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Background: Hypertrophic scars cause impaired skin appearance and function, seriously affecting physical and mental health. Due to medical ethics and clinical accessibility, the collection of human scar specimens is frequently restricted, and the establishment of scar experimental animal models for scientific research is urgently needed. The four most commonly used animal models of hypertrophic scars have the following drawbacks: the rabbit ear model takes a long time to construct; the immunodeficient mouse hypertrophic scar model necessitates careful feeding and experimental operations; female Duroc pigs are expensive to purchase and maintain, and their large size makes it difficult to produce a significant number of models; and mouse scar models that rely on tension require special skin stretch devices, which are often damaged and shed, resulting in unstable model establishment. Our group overcame the shortcomings of previous scar animal models and created a new mouse model of hypertrophic scarring induced by suture anchoring at the wound edge., Methods: We utilized suture anchoring of incisional wounds to impose directional tension throughout the healing process, restrain wound contraction, and generate granulation tissue, thus inducing scar formation. Dorsal paired incisions were generated in mice, with wound edges on the upper back sutured to the rib cage and the wound edges on the lower back relaxed as a control. Macroscopic manifestation, microscopic histological analysis, mRNA sequencing, bioinformatics, and in vitro cell assays were also conducted to verify the reliability of this method., Results: Compared with those in relaxed controls, the fibrotic changes in stretched wounds were more profound. Histologically, the stretched scars were hypercellular, hypervascular, and hyperproliferative with disorganized extracellular matrix deposition, and displayed molecular hallmarks of hypertrophic fibrosis. In addition, the stretched scars exhibited transcriptional overlap with mechanically stretched scars, and human hypertrophic and keloid scars. Phosphatidylinositol 3-kinase-serine/threonine-protein kinase B signaling was implicated as a profibrotic mediator of apoptosis resistance under suture-induced tension., Conclusions: This straightforward murine model successfully induces cardinal molecular and histological features of pathological hypertrophic scarring through localized suture tension to inhibit wound contraction. The model enables us to interrogate the mechanisms of tension-induced fibrosis and evaluate anti-scarring therapies., Competing Interests: None declared., (© The Author(s) 2024. Published by Oxford University Press.)

The solar-driven interfacial evaporation (SDIE) technology serves as a clean and facile approach combining seawater desalination to address water shortage and energy crisis. Recently, porous organic framework materials have aroused great attention, and the development of MOF-based composites with significant performance in photothermal conversion and water activation has become one of the important focuses in this field. In this work, an MOF-based composite photothermal membrane (PON@MOF) was prepared via the in situ growth of metal-organic framework (MOF) nanoneedle arrays induced by a pyridine-based organic polymer nanowire network (PON). The PON@MOF membrane possessed an MOF array layer, a PON-induced layer, and a porous support layer. The PON is rich in coordinated nitrogen atoms for anchoring the metal-ion source, and the main role of the PON layer is to provide favorable nucleation sites and orient in situ growth of well-aligned MOF nanoneedle arrays. With the highly conjugated framework of PON@MOF and the light trapping of the nanoarrays strengthening the photothermal conversion as well as the hydrophilic chemical structure facilitating the decrease of the water evaporation enthalpy, the PON@MOF membrane realizes highly efficient thermal vapor conversion. Under solar irradiation (1.0 kW m -2 ), PON@MOF demonstrated an evaporation rate of 2.14 kg m -2 h -1 and a solar-to-vapor conversion efficiency of 98.5%. Meanwhile, the PON@MOF membrane has excellent salt resistance and stability, highlighting its potential application in desalination. Overall, this work provides a special idea for the structural design of photothermal composites for solar desalination and freshwater production.

Harnessing the developmental events of mesenchymal condensation to direct postnatal dental stem cell aggregation represents a cutting-edge and promising approach to tooth regeneration. Tooth avulsion is among the most prevalent and serious dental injuries, and odontogenic aggregates assembled by stem cells from human exfoliated deciduous teeth (SHED) have proven effective in revitalizing avulsed teeth after replantation in the clinical trial. However, whether and how SHED aggregates (SA) communicate with recipient components and promote synergistic tissue regeneration to support replanted teeth remains elusive. Here, it is shown that SA-mediated avulsed tooth regeneration involves periodontal restoration and recovery of recipient Gli1 + stem cells, which are mobilized and necessarily contribute to the reestablishment of the tooth-periodontal ligament-bone interface. Mechanistically, the release of extracellular vesicles (EVs) is revealed indispensable for the implanted SA to mobilize recipient Gli1 + cells and regenerate avulsed teeth. Furthermore, SHED aggregates-released EVs (SA-EVs) are featured with odontogenic properties linked to tissue regeneration, which enhance migration, proliferation, and differentiation of Gli1 + cells. Importantly, local application of SA-EVs per se empowers recipient Gli1 + cells and safeguards regeneration of avulsed teeth. Collectively, the findings establish a paradigm in which odontogenesis-featured EVs govern donor-recipient stem cell interplay to achieve tooth regeneration, inspiring cell-free translational regenerative strategies., (© 2024 Wiley‐VCH GmbH.)

Short-chain fatty acids (SCFAs) are the main metabolites produced by bacterial fermentation of dietary fiber within gastrointestinal tract. SCFAs produced by gut microbiotas (GMs) are absorbed by host, reach bloodstream, and are distributed to different organs, thus influencing host physiology. However, due to the limited budget or the poor sensitivity of instruments, most studies on GMs have incomplete blood SCFA data, limiting our understanding of the metabolic processes within the host. To address this gap, we developed an innovative multi-task multi-view integrative approach (M 2 AE, Multi-task Multi-View Attentive Encoders), to impute blood SCFA levels using gut metagenomic sequencing (MGS) data, while taking into account the intricate interplay among the gut microbiome, dietary features, and host characteristics, as well as the nuanced nature of SCFA dynamics within the body. Here, each view represents a distinct type of data input (i.e., gut microbiome compositions, dietary features, or host characteristics). Our method jointly explores both view-specific representations and cross-view correlations for effective predictions of SCFAs. We applied M 2 AE to two in-house datasets, which both include MGS and blood SCFAs profiles, host characteristics, and dietary features from 964 subjects and 171 subjects, respectively. Results from both of two datasets demonstrated that M 2 AE outperforms traditional regression-based and neural-network based approaches in imputing blood SCFAs. Furthermore, a series of gut bacterial species (e.g., Bacteroides thetaiotaomicron and Clostridium asparagiforme ), host characteristics (e.g., race, gender), as well as dietary features (e.g., intake of fruits, pickles) were shown to contribute greatly to imputation of blood SCFAs. These findings demonstrated that GMs, dietary features and host characteristics might contribute to the complex biological processes involved in blood SCFA productions. These might pave the way for a deeper and more nuanced comprehension of how these factors impact human health.

Osteoporosis, characterized by low BMD, is a highly heritable metabolic bone disorder. Although single nucleotide variations (SNVs) have been extensively studied, they explain only a fraction of BMD heritability. Although genomic structural variations (SVs) are large-scale genomic alterations that contribute to genetic diversity in shaping phenotypic variations, the role of SVs in osteoporosis susceptibility remains poorly understood. This study aims to identify and prioritize genes that harbor BMD-related SVs. We performed whole genome sequencing on 4982 subjects from the Louisiana Osteoporosis Study. To obtain high-confidence SVs, the detection of SVs was performed using an ensemble approach. The SVs were tested for association with BMD variation at the hip (HIP), femoral neck (FNK), and lumbar spine (SPN), respectively. Additionally, we conducted co-occurrence analysis using multi-omics approaches to prioritize the identified genes based on their functional importance. Stratification was employed to explore the sex- and ethnicity-specific effects. We identified significant SV-BMD associations: 125 for FNK-BMD, 99 for SPN-BMD, and 83 for HIP-BMD. We observed SVs that were commonly associated with both FNK and HIP BMDs in our combined and stratified analyses. These SVs explain 13.3% to 19.1% of BMD variation. Novel bone-related genes emerged, including LINC02370, ZNF family genes, and ZDHHC family genes. Additionally, FMN2, carrying BMD-related deletions, showed associations with FNK or HIP BMDs, with sex-specific effects. The co-occurrence analysis prioritized an RNA gene LINC00494 and ZNF family genes positively associated with BMDs at different skeletal sites. Two potential causal genes, IBSP and SPP1, for osteoporosis were also identified. Our study uncovers new insights into genetic factors influencing BMD through SV analysis. We highlight BMD-related SVs, revealing a mix of shared and specific genetic influences across skeletal sites and gender or ethnicity. These findings suggest potential roles in osteoporosis pathophysiology, opening avenues for further research and therapeutic targets., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Society for Bone and Mineral Research.)

