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BACKGROUND: Automated analysis of lung computed tomography (CT) scans may help characterize subphenotypes of acute respiratory illness. We integrated lung CT features measured via deep learning with clinical and laboratory data in spontaneously breathing subjects to enhance the identification of COVID-19 subphenotypes. METHODS: This is a multicenter observational cohort study in spontaneously breathing patients with COVID-19 respiratory failure exposed to early lung CT within 7 days of admission. We explored lung CT images using deep learning approaches to quantitative and qualitative analyses; latent class analysis (LCA) by using clinical, laboratory and lung CT variables; regional differences between subphenotypes following 3D spatial trajectories. RESULTS: Complete datasets were available in 559 patients. LCA identified two subphenotypes (subphenotype 1 and 2). As compared with subphenotype 2 (n = 403), subphenotype 1 patients (n = 156) were older, had higher inflammatory biomarkers, and were more hypoxemic. Lungs in subphenotype 1 had a higher density gravitational gradient with a greater proportion of consolidated lungs as compared with subphenotype 2. In contrast, subphenotype 2 had a higher density submantellar-hilar gradient with a greater proportion of ground glass opacities as compared with subphenotype 1. Subphenotype 1 showed higher prevalence of comorbidities associated with endothelial dysfunction and higher 90-day mortality than subphenotype 2, even after adjustment for clinically meaningful variables. CONCLUSIONS: Integrating lung-CT data in a LCA allowed us to identify two subphenotypes of COVID-19, with different clinical trajectories. These exploratory findings suggest a role of automated imaging characterization guided by machine learning in subphenotyping patients with respiratory failure. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04395482. Registration date: 19/05/2020.

BackgroundSoybean cyst nematodes (SCN) as animal parasites of plants are not usually interested in killing the host but are rather focused on completing their life cycle to increase population, resulting in substantial yield losses. Remarkably, some agricultural soils after long-term crop monoculture show a significant decline in SCN densities and suppress disease in a sustainable and viable manner. However, relatively little is known about the microbes and mechanisms operating against SCN in such disease-suppressive soils.ResultsGreenhouse experiments showed that suppressive soils (S) collected from two provinces of China and transplantation soils (CS, created by mixing 10% S with 90% conducive soils) suppressed SCN. However, SCN suppressiveness was partially lost or completely abolished when S soils were treated with heat (80 °C) and formalin. Bacterial community analysis revealed that the specific suppression in S and CS was mainly associated with the bacterial phylum Bacteroidetes, specifically due to the enrichment of Chitinophaga spp. and Dyadobacter sp., in the cysts. SCN cysts colonized by Chitinophaga spp. showed dramatically reduced egg hatching, with unrecognizable internal body organization of juveniles inside the eggshell due to chitinase activity. Whereas, Dyadobacter sp. cells attached to the surface coat of J2s increased soybean resistance against SCN by triggering the expression of defence-associated genes. The disease-suppressive potential of these bacteria was validated by inoculating them into conducive soil. The Dyadobacter strain alone or in combination with Chitinophaga strains significantly decreased egg densities after one growing cycle of soybeans. In contrast, Chitinophaga strains alone required more than one growing cycle to significantly reduce SCN egg hatching and population density.ConclusionThis study revealed how soybean monoculture for decades induced microbiota homeostasis, leading to the formation of SCN-suppressive soil. The high relative abundance of antagonistic bacteria in the cyst suppressed the SCN population both directly and indirectly. Because uncontrolled proliferation will likely lead to quick demise due to host population collapse, obligate parasites like SCN may have evolved to modulate virulence/proliferation to balance these conflicting needs. Video Abstract.

