Whole-genome profiling of DNA methylation and hydroxymethylation identifies distinct regulatory programs among innate lymphocytes.

Innate lymphocytes encompass a diverse array of phenotypic identities with specialized functions. DNA methylation and hydroxymethylation are essential for epigenetic fidelity and fate commitment. The landscapes of these modifications are unknown in innate lymphocytes. Here, we characterized the whole-genome distribution of methyl-CpG and 5-hydroxymethylcytosine (5hmC) in mouse innate lymphoid cell 3 (ILC3), ILC2 and natural killer (NK) cells. We identified differentially methylated regions (DMRs) and differentially hydroxymethylated regions (DHMRs) between ILC and NK cell subsets and correlated them with transcriptional signatures. We associated lineage-determining transcription factors (LDTFs) with demethylation and demonstrated unique patterns of DNA methylation/hydroxymethylation in relationship to open chromatin regions (OCRs), histone modifications and TF-binding sites. We further identified an association between hydroxymethylation and NK cell superenhancers (SEs). Using mice lacking the DNA hydroxymethylase TET2, we showed the requirement for TET2 in optimal production of hallmark cytokines by ILC3s and interleukin-17A (IL-17A) by inflammatory ILC2s. These findings provide a powerful resource for studying innate lymphocyte epigenetic regulation and decode the regulatory logic governing their identity.

CMOS-based cryogenic control of silicon quantum circuits.

The most promising quantum algorithms require quantum processors that host millions of quantum bits when targeting practical applications1. A key challenge towards large-scale quantum computation is the interconnect complexity. In current solid-state qubit implementations, an important interconnect bottleneck appears between the quantum chip in a dilution refrigerator and the room-temperature electronics. Advanced lithography supports the fabrication of both control electronics and qubits in silicon using technology compatible with complementary metal oxide semiconductors (CMOS)2. When the electronics are designed to operate at cryogenic temperatures, they can ultimately be integrated with the qubits on the same die or package, overcoming the 'wiring bottleneck'3-6. Here we report a cryogenic CMOS control chip operating at 3 kelvin, which outputs tailored microwave bursts to drive silicon quantum bits cooled to 20 millikelvin. We first benchmark the control chip and find an electrical performance consistent with qubit operations of 99.99 per cent fidelity, assuming ideal qubits. Next, we use it to coherently control actual qubits encoded in the spin of single electrons confined in silicon quantum dots7-9 and find that the cryogenic control chip achieves the same fidelity as commercial instruments at room temperature. Furthermore, we demonstrate the capabilities of the control chip by programming a number of benchmarking protocols, as well as the Deutsch-Josza algorithm10, on a two-qubit quantum processor. These results open up the way towards a fully integrated, scalable silicon-based quantum computer.

A map of cis-regulatory elements and 3D genome structures in zebrafish.

The zebrafish (Danio rerio) has been widely used in the study of human disease and development, and about 70% of the protein-coding genes are conserved between the two species1. However, studies in zebrafish remain constrained by the sparse annotation of functional control elements in the zebrafish genome. Here we performed RNA sequencing, assay for transposase-accessible chromatin using sequencing (ATAC-seq), chromatin immunoprecipitation with sequencing, whole-genome bisulfite sequencing, and chromosome conformation capture (Hi-C) experiments in up to eleven adult and two embryonic tissues to generate a comprehensive map of transcriptomes, cis-regulatory elements, heterochromatin, methylomes and 3D genome organization in the zebrafish Tübingen reference strain. A comparison of zebrafish, human and mouse regulatory elements enabled the identification of both evolutionarily conserved and species-specific regulatory sequences and networks. We observed enrichment of evolutionary breakpoints at topologically associating domain boundaries, which were correlated with strong histone H3 lysine 4 trimethylation (H3K4me3) and CCCTC-binding factor (CTCF) signals. We performed single-cell ATAC-seq in zebrafish brain, which delineated 25 different clusters of cell types. By combining long-read DNA sequencing and Hi-C, we assembled the sex-determining chromosome 4 de novo. Overall, our work provides an additional epigenomic anchor for the functional annotation of vertebrate genomes and the study of evolutionarily conserved elements of 3D genome organization.

Ultralow-dielectric-constant amorphous boron nitride.

