A virtual memory CD8+ T cell-originated subset causes alopecia areata through innate-like cytotoxicity.

Virtual memory T (TVM) cells are a T cell subtype with a memory phenotype but no prior exposure to foreign antigen. Although TVM cells have antiviral and antibacterial functions, whether these cells can be pathogenic effectors of inflammatory disease is unclear. Here we identified a TVM cell-originated CD44super-high(s-hi)CD49dlo CD8+ T cell subset with features of tissue residency. These cells are transcriptionally, phenotypically and functionally distinct from conventional CD8+ TVM cells and can cause alopecia areata. Mechanistically, CD44s-hiCD49dlo CD8+ T cells could be induced from conventional TVM cells by interleukin (IL)-12, IL-15 and IL-18 stimulation. Pathogenic activity of CD44s-hiCD49dlo CD8+ T cells was mediated by NKG2D-dependent innate-like cytotoxicity, which was further augmented by IL-15 stimulation and triggered disease onset. Collectively, these data suggest an immunological mechanism through which TVM cells can cause chronic inflammatory disease by innate-like cytotoxicity.

2D Materials in Flexible Electronics: Recent Advances and Future Prospectives.

Flexible electronics have recently gained considerable attention due to their potential to provide new and innovative solutions to a wide range of challenges in various electronic fields. These electronics require specific material properties and performance because they need to be integrated into a variety of surfaces or folded and rolled for newly formatted electronics. Two-dimensional (2D) materials have emerged as promising candidates for flexible electronics due to their unique mechanical, electrical, and optical properties, as well as their compatibility with other materials, enabling the creation of various flexible electronic devices. This article provides a comprehensive review of the progress made in developing flexible electronic devices using 2D materials. In addition, it highlights the key aspects of materials, scalable material production, and device fabrication processes for flexible applications, along with important examples of demonstrations that achieved breakthroughs in various flexible and wearable electronic applications. Finally, we discuss the opportunities, current challenges, potential solutions, and future investigative directions about this field.

Analysis of the human endogenous coregulator complexome.

Elucidation of endogenous cellular protein-protein interactions and their networks is most desirable for biological studies. Here we report our study of endogenous human coregulator protein complex networks obtained from integrative mass spectrometry-based analysis of 3290 affinity purifications. By preserving weak protein interactions during complex isolation and utilizing high levels of reciprocity in the large dataset, we identified many unreported protein associations, such as a transcriptional network formed by ZMYND8, ZNF687, and ZNF592. Furthermore, our work revealed a tiered interplay within networks that share common proteins, providing a conceptual organization of a cellular proteome composed of minimal endogenous modules (MEMOs), complex isoforms (uniCOREs), and regulatory complex-complex interaction networks (CCIs). This resource will effectively fill a void in linking correlative genomic studies with an understanding of transcriptional regulatory protein functions within the proteome for formulation and testing of future hypotheses.

Coherent many-body exciton in van der Waals antiferromagnet NiPS3.

An exciton is the bosonic quasiparticle of electron-hole pairs bound by the Coulomb interaction1. Bose-Einstein condensation of this exciton state has long been the subject of speculation in various model systems2,3, and examples have been found more recently in optical lattices and two-dimensional materials4-9. Unlike these conventional excitons formed from extended Bloch states4-9, excitonic bound states from intrinsically many-body localized states are rare. Here we show that a spin-orbit-entangled exciton state appears below the Néel temperature of 150 kelvin in NiPS3, an antiferromagnetic van der Waals material. It arises intrinsically from the archetypal many-body states of the Zhang-Rice singlet10,11, and reaches a coherent state assisted by the antiferromagnetic order. Using configuration-interaction theory, we determine the origin of the coherent excitonic excitation to be a transition from a Zhang-Rice triplet to a Zhang-Rice singlet. We combine three spectroscopic tools-resonant inelastic X-ray scattering, photoluminescence and optical absorption-to characterize the exciton and to demonstrate an extremely narrow excitonic linewidth below 50 kelvin. The discovery of the spin-orbit-entangled exciton in antiferromagnetic NiPS3 introduces van der Waals magnets as a platform to study coherent many-body excitons.

Influence of Procedural Volume on the Outcome of Gastric Endoscopic Submucosal Dissection: A Nationwide Population-Based Study Using Administrative Data.