Abstract Background Accurate breed identification is essential for the conservation and sustainable use of indigenous farm animal genetic resources. In this study, we evaluated the phylogenetic relationships and genomic breed compositions of 13 sheep breeds using SNP and InDel data from whole genome sequencing. The breeds included 11 Chinese indigenous and 2 foreign commercial breeds. We compared different strategies for breed identification with respect to different marker types, i.e. SNPs, InDels, and a combination of SNPs and InDels (named SIs), different breed-informative marker detection methods, and different machine learning classification methods. Results Using WGS-based SNPs and InDels, we revealed the phylogenetic relationships between 11 Chinese indigenous and two foreign sheep breeds and quantified their purities through estimated genomic breed compositions. We found that the optimal strategy for identifying these breeds was the combination of DFI_union for breed-informative marker detection, which integrated the methods of Delta, Pairwise Wright's FST, and Informativeness for Assignment (namely DFI) by merging the breed-informative markers derived from the three methods, and KSR for breed assignment, which integrated the methods of K-Nearest Neighbor, Support Vector Machine, and Random Forest (namely KSR) by intersecting their results. Using SI markers improved the identification accuracy compared to using SNPs or InDels alone. We achieved accuracies over 97.5% when using at least the 1000 most breed-informative (MBI) SI markers and even 100% when using 5000 SI markers. Conclusions Our results provide not only an important foundation for conservation of these Chinese local sheep breeds, but also general approaches for breed identification of indigenous farm animal breeds.

Abstract Objective This work aimed to investigate the potential correlation between chromosomal polymorphisms and various reproductive abnormalities. Methods We examined 21,916 patients affected by infertility who sought care at the Department of Reproductive Medicine, Affiliated Hospital of Shandong Second Medical University between January 2018 and December 2022. A total of 2227 individuals identified as chromosomal polymorphism carriers constituted the polymorphism group, and 2245 individuals with normal chromosome karyotypes were randomly selected to form a control group. Clinical manifestations, histories of spontaneous miscarriage, abnormal reproductive developments, fetal abnormalities, and male sperm quality anomalies were statistically compared between these two groups. Results Of the 21,916 patients analyzed, 2227 displayed chromosomal polymorphism, representing a 10.16% detection rate. Amongst the male patients, 1622 out of 10,827 exhibited polymorphisms (14.98%), whereas 605 out of 11,089 females showed polymorphisms (5.46%). Female carriers in the polymorphism group, showed statistically significant increased rates of spontaneous abortion (29.75% vs. 18.54%), fetal anomalies (1.32% vs. 0.81%), and uterine abnormalities compared with the control group (1.32% vs. 0.81%). Male carriers in the polymorphism group had higher rates of spontaneous abortion in partners (22.87% vs. 10.37%), fetal anomalies (1.97% vs. 0.25%), compromised sperm quality (41.74% vs. 7.18%), testicular underdevelopment (2.28% vs. 0.92%), and hypogonadotropic hypogonadism (0.62% vs. 0.37%) compared with the control group. Conclusion Chromosomal polymorphisms may have a certain negative effect on reproductive irregularities, including spontaneous abortions, fetal anomalies, and reduced sperm quality in males. Their clinical effects deserve further investigation.

ABSTRACT: Over 1 year, two KPC-producing and two non–KPC-producing Klebsiella pneumoniae strains were isolated from a patient. Genome and DNA hybridization analyses revealed the first three strains as a clonal lineage, with carbapenem resistance changes due to a Tn2-like transposon on an IncR/IncFII plasmid. The fourth strain, carrying three plasmids, caused a lethal infection and represented a different lineage. All strains belonged to the ST11-SL47-OL101 type. This study highlights the Tn2-like transposon's role in carbapenemase gene spread and the importance of distinguishing between bacterial colonization and infection.

Abstract Background R2R3-MYB transcription factors belong to one of the largest gene subfamilies in plants, and they are involved in diverse biological processes. However, the role of R2R3-MYB transcription factor subfamily genes in the response of rice (Oryza sativa L.) to salt stress has been rarely reported. Results In this study, we performed a genome-wide characterization and expression identification of rice R2R3-MYB transcription factor subfamily genes. We identified a total of 117 R2R3-MYB genes in rice and characterized their gene structure, chromosomal location, and cis-regulatory elements. According to the phylogenetic relationships and amino acid sequence homologies, the R2R3-MYB genes were divided into four groups. qRT-PCR of the R2R3-MYB genes showed that the expression levels of 10 genes significantly increased after 3 days of 0.8% NaCl treatment. We selected a high expression gene OsMYB2-115 for further analysis. OsMYB2-115 was highly expressed in the roots, stem, leaf, and leaf sheath. OsMYB2-115 was found to be localized in the nucleus, and the yeast hybrid assay showed that OsMYB2-115 has transcriptional activation activity. Conclusion This result provides important information for the functional analyses of rice R2R3-MYB transcription factor subfamily genes related to the salt stress response and reveals that OsMYB2-115 may be an important gene associated with salt tolerance in rice.