Decrease in processing speed due to increased resistance and capacitance delay is a major obstacle for the down-scaling of electronics1-3. Minimizing the dimensions of interconnects (metal wires that connect different electronic components on a chip) is crucial for the miniaturization of devices. Interconnects are isolated from each other by non-conducting (dielectric) layers. So far, research has mostly focused on decreasing the resistance of scaled interconnects because integration of dielectrics using low-temperature deposition processes compatible with complementary metal-oxide-semiconductors is technically challenging. Interconnect isolation materials must have low relative dielectric constants (κ values), serve as diffusion barriers against the migration of metal into semiconductors, and be thermally, chemically and mechanically stable. Specifically, the International Roadmap for Devices and Systems recommends4 the development of dielectrics with κ values of less than 2 by 2028. Existing low-κ materials (such as silicon oxide derivatives, organic compounds and aerogels) have κ values greater than 2 and poor thermo-mechanical properties5. Here we report three-nanometre-thick amorphous boron nitride films with ultralow κ values of 1.78 and 1.16 (close to that of air, κ = 1) at operation frequencies of 100 kilohertz and 1 megahertz, respectively. The films are mechanically and electrically robust, with a breakdown strength of 7.3 megavolts per centimetre, which exceeds requirements. Cross-sectional imaging reveals that amorphous boron nitride prevents the diffusion of cobalt atoms into silicon under very harsh conditions, in contrast to reference barriers. Our results demonstrate that amorphous boron nitride has excellent low-κ dielectric characteristics for high-performance electronics.

A genomic and epigenomic atlas of prostate cancer in Asian populations.

Prostate cancer is the second most common cancer in men worldwide1. Over the past decade, large-scale integrative genomics efforts have enhanced our understanding of this disease by characterizing its genetic and epigenetic landscape in thousands of patients2,3. However, most tumours profiled in these studies were obtained from patients from Western populations. Here we produced and analysed whole-genome, whole-transcriptome and DNA methylation data for 208 pairs of tumour tissue samples and matched healthy control tissue from Chinese patients with primary prostate cancer. Systematic comparison with published data from 2,554 prostate tumours revealed that the genomic alteration signatures in Chinese patients were markedly distinct from those of Western cohorts: specifically, 41% of tumours contained mutations in FOXA1 and 18% each had deletions in ZNF292 and CHD1. Alterations of the genome and epigenome were correlated and were predictive of disease phenotype and progression. Coding and noncoding mutations, as well as epimutations, converged on pathways that are important for prostate cancer, providing insights into this devastating disease. These discoveries underscore the importance of including population context in constructing comprehensive genomic maps for disease.

GPCR targeting of E3 ubiquitin ligase MDM2 by inactive ß-arrestin.

E3 ubiquitin ligase Mdm2 facilitates ß-arrestin ubiquitination, leading to the internalization of G protein-coupled receptors (GPCRs). In this process, ß-arrestins bind to Mdm2 and recruit it to the receptor; however, the molecular architecture of the ß-arrestin-Mdm2 complex has not been elucidated yet. Here, we identified the ß-arrestin-binding region (ABR) on Mdm2 and solved the crystal structure of ß-arrestin1 in complex with Mdm2ABR peptide. The acidic residues of Mdm2ABR bind to the positively charged concave side of the ß-arrestin1 N-domain. The C-tail of ß-arrestin1 is still bound to the N-domain, indicating that Mdm2 binds to the inactive state of ß-arrestin1, whereas the phosphorylated C-terminal tail of GPCRs binds to activate ß-arrestins. The overlapped binding site of Mdm2 and GPCR C-tails on ß-arrestin1 suggests that the binding of GPCR C-tails might trigger the release of Mdm2. Moreover, hydrogen/deuterium exchange experiments further show that Mdm2ABR binding to ß-arrestin1 induces the interdomain interface to be more dynamic and uncouples the IP6-induced oligomer of ß-arrestin1. These results show how the E3 ligase, Mdm2, interacts with ß-arrestins to promote the internalization of GPCRs.

Epigenomic analysis reveals prevalent contribution of transposable elements to cis-regulatory elements, tissue-specific expression, and alternative promoters in zebrafish.

Transposable elements (TEs) encode regulatory elements that impact gene expression in multiple species, yet a comprehensive analysis of zebrafish TEs in the context of gene regulation is lacking. Here, we systematically investigate the epigenomic and transcriptomic landscape of TEs across 11 adult zebrafish tissues using multidimensional sequencing data. We find that TEs contribute substantially to a diverse array of regulatory elements in the zebrafish genome and that 37% of TEs are positioned in active regulatory states in adult zebrafish tissues. We identify TE subfamilies enriched in highly specific regulatory elements among different tissues. We use transcript assembly to discover TE-derived transcriptional units expressed across tissues. Finally, we show that novel TE-derived promoters can initiate tissue-specific transcription of alternate gene isoforms. This work provides a comprehensive profile of TE activity across normal zebrafish tissues, shedding light on mechanisms underlying the regulation of gene expression in this widely used model organism.

