Safety Monitoring Research Articles

Leaflet modification before transcatheter aortic valve implantation in patients at risk for coronary obstruction: the ShortCut study.

This trial sought to assess the safety and efficacy of ShortCut, the first dedicated leaflet modification device, prior to transcatheter aortic valve implantation (TAVI) in patients at risk for coronary artery obstruction. This pivotal prospective study enrolled patients with failed bioprosthetic aortic valves scheduled to undergo TAVI and were at risk for coronary artery obstruction. The primary safety endpoint was procedure-related mortality or stroke at discharge or 7 days, and the primary efficacy endpoint was per-patient leaflet splitting success. Independent angiographic, echocardiographic, and computed tomography core laboratories assessed all images. Safety events were adjudicated by a clinical events committee and data safety monitoring board. Sixty eligible patients were treated (77.0 ± 9.6 years, 70% female, 96.7% failed surgical bioprosthetic valves, 63.3% single splitting and 36.7% dual splitting) at 22 clinical sites. Successful leaflet splitting was achieved in all (100%; 95% confidence interval [CI] 94-100.0%, p<0.001) patients. Procedure time, including imaging confirmation of leaflet splitting, was 30.6 ± 17.9 min. Freedom from the primary safety endpoint was achieved in 59 (98.3%; 95% CI [91.1-100%]) patients, with no mortality and one (1.7%) disabling stroke. At 30 days, freedom from coronary obstruction was 95% (95% CI 86.1-99.0%). Within 90 days, freedom from mortality was 95% (95% CI 86.1-99.0%]), without any cardiovascular deaths. Modification of failed bioprosthetic aortic valve leaflets using ShortCut was safe, achieved successful leaflet splitting in all patients, and was associated with favorable clinical outcomes in patients at risk for coronary obstruction undergoing TAVI.

Self-Adaptive Incremental PCA-Based DBSCAN of Acoustic Features for Anomalous Sound Detection

In modern industry, maintaining continuous machine operations is important for improving production efficiency and reducing costs. Therefore, the smart technology of acoustic monitoring to detect anomalous machine conditions earlier before breakdowns works as part of predictive maintenance and is applied not only in industry fault detection but also in safety monitoring and surveillance systems. This paper proposes a self-adaptive unsupervised machine learning algorithm with dimension-reduction technology to detect anomalous sounds after extracting acoustic machine features. Technically, the automatic EPS calculation algorithm-based genetic algorithm optimizes the automatic clustering algorithm’s configuration for incremental principal component analysis and density-based spatial clustering algorithms with noise. IPCA is enhanced by the sequential Karhunen–Loeve (SKL) algorithm, and the condensation algorithm works as the second layer of the algorithm to reduce the number of effective components. This architecture could select an optimized set of parameters based on different test environments and keeps performance quality with fewer computational requirements. In the experiments, 228 sets of normal sounds and 100 sets of anomaly sounds are used. The sound files are collected from the same machine type (stepper motors) at a real plant site. We compare the proposed algorithm with K-means++, one-class SVM, agglomerative clustering, DCGAN and DCNN-Autoencoder, and this new algorithm performs best, with an AUC of 0.84 and the shortest execution time. The algorithm is generic and can be applied to detect anomalies in machines to provide early warning to people to avoid serious accidents or disasters.

Measuring the Critical Success Factor of Safety Program in Drilling and Well Intervention Operation at PT. MHK, Ltd.

The drilling and well intervention division implements various safety initiatives to prevent incidents. Despite its multiple activities, an assessment has yet to be conducted to determine the program’s efficacy. This paper aims to determine the elements that impact the effectiveness of safety program execution in drilling and well intervention operations in MHK, Ltd. The Author conducted measurements utilizing variables related to the critical success factor of the safety program, as outlined in the cross-reference research article. Subsequently, the author introduces a system of incentives in variables quantified as a novelty of this research based on input from subject matter experts. By understanding the variables that influence the efficacy of the safety campaign, the safety campaign initiatives will be more focused on mitigating accidents within the drilling and well intervention divisions. The research was conducted utilizing a quantitative approach, where data was gathered through a questionnaire that employed a Likert scale. The population comprises individuals employed in the Drilling and Well Intervention roles at many locations, including the site, field, barge, rig, and town. The data is further analyzed using the Partial Least Squares Structural Equation Modelling (PLS-SEM) technique, employing the smartPLS4 software. The study findings indicated that three out of five variables examined had a favorable and significant impact on the efficacy of the safety program at DWI MHK, Ltd.: management commitment, reward and punishment, and safety arrangement. We propose strengthening identified elements and addressing weaknesses, including utilizing digitalization and artificial intelligence for safety monitoring.

