bluuebunny/crossref_metadata_2025 huggingface dataset:
Excessive Mitochondrial Fission Suppresses Mucosal Repair by Impairing Butyrate Metabolism in Colonocytes
Authors: Fu Shi-Chen , Qu Jun-Yan , Li Li-Xiang , Yang Xiao-Xiao , Li Yan-Qing , Zuo Xiu-Li
Published: July 2023

Background

Mucosal healing is one of the principal therapeutic targets for ulcerative colitis (UC). Mitochondria are dynamic organelles that undergo constant fusion and fission; however, the process that is most conducive to mucosal healing remains unclear. This study investigated the role of mitochondrial fission in mucosal healing in UC patients.

Methods

Quantitative polymerase chain reaction, Western blotting, and immunostaining were used to detect mitochondrial fission in UC patients and a dextran sulfate sodium–induced colitis model. Colonic organoids were used to investigate the role of mitochondrial fission in butyrate metabolism. Enzyme activity assays were performed to identify the key proteins involved in this mechanism.

Results

It was found that inhibition of mitochondrial fission promoted mucosal healing in mice and that there was an increase in mitochondrial fission in colonic epithelial cells of UC patients. Excessive fission inhibits stem cell proliferation by impairing butyrate metabolism in colonic organoids. The mitochondrial fission antagonist P110 failed to promote mucosal healing in antibiotic-treated mice, and the addition of exogenous butyrate reversed this effect. Increased butyrate exposure in the colonic stem cell niche has also been observed in UC patients. Mechanistically, enzyme activity assays on colonic organoids revealed that excessive fission inhibits mitochondrial acetoacetyl-CoA thiolase activity via reactive oxygen species.

Conclusions

Collectively, these data indicate that excessive mitochondrial fission suppresses mucosal repair by inhibiting butyrate metabolism and provides a potential target for mucosal healing in patients with ulcerative colitis.
LAMB3 Promotes Intestinal Inflammation Through SERPINA3 and Is Directly Transcriptionally Regulated by P65 in Inflammatory Bowel Disease
Authors: Liu Fangyuan , Xu Weimin , Wang Yaosheng , Huang Zhenyu , Zhu Zhehui , Ou Weijun , Tang Wenbo , Fu Jihong , Liu Chenying , Gu Yubei , Liu Yun , Du Peng
Published: July 2023

Background

Various extracellular matrix (ECM) reshaping events are involved in inflammatory bowel disease (IBD). LAMB3 is a vital subunit of laminin-332, an important ECM component. Data on the biological function of LAMB3 in intestinal inflammation are lacking. Our aim is to discuss the effect of LAMB3 in IBD.

Methods

LAMB3 expression was assessed in cultured intestinal epithelial cells, inflamed mucosal tissues of patients and mouse colitis models. RNA sequencing, quantitative real-time polymerase chain reaction and Western blotting were used to detect the LAMB3 expression distribution and potential downstream target genes. Dual-luciferase assays and chromatin immunoprecipitation-quantitative polymerase chain reaction were used to determine whether P65 could transcriptionally activate LAMB3 under tumor necrosis factor α stimulation.

Results

LAMB3 expression was increased in inflammatory states in intestinal epithelial cells and colonoids and was associated with adverse clinical outcomes in Crohn’s disease. Knockdown of LAMB3 inhibited the expression of proinflammatory cytokines. Mechanistically, LAMB3 expression was directly transcriptionally activated by P65 and was inhibited by nuclear factor kappa B inhibitors under tumor necrosis factor α stimulation. Furthermore, RNA sequencing and replenishment experiments revealed that LAMB3 upregulated SERPINA3 to promote intestinal inflammation via the integrin α3β1/FAK pathway.

Conclusion

We propose that LAMB3 could serve as a potential therapeutic target of IBD and a predictor of intestinal stenosis of Crohn’s disease. Our findings demonstrate the important role of ECM in the progression of IBD and offer an experimental basis for the treatment and prognosis of IBD.
Use of a reference text by pharmacists, intern pharmacists and pharmacy students: a national cross-sectional survey
Authors: Mill Deanna , Johnson Jacinta L , Corre Lauren , Lee Kenneth , D’Lima Danielle , Clifford Rhonda , Seubert Liza , Salter Sandra M , Page Amy Theresa
Published: July 2023

Background

Reference texts assist pharmacists by addressing knowledge gaps and enabling evidence-based decisions when providing patient care. It is unknown whether reference text utilisation patterns differ between pharmacists, intern pharmacists and pharmacy students. To describe and compare the self-reported use and perceptions of a reference text, namely the national formulary, by pharmacists, intern pharmacists and pharmacy students.

Methods

Registered pharmacists, intern pharmacists and pharmacy students living in Australia were surveyed in July 2020. The survey was electronic and self-administered. Questions considered self-reported use of a specific reference text in the preceding 12 months.

