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Multi-detector CT (MDCT) is a highly accurate diagnostic tool that is commonly used to evaluate appendicitis and its complications. The diagnosis of appendicitis based on MDCT findings can be difficult and challenging when the observed findings are inconsistent with the typical features. Atypical appendicitis includes a wide spectrum of features, such as variable positions of the appendix and cecum, complications, and unusual pathological findings of secondary appendicitis that mimic or induce appendicitis. Our pictorial essay describes the diverse spectrum of atypical appendicitis and appendicitis-like conditions in terms of location abnormalities, complications, and uncommon pathologies, including related tumors, reactive appendicitis, appendiceal diverticulitis, and IgG4-related disease. Through this essay, the readers can become more familiar with MDCT findings of atypical appendicitis.

The role of community building portfolios in socioeconomic development and the growth of the built environment cannot be overstated. Damage to these structures can have far-reaching consequences on socioeconomic and environmental aspects, requiring a long-term perspective for recovery. As communities aim to enhance their resilience and sustainability, there is a cost burden that needs to be considered. To address this issue, this paper proposes a community-level performance enhancement approach that focuses on optimizing the long-term resilience and sustainability of community building portfolios, taking into account recurrent seismic hazards. A Gaussian process surrogate-based multi-objective optimization framework is utilized to optimize the cost objective while considering performance indicators for resilience and sustainability. The proposed framework involves using performance-based assessment methods to evaluate the socioeconomic and environmental consequences under stochastic and recurrent seismic hazard scenarios. These evaluated indicators are then used to efficiently optimize the community resilience and sustainability, taking into account the retrofit costs. Finally, approximate Pareto-optimal solutions are extracted and utilized for decision-making. In summary, this paper presents a novel approach for optimizing the long-term resilience and sustainability of community building portfolios by considering recurrent seismic hazards. The proposed framework incorporates performance-based assessment methods and multi-objective optimization techniques to achieve an optimal balance between cost, resilience, and sustainability, with the ultimate goal of enhancing community well-being and decision-making in the face of seismic hazards.

Abstract Coastal bridges are susceptible to severe damage when subjected to successive earthquake-tsunami events. Previous studies mainly consider the tsunami loadings as hydrodynamic forces, whereas other hydrodynamic forces such as uplift and slamming forces, are not fully investigated. Moreover, there are limited studies on the dynamic performance of simply supported girder bridges under the earthquake-tsunami sequences. To this end, this paper aims to conduct an in-depth investigation on dynamic performance of simply supported bridges subjected to sequential earthquake and tsunami hazards by means of a high-fidelity wave force simulation approach. More specifically, a typical, already constructed simply supported girder bridge is taken as the example bridge, and the numerical model of this bridge is built using the analytical platform OpenSees. The applied time series of tsunami wave force with five wave heights are generated based on a refined computational fluid dynamics (CFD) model, and are separately combined with the time histories of 21 pairs of far-field earthquake records to generate the sequential earthquake and tsunami loadings. Subsequently, nonlinear time history analyses (NTHAs) are carried out to obtain the structural dynamic responses, and the effects of preceding earthquakes and wave heights on the performance are investigated. Results indicate that the bearings are susceptible to the damage in the longitudinal direction of the bridge under the sequential earthquake and tsunami loadings, while the piers sustain more damage in the transverse direction. The preceding earthquakes have a significant effect on the bridge performance, and the effect becomes more pronounced with the increase of the ground motion intensity. The contribution of the tsunami loadings to the bridge response increases as the wave height rises. In particular, the transverse pier drift is dominated by the tsunami loadings when the wave height is higher than 7 m. The outcome of this study could aid the design and management of coastal bridge subjected to successive earthquake-tsunami events.