The extra-embryonic area opaca plays a role in positioning the primitive streak of the early chick embryo.

Classical studies have established that the marginal zone, a ring of extra-embryonic epiblast immediately surrounding the embryonic epiblast (area pellucida) of the chick embryo, is important in setting embryonic polarity by positioning the primitive streak, the site of gastrulation. The more external extra-embryonic region (area opaca) was thought to have only nutritive and support functions. Using experimental embryology approaches, this study reveals three separable functions for this outer region. First, juxtaposition of the area opaca directly onto the area pellucida induces a new marginal zone from the latter; this induced domain is entirely posterior in character. Second, ablation and grafting experiments using an isolated anterior half of the blastoderm and pieces of area opaca suggest that the area opaca can influence the polarity of the adjacent marginal zone. Finally, we show that the loss of the ability of such isolated anterior half-embryos to regulate (re-establish polarity spontaneously) at the early primitive streak stage can be rescued by replacing the area opaca by one from a younger stage. These results uncover new roles of chick extra-embryonic tissues in early development.

'Neighbourhood watch' model: embryonic epiblast cells assess positional information in relation to their neighbours.

In many developing and regenerating systems, tissue pattern is established through gradients of informative morphogens, but we know little about how cells interpret these. Using experimental manipulation of early chick embryos, including misexpression of an inducer (VG1 or ACTIVIN) and an inhibitor (BMP4), we test two alternative models for their ability to explain how the site of primitive streak formation is positioned relative to the rest of the embryo. In one model, cells read morphogen concentrations cell-autonomously. In the other, cells sense changes in morphogen status relative to their neighbourhood. We find that only the latter model can account for the experimental results, including some counter-intuitive predictions. This mechanism (which we name the 'neighbourhood watch' model) illuminates the classic 'French Flag Problem' and how positional information is interpreted by a sheet of cells in a large developing system.

Tumour-associated myeloid cells expressing IL-10R2/IL-22R1 as a potential biomarker for diagnosis and recurrence of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a poor survival rate, largely due to the lack of early diagnosis. Although myeloid cells are crucial in the tumour microenvironment, whether their specific subset can be a biomarker of PDAC progression is unclear. METHODS: We analysed IL-22 receptor expression in PDAC and peripheral blood. Additionally, we analysed gene expression profiles of IL-10R2+/IL-22R1+ myeloid cells and the presence of these cells using single-cell RNA sequencing and murine orthotropic PDAC models, respectively, followed by examining the immunosuppressive function of IL-10R2+/IL-22R1+ myeloid cells. Finally, the correlation between IL-10R2 expression and PDAC progression was evaluated. RESULTS: IL-10R2+/IL-22R1+ myeloid cells were present in PDAC and peripheral blood. Blood IL-10R2+ myeloid cells displayed a gene expression signature associated with tumour-educated circulating monocytes. IL-10R2+/IL-22R1+ myeloid cells from human myeloid cell culture inhibited T cell proliferation. By mouse models for PDAC, we found a positive correlation between pancreatic tumour growth and increased blood IL-10R2+/IL-22R1+ myeloid cells. IL-10R2+/IL-22R1+ myeloid cells from an early phase of the PDAC model suppressed T cell proliferation and cytotoxicity. IL-10R2+ myeloid cells indicated tumour recurrence 130 days sooner than CA19-9 in post-pancreatectomy patients. CONCLUSIONS: IL-10R2+/IL-22R1+ myeloid cells in the peripheral blood might be an early marker of PDAC prognosis.

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.

Biodegradation of low-density polyethylene by mixed fungi composed of Alternaria sp. and Trametes sp. isolated from landfill sites.