Background: Missing data is a common challenge in mass spectrometry-based metabolomics, which can lead to biased and incomplete analyses. The integration of whole-genome sequencing (WGS) data with metabolomics data has emerged as a promising approach to enhance the accuracy of data imputation in metabolomics studies., Method: In this study, we propose a novel method that leverages the information from WGS data and reference metabolites to impute unknown metabolites. Our approach utilizes a multi-scale variational autoencoder to jointly model the burden score, polygenetic risk score (PGS), and linkage disequilibrium (LD) pruned single nucleotide polymorphisms (SNPs) for feature extraction and missing metabolomics data imputation. By learning the latent representations of both omics data, our method can effectively impute missing metabolomics values based on genomic information., Results: We evaluate the performance of our method on empirical metabolomics datasets with missing values and demonstrate its superiority compared to conventional imputation techniques. Using 35 template metabolites derived burden scores, PGS and LD-pruned SNPs, the proposed methods achieved R 2 -scores > 0.01 for 71.55 % of metabolites., Conclusion: The integration of WGS data in metabolomics imputation not only improves data completeness but also enhances downstream analyses, paving the way for more comprehensive and accurate investigations of metabolic pathways and disease associations. Our findings offer valuable insights into the potential benefits of utilizing WGS data for metabolomics data imputation and underscore the importance of leveraging multi-modal data integration in precision medicine research., Competing Interests: Declaration of competing interest The authors declare no conflicts of interest., (Published by Elsevier Ltd.)

Background: Osteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations., Methods and Findings: We developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB. With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10-6 and 5×10-7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P < 0.001). The hazard ratios (HRs) of the predicted FNK-BMD and SPN-BMD were 0.83 (95% CI [0.79, 0.88], corresponding to a 1.44% difference in 10-year absolute risk) and 0.72 (95% CI [0.68, 0.76], corresponding to a 1.64% difference in 10-year absolute risk), respectively, indicating that for every increase of one standard deviation in BMD, the fracture risk will decrease by 17% and 28%, respectively. However, the model's performance declined in other ethnic groups and independent cohorts. The limitations of this study include differences in clinical factors distribution and the use of only SNPs as genetic factors., Conclusions: In this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10-6 or 5×10-7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model's explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Liu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Host-microbe interactions are virtually bidirectional, but how the host affects their microbiome is poorly understood. Here, we report that the host is a critical modulator to regulate the lifestyle switch and pathogenicity heterogeneity of the opportunistic pathogens Serratia marcescens utilizing the Drosophila and bacterium model system. First, we find that Drosophila larvae efficiently outcompete S. marcescens and typically drive a bacterial switch from pathogenicity to commensalism toward the fly. Furthermore, Drosophila larvae reshape the transcriptomic and metabolic profiles of S. marcescens characterized by a lifestyle switch. More importantly, the host alters pathogenicity and heterogeneity of S. marcescens in the single-cell resolution. Finally, we find that larvae-derived AMPs are required to recapitulate the response of S. marcescens to larvae. Altogether, our findings provide an insight into the pivotal roles of the host in harnessing the life history and heterogeneity of symbiotic bacterial cells, advancing knowledge of the reciprocal relationships between the host and pathogen., Competing Interests: ZW, SL, SZ, TZ, YW, AL, KW, XJ, HC, YZ, ET, YW, YW, WL No competing interests declared, (© 2024, Wang, Li et al.)

Spatial transcriptomics (ST) revolutionizes RNA quantification with high spatial resolution. Hematoxylin and eosin (H&E) images, the gold standard in medical diagnosis, offer insights into tissue structure, correlating with gene expression patterns. Current methods for predicting spatial gene expression from H&E images often overlook spatial relationships. We introduce ResSAT (Residual networks - Self-Attention Transformer), a framework generating spatially resolved gene expression profiles from H&E images by capturing tissue structures and using a self-attention transformer to enhance prediction.Benchmarking on 10× Visium datasets, ResSAT significantly outperformed existing methods, promising reduced ST profiling costs and rapid acquisition of numerous profiles., Competing Interests: Competing interests The authors declare that they have no competing interests.

Background: Asthma severely interferes with people's lives through coughing, wheezing and inflammation of the lungs. Herbacetin is a class of natural compounds that inhibit the development of inflammation. However, whether Herbacetin inhibits asthma has not been definitively studied., Methods: Lipopolysaccharides (LPS)-induced lung epithelial (BASE-2B) cells injury model was established, and then the relief of damaged BASE-2B cells with different concentrations of Herbacetin was examined. The cell counting kit (CCK8) was used to detect the effect of Herbacetin on the proliferation ability in ovalbumin (OVA)-induced asthma mice model, and Western Blot and flow cytometry were used to detect the effect of Herbacetin on the apoptosis in OVA-induced asthma mice model. Additionally, pulmonary pathology was detected by HE and Masson staining, and serum inflammatory factors were detected by alveolar lavage fluid., Results: Herbacetin reduces BESA-2B cells induced by LPS level of inflammation, and reactive oxygen species (ROS) generation, inhibits cell apoptosis, promotes cell proliferation, OVA-induced mice lung histopathology test HE staining, serum inflammatory factors show the same results. Western Blot shows that Herbacetin regulates the expression of Caspase-3, Bax, and Bcl-2. SGK1 overexpression increased the rate of apoptosis, and Herbacetin reversed this phenomenon. By silencing the expression of SGK1, it was found that Herbacetin was an inhibitor of SGK1, which could inhibit the NF-κB/p-P65 pathway in asthmatic airway inflammation., Conclusion: Herbacetin reduces pro-inflammatory cytokine levels by inhibiting the SGK1/NF-κB pathway. Our data suggest that Herbacetin has a significant anti-inflammatory effect on asthma and can be used as a potential therapeutic agent., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationship that could be construed as a potential conflict of interest., (© 2024 Chen et al.)

Detection of pavement diseases is crucial for road maintenance. Traditional methods are costly, time-consuming, and less accurate. This paper introduces an enhanced pavement disease recognition algorithm, MS-YOLOv8, which modifies the YOLOv8 model by incorporating three novel mechanisms to improve detection accuracy and adaptability to varied pavement conditions. The Deformable Large Kernel Attention (DLKA) mechanism adjusts convolution kernels dynamically, adapting to multi-scale targets. The Large Separable Kernel Attention (LSKA) enhances the SPPF feature extractor, boosting multi-scale feature extraction capabilities. Additionally, Multi-Scale Dilated Attention in the network's neck performs Spatially Weighted Dilated Convolution ( SWDA ) across different dilatation rates, enhancing background distinction and detection precision. Experimental results show that MS-YOLOv8 increases background classification accuracy by 6%, overall precision by 1.9%, and mAP by 1.4%, with specific disease detection mAP up by 2.9%. Our model maintains comparable detection speeds. This method offers a significant reference for automatic road defect detection.

Exploring a novel natural cryoprotectant and understanding its antifreeze mechanism allows the rational design of future sustainable antifreeze analogues. In this study, various antifreeze polysaccharides were isolated from wheat bran, and the antifreeze activity was comparatively studied in relation to the molecular structure. The antifreeze mechanism was further revealed based on the interactions of polysaccharides and water molecules through dynamic simulation analysis. The antifreeze polysaccharides showed distinct ice recrystallization inhibition activity, and structural analysis suggested that the polysaccharides were arabinoxylan, featuring a xylan backbone with a majority of Ara f and minor fractions of Man p , Gal p , and Glc p involved in the side chain. The antifreeze arabinoxylan, characterized by lower molecular weight, less branching, and more flexible conformation, could weaken the hydrogen bonding of the surrounding water molecules more evidently, thus retarding the transformation of water molecules into the ordered ice structure.

