Deep learning-based prognostication in idiopathic pulmonary fibrosis using chest radiographs.

OBJECTIVES: To develop and validate a deep learning-based prognostic model in patients with idiopathic pulmonary fibrosis (IPF) using chest radiographs. METHODS: To develop a deep learning-based prognostic model using chest radiographs (DLPM), the patients diagnosed with IPF during 2011-2021 were retrospectively collected and were divided into training (n = 1007), validation (n = 117), and internal test (n = 187) datasets. Up to 10 consecutive radiographs were included for each patient. For external testing, three cohorts from independent institutions were collected (n = 152, 141, and 207). The discrimination performance of DLPM was evaluated using areas under the time-dependent receiver operating characteristic curves (TD-AUCs) for 3-year survival and compared with that of forced vital capacity (FVC). Multivariable Cox regression was performed to investigate whether the DLPM was an independent prognostic factor from FVC. We devised a modified gender-age-physiology (GAP) index (GAP-CR), by replacing DLCO with DLPM. RESULTS: DLPM showed similar-to-higher performance at predicting 3-year survival than FVC in three external test cohorts (TD-AUC: 0.83 [95% CI: 0.76-0.90] vs. 0.68 [0.59-0.77], p < 0.001; 0.76 [0.68-0.85] vs. 0.70 [0.60-0.80], p = 0.21; 0.79 [0.72-0.86] vs. 0.76 [0.69-0.83], p = 0.41). DLPM worked as an independent prognostic factor from FVC in all three cohorts (ps < 0.001). The GAP-CR index showed a higher 3-year TD-AUC than the original GAP index in two of the three external test cohorts (TD-AUC: 0.85 [0.80-0.91] vs. 0.79 [0.72-0.86], p = 0.02; 0.72 [0.64-0.80] vs. 0.69 [0.61-0.78], p = 0.56; 0.76 [0.69-0.83] vs. 0.68 [0.60-0.76], p = 0.01). CONCLUSIONS: A deep learning model successfully predicted survival in patients with IPF from chest radiographs, comparable to and independent of FVC. CLINICAL RELEVANCE STATEMENT: Deep learning-based prognostication from chest radiographs offers comparable-to-higher prognostic performance than forced vital capacity. KEY POINTS: • A deep learning-based prognostic model for idiopathic pulmonary fibrosis was developed using 6063 radiographs. • The prognostic performance of the model was comparable-to-higher than forced vital capacity, and was independent from FVC in all three external test cohorts. • A modified gender-age-physiology index replacing diffusing capacity for carbon monoxide with the deep learning model showed higher performance than the original index in two external test cohorts.

Prognostication of lung adenocarcinomas using CT-based deep learning of morphological and histopathological features: a retrospective dual-institutional study.

OBJECTIVES: To develop and validate CT-based deep learning (DL) models that learn morphological and histopathological features for lung adenocarcinoma prognostication, and to compare them with a previously developed DL discrete-time survival model. METHODS: DL models were trained to simultaneously predict five morphological and histopathological features using preoperative chest CT scans from patients with resected lung adenocarcinomas. The DL score was validated in temporal and external test sets, with freedom from recurrence (FFR) and overall survival (OS) as outcomes. Discrimination was evaluated using the time-dependent area under the receiver operating characteristic curve (TD-AUC) and compared with the DL discrete-time survival model. Additionally, we performed multivariable Cox regression analysis. RESULTS: In the temporal test set (640 patients; median age, 64 years), the TD-AUC was 0.79 for 5-year FFR and 0.73 for 5-year OS. In the external test set (846 patients; median age, 65 years), the TD-AUC was 0.71 for 5-year OS, equivalent to the pathologic stage (0.71 vs. 0.71 [p = 0.74]). The prognostic value of the DL score was independent of clinical factors (adjusted per-percentage hazard ratio for FFR (temporal test), 1.02 [95% CI: 1.01-1.03; p < 0.001]; OS (temporal test), 1.01 [95% CI: 1.002-1.02; p = 0.01]; OS (external test), 1.01 [95% CI: 1.005-1.02; p < 0.001]). Our model showed a higher TD-AUC than the DL discrete-time survival model, but without statistical significance (2.5-year OS: 0.73 vs. 0.68; p = 0.13). CONCLUSION: The CT-based prognostic score from collective deep learning of morphological and histopathological features showed potential in predicting survival in lung adenocarcinomas. CLINICAL RELEVANCE STATEMENT: Collective CT-based deep learning of morphological and histopathological features presents potential for enhancing lung adenocarcinoma prognostication and optimizing pre-/postoperative management. KEY POINTS: • A CT-based prognostic model was developed using collective deep learning of morphological and histopathological features from preoperative CT scans of 3181 patients with resected lung adenocarcinoma. • The prognostic performance of the model was comparable-to-higher performance than the pathologic T category or stage. • Our approach yielded a higher discrimination performance than the direct survival prediction model, but without statistical significance (0.73 vs. 0.68; p=0.13).

