Distinct lineage-dependent structural and functional organization of the hippocampus.

The hippocampus, as part of the cerebral cortex, is essential for memory formation and spatial navigation. Although it has been extensively studied, especially as a model system for neurophysiology, the cellular processes involved in constructing and organizing the hippocampus remain largely unclear. Here, we show that clonally related excitatory neurons in the developing hippocampus are progressively organized into discrete horizontal, but not vertical, clusters in the stratum pyramidale, as revealed by both cell-type-specific retroviral labeling and mosaic analysis with double markers (MADM). Moreover, distinct from those in the neocortex, sister excitatory neurons in the cornu ammonis 1 region of the hippocampus rarely develop electrical or chemical synapses with each other. Instead, they preferentially receive common synaptic input from nearby fast-spiking (FS), but not non-FS, interneurons and exhibit synchronous synaptic activity. These results suggest that shared inhibitory input may specify horizontally clustered sister excitatory neurons as functional units in the hippocampus.

Centrosome anchoring regulates progenitor properties and cortical formation.

Radial glial progenitor cells (RGPs) are the major neural progenitor cells that generate neurons and glia in the developing mammalian cerebral cortex1-4. In RGPs, the centrosome is positioned away from the nucleus at the apical surface of the ventricular zone of the cerebral cortex5-8. However, the molecular basis and precise function of this distinctive subcellular organization of the centrosome are largely unknown. Here we show in mice that anchoring of the centrosome to the apical membrane controls the mechanical properties of cortical RGPs, and consequently their mitotic behaviour and the size and formation of the cortex. The mother centriole in RGPs develops distal appendages that anchor it to the apical membrane. Selective removal of centrosomal protein 83 (CEP83) eliminates these distal appendages and disrupts the anchorage of the centrosome to the apical membrane, resulting in the disorganization of microtubules and stretching and stiffening of the apical membrane. The elimination of CEP83 also activates the mechanically sensitive yes-associated protein (YAP) and promotes the excessive proliferation of RGPs, together with a subsequent overproduction of intermediate progenitor cells, which leads to the formation of an enlarged cortex with abnormal folding. Simultaneous elimination of YAP suppresses the cortical enlargement and folding that is induced by the removal of CEP83. Together, these results indicate a previously unknown role of the centrosome in regulating the mechanical features of neural progenitor cells and the size and configuration of the mammalian cerebral cortex.

HIV-1 usurps transcription start site heterogeneity of host RNA polymerase II to maximize replication fitness.

HIV-1 relies on host RNA polymeraseII (Pol II) to transcribe its genome and uses multiple transcription start sites (TSS), including three consecutive guanosines located near the U3-R junction, to generate transcripts containing three, two, and one guanosine at the 5' end, referred to as 3G, 2G, and 1G RNA, respectively. The 1G RNA is preferentially selected for packaging, indicating that these 99.9% identical RNAs exhibit functional differences and highlighting the importance of TSS selection. Here, we demonstrate that TSS selection is regulated by sequences between the CATA/TATA box and the beginning of R. Furthermore, we have generated two HIV-1 mutants with distinct 2-nucleotide modifications that predominantly express 3G RNA or 1G RNA. Both mutants can generate infectious viruses and undergo multiple rounds of replication in T cells. However, both mutants exhibit replication defects compared to the wild-type virus. The 3G-RNA-expressing mutant displays an RNA genome-packaging defect and delayed replication kinetics, whereas the 1G-RNA-expressing mutant exhibits reduced Gag expression and a replication fitness defect. Additionally, reversion of the latter mutant is frequently observed, consistent with sequence correction by plus-strand DNA transfer during reverse transcription. These findings demonstrate that HIV-1 maximizes its replication fitness by usurping the TSS heterogeneity of host RNA Pol II to generate unspliced RNAs with different specialized roles in viral replication. The three consecutive guanosines at the junction of U3 and R may also maintain HIV-1 genome integrity during reverse transcription. These studies reveal the intricate regulation of HIV-1 RNA and complex replication strategy.

T-S2Inet: Transformer-based sequence-to-image network for accurate nanopore sequence recognition.