BACKGROUND & AIMS: Endoscopic submucosal dissection (ESD) is a well-established treatment modality for gastric neoplasms. We aimed to investigate the effect of procedural volume on the outcome of ESD for gastric cancer or adenoma. METHODS: In this population-based cohort study, patients who underwent ESD for gastric cancer or adenoma from November 2011 to December 2017 were identified using the Korean National Health Insurance Service database. Operational definitions to identify the target population and post-procedural complications were created using diagnosis and procedure codes and were validated using hospital medical record data. Outcomes included hemorrhage, perforation, pneumonia, 30-day mortality, a composite outcome comprising all of these adverse outcomes, and additional resection. Hospital volume was categorized into 3 groups based on the results of the threshold analysis: high-, medium-, low-volume centers (HVCs, MVCs, and LVCs, respectively). Inverse probability of treatment weighting analysis was applied to enhance comparability across the volume groups. RESULTS: There were 94,246 procedures performed in 88,687 patients during the study period. There were 5886 composite events including 4925 hemorrhage, 447 perforation, and 703 pneumonia cases. There were significant differences in ESD-related adverse outcomes among the 3 hospital volume categories, showing that HVCs and MVCs were associated with a lower risk of a composite outcome than LVCs (inverse probability of treatment-weighted odds ratio [OR], 0.651; 95% CI, 0.521-0.814; inverse probability of treatment-weighted OR, 0.641; 95% CI, 0.534-0.769). Similar tendencies were also shown for hemorrhage, perforation, and pneumonia; however, these were not evident for additional resection. CONCLUSIONS: Procedural volume was closely associated with clinical outcome in patients undergoing ESD for gastric cancer or adenoma.

Effect of Statin Therapy on Cardiovascular Outcome in Stroke Patients with Low Baseline Low-Density Lipoprotein Cholesterol.

OBJECTIVES: To investigate whether post-stroke statin therapy reduces subsequent major vascular events in statin-naïve patients with pretreatment low-density lipoprotein cholesterol (LDL-C) below the recommended target (≤70 mg/dL for atherosclerotic stroke and ≤100 mg/dL for non-atherosclerotic stroke) at stroke onset. METHODS: Patients from an ongoing stroke registry who had an ischemic stroke between 2011 and 2020 were screened. Statin naïve patients with baseline LDL-C below the target were assessed. The effect of post-stroke statin therapy on major vascular events (composite of recurrent stroke, myocardial infarction, and death) was investigated using weighted Cox regression analyses using stabilized inverse probability treatment weighting. RESULTS: The baseline LDL-C level of the 1,858 patients (mean age 67.9 ± 15.3 years, 61.4% men, 13.2% atherosclerotic stroke) included in the study was 75.7 ± 17.0 mg/dL. Statins were prescribed to 1,256 (67.7%) patients (low-to-moderate intensity, 23.5%; high intensity, 44.1%). Post-stroke statin therapy was associated with a lower risk of major vascular events during 1-year follow-up (weighted hazard ratio 0.55, 95% confidence interval 0.42-0.71). In a subgroup of patients who were at very high risk of atherosclerotic cardiovascular disease with LDL-C <55 mg/dL or patients who were not at very high risk of atherosclerotic cardiovascular disease with LDL-C <70 mg/dL, post-stroke statin therapy was also associated with a reduction in major vascular events (weighted hazard ratio 0.45, 95% confidence interval 0.29-0.70). The intensity of the most beneficial statin varied by subtype of stroke. INTERPRETATION: Statin therapy may improve vascular outcomes after ischemic stroke, even in cases of LDL-C below the target without pre-stroke lipid-lowering therapy. ANN NEUROL 2024;95:876-885.

Efficacy of Endovascular Thrombectomy in Acute Basilar Artery Occlusion with Low PC-ASPECTS: A Nationwide Prospective Registry-Based Study.

