parameters, and patient selection are ongoing areas of investigation., (© 2024 International League Against Epilepsy.)

Consistent observations across recording modalities, experiments, and neural systems find neural field spectra with 1/f-like scaling, eliciting many alternative theories to explain this universal phenomenon. We show that a general dynamical system with stochastic drive and minimal assumptions generates 1/f-like spectra consistent with the range of values observed in vivo without requiring a specific biological mechanism or collective critical behavior., (© 2024 Massachusetts Institute of Technology.)

We evaluated whether spike ripples, the combination of epileptiform spikes and ripples, provide a reliable and improved biomarker for the epileptogenic zone compared with other leading interictal biomarkers in a multicentre, international study. We first validated an automated spike ripple detector on intracranial EEG recordings. We then applied this detector to subjects from four centres who subsequently underwent surgical resection with known 1-year outcomes. We evaluated the spike ripple rate in subjects cured after resection [International League Against Epilepsy Class 1 outcome (ILAE 1)] and those with persistent seizures (ILAE 2-6) across sites and recording types. We also evaluated available interictal biomarkers: spike, spike-gamma, wideband high frequency oscillation (HFO, 80-500 Hz), ripple (80-250 Hz) and fast ripple (250-500 Hz) rates using previously validated automated detectors. The proportion of resected events was computed and compared across subject outcomes and biomarkers. Overall, 109 subjects were included. Most spike ripples were removed in subjects with ILAE 1 outcome (P < 0.001), and this was qualitatively observed across all sites and for depth and subdural electrodes (P < 0.001 and P < 0.001, respectively). Among ILAE 1 subjects, the mean spike ripple rate was higher in the resected volume (0.66/min) than in the non-removed tissue (0.08/min, P < 0.001). A higher proportion of spike ripples were removed in subjects with ILAE 1 outcomes compared with ILAE 2-6 outcomes (P = 0.06). Among ILAE 1 subjects, the proportion of spike ripples removed was higher than the proportion of spikes (P < 0.001), spike-gamma (P < 0.001), wideband HFOs (P < 0.001), ripples (P = 0.009) and fast ripples (P = 0.009) removed. At the individual level, more subjects with ILAE 1 outcomes had the majority of spike ripples removed (79%, 38/48) than spikes (69%, P = 0.12), spike-gamma (69%, P = 0.12), wideband HFOs (63%, P = 0.03), ripples (45%, P = 0.01) or fast ripples (36%, P < 0.001) removed. Thus, in this large, multicentre cohort, when surgical resection was successful, the majority of spike ripples were removed. Furthermore, automatically detected spike ripples localize the epileptogenic tissue better than spikes, spike-gamma, wideband HFOs, ripples and fast ripples., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Background/aims: The performance of machine learning (ML) in predicting the outcomes of patients with hepatocellular carcinoma (HCC) remains uncertain. We aimed to develop risk scores using conventional methods and ML to categorize early-stage HCC patients into distinct prognostic groups., Methods: The study retrospectively enrolled 1,411 consecutive treatment-naïve patients with the Barcelona Clinic Liver Cancer (BCLC) stage 0 to A HCC from 2012 to 2021. The patients were randomly divided into a training cohort (n=988) and validation cohort (n=423). Two risk scores (CATS-IF and CATS-INF) were developed to predict overall survival (OS) in the training cohort using the conventional methods (Cox proportional hazards model) and ML-based methods (LASSO Cox regression), respectively. They were then validated and compared in the validation cohort., Results: In the training cohort, factors for the CATS-IF score were selected by the conventional method, including age, curative treatment, single large HCC, serum creatinine and alpha-fetoprotein levels, fibrosis-4 score, lymphocyte-tomonocyte ratio, and albumin-bilirubin grade. The CATS-INF score, determined by ML-based methods, included the above factors and two additional ones (aspartate aminotransferase and prognostic nutritional index). In the validation cohort, both CATS-IF score and CATS-INF score outperformed other modern prognostic scores in predicting OS, with the CATSINF score having the lowest Akaike information criterion value. A calibration plot exhibited good correlation between predicted and observed outcomes for both scores., Conclusion: Both the conventional Cox-based CATS-IF score and ML-based CATS-INF score effectively stratified patients with early-stage HCC into distinct prognostic groups, with the CATS-INF score showing slightly superior performance.