Background: Attention deficit hyperactivity disorder (ADHD), a prevalent neurodevelopmental disorder in children, can be effectively alleviated by the herbal preparation Long Mu Qing Xin Mixture (LMQXM), but its mechanism has not been fully elucidated., Objective: To scrutinize the potential pharmacological mechanisms by which LMQXM improves behavior in spontaneously hypertensive rats (SHR/NCrl)., Methods: The SHR/NCrl rats were randomly stratified into the model (SHR) group, the methylphenidate hydrochloride (MPH) group, and groups subjected to varying dosages of LMQXM into the medium dose (MD) group with a clinically effective dose, the low dose (LD) group with 0.5 times the clinically effective dose, and high dose (HD) group with 2 times the clinically effective dose. Furthermore, the WKY/NCrl rats constituted the control group. The evaluation of behavior involved the open field test and the Morris water maze test. HPLC, LC-MS, ELISA, immunohistochemistry, Western blot, and RT-qPCR were utilized to scrutinize the catecholamine neurotransmitter content and the expression of proteins and genes associated with the dopamine receptor D1 (DRD1)/cAMP/protein kinase A (PKA)-cAMP response element-binding (CREB) pathway in prefrontal cortex (PFC) and striatum., Results: MPH and LMQXM ameliorated hyperactivity and learning and memory deficits of SHR/NCrl rats. Among them, LMQXM-MD and MPH also upregulated dopamine (DA), norepinephrine (NE), adenylate cyclase (AC) and cAMP levels, and the expression of proteins and genes associated with the DRD1/cAMP/PKA-CREB pathway in PFC and striatum of SHR/NCrl rats. PFC and striatum DA levels were also upregulated in the LMQXM-LD group as well as the striatum DA levels in the LMQXM-HD group, but there were no statistically significant differences in their NE levels compared to the SHR group. LMQXM-LD and LMQXM-HD also upregulated some DRD1/cAMP/PKA-CREB pathway-related proteins and gene expression, but the effects were discernibly disparate in PFC and striatum. Upon comprehensive analysis, LMQXM-MD appeared to be the most effective dose., Conclusion: Our study tentatively suggests that LMQXM may rectify hyperactivity and learning and memory deficits of SHR/NCrl rats by elevating catecholamine neurotransmitters in the PFC and striatum. This effect may be attributed to the potential activation of the DRD1/cAMP/PKA-CREB signaling pathway, which appears to achieve an optimal response at moderate doses., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Xiao, Jiang, Pu, Chen, Wang, Liu, Zhang and Xu.)

Zimmermann-Laband Syndrome (ZLS; MIM 135500) is a rare genetic disorder with the main clinical manifestations of gingival fibromatosis and finger/toe nail hypoplasia. KCNH1 (potassium channel, voltage-gated, subfamily H, member-1), KCNN3 (potassium channel, voltage-gated, subfamily H, member-3) and ATP6V1B2 (ATPase H + transporting V1 subunit B2) genes are considered causative genes for ZLS. However, there are limited reports about the diverse clinical presentation and genetic heterogeneity. Reporting more information on phenotype-genotype correlation and the treatment of ZLS is necessary. This case reported a 2-year-old patient with gingival enlargement that failure of eruption of the deciduous teeth and severe hypoplasia of nails. Based on a systemic examination and a review of the relevant literature, we made an initial clinical diagnosis of ZLS. A novel pathogenic variant in the KCNH1 gene was identified using whole-exome sequencing to substantiate our preliminary diagnosis. The histopathological results were consistent with gingival fibromatosis. Gingivectomy and gingivoplasty were performed under general anesthesia. After surgery, the gingival appearance improved significantly, and the masticatory function of the teeth was restored. After 2-year follow-up, the gingival showed slightly hyperplasia. Systemic examination and gene sequencing firstly contribute to provide information for an early diagnosis for ZLS, then timely removal of the hyperplastic gingival facilitates the establishment of a normal occlusal relationship and improves oral aesthetics., Competing Interests: The authors declare no conflict of interest., (©2024 The Author(s). Published by MRE Press.)

Objective: To investigate the correlation between gender differences in plasma lipoprotein phospholipase A2 (Lp-PLA2) levels and the risk of recurrent stroke in patients with acute ischaemic stroke in China., Methods: We conducted a prospective follow-up study that included baselineLp-PLA2 levels and NIH Stroke Scale (NIHSS) scores in patients with ischaemic stroke upon admission. The diagnostic efficacy of the baseline Lp-PLA2 level for stroke recurrence was evaluated. And Kaplan‒Meier method was used to analyse the difference in the risk of recurrent stroke between these two groups among males and females. A paired t test was used to analyse the difference in Lp-PLA2 levels in male and female patients after follow-up., Results: Baseline plasma Lp-PLA2 was higher in men and women with recurrent stroke than in those without recurrent stroke. The correlation between baseline Lp-PLA2 and neurological impairment was higher in female than male stroke patients ( R  = 0.338 and 0.253, respectively). Although weakly correlated with neurological impairment, baseline Lp-PLA2 was more effective in predicting recurrent stroke (AUC = 0.705 in men, 0.788 in women). A Cox model was used to compare the risk of stroke between the high- and low-Lp-PLA2 groups (OR = 3.98 in men, 2.61 in women). According to the follow-up time of 6 months as the node, Lp-PLA2 will give different risk indicators., Conclusion: Elevated plasma Lp-PLA2 is an independent risk factor for recurrent ischaemic stroke but is not strongly associated with the degree of cerebral damage. The predictive value of baseline Lp-PLA2 for stroke recurrence risk was higher in females than in males.

Objective: (MSU) crystals usually in the kidney tubules especially collecting ducts in the medulla. Previous animal models have not fully reproduced the impact of MSU on kidneys under non-hyperuricemic conditions., Methods: In the group treated with MSU, the upper pole of the rat kidney was injected intrarenally with 50 mg/kg of MSU, while the lower pole was injected with an equivalent volume of PBS solution. The body weight and kidney mass of the rats were observed and counted. H&E staining was used to observe the pathological damage of the kidney and to count the number of inflammatory cells. Masoon staining was used to observe the interstitial fibrosis in the kidneys of the rat model. Flow cytometric analysis was used for counting inflammatory cells in rats. ElISA was used to measure the concentration of serum and urine uric acid, creatinine and urea nitrogen in rats., Results: At the MSU injection site, a significantly higher infiltration of inflammatory cells and a substantial increase in the area of interstitial fibrosis compared to the control group and the site of PBS injection were observed. The serum creatinine level was significantly increased in the MSU group. However, there were no significant differences in the rats' general conditions or blood inflammatory cell counts when compared to the control group., Conclusion: The injection of urate crystals into the kidney compromised renal function, caused local pathological damage, and increased inflammatory cell infiltration and interstitial fibrosis. Intrarenal injection of MSU crystals may result in urate nephropathy. The method of intrarenal injection did not induce surgical infection or systemic inflammatory response., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Li, Chen, Ouyang, Lin, Wu, Fu, Quan, Wang, Wu, Yuan, Liu, Zhao, Liu, Zhu, Li and Mao.)

Organophosphate pesticides (OPs) are widely utilized in agricultural production, and the residues threaten public health and environmental safety due to their toxicity. Herein, a novel and simple DNA aptamer-based sensor has been fabricated for the rapid, visual, and quantitative detection of profenofos and isocarbophos. The proposed DNA aptamers with a G-quadruplex spatial structure could be recognized by SYBR Green I (SG-I), resulting in strong green fluorescence emitted by SG-I. The DNA aptamers exhibit a higher specific binding ability to target OP molecules through aromatic ring stacking, disrupting the interaction between SG-I and DNA aptamers to induce green fluorescence quenching. Meanwhile, the fluorescence wavelength of G-quadruplex fluorescence emission peaks changes, accompanied by an obvious fluorescence variation from green to blue. SG-I-modified aptasensor without any additive reference fluorescence units for use in multicolor fluorescence assay for selective monitoring of OPs was first developed. The developed aptasensor provides a favorable linear range from 0 to 200 nM, with a low detection limit of 2.48 and 3.01 nM for profenofos and isocarbophos, respectively. Moreover, it offers high selectivity and stability in real sample detection with high recoveries. Then, a self-designed portable smartphone sensing platform was successfully used for quantitative result outputs, demonstrating experience in designing a neotype sensing strategy for point-of-care pesticide monitoring.

The interplay between bacteria and their host influences the homeostasis of the human immune microenvironment, and this reciprocal interaction also affects the process of tissue damage repair. A variety of immunomodulatory commensal bacteria reside in the body, capable of delivering membrane vesicles (MVs) to host cells to regulate the local immune microenvironment. This research revealed, for the initial time, the significant enhancement of mucosal and cutaneous wound healing by MVs secreted by the human commensal Lactobacillus reuteri (RMVs) through modulation of the inflammatory environment in wound tissue. Local administration of RMVs reduces the proportion of pro-inflammatory macrophages in inflamed tissues and mitigates the level of local inflammation, thereby facilitating the healing of oral mucosa and cutaneous wounds. The elevated oxidative stress levels in activated pro-inflammatory macrophages can be modulated by RMVs, resulting in phenotypic transformation of macrophages. Furthermore, 3-hydroxypropionaldehyde present in RMVs can decrease the mitochondrial permeability of macrophages and stabilize the mitochondrial membrane potential, thereby promoting the conversion of macrophages to an anti-inflammatory phenotype. This study pioneers the significance of commensal bacterial MVs in tissue injury repair and presents a novel concept for the repair of tissue damage., (© 2024 The Authors. Advanced Science published by Wiley‐VCH GmbH.)