OBJECTIVE: We evaluated the efficacy of endovascular thrombectomy (EVT) on the functional outcome of patients with acute basilar artery occlusion and low posterior circulation acute stroke prognosis early computed tomography score (PC-ASPECTS). METHODS: We identified patients with acute ischemic stroke due to basilar artery occlusion and PC-ASPECTS of 6 or less, presenting within 24 h between August 2008 and April 2022. The primary outcome was a favorable functional outcome, defined as a modified Rankin Scale (mRS) score of 0-3 at 90 days. The secondary outcomes included an mRS score of 0-2, a favorable shift in the ordinal mRS scale, the occurrence of symptomatic intracranial hemorrhage (sICH), and mortality at 90 days. We compared the outcome of patients treated with EVT and those without EVT, using the inverse probability of treatment weighting methods. RESULTS: Out of 566 patients, 55.5% received EVT. In the EVT group, 106 (33.8%) achieved favorable outcomes, compared to 56 patients (22.2%) in the conservative group. EVT significantly increased the likelihood of achieving a favorable outcome compared to conservative treatment (relative risk [RR] 1.39, 95% confidence interval [CI], 1.11-1.74, p = 0.004). EVT was associated with a favorable shift in the mRS (RR 1.85, 95% CI, 1.49-2.29, p < 0.001) and reduced mortality without an increase in the risk of sICH. It did not have an impact on achieving an mRS score of 0-2. INTERPRETATION: Patients with acute basilar artery occlusion and a PC-ASPECTS of 6 or less might benefit from EVT without an increasing sICH. ANN NEUROL 2024;95:788-799.

Fibrotic Burden in Patients With Hepatitis B Virus-Related Cirrhosis Is Independently Associated With Poorer Kidney Outcomes.

BACKGROUND: We investigated whether higher fibrotic burden was independently associated with poorer kidney outcomes in patients with hepatitis B virus (HBV)-related cirrhosis. METHODS: A total of 1691 patients with radiologically diagnosed HBV-related cirrhosis but without baseline chronic kidney disease (CKD) who underwent transient elastography (TE) between March 2012 and August 2018 were selected. The study outcome was the composite of development of incident CKD, defined as the occurrence of estimated glomerular filtration rate (eGFR) <60 mL/minute/1.73 m2 or proteinuria (≥1+ on dipstick test) on 2 consecutive measurements during follow-up, 50% decline in eGFR or onset of end-stage kidney disease (initiation of chronic dialysis), or all-cause mortality. RESULTS: The mean age was 53.4 years and 1030 (60.9%) patients were male. During 8379 person-years of follow-up (median 5.2 years), 60 (3.5%) patients experienced study outcomes. When stratified according to TE-defined fibrotic burden, multivariable Cox models revealed that risk of poorer kidney outcomes was 2.77-fold (95% confidence interval, 1.16-6.63; P < .001) higher in patients with liver stiffness range indicating cirrhosis (≥11.7 kPa), compared to those without significant liver fibrosis (<7.9 kPa). These associations remained significant even after adjusting for vigorous confounders. CONCLUSIONS: Higher fibrotic burden assessed using TE was independently associated with poorer kidney outcomes in patients with HBV-related cirrhosis.

Automated Prediction of Proximal Middle Cerebral Artery Occlusions in Noncontrast Brain Computed Tomography.

BACKGROUND: Early identification of large vessel occlusion (LVO) in patients with ischemic stroke is crucial for timely interventions. We propose a machine learning-based algorithm (JLK-CTL) that uses handcrafted features from noncontrast computed tomography to predict LVO. METHODS: We included patients with ischemic stroke who underwent concurrent noncontrast computed tomography and computed tomography angiography in seven hospitals. Patients from 5 of these hospitals, admitted between May 2011 and March 2015, were randomly divided into training and internal validation (9:1 ratio). Those from the remaining 2 hospitals, admitted between March 2021 and September 2021, were designated for external validation. From each noncontrast computed tomography scan, we extracted differences in volume, tissue density, and Hounsfield unit distribution between bihemispheric regions (striatocapsular, insula, M1-M3, and M4-M6, modified from the Alberta Stroke Program Early Computed Tomography Score). A deep learning algorithm was used to incorporate clot signs as an additional feature. Machine learning models, including ExtraTrees, random forest, extreme gradient boosting, support vector machine, and multilayer perceptron, as well as a deep learning model, were trained and evaluated. Additionally, we assessed the models' performance after incorporating the National Institutes of Health Stroke Scale scores as an additional feature. RESULTS: Among 2919 patients, 83 were excluded. Across the training (n=2463), internal validation (n=275), and external validation (n=95) datasets, the mean ages were 68.5±12.4, 67.6±13.8, and 67.9±13.6 years, respectively. The proportions of men were 57%, 53%, and 59%, with LVO prevalences of 17.0%, 16.4%, and 26.3%, respectively. In the external validation, the ExtraTrees model achieved a robust area under the curve of 0.888 (95% CI, 0.850-0.925), with a sensitivity of 80.1% (95% CI, 72.0-88.1) and a specificity of 88.6% (95% CI, 84.7-92.5). Adding the National Institutes of Health Stroke Scale score to the ExtraTrees model increased sensitivity (from 80.1% to 92.1%) while maintaining specificity. CONCLUSIONS: Our algorithm provides reliable predictions of LVO using noncontrast computed tomography. By enabling early LVO identification, our algorithm has the potential to expedite the stroke workflow.

