OBOX regulates mouse zygotic genome activation and early development.

Zygotic genome activation (ZGA) activates the quiescent genome to enable the maternal-to-zygotic transition1,2. However, the identity of transcription factors that underlie mammalian ZGA in vivo remains elusive. Here we show that OBOX, a PRD-like homeobox domain transcription factor family (OBOX1-OBOX8)3-5, are key regulators of mouse ZGA. Mice deficient for maternally transcribed Obox1/2/5/7 and zygotically expressed Obox3/4 had a two-cell to four-cell arrest, accompanied by impaired ZGA. The Obox knockout defects could be rescued by restoring either maternal and zygotic OBOX, which suggests that maternal and zygotic OBOX redundantly support embryonic development. Chromatin-binding analysis showed that Obox knockout preferentially affected OBOX-binding targets. Mechanistically, OBOX facilitated the 'preconfiguration' of RNA polymerase II, as the polymerase relocated from the initial one-cell binding targets to ZGA gene promoters and distal enhancers. Impaired polymerase II preconfiguration in Obox mutants was accompanied by defective ZGA and chromatin accessibility transition, as well as aberrant activation of one-cell polymerase II targets. Finally, ectopic expression of OBOX activated ZGA genes and MERVL repeats in mouse embryonic stem cells. These data thus demonstrate that OBOX regulates mouse ZGA and early embryogenesis.

Inferring gene regulatory network from single-cell transcriptomes with graph autoencoder model.

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition.

AutoEdge-CCP: A novel approach for predicting cancer-associated circRNAs and drugs based on automated edge embedding.

The unique expression patterns of circRNAs linked to the advancement and prognosis of cancer underscore their considerable potential as valuable biomarkers. Repurposing existing drugs for new indications can significantly reduce the cost of cancer treatment. Computational prediction of circRNA-cancer and drug-cancer relationships is crucial for precise cancer therapy. However, prior computational methods fail to analyze the interaction between circRNAs, drugs, and cancer at the systematic level. It is essential to propose a method that uncover more valuable information for achieving cancer-centered multi-association prediction. In this paper, we present a novel computational method, AutoEdge-CCP, to unveil cancer-associated circRNAs and drugs. We abstract the complex relationships between circRNAs, drugs, and cancer into a multi-source heterogeneous network. In this network, each molecule is represented by two types information, one is the intrinsic attribute information of molecular features, and the other is the link information explicitly modeled by autoGNN, which searches information from both intra-layer and inter-layer of message passing neural network. The significant performance on multi-scenario applications and case studies establishes AutoEdge-CCP as a potent and promising association prediction tool.

Unveiling the Intrinsic Photophysics in Quasi-Two-Dimensional Perovskites.

The two-dimensional (2D) perovskites have drawn intensive attention due to their unique stability and outstanding optoelectronic properties. However, the debate surrounding the spatial phase distribution and band alignment among different 2D phases in the quasi-2D perovskite has created complexities in understanding the carrier dynamics, hindering material and device development. In this study, we employed highly sensitive transient absorption spectroscopy to investigate the carrier dynamics of (BA)2(MA)n-1PbnI3n+1 quasi-2D Ruddlesden-Popper perovskite thin films, nominally prepared as n = 4. We observed the carrier-density-dependent electron and hole transfer dynamics between the 2D and three-dimensional (3D) phases. Under a low carrier density within the linear response range, we successfully resolved three ultrafast processes of both electron and hole transfers, spanning from hundreds of femtoseconds to several picoseconds, tens to hundreds of picoseconds, and hundreds of picoseconds to several nanoseconds, which can be attributed to lateral-epitaxial, partial-epitaxial, and disordered-interface heterostructures between 2D and 3D phases. By considering the interplay among the phase structure, band alignment, and carrier dynamics, we have proposed material synthesis strategies aimed at enhancing the carrier transport. Our results not only provide deep insights into an accurate intrinsic photophysics of quasi-2D perovskites but also inspire advancements in the practical application of these materials.

A Durable and High-Voltage Mn-Graphite Dual-Ion Battery Using Mn-Based Hybrid Electrolytes.