Aging significantly elevates the risk for Alzheimer's disease (AD), contributing to the accumulation of AD pathologies, such as amyloid-β (Aβ), inflammation, and oxidative stress. The human prefrontal cortex (PFC) is highly vulnerable to the impacts of both aging and AD. Unveiling and understanding the molecular alterations in PFC associated with normal aging (NA) and AD is essential for elucidating the mechanisms of AD progression and developing novel therapeutics for this devastating disease. In this study, for the first time, we employed a cutting-edge spatial transcriptome platform, STOmics ® SpaTial Enhanced Resolution Omics-sequencing (Stereo-seq), to generate the first comprehensive, subcellular resolution spatial transcriptome atlas of the human PFC from six AD cases at various neuropathological stages and six age, sex, and ethnicity matched controls. Our analyses revealed distinct transcriptional alterations across six neocortex layers, highlighted the AD-associated disruptions in laminar architecture, and identified changes in layer-to-layer interactions as AD progresses. Further, throughout the progression from NA to various stages of AD, we discovered specific genes that were significantly upregulated in neurons experiencing high stress and in nearby non-neuronal cells, compared to cells distant from the source of stress. Notably, the cell-cell interactions between the neurons under the high stress and adjacent glial cells that promote Aβ clearance and neuroprotection were diminished in AD in response to stressors compared to NA. Through cell-type specific gene co-expression analysis, we identified three modules in excitatory and inhibitory neurons associated with neuronal protection, protein dephosphorylation, and negative regulation of Aβ plaque formation. These modules negatively correlated with AD progression, indicating a reduced capacity for toxic substance clearance in AD subject samples. Moreover, we have discovered a novel transcription factor, ZNF460, that regulates all three modules, establishing it as a potential new therapeutic target for AD. Overall, utilizing the latest spatial transcriptome platform, our study developed the first transcriptome-wide atlas with subcellular resolution for assessing the molecular alterations in the human PFC due to AD. This atlas sheds light on the potential mechanisms underlying the progression from NA to AD., Competing Interests: CONFLICT OF INTEREST All authors have no conflicts of interest to declare.

Optical camera communication (OCC) shows promise for optical wireless communication (OWC) in vehicular networks. However, vehicle mobility-induced angular distortions hinder system throughput by degrading non-isotropic vehicular OCC channel gain. Few of the prior works have ever made a comprehensive analysis of their impact, especially based on the pixel value which reflects the camera imaging features. To address this knowledge gap, a pixel value-described vehicular OCC system model accounting for transmitter imaging location and intensity from the geometry and radiometry aspects is presented in this paper with common types of the offset and rotation angles included. We integrate a MATLAB-based simulated vehicular OCC system with an experimentally designed testbed for validation and performance analysis. For a single-time snapshot, we investigate the impacts of common angular distortion types in vehicular OCC systems on maximum pixel value, imaging location, and communication-related metrics. Furthermore, we statistically analyze their influences by considering two driving scenarios with respective angular distributions. The angular distortion characterization from this work is expected to lay a stepping stone to addressing mobility in vehicular OCC systems.

Beta-lactoglobulin (β-Lg), a prominent milk protein, is a major contributor to milk allergies. The quantitative assessment of β-Lg is a valuable method for assessing the allergenic potential of dairy products. In this study, a specific aptamer, β-Lg-01, with an affinity constant ( K D ) of 28.6 nM for β-Lg was screened through seven rounds of magnetic bead SELEX (MB-SELEX). A novel bio-layer interferometry (BLI)-based aptasensor was developed, which had a limit of detection (LOD) of 0.3 ng mL -1 , a linear range of 1.5 ng mL -1 -15 μg mL -1 , and a recovery rate of 102-116% among the milk samples. This aptasensor provides a potential tool for the detection and risk assessment of β-Lg within 10 min.

Hip fracture risk assessment is an important but challenging task. Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur. We developed a global FEA-computed fracture risk index to increase the prediction accuracy of hip fracture incidence., Purpose: Quantitative CT-based patient-specific finite element (FE) analysis (FEA) incorporates bone geometry and bone density in the proximal femur to compute the force (fracture load) and energy necessary to break the proximal femur in a particular loading condition. The fracture loads and energies-to-failure are individually associated with incident hip fracture, and provide different structural information about the proximal femur., Methods: We used principal component analysis (PCA) to develop a global FEA-computed fracture risk index that incorporates the FEA-computed yield and ultimate failure loads and energies-to-failure in four loading conditions of 110 hip fracture subjects and 235 age- and sex-matched control subjects from the AGES-Reykjavik study. Using a logistic regression model, we compared the prediction performance for hip fracture based on the stratified resampling., Results: We referred the first principal component (PC1) of the FE parameters as the global FEA-computed fracture risk index, which was the significant predictor of hip fracture (p-value < 0.001). The area under the receiver operating characteristic curve (AUC) using PC1 (0.776) was higher than that using all FE parameters combined (0.737) in the males (p-value < 0.001)., Conclusions: The global FEA-computed fracture risk index increased hip fracture risk prediction accuracy in males., (© 2024. International Osteoporosis Foundation and Bone Health and Osteoporosis Foundation.)

Hydroformylation is one of the most important homogeneous reactions in industrial production. Herein, a density functional theory (DFT) method was employed to investigate two proposed reaction mechanisms of hydroformylation catalyzed by cationic cobalt(II) complexes, the carbonyl dissociative mechanism and the associative mechanism. The calculated results showed that the heterolytic H 2 activation is the rate-determining step for both the dissociative mechanism and the associative mechanism, with energy barriers of 26.8 kcal mol -1 and 40.5 kcal mol -1 , respectively. Meanwhile, the regioselectivity, the spin multiplicity of the catalyst and the substituent effects on the reaction were also investigated. The most stable cobalt(II) catalyst has a doublet state and the linear aldehyde is the dominant product. In addition, it was found that the energy barrier of the reaction decreased when the electron density of the Co center of the catalyst was increased by changing the ligand. The catalytic activity of the catalyst was proposed to be the best when the PEt 2 group of the ligand is replaced by the P( t Bu) 2 group. This study might not only provide new insights for hydroformylation catalyzed by cobalt but also facilitate theory-guided design of novel transition metal catalysts for hydroformylation.

Background: Adenoid hypertrophy (AH) is a common pediatric disease that significantly impacts the growth and quality of life of children. However, there is no replicable and valid model for AH., Methods: An AH rat model was developed via comprehensive allergic sensitization, chronic inflammation induction, and chronic intermittent hypoxia (CIH). The modeling process involved three steps: female Sprague-Dawley rats (aged 4-5 weeks) were used for modeling. Allergen sensitization was induced via intraperitoneal administration and intranasal provocation using ovalbumin (OVA); chronic nasal inflammation was induced through intranasal lipopolysaccharide (LPS) administration for sustained nasal irritation; CIH akin to obstructive sleep apnea/hypopnea syndrome was induced using an animal hypoxia chamber. Postmodel establishment, behaviors, and histological changes in nasopharynx-associated lymphoid tissue (NALT) and nasal mucosa were assessed. Arterial blood gas analysis and quantification of serum and tissue levels of (interleukin) IL-4 and IL-13, OVA-specific immunoglobulin E (sIgE), eosinophil cationic protein (ECP), tumor necrosis factor (TNF-α), IL-17, and transforming growth factor (TGF)-β were conducted for assessment. The treatment group received a combination of mometasone furoate and montelukast sodium for a week and then was evaluated., Results: Rats exhibited notable nasal symptoms and hypoxia after modeling. Histopathological analysis revealed NALT follicle hypertrophy and nasal mucosa inflammatory cell infiltration. Elevated IL-4, IL-13, IL-17, OVA-sIgE, ECP, and TNF-α levels and reduced TGF-β levels were observed in the serum and tissue of model-group rats. After a week of treatment, the treatment group exhibited symptom and inflammatory factor improvement., Conclusion: The model effectively simulates AH symptoms and pathological changes. But it should be further validated for genetic, immunological, and hormonal backgrounds in the currently used and other strains and species., (© 2024 The Authors. Animal Models and Experimental Medicine published by John Wiley & Sons Australia, Ltd on behalf of The Chinese Association for Laboratory Animal Sciences.)