Abstract Quantifying economic losses of civil infrastructures subjected to various hazards under a life-cycle context is of vital importance for risk assessment and management. In previous studies, the expected long-term loss has been widely applied as a standard decision criterion during the life-cycle analysis. However, the expectation may not be informative enough to illustrate uncertainties associated with the long-term loss. Therefore, the higher-order moments and the probability distribution should be investigated. In this paper, a probabilistic analysis framework is proposed to construct the probability density function and cumulative distribution function of long-term loss by assessing the analytical statistical moments. The stochastic renewal process is utilized to assess the long-term loss by considering uncertainties associated with stochastic occurrence and frequency of the hazards. Based on the maximum entropy method, the proposed approach shows superior efficiency to assess the probability distribution of long-term loss than crude Monte Carlo simulation. The probability distribution can be essential information for decision-making process of risk management. An illustrative example is investigated to show the probability density function of long-term loss of civil infrastructure subjected to hurricane hazards. A good agreement of results obtained by the proposed approach and Monte Carlo simulation has verified the accuracy and effectiveness of the proposed method.

Abstract Background MicroRNAs (miRNAs) are abundant in tumor-derived extracellular vesicles (EVs) and the functions of extracellular miRNA to recipient cells have been extensively studied with tumorigenesis. However, the role of miRNA in EV secretion from cancer cells remains unknown. Methods qPCR and bioinformatics analysis were applied for determining extracellular let-7a expression from CRC patient serum and cells. Nanosight particle tracking analysis was performed for investigating the effect of let-7a on EV secretion. Luciferase reporter assays was used for identifying targeted genes synaptosome-associated protein 23 (SNAP23). In vitro and in vivo assays were used for exploring the function of let-7a/SNAP23 axis in CRC progression. Bioenergetic assays were performed for investigating the role of let-7a/SNAP23 in cellular metabolic reprogramming. Results let-7a miRNA was elevated in serum EVs from CRC patients and was enriched in CRC cell-derived EVs. We determined that let-7a could suppress EV secretion directly targeting SNAP23. In turn, SNAP23 promotes EV secretion of let-7a to downregulate the intracellular let-7a expression. In addition, we found a novel mechanism of let-7a/SNAP23 axis by regulating mitochondrial oxidative phosphorylation (OXPHOS) through Lin28a/SDHA signaling pathway. Conclusions Let-7a plays an essential role in not only inhibiting EV secretion, but also suppressing OXPHOS through SNAP23, resulting in the suppression of CRC progression, suggesting that let-7a/SNAP23 axis could provide not only effective tumor biomarkers but also novel targets for tumor therapeutic strategies.

Simply supported bridges comprise the majority of bridge systems in coastal communities and are susceptible to severe damage from extreme waves induced by storms or tsunamis. However, the effects of oblique wave impacts have been less investigated due to the lack of appropriate numerical models. To address this issue, this study investigates the effects of wave incident angles on coastal bridge superstructures by developing an advanced computational fluid dynamics (CFD) model. Different wave scenarios, including wave height, relative clearance, incident angle, and wavelength are tested. It is found that the maximum wave forces in the horizontal and longitudinal directions could reach 1901 and 862 kN under extreme conditions, respectively, destroying bearing connections. Three surrogate models, i.e., the Gaussian Kriging surrogate model, the Artificial Neural Network (ANN), and the Polynomial Chaos Expansion (PCE), are established by correlating the wave parameters with the maximum wave forces. Through comparisons among the three surrogate models, it is found that the 3-order PCE model has better performance in predicting loads in vertical and horizontal directions, while the ANN model is more suitable for results in the longitudinal direction. This study contributes to the optimized design of coastal bridges and also offers an opportunity for future studies to investigate hazard damage-mitigation measures.

Coastal civil infrastructure can be susceptible to damages caused by hurricanes throughout its service life. Vulnerability assessment is a key component in hazard risk management of civil infrastructure systems. Previously, most studies assume storm parameters are independent when computing the vulnerability of infrastructure to hurricanes. Due to the complicated interactive effects between storm parameters during hurricanes, the independent model may mis-specify such intercorrelation, thus resulting in inaccurate estimation of the probability of failure. This paper proposes a copula-based vulnerability assessment framework to investigate the impact of dependent storm parameters on the vulnerability of civil infrastructure subjected to hurricanes. The developed framework is applied to a typical simply supported bridge to compute the probability with respect to deck unseating failure under hurricane hazards. The copula approach provides superior efficiency in modeling dependency between the maximum wave height and peak water level, by separately considering marginal distributions and the joint effects. Probabilistic wave-induced load acting on the bridge deck is computed using a three-dimensional computational fluid dynamics model, incorporating uncertainties associated with hazard parameters. The effect of correlated storm parameters and the tail dependence characteristics on the bridge vulnerability is investigated by using different copula models, including Clayton, Gaussian, and Hüsler-Reiss copula functions. By incorporating dependent hazard parameters, the failure probability of the bridge may be significantly increased for non-major hurricanes (e.g., with a 50-year return period), while the failure probability under major hurricanes (e.g., with a 500-year return period) may not be severely affected.

