[Effects of Vermicomposting on Compost Quality and Heavy Metals：A Meta-analysis].

In order to comprehensively evaluate the effects of vermicomposting on compost quality and the conversion of heavy metals under different control conditions, 109 studies were reviewed. The effects of earthworm species, pre-compost time, ventilation methods, initial C/N, initial pH, and initial moisture of the raw materials on compost quality and the heavy metal toxicity were quantitatively discussed during the vermicomposting process through Meta-analysis. The results showed that the six subgroups of factors all showed obvious influences on the compost quality and heavy metal toxicity. After vermicomposting, the contents of NO3--N (116.2%), TN (29.1%), TP (31.2%), and TK (15.0%) were significantly increased, whereas NH4+-N (-14.8%) and C/N (-36.3%) were significantly decreased. Meanwhile, the total amount of Cu and Cr of the final compost and their bioavailability were significantly reduced. Considering the influences of grouping factors on compost quality and heavy metals, it is recommended to adjust the initial moisture of pile materials to 70%-80%, C/N to 30-85, and pH to 6-7 and to conduct pre-composting for 0-15 d; additionally, vermicomposting should be naturally placed when the composting is aimed at promoting the compost quality. If the main purpose is to weaken the perniciousness of heavy metals in the raw material, it is recommended to adjust the initial moisture of the material to 50%-60%, C/N to less than 30, and pH to 7-8; to conduct no pre-compost; regularly turn the piles; and use the earthworm Eudrilus eugeniae for vermicomposting.

Lipid-Lowering Meroterpenoids Penihemeroterpenoids A-F from Penicillium herquei GZU-31-6 via Targeting the AMPK/ACC/SREBP-1c Signaling Pathway.

Penihemeroterpenoids A-C, the first meroterpenoids with an unprecedented 6/5/6/5/5/6/5 heptacyclic ring system, together with precursors penihemeroterpenoids D-F, were co-isolated from the fungus Penicillium herquei GZU-31-6. Among them, penihemeroterpenoids C-F exhibited lipid-lowering effects comparable to those of the positive control simvastatin by the activation of the AMPK/ACC/SREBP-1c signaling pathway, downregulated the mRNA levels of lipid synthesis genes FAS and PNPLA3, and increased the level of mRNA expression of the lipid export gene MTTP.

Associations of Sedentary Behavior with Risks of Cardiovascular Disease Events among Chinese Adults.

AIMS: Evidence regarding the modification effects of age, sex, ethnicity, socioeconomic status, or weight status on the associations of sedentary behavior (SB) with cardiovascular diseases (CVDs) is limited. Moreover, the mechanisms for the associations also remain unclear. We aimed to investigate the possible influence of these factors on the associations of SB with CVD events and whether the associations are mediated by metabolic phenotypes. METHODS: This study included 42,619 participants aged 20-74 years, recruited from the Shanghai Suburban Adult Cohort and Biobank study. SB was assessed at baseline and integrated with health information systems to predict future CVD events. Cox proportional hazards models, interaction analyses, restricted cubic splines and causal mediation analyses were used for assessments. RESULTS: Compared to those with ＜3 h/d sedentary time, participants having SB ≥ 5 h/d had significantly higher risks of CVD (HR[95%CI]: 1.27[1.12-1.44]), coronary heart disease (CHD, 1.35[1.14-1.60]), and ischemic stroke (IS, 1.30[1.06-1.60]). The association of CHD was more pronounced in the retired individuals than their counterparts (1.45[1.20-1.76] versus 1.06[0.74-1.52], pinteraction=0.046). When SB was expressed as a continuous variable, a 1 h/d increment in SB was positively associated with risks of CVD (1.03[1.01-1.05]), CHD (1.04[1.01-1.07]), and IS (1.05[1.01-1.08]). High-density lipoprotein cholesterol (HDL-C, proportion mediated: 12.54%, 12.23%, and 11.36%, all p＜0.001), followed by triglyceride (TG, 5.28%, 4.77%, and 4.86%, all p＜0.01) and serum uric acid (SUA, 3.64%, 4.24%, and 2.29%, all p＜0.05) were major mediators through metabolic phenotypes. CONCLUSIONS: Higher SB was associated with elevated risks of CVD events. The detrimental effect of SB on CHD risk was more pronounced among retired individuals. Moreover, HDL-C, TG and SUA partially mediated the relationships between SB and CVD events. Our findings may have implications for preventing and controlling CVD associated with SB.

