Toll-like receptor 9 interaction with CpG ODN – An in silico analysis approach

BACKGROUND: Toll-like receptor 9 (TLR9) recognises unmethylated CpG DNA and activates a signalling cascade, leading to the production of inflammatory cytokines such as TNF-α, IL-1, IL-6 and IL-12 via the adaptor protein MyD88. However, the specific sequence and structural requirements of the CpG DNA for the recognition of and binding to TLR9 are unknown. Moreover, the 3D structures of TLR9 and the TLR9-ODN complex have not been determined. In this study, we propose a reliable model of the interaction of the TLR9 ECD with CpG ODN using bioinformatics tools. RESULTS: The three-dimensional structures of two TLR9 ECD-CpG ODN complexes were constructed using a homology modelling and docking strategy. Based on the models of these complexes, the TLR9 ECD-CpG ODN interaction patterns were calculated. The results showed that the interface between the human TLR9 and the CpG ODN molecule is geometrically complementary. The computed molecular interactions indicated that LRR11 is the main region of TLR9 that binds to CpG ODN and that five positively charged residues within LRR11 are involved in the binding of the TLR9 ECD to the CpG ODN. Observations in the close-up view of these interactions indicated that these five positively charged residues contribute differently to the binding region within the TLR9 ECD-CpG ODN complex. 337Arg and 338Lys reside in the binding sites of ODN, forming hydrogen bonds and direct contacts with the CpG ODN, whereas 347Lys, 348Arg, and 353His do not directly contact the CpG ODN. These results are in agreement with previously reported experimental data. CONCLUSION: In this study, we present two structural models for the human and mouse TLR9 ECD in a complex with CpG ODN. Some features predicted by this model are consistent with previously reported experimental data. This complex model may lead to a better understanding of the function of TLR9 and its interaction with CpG ODN and will improve our understanding of TLR9-ligand interaction in general

MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation

The Segment Anything Model (SAM), a foundation model for general image segmentation, has demonstrated impressive zero-shot performance across numerous natural image segmentation tasks. However, SAM's performance significantly declines when applied to medical images, primarily due to the substantial disparity between natural and medical image domains. To effectively adapt SAM to medical images, it is important to incorporate critical third-dimensional information, i.e., volumetric or temporal knowledge, during fine-tuning. Simultaneously, we aim to harness SAM's pre-trained weights within its original 2D backbone to the fullest extent. In this paper, we introduce a modality-agnostic SAM adaptation framework, named as MA-SAM, that is applicable to various volumetric and video medical data. Our method roots in the parameter-efficient fine-tuning strategy to update only a small portion of weight increments while preserving the majority of SAM's pre-trained weights. By injecting a series of 3D adapters into the transformer blocks of the image encoder, our method enables the pre-trained 2D backbone to extract third-dimensional information from input data. The effectiveness of our method has been comprehensively evaluated on four medical image segmentation tasks, by using 10 public datasets across CT, MRI, and surgical video data. Remarkably, without using any prompt, our method consistently outperforms various state-of-the-art 3D approaches, surpassing nnU-Net by 0.9%, 2.6%, and 9.9% in Dice for CT multi-organ segmentation, MRI prostate segmentation, and surgical scene segmentation respectively. Our model also demonstrates strong generalization, and excels in challenging tumor segmentation when prompts are used. Our code is available at: https://github.com/cchen-cc/MA-SAM

Artificial General Intelligence for Medical Imaging

In this review, we explore the potential applications of Artificial General Intelligence (AGI) models in healthcare, focusing on foundational Large Language Models (LLMs), Large Vision Models, and Large Multimodal Models. We emphasize the importance of integrating clinical expertise, domain knowledge, and multimodal capabilities into AGI models. In addition, we lay out key roadmaps that guide the development and deployment of healthcare AGI models. Throughout the review, we provide critical perspectives on the potential challenges and pitfalls associated with deploying large-scale AGI models in the medical field. This comprehensive review aims to offer insights into the future implications of AGI in medical imaging, healthcare and beyond

Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions

Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education. This study evaluated the capabilities of leading LLMs, including GPT-4, GPT-3.5, PaLM2, Claude2, and SenseNova, in answering conceptual biology questions. The models were tested on a 108-question multiple-choice exam covering biology topics in molecular biology, biological techniques, metabolic engineering, and synthetic biology. Among the models, GPT-4 achieved the highest average score of 90 and demonstrated the greatest consistency across trials with different prompts. The results indicated GPT-4's proficiency in logical reasoning and its potential to aid biology research through capabilities like data analysis, hypothesis generation, and knowledge integration. However, further development and validation are still required before the promise of LLMs in accelerating biological discovery can be realized