Background and Objectives: Sleep spindles are prominent thalamocortical brain oscillations during sleep that have been mechanistically linked to sleep-dependent memory consolidation in animal models and healthy controls. Sleep spindles are decreased in Rolandic epilepsy and related sleep-activated epileptic encephalopathies. We investigate the relationship between sleep spindle deficits and deficient sleep dependent memory consolidation in children with Rolandic epilepsy., Methods: In this prospective case-control study, children were trained and tested on a validated probe of memory consolidation, the motor sequence task (MST). Sleep spindles were measured from high-density EEG during a 90-minute nap opportunity between MST training and testing using a validated automated detector., Results: Twenty-three children with Rolandic epilepsy (14 with resolved disease), and 19 age- and sex-matched controls were enrolled. Children with active Rolandic epilepsy had decreased memory consolidation compared to control children (p=0.001, mean percentage reduction: 25.7%, 95% CI [10.3, 41.2]%) and compared to children with resolved Rolandic epilepsy (p=0.007, mean percentage reduction: 21.9%, 95% CI [6.2, 37.6]%). Children with active Rolandic epilepsy had decreased sleep spindle rates in the centrotemporal region compared to controls (p=0.008, mean decrease 2.5 spindles/min, 95% CI [0.7, 4.4] spindles/min). Spindle rate positively predicted sleep-dependent memory consolidation (p=0.004, mean MST improvement of 3.9%, 95% CI [1.3, 6.4]%, for each unit increase in spindles per minute)., Discussion: Children with Rolandic epilepsy have a sleep spindle deficit during the active period of disease which predicts deficits in sleep dependent memory consolidation. This finding provides a mechanism and noninvasive biomarker to aid diagnosis and therapeutic discovery for cognitive dysfunction in Rolandic epilepsy and related sleep activated epilepsy syndromes.

Purpose: To review the current and future approaches to investigating the intraocular immune response in human uveitis., Design: Perspective., Methods: Review of currently available methods for investigating the immune response in ocular tissues and fluids in patients with intraocular inflammation/ uveitis. The advantages and disadvantages of human studies have been compared to those of animal models of uveitis., Results: Animal models, while being excellent tools for mechanistic studies, do not replicate the clinical and immunologic heterogeneity of human uveitis. Opportunities for immunological studies in human uveitis are mostly limited to histological studies, or sampling of intraocular fluids and peripheral blood. Histopathological studies can be enhanced by revisiting published historical data, tissue repositories, or autopsy specimens. Intraocular fluids can be investigated by a variety of techniques. Among these, flow cytometry and single-cell RNA sequencing (scRNAseq) provide single-cell resolution. While the current technology is costly and labor-intensive, scRNAseq is less limited by the low cellular yield from intraocular fluids and allows unbiased immune profiling enabling discovery of new cellular subsets. Immunological phenotypes uncovered from human data can be further investigated in animal studies., Conclusion: The diversity of the intraocular immune response in uveitis patients remains challenging but can be studied by multiple techniques including histopathology, flow cytometry, and scRNAseq. Human data can be combined with animal studies for translating uveitis research into novel therapies., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Introduction: Field factors play more important roles in predicting the outcomes of patients compared with tumor factors in early-stage hepatocellular carcinoma (HCC). However, the prognostic ability of noninvasive serum marker scores for hepatic fibrosis and liver functional reserve on very early-stage HCC is still not yet determined. We aimed to investigate the performance of these serum marker scores in predicting the prognoses of patients with very early-stage HCC., Methods: A total of 446 patients with very early-stage HCC from 2012 to 2022 were retrospectively enrolled. Serum biomarkers and prognostic scores determining overall survival (OS) were analyzed by Cox proportional hazards model. We compared the Akaike information criterion among the prognostic nutritional index (PNI), aspartate aminotransferase-to-platelet ratio index, albumin-bilirubin (ALBI) score, EZ (easy)-ALBI score, modified ALBI score, fibrosis-4 score, and lymphocyte-to-monocyte ratio to determine the predictability on the OS., Results: After a median follow-up of 41.0 months (interquartile range 36.9-45.1 months), 81 patients died, with a 5-year OS rate of 71.0%. Among the noninvasive serum marker scores, PNI had the best performance in predicting the OS with the lowest Akaike information criterion (846.407) compared with other scores. Moreover, we stratified the patients into high-risk (PNI <45) and low-risk (PNI ≥45) groups. It showed that the 5-year OS rates were 83.4% and 60.8% in the low-risk and high-risk PNI groups, respectively ( P < 0.001)., Discussion: PNI had the best performance in predicting the OS for patients with very early-stage HCC., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of The American College of Gastroenterology.)