Rationale: With the accelerating process of population aging, the comorbidity of chronic disease (CCD) has become a major public health problem that threatens the health of older adults., Objective: This study aimed to assess whether CCD is associated with basic activities of daily living (BADL) and explore the factors influencing BADL in older adults., Method: A cross-sectional community health survey with stratified random sampling among older residents (≥60 years old) was conducted in 2022. A questionnaire was used to collect information on BADL, chronic diseases, and other relevant aspects. Propensity score matching (PSM) was used to match the older adults with and without CCD. Univariate and multivariate logistic regression analyses were used to explore the factors influencing BADL. PSM was used to match participants with single-chronic disease (SCD) and CCD., Results: Among the 47,720 participants, those with CCD showed a higher prevalence of BADL disability (13.07%) than those with no CCD (6.33%) and SCD (7.39%). After adjusting for potential confounders with PSM, 6,513 pairs of cases with and without CCD were matched. The univariate analysis found that the older adults with CCD had a significantly higher prevalence of BADL disability (13.07%, 851 of 6,513) than those without CCD (9.83%, 640 of 6,513, P < 0.05). The multivariate logistic regression analysis revealed that CCD was a risk factor for BADL in older adults [OR = 1.496, 95% CI: 1.393-1.750, P < 0.001]. In addition, age, educational level, alcohol intake, social interaction, annual physical examination, retirement benefits, depression, weekly amount of exercise, and years of exercise were related to BADL disability ( P < 0.05). PSM matching was performed on participants with CCD and SCD and showed that the older adults with CCD had a significantly higher prevalence of BADL disability (13.07%, 851 of 6,513) than those with SCD (11.39%, 742 of 6,513, P < 0.05)., Conclusion: The older adults with CCD are at a higher risk of BADL disability than their counterparts with no CCD or SCD. Therefore, we advocate paying attention to and taking measures to improve the health and quality of life of these individuals., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Zeng, Miao, Wang, Zhao, Wang, Liu, Wei, Liu, Jia, Li, Zhou, Chen and Tian.)

Sepsis is the third leading cause of death worldwide. Antibiotics are an important component in the treatment of sepsis. The use of antibiotics is currently facing the challenge of increasing antibiotic resistance (Evans et al., 2021). Sepsis medication prediction can be modeled as a Markov decision process, but existing methods fail to integrate with medical knowledge, making the decision process potentially deviate from medical common sense and leading to underperformance. (Wang et al., 2021). In this paper, we use Deep Q-Network (DQN) to construct a Sepsis Anti-infection DQN (SAI-DQN) model to address the challenge of determining the optimal combination and duration of antibiotics in sepsis treatment. By setting sepsis clinical knowledge as reward functions to guide DQN complying with medical guidelines, we formed personalized treatment recommendations for antibiotic combinations. The results showed that our model had a higher average value for decision-making than clinical decisions. For the test set of patients, our model predicts that 79.07% of patients will achieve a favorable prognosis with the recommended combination of antibiotics. By statistically analyzing decision trajectories and drug action selection, our model was able to provide reasonable medication recommendations that comply with clinical practices. Our model was able to improve patient outcomes by recommending appropriate antibiotic combinations in line with certain clinical knowledge., Competing Interests: Declaration of competing interest All authors declare no conflicts of interest in this research., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Toxoplasma gondii is a zoonotic parasite infecting humans and nearly all warm-blooded animals. Successful parasitism in diverse hosts at various developmental stages requires the parasites to fine tune their metabolism according to environmental cues and the parasite's needs. By manipulating the β and γ subunits, we have previously shown that AMP-activated protein kinase (AMPK) has critical roles in regulating the metabolic and developmental programmes. However, the biological functions of the α catalytic subunit have not been established. T. gondii encodes a canonical AMPKα, as well as a KIN kinase whose kinase domain has high sequence similarities to those of classic AMPKα proteins. Here, we found that TgKIN is dispensable for tachyzoite growth, whereas TgAMPKα is essential. Depletion of TgAMPKα expression resulted in decreased ATP levels and reduced metabolic flux in glycolysis and the tricarboxylic acid cycle, confirming that TgAMPK is involved in metabolic regulation and energy homeostasis in the parasite. Sequential truncations at the C-terminus found an α-helix that is key for the function of TgAMPKα. The amino acid sequences of this α-helix are not conserved among various AMPKα proteins, likely because it is involved in interactions with TgAMPKβ, which only have limited sequence similarities to AMPKβ in other eukaryotes. The essential role of the less conserved C-terminus of TgAMPKα provides opportunities for parasite specific drug designs targeting TgAMPKα., (© 2024 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd.)

Integration of heterogeneous and high-dimensional multi-omics data is becoming increasingly important in understanding etiology of complex genetic diseases. Each omics technique only provides a limited view of the underlying biological process and integrating heterogeneous omics layers simultaneously would lead to a more comprehensive and detailed understanding of diseases and phenotypes. However, one obstacle faced when performing multi-omics data integration is the existence of unpaired multi-omics data due to instrument sensitivity and cost. Studies may fail if certain aspects of the subjects are missing or incomplete. In this paper, we propose a deep learning method for multi-omics integration with incomplete data by Cross-omics Linked unified embedding with Contrastive Learning and Self Attention (CLCLSA). Utilizing complete multi-omics data as supervision, the model employs cross-omics autoencoders to learn the feature representation across different types of biological data. The multi-omics contrastive learning is employed, which maximizes the mutual information between different types of omics. In addition, the feature-level self-attention and omics-level self-attention are employed to dynamically identify the most informative features for multi-omics data integration. Finally, a Softmax classifier is employed to perform multi-omics data classification. Extensive experiments were conducted on four public multi-omics datasets. The experimental results indicate that our proposed CLCLSA produces promising results in multi-omics data classification using both complete and incomplete multi-omics data., Competing Interests: Declaration of competing interest The authors declare that there are no conflicts of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Atmospheric deposition of Cd poses a serious threat to ecosystem security. Biochar is widely used for polluted soil remediation, however, whether biochar already applied to the soil can reduce the hazards of newly deposited Cd remains to be studied. Thus, an indoor cultural experiment and static adsorption method were conducted to study the isothermal and kinetic adsorption processes of three types of biochar (rice husk, rubber wood, and tobacco stem biochars) on Cd in iron rich soils and the effect of biochar on the morphological distribution of Cd in the soil and the soil pH. The results showed that the soil with biochar in our study could quickly fix "the new deposited Cd" in the soil in 3 h with the maximum adsorption capacity in rubber wood biochar-treated sample (3227.34 mg/kg）. The addition of all three biochar treatments significantly increased the soil pH and reduced the soil exchange state Cd content, with a 13.69-17.32% increase in the pH and a 13.22-54.39% reduction in the exchange state Cd content when contrasted with the control, which could promote those Cd converting into unavailable Cd (carbonate-bound form Cd, Fe-Mn oxide-bound form Cd, or residual form Cd) for crops. In summary, the addition of three kinds of biochar treatments could effectively reduce the ecological and environmental risk of soil that was contaminated by Cd and could provide a reliable theoretical basis for the effect of biochar on the improvement of the quality of soil that is contaminated by heavy metals., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Although colloidal perovskite nanocrystal (PNC) solution has exhibited near-unity photoluminescence quantum yield (PLQY), the luminance would be severely quenched when the PNC solution is assembled into thin films due to the agglomeration and fusion of NCs caused by the exfoliation of surface ligands and non-radiative Förster resonance energy transfer (FRET) from small to large particle sizes, which seriously affected the performances of light-emitting diodes (LEDs). Here, we used Guanidine thiocyanate (GASCN) and Sodium thiocyanate (NaSCN) to achieve effective CsPbI 3 PNC surface reconstruction. Due to the strong coordination ability of these small molecules with the anions and cations on the surface of the PNCs, they can provide strong surface protection against PNC fusion during centrifugal purification process and repair the surface defects of PNCs, so that the original uniform size distribution of PNCs can be maintained and FRET between close-packed PNC films is effectively suppressed, which allows the emission characteristics of the films to be preserved. As a result, highly oriented, smooth and nearly defect-free high-quality PNC thin films are obtained, with PLQY as high as 95.1 %, far exceeding that of the original film, and corresponding LEDs exhibit a maximum external quantum efficiency of 24.5 %., (© 2024 Wiley-VCH GmbH.)

Cavity-enhanced direct frequency comb spectroscopy (CE-DFCS) is widely used as a highly sensitive gas sensing technology in various gas detection fields. For the on-axis coupling incidence scheme, the detection accuracy and stability are seriously affected by the cavity-mode noise, and therefore, stable operation inevitably requires external electronic mode-locking and sweeping devices, substantially increasing system complexity. To address this issue, we propose off-axis cavity-enhanced optical frequency comb spectroscopy from both theoretical and experimental aspects, which is applied to the detection of single- and dual-gas of carbon monoxide (CO) and carbon dioxide (CO 2 ) in the near-infrared. An erbium-doped fiber frequency comb with a repetition frequency of ∼41.709 MHz is coupled into a resonant cavity with a length of ∼360 mm in an off-axis manner, exciting numerous high-order modes to effectively suppress cavity-mode noise. The performance of multiple machine learning models is compared for the inversion of a single/dual gas concentration. A few absorbance spectra are collected to build a sample data set, which is then utilized for model training and learning. The results demonstrate that the Particle Swarm Optimization Support Vector Machine (PSO-SVM) model achieves the highest predictive accuracy for gas concentration and is ultimately applied to the detection system. Based on Allan deviation, the detection limit for CO in single-gas detection can reach 8.247 parts per million by volume (ppmv) by averaging 87 spectra. Meanwhile, for simultaneous CO 2 /CO measurement with highly overlapping absorbance spectra, the LoD can be reduced to 13.196 and 4.658 ppmv, respectively. The proposed optical gas sensing technique indicates the potential for the development of a field-deployable and intelligent sensor system capable of simultaneous detection of multiple gases.