Simultaneous measurement of electrostatic charge and its effect on particle motions by electrostatic sensors array in gas-solid fluidized beds

Repeated particle-particle and particle-wall collisions and frictions lead to the generation and accumulation of electrostatic charges in the gas-solid fluidized beds. Variations of electrostatic signals are a rich source of information on particle motions and charging, which have rarely been explored and interpreted. To gain a more comprehensive understanding of the induced electrostatic signals in the fluidized beds, an array of arc-shaped induced electrostatic sensors were attached to the outer wall of a fluidized bed. Combined with cross-correlation method, induced electrostatic voltage signals and correlation velocity of particles were measured simultaneously. It was found that electrostatic charges accumulation restrained the particle motions while the average correlation velocity of particles increased with the amount of injecting liquid antistatic agent. Based on the analyses of induced electrostatic signals, the particle correlation velocity, and the particles charge-to-mass ratio under different charging levels, a predictive model of the average particles charge-to-mass ratio was established. Compared with the results obtained from Faraday cup, the estimated results showed a relative error no more than 40%. Simultaneous measurement of particle correlation velocity and particles charge-to-mass ratio were complemented by arc-shaped induced electrostatic sensors array combined with cross-correlation method

Transcriptome analysis of orange-spotted grouper (Epinephelus coioides) spleen in response to Singapore grouper iridovirus

<p>Abstract</p> <p>Background</p> <p>Orange-spotted grouper (<it>Epinephelus coioides</it>) is an economically important marine fish cultured in China and Southeast Asian countries. The emergence of infectious viral diseases, including iridovirus and betanodavirus, have severely affected food products based on this species, causing heavy economic losses. Limited available information on the genomics of <it>E. coioides </it>has hampered the understanding of the molecular mechanisms that underlie host-virus interactions. In this study, we used a 454 pyrosequencing method to investigate differentially-expressed genes in the spleen of the <it>E. coioides </it>infected with Singapore grouper iridovirus (SGIV).</p> <p>Results</p> <p>Using 454 pyrosequencing, we obtained abundant high-quality ESTs from two spleen-complementary DNA libraries which were constructed from SGIV-infected (V) and PBS-injected fish (used as a control: C). A total of 407,027 and 421,141 ESTs were produced in control and SGIV infected libraries, respectively. Among the assembled ESTs, 9,616 (C) and 10,426 (V) ESTs were successfully matched against known genes in the NCBI non-redundant (nr) database with a cut-off E-value above 10^-5. Gene ontology (GO) analysis indicated that "cell part", "cellular process" and "binding" represented the largest category. Among the 25 clusters of orthologous group (COG) categories, the cluster for "translation, ribosomal structure and biogenesis" represented the largest group in the control (185 ESTs) and infected (172 ESTs) libraries. Further KEGG analysis revealed that pathways, including cellular metabolism and intracellular immune signaling, existed in the control and infected libraries. Comparative expression analysis indicated that certain genes associated with mitogen-activated protein kinase (MAPK), chemokine, toll-like receptor and RIG-I signaling pathway were alternated in response to SGIV infection. Moreover, changes in the pattern of gene expression were validated by qRT-PCR, including cytokines, cytokine receptors, and transcription factors, apoptosis-associated genes, and interferon related genes.</p> <p>Conclusion</p> <p>This study provided abundant ESTs that could contribute greatly to disclosing novel genes in marine fish. Furthermore, the alterations of predicted gene expression patterns reflected possible responses of these fish to the virus infection. Taken together, our data not only provided new information for identification of novel genes from marine vertebrates, but also shed new light on the understanding of defense mechanisms of marine fish to viral pathogens.</p

Monitoring of particle motions in gas-solid fluidized beds by electrostatic sensors

Gas-solid fluidized beds are widely applied in numerous industrial processes. Particle motions significantly affect the performance of fluidized bed reactors and the characterization of particle movements is therefore important for fluidization quality monitoring and scale-up of reactors. Electrostatic charge signals in the fluidized bed contain much dynamic information on particle motions, which are poorly understood and explored. In this work, correlation velocities of Geldart B and D particles were measured, analyzed and compared by induced electrostatic sensors combined with cross-correlation method in the fluidized bed. The results indicated that the average correlation velocity of particle clouds increased and the normalized probability density distributions of correlation velocities broadened when the superficial gas velocity increased in the dense-phase region. Both upward and downward correlation velocities could be acquired in the dynamic bed level region. Under the same excess gas velocity, the average correlation velocity of Geldart D particles was significantly smaller than that of Geldart B particles, which was caused by the smaller bubble sizes caused by the dominant bubble split over coalescence and less volume of gas forming bubbles for Geldart D particles. The experimental results verified the reliability and repeatability of particle correlation velocity measurement by induced electrostatic sensors in the gas-solid fluidized bed, which provides definite potential in monitoring of particle motions