It is essential to assess the performance of a community under probable hazard scenarios and to provide possible performance enhancements. This requires establishing performance indicators, an assessment method, and an optimization technique to provide mitigation alternatives. In this paper, multiple performance indicators are utilized to assess the performance of a community building portfolio including loss, downtime, and environmental impact (e.g., CO2 emissions). The performance of a community is assessed by utilizing a performance-based assessment methodology. Then, the performance indicators are utilized as performance objectives to be optimized considering non-dominated sorting and crowding distance evolutionary optimization techniques. The framework utilizes retrofit alternatives for each building in a community and provides Pareto-optimal solutions for considered performance objectives given retrofit cost. This process of performance assessment and optimization is repeated by utilizing the Monte Carlo approach to consider uncertainties. Finally, the Pareto-optimal solutions are utilized to evaluate the retrofit programs for community building portfolios in terms of considered performance indicators.

The typical presentation of intussusception includes intermittent severe abdominal pain, vomiting, rectal bleeding, and the presence of an abdominal mass. We present a case of intussusception after abdominal blunt trauma along with a literature review. A 4-year-old girl was admitted to the emergency department after a bicycle accident. She complained of progressively worsening abdominal pain, but there was no vomiting, fever, bloody stool, or abdominal mass. She was finally diagnosed with traumatic intussusception by ultrasonography and treated with air reduction. Because the typical symptoms are unusual in traumatic intussusception, close attention must be paid to avoid a delayed diagnosis.

The random traffic flow model which considers parameters of all the vehicles passing through the bridge, including arrival time, vehicle speed, vehicle type, vehicle weight, and horizontal position as well as the bridge deck roughness, is input into the vehicle-bridge coupling vibration program. In this way, vehicle-bridge coupling vibration responses with considering the random traffic flow can be numerically simulated. Experimental test is used to validate the numerical simulation, and they had the consistent changing trends. This result proves the reliability of the vehicle-bridge coupling model in this paper. However, the computational process of this method is complicated and proposes high requirements for computer performance and resources. Therefore, this paper considers using a more advanced intelligent method to predict vibration responses of the long-span bridge. The PSO-BP (particle swarm optimization-back propagation) neural network model is proposed to predict vibration responses of the long-span bridge. Predicted values and real values at each point basically have the consistent changing trends, and the maximum error is less than 10%. Hence, it is feasible to predict vibration responses of the long-span bridge using the PSO-BP neural network model. In order to verify advantages of the predicting model, it is compared with the BP neural network model and GA-BP neural network model. The PSO-BP neural network model converges to the set critical error after it is iterated to the 226th generation, while the other two neural network models are not converged. In addition, the relative error of predicted values using PSO-BP neural network is only 2.71%, which is obviously less than the predicted results of other two neural network models. We can find that the PSO-BP neural network model proposed by the paper in predicting vibration responses is highly efficient and accurate.

Objective Among infants and preschool children with complaint of upper extremity immobility, pulled elbow, also known as nursemaid’s elbow or radial head subluxation is the most common cause presenting to pediatric emergency departments. However, proper tools to diagnose pulled elbow remain limited. We conducted a study to determine the feasibility of ultrasonography in diagnosing pulled elbow. Methods Infants and preschool children presenting to an urban emergency department with the complaint of upper extremity immobility between April and July 2013 were enrolled. The following ultrasonographic information was recorded: (1) whether there was a change in the shape of the supinator muscle, (2) whether there was an annular ligament in place, and (3) whether there was an enlargement of the synovial fringe. We used the affected arms’ ultrasonographic images as the study group and opposite arms’ ultrasonographic images as the control group. Results When we diagnosed pulled elbow using ultrasonographic findings (i.e., the annular ligament was not in place), we found the following results: sensitivity, 64.9% (95% CI, 47.5% to 79.8%); specificity, 100.0% (95% CI, 90.5% to 100.0%); positive predictive value, 100.0% (95% CI, 85.8% to 100.0%); and negative predictive, 74.0% (95% CI, 59.7% to 85.4%). Conclusion A pulled elbow can easily be confirmed by ultrasonography when the annular ligament is displaced.