Key findings

There were 554 eligible responses out of 774 who commenced the survey: 430 (78%) pharmacists, 45 (8%) intern pharmacists and 79 (14%) pharmacy students. Most participants (529/554, 96%) reported historical use of the text, though pharmacists were significantly less likely than intern pharmacists and students to use it frequently (52/422, 12% versus 16/43, 37% versus 23/76, 30%, P < 0.001). Pharmacists (44%, 177/404) reported using the text as a tool to resolve a situation when providing a service or patient care (177/404, 44%) or as a teaching resource (150/404, 38%). In contrast, intern pharmacists and students most commonly use these to familiarise themselves with the contents (30/43, 70%; 46/76, 61%) or update their knowledge (34/43, 79%; 53/76, 70%).

Conclusions

Access and use patterns varied significantly across career stages. A broader understanding of the use of reference texts may help develop interventions to optimise the content and usability. Varying usage patterns across the groups may inform the tailoring of texts for future use.
A nationwide survey of methods and barriers to adverse drug reaction monitoring and reporting among hospital pharmacists in Thailand
Authors: Jarernsiripornkul Narumol , Kayrash Sasina , Homket Prangwalai , Srisuriyachanchai Warisara
Published: July 2023

Objectives

To explore the methods of adverse drug reaction identification and monitoring used by hospital pharmacists in all regions of Thailand, to explore barriers to and pharmacists’ attitudes towards adverse drug reaction monitoring and reporting, and to assess the factors related to these aspects.

Methods

Pharmacists in 480 hospitals in Thailand were selected by stratified sampling. Self-administered questionnaires were sent by post and pharmacists returned completed questionnaires via QR code.

Key findings

In total, pharmacists at 286 hospitals returned the questionnaire (response rate = 59.6%). The most common adverse drug reaction (ADR) monitoring methods were the spontaneous reporting system (SRS), which was used by 100% of respondents, followed by high alert drug lists (73.1%) and routine adverse drug reaction monitoring (64.0%). The most frequently used methods of adverse drug reaction prevention were providing drug allergy card and recording allergy history in database. The major barrier to adverse drug reaction monitoring and reporting among respondents was uncertainty about whether the suspected drug caused the reaction (49.3%). Pharmacists had a good attitude towards adverse drug reaction monitoring and reporting (60.5%). Longer work experience (≥10 years) was negatively related with good attitudes towards adverse drug reaction monitoring and reporting (OR = 0.535, P = 0.040), whereas higher education level was positively related (OR = 2.201, P = 0.025).

Conclusions

Spontaneous reporting system is the main method used for adverse drug reaction monitoring and reporting among hospital pharmacists in Thailand. Pharmacists had good attitudes towards adverse drug reaction monitoring and reporting, however, barriers remain.
How Not to Be a Fallibilist
Authors: Kyriacou Christos
Published: July 2023

I develop one partial explanation of the origins of our fallibilist intuitions about knowledge in ordinary language fallibilism and argue that this explanation indicates that our epistemic methodology should be more impartial and theory-neutral. First, I explain why the so-called Moorean constraint (cf. Hawthorne 2005, 111) that encapsulates fallibilist intuitions is fallibilism’s cornerstone. Second, I describe a pattern of fallibilist reasoning in light of the influential dual processing and heuristics and biases approach to cognition (cf. Kahneman 2011; Thaler and Sunstein 2008; Evans 2017). I suggest that this pattern of reasoning involves the question-substitution heuristic, the availability and representativeness heuristics, the focusing bias as well as framing effects, priming and the anchoring and adjusting heuristic. Third, I argue that this fallibilist pattern of reasoning is methodologically dubious because it involves a vicious circularity and briefly outline an alternative, more impartial and theory-neutral abductive methodology for the theory of knowledge. Finally, I briefly explain how this analysis sheds light on the ordinary language fallibilism of Moore (1939), Austin (1961), Wittgenstein (1969) and Chisholm (1982).
Can Voluntary Health Insurance for Non-reimbursed Expensive New Treatments Be Just?
Authors: Smids Jilles , Bunnik Eline M
Published: July 2023

Public healthcare systems are increasingly refusing (temporarily) to reimburse newly approved medical treatments of insufficient or uncertain cost-effectiveness. As both patient demand for these treatments and their list prices increase, a market might arise for voluntary additional health insurance (VHI) that covers effective but (very) expensive medical treatments. In this paper, we evaluate such potential future practices of VHI in public healthcare systems from a justice perspective. We find that direct (telic) egalitarian objections to unequal access to expensive treatments based on different ability to afford VHI do not stand up to scrutiny. However, such unequal access might lead to loss of self-respect among individuals, or loss of fraternity within society, rendering it more difficult for citizens to interact on equal moral footing. This would be problematic from a relational egalitarian perspective. Moreover, the introduction of VHI might turn out to have negative consequences for the comprehensiveness and/or the quality of the public healthcare services that are offered to all patients equally through basic health insurance. These consequences must be weighed against potential health gains and the value of liberty. We conclude that governments should be careful when considering the introduction of VHI in public healthcare systems.
Major Susceptibility Gene Epistasis over Minor Gene Resistance to Spot Form Net Blotch in a Commercial Barley Cultivar
Authors: Muria-Gonzalez Mariano Jordi , Lawrence Julie A. , Palmiero Elzette , D'Souza Nola K. , Gupta Sanjiv , Ellwood Simon R.
Published: July 2023