Unlocking the Potential of A-Site Ca-Doped LaCo0.2Fe0.8O3-δ: A Redox-Stable Cathode Material Enabling High Current Density in Direct CO2 Electrolysis.

Massive carbon dioxide (CO2) emission from recent human industrialization has affected the global ecosystem and raised great concern for environmental sustainability. The solid oxide electrolysis cell (SOEC) is a promising energy conversion device capable of efficiently converting CO2 into valuable chemicals using renewable energy sources. However, Sr-containing cathode materials face the challenge of Sr carbonation during CO2 electrolysis, which greatly affects the energy conversion efficiency and long-term stability. Thus, A-site Ca-doped La1-xCaxCo0.2Fe0.8O3-δ (0.2 ≤ x ≤ 0.6) oxides are developed for direct CO2 conversion to carbon monoxide (CO) in an intermediate-temperature SOEC (IT-SOEC). With a polarization resistance as low as 0.18 Ω cm2 in pure CO2 atmosphere, a remarkable current density of 2.24 A cm-2 was achieved at 1.5 V with La0.6Ca0.4Co0.2Fe0.8O3-δ (LCCF64) as the cathode in La0.8Sr0.2Ga0.83Mg0.17O3-δ (LSGM) electrolyte (300 µm) supported electrolysis cells using La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF) as the air electrode at 800 °C. Furthermore, symmetrical cells with LCCF64 as the electrodes also show promising electrolysis performance of 1.78 A cm-2 at 1.5 V at 800 °C. In addition, stable cell performance has been achieved on direct CO2 electrolysis at an applied constant current of 0.5 A cm-2 at 800 °C. The easily removable carbonate intermediate produced during direct CO2 electrolysis makes LCCF64 a promising regenerable cathode. The outstanding electrocatalytic performance of the LCCF64 cathode is ascribed to the highly active and stable metal/perovskite interfaces that resulted from the in situ exsolved Co/CoFe nanoparticles and the additional oxygen vacancies originated from the Ca2Fe2O5 phase synergistically providing active sites for CO2 adsorption and electrolysis. This study offers a novel approach to design catalysts with high performance for direct CO2 electrolysis.

Deep learning-based iodine contrast-augmenting algorithm for low-contrast-dose liver CT to assess hypovascular hepatic metastasis.