Background: The introduction of direct-acting antiviral agents (DAAs) has revolutionized the therapeutic landscape of chronic hepatitis C (CHC), however real-world data on the risk factors of hepatocellular carcinoma (HCC) recurrence following DAA treatment in CHC-HCC patients are limited in Taiwan. We aimed to evaluate the therapeutic efficacy of DAAs in Taiwanese patients with prior hepatitis C virus (HCV)-induced HCC and identify the posttreatment risk factors for HCC recurrence., Methods: Between January 2017 and August 2021, 208 CHC-HCC patients underwent DAA treatment at Taipei Veterans General Hospital. Among them, 94 patients met the inclusion criteria (Barcelona clinic liver cancer [BCLC] stage 0/A after treatment with complete radiological response) for analysis. Comprehensive demographic, clinical, and laboratory data were collected before and after DAA treatment. The primary outcome was HCC recurrence post-DAA treatment, and independent variables were assessed using multivariate Cox proportional hazards models., Results: The mean age of the enrolled patients was 75.9 ± 8.9 years; 44.7% were male, and 94.7% were Child-Pugh class A. Before DAA treatment, 31.9% experienced HCC recurrence. The median follow-up after DAA treatment was 22.1 months (interquartile range, 8.6-35.9 months). After treatment, 95.7% of the patients achieved a sustained virological response (SVR 12 ), but HCC recurrence occurred in 54.3%. Cumulative HCC recurrence rates after treatment were 31.1% at 1 year, 57.3% at 3 years, and 68.5% at up to 5.69 years. Multivariate analysis revealed that prior HCC recurrence before DAA treatment (hazard ratio [HR] = 3.15, p = 0.001), no SVR 12 after treatment (HR = 6.829, p = 0.016), 12-week posttreatment alpha-fetoprotein (AFP) level >10 ng/mL (HR = 2.34, p = 0.036), and BCLC A3 lesions (two or three nodules without any tumor exceeding 3 cm) (HR = 2.31, p = 0.039) were independent risk factors for HCC recurrence. We further developed a risk stratification system based on these significant independent factors., Conclusion: This investigation underscores the critical influence of factors such as prior HCC recurrence, successful attainment of SVR 12 , posttreatment AFP level, and specific tumor characteristics in determining the risk of HCC recurrence after treatment with DAAs. Our proposed innovative risk stratification system may not only contribute to enhanced personalized care but also holds the potential to optimize treatment outcomes., Competing Interests: Conflicts of interest: Dr. Chien-Wei Su, Dr. Fa-Yauh Lee, Dr. Yi-Hsiang Huang, and Dr. Ming-Chih Hou, editorial board members at Journal of the Chinese Medical Association , have no roles in the peer review process of or decision to publish this article. The other authors declare that they have no conflicts of interest related to the subject matter or materials discussed in this article., (Copyright © 2024, the Chinese Medical Association.)

Machine learning (ML) models for predicting 72-hour unscheduled return visits (URVs) for patients with abdominal pain in the emergency department (ED) were developed in a previous study. This study refined the data to adjust previous prediction models and evaluated the model performance in future data validation during the COVID-19 era. We aimed to evaluate the practicality of the ML models and compare the URVs before and during the COVID-19 pandemic. We used electronic health records from Chang Gung Memorial Hospital from 2018 to 2019 as a training dataset, and various machine learning models, including logistic regression (LR), random forest (RF), extreme gradient boosting (XGB), and voting classifier (VC) were developed and subsequently used to validate against the 2020 to 2021 data. The models highlighted several determinants for 72-hour URVs, including patient age, prior ER visits, specific vital signs, and medical interventions. The LR, XGB, and VC models exhibited the same AUC of 0.71 in the testing set, whereas the VC model displayed a higher F1 score (0.21). The XGB model demonstrated the highest specificity (0.99) and precision (0.64) but the lowest sensitivity (0.01). Among these models, the VC model showed the most favorable, balanced, and comprehensive performance. Despite the promising results, the study illuminated challenges in predictive modeling, such as the unforeseen influences of global events, such as the COVID-19 pandemic. These findings not only highlight the significant potential of machine learning in augmenting emergency care but also underline the importance of iterative refinement in response to changing real-world conditions., Competing Interests: The authors have no conflicts of interest to disclose., (Copyright © 2024 the Author(s). Published by Wolters Kluwer Health, Inc.)