Biological Interactions Mediate Soil Functions by Altering Rare Microbial Communities.

Soil microbes, the main driving force of terrestrial biogeochemical cycles, facilitate soil organic matter turnover. However, the influence of the soil fauna on microbial communities remains poorly understood. We investigated soil microbiota dynamics by introducing competition and predation among fauna into two soil ecosystems with different fertilization histories. The interactions significantly affected rare microbial communities including bacteria and fungi. Predation enhanced the abundance of C/N cycle-related genes. Rare microbial communities are important drivers of soil functional gene enrichment. Key rare microbial taxa, including SM1A02, Gammaproteobacteria, and HSB_OF53-F07, were identified. Metabolomics analysis suggested that increased functional gene abundance may be due to specific microbial metabolic activity mediated by soil fauna interactions. Predation had a stronger effect on rare microbes, functional genes, and microbial metabolism compared to competition. Long-term organic fertilizer application increased the soil resistance to animal interactions. These findings provide a comprehensive understanding of microbial community dynamics under soil biological interactions, emphasizing the roles of competition and predation among soil fauna in terrestrial ecosystems.

Exploiting Geometric Features via Hierarchical Graph Pyramid Transformer for Cancer Diagnosis using Histopathological Images.

Cancer is widely recognized as the primary cause of mortality worldwide, and pathology analysis plays a pivotal role in achieving accurate cancer diagnosis. The intricate representation of features in histopathological images encompasses abundant information crucial for disease diagnosis, regarding cell appearance, tumor microenvironment, and geometric characteristics. However, recent deep learning methods have not adequately exploited geometric features for pathological image classification due to the absence of effective descriptors that can capture both cell distribution and gathering patterns, which often serve as potent indicators. In this paper, inspired by clinical practice, a Hierarchical Graph Pyramid Transformer (HGPT) is proposed to guide pathological image classification by effectively exploiting a geometric representation of tissue distribution which was ignored by existing state-of-the-art methods. First, a graph representation is constructed according to morphological feature of input pathological image and learn geometric representation through the proposed multi-head graph aggregator. Then, the image and its graph representation are feed into the transformer encoder layer to model long-range dependency. Finally, a locality feature enhancement block is designed to enhance the 2D local representation of feature embedding, which is not well explored in the existing vision transformers. An extensive experimental study is conducted on Kather-5K, MHIST, NCT-CRC-HE, and GasHisSDB for binary or multi-category classification of multiple cancer types. Results demonstrated that our method is capable of consistently reaching superior classification outcomes for histopathological images, which provide an effective diagnostic tool for malignant tumors in clinical practice.

Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs.

As the long non-coding RNAs (lncRNAs) play important roles during the incurrence and development of various human diseases, identifying disease-related lncRNAs can contribute to clarifying the pathogenesis of diseases. Most of the recent lncRNA-disease association prediction methods utilized the multi-source data about the lncRNAs and diseases. A single lncRNA may participate in multiple disease processes, and multiple lncRNAs usually are involved in the same disease process synergistically. However, the previous methods did not completely exploit the biological characteristics to construct the informative prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode the biological characteristics about lncRNA-disease associations, the topological features from the entire heterogeneous graph perspective, and the gated enhanced pairwise features. First, the strategy for constructing hyperedges is designed to reflect the biological characteristic that multiple lncRNAs are involved in multiple disease processes. Furthermore, each hyperedge has its own biological perspective, and multiple hyperedges are beneficial for revealing the diverse relationships among multiple lncRNAs and diseases. Second, we encode the biological features of each lncRNA (disease) node using a strategy based on dynamic hypergraph convolutional networks. The strategy may adaptively learn the features of the hyperedges and formulate the dynamically evolved hypergraph topological structure. Third, a group convolutional network is established to integrate the entire heterogeneous topological structure and multiple types of node attributes within an lncRNA-disease-miRNA graph. Finally, a gated convolutional strategy is proposed to enhance the informative features of the lncRNA-disease node pairs. The comparison experiments indicate that AGLDA outperforms seven advanced prediction methods. The ablation studies confirm the effectiveness of major innovations, and the case studies validate AGLDA's ability in application for discovering potential disease-related lncRNA candidates.