Evaluation of bridge structure damage based on deep learning identification model

Safety monitoring is an important part of bridge engineering construction and operation. At present, there is room for promoting the health monitoring and evaluation of small and medium-sized concrete bridges. In view of this, the study first models the spatial model and physical parameters of the bridge, and then builds the data of vehicle load and vehicle type. To reduce the complexity of data mapping, wavelet packet decomposition is used to analyze the data structure. And the physical field effect analysis is abandoned to directly mine the data relationship at both ends by using deep neural network. The data decomposition results show that the method can discard the temperature-induced effect. And the local decomposition results of the data meet the input of the neural network. The data measured by the sensor is added to the depth learning model for fitting. The overall and local fitting rates are more than 92%. The loss function converges quickly, and there is no gradient explosion. The model predicts the bridge structural damage caused by vehicle stress of four load categories, and the results show that the average fitting rate is 89.72%. Therefore, the identification path of the proposed deep learning model has positive significance for the evaluation of bridge structural damage. The main contribution of the study is to propose a deep learning-based method for bridge structural damage assessment. By modeling the spatial model and physical parameters of the bridge and combining data from vehicle load and vehicle type, the data structure was analyzed using the wavelet packet decomposition method to eliminate temperature-induced effects and data from sensor measurements were added to the deep learning model for fitting. This finding has positive implications for bridge structural damage assessment and can provide effective pathways and methods to monitor and evaluate the health status of small and medium-sized concrete bridges. This has important practical application value for the construction and operation of bridge engineering.

Pilot study with randomised control of dual site theta burst transcranial magnetic stimulation (TMS) for methamphetamine use disorder: a protocol for the TARTAN study

BackgroundTranscranial magnetic stimulation (TMS) (including the theta burst stimulation (TBS) form of TMS used in this study) is a non-invasive means to stimulate nerve cells in superficial areas of the brain. In recent years, there has been a growth in the application of TMS to investigate the modulation of neural networks involved in substance use disorders. This study examines the feasibility of novel TMS protocols for the treatment of methamphetamine (MA) use disorder in an ambulatory drug and alcohol treatment setting.MethodsThirty participants meeting the criteria for moderate to severe MA use disorder will be recruited in community drug and alcohol treatment settings and randomised to receive active TMS or sham (control) intervention. The treatment is intermittent TBS (iTBS) applied to the left dorsolateral prefrontal cortex (DLPFC), then continuous TBS (cTBS) to the left orbitofrontal cortex (OFC). Twelve sessions are administered over 4 weeks with opt-in weekly standardized cognitive behaviour therapy (CBT) counselling and a neuroimaging sub-study offered to participants. Primary outcomes are feasibility measures including recruitment, retention and acceptability of the intervention. Secondary outcomes include monitoring of safety and preliminary efficacy data including changes in substance use, cravings (cue reactivity) and cognition (response inhibition).DiscussionThis study examines shorter TBS protocols of TMS for MA use disorder in real-world drug and alcohol outpatient settings where withdrawal and abstinence from MA, or other substances, are not eligibility requirements. TMS is a relatively affordable treatment and staff of ambulatory health settings can be trained to administer TMS. It is a potentially scalable and translatable treatment for existing drug and alcohol clinical settings. TMS has the potential to provide a much-needed adjuvant treatment to existing psychosocial interventions for MA use disorder. A limitation of this protocol is that the feasibility of follow-up is only examined at the end of treatment (4 weeks).Trial registrationAustralia New Zealand Clinical Trial Registry ACTRN12622000762752. Registered on May 27, 2022, and retrospectively registered (first participant enrolled) on May 23, 2022, with protocol version 7 on February 24, 2023.

A novel reconstruction method for displacement missing data of arch dam via hierarchical clustering and deep learning

The absence of displacement monitoring data presents a challenge to real-time dam safety monitoring. This article introduces a novel method for reconstructing missing displacements, comprehensively taking into account the spatiotemporal correlation and causal effect mechanism of displacement. Firstly, adaptive weighted derivative dynamic time warping (AWDDTW) and adaptive weighted dynamic time warping (AWDTW), in conjunction with clustering algorithms, are proposed to extract spatiotemporal correlation among dam displacements. Secondly, by stacking residual blocks within the residual network and densifying the shortcut connections between residual blocks, the deep stacked residual network (DS-ResNet) is established to effectively capture the intricate mappings between displacements and various factors. Finally, using two arch dams, as examples, we simulated scenarios of continuous long-term and multiple short-term missing data to validate the new method proposed in this study. The results indicate that the proposed clustering algorithm can accurately compute the similarity between displacement sequences of different lengths and monitoring frequencies, thereby providing more precise displacement point partitioning results. Compared to models such as random forests, relevance vector machines, multilayer perceptron, ResNet and Transformer, the DS-ResNet demonstrates superior capability in identifying and extracting effective information from displacement sequences under both continuous long-term and multiple short-term missing data scenarios. Compared to methods that solely consider spatiotemporal correlation or causal effect mechanism of displacement, the proposed method can more comprehensively reflect the operating patterns of arch dams and more accurately reconstruct missing displacement values. The research presented herein offers new technical means and solution approaches for dam safety monitoring and operational management.