Spot form net blotch, caused by Pyrenophora teres f. maculata, is a significant global disease of barley ( Hordeum vulgare). Baudin, a barley cultivar that was until recently extensively grown in Western Australia, was reported as having minor seedling resistance. However, Baudin was highly susceptible to a local isolate, M3, suggesting that this isolate had gained virulence against a major susceptibility gene. M3 causes atypical lesions with pale centers early in the infection, with initial screens of a segregating population indicating that this was determined by a single locus in the Baudin genome. The susceptibility was semidominant in F1 progeny and the susceptibility gene, designated Spm1 ( Susceptibility to P. teres f. maculata 1), mapped to a 190-kb section of the resistance gene-rich Mla region of chromosome 1H. Phenotyping with Ptm SP1, a non-M3 pathotype, identified a seedling resistance locus on 2H. Minor gene resistance is generally regarded as potentially durable, but our findings suggest the resistance to spot form net blotch in Baudin is nullified by strong susceptibility conferred by a separate locus on 1H.

[Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license .
Detection of Secreted Effector Proteins fromPhelipanche aegyptiacaDuring Invasion of Melon Roots
Authors: Xiao Lifeng , Liu Qianqian , Cao Xiaolei , Chen Meixiu , Zhang Lu , Yao Zhaoqun , Zhao Sifeng
Published: July 2023

Parasites can interact with their host plants through the induction and delivery of secreted effector proteins that facilitate plant colonization by decomposing plant cell walls and inhibiting plant immune response to weaken the defense ability of the host. Yet effectors mediating parasitic plant–host interactions are poorly understood. Phelipanche aegyptiaca is an obligate root parasite plant causing severe yield and economic losses in agricultural fields worldwide. Host resistance against P. aegyptiaca occurred during the attachment period of parasitism. Comparative transcriptomics was used to assess resistant and susceptible interactions simultaneously between P. aegyptiaca and two contrasting melon cultivars. In total, 2,740 secreted proteins from P. aegyptiaca were identified here. Combined with transcriptome profiling, 209 candidate secreted effector proteins (CSEPs) were predicted, with functional annotations such as cell wall degrading enzymes, protease inhibitors, transferases, kinases, and elicitor proteins. A heterogeneous expression system in Nicotiana benthamiana was used to investigate the functions of 20 putatively effector genes among the CSEPs. Cluster 15140.0 can suppress BAX-triggered programmed cell death in N. benthamiana. These findings showed that the prediction of P. aegyptiaca effector proteins based on transcriptomic analysis and multiple bioinformatics software is effective and more accurate, providing insights into understanding the essential molecular nature of effectors and laying the foundation of revealing the parasite mechanism of P. aegyptiaca, which is helpful in understanding parasite–host plant interaction.
Dielectric model of asphalt pavement materials towards the future electrified road
Authors: Wang Dawei , Ye Chengsen , Lv Haotian , Meng Luopei , Tang Fujiao , Ni Yaowei , Liu Pengfei
Published: July 2023

The dielectric properties of asphalt mixture are crucial for future electrified road (e-road) and pavement non-destructive detection. Few investigations have been conducted on the temperature and frequency influencing the dielectric properties of asphalt pavement materials. The development of e-road requires more accurate prediction models of pavement dielectric properties. To quantify the influence of temperature and frequency on the dielectric properties of asphalt mixtures, the dielectric constants, dielectric loss factor and dielectric loss tangents of aggregate, asphalt binders and asphalt mixtures were tested over the temperature range of −30 to 60°C and frequency range of 200 to 2 000 000 Hz. The results showed that the dielectric constants and dielectric loss factors of aggregate, asphalt binders and asphalt mixtures vary linearly with temperature, while the growth rates vary with the frequency. A model based on nonlinear fitting was first presented to estimate the dielectric loss factor, and another prediction model of the dielectric constant of asphalt mixtures considering the temperature impact was proposed afterwards. Compared with classical models, the average relative error of the proposed model of the dielectric constant is the smallest and is less sensitive to the asphalt mixture. This investigation can cast light on the utilization of non-destructive pavement testing and is potentially valuable for e-road using the electromagnetic properties of asphalt pavement materials.

This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Automatic pavement texture recognition using lightweight few-shot learning
Authors: Pan Shuo , Yan Hai , Liu Zhuo , Chen Ning , Miao Yinghao , Hou Yue
Published: July 2023

Texture is a crucial characteristic of roads, closely related to their performance. The recognition of pavement texture is of great significance for road maintenance professionals to detect potential safety hazards and carry out necessary countermeasures. Although deep learning models have been applied for recognition, the scarcity of data has always been a limitation. To address this issue, this paper proposes a few-shot learning model based on the Siamese network for pavement texture recognition with a limited dataset. The model achieved 89.8% accuracy in a four-way five-shot task classifying the pavement textures of dense asphalt concrete, micro surface, open-graded friction course and stone matrix asphalt. To align with engineering practice, global average pooling (GAP) and one-dimensional convolution are implemented, creating lightweight models that save storage and training time. Comparative experiments show that the lightweight model with GAP implemented on dense layers and one-dimensional convolution on convolutional layers reduced storage volume by 94% and training time by 99%, despite a 2.9% decrease in classification accuracy. Moreover, the model with only GAP implemented on dense layers achieved the highest accuracy at 93.5%, while reducing storage volume and training time by 83% and 6%, respectively.