PURPOSE: To investigate the image quality and diagnostic performance of low-contrast-dose liver CT using a deep learning-based iodine contrast-augmenting algorithm (DLICA) for hypovascular hepatic metastases. METHODS: This retrospective study included 128 patients who underwent contrast-enhanced dual-energy CT for hepatic metastasis surveillance between July 2019 and June 2022 using a 30% reduced iodine contrast dose in the portal phase. Three image types were reconstructed: 50-keV virtual monoenergetic images (50-keV VMI); linearly blended images simulating 120-kVp images (120-kVp); and post-processed 120-kVp images using DLICA (DLICA 120-kVp). Three reviewers evaluated lesion conspicuity, image contrast, and subjective image noise. We also measured image noise, contrast-to-noise ratios (CNRs), and signal-to-noise ratios (SNRs). The diagnostic performance for hepatic metastases was evaluated using a jackknife alternative free-response receiver operating characteristic method with the consensus of two independent radiologists as the reference standard. RESULTS: DLICA 120-kVp demonstrated significantly higher CNR of lesions to liver (5.7 ± 3.1 vs. 3.8 ± 2.1 vs. 3.8 ± 2.1) and higher SNR compared with 50-keV VMI and 120-kVp (p < 0.001 for all). DLICA 120-kVp had significantly lower image noise than 50-kVp VMI for all regions (p < 0.001 for all). DLICA 120-kVp also exhibited superior lesion conspicuity (4.0 [3.3-4.3] vs. 3.7 [3.0-4.0] vs. 3.7 [3.0-4.0]), higher image contrast, and lower subjective image noise compared with 50-keV VMI and 120-kVp (p < 0.001 for all). Although there was no significant difference in the figure of merit for lesion diagnosis among the three methods (p = 0.11), DLICA 120-kVp had a significantly higher figure of merit for lesions with a diameter < 20 mm than 50-keV VMI (0.677 vs. 0.648, p = 0.007). On a per-lesion basis, DLICA 120-kVp also demonstrated higher sensitivity than the 50-keV VMI (81.2% vs. 72.9%, p < 0.001). The specificities per lesion were not significantly different among the three algorithms (p = 0.15). CONCLUSION: DLICA at 120-kVp provided superior lesion conspicuity and image quality and similar diagnostic performance for hypovascular hepatic metastases compared with 50-keV VMI.

Histone H3 Tail Modifications Alter Structure and Dynamics of the H1 C-Terminal Domain Within Nucleosomes.

The highly positively charged and intrinsically disordered H1 C-terminal domain (CTD) undergoes extensive condensation upon binding to nucleosomes, and stabilizes nucleosomes and higher-order chromatin structures but its interactions in chromatin are not well defined. Using single-molecule FRET we found that about half of the H1 CTDs in H1-nucleosome complexes exhibit well-defined FRET values indicative of distinct, static conformations, while the remainder of the population exhibits exchange between multiple defined FRET structures. Moreover, crosslinking studies indicate that the first 30 residues of the H1 CTD participate in relatively localized contacts with the first ∼25 bp of linker DNA, and that two separate regions in the CTD contribute to H1-dependent organization of linker DNA. Finally, we show that acetylation mimetics within the histone H3 tail markedly reduce the overall extent of H1 CTD condensation and significantly increase the fraction of H1 CTDs undergoing dynamic exchange between FRET states. Our results indicate the nucleosome-bound H1 CTD adopts loosely defined structures that exhibit significantly enhanced dynamics and decondensation upon epigenetic acetylation within the H3 tail.

Circadian control of cisplatin-DNA adduct repair and apoptosis in culture cells.

Cisplatin, a widely prescribed chemotherapeutic agent for treating solid tumors, induces DNA adducts and activates cellular defense mechanisms, including DNA repair, cell cycle checkpoint control, and apoptosis. Considering the circadian rhythmicity displayed by most chemotherapeutic agents and their varying therapeutic efficacy based on treatment timing, our study aimed to investigate whether the circadian clock system influences the DNA damage responses triggered by cisplatin in synchronized cells. We examined the DNA damage responses in circadian-synchronized wild-type mouse embryonic fibroblasts (WT-MEF; clock-proficient cells), cryptochrome1 and 2 double knock-out MEF (CRYDKO; clock-deficient cells), and mouse hepatocarcinoma Hepa1c1c7 cells. Varying the treatment time resulted in a significant difference in the rate of platinum-DNA adduct removal specifically in circadian-synchronized WT-MEF, while CRYDKO did not exhibit such variation. Moreover, diurnal variation in other DNA damage responses, such as cell cycle checkpoint activity indicated by p53 phosphorylation status and apoptosis measured by DNA break frequency, was observed only in circadian-synchronized WT-MEF, not in CRYDKO or mouse hepatocarcinoma Hepa1c1c7 cells. These findings highlight that the DNA damage responses triggered by cisplatin are indeed governed by circadian control exclusively in clock-proficient cells. This outcome bears potential implications for enhancing or devising chronotherapy approaches for cancer patients.