Abstract Background There is growing evidence for a relationship between gut microbiota and hepatic encephalopathy (HE). However, the causal nature of the relationship between gut microbiota and HE has not been thoroughly investigated. Method This study utilized the large-scale genome-wide association studies (GWAS) summary statistics to evaluate the causal association between gut microbiota and HE risk. Specifically, two-sample Mendelian randomization (MR) approach was used to identify the causal microbial taxa for HE. The inverse variance weighted (IVW) method was used as the primary MR analysis. Sensitive analyses were performed to validate the robustness of the results. Results The IVW method revealed that the genus Bifidobacterium (OR = 0.363, 95% CI: 0.139-0.943, P = 0.037), the family Bifidobacteriaceae (OR = 0.359, 95% CI: 0.133-0.950, P = 0.039), and the order Bifidobacteriales (OR = 0.359, 95% CI: 0.133-0.950, P = 0.039) were negatively associated with HE. However, no causal relationship was observed among them after the Bonferroni correction test. Neither heterogeneity nor horizontal pleiotropy was found in the sensitivity analysis. Conclusion Our MR study demonstrated a potential causal association between Bifidobacterium, Bifidobacteriaceae, and Bifidobacteriales and HE. This finding may provide new therapeutic targets for patients at risk of HE in the future.

Abstract Background Due to the high prevalence of multimorbidity and realistic health service demands for fall prevention, there is growing interest in the association between multimorbidity and falls. Our study aimed to identify multimorbidity patterns among Chinese older adults and explore the association between multimorbidity patterns and falls. Methods Data from 4,579 Chinese community-dwelling older adults was included in this analysis. Information regarding falls and 10 chronic conditions was collected. An exploratory factor analysis was performed to determine multimorbidity patterns. Regression models were fitted to explore the associations of individual chronic disease or multimorbidity patterns with falls. Results Among 4,579 participants, 368 (8.0%) were defined as fallers, including 92 (2.0%) frequent fallers, and multimorbidity affected 2,503 (54.7%) participants. Older adults with multimorbidity were more likely to be fallers [odds ratio (OR) = 1.3, P = 0.02] and frequent fallers (OR = 1.7, P = 0.04). Three multimorbidity patterns were identified (i.e., cardiovascular-metabolic diseases, psycho-cognitive diseases and organic diseases), and the associations between psycho-cognitive diseases/organic diseases and prevalent falls or frequent falls were found to be significant. Conclusions The psycho-cognitive disease pattern and organic disease pattern are significantly associated with falls. Therefore, more attention should be paid to patients with psycho-cognitive diseases and timely, targeted diagnostic and treatment services should be provided in fall prevention.

Abstract Schizophrenic patients’ brain tumor magnetic resonance imaging (MRI) images are important references for doctors to diagnose and treat schizophrenia. However, automatic segmentation of these images is a professional and tedious task. Existing methods suffer from problems such as large model parameters, long computation time, and inadequate image processing. To achieve more accurate segmentation of brain tumors, we propose brain tumor MRI images for automatic segmentation using self-supervised contrastive learning in schizophrenia patients (BTCSSSP). First, a denoising algorithm based on progressive principal component analysis approximation and adaptive clustering is designed to process the noisy MRI images. Second, a brightness-aware image enhancement algorithm is developed to address the problems of non-uniformity, unclear boundaries, and poor spatial resolution of the MRI images. Finally, a cross-scale U-Net network with selective feature fusion attention module is designed based on self-supervised contrastive learning to achieve automatic segmentation of brain tumor MRI images. The results show that the BTCSSSP method yields higher Recall and Precision than existing methods. The maximum recall is 95%, and the image segmentation precision is 95%, thus indicating good practical applicability.