MOTIVATION: Nanopore sequencing is a new macromolecular recognition and perception technology that enables high-throughput sequencing of DNA, RNA, even protein molecules. The sequences generated by nanopore sequencing span a large time frame, and the labor and time costs incurred by traditional analysis methods are substantial. Recently, research on nanopore data analysis using machine learning algorithms has gained unceasing momentum, but there is often a significant gap between traditional and deep learning methods in terms of classification results. To analyze nanopore data using deep learning technologies, measures such as sequence completion and sequence transformation can be employed. However, these technologies do not preserve the local features of the sequences. To address this issue, we propose a sequence-to-image (S2I) module that transforms sequences of unequal length into images. Additionally, we propose the Transformer-based T-S2Inet model to capture the important information and improve the classification accuracy. RESULTS: Quantitative and qualitative analysis shows that the experimental results have an improvement of around 2% in accuracy compared to previous methods. The proposed method is adaptable to other nanopore platforms, such as the Oxford nanopore. It is worth noting that the proposed method not only aims to achieve the most advanced performance, but also provides a general idea for the analysis of nanopore sequences of unequal length. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/guanxiaoyu11/S2Inet.

The centrosome protein AKNA regulates neurogenesis via microtubule organization.

The expansion of brain size is accompanied by a relative enlargement of the subventricular zone during development. Epithelial-like neural stem cells divide in the ventricular zone at the ventricles of the embryonic brain, self-renew and generate basal progenitors1 that delaminate and settle in the subventricular zone in enlarged brain regions2. The length of time that cells stay in the subventricular zone is essential for controlling further amplification and fate determination. Here we show that the interphase centrosome protein AKNA has a key role in this process. AKNA localizes at the subdistal appendages of the mother centriole in specific subtypes of neural stem cells, and in almost all basal progenitors. This protein is necessary and sufficient to organize centrosomal microtubules, and promote their nucleation and growth. These features of AKNA are important for mediating the delamination process in the formation of the subventricular zone. Moreover, AKNA regulates the exit from the subventricular zone, which reveals the pivotal role of centrosomal microtubule organization in enabling cells to both enter and remain in the subventricular zone. The epithelial-to-mesenchymal transition is also regulated by AKNA in other epithelial cells, demonstrating its general importance for the control of cell delamination.

Circumventing drug resistance in gastric cancer: A spatial multi-omics exploration of chemo and immuno-therapeutic response dynamics.

BACKGROUND: Gastric Cancer (GC) characteristically exhibits heterogeneous responses to treatment, particularly in relation to immuno plus chemo therapy, necessitating a precision medicine approach. This study is centered around delineating the cellular and molecular underpinnings of drug resistance in this context. METHODS: We undertook a comprehensive multi-omics exploration of postoperative tissues from GC patients undergoing the chemo and immuno-treatment regimen. Concurrently, an image deep learning model was developed to predict treatment responsiveness. RESULTS: Our initial findings associate apical membrane cells with resistance to fluorouracil and oxaliplatin, critical constituents of the therapy. Further investigation into this cell population shed light on substantial interactions with resident macrophages, underscoring the role of intercellular communication in shaping treatment resistance. Subsequent ligand-receptor analysis unveiled specific molecular dialogues, most notably TGFB1-HSPB1 and LTF-S100A14, offering insights into potential signaling pathways implicated in resistance. Our SVM model, incorporating these multi-omics and spatial data, demonstrated significant predictive power, with AUC values of 0.93 and 0.84 in the exploration and validation cohorts respectively. Hence, our results underscore the utility of multi-omics and spatial data in modeling treatment response. CONCLUSION: Our integrative approach, amalgamating mIHC assays, feature extraction, and machine learning, successfully unraveled the complex cellular interplay underlying drug resistance. This robust predictive model may serve as a valuable tool for personalizing therapeutic strategies and enhancing treatment outcomes in gastric cancer.

A diffusion MRI-based spatiotemporal continuum of the embryonic mouse brain for probing gene-neuroanatomy connections.