Mutually enhanced multi-view information learning for segmentation of lung tumor in CT images.

Objective.The accurate automatic segmentation of tumors from computed tomography (CT) volumes facilitates early diagnosis and treatment of patients. A significant challenge in tumor segmentation is the integration of the spatial correlations among multiple parts of a CT volume and the context relationship across multiple channels.Approach.We proposed a mutually enhanced multi-view information model (MEMI) to propagate and fuse the spatial correlations and the context relationship and then apply it to lung tumor CT segmentation. First, a feature map was obtained from segmentation backbone encoder, which contained many image region nodes. An attention mechanism from the region node perspective was presented to determine the impact of all the other nodes on a specific node and enhance the node attribute embedding. A gated convolution-based strategy was also designed to integrate the enhanced attributes and the original node features. Second, transformer across multiple channels was constructed to integrate the channel context relationship. Finally, since the encoded node attributes from the gated convolution view and those from the channel transformer view were complementary, an interaction attention mechanism was proposed to propagate the mutual information among the multiple views.Main results.The segmentation performance was evaluated on both public lung tumor dataset and private dataset collected from a hospital. The experimental results demonstrated that MEMI was superior to other compared segmentation methods. Ablation studies showed the contributions of node correlation learning, channel context relationship learning, and mutual information interaction across multiple views to the improved segmentation performance. Utilizing MEMI on multiple segmentation backbones also demonstrated MEMI's generalization ability.Significance.Our model improved the lung tumor segmentation performance by learning the correlations among multiple region nodes, integrating the channel context relationship, and mutual information enhancement from multiple views.

Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs.

Inferring the latent disease-related miRNAs is helpful for providing a deep insight into observing the disease pathogenesis. We propose a method, CMMDA, to encode and integrate the context relationship among multiple heterogeneous networks, the complementary information across these networks, and the pairwise multimodal attributes. We first established multiple heterogeneous networks according to the diverse disease similarities. The feature representation embedding the context relationship is formulated for each miRNA (disease) node based on transformer. We designed a co-attention fusion mechanism to encode the complementary information among multiple networks. In terms of a pair of miRNA and disease nodes, the pairwise attributes from multiple networks form a multimodal attribute embedding. A module based on depthwise separable convolution is constructed to enhance the encoding of the specific features from each modality. The experimental results and the ablation studies show that CMMDA's superior performance and the effectiveness of its major innovations.

HPPO-Derived Meroterpenoids from the Marine-Derived Fungus Penicillium sp. SCSIO 41691.

Seven new 4-hydroxy-6-phenyl-2H-pyran-2-one (HPPO) derived meroterpenoids, 1-methyl-12a,12b-epoxyarisugacin M (1), 1-methyl-4a,12b-epoxyarisugacin M (2), 2,3-dihydroxy-3,4a-epoxy-12a-dehydroxyisoterreulactone A (3), 2-hydroxy-12a-dehydroxyisoterreulactone A (4), 3'-demethoxyterritrems B' (5), 4a-hydroxyarisugacin P (6), and 1-epi-arisugacin H (7), together with two known analogues (8 and 9), were isolated from the marine-derived fungal strain Penicillium sp. SCSIO 41691. Their structures were elucidated by spectroscopic methods, and the absolute configurations of compounds 1 and 3 were determined by single-crystal X-ray diffraction. Among them, 1 and 2 had a unique methyl migration in the basic meroterpenoid skeleton with a 12a,12b-epoxy or 4a,12b-epoxy group, and 3 was a highly oxygenated HPPO-derived meroterpenoid featuring a rare 6/5/6/6/6/6 hexacyclic system with a 3,4a-epoxy group. Biologically, 5 exhibited inhibitory activity against lipopolysaccharide-induced nitric oxide production in RAW 264.7 cells with an IC50 value of 21 µM, more potent than the positive control indomethacin.