This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Feasibility of determining asphalt pavement condition from falling weight deflectometer test and finite element model updating
Authors: Deng Yong , Zhang Yazhou , Shi Xianming
Published: July 2023

Determination of pavement internal condition from a non-destructive field test is a persistent topic for its practical necessity and difficulty. It is essentially an inverse problem calibrating pavement material and structural properties from pavement responses. Considering the intrinsic complexity of asphalt pavement materials (e.g., time and temperature dependencies of asphalt mixture and stress dependency of unbound granular materials), this problem has become a typical high-dimensional optimization problem with a large and diverse set of calibrated parameters. This study investigated the feasibility of artificial intelligence-based finite element model updating in addressing this problem, and focused on the accuracy as well as stability of the backcalculated results. For a comprehensive evaluation of this method, the effects of its components such as the surrogate model representing the pavement system, the applied pavement response, the optimization algorithm and the backcalculation scheme were characterized. Finally, we found that the sensitivity of applied pavement responses to thebackcalculated pavement condition, the number of applied pavement responses and the balance between the backcalculated pavement condition and the applied test were of significant importance to achieving accurate and stable backcalculation results. Corresponding modifications were recommended to be conducted in future research for improving the performance of the proposed backcalculation method.

This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
Automatic identification of pavement cracks in public roads using an optimized deep convolutional neural network model
Authors: Lv Zhihan , Cheng Chen , Lv Haibin
Published: July 2023

The current study aims to improve the efficiency of automatic identification of pavement distress and improve the status quo of difficult identification and detection of pavement distress. First, the identification method of pavement distress and the types of pavement distress are analysed. Then, the design concept of deep learning in pavement distress recognition is described. Finally, the mask region-based convolutional neural network (Mask R-CNN) model is designed and applied in the recognition of road crack distress. The results show that in the evaluation of the model's comprehensive recognition performance, the highest accuracy is 99%, and the lowest accuracy is 95% after the test and evaluation of the designed model in different datasets. In the evaluation of different crack identification and detection methods, the highest accuracy of transverse crack detection is 98% and the lowest accuracy is 95%. In longitudinal crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. In mesh crack detection, the highest accuracy is 98% and the lowest accuracy is 92%. This work not only provides an in-depth reference for the application of deep CNNs in pavement distress recognition but also promotes the improvement of road traffic conditions, thus contributing to the progression of smart cities in the future.

This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
TransCrack: revisiting fine-grained road crack detection with a transformer design
Authors: Lin Chunmian , Tian Daxin , Duan Xuting , Zhou Jianshan
Published: July 2023

Prior convolution-based road crack detectors typically learn more abstract visual representation with increasing receptive field via an encoder–decoder architecture. Despite the promising accuracy, progressive spatial resolution reduction causes semantic feature blurring, leading to coarse and incontiguous distress detection. To these ends, an alternative sequence-to-sequence perspective with a transformer network termed TransCrack is introduced for road crack detection. Specifically, an image is decomposed into a grid of fixed-size crack patches, which is flattened with position embedding into a sequence. We further propose a pure transformer-based encoder with multi-head reduced self-attention modules and feed-forward networks for explicitly modelling long-range dependencies from the sequential input in a global receptive field. More importantly, a simple decoder with cross-layer aggregation architecture is developed to incorporate global with local attentions across different regions for detailed feature recovery and pixel-wise crack mask prediction. Empirical studies are conducted on three publicly available damage detection benchmarks. The proposed TransCrack achieves a state-of-the-art performance over all counterparts by a substantialmargin, and qualitative results further demonstrate its superiority in contiguous crack recognition and fine-grained profile extraction.

This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
A hybrid approach for fatigue life prediction of in-service asphalt pavement
Authors: Luo Xue , Wang Hang , Cao Shunqin , Ling Jian , Yang Siyuan , Zhang Yuqing
Published: July 2023

Fatigue cracking is one of the main pavement failures, which makes accurate fatigue life prediction for the design and maintenance of asphalt pavements crucial. The majority of traditional prediction methods are based entirely on the laboratory fatigue test, without considering the field condition and maintenance data. This paper aims to propose a hybrid approach to fill this gap. The key idea is that the damage condition is back-calculated by an artificial intelligence-based finite-element (FE) model updating using field-monitoring information (data-driven component), which is used to update the parameters in the mechanistic composition-specific fatigue life prediction equation (model-driven component). The laboratory test of field cores gives the material non-destructive properties. The simulated pavement response subjected to truck loading shows good agreement with measured values, which indicates that the verified constitutive relationship could be used in the data-driven component. Furthermore, in view that the fatigue test is time- and money-consuming, this paper proposes a non-test estimation of the fatigue characteristic curve based on FE simulation of a repeated direct tension test. Three test pavement sections were employed as case studies. Results showed that the predicted fatigue life changes with the service time. At the early age, semi-rigid pavement has a larger fatigue life than flexible and inverted pavements.