The embryonic mouse brain undergoes drastic changes in establishing basic anatomical compartments and laying out major axonal connections of the developing brain. Correlating anatomical changes with gene-expression patterns is an essential step toward understanding the mechanisms regulating brain development. Traditionally, this is done in a cross-sectional manner, but the dynamic nature of development calls for probing gene-neuroanatomy interactions in a combined spatiotemporal domain. Here, we present a four-dimensional (4D) spatiotemporal continuum of the embryonic mouse brain from E10.5 to E15.5 reconstructed from diffusion magnetic resonance microscopy (dMRM) data. This study achieved unprecedented high-definition dMRM at 30- to 35-µm isotropic resolution, and together with computational neuroanatomy techniques, we revealed both morphological and microscopic changes in the developing brain. We transformed selected gene-expression data to this continuum and correlated them with the dMRM-based neuroanatomical changes in embryonic brains. Within the continuum, we identified distinct developmental modes comprising regional clusters that shared developmental trajectories and similar gene-expression profiles. Our results demonstrate how this 4D continuum can be used to examine spatiotemporal gene-neuroanatomical interactions by connecting upstream genetic events with anatomical changes that emerge later in development. This approach would be useful for large-scale analysis of the cooperative roles of key genes in shaping the developing brain.

Syphilis and cardiovascular risk: a Taiwanese registry.

BACKGROUND AND AIMS: Studies on the impact of syphilis on the cardiovascular system in large populations are limited. This study investigated the effects of syphilis on cardiovascular outcomes. METHODS: Medical records from 2010 to 2015 were retrieved from the Taiwan National Health Insurance Research Database, linked to the Notifiable Infectious Diseases database from the Taiwan Centers for Disease Control. Patients with syphilis were identified, excluding those with missing information, under 20 years of age, or with a history of human immunodeficiency virus infection, acute myocardial infarction, heart failure, aortic regurgitation, replacement of the aortic valve, aneurysm and/or dissection of the aorta, atrial fibrillation, ischaemic stroke, haemorrhagic stroke, and venous thromboembolism. Primary outcomes included new-onset acute myocardial infarction, heart failure, aortic regurgitation, aneurysm and dissection of the aorta, atrial fibrillation, ischaemic stroke, haemorrhagic stroke, venous thromboembolism, cardiovascular death, and all-cause mortality. RESULTS: A total of 28 796 patients with syphilis were identified from 2010 to 2015. After exclusions and frequency matching, 20 601 syphilis patients and 20 601 non-syphilis patients were analysed. The relative rate (RR) was utilized in the analysis, as the competing risk of death was not considered. Compared with patients without syphilis, patients with syphilis had increased risks of acute myocardial infarction (RR 38%, 95% confidence interval [CI] 1.19-1.60, P < .001), heart failure (RR 88%, 95% CI 1.64-2.14, P < .001), aortic regurgitation (RR 81%, 95% CI 1.18-2.75, P = .006), atrial fibrillation (RR 45%, 95% CI 1.20-1.76, P < .001), ischaemic stroke (RR 68%, 95% CI 1.52-1.87, P < .001), haemorrhagic stroke (RR 114%, 95% CI 1.74-2.64, P < .001), venous thromboembolism (RR 67%, 95% CI 1.23-2.26, P = .001), cardiovascular death (RR 155%, 95% CI 2.11-3.08, P < .001), and all-cause death (RR 196%, 95% CI 2.74-3.19, P < .001) but not for aneurysm and dissection of the aorta. CONCLUSIONS: This study demonstrates that patients with syphilis have a higher risk of cardiovascular events and all-cause mortality compared with those without syphilis.

Revolutionizing the structural design and determination of covalent-organic frameworks: principles, methods, and techniques.

Covalent organic frameworks (COFs) represent an important class of crystalline porous materials with designable structures and functions. The interconnected organic monomers, featuring pre-designed symmetries and connectivities, dictate the structures of COFs, endowing them with high thermal and chemical stability, large surface area, and tunable micropores. Furthermore, by utilizing pre-functionalization or post-synthetic functionalization strategies, COFs can acquire multifunctionalities, leading to their versatile applications in gas separation/storage, catalysis, and optoelectronic devices. Our review provides a comprehensive account of the latest advancements in the principles, methods, and techniques for structural design and determination of COFs. These cutting-edge approaches enable the rational design and precise elucidation of COF structures, addressing fundamental physicochemical challenges associated with host-guest interactions, topological transformations, network interpenetration, and defect-mediated catalysis.