In mammals, parental exposure to amitriptyline (AMI) has been proven to contribute to congenital disabilities in their offspring. However, no studies have paid attention to the adverse effects of parental exposure to amitriptyline on fish offspring. In this study, we exposed adult zebrafish (F0) to AMI (0.8 μg/L) for 21 days. Subsequently, these zebrafish (F0) were allowed to mate, and their offspring (F1) were collected to culture in clean water for 5 days. The mortality rate, average hatching time, and heart rate at 48 h post-fertilization (hpf) of F1 were investigated. Our results showed that parental exposure to AMI induced tachycardia and increased mortality in F1 zebrafish. Under a light/dark transition test, F1 larvae born from AMI-exposed parents exhibited lower locomotor activity in the dark period and decreased thigmotaxis in the light period. The transcriptome analysis showed that parental AMI exposure dysregulated some key pathways in their offspring. Through the prediction of key driver analysis, six differentially expressed genes (DEGs) were revealed as key driver genes involved in protein processing in endoplasmic reticulum (hspa5, hsp70.1, hsp90a), ribosome (rps27a) and PPAR signaling pathway (pparab and fabp2). Considering that the concentration of AMI residual components in natural water bodies may be over our test concentration (0.8 μg/L), our findings suggested that toxicity of parental exposure to the offspring of fish should receive greater attention., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Sparse CT reconstruction continues to be an area of interest in a number of novel imaging systems. Many different approaches have been tried including model-based methods, compressed sensing approaches, and most recently deep-learning-based processing. Diffusion models, in particular, have become extremely popular due to their ability to effectively encode rich information about images and to allow for posterior sampling to generate many possible outputs. One drawback of diffusion models is that their recurrent structure tends to be computationally expensive. In this work we apply a new Fourier diffusion approach that permits processing with many fewer time steps than the standard scalar diffusion model. We present an extension of the Fourier diffusion technique and evaluate it in a simulated breast cone-beam CT system with a sparse view acquisition.

Background: Pulmonary artery sarcoma (PAS) is a very rare malignancy with a poor prognosis; however, its clinical manifestations and imaging findings are often indistinguishable from pulmonary thromboembolism (PTE). We thus aimed to accurately diagnose PAS by comparing the clinical and computed tomography pulmonary angiography (CTPA) and magnetic resonance imaging (MRI) imaging characteristics of PAS and PTE., Methods: This case-control study retrospectively enrolled 20 patients with PAS (from March 2017 to September 2022), 40 patients with central acute PTE, and 40 patients with central chronic PTE (from January 2021 to December 2022) in the China-Japan Friendship Hospital. The following clinical and imaging findings were compared between the three groups: initial symptoms; D-dimer, C-reactive protein, and N-terminal pro B-type natriuretic peptide levels; wall-eclipsing sign (WES); scope of lesion involvement; and morphological characteristics. Signal intensity was also observed on different MRI sequences., Results: The D-dimer level in PAS was significantly lower than that in central acute PTE (P<0.001). The WES was present in 17 cases of PAS (85.0%), which was a greater proportion than that of the central acute PTE and chronic PTE groups (all P values <0.001). The involvement of the pulmonary valve or right ventricular outflow tract was observed in five PAS cases but none of the central acute PTE or chronic PTE cases (all P values =0.001). In 19 PAS cases (95.0%), the lesions grew expansively in the central pulmonary artery. The proximal margin of 18 patients with PAS (90.0%) was bulging or lobulated. Nine cases of PAS (45.0%) showed aneurysm-like dilatation (grape-like sign) of the distal pulmonary artery, representing significantly greater proportion than that of the central acute PTE and chronic PTE groups (all P values <0.001). In 37 patients with central acute PTE (92.5%), the clots were observed to be floating in the pulmonary artery lumen with saddle, tubular or polypoid shape. Eccentric filling defects attached to the pulmonary artery wall were observed in 32 cases of central chronic PTE (80.0%). On MRI, PAS lesions were hyperintense on fat-suppressed T2-weighted imaging and diffusion-weighted imaging, demonstrating heterogeneous enhancement., Conclusions: Comprehensive analysis of the clinical data and imaging features on CTPA and MRI can aid in the accurate differential diagnosis of PAS and PTE., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-992/coif). The authors have no conflicts of interest to declare., (2024 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Accurate detection of fibrotic interstitial lung disease (f-ILD) is conducive to early intervention. Our aim was to develop a lung graph-based machine learning model to identify f-ILD. A total of 417 HRCTs from 279 patients with confirmed ILD (156 f-ILD and 123 non-f-ILD) were included in this study. A lung graph-based machine learning model based on HRCT was developed for aiding clinician to diagnose f-ILD. In this approach, local radiomics features were extracted from an automatically generated geometric atlas of the lung and used to build a series of specific lung graph models. Encoding these lung graphs, a lung descriptor was gained and became as a characterization of global radiomics feature distribution to diagnose f-ILD. The Weighted Ensemble model showed the best predictive performance in cross-validation. The classification accuracy of the model was significantly higher than that of the three radiologists at both the CT sequence level and the patient level. At the patient level, the diagnostic accuracy of the model versus radiologists A, B, and C was 0.986 (95% CI 0.959 to 1.000), 0.918 (95% CI 0.849 to 0.973), 0.822 (95% CI 0.726 to 0.904), and 0.904 (95% CI 0.836 to 0.973), respectively. There was a statistically significant difference in AUC values between the model and 3 physicians (p < 0.05). The lung graph-based machine learning model could identify f-ILD, and the diagnostic performance exceeded radiologists which could aid clinicians to assess ILD objectively., (© 2024. The Author(s).)

Objective: To assess lung deformation in patients with idiopathic pulmonary fibrosis (IPF) using with elastic registration algorithm applied to three-dimensional ultrashort echo time (3D-UTE) MRI and analyze relationship of lung deformation with the severity of IPF., Methods: Seventy-six patients with IPF (mean age: 62 ± 6 years) and 62 age- and gender-matched healthy controls (mean age: 58 ± 4 years) were prospectively enrolled. End-inspiration and end-expiration images acquired with a single breath-hold 3D-UTE sequence were registered using elastic registration algorithm. Jacobian determinants were calculated from deformation fields and represented on color maps. Jac-mean (absolute value of the log means of Jacobian determinants) and the Dice similarity coefficient (Dice) were compared between different groups., Results: Compared with healthy controls, the Jac-mean of IPF patients significantly decreased (0.21 ± 0.08 vs. 0.27 ± 0. 07, p < 0.001). Furthermore, the Jac-mean and Dice correlated with the metrics of pulmonary function tests and the composite physiological index. The lung deformation in IPF patients with dyspnea Medical Research Council (MRC) ≥ 3 (Jac-mean: 0.16 ± 0.03; Dice: 0.06 ± 0.02) was significantly lower than MRC1 (Jac-mean: 0. 25 ± 0.03, p < 0.001; Dice: 0.10 ± 0.01, p < 0.001) and MRC 2 (Jac-mean: 0.22 ± 0.11, p = 0.001; Dice: 0.08 ± 0.03, p = 0.006). Meanwhile, Jac-mean and Dice correlated with health-related quality of life, 6 min-walk distance, and the extent of pulmonary fibrosis. Jac-mean correlated with pulmonary vascular-related indexes on high-resolution CT., Conclusion: The decreased lung deformation in IPF patients correlated with the clinical severity of IPF patients. Elastic registration of inspiratory-to-expiratory 3D UTE MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF., Critical Relevance Statement: This prospective study demonstrated that lung deformation decreased in idiopathic pulmonary fibrosis (IPF) patients and correlated with the severity of IPF. Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI may be a new morphological and functional marker for non-radiation and noninvasive evaluation of IPF., Key Points: • Elastic registration of inspiratory-to-expiratory three-dimensional ultrashort echo time (3D UTE) MRI could evaluate lung deformation. • Lung deformation significantly decreased in idiopathic pulmonary fibrosis (IPF) patients, compared with the healthy controls. • Reduced lung deformation of IPF patients correlated with worsened pulmonary function and the composite physiological index (CPI)., (© 2024. The Author(s).)