Rechargeable Mn-metal batteries (MMBs) can attract considerable attention because Mn has the intrinsic merits including high energy density (976 mAh g-1), high air stability, and low toxicity. However, the application of Mn in rechargeable batteries is limited by the lack of proper cathodes for reversible Mn2+ intercalation/de-intercalation, thus leading to low working voltage (<1.8 V) and poor cycling stability (≤200 cycles). Herein, a high-voltage and durable MMB with graphite as the cathode is successfully constructed using a LiPF6-Mn(TFSI)2 hybrid electrolyte, which shows a high discharge voltage of 2.34 V and long-term stability of up to 1000 cycles. Mn(TFSI)2 can reduce the plating/stripping overpotential of Mn ions, while LiPF6 can efficiently improve the conductivity of the electrolyte. Electrochemical in-situ characterization implies the dual-anions intercalation/de-intercalation at the cathode and Mn2+ plating/stripping reaction at the anode. Theoretical calculations unveil the top site of graphite is the energetically favorable for anions intercalation and TFSI- shows the low migration barrier. This work paves an avenue for designing high-performance rechargeable MMBs towards electricity storage.

Vertical Cross-Alignments of 2D Semiconductors with Steered Internal Electric Field for Urea Electrooxidation via Balancing Intermediates Adsorption.

Single-component electrocatalysts generally lead to unbalanced adsorption of OH- and urea during urea oxidation reaction (UOR), thus obtaining low activity and selectivity especially when oxygen evolution reaction (OER) competes at high potentials (>1.5 V). Herein, a cross-alignment strategy of in situ vertically growing Ni(OH)2 nanosheets on 2D semiconductor g-C3N4 is reported to form a hetero-structured electrocatalyst. Various spectroscopy measurements including in situ experiments indicate the existence of enhanced internal electric field at the interfaces of vertical Ni(OH)2 and g-C3N4 nanosheets, favorable for balancing adsorption of reaction intermediates. This heterojunction electrocatalyst shows high-selectivity UOR compared to pure Ni(OH)2, even at high potentials (>1.5 V) and large current density. The computational results show the vertical heterojunction could steer the internal electric field to increase the adsorption of urea, thus efficiently avoiding poisoning of strongly adsorbed OH- on active sites. A membrane electrode assembly (MEA)-based electrolyzer with the heterojunction anode could operate at an industrial-level current density of 200 mA cm-2. This work paves an avenue for designing high-performance electrocatalysts by vertical cross-alignments of active components.

A comparison of deep learning-based pre-processing and clustering approaches for single-cell RNA sequencing data.

The emergence of single cell RNA sequencing has facilitated the studied of genomes, transcriptomes and proteomes. As available single-cell RNA-seq datasets are released continuously, one of the major challenges facing traditional RNA analysis tools is the high-dimensional, high-sparsity, high-noise and large-scale characteristics of single-cell RNA-seq data. Deep learning technologies match the characteristics of single-cell RNA-seq data perfectly and offer unprecedented promise. Here, we give a systematic review for most popular single-cell RNA-seq analysis methods and tools based on deep learning models, involving the procedures of data preprocessing (quality control, normalization, data correction, dimensionality reduction and data visualization) and clustering task for downstream analysis. We further evaluate the deep model-based analysis methods of data correction and clustering quantitatively on 11 gold standard datasets. Moreover, we discuss the data preferences of these methods and their limitations, and give some suggestions and guidance for users to select appropriate methods and tools.

Deep learning models for disease-associated circRNA prediction: a review.

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.

A co-adaptive duality-aware framework for biomedical relation extraction.

MOTIVATION: Biomedical relation extraction is a vital task for electronic health record mining and biomedical knowledge base construction. Previous work often adopts pipeline methods or joint methods to extract subject, relation, and object while ignoring the interaction of subject-object entity pair and relation within the triplet structure. However, we observe that entity pair and relation within a triplet are highly related, which motivates us to build a framework to extract triplets that can capture the rich interactions among the elements in a triplet. RESULTS: We propose a novel co-adaptive biomedical relation extraction framework based on a duality-aware mechanism. This framework is designed as a bidirectional extraction structure that fully takes interdependence into account in the duality-aware extraction process of subject-object entity pair and relation. Based on the framework, we design a co-adaptive training strategy and a co-adaptive tuning algorithm as collaborative optimization methods between modules to promote better mining framework performance gain. The experiments on two public datasets show that our method achieves the best F1 among all state-of-the-art baselines and provides strong performance gain on complex scenarios of various overlapping patterns, multiple triplets, and cross-sentence triplets. AVAILABILITY AND IMPLEMENTATION: Code is available at https://github.com/11101028/CADA-BioRE.