This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Use of recurrent neural networks considering maintenance to predict urban road performance in Beijing, China
Authors: Deng Yutong , Li Feng , Zhou Siqi , Zhang Song , Yang Yang , Zhang Qiang , Li Yanfei
Published: July 2023

A correct understanding of the pavement performance change law forms the premise of the scientific formulation of maintenance decisions. This paper aims to develop a predictive model taking into account the costs of different types of maintenance works that reflects the continuous true usage performance of the pavement. The model proposed in this study was trained on a dataset containing five-year maintenance work data on urban roads in Beijing with pavement performance indicators for the corresponding years. The same roads were matched and combined to obtain a set of sequences of pavement performance changes with the features of the current year; with the recurrent-neural-network-based long short-term memory (LSTM) network and gate recurrent unit (GRU) network, the prediction accuracy of highway pavement performance on the test set was significantly increased. The prediction result indicates that the generalization ability of the improved recurrent neural network model is satisfactory, with the
R
2
achieving 0.936, and of the two models the GRU model is more efficient, with an accuracy that reaches almost the same level as LSTM but with the training convergence time reduced to 25 s. This study demonstrates that data generated by the work of maintenance units can be used effectively in the prediction of pavement performance.

This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
A machine learning-based approach to assess impacts of autonomous vehicles on pavement roughness
Authors: Chen Chenxi , Song Yang , Wang Yizhuang David , Hu Xianbiao , Liu Jenny
Published: July 2023

Studies have been initiated to investigate the potential impact of connected and automated vehicles (CAVs) on transportation infrastructure. However, most existing research only focuses on the wandering patterns of CAVs. To bridge this gap, an apple-to-apple comparison is first performed to systematically reveal the behavioural differences between the human-driven vehicle (HDV) and CAV trajectory patterns for the first time, with the data collected from the camera-based next generation simulation dataset and autonomous driving co-simulation platform, CARLA and SUMO, respectively. A gradient boosting-based ensemble learning model for pavement performance (i.e. international roughness index) prediction is then developed with the input features including three driving pattern features, namely, lateral wandering deviation, longitudinal car-following distance and driving speed, plus 20 other context variables. A total of 1707 observations is extracted from the long-term pavement performance database for model training purposes. The result indicates that the trained model can accurately predict pavement deterioration and that CAV deteriorates pavement faster than HDV by 8.1% on average. According to the sensitivity analysis, CAV deployment will create a greater impact on the younger pavements, and the rate of pavement deterioration is found to be stable under light traffic, whereas it will increase under congested traffic.

This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Introduction to ‘Artificial intelligence in failure analysis of transportation infrastructure and materials'
Authors: Hou Yue , Dong Qiao , Wang Dawei , Liu Jenny
Published: July 2023

Transportation infrastructures, including roads, bridges, tunnels, stations, airports and subways, play fundamental roles in modern society. Engineering failures of transportation infrastructures may result in significant damage to the public. The traditional methods are to monitor, store and analyse the information during the infrastructure and material design, testing, construction, numerical simulations, evaluation, operation, maintenance and preservation, using mechanistic-based, material-based and statistics-based approaches. In recent decades, artificial intelligence (AI) has drawn the attention of many researchers and has been used as a powerful tool to understand and analyse the engineering failures in transportation infrastructure and materials. AI has the advantages of conveniently characterizing infrastructure materials in multi-scale, extracting failure information from images and cloud points, evaluating performance from the signals of sensors, predicting the long-term performance of infrastructure based on big data and optimizing infrastructure maintenance strategies, etc. In the future, AI techniques will be more effective and promising for data collection, transmission, fusion, mining and analysis, which will help engineers quickly detect, analyse and finally prevent the engineering failures of transportation infrastructure and materials. This theme issue presents the latest developments of AI in failure analysis of transportation infrastructure and materials.

This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Passenger flow anomaly detection in urban rail transit networks with graph convolution network–informer and Gaussian Bayes models
Authors: Liu Bing , Ma Xiaolei , Tan Erlong , Ma Zhenliang
Published: July 2023

Passenger flow anomaly detection in urban rail transit networks (URTNs) is critical in managing surging demand and informing effective operations planning and controls in the network. Existing studies have primarily focused on identifying the source of anomalies at a single station by analysing the time-series characteristics of passenger flow. However, they ignored the high-dimensional and complex spatial features of passenger flow and the dynamic behaviours of passengers in URTNs during anomaly detection. This article proposes a novel anomaly detection methodology based on a deep learning framework consisting of a graph convolution network (GCN)–informer model and a Gaussian naive Bayes model. The GCN–informer model is used to capture the spatial and temporal features of inbound and outbound passenger flows, and it is trained on normal datasets. The Gaussian naive Bayes model is used to construct a binary classifier for anomaly detection, and its parameters are estimated by feeding the normal and abnormal test data into the trained GCN–informer model. Experiments are conducted on a real-world URTN passenger flow dataset from Beijing. The results show that the proposed framework has superior performance compared to existing anomaly detection algorithms in detecting network-level passenger flow anomalies.