Spontaneous histone exchange between nucleosomes.

The nucleosome is the fundamental gene-packing unit in eukaryotes. Nucleosomes comprise ∼147 bp DNA wrapped around an octameric histone protein core composed of two H2A-H2B dimers and one (H3-H4)2 tetramer. The strong yet flexible DNA-histone interactions are the physical basis of the dynamic regulation of genes packaged in chromatin. The dynamic nature of DNA-histone interactions also implies that nucleosomes dissociate DNA-histone contacts both transiently and repeatedly. This kinetic instability may lead to spontaneous nucleosome disassembly or histone exchange between nucleosomes. At high nucleosome concentrations, nucleosome-nucleosome collisions and subsequent histone exchange would be a more likely event, where nucleosomes could act as their own histone chaperone. This spontaneous histone exchange could serve as a mechanism for maintaining overall chromatin stability, although it has never been reported. Here we employed three-color single-molecule FRET (smFRET) to demonstrate that histone H2A-H2B dimers are exchanged spontaneously between nucleosomes on a time scale of a few tens of seconds at a physiological nucleosome concentration. We show that the rate of histone exchange increases at a higher monovalent salt concentration, with histone-acetylated nucleosomes, and in the presence of histone chaperone Nap1, while it remains unchanged at a higher temperature, and decreases upon DNA methylation. These results support the notion of histone exchange via transient and repetitive partial disassembly of the nucleosome and corroborate spontaneous histone diffusion in a compact chromatin context, modulating the local concentrations of histone modifications and variants.

Methods to investigate nucleosome structure and dynamics with single-molecule FRET.

The nucleosome is the fundamental building block of chromatin. Changes taking place at the nucleosome level are the molecular basis of chromatin transactions with various enzymes and factors. These changes are directly and indirectly regulated by chromatin modifications such as DNA methylation and histone post-translational modifications including acetylation, methylation, and ubiquitylation. Nucleosomal changes are often stochastic, unsynchronized, and heterogeneous, making it very difficult to monitor with traditional ensemble averaging methods. Diverse single-molecule fluorescence approaches have been employed to investigate the structure and structural changes of the nucleosome in the context of its interactions with various enzymes such as RNA Polymerase II, histone chaperones, transcription factors, and chromatin remodelers. We utilize diverse single-molecule fluorescence methods to study the nucleosomal changes accompanying these processes, elucidate the kinetics of these processes, and eventually learn the implications of various chromatin modifications in directly regulating these processes. The methods include two- and three-color single-molecule fluorescence resonance energy transfer (FRET), single-molecule fluorescence correlation spectroscopy, and fluorescence (co-)localization. Here we report the details of the two- and three-color single-molecule FRET methods we currently use. This report will help researchers design their single-molecule FRET approaches to investigating chromatin regulation at the nucleosome level.

Histone H3 tail modifications regulate structure and dynamics of the H1 C-terminal domain within nucleosomes.

Despite their importance, how linker histone H1s interact in chromatin and especially how the highly positively charged and intrinsically disordered H1 C-terminal domain (CTD) binds and stabilizes nucleosomes and higher-order chromatin structures remains unclear. Using single-molecule FRET we found that about half of the H1 CTDs in H1-nucleosome complexes exhibit well-defined FRET values indicative of distinct, static conformations, while the remainder of the population exhibits dynamically changing values, similar to that observed for H1 in the absence of nucleosomes. We also find that the first 30 residues of the CTD participate in relatively localized interactions with the first ∼20 bp of linker DNA, and that two separate regions in the CTD contribute to H1-dependent organization of linker DNA, consistent with some non-random CTD-linker DNA interactions. Finally, our data show that acetylation mimetics within the histone H3 tail induce decondensation and enhanced dynamics of the nucleosome-bound H1 CTD. (148 words).

Spontaneous Histone Exchange Between Nucleosomes.