Pinpointing the Cl Coordination Effect on Mn-N3-Cl Moiety Toward Boosting Reaction Kinetics and Suppressing Shuttle Effect in Li-S Batteries.

Single atom catalysts (SACs) are highly favored in Li-S batteries due to their excellent performance in promoting the conversion of lithium polysulfides (LiPSs) and inhibiting their shuttling. However, the intricate and interrelated microstructures pose a challenge in deciphering the correlation between the chemical environment surrounding the active site and its catalytic activity. Here, a novel SAC featuring a distinctive Mn-N3-Cl moiety anchored on B, N co-doped carbon nanotubes (MnN3Cl@BNC) is synthesized. Subsequently, the selective removal of the Cl ligands while inheriting other microstructures is performed to elucidate the effect of Cl coordination on catalytic activity. The Cl coordination effectively enhances the electron cloud density of the Mn-N3-Cl moiety, reducing the band gap and increasing the adsorption capacity and redox kinetics of LiPSs. As a modified separator for Li-S batteries, MnN3Cl@BNC exhibits high capacities of 1384.1 and 743 mAh g-1 at 0.1 and 3C, with a decay rate of only 0.06% per cycle over 700 cycles at 1 C, which is much better than that of MnN3OH@BNC. This study reveals that Cl coordination positively contributes to improving the catalytic activity of the Mn-N3-Cl moiety, providing a fresh perspective for the design of high-performance SACs.

S2Snet: deep learning for low molecular weight RNA identification with nanopore.

Ribonucleic acid (RNA) is a pivotal nucleic acid that plays a crucial role in regulating many biological activities. Recently, one study utilized a machine learning algorithm to automatically classify RNA structural events generated by a Mycobacterium smegmatis porin A nanopore trap. Although it can achieve desirable classification results, compared with deep learning (DL) methods, this classic machine learning requires domain knowledge to manually extract features, which is sophisticated, labor-intensive and time-consuming. Meanwhile, the generated original RNA structural events are not strictly equal in length, which is incompatible with the input requirements of DL models. To alleviate this issue, we propose a sequence-to-sequence (S2S) module that transforms the unequal length sequence (UELS) to the equal length sequence. Furthermore, to automatically extract features from the RNA structural events, we propose a sequence-to-sequence neural network based on DL. In addition, we add an attention mechanism to capture vital information for classification, such as dwell time and blockage amplitude. Through quantitative and qualitative analysis, the experimental results have achieved about a 2% performance increase (accuracy) compared to the previous method. The proposed method can also be applied to other nanopore platforms, such as the famous Oxford nanopore. It is worth noting that the proposed method is not only aimed at pursuing state-of-the-art performance but also provides an overall idea to process nanopore data with UELS.

Deep learning-based pseudo-mass spectrometry imaging analysis for precision medicine.

Liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics provides systematic profiling of metabolic. Yet, its applications in precision medicine (disease diagnosis) have been limited by several challenges, including metabolite identification, information loss and low reproducibility. Here, we present the deep-learning-based Pseudo-Mass Spectrometry Imaging (deepPseudoMSI) project (https://www.deeppseudomsi.org/), which converts LC-MS raw data to pseudo-MS images and then processes them by deep learning for precision medicine, such as disease diagnosis. Extensive tests based on real data demonstrated the superiority of deepPseudoMSI over traditional approaches and the capacity of our method to achieve an accurate individualized diagnosis. Our framework lays the foundation for future metabolic-based precision medicine.

Active learning for efficient analysis of high-throughput nanopore data.