The BTB/TAZ domain-containing protein CmBT1-mediated CmANR1 ubiquitination negatively regulates root development in chrysanthemum.

Roots are fundamental for plants to adapt to variable environmental conditions. The development of a robust root system is orchestrated by numerous genetic determinants and, among them, the MADS-box gene ANR1 has garnered substantial attention. Prior research has demonstrated that, in chrysanthemum, CmANR1 positively regulates root system development. Nevertheless, the upstream regulators involved in the CmANR1-mediated regulation of root development remain unidentified. In this study, we successfully identified bric-a-brac, tramtrack and broad (BTB) and transcription adapter putative zinc finger (TAZ) domain protein CmBT1 as the interacting partner of CmANR1 through a yeast-two-hybrid (Y2H) screening library. Furthermore, we validated this physical interaction through bimolecular fluorescence complementation and pull-down assays. Functional assays revealed that CmBT1 exerted a negative influence on root development in chrysanthemum. In both in vitro and in vivo assays, it was evident that CmBT1 mediated the ubiquitination of CmANR1 through the ubiquitin/26S proteasome pathway. This ubiquitination subsequently led to the degradation of the CmANR1 protein and a reduction in the transcription of CmANR1-targeted gene CmPIN2, which was crucial for root development in chrysanthemum. Genetic analysis suggested that CmBT1 modulated root development, at least in part, by regulating the level of CmANR1 protein. Collectively, these findings shed new light on the regulatory role of CmBT1 in degrading CmANR1 through ubiquitination, thereby repressing the expression of its targeted gene and inhibiting root development in chrysanthemum.

Two successful pregnancies in a membranous nephropathy patient: Case report and literature review.

BACKGROUND: Pregnancy in patients with nephrotic syndrome presents enormous challenges to both the mother and fetus, and there are no treatment guidelines for these patients. METHODS: We show a case of a woman with anti-PLA2R antibody-positive membranous nephropathy who did not have a kidney biopsy. Her clinical course during both pregnancies was closely followed and her medications were guided. RESULTS: She gave birth to 2 healthy babies and her condition was very well controlled with the help of medication. CONCLUSION: Patients with nephrotic syndrome can have successful pregnancies after drug treatment. In addition, similar to the non-pregnant population, percutaneous kidney biopsy is not required for the diagnosis of idiopathic membranous nephropathy (IMN) in pregnant nephrotic syndrome patients with anti-PLA2R antibody positive, but the etiology of secondary MN should be excluded.

Meroterpenoids from the marine-derived fungus Aspergillus terreus GZU-31-1 exerts anti-liver fibrosis effects by targeting the Nrf2 signaling in vitro.

Six undescribed meroterpenoids aspertermeroterpenes A-F and four known analogues were isolated from the marine-derived fungus Aspergillus terreus GZU-31-1. Their structures were elucidated based on spectroscopic methods and electronic circular dichroism calculations. All meroterpenoids possessed the unique acetyl group at C-11, and also aspertermeroterpene A featured the rare C-14 decarboxylated in DMOA meroterpenoids. In the bioassays, aspermeroterpene B exhibited a potent inhibitory effect on the activation of hepatic stellate cells at the concentration of 5 µM via targeting the Nrf2 signaling. This is the first time reported that aspermeroterpene B as a previously undescribed carbon skeleton of meroterpenoid possessed anti-liver fibrosis effect.

Two new 2-(2-phenylethyl)chromone derivatives and two sesquiterpenes from agarwood of Aquilaria sinensis with anti-inflammatory activity.