Secular Trends in Outcomes and Impact of Novel Oral Anticoagulants in Atrial Fibrillation-Related Acute Ischemic Stroke.

BACKGROUND: Novel oral anticoagulants (NOACs) are currently recommended for the secondary prevention of stroke in patients with acute ischemic stroke (AIS) accompanied by atrial fibrillation (AF). However, the impact of NOACs on clinical outcomes in real-world practice remains ambiguous. This study analyzes the trend of clinical events in patients with AF-related AIS and determines how much the introduction of NOACs has mediated this trend. METHODS: We identified patients with AIS and AF between January 2011 and December 2019 using a multicenter stroke registry. Annual rates of NOAC prescriptions and clinical events within 1 year were evaluated. The primary outcome was a composite of recurrent stroke, myocardial infarction, and all-cause mortality. To assess the mediation effect of NOACs on the relationship between the calendar year and these outcomes, we used natural effect models and conducted exposure-mediator, exposure-outcome, and mediator-outcome analyses using multivariable regression models or accelerated failure time models, adjusting for potential confounders. RESULTS: Among the 12 977 patients with AF-related AIS, 12 500 (average age: 74.4 years; 51.3% male) were analyzed after excluding cases of valvular AF. Between 2011 and 2019, there was a significant decrease in the 1-year incidence of the primary composite outcome from 28.3% to 21.7%, while the NOAC prescription rate increased from 0% to 75.6%. A 1-year increase in the calendar year was independently associated with delayed occurrence of the primary outcome (adjusted time ratio, 1.10 [95% CI, 1.07-1.14]) and increased NOAC prescription (adjusted odds ratio, 2.20 [95% CI, 2.14-2.27]). Increased NOAC prescription was associated with delayed occurrence of the primary outcome (adjusted time ratio, 3.82 [95% CI, 3.17 to 4.61]). Upon controlling for NOAC prescription (mediator), the calendar year no longer influenced the primary outcome (adjusted time ratio, 0.97 [95% CI, 0.94-1.00]). This suggests that NOAC prescription mediates the association between the calendar year and the primary outcome. CONCLUSIONS: Our study highlights a temporal reduction in major clinical events or death in Korean patients with AF-related AIS, mediated by increased NOAC prescription, emphasizing NOAC use in this population.

A machine learning model to predict liver-related outcomes after the functional cure of chronic hepatitis B.