Purpose: This study aimed to explore the effects of additional visual tasks in physical exercise on the vision and balance ability of children, and to verify whether children's vision mediated the influence of physical exercise on their balance ability., Methods: The study randomly selected 86 students aged 9-10 years old from a school in Suzhou city, dividing them into an experimental group ( n  = 43) and a control group ( n  = 43). The experimental group participated in physical exercise with additional visual tasks, while the control group engaged in routine physical exercise. The experiment lasted for 16 weeks, with kinetic visual acuity (KVA), uncorrected distance visual acuity (UDVA), static balance, and dynamic balance measured before and after the experiment., Results: The results showed that after the experiment, the experimental group had significantly improved kinetic visual acuity (KVA), uncorrected distance visual acuity (UDVA), static balance, and dynamic balance. In contrast, the control group had significantly decreased kinetic visual acuity, no significant improvement in uncorrected distance visual acuity, and no significant difference in dynamic balance and static balance. In the experimental group, there was a moderate positive correlation between kinetic visual acuity and uncorrected distance visual acuity, and a moderate positive correlation between uncorrected distance visual acuity and both static and dynamic balance. The study also found that uncorrected distance visual acuity partially mediated the effect of additional visual tasks during physical exercise on static and dynamic balance among children., Conclusion: In conclusion, adding visual tasks to physical exercise had a positive effect on improving children's vision and balance ability. Kinetic visual acuity and uncorrected distance visual acuity were positively correlated, and uncorrected distance visual acuity was positively correlated with both static and dynamic balance. Uncorrected distance visual acuity partially mediated the effect of physical exercise on children's balance ability., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Yin, Zhu, Liu, Wang, Li and Dai.)

Accurate prognosis for cancer patients can provide critical information for optimizing treatment plans and improving life quality. Combining omics data and demographic/clinical information can offer a more comprehensive view of cancer prognosis than using omics or clinical data alone and can also reveal the underlying disease mechanisms at the molecular level. In this study, we developed and validated a deep learning framework to extract information from high-dimensional gene expression and miRNA expression data and conduct prognosis prediction for breast cancer and ovarian-cancer patients using multiple independent multi-omics datasets. Our model achieved significantly better prognosis prediction than the current machine learning and deep learning approaches in various settings. Moreover, an interpretation method was applied to tackle the "black-box" nature of deep neural networks and we identified features (i.e., genes, miRNA, demographic/clinical variables) that were important to distinguish predicted high- and low-risk patients. The significance of the identified features was partially supported by previous studies., (© 2024. The Author(s).)

Statement of Problem: Post and core (PC) restoration has been widely used to restore endodontically treated teeth. Nevertheless, bibliometric studies focusing on PC research are lacking., Purpose: The purpose of this bibliometric analysis was to review the general situation and determine the research direction of PC restoration by means of visualization, including the time development, countries, institutions, authors, journals, research categories, and the information of references with the strongest citation burst., Material and Methods: The search was carried out within the topic field of the Web of Science (WoS) database. CiteSpace, VOSviewer, and R language were used to analyze the literature. Bibliometric indicators in terms of title, keyword, reference, publication time, institution, country and citation information were analyzed., Results: Between 1966 and 2022, dental research on PC trended upward, especially in 2011 and up to 2022. Scholars in Brazil, the United States, and Italy actively participated in PC research. The country that published the most was Brazil. Italy both ranked first in the H-index and average citations per item and had cooperative relations with several countries. The United States ranked first in the world in total citation count. The University of Siena published the most articles. The Journal of Prosthetic Dentistry (JPD) was the major contributing journal. The top three authors in this field were Ferrari M, Goracci C and Naumann M. The largest cooperative network of authors consisted of four Italian scholars. There were six main clusters of research topics on PC through CiteSpace co-citations. In addition, CiteSpace co-citations, CiteSpace Burst detection and VOSviewer keyword analysis showed the most important research interests, such as bond strength, fiber post, fracture resistance, and finite element analysis. Photodynamic therapy is the latest research hotspot. Finally, there were the top 25 references with the strongest citation bursts, which mainly focused on four aspects, namely, studies related to fiber posts, PC bonding strength, finite element analysis, and residual teeth and the ferrule effect., Conclusions: This bibliometric analysis provides a comprehensive overview of PC research, including the research time, country, institution, author, journal, article clustering, article keywords, and important literature analysis, so that researchers can have a clear understanding of the research situation in this field., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Quasi-2D perovskites, multiquantum well materials with the energy cascade structure, exhibit impressive optoelectronic properties and a wide range of applications in various optoelectronic devices. However, the insufficient exciton energy transfer caused by the excess of small- n phases that induce nonradiative recombination and the spatially random phase distribution that impedes charge transport severely inhibit the device performance of light-emitting diodes (LEDs). Here, a faster energy transfer process and efficient carrier recombination are achieved by introducing the multifunctional additive 2-(methylsulfonyl)-4-(trifluoromethyl)benzoic acid (MTA) to manipulate the crystallization process of perovskites. The introduction of MTA not only constrains the PEA and restrains the formation of small- n phases to improve the energy transfer process but also optimizes the crystal orientation to promote charge transport. As a result, highly efficient pure green quasi-2D perovskite LEDs with a peak EQE of 25.9%, a peak current efficiency of 108.1 cd A -1 , and a maximum luminance of 288798 cd m -2 are achieved.

Background: In the 2022 European Society of Cardiology (ESC) and the European Respiratory Society (ERS) guidelines, the diagnostic criteria for pulmonary hypertension (PH) included a reduced mean pulmonary artery pressure (mPAP) of 20 mmHg (mPAP >20 mmHg). This study aimed to reassess cardiovascular metrics on computed tomography pulmonary angiography (CTPA) for chronic thromboembolic pulmonary hypertension (CTEPH) to optimize the timely diagnosis of patients with suspected PH., Methods: Patients with suspected CTEPH who underwent CTPA and right heart catheterization (RHC) between January 2019 and December 2022 in China-Japan Friendship Hospital were retrospectively included. They were grouped into CTEPH and non-PH groups according to the new and old criteria (2022 and 2015 ESC/ERS guidelines) for the diagnosis of PH. Cardiovascular metrics including the main pulmonary artery diameter (MPAd), Cobb angle, and right ventricular free wall thickness (RVWT), among others, were measured. The correlation of these metrics with hemodynamic data was analyzed with Spearman rank correlation analysis, while the differences in cardiovascular metrics between the updated (mPAP >20 mmHg) and old PH criteria (mPAP ≥25 mmHg) were compared with independent samples t -test or the Mann-Whitney test. Receiver operator characteristic (ROC) curve analysis was performed for the prediction model., Results: The study enrolled 180 patients (males n=86; age 55.5±12.0 years old). According to the old guidelines, 119 patients were placed into the PH group (mPAP ≥25 mmHg) , while according to the new guidelines, 130 patients were placed into the PH group (mPAP >20 mmHg). Cardiovascular metrics on CTPA between the updated and old guidelines were comparable (P>0.05). Compared to other metrics, an MPAd of 30.4 mm exhibited the highest area under the curve (AUC: 0.934±0.021), with a sensitivity of 0.88 and specificity of 0.90. MPAd [odds ratio (OR) =1.271], transverse diameter of the right ventricle (RVtd; OR =1.176), Cobb angle (OR =1.108), and RVWT (OR =3.655) were independent factors for diagnosing CTEPH (P<0.05). Cobb angle, right and left ventricular transverse diameter ratio, and right and left ventricular area ratio moderately correlated with mPAP (r=0.586, r=0.583, r=0.629) and pulmonary vascular resistance (PVR) (r=0.613, r=0.593, r=0.642)., Conclusions: Cardiovascular metrics on CTPA were comparable between the new and old guidelines for CTEPH diagnosis. Cardiovascular metrics on CTPA can noninvasively assess the hemodynamics of patients with CTEPH., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-250/coif). The authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objective: To evaluate whether the revised US Preventive Services Task Force (USPSTF) criteria reduced inequities in lung cancer screening (LCS) eligibility among a racially diverse sample of patients with lung cancer., Methods: This is a retrospective analysis of adults diagnosed with primary lung malignancies at an urban safety net hospital. For all patients and exclusively ever-smokers, χ 2 tests were used to evaluate differences in LCS eligibility among socio-demographic variables using the 2013 and 2021 USPSTF criteria. Patients who were ineligible for LCS were categorized by reason for exclusion., Results: Among 678 lung cancer patients (46% female, mean age 66 ± 10 years), 51% were White, and 39% were Black. Using the 2013 guidelines, White patients (57%) would have been more likely to be eligible than Black (37%) and other-race patients (35%) (P < 0.0001) at time of cancer diagnosis. Under the 2021 guidelines, White patients (68%) remained more likely to be eligible for LCS than Black (54%) and other-race patients (48%) (P = 0.0002). Among exclusively ever-smoking patients, we did not observe a significant difference in eligibility by race under the 2021 USPSTF guidelines (White [73%], Black [65%], and other-race [65%]; [P = 0.48]). Sex, ethnicity, education level, and insurance type were not associated with differential screening eligibility under either the 2013 or 2021 guidelines., Conclusion: The revised 2021 USPSTF LCS guidelines may not be sufficient to eliminate racial inequities in LCS eligibility among patients who go on to be diagnosed with primary lung cancer. Differential rates of lung cancer among never-smokers may contribute to this inequity., (© 2022. W. Montague Cobb-NMA Health Institute.)