MOTIVATION: As the third-generation sequencing technology, nanopore sequencing has been used for high-throughput sequencing of DNA, RNA, and even proteins. Recently, many studies have begun to use machine learning technology to analyze the enormous data generated by nanopores. Unfortunately, the success of this technology is due to the extensive labeled data, which often suffer from enormous labor costs. Therefore, there is an urgent need for a novel technology that can not only rapidly analyze nanopore data with high-throughput, but also significantly reduce the cost of labeling. To achieve the above goals, we introduce active learning to alleviate the enormous labor costs by selecting the samples that need to be labeled. This work applies several advanced active learning technologies to the nanopore data, including the RNA classification dataset (RNA-CD) and the Oxford Nanopore Technologies barcode dataset (ONT-BD). Due to the complexity of the nanopore data (with noise sequence), the bias constraint is introduced to improve the sample selection strategy in active learning. Results: The experimental results show that for the same performance metric, 50% labeling amount can achieve the best baseline performance for ONT-BD, while only 15% labeling amount can achieve the best baseline performance for RNA-CD. Crucially, the experiments show that active learning technology can assist experts in labeling samples, and significantly reduce the labeling cost. Active learning can greatly reduce the dilemma of difficult labeling of high-capacity nanopore data. We hope active learning can be applied to other problems in nanopore sequence analysis. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://github.com/guanxiaoyu11/AL-for-nanopore. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Efficient and Accurate Computed Tomography-Based Stone Volume Determination: Development of an Automated Artificial Intelligence Algorithm.

PURPOSE: Given the shortcomings of current stone burden characterization (maximum diameter or ellipsoid formulas), we sought to investigate the diagnostic accuracy and precision of a University of California, Irvine-developed artificial intelligence (AI) algorithm for determining stone volume determination. MATERIALS AND METHODS: A total of 322 noncontrast CT scans were retrospectively obtained from patients with a diagnosis of urolithiasis. The largest stone in each noncontrast CT scan was designated the "index stone." The 3D volume of the index stone using 3D Slicer technology was determined by a validated reviewer; this was considered the "ground truth" volume. The AI-calculated index stone volume was subsequently compared with ground truth volume as well with the scalene, prolate, and oblate ellipsoid formulas estimated volumes. RESULTS: There was a nearly perfect correlation between the AI-determined volume and the ground truth (R=0.98). While the AI algorithm was efficient for determining the stone volume for all sizes, its accuracy improved with larger stone size. Moreover, the AI stone volume produced an excellent 3D pixel overlap with the ground truth (Dice score=0.90). In comparison, the ellipsoid formula-based volumes performed less well (R range: 0.79-0.82) than the AI algorithm; for the ellipsoid formulas, the accuracy decreased as the stone size increased (mean overestimation: 27%-89%). Lastly, for all stone sizes, the maximum linear stone measurement had the poorest correlation with the ground truth (R range: 0.41-0.82). CONCLUSIONS: The University of California, Irvine AI algorithm is an accurate, precise, and time-efficient tool for determining stone volume. Expanding the clinical availability of this program could enable urologists to establish better guidelines for both the metabolic and surgical management of their urolithiasis patients.

Volumetric Analysis of Acute Uncomplicated Type B Aortic Dissection Using an Automated Deep Learning Aortic Zone Segmentation Model.

INTRODUCTION: Machine learning techniques have shown excellent performance in 3D medical image analysis, but have not been applied to acute uncomplicated type B aortic dissection (auTBAD) utilizing SVS/STS-defined aortic zones. The purpose of this study was to establish a trained, automatic machine learning aortic zone segmentation model to facilitate performance of an aortic zone volumetric comparison between auTBAD patients based on rate of aortic growth. METHODS: Patients with auTBAD and serial imaging were identified. For each patient, imaging characteristics from two CT scans were analyzed: (1) the baseline CTA at index admission, and (2) either the most recent surveillance CTA, or the most recent CTA prior to an aortic intervention. Patients were stratified into two comparative groups based on aortic growth: rapid growth (diameter increase ≥5mm/year) and no/slow growth (diameter increase <5mm/year). Deidentified images were imported into an open-source software package for medical image analysis and images were annotated based on SVS/STS criteria for aortic zones. Our model was trained using 4-fold cross-validation. The segmentation output was used to calculate aortic zone volumes from each imaging study. RESULTS: Of 59 patients identified for inclusion, rapid growth was observed in 33 (56%) patients and no/slow growth was observed in 26 (44%) patients. There were no differences in baseline demographics, comorbidities, admission mean arterial pressure, number of discharge antihypertensives, or high-risk imaging characteristics between groups (p>0.05 for all). Median duration between baseline and interval CT was 1.07 years (IQR 0.38-2.57). Post-discharge aortic intervention was performed in 13 (22%) of patients at a mean of 1.5±1.2 years, with no difference between groups (p>0.05). Among all patients, the largest relative percent increases in zone volumes over time were found in zone 4 (13.9% IQR -6.82-35.1) and zone 5 (13.4% IQR -7.78-37.9). There were no differences in baseline zone volumes between groups (p>0.05 for all). Average Dice coefficient, a performance measure of the model output, was 0.73. Performance was best in zone 5 (0.84) and zone 9 (0.91). CONCLUSIONS: We describe an automatic deep learning segmentation model incorporating SVS-defined aortic zones. The open-source, trained model demonstrates concordance to the manually segmented aortas with the strongest performance in zones 5 and 9, providing a framework for further clinical applications. In our limited sample, there were no differences in baseline aortic zone volumes between rapid growth and no/slow growth patients.