BACKGROUND & AIMS: The risk of hepatocellular carcinoma (HCC) and hepatic decompensation persists after hepatitis B surface antigen (HBsAg) seroclearance. This study aimed to develop and validate a machine learning model to predict the risk of liver-related outcomes (LROs) following HBsAg seroclearance. METHODS: A total of 4,787 consecutive patients who achieved HBsAg seroclearance between 2000 and 2022 were enrolled from six centers in South Korea and a territory-wide database in Hong Kong, comprising the training (n = 944), internal validation (n = 1,102), and external validation (n = 2,741) cohorts. Three machine learning-based models were developed and compared in each cohort. The primary outcome was the development of any LRO, including HCC, decompensation, and liver-related death. RESULTS: During a median follow-up of 55.2 (IQR 30.1-92.3) months, 123 LROs were confirmed (1.1%/person-year) in the Korean cohort. The model with the best predictive performance in the training cohort was selected as the final model (designated as PLAN-B-CURE), which was constructed using a gradient boosting algorithm and seven variables (age, sex, diabetes, alcohol consumption, cirrhosis, albumin, and platelet count). Compared to previous HCC prediction models, PLAN-B-CURE showed significantly superior accuracy in the training cohort (c-index: 0.82 vs. 0.63-0.70, all p <0.001; area under the receiver-operating characteristic curve: 0.86 vs. 0.62-0.72, all p <0.01; area under the precision-recall curve: 0.53 vs. 0.13-0.29, all p <0.01). PLAN-B-CURE showed a reliable calibration function (Hosmer-Lemeshow test p >0.05) and these results were reproduced in the internal and external validation cohorts. CONCLUSION: This novel machine learning model consisting of seven variables provides reliable risk prediction of LROs after HBsAg seroclearance that can be used for personalized surveillance. IMPACT AND IMPLICATIONS: Using large-scale multinational data, we developed a machine learning model to predict the risk of liver-related outcomes (i.e., hepatocellular carcinoma, decompensation, and liver-related death) after the functional cure of chronic hepatitis B (CHB). The new model named PLAN-B-CURE was constructed using seven variables (age, sex, alcohol consumption, diabetes, cirrhosis, serum albumin, and platelet count) and a gradient boosting machine algorithm, and it demonstrated significantly better predictive accuracy than previous models in both the training and validation cohorts. The inclusion of diabetes and significant alcohol intake as model inputs suggests the importance of metabolic risk factor management after the functional cure of CHB. Using seven readily available clinical factors, PLAN-B-CURE, the first machine learning-based model for risk prediction after the functional cure of CHB, may serve as a basis for individualized risk stratification.

Adrenalectomy Improves Body Weight, Glucose, and Blood Pressure Control in Patients With Mild Autonomous Cortisol Secretion: Results of an Randomized Controlled Trial by the Co-work of Adrenal Research (COAR) Study.

OBJECTIVE: To assess the metabolic effects of adrenalectomy in patients with mild autonomous cortisol secretion (MACS). BACKGROUND: Despite retrospective studies showing the association of adrenalectomy for MACS with beneficial metabolic effects, there have been only 2 randomized prospective studies with some limitations to date. METHODS: A prospective, multicenter study randomized 132 patients with adrenal incidentaloma without any features of Cushing syndrome but with serum cortisol >50 nmol/L after a 1 mg overnight dexamethasone suppression test into an adrenalectomy group (n = 66) or control group (n = 66). The primary outcomes were changes in body weight, glucose, and blood pressure (BP). RESULTS: Among the 118 participants who completed the study with a median follow-up duration of 48 months (range: 3-66), the adrenalectomy group (n = 46) exhibited a significantly higher frequency of improved weight control, glucose control, and BP control (32.6%, 45.7%, and 45.7%, respectively) compared with the control group (n = 46; 6.5%, P = 0.002; 15.2%, P = 0.002; and 23.9%, P = 0.029, respectively) after matching for age and sex. Adrenalectomy [odds ratio (OR) = 10.38, 95% CI = 2.09-51.52, P = 0.004], body mass index (OR = 1.39, 95% CI = 1.08-1.79, P = 0.010), and cortisol after a 1 mg overnight dexamethasone suppression test levels (OR = 92.21, 95% CI = 5.30-1604.07, P = 0.002) were identified as independent factors associated with improved weight control. Adrenalectomy (OR = 5.30, 95% CI = 1.63-17.25, P = 0.006) and diabetes (OR = 8.05, 95% CI = 2.34-27.65, P = 0.001) were independently associated with improved glucose control. Adrenalectomy (OR = 2.27, 95% CI = 0.87-5.94, P = 0.095) and hypertension (OR = 10.77, 95% CI = 3.65-31.81, P < 0.001) demonstrated associations with improved BP control. CONCLUSIONS: adrenalectomy improved weight, glucose, and BP control in patients with MACS.

A Liver Stiffness-Based Etiology-Independent Machine Learning Algorithm to Predict Hepatocellular Carcinoma.