This article is part of the theme issue 'Artificial intelligence in failure analysis of transportation infrastructure and materials'.
Intelligent decision method for stability assessment of shield tunnel based on multi-objective data mining
Authors: Li Xin , Xue Yiguo , Li Zhiqiang , Kong Fanmeng , Zhou Binghua
Published: July 2023

Due to improper operation of the shield construction process and unknown geological surveys, shield construction faces many risks in passing through complex strata, among which the excavation face instability is the most serious, potentially leading to disastrous accidents. To address these issues, this research focuses on the limit support pressure and the excavation face stability in the soil when crossing the Yangtze River. First, an analytical formula for the limit support pressure of the excavation face is established through the wedge model. The support safety coefficient is used to assess the excavation face stability quantitatively. Then the rough set algorithm is used to analyse the sensitivity of each index to establish the reduced evaluation index system for the excavation face stability. The back propagation (BP) neural network is used to train the learning data, and a neural network evaluation model with a prediction error of 5.7675 × 10−4is established. The prediction performance of BP is verified by comparison with the TOPSIS prediction model and the cloud model. The evaluation method proposed in this paper provides an essential reference for evaluating the underwater shield tunnel excavation face stability.

This article is part of the theme issue ‘Artificial intelligence in failure analysis of transportation infrastructure and materials’.
Global Safety Assessment of Adverse Events of Special Interest Following 2 Years of Use and 772 Million Administered Doses of mRNA-1273
Authors: Urdaneta Veronica , Esposito Daina B. , Dharia Priyadarshani , Moraga Margot Stam , Anteyi Kate , Oduyebo-Omotosho Titi , Rossi Melissa , Burton Paul , Vega José M. , Dawson Rachel , Straus Walter
Published: July 2023

Background

The large-scale use of mRNA COVID-19 vaccines during the pandemic was associated with enhanced safety monitoring to ensure accurate and timely review of safety. We reviewed the mRNA-1273 (original strain) safety profile following 2 years of use (>772 million administered doses), primarily focusing upon predefined safety topics (ie, adverse events of special interest [AESI]) proposed in advance of COVID-19 vaccine use.

Methods

Cumulative mRNA-1273 safety data from spontaneous adverse event (AE) cases reported to the Moderna, Inc., global safety database (GSDB) between December 18, 2020, and December 17, 2022, were included. Reporting rates of AESI were calculated per 1 million doses of mRNA-1273 administered. Observed-to-expected (OE) ratios were computed by comparing observed rates of AESI to the background/expected rate for these events to evaluate potential associations with mRNA-1273.

Results

There were 658,767 identified case reports, associated with 2,517,669 AEs. Most AEs were non-serious (83.4%); 0.7% were fatal. AESI represented 13.7% of all AEs, with reporting rates for most AESI below the expected background incidence. Exceptions included anaphylaxis (OE ratio 3 days after vaccination, 2.19 [95% CI, 2.02-2.37]), myocarditis (OE ratio 7 days after vaccination, 1.41 [1.32-1.51]; among men aged 12-40 years, 9.75 [7.74-12.3]; and individuals aged 12-40 years, 3.51 [3.19-3.86]), and pericarditis (OE ratio 7 days after vaccination in individuals aged 12-40 years, 2.54 [2.16-2.99]).

Conclusions

With the exceptions of anaphylaxis, myocarditis, and pericarditis, this safety analysis of mRNA-1273 did not find evidence to suggest an increased risk for AESI identified for enhanced monitoring ahead of COVID-19 vaccine use.
ADMINISTRATION OF LEVODOPA IN A PARKINSON’S PATIENT USING A NOVEL ORAL AND MAXILLOFACIAL ROUTE – A FIRST IN HUMAN STUDY
Authors: Thirunavukarasu Suresh , Ramanan Balasubramanian Bala Venkata , Suresh Sathya Krishnan , Antonisamy Vincent Jayakumar , Varadharaj Devi , Shanmugam Paranjothi , Verma Kavita , Elumalai Canmany , Selvakumar Gladson , Elumalai Ahila , Prabahar Lydia , Janardhanan Hridwik Adiyeri , UR Anoop
Published: July 2023

Parkinson’s disease is characterized by loss of nigrostriatal dopaminergic neurons in the brain. Dopamine cannot be administered systemically as a treatment because dopamine does not cross the blood-brain-barrier. Oral levodopa is the gold standard till date. Currently, in patients who show poor response to oral levodopa, the drug can be delivered through alternate routes like inhalation and continuously by intestinal and subcutaneous routes. In this report, a novel oral and maxillofacial route was used for the first time in the world to administer levodopa in a Parkinson’s patient and its efficacy was compared with the oral route of administration.
Validity and reliability of an app-based medical device to empower individuals in evaluating their physical capacities
Authors: Mazéas Alexandre , Blond Marine , Chalabaev Aïna , Duclos Martine
Published: July 2023

Background

Cardiorespiratory fitness and muscle strength are valid markers of health and strong predictors of mortality and morbidity. The tests used to measure these variables require in-person visits with specialized equipment and trained personnel – leading to organizational constraints both for patients and hospitals, and making them difficult to implement at a large scale. In this context, technologies embedded in smartphones offer new opportunities to develop remote tests.