Risk Factors of Distant Metastatic Parathyroid Carcinoma and Insights into Therapeutic Perspectives.

BACKGROUND: Distant metastatic parathyroid carcinoma (DM-PC) is a rare but often lethal entity with limited data about prognostic indicators. We sought to investigate the risk factors, patterns, and outcomes of DM-PC. METHODS: In this observational cohort study, 126 patients who underwent surgery for PC at a tertiary referral center from 2010 to 2023 were enrolled, among whom 38 had DMs. Univariate and multivariate Cox regression analyses were used to assess the effects of prognostic factors on DM. RESULTS: The cumulative incidence of DM was 14.1%, 33.8%, and 66.9% at 5, 10, and 20 years in the duration of disease course, respectively. DM-PC patients suffered a worse 5-year overall survival of 37.1% compared with 89.8% in the non-DM patients (p < 0.001). DM-PC patients also suffered more previous operations (p < 0.001), higher preoperative serum calcium (p<0.001) and parathyroid hormone (PTH) levels (p < 0.001), lower frequencies of R0 resection (p < 0.001), higher rates of pathological vascular invasion (p = 0.020), thyroid infiltration (p = 0.027), extraglandular extension (p = 0.001), upper aerodigestive tract (UAT) invasion (p < 0.001), and lymph node metastasis (p < 0.001). Multivariate Cox regression revealed that non-R0 resection (HR 6.144, 95% CI 2.881-13.106, p < 0.001), UAT invasion (HR 3.718, 95% CI 1.782-7.756, p < 0.001), and higher preoperative PTH levels (HR 1.001, 95% CI 1.000-1.001, p = 0.012) were independent risk factors of DM. CONCLUSIONS: Upper aerodigestive tract invasion and higher preoperative PTH levels might be risk factors for possible metastatic involvement of PC. R0 resection and closer surveillance should be considered in such cases to minimize the risk of DM and to optimize patient care.

Lipid metabolism mediates the association between body mass index change and bone mineral density: The Taizhou imaging study.

BACKGROUND: Limited research explores the impact of body mass index (BMI) change on osteoporosis, regarding the role of lipid metabolism. We aimed to cross-sectionally investigate these relationships in 820 Chinese participants aged 55-65 from the Taizhou Imaging Study. METHODS: We used the baseline data collected between 2013 and 2018. T-score was calculated by standardizing bone mineral density and was used for osteoporosis and osteopenia diagnosis. Multinomial logistic regression was used to examine the effect of BMI change on bone health status. Multivariable linear regression was employed to identify the metabolites corrected with BMI change and T-score. Exploratory factor analysis (EFA) and mediation analysis were conducted to ascertain the involvement of the metabolites. RESULTS: BMI increase served as a protective factor against osteoporosis (OR = 0.79[0.71-0.88], P-value<0.001) and osteopenia (OR = 0.88[0.82-0.95], P-value<0.001). Eighteen serum metabolites were associated with both BMI change and T-score. Specifically, high-density lipoprotein (HDL) substructures demonstrated negative correlations (ß = -0.08 to -0.06 and - 0.12 to -0.08, respectively), while very low-density lipoprotein (VLDL) substructions showed positive correlations (ß = 0.09 to 0.10 and 0.10 to 0.11, respectively). The two lipid factors (HDL and VLDL) extracted by EFA acted as mediators between BMI change and T-score (Prop. Mediated = 8.16% and 10.51%, all P-value<0.01). CONCLUSION: BMI gain among Chinese aged 55-65 is beneficial for reducing the risk of osteoporosis. The metabolism of HDL and VLDL partially mediates the effect of BMI change on bone loss. Our research offers novel insights into the prevention of osteoporosis, approached from the perspective of weight management and lipid metabolomics.

Isolation and functional identification of immune cells in hemolymph of blood clams Tegillarca granosa.

Blood clam Tegillarca granosa is a type of economically cultivated bivalve mollusk with red blood, and it primarily relies on hemocytes in its hemolymph for immune defense. However, there are currently no reports on the isolation and identification of immune cells in T. granosa, which hinders our understanding of their immune defense. In this study, we employed single-cell transcriptome sequencing (scRNA-seq) to visualize the molecular profile of hemocytes in T. granosa. Based on differential expression of immune genes and hemoglobin genes, hemocytes can be molecularly classified into immune cells and erythrocytes. In addition, we separated immune cells using density gradient centrifugation and demonstrated their stronger phagocytic capacity compared to erythrocytes, as well as higher levels of ROS and NO. In summary, our experiments involved the isolation and functional identification of immune cells in hemolymph of T. granosa. This study will provide valuable insights into the innate immune system of red-blood mollusks and further deepen the immunological research of mollusks.