Purpose: Periodontitis, a ubiquitous severe gum disease affecting the teeth and surrounding alveolar bone, can heighten systemic inflammation. We investigated the association between very severe periodontitis and early biomarkers of age-related macular degeneration (AMD), in individuals with no eye disease., Design: Cross-sectional analysis of the prospective community-based cohort United Kingdom (UK) Biobank., Participants: Sixty-seven thousand three hundred eleven UK residents aged 40 to 70 years recruited between 2006 and 2010 underwent retinal imaging., Methods: Macular-centered OCT images acquired at the baseline visit were segmented for retinal sublayer thicknesses. Very severe periodontitis was ascertained through a touchscreen questionnaire. Linear mixed effects regression modeled the association between very severe periodontitis and retinal sublayer thicknesses, adjusting for age, sex, ethnicity, socioeconomic status, alcohol consumption, smoking status, diabetes mellitus, hypertension, refractive error, and previous cataract surgery., Main Outcome Measures: Photoreceptor layer (PRL) and retinal pigment epithelium-Bruch's membrane (RPE-BM) thicknesses., Results: Among 36 897 participants included in the analysis, 1571 (4.3%) reported very severe periodontitis. Affected individuals were older, lived in areas of greater socioeconomic deprivation, and were more likely to be hypertensive, diabetic, and current smokers (all P < 0.001). On average, those with very severe periodontitis were hyperopic (0.05 ± 2.27 diopters) while those unaffected were myopic (-0.29 ± 2.40 diopters, P < 0.001). Following adjusted analysis, very severe periodontitis was associated with thinner PRL (-0.55 μm, 95% confidence interval [CI], -0.97 to -0.12; P  = 0.022) but there was no difference in RPE-BM thickness (0.00 μm, 95% CI, -0.12 to 0.13; P  = 0.97). The association between PRL thickness and very severe periodontitis was modified by age ( P < 0.001). Stratifying individuals by age, thinner PRL was seen among those aged 60 to 69 years with disease (-1.19 μm, 95% CI, -1.85 to -0.53; P < 0.001) but not among those aged < 60 years., Conclusions: Among those with no known eye disease, very severe periodontitis is statistically associated with a thinner PRL, consistent with incipient AMD. Optimizing oral hygiene may hold additional relevance for people at risk of degenerative retinal disease., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (© 2024 by the American Academy of Ophthalmology.)

The study of cell signaling within tissues can be enhanced using highly multiplexed immunohistochemistry to localize the presence and spatial distribution of numerous pathways of interest simultaneously. Additional data can also be gained by placing the identified proteins into the context of adjacent structures, stroma, and interacting partners. Here, we outline a protocol for using the recently described IBEX method on tissues. This is an open and simple cyclic immunohistochemistry approach suited to this application. We describe a simplified protocol and provide guidance on the method, using a 12-marker panel on human retina to demonstrate the approach., (© 2024. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Epileptic encephalopathies are defined by the presence of frequent epileptiform activity that causes neurodevelopmental slowing or regression. Here, we review evidence that epilepsy surgery improves neurodevelopment in children with epileptic encephalopathies. We describe an example patient with epileptic encephalopathy without drug refractory seizures, who underwent successful diagnostic and therapeutic surgeries. In patients with epileptic encephalopathy, cognitive improvement alone is a sufficient indication to recommend surgical intervention in experienced centers., Competing Interests: Disclosure Dr C.J. Chu is a coinvestigator in the Neuropace Nautilus trial. Dr C.J. Chu receives research support from NIHNINDS, United States, NIHNIMH, United States, and the Epilepsy Foundation, United States New England and is a coinvestigator in the Neuropace Nautilus trial. Dr C.J. Chu has served as a consultant for Biogen Inc, Ionis Pharmaceuticals, Sun Pharmaceuticals, and Novartis within the last 2 years. Dr R.M. Richardson is PI in the Neuropace Nautilus trial and coinvestigator in the RNS System LGS Feasibility Study. Dr R.M. Richardson receives research support from NIH NINDS. Dr K.T. Kahle receives research support from NIH NINDS., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Introduction: Hepatitis B virus (HBV) infection is prevalent in Asia including Taiwan. We retrospectively evaluated the risk of HBV reactivation and clinical outcomes in HBV+ and HBV- kidney transplant recipients., Methods: Patients who underwent kidney transplantation between January 2004 and December 2021 were reviewed. The outcomes of interest included risks of HBV reactivation and patient/graft survival., Results: We identified 337 patients (47.5 ± 12 years) in our final cohort. Fifty-two (15.4%) had hepatitis B surface antigen (HBsAg) positive at the time of transplantation. Seventeen developed viral reactivations, with 41.2% of them accompanied by active hepatitis. The graft survival, acute rejection rate, and cancer development after kidney transplantation did not differ in terms of HBsAg status. The Cox multivariate analysis indicated the HBV reactivation risk was increased by a lack of pretransplant anti-HBV medication (hazard ratio [HR], 5.95; 95% confidence interval [CI], 1.31-27.02; p = 0.021) or an absence of lifelong antiviral therapy (HR: 3.14; 95% CI: 1.01-9.74; p = 0.047)., Conclusion: Individuals, independent of HBsAg status, had similar prognosis in terms of patient and graft survival, acute rejection rate, and cancer development. The absence of either pretransplant anti-HBV medication or lifelong antiviral therapy was significantly associated with an increased risk of HBV reactivation., (© 2024 S. Karger AG, Basel.)