With the development of industry and modern manufacturing, nondegradable low-density polyethylene (LDPE) has been widely used, posing a rising environmental hazard to natural ecosystems and public health. In this study, we isolated a series of LDPE-degrading fungi from landfill sites and carried out LDPE degradation experiments by combining highly efficient degrading fungi in pairs. The results showed that the mixed microorganisms composed of Alternaria sp. CPEF-1 and Trametes sp. PE2F-4 (H-3 group) had a greater degradation effect on heat-treated LDPE (T-LDPE). After 30 days of inoculation with combination strain H-3, the weight loss rate of the T-LDPE film was approximately 154% higher than that of the untreated LDPE (U-LDPE) film, and the weight loss rate reached 0.66 ± 0.06%. Environmental scanning electron microscopy (ESEM) and Fourier transform infrared spectroscopy (FTIR) were used to further investigate the biodegradation impacts of T-LDPE, including the changes on the surface and depolymerization of the LDPE films during the fungal degradation process. Our findings revealed that the combined fungal treatment is more effective at degrading T-LDPE than the single strain treatment, and it is expected that properly altering the composition of the microbial community can help lessen the detrimental impact of plastics on the environment.

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.

Impact of Fat Mass on Osteoporosis, Sarcopenia, and Osteosarcopenia in Peritoneal Dialysis Patients.

INTRODUCTION: The relationship between fat mass and osteoporosis, sarcopenia, and osteosarcopenia is complex. While higher fat mass generally has a negative impact on bone and muscle health in the general population, the impact in peritoneal dialysis (PD) patients is less well understood. METHODS: In this study of 359 PD patients, sarcopenia was identified using appendicular skeletal muscle per square meter (ASM/m2), with cut-off values of <7.0 kg/m2 for men and <5.5 kg/m2 for women. Fat tissue index (FTI) and lean tissue index (LTI) were determined using body composition monitoring, with the lowest tertile classified as low FTI and low LTI. Bone mineral density was measured, with a T-score below -2.5 indicating osteoporosis. RESULTS: The prevalence of osteoporosis, sarcopenia, and osteosarcopenia was 25%, 32%, and 15%, respectively. Notably, 60% of osteoporotic patients had sarcopenia, and about 45% of sarcopenic patients had osteoporosis. Patients with osteoporosis were older and had significantly lower LTI (15.3 vs. 12.7 kg/m2, p < 0.001) and ASM (7.3 vs. 5.8 kg/m2, p < 0.001). Osteoporotic patients also had lower FTI, but this was more pronounced in men than in women. Patients with both sarcopenia and osteoporosis had the lowest LTI and FTI compared to those with only one or neither condition. Low FTI was a significant determinant for osteoporosis (OR, 2.34; 95% CI, 1.43-3.85; p = 0.001), sarcopenia (OR, 2.91; 95% CI, 1.82-4.64; p < 0.001), and osteosarcopenia (OR, 2.34; 95% CI, 1.30-4.24; p = 0.005) in univariate analysis, and these associations remained significant after adjustment for age and body mass index. CONCLUSION: Osteoporosis and sarcopenia are common and interrelated in PD patients. Low fat mass, but not normal/high fat mass, was significantly associated with these conditions, suggesting the importance of maintaining adequate fat mass in PD patients.

The collateral map: prediction of lesion growth and penumbra after acute anterior circulation ischemic stroke.

OBJECTIVES: This study evaluated the collateral map's ability to predict lesion growth and penumbra after acute anterior circulation ischemic strokes. METHODS: This was a retrospective analysis of selected data from a prospectively collected database. The lesion growth ratio was the ratio of the follow-up lesion volume to the baseline lesion volume on diffusion-weighted imaging (DWI). The time-to-maximum (Tmax)/DWI ratio was the ratio of the baseline Tmax > 6 s volume to the baseline lesion volume. The collateral ratio was the ratio of the hypoperfused lesion volume of the phase_FU (phase with the hypoperfused lesions most approximate to the follow-up DWI lesion) to the hypoperfused lesion volume of the phase_baseline of the collateral map. Multiple logistic regression analyses were conducted to identify independent predictors of lesion growth. The concordance correlation coefficients of Tmax/DWI ratio and collateral ratio for lesion growth ratio were analyzed. RESULTS: Fifty-two patients, including twenty-six males (mean age, 74 years), were included. Intermediate (OR, 1234.5; p < 0.001) and poor collateral perfusion grades (OR, 664.7; p = 0.006) were independently associated with lesion growth. Phase_FUs were immediately preceded phases of the phase_baselines in intermediate or poor collateral perfusion grades. The concordance correlation coefficients of the Tmax/DWI ratio and collateral ratio for the lesion growth ratio were 0.28 (95% CI, 0.17-0.38) and 0.88 (95% CI, 0.82-0.92), respectively. CONCLUSION: Precise prediction of lesion growth and penumbra can be possible using collateral maps, allowing for personalized application of recanalization treatments. Further studies are needed to generalize the findings of this study. CLINICAL RELEVANCE STATEMENT: Precise prediction of lesion growth and penumbra can be possible using collateral maps, allowing for personalized application of recanalization treatments. KEY POINTS: • Cell viability in cerebral ischemia due to proximal arterial steno-occlusion mainly depends on the collateral circulation. • The collateral map shows salvageable brain extent, which can survive by recanalization treatments after acute anterior circulation ischemic stroke. • Precise estimation of salvageable brain makes it possible to make patient-specific treatment decision.

Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation.

BACKGROUND: A real-time model for predicting short-term mortality in critically ill patients is needed to identify patients at imminent risk. However, the performance of the model needs to be validated in various clinical settings and ethnicities before its clinical application. In this study, we aim to develop an ensemble machine learning model using routinely measured clinical variables at a single academic institution in South Korea. METHODS: We developed an ensemble model using deep learning and light gradient boosting machine models. Internal validation was performed using the last two years of the internal cohort dataset, collected from a single academic hospital in South Korea between 2007 and 2021. External validation was performed using the full Medical Information Mart for Intensive Care (MIMIC), eICU-Collaborative Research Database (eICU-CRD), and Amsterdam University Medical Center database (AmsterdamUMCdb) data. The area under the receiver operating characteristic curve (AUROC) was calculated and compared to that for the National Early Warning Score (NEWS). RESULTS: The developed model (iMORS) demonstrated high predictive performance with an internal AUROC of 0.964 (95% confidence interval [CI] 0.963-0.965) and external AUROCs of 0.890 (95% CI 0.889-0.891) for MIMIC, 0.886 (95% CI 0.885-0.887) for eICU-CRD, and 0.870 (95% CI 0.868-0.873) for AmsterdamUMCdb. The model outperformed the NEWS with higher AUROCs in the internal and external validation (0.866 for the internal, 0.746 for MIMIC, 0.798 for eICU-CRD, and 0.819 for AmsterdamUMCdb; p < 0.001). CONCLUSIONS: Our real-time machine learning model to predict short-term mortality in critically ill patients showed excellent performance in both internal and external validations. This model could be a useful decision-support tool in the intensive care units to assist clinicians.

Non-invasive prediction of massive transfusion during surgery using intraoperative hemodynamic monitoring data.

OBJECTIVE: Failure to receive prompt blood transfusion leads to severe complications if massive bleeding occurs during surgery. For the timely preparation of blood products, predicting the possibility of massive transfusion (MT) is essential to decrease morbidity and mortality. This study aimed to develop a model for predicting MT 10 min in advance using non-invasive bio-signal waveforms that change in real-time. METHODS: In this retrospective study, we developed a deep learning-based algorithm (DLA) to predict intraoperative MT within 10 min. MT was defined as the transfusion of 3 or more units of red blood cells within an hour. The datasets consisted of 18,135 patients who underwent surgery at Seoul National University Hospital (SNUH) for model development and internal validation and 621 patients who underwent surgery at the Boramae Medical Center (BMC) for external validation. We constructed the DLA by using features extracted from plethysmography (collected at 500 Hz) and hematocrit measured during surgery. RESULTS: Among 18,135 patients in SNUH and 621 patients in BMC, 265 patients (1.46%) and 14 patients (2.25%) received MT during surgery, respectively. The area under the receiver operating characteristic curve (AUROC) of DLA predicting intraoperative MT before 10 min was 0.962 (95% confidence interval [CI], 0.948-0.974) in internal validation and 0.922 (95% CI, 0.882-0.959) in external validation, respectively. CONCLUSION: The DLA can successfully predict intraoperative MT using non-invasive bio-signal waveforms.

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from early accurate identification of high-risk patients. We developed and validated a machine learning model to predict postoperative respiratory failure, defined as prolonged (>48 h) mechanical ventilation or reintubation after surgery. METHODS: Easily extractable electronic health record (EHR) variables that do not require subjective assessment by clinicians were used. From EHR data of 307,333 noncardiac surgical cases, the model, trained with a gradient boosting algorithm, utilised a derivation cohort of 99,025 cases from Seoul National University Hospital (2013-9). External validation was performed using three separate cohorts A-C from different hospitals comprising 208,308 cases. Model performance was assessed by area under the receiver operating characteristic (AUROC) curve and area under the precision-recall curve (AUPRC), a measure of sensitivity and precision at different thresholds. RESULTS: The model included eight variables: serum albumin, age, duration of anaesthesia, serum glucose, prothrombin time, serum creatinine, white blood cell count, and body mass index. Internally, the model achieved an AUROC of 0.912 (95% confidence interval [CI], 0.908-0.915) and AUPRC of 0.113. In external validation cohorts A, B, and C, the model achieved AUROCs of 0.879 (95% CI, 0.876-0.882), 0.872 (95% CI, 0.870-0.874), and 0.931 (95% CI, 0.925-0.936), and AUPRCs of 0.029, 0.083, and 0.124, respectively. CONCLUSIONS: Utilising just eight easily extractable variables, this machine learning model demonstrated excellent discrimination in both internal and external validation for predicting postoperative respiratory failure. The model enables personalised risk stratification and facilitates data-driven clinical decision-making.