The emerging field of liquid biopsy stands at the forefront of novel diagnostic strategies for cancer and other diseases. Liquid biopsy allows minimally invasive molecular characterization of cancers for diagnosis, patient stratification to therapy, and longitudinal monitoring. Liquid biopsy strategies include detection and monitoring of circulating tumor cells, cell-free DNA, and extracellular vesicles. In this review, we address the current understanding and the role of existing liquid-biopsy-based modalities in cancer diagnostics and monitoring. We specifically focus on the technical and clinical challenges associated with liquid biopsy and biomarker development being addressed by the Liquid Biopsy Consortium, established through the National Cancer Institute. The Liquid Biopsy Consortium has developed new methods/assays and validated existing methods/technologies to capture and characterize tumor-derived circulating cargo, as well as addressed existing challenges and provided recommendations for advancing biomarker assays.

Objective International Liaison Committee on Resuscitation guidelines advocate an arterial saturation of 94% to 96% after return of spontaneous circulation (ROSC). However, a few clinical trials have investigated the impact of postresuscitative O2 therapy after cardiac arrest. We studied whether early hyperoxemia is associated with a poor post-ROSC outcome after in-hospital cardiac arrest. Methods We retrospectively reviewed patients who experienced an in-hospital cardiac arrest from January 2005 to January 2011. Based on the results of the first arterial blood gas analysis (ABGA) within 10 minutes and a second ABGA from 60 to 120 minutes after ROSC, patients were classified into three groups: hyperoxemia (PaO2 ≥ 300 mmHg), normoxemia (300 mmHg > PaO2 ≥ 60 mmHg), and hypoxemia (PaO2 < 60 mmHg or ratio of PaO2 to fraction of inspired oxygen < 300). We examined whether early hyperoxemia was associated with survival and neurological outcome. Results There were 792 patients who met the inclusion criteria: 638 (80.6%) in the hypoxemia group, 62 (7.8%) in the normoxemia group, and 92 (11.6%) in the hyperoxemia group. Multiple logistic regression analysis showed that hyperoxemia was not associated with survival (odds ratio [OR], 1.07; 95% confidence interval [CI], 0.30 to 3.84) or neurological outcome (OR, 1.03; 95% CI, 0.31 to 3.40). Conclusion Postresuscitation hyperoxemia was not associated with survival or neurological outcome in patients with ROSC after in-hospital cardiac arrest.

A lightweight composite bridge deck system composed of steel orthotropic deck stiffened with thin Ultra-High Performance Concrete (UHPC) layer has been proposed to eliminate fatigue cracks in orthotropic steel decks. The debonding between steel deck and UHPC layer may be introduced during construction and operation phases, which could cause adverse consequences, such as crack-induced water invasion and distinct reduction of the shear resistance. The piezoelectric lead zirconate titanate (PZT)-based technologies are used to detect interfacial debonding defects between the steel deck and the UHPC layer. Both impedance analysis and wave propagation method are employed to extract debonding features of the steel-UHPC composite slab with debonding defect in different sizes and thicknesses. Experimental tests are performed on two steel-UHPC composite slabs and a conventional steel-concrete composite deck. Additionally, an improved Particle Swarm Optimization (PSO)-k-means clustering algorithm is adopted to obtain debonding patterns based on the feature data set. The laboratory tests demonstrate that the proposed approach provides an effective way to detect interfacial debonding of steel-UHPC composite deck.