The nucleosome is the fundamental gene-packing unit in eukaryotes. Nucleosomes comprise ∼147 bp DNA wrapped around an octameric histone protein core composed of two H2A-H2B dimers and one (H3-H4) 2 tetramer. The strong yet flexible DNA-histone interactions are a physical basis of the dynamic regulation of genes packaged in chromatin. The dynamic nature of DNA-histone interactions implies that nucleosomes dissociate DNA-histone contacts transiently and repeatedly. This kinetic instability may lead to spontaneous nucleosome disassembly or histone exchange between nucleosomes. At a high nucleosome concentration, nucleosome-nucleosome collisions and subsequent histone exchange would be a more likely pathway, where nucleosomes act as their own histone chaperone. The spontaneous histone exchange would serve as a mechanism for maintaining the overall chromatin stability although it has never been reported. We employed three-color single-molecule FRET (smFRET) to demonstrate that histone H2A-H2B dimers are exchanged spontaneously between nucleosomes and that the time scale is on a few tens of seconds at a physiological nucleosome concentration. The rate of histone exchange increases at a higher monovalent salt concentration, with histone acetylated nucleosomes, and in the presence of histone chaperone Nap1, while it remains unchanged at a higher temperature, and decreases upon DNA methylation. These results support histone exchange via transient and repetitive partial disassembly of the nucleosome and corroborate spontaneous histone diffusion in a compact chromatin context, modulating the local concentrations of histone modifications and variants.

Effects of Histone H2B Ubiquitylations and H3K79me3 on Transcription Elongation.

Post-translational modifications of histone proteins often mediate gene regulation by altering the global and local stability of the nucleosome, the basic gene-packing unit of eukaryotes. We employed semisynthetic approaches to introduce histone H2B ubiquitylations at K34 (H2BK34ub) and K120 (H2BK120ub) and H3K79 trimethylation (H3K79me3). With these modified histones, we investigated their effects on the kinetics of transcription elongation by RNA polymerase II (Pol II) using single-molecule FRET. Pol II pauses at several locations within the nucleosome for a few seconds to minutes, which governs the overall transcription efficiency. We found that H2B ubiquitylations suppress pauses and shorten the pause durations near the nucleosome entry while H3K79me3 shortens the pause durations and increases the rate of RNA elongation near the center of the nucleosome. We also found that H2BK34ub facilitates partial rewrapping of the nucleosome upon Pol II passage. These observations suggest that H2B ubiquitylations promote transcription elongation and help maintain the chromatin structure by inducing and stabilizing nucleosome intermediates and that H3K79me3 facilitates Pol II progression possibly by destabilizing the local structure of the nucleosome. Our results provide the mechanisms of how these modifications coupled by a network of regulatory proteins facilitate transcription in two different regions of the nucleosome and help maintain the chromatin structure during active transcription.

Deep Learning-Based Computer-Aided Detection System for Preoperative Chest Radiographs to Predict Postoperative Pneumonia.

RATIONALE AND OBJECTIVES: The role of preoperative chest radiography (CR) for prediction of postoperative pneumonia remains uncertain. We aimed to develop and validate a prediction model for postoperative pneumonia incorporating findings of preoperative CRs evaluated by a deep learning-based computer-aided detection (DL-CAD) system MATERIALS AND METHODS: This retrospective study included consecutive patients who underwent surgery between January 2019 and March 2020 and divided into development (surgery in 2019) and validation (surgery between January and March 2020) cohorts. Preoperative CRs obtained within 1-month before surgery were analyzed with a commercialized DL-CAD that provided probability values for the presence of 10 different abnormalities in CRs. Logistic regression models to predict postoperative pneumonia were built using clinical variables (clinical model), and both clinical variables and DL-CAD results for preoperative CRs (DL-CAD model). The discriminative performances of the models were evaluated by area under the receiver operating characteristic curves. RESULTS: In development cohort (n = 19,349; mean age, 57 years; 11,392 men), DL-CAD results for pulmonary nodules (odds ratio [OR, for 1% increase in probability value], 1.007; p = 0.021), consolidation (OR, 1.019; p < 0.001), and cardiomegaly (OR, 1.013; p < 0.001) were independent predictors of postoperative pneumonia and were included in the DL-CAD model. In validation cohort (n = 4957; mean age, 56 years; 2848 men), the DL-CAD model exhibited a higher AUROC than the clinical model (0.843 vs. 0.815; p = 0.012). CONCLUSION: Abnormalities in preoperative CRs evaluated by a DL-CAD were independent risk factors for postoperative pneumonia. Using DL-CAD results for preoperative CRs led to an improved prediction of postoperative pneumonia.