Ambient PM2.5 exposure and rapid population aging: A double threat to public health in the Republic of Korea.

Particulate matter with an aerodynamic diameter of ≤2.5 µm (PM2.5) can infiltrate deep into the respiratory system, posing significant health risks. Notably, the health burden of PM2.5 is more pronounced among the older adult population. With an aging population, the public health burden attributable to PM2.5 could escalate even if the current PM2.5 level remains stable. This study evaluated the number of deaths attributable to long-term PM2.5 exposure in the Republic of Korea between 2020 and 2050 and identified the PM2.5 concentration required at least to maintain the current PM2.5 health burden. To calculate mortality for 2020-2050, we performed a health impact assessment using 3-year (2019-2021) average population-weighted PM2.5 concentrations, age-specific population and mortality rates. In 2020, 33,578 [95% confidence interval (CI) = 31,708-35,448] deaths were attributable to PM2.5 exposure. Projecting forward, if the 2019-2021 average PM2.5 level remains constant, mortality is projected to be 112,953 (95% CI = 109,963-115,943) in 2050, more than three times higher than in 2020. To maintain the same level of health burden in 2050 as in 2020, the PM2.5 concentration needs to be immediately reduced to 5.8 µg/m3. In an age-specific analysis, the proportion of older adults (ages 65+) to total mortality would increase from 83% (2020) to 96% (2050), indicating that the rising mortality is predominantly driven by the aging population. By region, the reduction of PM2.5 concentrations, which is required immediately in 2020 to have the health burden in 2050 equal to that in 2020, varied from 3.6 µg/m3 in Goheung-gun (25% reduction) to 20.8 µg/m3 in Heungdeok-gu (82% reduction). Our study emphasizes the critical need for air quality management to consider aging populations when establishing PM2.5 air quality standards, as well as their associated policies and regulations.

Capabilities of satellite Geostationary Environment Monitoring Spectrometer (GEMS) NO2 data for hourly ambient NO2 exposure modeling.

The Geostationary Environment Monitoring Spectrometer (GEMS) is the world's first geostationary instrument that monitors hourly gaseous air pollutant levels, including nitrogen dioxide (NO2). Using the first-of-its-kind capabilities of GEMS NO2 data, we examined how well GEMS NO2 levels can explain the spatiotemporal variabilities in hourly NO2 concentrations in the Republic of Korea for the year 2022. A correlation analysis between hourly GEMS NO2 levels and ground NO2 concentrations showed a higher spatial correlation [Pearson r = 0.56 (SD = 0.20)] than a temporal one [Pearson r = 0.42 (SD = 0.14)], on average. To take advantage of the enhanced spatial predictability of GEMS NO2 data, we employed a mixed effects model to allow hour-specific relationships between GEMS NO2 and NO2 concentrations on a given day in each region and subsequently estimated hourly NO2 concentrations in all urban and rural areas. The 10-fold cross validation demonstrated R2 = 0.72, mean absolute error (MAE) = 3.7 ppb, and root mean squared error (RMSE) = 5.5 ppb. The hourly variations of the relationships were attributed particularly to those of wind speed among meteorological parameters considered in this study. The spatial distributions of hourly estimated NO2 concentrations were highly correlated between hours [average r = 0.91 (SD = 0.06)]. Nonetheless, they represented the diurnal patterns of urban versus rural NO2 contrasts during the day [urban/rural NO2 ratios from 1.22 (5 p.m.) to 1.37 (12 p.m.)]. The newly retrieved GEMS NO2 data enable temporally as well as spatially resolved NO2 exposure assessment. In combination with the time-activity patterns of individual subjects, the GEMS NO2 data can generate 'sub-population' exposure estimates and therefore enhance health effect studies.