Abstract During oxidative phosphorylation, mitochondria continuously produce reactive oxygen species (ROS), and untimely ROS clearance can subject mitochondria to oxidative stress, ultimately resulting in mitochondrial damage. Mitophagy is essential for maintaining cellular mitochondrial quality control and homeostasis, with activation involving both ubiquitin-dependent and ubiquitin-independent pathways. Over the past decade, numerous studies have indicated that different forms of regulated cell death (RCD) are connected with mitophagy. These diverse forms of RCD have been shown to be regulated by mitophagy and are implicated in the pathogenesis of a variety of diseases, such as tumors, degenerative diseases, and ischemia‒reperfusion injury (IRI). Importantly, targeting mitophagy to regulate RCD has shown excellent therapeutic potential in preclinical trials, and is expected to be an effective strategy for the treatment of related diseases. Here, we present a summary of the role of mitophagy in different forms of RCD, with a focus on potential molecular mechanisms by which mitophagy regulates RCD. We also discuss the implications of mitophagy-related RCD in the context of various diseases.

Understanding the intention of community residents to seek help from mental health professionals (MHPs) is essential in targeting interventions that promote the prevention and treatment of depression. This study aimed to investigate the current status of Chinese community populations' depression help-seeking intentions from MHPs and explore factors influencing the intentions. Data were used from a survey conducted in a city in central China (n = 919 aged 38.68 ± 17.34, 72.1% female). Help-seeking intentions, help-seeking attitude, depression stigma, family function and depressive symptoms were measured. The total mean score on the intent to seek help from MHPs was 11.01 ± 7.78 and most of respondents were unwilling to seek professional help. Multiple linear regression showed that participants who were students, held a positive help-seeking attitude and had low personal stigma were more likely to have the intention to seek help from MHPs. It is necessary to utilize effective interventions to improve community residents' intention to seek professional help. These include promoting the importance of seeking professional assistance, optimizing the quality of mental health services and altering residents' prejudice to seeking professional help.

When gravitational waves (GWs) pass by a massive object on its way to Earth, a strong gravitational lensing effect will happen. Thus, the GW signal will be amplified, deflected, and delayed in time. Through analyzing the lensed GW waveform, physical properties of the lens can be inferred. On the other hand, neglecting lensing effects in the analysis of GW data may induce systematic errors in the estimating of source parameters. As a space-borne GW detector, TianQin will be launched in the 2030s. It is expected to detect dozens of mergers of massive black hole binaries (MBHBs) as far as z=15 and thus will have high probability to detect at least one lensed event during the mission lifetime. In this article, we discuss the capability of TianQin to detect lensed MBHB signals. Three lens models are considered in this work: the point mass model, the singular isothermal sphere (SIS) model, and the Navarro-Frenk-White (NFW) model. The sensitive frequency band for space-borne GW detectors is around millihertz, and the corresponding GW wavelength could be comparable to the lens gravitational length scale, which requires us to account for wave diffraction effects. In calculating lensed waveforms, we adopt the approximation of geometric optics at high frequencies to accelerate computation, while precisely evaluating the diffraction integral at low frequencies. Through a Fisher analysis, we analyze the accuracy to estimate the lens parameters. We find that the accuracy can reach to the level of 10-3 for the mass of point mass and SIS lens and to the level of 10-5 for the density of the NFW lens. We also assess the impact on the accuracy of estimating the source parameters and find that the improvement of the accuracy is dominated by the increasing of signal-to-noise ratio.

The lithium-ion battery is currently the preferred power source for applications ranging from smart phones to electric vehicles. Imaging the chemical reactions governing its function as they happen, with nanoscale spatial resolution and chemical specificity, is a long-standing open problem. Here, we demonstrate operando spectrum imaging of a Li-ion battery anode over multiple charge-discharge cycles using electron energy-loss spectroscopy (EELS) in a scanning transmission electron microscope (STEM). Using ultrathin Li-ion cells, we acquire reference EELS spectra for the various constituents of the solid-electrolyte interphase (SEI) layer and then apply these "chemical fingerprints" to high-resolution, real-space mapping of the corresponding physical structures. We observe the growth of Li and LiH dendrites in the SEI and fingerprint the SEI itself. High spatial- and spectral-resolution operando imaging of the air-sensitive liquid chemistries of the Li-ion cell opens a direct route to understanding the complex, dynamic mechanisms that affect battery safety, capacity, and lifetime.