Two new 2-(2-phenylethyl)chromones (1-2), two new sesquiterpenes (12-13), and twelve known compounds (3-11, 14-16) were isolated from agarwood of Aquilaria sinensis. These structures were confirmed by HRESIMS, 1D and 2D NMR spectra. The absolute configurations of two new sesquiterpenes were determined by comparing the experimental and calculated ECD spectra. Among them, 7,8-dihydroxy-2-[2-(4'-methoxyphenyl)ethyl]chromone (2) was the first time found that the hydroxyl groups at both C-7/C-8 in agarwood. And Aseudesm B (13), the aldehyded methyl group at C-5 of eucalyptane sesquiterpenes was first discovered in natural products. In the bioassays, all compounds were evaluated for their inhibitory activity against lipopolysaccharide-activated nitric oxide (NO) production in RAW264.7 cells. Compounds 2-5, 7, 9-10, and 13-14 revealed notable inhibitory effects against NO production with IC50 values ranging from 4.0 to 13.0 µM.

Novel 6/7/6 ring system diterpenoids and cytochalasins from the fungus Eutypella scoparia GZU-4-19Y and their anti-inflammatory activity.

Two new compounds eutyditerpenoid A (1) and seco-phenochalasin B (5), together with seven known compounds diaporthein A (2), aspergillon A (3), phenochalasin B (4), cytochalasins Z24 and Z25 (6 and 7), scoparasins A and B (8 and 9) were isolated from marine-derived Eutypella scoparia GZU-4-19Y. Among them, eutyditerpenoid A (1) with a rare 6/7/6 ring system possesing an anhydride moiety was the first example in the pimarane-type diterpenoids. Their structures were determined based on spectroscopic methods and the electronic circular dichroism (ECD) calculations. In the bioassays, all of the isolates were evaluated for their inhibitory activity against NO production induced by lipopolysaccharide in RAW 264.7 cells. Compounds 3 and 7 showed potent NO inhibition activity with IC50 values of 2.1 and 17.1 µM respectively, and the former also significantly suppressed the protein expression of iNOS and COX-2 at the concentration of 2.5 µM.

Multi-scale topology and position feature learning and relationship-aware graph reasoning for prediction of drug-related microbes.

MOTIVATION: The human microbiome may impact the effectiveness of drugs by modulating their activities and toxicities. Predicting candidate microbes for drugs can facilitate the exploration of the therapeutic effects of drugs. Most recent methods concentrate on constructing of the prediction models based on graph reasoning. They fail to sufficiently exploit the topology and position information, the heterogeneity of multiple types of nodes and connections, and the long-distance correlations among nodes in microbe-drug heterogeneous graph. RESULTS: We propose a new microbe-drug association prediction model, NGMDA, to encode the position and topological features of microbe (drug) nodes, and fuse the different types of features from neighbors and the whole heterogeneous graph. First, we formulate the position and topology features of microbe (drug) nodes by t-step random walks, and the features reveal the topological neighborhoods at multiple scales and the position of each node. Second, as the features of nodes are high-dimensional and sparse, we designed an embedding enhancement strategy based on supervised fully connected autoencoders to form the embeddings with representative features and the more discriminative node distributions. Third, we propose an adaptive neighbor feature fusion module, which fuses features of neighbors by the constructed position- and topology-sensitive heterogeneous graph neural networks. A novel self-attention mechanism is developed to estimate the importance of the position and topology of each neighbor to a target node. Finally, a heterogeneous graph feature fusion module is constructed to learn the long-distance correlations among the nodes in the whole heterogeneous graph by a relationship-aware graph transformer. Relationship-aware graph transformer contains the strategy for encoding the connection relationship types among the nodes, which is helpful for integrating the diverse semantics of these connections. The extensive comparison experimental results demonstrate NGMDA's superior performance over five state-of-the-art prediction methods. The ablation experiment shows the contributions of the multi-scale topology and position feature learning, the embedding enhancement strategy, the neighbor feature fusion, and the heterogeneous graph feature fusion. Case studies over three drugs further indicate that NGMDA has ability in discovering the potential drug-related microbes. AVAILABILITY AND IMPLEMENTATION: Source codes and Supplementary Material are available at https://github.com/pingxuan-hlju/NGMDA.

Prevalence and risk factors of low vitamin D levels in children and adolescents with familial hypokalemic periodic paralysis.