BACKGROUND & AIMS: The existing hepatocellular carcinoma (HCC) risk scores have modest accuracy, and most are specific to chronic hepatitis B infection. In this study, we developed and validated a liver stiffness-based machine learning algorithm (ML) for prediction and risk stratification of HCC in various chronic liver diseases (CLDs). METHODS: MLs were trained for prediction of HCC in 5155 adult patients with various CLDs in Korea and further tested in 2 prospective cohorts from Hong Kong (HK) (N = 2732) and Europe (N = 2384). Model performance was assessed according to Harrell's C-index and time-dependent receiver operating characteristic (ROC) curve. RESULTS: We developed the SMART-HCC score, a liver stiffness-based ML HCC risk score, with liver stiffness measurement ranked as the most important among 9 clinical features. The Harrell's C-index of the SMART-HCC score in HK and Europe validation cohorts were 0.89 (95% confidence interval, 0.85-0.92) and 0.91 (95% confidence interval, 0.87-0.95), respectively. The area under ROC curves of the SMART-HCC score for HCC in 5 years was ≥0.89 in both validation cohorts. The performance of SMART-HCC score was significantly better than existing HCC risk scores including aMAP score, Toronto HCC risk index, and 7 hepatitis B-related risk scores. Using dual cutoffs of 0.043 and 0.080, the annual HCC incidence was 0.09%-0.11% for low-risk group and 2.54%-4.64% for high-risk group in the HK and Europe validation cohorts. CONCLUSIONS: The SMART-HCC score is a useful machine learning-based tool for clinicians to stratify HCC risk in patients with CLDs.

Liver Cancer Risk Across Metabolic Dysfunction-Associated Steatotic Liver Disease and/or Alcohol: A Nationwide Study.

INTRODUCTION: New terminologies of metabolic dysfunction-associated steatotic liver disease (MASLD) have been developed. We assessed hepatocellular carcinoma (HCC) risk across MASLD and/or alcohol intake. METHODS: We included participants aged 40-79 years receiving a national health checkup from 2009 to 2010 in the Republic of Korea, classified as follows: non-MASLD, MASLD, MASLD with increased alcohol intake (MetALD; weekly alcohol 210-420 g for male and 140-350 g for female individuals), and alcohol-associated liver disease (ALD; excessive alcohol intake with weekly alcohol ≥420 g for male or ≥350 g for female individuals). The primary outcome was HCC incidence. HCC risk was estimated using multivariable Cox proportional hazard models. RESULTS: Among 6,412,209 participants, proportions of non-MASLD, MASLD, MetALD, and ALD cases were 59.5%, 32.4%, 4.8%, and 3.4%, respectively. During follow-up (median 13.3 years), 27,118 had newly developed HCC. Compared with non-MASLD, the HCC risk increased from MASLD (adjusted hazard ratio [aHR] 1.66, 95% confidence interval [CI] 1.62-1.71) and MetALD (aHR 2.17, 95% CI 2.08-2.27) to ALD (aHR 2.34, 95% CI 2.24-2.45) in a stepwise manner. Furthermore, the older and non-cirrhosis subgroups were more vulnerable to detrimental effects of MASLD and/or alcohol intake, concerning HCC risk. Among the older, female, and cirrhosis subgroups, MetALD poses similar HCC risks as ALD. DISCUSSION: HCC risk increased from MASLD and MetALD to ALD in a stepwise manner, compared with non-MASLD. For an effective primary prevention of HCC, a comprehensive approach should be required to modify both metabolic dysfunction and alcohol intake habit.

Ultrathin Serpentine Insulation Layer Architecture for Ultralow Power Gas Sensor.

Toxic gases have surreptitiously influenced the health and environment of contemporary society with their odorless/colorless characteristics. As a result, a pressing need for reliable and portable gas-sensing devices has continuously increased. However, with their negligence to efficiently microstructure their bulky supportive layer on which the sensing and heating materials are located, previous semiconductor metal-oxide gas sensors have been unable to fully enhance their power efficiency, a critical factor in power-stringent portable devices. Herein, an ultrathin insulation layer with a unique serpentine architecture is proposed for the development of a power-efficient gas sensor, consuming only 2.3 mW with an operating temperature of 300 °C (≈6% of the leading commercial product). Utilizing a mechanically robust serpentine design, this work presents a fully suspended standalone device with a supportive layer thickness of only ≈50 nm. The developed gas sensor shows excellent mechanical durability, operating over 10 000 on/off cycles and ≈2 years of life expectancy under continuous operation. The gas sensor detected carbon monoxide concentrations from 30 to 1 ppm with an average response time of ≈15 s and distinguishable sensitivity to 1 ppm (ΔR/R0 = 5%). The mass-producible fabrication and heating efficiency presented here provide an exemplary platform for diverse power-efficient-related devices.

Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions.

Three-dimensional (3D) hetero-integration technology is poised to revolutionize the field of electronics by stacking functional layers vertically, thereby creating novel 3D circuity architectures with high integration density and unparalleled multifunctionality. However, the conventional 3D integration technique involves complex wafer processing and intricate interlayer wiring. Here we demonstrate monolithic 3D integration of two-dimensional, material-based artificial intelligence (AI)-processing hardware with ultimate integrability and multifunctionality. A total of six layers of transistor and memristor arrays were vertically integrated into a 3D nanosystem to perform AI tasks, by peeling and stacking of AI processing layers made from bottom-up synthesized two-dimensional materials. This fully monolithic-3D-integrated AI system substantially reduces processing time, voltage drops, latency and footprint due to its densely packed AI processing layers with dense interlayer connectivity. The successful demonstration of this monolithic-3D-integrated AI system will not only provide a material-level solution for hetero-integration of electronics, but also pave the way for unprecedented multifunctional computing hardware with ultimate parallelism.

Magnetic resonance elastography-based prediction model for hepatic decompensation in NAFLD: A multicenter cohort study.

BACKGROUND AND AIMS: Magnetic resonance elastography (MRE) is an accurate, continuous biomarker of liver fibrosis; however, the optimal combination with clinical factors to predict the risk of incident hepatic decompensation is unknown. Therefore, we aimed to develop and validate an MRE-based prediction model for hepatic decompensation for patients with NAFLD. APPROACH AND RESULTS: This international multicenter cohort study included participants with NAFLD undergoing MRE from 6 hospitals. A total of 1254 participants were randomly assigned as training (n = 627) and validation (n = 627) cohorts. The primary end point was hepatic decompensation, defined as the first occurrence of variceal hemorrhage, ascites, or HE. Covariates associated with hepatic decompensation on Cox-regression were combined with MRE to construct a risk prediction model in the training cohort and then tested in the validation cohort. The median (IQR) age and MRE values were 61 (18) years and 3.5 (2.5) kPa in the training cohort and 60 (20) years and 3.4 (2.5) kPa in the validation cohort, respectively. The MRE-based multivariable model that included age, MRE, albumin, aspartate aminotransferase, and platelets had excellent discrimination for the 3- and 5-year risk of hepatic decompensation (c-statistic 0.912 and 0.891, respectively) in the training cohort. The diagnostic accuracy remained consistent in the validation cohort with a c-statistic of 0.871 and 0.876 for hepatic decompensation at 3 and 5 years, respectively, and was superior to Fibrosis-4 in both cohorts ( p < 0.05). CONCLUSIONS: An MRE-based prediction model allows for accurate prediction of hepatic decompensation and assists in the risk stratification of patients with NAFLD.

Outcome of untreated low-level viremia versus antiviral therapy-induced or spontaneous undetectable HBV-DNA in compensated cirrhosis.

BACKGROUND: Comparative outcomes of HBV-infected compensated cirrhosis with low-level viremia (LLV) versus maintained virological response (MVR) are unclear. We conducted a large, multiethnic, multicenter study to examine the natural history of LLV versus MVR in compensated cirrhosis. PATIENTS AND METHODS: We enrolled patients with HBV-infected compensated cirrhosis (n=2316) from 19 hospitals in South Korea, Singapore, and Japan. We defined the LLV group as untreated patients with ≥1 detectable serum HBV-DNA (20-2000 IU/mL), Spontaneous-MVR group as untreated patients with spontaneously achieved MVR, and antiviral therapy (AVT)-MVR group as patients achieving AVT-induced MVR. Study end points were HCC or hepatic decompensation. RESULTS: The annual HCC incidence was 2.7/100 person-years (PYs), 2.6/100 PYs, and 3.3/100 PYs for LLV (n=742), Spontaneous-MVR (n=333), and AVT-MVR (n=1241) groups, respectively ( p = 0.81 between LLV vs. Spontaneous-MVR groups and p = 0.37 between LLV vs. AVT-MVR groups). Similarly, the annual decompensation incidence was 1.6/100 PYs, 1.9/100 PYs, and 1.6/100 PYs for LLV, Spontaneous-MVR, and AVT-MVR groups, respectively ( p = 0.40 between LLV vs. Spontaneous-MVR groups and p = 0.83 between LLV vs. AVT-MVR groups). Multivariable analyses determined that HCC and decompensation risks in the LLV group were comparable to those with Spontaneous-MVR and AVT-MVR groups (all p >0.05). Propensity score matching also reproduced similar results for HCC and decompensation risks (all p >0.05 between LLV vs. Spontaneous-MVR groups and between LLV vs. AVT-MVR groups). CONCLUSIONS: Untreated LLV in HBV-infected compensated cirrhosis is not associated with increased risk of disease progression compared with Spontaneous-MVR and AVT-MVR. These data have important implications for practice and further research.