DNA-based nanosized functional ruthenium(II) complex as potential phosphorescent probe to track tumor cells.

Identifying tumor cells can be challenging due to cancer's complex and heterogeneous nature. Here, an efficacious phosphorescent probe that can precisely highlight tumor cells has been created. By combining the ruthenium(II) complex with oligonucleotides, we have developed a nanosized functional ruthenium(II) complex (Ru@DNA) with dimensions ranging from 300 to 500 nm. Our research demonstrates that Ru@DNA can readily traverse biomembranes via ATP-dependent endocytosis without carriers. Notably, the nanosized ruthenium(II) complex exhibits rapid and selective accumulation within tumor cells, possibly attributed to the nanoparticles' enhanced permeation and retention (EPR) effect. Ru@DNA can also effectively discern and label the transplanted cancer cells in the zebrafish model. Moreover, Ru@DNA is efficiently absorbed into the intestine and further distributed in the pancreas. Our findings underscore the potential of Ru@DNA as a DNA-based nanodevice derived from a functional ruthenium(II) complex. This innovative nanodevice holds promise as an efficient phosphorescent probe for both in vitro and in vivo imaging of living tumor cells.

Induction of therapeutic immunity and cancer eradication through biofunctionalized liposome-like nanovesicles derived from irradiated-cancer cells.

Immunotherapy has revolutionized the treatment of cancer. However, its efficacy remains to be optimized. There are at least two major challenges in effectively eradicating cancer cells by immunotherapy. Firstly, cancer cells evade immune cell killing by down-regulating cell surface immune sensors. Secondly, immune cell dysfunction impairs their ability to execute anti-cancer functions. Radiotherapy, one of the cornerstones of cancer treatment, has the potential to enhance the immunogenicity of cancer cells and trigger an anti-tumor immune response. Inspired by this, we fabricate biofunctionalized liposome-like nanovesicles (BLNs) by exposing irradiated-cancer cells to ethanol, of which ethanol serves as a surfactant, inducing cancer cells pyroptosis-like cell death and facilitating nanovesicles shedding from cancer cell membrane. These BLNs are meticulously designed to disrupt both of the aforementioned mechanisms. On one hand, BLNs up-regulate the expression of calreticulin, an "eat me" signal on the surface of cancer cells, thus promoting macrophage phagocytosis of cancer cells. Additionally, BLNs are able to reprogram M2-like macrophages into an anti-cancer M1-like phenotype. Using a mouse model of malignant pleural effusion (MPE), an advanced-stage and immunotherapy-resistant cancer model, we demonstrate that BLNs significantly increase T cell infiltration and exhibit an ablative effect against MPE. When combined with PD-1 inhibitor (α-PD-1), we achieve a remarkable 63.6% cure rate (7 out of 11) among mice with MPE, while also inducing immunological memory effects. This work therefore introduces a unique strategy for overcoming immunotherapy resistance.

Severe megaloblastic anemia in a patient with advanced lung adenocarcinoma during treatment with erlotinib: a case report and literature review.

BACKGROUND: Erlotinib is a first-generation, tyrosine kinase inhibitor of the epidermal growth factor receptor (EGFR-TKI) used for the treatment patients with NSCLC. Erlotinib is considered as a safe and effective treatment option, with generally good tolerance. Diarrhea and rash are the most common side effects, and more rare side effects appear in long-term real-world applications. Severe erlotinib related megaloblastic anemia is rare and remains unreported. This is the first case report of severe megaloblastic anemia in a patient with advanced lung adenocarcinoma with an EGFR L858R mutation treated with erlotinib. In this report, the clinical manifestations, diagnosis and treatment of erlotinib related severe megaloblastic anemia are described, and the possible pathogenesis and related treatment options are discussed. CASE DESCRIPTION: Herein, we present a 57- year-old non-smoking female diagnosed with metastatic lung adenocarcinoma harboring an EGFR L858R mutation, who had received erlotinib as the first-line therapy. After 44 weeks of treatment, the patient developed severe anemia. Anemia was manifested as megaloblastic anemia with elevated mean corpuscular volume and mean corpuscular hemoglobin. The total vitamin B12 level was below the detection limit of 50.00 pg /mL. Bone marrow smear suggested megaloblastic anemia. Her hematologic parameters were markedly recovered following the withdrawal of erlotinib and vitamin B12 supplement. As a result, the patient was diagnosed with erlotinib-associated megaloblastic anemia. CONCLUSIONS: This is the first case of severe megaloblastic anemia reported with erlotinib. Few of these hematologic adverse effects have been observed in studies on erlotinib, this case report highlights this possibility for long-term erlotinib administration. Close clinical and blood monitoring is recommended for patients receiving long-term TKI therapy.