Early reduction in albuminuria is associated with a steeper 'dip' in initial estimated glomerular filtration rate but favourable long-term kidney outcomes in people with diabetes receiving sodium-glucose cotransporter-2 inhibitors.

AIM: To assess if early change in albuminuria was linked to an initial change in estimated glomerular filtration rate (eGFR) and long-term kidney outcomes in people with type 2 diabetes (T2D) receiving sodium-glucose cotransporter-2 (SGLT2) inhibitors. METHODS: Using a medical database from a multicentre healthcare institute in Taiwan, we retrospectively enrolled 8310 people receiving SGLT2 inhibitors from 1 June 2016 to 31 December 2021. We compared the risks of initial eGFR decline, major adverse renal events (MARE; >50% eGFR reduction or development of end-stage kidney disease), major adverse cardiovascular events (MACE), or hospitalization for heart failure (HHF) using a Cox proportional hazards model. RESULTS: In all, 36.8% (n = 3062) experienced a >30% decrease, 21.0% (n = 1743) experienced a 0%-30% decrease, 14.4% (n = 1199) experienced a 0%-30% increase, and 27.7% (n = 2306) experienced a >30% increase in urine albumin-to-creatine ratio (UACR) after 3 months of SGLT2 inhibitor treatment. Greater acute eGFR decline at 3 months correlated with greater UACR reduction: -3.6 ± 10.9, -2.0 ± 9.5, -1.1 ± 8.6, and -0.3 ± 9.7 mL/min/1.73 m2 for the respective UACR change groups (p < 0.001). Over a median of 29.0 months, >30% UACR decline was associated with a higher risk of >30% initial eGFR decline (hazard ratio [HR] 2.68, 95% confidence interval [CI] 1.61-4.47]), a lower risk of MARE (HR 0.66, 95% CI 0.48-0.89), and a comparable risk of MACE or HHF after multivariate adjustment (p < 0.05). The nonlinear analysis showed early UACR decline was linked to a lower risk of MARE but a higher risk of initial steep eGFR decline of >30%. CONCLUSION: Physicians should be vigilant for the potential adverse effects of abrupt eGFR dipping associated with a profound reduction in UACR, despite the favourable long-term kidney outcomes in the population with T2D receiving SGLT2 inhibitor treatment.

Revealing the Structure-Luminescence Relationship in Robust Sn(IV)-Based Metal Halides by Sb3+ Doping.

Low-dimensional hybrid metal halides are an emerging class of materials with highly efficient photoluminescence (PL), but the problems of poor stability remain challenging. Sn(IV)-based metal halides show robust structure but exhibit poor PL properties, and the structure-luminescence relationship is elusive. Herein, two Sn(IV)-based metal halides (compounds 1 and 2) with the same constituent ((C6H16N2)SnCl6) but different crystal structures have been prepared, which however show poor PL properties at room temperature due to the absence of active ns2 electrons. To improve materials' PL properties, Sb3+ with active 5s2 electrons was embedded into the lattice of Sn4+-based hosts. As a result, efficient emissions were achieved for Sb3+-doped compounds 1 and 2 with a maximum PL efficiency of 14.28 and 62%, respectively. Experimental and calculation results reveal that the smaller distorted lattice structure of the host could result in the blueshift of the emission from Sb3+. Thus, a tunable color from red to orange was realized. Benefiting from the broadband efficient emission from Sb3+-doped compound 2, an efficient white light-emitting diode with a high color rendering index of up to 92.3 was fabricated to demonstrate its lighting application potential. This work promotes the understanding of the influence of robust Sn(IV)-based host lattice on the PL properties of Sb3+, advancing the development of environmentally friendly, low-cost, and high-efficiency Sn(IV)-based metal halides.