Objectives

This study aimed to test the validity and reliability ofMediEval, a newly developed app-based medical device that allows individuals to perform the 6-minute walk test (6MWT) and the 30-second sit-to-stand (30s-STS) test on their own using GPS signal and camera detection with a skeleton extraction algorithm.

Methods

A total of 53 healthy adults performed the two tests in three different sessions to determine the intra- and inter-day reproducibility. Test validity was assessed by comparing the results obtained from the app to gold standard measures. Pearson correlations and concordance correlation coefficients, the relative measurement error, intraclass correlation coefficients, the standard error of measure and the minimal detectable change were computed for each test.

Results

The results revealed high to excellent validity of the app in comparison to gold standards (ρ=0.84 for the 6MWT andρ=0.99 for the 30s-STS test) with low relative measurement error. The mean differences between the app and the gold standard measures were 8.96m for the 6MWT and 0.28 repetition for the 30s-STS test. Both tests had good test-retest reliability (ICCs = 0.77). The minimal detectable changes were respectively 97.56 meters for the 6MWT and 7.37 repetitions for the 30s-STS test.

Discussion

TheMediEvalmedical device proposes valid and reproducible measures of the 6MWT and the 30s-STS test. This device holds promise for monitoring the physical activity of large epidemiologic cohorts while refining patient experience and improving the scalability of the healthcare system. Considering minimal detectable change values, it may be important to ask participants to perform several tests and average them to improve accuracy. Future studies in clinical context are needed to evaluate the responsiveness and the smallest detectable changes of the device for specific populations with chronic diseases.

Data, code & supplemental materials

https://osf.io/4hzke/
Neural Correlates of Behavioral Recovery Following Ultrasonic Thalamic Stimulation in Chronic Disorders of Consciousness
Authors: Cain Josh A. , Spivak Norman M. , Coetzee John P. , Crone Julia S. , Johnson Micah A. , Lutkenhoff Evan S. , Real Courtney , Buitrago-Blanco Manuel , Vespa Paul M. , Schnakers Caroline , Monti Martin M.
Published: July 2023

Background

Treatments aimed at hastening recovery from disorders of consciousness (DOC; e.g., coma, the vegetative state) have lagged behind a rapidly advancing science of these conditions. In part, this is due to the difficulty in selectively targeting the many deep regions of the brain known to be key for recovery from DOC. The (re)emergence of low intensity focused ultrasound (LIFU) neuromodulation addresses this gap by providing a non-invasive, safe, and relatively low-cost means to exert neuromodulatory effects, anywhere in the brain, with relatively high spatial precision.

Methods

As part of this first-in-man clinical trial, a cohort of 10 patients with chronic DOC underwent two sessions of MR-guided thalamic LIFU, with concomitant functional neuroimaging, one week apart. Behavioral responsiveness, measured with the Coma Recovery Scale Revised (CRS-R), was assessed at multiple time-points both before and after each LIFU session. Changes in clinical score before and after each session were compared within subjects.

Results

This convenience sample of sample of chronic DOC patients included, at entry, 4 Minimally Conscious State plus (MCS+), 4 Minimally Conscious State minus (MCS-) and 2 Vegetative State (VS) patients (6 male; mean age = 39.1, mean time since injury = 56.75 months; 4 anoxic and 6 traumatic injuries). We find a significant linear increase over time in CRS-R total score with thalamic LIFU exposure. Functional imaging reveals changes in brain-wide activity and thalamo-cortical connectivity of the targeted thalamus (but not the contralateral, non-targeted, thalamus), during LIFU administration. Strikingly, these effects are associated with the degree of behavioral recovery observed following exposure.

Discussion

Collectively, these results are the first to suggest the efficacy of thalamic LIFU for the treatment of chronic DOC and extend our previous investigations in acute DOC populations. Indeed, results from both cohorts support the safety, feasibility, and preliminary efficacy of LIFU, as evaluated by gold-standard clinical assessments. Moreover, imaging results in both datasets provide a convergent biological link uniting neuromodulatory thalamic LIFU and the observed behavioral recovery. These first-in-man findings provide a key foundation to motivate further exploration of this technique (e.g., LIFU parameterization, optimal number and timing of exposures) and invite a sham-control clinical trial, in a larger cohort, to assess, in a blinded fashion, the technique’s efficacy.

Clinical Trial number, date of submission, date of first enrollment, registration link:

NCT02522429

August 13, 2015

March 10, 2016

https://clinicaltrials.gov/ct2/show/NCT02522429
Coding Inequity: Assessing GPT-4’s Potential for Perpetuating Racial and Gender Biases in Healthcare
Authors: Zack Travis , Lehman Eric , Suzgun Mirac , Rodriguez Jorge A. , Celi Leo Anthony , Gichoya Judy , Jurafsky Dan , Szolovits Peter , Bates David W. , Abdulnour Raja-Elie E. , Butte Atul J. , Alsentzer Emily
Published: July 2023

Background

Large language models (LLMs) such as GPT-4 hold great promise as transformative tools in healthcare, ranging from automating administrative tasks to augmenting clinical decision- making. However, these models also pose a serious danger of perpetuating biases and delivering incorrect medical diagnoses, which can have a direct, harmful impact on medical care.