Methods of Visualizing the Results of an Artificial-Intelligence-Based Computer-Aided Detection System for Chest Radiographs: Effect on the Diagnostic Performance of Radiologists.

It is unclear whether the visualization methods for artificial-intelligence-based computer-aided detection (AI-CAD) of chest radiographs influence the accuracy of readers' interpretation. We aimed to evaluate the accuracy of radiologists' interpretations of chest radiographs using different visualization methods for the same AI-CAD. Initial chest radiographs of patients with acute respiratory symptoms were retrospectively collected. A commercialized AI-CAD using three different methods of visualizing was applied: (a) closed-line method, (b) heat map method, and (c) combined method. A reader test was conducted with five trainee radiologists over three interpretation sessions. In each session, the chest radiographs were interpreted using AI-CAD with one of the three visualization methods in random order. Examination-level sensitivity and accuracy, and lesion-level detection rates for clinically significant abnormalities were evaluated for the three visualization methods. The sensitivity (p = 0.007) and accuracy (p = 0.037) of the combined method are significantly higher than that of the closed-line method. Detection rates using the heat map method (p = 0.043) and the combined method (p = 0.004) are significantly higher than those using the closed-line method. The methods for visualizing AI-CAD results for chest radiographs influenced the performance of radiologists' interpretations. Combining the closed-line and heat map methods for visualizing AI-CAD results led to the highest sensitivity and accuracy of radiologists.

The Effects of Histone H2B ubiquitylations and H3K79me 3 on Transcription Elongation.

Post-translational modifications of histone proteins often mediate gene regulation by altering the global and local stability of the nucleosome, the basic gene-packing unit of eukaryotes. We employed semi-synthetic approaches to introduce histone H2B ubiquitylations at K34 (H2BK34ub) and K120 (H2BK120ub) and H3 K79 trimethylation (H3K79me3). With these modified histones, we investigated their effects on the kinetics of transcription elongation by RNA Polymerase II (Pol II) using single-molecule FRET. Pol II pauses at several locations within the nucleosome for a few seconds to minutes, which governs the overall transcription efficiency. We found that H2B ubiquitylations suppress pauses and shorten the pause durations near the nucleosome entry while H3K79me3 shortens the pause durations and increases the rate of RNA elongation near the center of the nucleosome. We also found that H2BK34ub facilitates partial rewrapping of the nucleosome upon Pol II passage. These observations suggest that H2B ubiquitylations promote transcription elongation and help maintain the chromatin structure by inducing and stabilizing nucleosome intermediates and that H3K79me3 facilitates Pol II progression possibly by destabilizing the local structure of the nucleosome. Our results provide the mechanisms of how these modifications coupled by a network of regulatory proteins facilitate transcription in two different regions of the nucleosome and help maintain the chromatin structure during active transcription.

Acute frataxin knockdown in induced pluripotent stem cell-derived cardiomyocytes activates a type I interferon response.

Friedreich ataxia, the most common hereditary ataxia, is a neuro- and cardio-degenerative disorder caused, in most cases, by decreased expression of the mitochondrial protein frataxin. Cardiomyopathy is the leading cause of premature death. Frataxin functions in the biogenesis of iron-sulfur clusters, which are prosthetic groups that are found in proteins involved in many biological processes. To study the changes associated with decreased frataxin in human cardiomyocytes, we developed a novel isogenic model by acutely knocking down frataxin, post-differentiation, in cardiomyocytes derived from induced pluripotent stem cells (iPSCs). Transcriptome analysis of four biological replicates identified severe mitochondrial dysfunction and a type I interferon response as the pathways most affected by frataxin knockdown. We confirmed that, in iPSC-derived cardiomyocytes, loss of frataxin leads to mitochondrial dysfunction. The type I interferon response was activated in multiple cell types following acute frataxin knockdown and was caused, at least in part, by release of mitochondrial DNA into the cytosol, activating the cGAS-STING sensor pathway.