UiO-66/PIM-1 Mixed-Matrix Membrane for Hexane Isomer Separation.

The separation of high-octane dibranched alkanes from naphtha is critical in the refining of gasoline. To date, research on the membrane-based separation of alkane isomers has been limited, with a particular paucity of investigations into mixed-matrix membranes. Herein, the continuous and dense UiO-66/PIM-1 mixed-matrix membrane, which was prepared through precise control of the interfacial structure, was first applied to the differentiation of C6 alkane isomers. Due to the synergistic combination of UiO-66 with differential adsorption capabilities for alkanes and PIM-1 that possesses a cross-linkable structure, the resulting UiO-66/PIM-1-(20) membrane demonstrated remarkable separation performance and high stability. Pervaporation measurements showed that the mass fraction of 2,2-dimethylbutane in the feed side was increased from 50.0 to 75.8 wt % while an excellent flux of 1700 g m-2 h-1 was maintained over a continuous 40 h period. The UiO-66/PIM-1-(20) membrane, characterized by its facile replication and processing, shows potential for large-scale fabrication. This study offers a new approach to the membrane separation of alkane isomers.

Association of Valve Size and Hemodynamic Performance With Clinical Outcomes in Aortic Valve Replacement　- A Long-Term Follow-up in an Asian Population.

BACKGROUND: Studies of the influence of smaller body type on the severity of prosthesis-patient mismatch (PPM) after small-sized surgical aortic valve replacement (SAVR) are few, but the issue is particularly relevant for Asian patients.Methods and Results: 695 patients who underwent SAVR with bioprosthetic valves had their hemodynamic valve performance analyzed at 3 months, 1 year, 3 years, and 5 years after operation, and clinical outcomes were assessed. The patients were stratified into 3 valve size groups: 19/21, 23, and 25/27 mm. A smaller valve was associated with higher mean pressure gradients at the 4 time points after operation (P trend <0.05). However, the 3 valve size groups demonstrated no significant differences in the risk of clinical events. At none of the time points did patients with projected PPM show increased mean pressure gradients (P>0.05), whereas patients with measured PPM did (P<0.05). Compared with patients with projected PPM, those with measured PPM demonstrated higher rates of infective endocarditis readmission (adjusted hazard ratio [aHR] 3.31, 95% confidence interval [CI] 1.06-10.39) and a higher risk of composite outcomes (aHR 1.45, 95% CI 0.95-2.22, P=0.087). CONCLUSIONS: Relative to those receiving larger valves, patients receiving small bioprosthetic valves had poorer hemodynamic performance but did not demonstrate differences in clinical events in long-term follow-up.

Sex Differences in Epidemiological Distribution and Outcomes of Surgical Mitral Valve Disease.

BACKGROUND: Mitral valve (MV) disease is the most common form of valvular heart disease. Findings that indicate women have a higher risk for unfavorable outcomes than men remain controversial. This study aimed to determine the sex-based differences in epidemiological distributions and outcomes of surgery for MV disease.Methods and Results: Overall, 18,572 patients (45.3% women) who underwent MV surgery between 2001 and 2018 were included. Outcomes included in-hospital death and all-cause mortality during follow up. Subgroup analysis was conducted across different etiologies, including infective endocarditis (IE), degenerative, ischemic, and rheumatic mitral pathology. The overall MV repair rate was lower in women than in men (20.5% vs. 30.6%). After matching, 6,362 pairs (woman : man=1 : 1) of patients were analyzed. Women had a slightly higher risk for in-hospital death than men (10.8% vs. 9.8%; odds ratio [OR]: 1.11, 95% confidence interval [CI]: 0.99-1.24; P=0.075). Women tended to have a higher incidence of de novo dialysis (9.8% vs. 8.6%; P=0.022) and longer intensive care unit stay (8 days vs. 7.1 days; P<0.001). Women with IE had poorer in-hospital outcomes than men; however, there were no sex differences in terms of all-cause mortality. CONCLUSIONS: Sex-based differences of MV intervention still persist. Although long-term outcomes were comparable between sexes, women, especially those with IE, had worse perioperative outcomes than men.