Patients with familial hypokalemic periodic paralysis (HOKPP) experience episodes of reversible immobility and are at an increased risk of limited sunlight exposure, potentially leading to vitamin D deficiency. However, there is a lack of data on vitamin D levels in this population. We investigated serum vitamin D levels and their associated factors in children with HOKPP. This study included 170 genetically-confirmed children with HOKPP, aged 3-18 years, and 170 age-, sex-, and body mass index (BMI)-matched healthy controls from the Korean Channelopathy Study, a prospective controlled investigation. Anthropometric and clinical characteristics were recorded, and serum levels of calcium, ionized calcium, phosphorus, alkaline phosphatase, 25-hydroxyvitamin D, and intact parathyroid hormone (PTH) were analyzed. Vitamin D deficiency (< 20 ng/mL) was observed in 87.0% of the patients compared to 45.5% of the controls (P < 0.05) during the summer-fall season. During the winter-spring season, 91.7% of the patients and 73.4% of the controls were deficient (P < 0.05). A strong positive correlation was found between onset age of the first paralytic attack and vitamin D levels (r = 0.78, P < 0.01). Conversely, the frequency and duration of paralytic attacks were negatively correlated with vitamin D levels (r = -0.82 and r = -0.65, P < 0.01, respectively). Age, BMI, age at onset, frequency and duration of attacks, and PTH levels were independently associated with vitamin D levels (ß = -0.10, -0.12, 0.19, -0.27, -0.21, and -0.13, P < 0.05, respectively). CONCLUSIONS: Vitamin D deficiency was highly prevalent in children with HOKPP, and vitamin D levels correlated with various disease characteristics. We recommend routine screening for vitamin D levels in these patients to address this prevalent deficiency. Considering the high prevalence of vitamin D deficiency observed, further research on other diseases characterized by reversible immobility is warranted. WHAT IS KNOWN: • A correlation between immobility and low serum vitamin D levels has been established. However, the vitamin D status of patients with familial hypokalemic periodic paralysis (HOKPP) who experience periods of reversible immobility remains unknown. WHAT IS NEW: • Vitamin D deficiency was highly prevalent in children with HOKPP, and vitamin D levels correlated with various disease characteristics.

MSCT-UNET: multi-scale contrastive transformer within U-shaped network for medical image segmentation.

Objective.Automatic mutli-organ segmentation from anotomical images is essential in disease diagnosis and treatment planning. The U-shaped neural network with encoder-decoder has achieved great success in various segmentation tasks. However, a pure convolutional neural network (CNN) is not suitable for modeling long-range relations due to limited receptive fields, and a pure transformer is not good at capturing pixel-level features.Approach.We propose a new hybrid network named MSCT-UNET which fuses CNN features with transformer features at multi-scale and introduces multi-task contrastive learning to improve the segmentation performance. Specifically, the multi-scale low-level features extracted from CNN are further encoded through several transformers to build hierarchical global contexts. Then the cross fusion block fuses the low-level and high-level features in different directions. The deep-fused features are flowed back to the CNN and transformer branch for the next scale fusion. We introduce multi-task contrastive learning including a self-supervised global contrast learning and a supervised local contrast learning into MSCT-UNET. We also make the decoder stronger by using a transformer to better restore the segmentation map.Results.Evaluation results on ACDC, Synapase and BraTS datasets demonstrate the improved performance over other methods compared. Ablation study results prove the effectiveness of our major innovations.Significance.The hybrid encoder of MSCT-UNET can capture multi-scale long-range dependencies and fine-grained detail features at the same time. The cross fusion block can fuse these features deeply. The multi-task contrastive learning of MSCT-UNET can strengthen the representation ability of the encoder and jointly optimize the networks. The source code is publicly available at:https://github.com/msctunet/MSCT_UNET.git.

A2Hgx(SeO3)y (A = K, Rb, Cs): Three Alkali Metal Mercury Selenites Featuring Unique 1D [HgOm(SeO3)n]∞ Chains.