Methods

Using the Azure OpenAI API, we tested whether GPT-4 encodes racial and gender biases and examined the impact of such biases on four potential applications of LLMs in the clinical domain—namely, medical education, diagnostic reasoning, plan generation, and patient assessment. We conducted experiments with prompts designed to resemble typical use of GPT-4 within clinical and medical education applications. We used clinical vignettes from NEJM Healer and from published research on implicit bias in healthcare. GPT-4 estimates of the demographic distribution of medical conditions were compared to true U.S. prevalence estimates. Differential diagnosis and treatment planning were evaluated across demographic groups using standard statistical tests for significance between groups.

Findings

We find that GPT-4 does not appropriately model the demographic diversity of medical conditions, consistently producing clinical vignettes that stereotype demographic presentations. The differential diagnoses created by GPT-4 for standardized clinical vignettes were more likely to include diagnoses that stereotype certain races, ethnicities, and gender identities. Assessment and plans created by the model showed significant association between demographic attributes and recommendations for more expensive procedures as well as differences in patient perception.

Interpretation

Our findings highlight the urgent need for comprehensive and transparent bias assessments of LLM tools like GPT-4 for every intended use case before they are integrated into clinical care. We discuss the potential sources of these biases and potential mitigation strategies prior to clinical implementation.
Validation of a 9-item Perceived Suicide Awareness Scale (PSAS-9) for adolescents
Authors: Baggio Stéphanie , Nsingi Neslie , Iglesias Katia , Sapin Marlène
Published: July 2023

Introduction

Robust empirical data on suicide awareness are needed, to better plan and evaluate suicide prevention interventions. However, there is a lack of validated measures of suicide awareness. This is especially true for perceived suicide awareness, which focuses on perceived knowledge about suicide, willingness, and confidence to talk about suicide and get help. This study aimed to validate a measure of perceived suicide awareness.

Methods

We re-used data from a suicide prevention trial conducted in Swiss secondary schools (n=366). Baseline and one-month follow-up data were used to validate the scale. The main measure was an initial 14-item perceived suicide awareness scale (PSAS). Perceived knowledge of help-seeking resources, suicide-related knowledge, and support networks were used to assess convergent validity.

Results

A nine-item version, the PSAS-9, showed satisfactory psychometric properties, including high internal consistency (α=.78), acceptable test-retest (r=.68), and a one-factor structure explaining 95% of the variance. The convergent validity was acceptable (.19≤r≤.40). The PSAS-9 was not correlated with suicide-related knowledge (r=.02).

Conclusion

This study was an important step towards validating a perceived suicide awareness scale, distinct from suicide-related knowledge, to be used in future studies focused on suicide prevention, and, more generally, studies interested in measuring suicide awareness.
Image-based models of T-cell distribution identify a clinically meaningful response to a dendritic cell vaccine in patients with glioblastoma
Authors: Bond Kamila M. , Curtin Lee , Hawkins-Daarud Andrea , Urcuyo Javier C. , De Leon Gustavo , Singleton Kyle W. , Afshari Ariana E. , Paulson Lisa E. , Sereduk Christopher P. , Smith Kris A. , Nakaji Peter , Baxter Leslie C. , Patra Devi Prasad , Gustafson Michael P. , Dietz Allan B. , Zimmerman Richard S. , Bendok Bernard R. , Tran Nhan L. , Hu Leland S. , Parney Ian F. , Rubin Joshua B. , Swanson Kristin R.
Published: July 2023

BACKGROUND

Glioblastoma is an extraordinarily heterogeneous tumor, yet the current treatment paradigm is a “one size fits all” approach. Hundreds of glioblastoma clinical trials have been deemed failures because they did not extend median survival, but these cohorts are comprised of patients with diverse tumors. Current methods of assessing treatment efficacy fail to fully account for this heterogeneity.

METHODS

Using an image-based modeling approach, we predicted T-cell abundance from serial MRIs of patients enrolled in the dendritic cell (DC) vaccine clinical trial. T-cell predictions were quantified in both the contrast-enhancing and non-enhancing regions of the imageable tumor, and changes over time were assessed.

RESULTS

A subset of patients in a DC vaccine clinical trial, who had previously gone undetected, were identified as treatment responsive and benefited from prolonged survival. A mere two months after initial vaccine administration, responsive patients had a decrease in model-predicted T-cells within the contrast-enhancing region, with a simultaneous increase in the T2/FLAIR region.

CONCLUSIONS

In a field that has yet to see breakthrough therapies, these results highlight the value of machine learning in enhancing clinical trial assessment, improving our ability to prospectively prognosticate patient outcomes, and advancing the pursuit towards individualized medicine.