Synergistic Lewis and Brønsted Acid Sites Promote OH* Formation and Enhance Formate Selectivity: Towards High-efficiency Glycerol Valorization.

As a sustainable valorization route, electrochemical glycerol oxidation reaction (GOR) involves in formation of key OH* and selective adsorption/cleavage of C-C(O) intermediates with multi-step electron transfer, thus suffering from high potential and poor formate selectivity for most non-noble-metal-based electrocatalysts. So, it remains challenging to understand the structure-property relationship as well as construct synergistic sites to realize high-activity and high-selectivity GOR. Herein, we successfully achieve dual-high performance with low potentials and superior formate selectivity for GOR by forming synergistic Lewis and Brønsted acid sites in Ni-alloyed Co-based spinel. The optimized NiCo oxide solid-acid electrocatalyst exhibits low reaction potential (1.219 V@10 mA/cm2) and high formate selectivity (94.0 %) toward GOR. In situ electrochemical impedance spectroscopy and pH-dependence measurements show that the Lewis acid centers could accelerate OH* production, while the Brønsted acid centers are proved to facilitate high-selectivity formation of formate. Theoretical calculations reveal that NiCo alloyed oxide shows appropriate d-band center, thus balancing adsorption/desorption of C-O intermediates. This study provides new insights into rationally designing solid-acid electrocatalysts for biomass electro-upcycling.

Strategy of In Situ Electrochemical Regulation for Highly Enhanced Nonenzymatic Sensing of Carbaryl.

Specific and sensitive sensing of most pesticide residues relies on enzymes such as acetylcholinesterase and advanced materials, which need to be loaded on the surface of working electrodes, leading to instability, uneven surface, tedious process, and high cost. Meanwhile, employing certain potential or current in electrolyte solution could also modify the surface in situ and overcome these drawbacks. However, this method is only regarded as electrochemical activation widely applied in the pretreatment of electrodes. In this paper, by means of regulating the electrochemical technique and its parameters, we prepared a proper sensing interface and derivatized the carbaryl (a carbamate pesticide) hydrolyzed form (1-naphthol) to enhance sensing by 100 times within several minutes. After regulation I by chronopotentiometry with 0.2 mA for 20 s or chronoamperometry with 2 V for 10 s, abundant oxygen-containing groups form and the ordered carbon structure is destroyed. Sweeping from -0.5 to 0.9 V through cyclic voltammetry for only one segment, following regulation II, the composition of oxygen-containing groups changes and the disordered structure is alleviated. Finally, on the constructed sensing interface, test by regulation III through differential pulse voltammetry from 0.8 to -0.4 V, resulting in derivatization of 1-naphthol during 0.8-0 V, followed by electroreduction of the derivative at around -0.17 V. Compared with the electro-oxidation peak at 0.5 V in previous reports, it is essential to improve specificity, even toward several other carbamate pesticides with similar structures. Hence, the in situ electrochemical regulation strategy has demonstrated great potential for effective sensing of electroactive molecules.

Modulating Surface Electron Density of Heterointerface with Bio-Inspired Light-Trapping Nano-Structure to Boost Kinetics of Overall Water Splitting.

Herein, inspired by natural sunflower heads' properties increasing the temperature of dish-shaped flowers by tracking the sun, a novel hybrid heterostructure (MoS2 /Ni3 S2 @CA, CA means carbon nanowire arrays) with the sunflower-like structure to boost the kinetics of water splitting is proposed. Density functional theory (DFT) reveals that it can modulate the active electronic states of NiMo atoms around the Fermi-level through the charge transfer between the metallic atoms of Ni3 S2 and MoMo bonds of MoS2 to boost overall water splitting. Most importantly, the finite difference time domain (FDTD) could find that its unique bio-inspired micro-nano light-trapping structure has high solar photothermal conversion efficiency. With the assistance of the photothermal field, the kinetics of water-splitting is improved, affording low overpotentials of 96 and 229 mV at 10 mA cm-2 for HER and OER, respectively. Moreover, the Sun-MoS2 /Ni3 S2 @CA enables the overall alkaline water splitting at a low cell voltage of 1.48 and 1.64 V to achieve 10 and 100 mA cm-2 with outstanding catalytic durability. This study may open up a new route for rationally constructing bionic sunflower micro-nano light-trapping structure to maximize their photothermal conversion and electrochemical performances, and accelerate the development of nonprecious electrocatalysts for overall water splitting.