Blast resistance of RC tubular structure under internal ANFO explosion.

The degree of structural damage is significantly more severe when a blast occurs inside than outside of a structure. However, existing designs for RC structures such as reinforced concrete containment vessels (RCCV) do not include design features to protect the structure for internal blast. Therefore, the internal blast resistance capacity of RC structures is evaluated by performing internal blast tests on RC tubular structures. The main objective of the study was to observe and document the basic structural behavior data obtained from internal detonation tests. ANFO explosive charge weights of 15.88, 20.41, 22.68 and 24.95 kg were selected for a charge detonating at a cross section center of the mid-span of the specimen, giving a standoff distance to the inner wall surface of 1000 mm. The data acquisitions include blast pressure, deflection, strain, and crack pattern. When the explosive charge weight increased from 15.88 to 24.95 kg, the peak incident pressure and time duration increased from 0.1718 to 0.3394 MPa and from 5.856 to 5.981 ms, respectively. Then, the test data were used to predict the internal charge weight required to fail a real scale RCCV using simple assumptions and the test data. The results of the study are discussed in detail in the paper.

The Effects of Histone H2B Ubiquitylations on the Nucleosome Structure and Internucleosomal Interactions.

Eukaryotic gene compaction takes place at multiple levels to package DNA to chromatin and chromosomes. Two of the most fundamental levels of DNA packaging are at the nucleosome and dinucleosome stacks. The nucleosome is the basic gene-packing unit and is composed of DNA wrapped around a histone core. Nucleosomes stack with one another for further compaction of DNA. The first stacking step leads to dinucleosome formation, which is driven by internucleosomal interactions between various parts of two nucleosomes. Histone proteins are rich targets for post-translational modifications, some of which affect the structure of the nucleosome and the interactions between nucleosomes. These effects are often implicated in the regulation of various genomic transactions. In particular, histone H2B ubiquitylation has been associated with facilitated transcription and hexasome formation. Here, we employed semi-synthetically ubiquitylated histone H2B and single-molecule FRET to investigate the effects of H2B ubiquitylations at lysine 34 (H2BK34) and lysine 120 (H2BK120) on the structure of the nucleosome and the interactions between two nucleosomes. Our results suggest that H2BK34 ubiquitylation widens the DNA gyre gap in the nucleosome and stabilizes long- and short-range internucleosomal interactions while H2BK120 ubiquitylation does not affect the nucleosome structure or internucleosomal interactions. These results suggest potential roles for H2B ubiquitylations in facilitated transcription and hexasome formation while maintaining the structural integrity of chromatin.

Machine-Learning-Based Clinical Biomarker Using Cell-Free DNA for Hepatocellular Carcinoma (HCC).

(1) Background: Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide. Although various serum enzymes have been utilized for the diagnosis and prognosis of HCC, the currently available biomarkers lack the sensitivity needed to detect HCC at early stages and accurately predict treatment responses. (2) Methods: We utilized our highly sensitive cell-free DNA (cfDNA) detection system, in combination with a machine learning algorithm, to provide a platform for improved diagnosis and prognosis of HCC. (3) Results: cfDNA, specifically alpha-fetoprotein (AFP) expression in captured cfDNA, demonstrated the highest accuracy for diagnosing malignancies among the serum/plasma biomarkers used in this study, including AFP, aspartate aminotransferase, alanine aminotransferase, albumin, alkaline phosphatase, and bilirubin. The diagnostic/prognostic capability of cfDNA was further improved by establishing a cfDNA score (cfDHCC), which integrated the total plasma cfDNA levels and cfAFP-DNA expression into a single score using machine learning algorithms. (4) Conclusion: The cfDHCC score demonstrated significantly improved accuracy in determining the pathological features of HCC and predicting patients' survival outcomes compared to the other biomarkers. The results presented herein reveal that our cfDNA capture/analysis platform is a promising approach to effectively utilize cfDNA as a biomarker for the diagnosis and prognosis of HCC.