Abstract Background The unfolding speed of some hyperthermophilic proteins is dramatically lower than that of their mesostable homologs. Ribonuclease HII from the hyperthermophilic archaeon Thermococcus kodakaraensis (Tk-RNase HII) is stabilized by its remarkably slow unfolding rate, whereas RNase HI from the thermophilic bacterium Thermus thermophilus (Tt-RNase HI) unfolds rapidly, comparable with to that of RNase HI from Escherichia coli (Ec-RNase HI). Results To clarify whether the difference in the unfolding rate is due to differences in the types of RNase H or differences in proteins from archaea and bacteria, we examined the equilibrium stability and unfolding reaction of RNases HII from the hyperthermophilic bacteria Thermotoga maritima (Tm-RNase HII) and Aquifex aeolicus (Aa-RNase HII) and RNase HI from the hyperthermophilic archaeon Sulfolobus tokodaii (Sto-RNase HI). These proteins from hyperthermophiles are more stable than Ec-RNase HI over all the temperature ranges examined. The observed unfolding speeds of all hyperstable proteins at the different denaturant concentrations studied are much lower than those of Ec-RNase HI, which is in accordance with the familiar slow unfolding of hyperstable proteins. However, the unfolding rate constants of these RNases H in water are dispersed, and the unfolding rate constant of thermophilic archaeal proteins is lower than that of thermophilic bacterial proteins. Conclusions These results suggest that the nature of slow unfolding of thermophilic proteins is determined by the evolutionary history of the organisms involved. The unfolding rate constants in water are related to the amount of buried hydrophobic residues in the tertiary structure.

Despite noteworthy advances in cancer immunotherapy, the understanding of effective strategies to harness eosinophils in anti‐cancer immunity, which is critical for cytotoxic T‐cell recruitment, remains limited. In particular, interleukin‐33, which is essential for eosinophil activation, is chromatin‐bound and lacks efficient extracellular release mechanisms for its intact form. Here, a nanobubble‐based acoustical cancer cell‐bursting strategy (NanoBurst) is reported to elicit eosinophil‐mediated cancer immunity by releasing damage‐associated molecular patterns (DAMPs). NanoBurst consists of polymeric nanoparticles encapsulating a gas precursor, designed to effectively disrupt cancer cell membranes upon exposure to ultrasound. This process triggers the efficient release of both nucleus‐derived (e.g., high‐mobility group nucleosome‐binding domain‐1, gasdermin E, interleukin‐33) and cytoplasm‐derived DAMPs, initiating the eosinophil‐mediated immune cascade. Consequently, NanoBurst induces direct tumoricidal effects by activating eosinophils and enhances anti‐cancer immunity by recruiting NK cells and cytotoxic T‐cells in vivo. When combined with a dipeptidyl peptidase‐4 (DPP4) inhibitor, an inducer of eosinophil‐mediated chemotaxis, NanoBurst effectively inhibits the growth of primary tumors and the metastasis to the lung in a CT26 tumor‐bearing mouse model. Notably, boosting eosinophil‐mediated anti‐cancer immunity with NanoBurst‐DPP4 inhibitor shows superior efficacy for tumor suppression compared to immune checkpoint inhibitors. [ABSTRACT FROM AUTHOR]

In response to global climate challenges, urban low-carbon transformation has become a critical strategy for sustainable development. This study constructs a theoretical model for urban low-carbon transformation using the multi-level perspective framework. We focused on three batches of low-carbon pilot cities in China and employed fuzzy set qualitative comparative analysis to investigate the transformation pathways and impact mechanisms during the periods 2010–2012, 2012–2017, and 2017–2019. The results indicate that none of the six antecedent conditions is necessary for urban low-carbon transformation. Initially, the transformation is primarily driven by a pathway led by low-carbon industries. In the mid-stage, two pathways emerge: one dominated by the combination of low-carbon industries and research and development (R&D) human capital and another led by low-carbon consumption awareness and economic development levels. In the later stage, the influencing factors involve a combination across micro, meso, and macro levels, reflecting an increasingly diversified and intricate configuration. The regional industrial structure consistently plays a dominant role, while awareness of low-carbon consumption has grown over time. This study not only enhances our understanding of the underlying mechanisms but also provides practical policy recommendations for local governments to tailor their strategies for effective low-carbon transformation. [ABSTRACT FROM AUTHOR]