BMI trajectory of rapid and excessive weight gain during adulthood is associated with bone loss: a cross-sectional study from NHANES 2005-2018.

BACKGROUND: Studies have examined the effect of weight change on osteoporosis, but the results were controversial. Among them, few had looked at weight change over the life span. This study aimed to fill this gap and investigate the association between lifetime body mass index (BMI) trajectories and bone loss. METHODS: In this cross-sectional study, participants at age 50 and above were selected from the National Health and Nutrition Examination Survey (NHANES) 2005-2018. Dual-energy X-ray Absorptiometry was used to measure the bone mineral density at the femoral neck and lumbar spine. Standard BMI criteria were used, with < 25 kg/m2 for normal, 25-29.9 kg/m2 for overweight, and ≥ 30 kg/m2 for obesity. The latent class trajectory model (LCTM) was used to identify BMI trajectories. Multinomial logistic regression models were fitted to evaluate the association between different BMI trajectories and osteoporosis or osteopenia. RESULTS: For the 9,706 eligible participants, we identified four BMI trajectories, including stable (n = 7,681, 70.14%), slight increase (n = 1253, 12.91%), increase to decrease (n = 195, 2.01%), and rapid increase (n = 577, 5.94%). Compared with individuals in the stable trajectory, individuals in the rapid increase trajectory had higher odds of osteoporosis (OR = 2.25, 95% CI 1.19-4.23) and osteopenia (OR = 1.49, 95% CI 1.02-2.17). This association was only found in the lumbar spine (OR = 2.11, 95% CI 1.06-4.2) but not in the femoral neck. In early-stage (age 25-10 years ago) weight change, staying an obesity and stable weight seemed to have protective effects on osteoporosis (OR = 0.26, 95% CI 0.08-0.77) and osteopenia (OR = 0.46, 95% CI 0.25-0.84). Meanwhile, keeping an early-stage stable and overweight was related to lower odds of osteopenia (OR = 0.53, 95% CI 0.34-0.83). No statistically significant association between recent (10 years ago to baseline) weight change and osteoporosis was found. CONCLUSIONS: Rapid and excess weight gain during adulthood is associated with a higher risk of osteoporosis. But this association varies by skeletal sites. Maintaining stable overweight and obesity at an early stage may have potentially beneficial effects on bone health.

Spiribacter salilacus sp. nov., a novel moderately halophilic bacterium isolated from a saline lake in China.

A Gram-staining-negative, non-motile, rod-shaped or curved rod-shaped, moderately halophilic bacterium, designated C176T, was isolated from Yuncheng Salt Lake in Shanxi Province, P.R. China. The optimal temperature, salinity and pH for growth of strain C176T was 37 °C, 6% (w/v) NaCl and 7.5. Phylogenetic analysis using 16S rRNA gene sequences indicated strain C176T has the highest similarity with Spiribacter salinus LMG 27464 T (97.7%), following by the S. halobius E85T (97.6%), S. curvatus DSM 28542 T (97.2%), S. roseus CECT 9117 T (97.0%) and S. vilamensis DSM 21056 T (96.9%). The ANI and dDDH values between strain C176T and S. salinus LMG 27464 T were 69.8 and 17.7%, respectively. The DNA G + C content of genome for strain C176T was 54.1%. Summed feature 8 (C18:1 ω7c and/or C18:1 ω6c) and C16:0 were detected as its major fatty acids, with content of 38.7 and 28.6% respectively, while Q-8 was the predominant ubiquinone. The major polar lipids of strain C176T contained phospholipid, phosphatidylglycerol and phosphoglycolipid. In accordance with results of polyphasic taxonomy, strain C176T is considered as a novel species of the genus Spiribacter, for which the name Spiribacter salilacus sp. nov. is proposed. The type strain is C176T (= MCCC 1H00417T = KCTC 72692 T).