Kinase inhibitor pulldown assay (KiP) for clinical proteomics.

Protein kinases are frequently dysregulated and/or mutated in cancer and represent essential targets for therapy. Accurate quantification is essential. For breast cancer treatment, the identification and quantification of the protein kinase ERBB2 is critical for therapeutic decisions. While immunohistochemistry (IHC) is the current clinical diagnostic approach, it is only semiquantitative. Mass spectrometry-based proteomics offers quantitative assays that, unlike IHC, can be used to accurately evaluate hundreds of kinases simultaneously. The enrichment of less abundant kinase targets for quantification, along with depletion of interfering proteins, improves sensitivity and thus promotes more effective downstream analyses. Multiple kinase inhibitors were therefore deployed as a capture matrix for kinase inhibitor pulldown (KiP) assays designed to profile the human protein kinome as broadly as possible. Optimized assays were initially evaluated in 16 patient derived xenograft models (PDX) where KiP identified multiple differentially expressed and biologically relevant kinases. From these analyses, an optimized single-shot parallel reaction monitoring (PRM) method was developed to improve quantitative fidelity. The PRM KiP approach was then reapplied to low quantities of proteins typical of yields from core needle biopsies of human cancers. The initial prototype targeting 100 kinases recapitulated intrinsic subtyping of PDX models obtained from comprehensive proteomic and transcriptomic profiling. Luminal and HER2 enriched OCT-frozen patient biopsies subsequently analyzed through KiP-PRM also clustered by subtype. Finally, stable isotope labeled peptide standards were developed to define a prototype clinical method. Data are available via ProteomeXchange with identifiers PXD044655 and PXD046169.

Prevalence of Mutations in Mendelian Stroke Genes in Early Onset Stroke Patients.

OBJECTIVE: Heritability of stroke is assumed not to be low, especially in the young stroke population. However, most genetic studies have been performed in highly selected patients with typical clinical or neuroimaging characteristics. We investigated the prevalence of 15 Mendelian stroke genes and explored the relationships between variants and the clinical and neuroimaging characteristics in a large, unselected, young stroke population. METHODS: We enrolled patients aged ≤55 years with stroke or transient ischemic attack from a prospective, nationwide, multicenter stroke registry. We identified clinically relevant genetic variants (CRGVs) in 15 Mendelian stroke genes (GLA, NOTCH3, HTRA1, RNF213, ACVRL1, ENG, CBS, TREX1, ABCC6, COL4A1, FBN1, NF1, COL3A1, MT-TL1, and APP) using a customized, targeted next generation sequencing panel. RESULTS: Among 1,033 patients, 131 (12.7%) had 28 CRGVs, most frequently in RNF213 (n = 59), followed by ABCC6 (n = 53) and NOTCH3 (n = 15). The frequency of CRGVs differed by ischemic stroke subtypes (p < 0.01): the highest in other determined etiology (20.1%), followed by large artery atherosclerosis (13.6%). It also differed between patients aged ≤35 years and those aged 51 to 55 years (17.1% vs 9.3%, p = 0.02). Only 27.1% and 26.7% of patients with RNF213 and NOTCH3 variants had typical neuroimaging features of the corresponding disorders, respectively. Variants of uncertain significance (VUSs) were found in 15.4% patients. INTERPRETATION: CRGVs in 15 Mendelian stroke genes may not be uncommon in the young stroke population. The majority of patients with CRGVs did not have typical features of the corresponding monogenic disorders. Clinical implications of having CRGVs or VUSs should be explored. ANN NEUROL 2023;93:768-782.