Herein, three alkali metal mercury selenites, K2Hg2(SeO3)3, Rb2Hg2(SeO3)3, and Cs2Hg3(SeO3)4, were successfully obtained by a hydrothermal method. The three compounds featured same one-dimensional (1D) [HgOm(SeO3)n]∞ chain structure that consisting of distorted Hg-O polyhedra and SeO3 triangular pyramids with stereochemically active lone pair (SCALP) electrons. Interestingly, the rich coordination environment of Hg atoms and the size difference of alkali metal cations lead to diverse arrangement of SeO3 groups, which makes them exhibit different birefringence. The band gaps of the three compounds indicate that they are potential ultraviolet (UV) optical materials. Detailed theoretical calculations demonstrate that the combined effects of SeO3 triangular pyramids and Hg-O polyhedra are responsible for the optical characteristics of the reported compounds.

Effects of Factors Influencing Scar Formation on the Scar Microbiome in Patients with Burns.

Skin microbiome dysbiosis has deleterious effects, and the factors influencing burn scar formation, which affects the scar microbiome composition, are unknown. Therefore, we investigated the effects of various factors influencing scar formation on the scar microbiome composition in patients with burns. We collected samples from the burn scar center and margin of 40 patients with burns, subgrouped by factors influencing scar formation. Scar microbiome composition-influencing factors were analyzed using univariate and multivariate analyses. Skin graft, hospitalization period, intensive care unit (ICU) admission, burn degree, sex, age, total body surface area burned (TBSA), time post-injury, transepidermal water loss, the erythrocyte sedimentation rate, and C-reactive protein levels were identified as factors influencing burn scar microbiome composition. Only TBSA and ICU admission were associated with significant differences in alpha diversity. Alpha diversity significantly decreased with an increase in TBSA and was significantly lower in patients admitted to the ICU than in those not admitted to the ICU. Furthermore, we identified microorganisms associated with various explanatory variables. Our cross-sectional systems biology study confirmed that various variables influence the scar microbiome composition in patients with burns, each of which is associated with various microorganisms. Therefore, these factors should be considered during the application of skin microbiota for burn scar management.

Varying Definitions of Carotid Intima-Media Thickness and Future Cardiovascular Disease: A Systematic Review and Meta-Analysis.

BACKGROUND: Carotid intima-media thickness (cIMT) has been widely used as a predictor of future cardiovascular disease (CVD); however, various definitions of cIMT exist. This study provides a systematic review and meta-analysis of the associations between different cIMT definitions and CVD. METHODS AND RESULTS: A systematic review of the different cIMT definitions used in prospective cohort studies was performed. The relationships between cIMT of different definitions (common carotid artery IMT [CCA-IMT], internal carotid artery IMT [ICA-IMT], combined segments [combined-IMT], mean CCA-IMT, and maximum CCA-IMT) with future stroke, myocardial infarction (MI), and CVD events were analyzed using random effects models. Among 2287 articles, 18 articles (14 studies) with >10 different cIMT definitions were identified and included in our meta-analysis. After adjusting for age and sex, a 1-SD increase in CCA-IMT was associated with future stroke (hazard ratio [HR], 1.32 [95% CI, 1.27-1.38]), MI (HR, 1.27 [95% CI, 1.22-1.33]), and CVD events (HR, 1.28 [95% CI, 1.19-1.37]). A 1-SD increase in ICA-IMT was related to future stroke (HR, 1.25 [95% CI, 1.11-1.42]) and CVD events (HR, 1.25 [95% CI, 1.04-1.50]) but not MI (HR, 1.26 [95% CI, 0.98-1.61]). A 1-SD increase in combined-IMT was associated with future stroke (HR, 1.30 [95% CI, 1.08-1.57]) and CVD events (HR, 1.36 [95% CI, 1.23-1.49]). Maximum CCA-IMT was more strongly related than mean CCA-IMT with risk of MI, and both measures were similarly associated with stroke and CVD events. CONCLUSIONS: Combined-IMT is more strongly associated with CVD events compared with single-segment cIMT definitions. Maximum CCA-IMT shows a stronger association with MI than mean CCA-IMT. Further research is warranted to validate our findings and to standardize the cIMT measurement protocol, as well as to explore underlying mechanisms.