Effect of antimicrobial de-escalation strategy on 14-day mortality among intensive care unit patients: a retrospective propensity score-matched cohort study with inverse probability-of-treatment weighting

Abstract Purpose This study aimed to investigate the prevalence of antimicrobial de-escalation (ADE) strategy and assess its effect on 14-day mortality among intensive care unit patients. Methods A single-center retrospective cohort study was conducted on patients admitted to the intensive care unit (ICU) with infectious diseases between January 2018 and December 2020. Patients were stratified into three groups based on the initial treatment regimen within 5 days of antimicrobial administration: ADE, No Change, and Other Change. Confounders between groups were screened using one-way ANOVA and Chi-square analysis. Univariate and multivariate analyses were performed to identify risk factors for 14-day mortality. Potential confounders were balanced using propensity score inverse probability of treatment weighting (IPTW), followed by multivariate logistic regression analysis to evaluate the effect of ADE strategy on 14-day mortality. Results A total of 473 patients met the inclusion criteria, with 53 (11.2%) in the ADE group, 173 (36.6%) in the No Change group, and 247 (52.2%) in the Other Change group. The 14-day mortality rates in the three groups were 9.4%, 11.6%, and 21.9%, respectively. After IPTW, the adjusted odds ratio for 14-day mortality comparing No Change with ADE was 1.557 (95% CI 1.078–2.247, P = 0.0181) while comparing Other Change with ADE was 1.282(95% CI 0.884–1.873, P = 0.1874). Conclusion The prevalence of ADE strategy was low among intensive care unit patients. The ADE strategy demonstrated a protective effect or no adverse effect on 14-day mortality compared to the No Change or Other Change strategies, respectively. These findings provide evidence supporting the implementation of the ADE strategy in ICU patients

Gravid females of Cephalcia chuxiongica (Hymenoptera, Pamphiliidae) are attracted to egg-carrying needles of Pinus yunnanensis

Cephalcia chuxiongica Xiao is one of the most dangerous defoliators of Pinus yunnanensis and other pine species in Yunnan province, resulting in serious losses. Its distinguishing characteristics are the females’ aggregation oviposition and larvae’s aggregation feeding. In order to explore the mechanism of aggregation oviposition in this sawfly, preliminary olfactory bioassay was conducted in laboratory. In in-cage choice tests, on average vast majority gravid females selected the shoots that had been loaded and oviposited by a ‘pioneer’ female. In one-choice tests in laboratory by a Y-tube olfactometer, the gravid females were attracted by the odors of eggs-carrying shoots (PE), shoots with one delivering female and her eggs (PGE), needles’ extract (NE), and fresh eggs’ eluent (EL); the virgin females were attracted by odors of fresh needles (P), PE, PGE, and NE, but repelled by odors of virgin and gravid females. In two-choice tests, the odors were tested in pairs for gravid females. When compared with odors of gravid females (G) or P, gravid females showed significantly more tendency to odors of PE or PGE. When given odors EL vs. NE, gravid females preferred the odors of NE, but they did not make obvious selection between G vs. P, and PE vs. PGE. Based on the results, our conjectures were: (1) Delivery female, as a pioneer, can summon her conspecific gravid females to aggregate in the same pine shoot; (2) Pine needles’ odors were attractive for both the virgin and gravid females; (3) Gravid females could be attracted by odors released by the pioneer gravid females; (4) The olfactory sensation of the females may be changed by mating

The Wind Loading Characteristics of MAN Type Dry Gas Storage Tank

The effects of Reynolds number (Re) and surface roughness on the wind pressure coefficient on a MAN type dry gas tank were analyzed in detail by wind tunnel tests. A wind load calculation model was then established, which is suitable for the wind resistant design of the gas tanks. The test results revealed that in the range of 7 × 105 < Re < 1.0 × 106 (supercritical regimes), the drag coefficient (Cd) and wind pressure coefficient remained constant, consistent with 2D cylinders in a uniform flow. However, in common with 2D cylinder flows, the surface roughness with the spacing ratio (λ) greater than 0.9 had a significant effect on the wind pressures coefficient. The minimum pressure coefficient (Cpmin) sharply increased from −2.3 to −0.65 with increasing surface roughness. The corresponding angle for the minimum pressure coefficient θmin was in between 140°and 90°, which was in a gradual decreasing trend with the increase in surface roughness of the model. The calculation method of the wind pressure coefficient with vary surface roughness was proposed, based on which, the calculation results were in good agreement with the test data