Most packages for the analysis of fMRI-based functional connectivity (FC) and genomic data are used with a programming language interface, lacking an easy-to-navigate GUI frontend. This exacerbates two problems found in these types of data: demographic confounds and quality control in the face of high dimensionality of features. The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset. FC in particular usually contains tens of thousands of features per subject, and can only be summarized and efficiently explored using visualizations. To remedy this situation, we have developed ImageNomer, a data visualization and analysis tool that allows inspection of both subject-level and cohort-level demographic, genomic, and imaging features. The software is Python-based, runs in a self-contained Docker image, and contains a browser-based GUI frontend. We demonstrate the usefulness of ImageNomer by identifying an unexpected race confound when predicting achievement scores in the Philadelphia Neurodevelopmental Cohort (PNC) dataset, which contains multitask fMRI and single nucleotide polymorphism (SNP) data of healthy adolescents. In the past, many studies have attempted to use FC to identify achievement-related features in fMRI. Using ImageNomer to visualize trends in achievement scores between races, we find a clear potential for confounding effects if race can be predicted using FC. Using correlation analysis in the ImageNomer software, we show that FCs correlated with Wide Range Achievement Test (WRAT) score are in fact more highly correlated with race. Investigating further, we find that whereas both FC and SNP (genomic) features can account for 10-15% of WRAT score variation, this predictive ability disappears when controlling for race. We also use ImageNomer to investigate race-FC correlation in the Bipolar and Schizophrenia Network for Intermediate Phenotypes (BSNIP) dataset. In this work, we demonstrate the advantage of our ImageNomer GUI tool in data exploration and confound detection. Additionally, this work identifies race as a strong confound in FC data and casts doubt on the possibility of finding unbiased achievement-related features in fMRI and SNP data of healthy adolescents., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Introduction: Compensatory mouth breathing, caused by nasopharyngeal obstructive diseases, is the main cause of hyperdivergent mandibular retrognathia in children. Such deformities require effective growth guidance before pubertal growth peaks. The traditional mandibular advancement device, twin block (TB), can guide the forward development of the mandible. However, the side effect of increasing the vertical dimension of the lower facial third, worsens the facial profile of children with divergent growth trends. To solve this problem, a modified TB (LLTB) appliance was designed to control the vertical dimension by intruding incisors and inhibiting the elongation of posterior teeth during the advancement of the mandible, which could avoid the side effects of traditional appliances and effectively guide the growth of the mandible in a normal direction., Methods and Analysis: The study was designed as a single-centre, single-blind, randomised, parallel controlled trial. We aim to enrol 60 children aged 9-14 years with hyperdivergent skeletal class II malocclusion, using a 1:1 allocation ratio. The participants were will be randomly assigned to receive either the TB or LLTB treatment. The primary outcome will be a change in the angle of the mandibular plane relative to the anterior cranial base. The secondary outcomes will include changes in the sagittal maxillomandibular relation, occlusal plane, facial height, morphology of the mandible and upper airway width. Safety endpoints will also be evaluated., Ethics and Dissemination: Ethical approval was obtained from the ethics committee of Shanghai Stomatological Hospital. Both participants and their guardians will be fully informed of the study and sign an informed consent form before participating in the trial. The results will be publicly available in peer-reviewed scientific journals., Trial Registration Number: ChiCTR2000035882., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: To re-assess cardiovascular metrics on computed tomography pulmonary angiography (CTPA) in predicting pulmonary hypertension (PH) under the 2022 ESC/ERS guidelines., Materials and Methods: This observational study retrospectively included 272 patients (female 143, mean age = 54.9 ± 12.5 years old) with suspected PH. 218 patients were grouped to evaluate cardiovascular metrics on CTPA and develop a binary logistic regression model. The other 54 patients were grouped into the validation group to assess the performance of the prediction model under the updated criteria. Based on mean pulmonary artery pressure (mPAP), patients were divided into three groups: group A consisted of patients with mPAP ≤ 20 mmHg, group B included patients with 20 mmHg < mPAP < 25 mmHg, and group C comprised patients with mPAP ≥ 25 mmHg. Cardiovascular metrics among the three groups were compared, and receiver operating characteristic curves (ROCs) were used to evaluate the performance of cardiovascular metrics in predicting mPAP > 20 mmHg., Results: The main pulmonary arterial diameter (MPAd), MPAd/ascending aorta diameter ratio (MPAd/AAd ratio), and right ventricular free wall thickness (RVFWT) showed significant differences among the three groups (p < 0.05). The area under curve (AUC) of MPAd was larger than MPAd/AAd ratio and RVFWT. A MPAd cutoff value of 30.0 mm has a sensitivity of 83.1% and a specificity of 90.4%. The AUC of the binary logistic regression model (Z =  - 12.98187 + 0.31053 MPAd + 1.04863 RVFWT) was 0.938 ± 0.018. In the validation group, the AUC, sensitivity, specificity, and accuracy of the prediction model were 0.878, 92.7%, 76.9%, and 88.9%, respectively., Conclusion: Under the updated criteria, MPAd with a threshold value of 30.0 mm has better sensitivity and specificity in predicting PH. The binary logistic regression model may improve the diagnostic accuracy., Critical Relevance Statement: Under the updated criteria, the main pulmonary arterial diameter with a threshold value of 30.0 mm has better sensitivity and specificity in predicting pulmonary hypertension. The binary logistic regression model may improve diagnostic accuracy., Key Points: • According to 2022 ESC/ERS guidelines, a MPAd cutoff value of 30.0 mm has better sensitivity and specificity in predicting mPAP > 20 mmHg • A binary logistic regression model (Z = - 12.98187 + 0.31053 MPAd + 1.04863 RVFWT) was developed and had a sensitivity, specificity, and accuracy of 92.7%, 76.9%, and 88.9% in predicting mPAP > 20 mmHg. • A binary logistic regression prediction model outperforms MPAd in predicting mPAP > 20 mmHg., (© 2023. European Society of Radiology (ESR).)

Background: Computed tomography pulmonary angiography (CTPA) is a first-line noninvasive method to diagnose acute pulmonary thromboembolism (APE); however, whether chest noncontrast CT (NC-CT) could aid in the diagnosis of APE remains unknown. The aim of this study was to build and evaluate a holistic lung graph-based machine learning (HLG-ML) using NC-CT for the diagnosis of APE and to compare its performance with that of radiologists and the YEARS algorithm., Methods: This study enrolled 178 cases (77 males; age 63.9±16.7 years) who underwent NC-CT and CTPA in the same day from January 2019 to December 2020. Of these patients, 133 (75% of cases; 58 males; age 65.4±15.6 years) were placed into a training group and 45 (25% of cases; 19 males; age 59.6±19.2 years) into a testing group. The other 43 cases (18 males; age 62.8±20.0 years) were used to externally validate the model between January 2021 and March 2022. A HLG was developed with a pulmonary radiomics descriptor derived from NC-CT images. The approach extracted local radiomics features and encoded these local features into a radiomics descriptor as a characterization of global radiomics feature distribution. Subsequently, 8 ML models were trained and compared based on the radiomics descriptor. In the validation group, area under the curves (AUCs) of the HLG-ML model in the diagnosis of APE were compared with those of the 3 radiologists and the YEARS algorithm., Results: Among the 8 ML models, gradient boosting decision tree demonstrated the best classification performance (AUC =0.772) on the training set. In the testing set, the AUC of gradient boosting decision trees was 0.857 [95% confidence intervals (CIs): 0.699-0.951]. In the validation set, the performance of gradient boosting decision tree (AUC =0.810; 95% CI: 0.669-0.952; Youden index =0.621) outperformed 3 radiologists (AUC =0.508, 95% CI: 0.335-0.681, Youden index =0.016; AUC =0.504, 95% CI: 0.354-0.654, Youden index =0.008; AUC =0.527, 95% CI: 0.363-0.691, Youden index =0.050) and the YEARS algorithm (AUC =0.618; 95% CI: 0.469-0.767; Youden index =0.237)., Conclusions: Compared to all 3 radiologists and the YEARS algorithm, the proposed HLG-based gradient boosting decision tree model achieved a superior performance in the diagnosis of APE on the NC-CT and may thus serve as a valuable tool for physicians in the diagnosis of APE., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-1059/coif). HK, PY and RZ are employees of the Institute of Advanced Research, Infervision Medical Technology Co., Ltd. The other authors have no conflicts of interest to declare., (2023 Quantitative Imaging in Medicine and Surgery. All rights reserved.)