Deep learning-based image reconstruction of 40-keV virtual monoenergetic images of dual-energy CT for the assessment of hypoenhancing hepatic metastasis.

OBJECTIVES: To evaluate the diagnostic value of deep learning model (DLM) reconstructed dual-energy CT (DECT) low-keV virtual monoenergetic imaging (VMI) for assessing hypoenhancing hepatic metastases. METHODS: This retrospective study included 131 patients who underwent contrast-enhanced DECT (80-kVp and 150-kVp with a tin filter) in the portal venous phase for hepatic metastasis surveillance. Linearly blended images simulating 100-kVp images (100-kVp), standard 40-keV VMI images (40-keV VMI), and post-processed 40-keV VMI using a vendor-agnostic DLM (i.e., DLM 40-keV VMI) were reconstructed. Lesion conspicuity and diagnostic acceptability were assessed by three independent reviewers and compared using the Wilcoxon signed-rank test. The contrast-to-noise ratios (CNRs) were also measured placing ROIs in metastatic lesions and liver parenchyma. The detection performance of hepatic metastases was assessed by using a jackknife alternative free-response ROC method. The consensus by two independent radiologists was used as the reference standard. RESULTS: DLM 40-keV VMI, compared to 40-keV VMI and 100-kVp, showed a higher lesion-to-liver CNR (8.25 ± 3.23 vs. 6.05 ± 2.38 vs. 5.99 ± 2.00), better lesion conspicuity (4.3 (4.0-4.7) vs. 3.7 (3.7-4.0) vs. 3.7 (3.3-4.0)), and better diagnostic acceptability (4.3 (4.0-4.3) vs. 3.0 (2.7-3.3) vs. 4.0 (4.0-4.3)) (p < 0.001 for all). For lesion detection (246 hepatic metastases in 68 patients), the figure of merit was significantly higher with DLM 40-keV VMI than with 40-keV VMI (0.852 vs. 0.822, p = 0.012), whereas no significant difference existed between DLM 40-keV VMI and 100-kVp (0.852 vs. 0.842, p = 0.31). CONCLUSIONS: DLM 40-keV VMI provided better image quality and comparable diagnostic performance for detecting hypoenhancing hepatic metastases compared to linearly blended images. KEY POINTS: • DLM 40-keV VMI provides a superior image quality compared with 40-keV or 100-kVp for assessing hypoenhancing hepatic metastasis. • DLM 40-keV VMI has the highest CNR and lesion conspicuity score for hypoenhancing hepatic metastasis due to noise reduction and structural preservation. • DLM 40-keV VMI provides higher lesion detectability than standard 40-keV VMI (p = 0.012).

DOT1L activity in leukemia cells requires interaction with ubiquitylated H2B that promotes productive nucleosome binding.

DOT1L methylates histone H3 lysine 79 during transcriptional elongation and is stimulated by ubiquitylation of histone H2B lysine 120 (H2BK120ub) in a classical trans-histone crosstalk pathway. Aberrant genomic localization of DOT1L is implicated in mixed lineage leukemia (MLL)-rearranged leukemias, an aggressive subset of leukemias that lacks effective targeted treatments. Despite recent atomic structures of DOT1L in complex with H2BK120ub nucleosomes, fundamental questions remain as to how DOT1L-ubiquitin and DOT1L-nucleosome acidic patch interactions observed in these structures contribute to nucleosome binding and methylation by DOT1L. Here, we combine bulk and single-molecule biophysical measurements with cancer cell biology to show that ubiquitin and cofactor binding drive conformational changes to stimulate DOT1L activity. Using structure-guided mutations, we demonstrate that ubiquitin and nucleosome acidic patch binding by DOT1L are required for cell proliferation in the MV4; 11 leukemia model, providing proof of principle for MLL targeted therapeutic strategies.