Rapid Initiation of Injection Naltrexone for Opioid Use Disorder

Injectable extended-release (XR)-naltrexone is an effective treatment option for opioid use disorder (OUD), but the need to withdraw patients from opioid treatment prior to initiation is a barrier to implementation. To compare the effectiveness of the standard procedure (SP) with the rapid procedure (RP) for XR-naltrexone initiation. The Surmounting Withdrawal to Initiate Fast Treatment with Naltrexone study was an optimized stepped-wedge cluster randomized trial conducted at 6 community-based inpatient addiction treatment units. Units using the SP were randomly assigned at 14-week intervals to implement the RP. Participants admitted with OUD received the procedure the unit was delivering at the time of their admission. Participant recruitment took place between March 16, 2021, and July 18, 2022. The last visit was September 21, 2022. Standard procedure, based on the XR-naltrexone package insert (approximately 5-day buprenorphine taper followed by a 7- to 10-day opioid-free period and RP, defined as 1 day of buprenorphine at minimum necessary dose, 1 opioid-free day, and ascending low doses of oral naltrexone and adjunctive medications (eg, clonidine, clonazepam, antiemetics) for opioid withdrawal. Receipt of XR-naltrexone injection prior to inpatient discharge (primary outcome). Secondary outcomes included opioid withdrawal scores and targeted safety events and serious adverse events. All analyses were intention-to-treat. A total of 415 participants with OUD were enrolled (mean [SD] age, 33.6 [8.48] years; 205 [49.4%] identified sex as male); 54 [13.0%] individuals identified as Black, 91 [21.9%] as Hispanic, 290 [69.9%] as White, and 22 [5.3%] as multiracial. Rates of successful initiation of XR-naltrexone among the RP group (141 of 225 [62.7%]) were noninferior to those of the SP group (68 of 190 [35.8%]) (odds ratio [OR], 3.60; 95% CI, 2.12-6.10). Withdrawal did not differ significantly between conditions (proportion of days with a moderate or greater maximum Clinical Opiate Withdrawal Scale score (>12) for RP vs SP: OR, 1.25; 95% CI, 0.62-2.50). Targeted safety events (RP: 12 [5.3%]; SP: 4 [2.1%]) and serious adverse events (RP: 15 [6.7%]; SP: 3 [1.6%]) were infrequent but occurred more often with RP than SP. In this trial, the RP of XR-naltrexone initiation was noninferior to the standard approach and saved time, although it required more intensive medical management and safety monitoring. The results of this trial suggest that rapid initiation could make XR-naltrexone a more viable treatment for patients with OUD. ClinicalTrials.gov Identifier: NCT04762537.

Challenges to Compliance and Adherence to Personal Hygiene and Food Safety Among Foodservice Workers in Hospitals of Mogalakwena Municipality, Limpopo Province

Background: Foodborne illnesses could result from food handlers acquiring bacteria that make them dangerous for humans to consume. Non-adherence to personal hygiene and food safety can impact how well patients' conditions are managed and aggravate illnesses during hospital stay. Therefore, this study seeks to explore factors that may impact adherence to personal hygiene and food safety among hospital food service workers. Methods: Qualitative exploratory study design was used to extract data from food handlers at Mokopane and Voortrekker hospitals in Mogalakwena municipality of Limpopo province, South Africa. Twenty-one participants were purposively sampled, and face-to-face interviews were conducted using voice recorders and field notes for non-verbal cues. Tesch’s eight steps, inductive, descriptive, and open coding techniques were used to analyse data. Results: Factors reportedly affecting personal hygiene and food safety include knowledge of the importance of adherence to personal hygiene and food safety, personal preferences and attitude towards personal hygiene, and lack of uniform and inspection. Non-adherence is affected by cultural norms regarding the wearing of wedding rings and bracelets on the wrist. Conclusion: This study recommends regular internal and external food safety compliance and monitoring. The leadership of the hospital's food service department should be strengthened, and food handlers should get regular in-service training.

Ultrasensitive and Highly Selective Detection of Staphylococcus aureus at the Single-Cell Level Using Bacteria-Imprinted Polymer and Vancomycin-Conjugated MnO2 Nanozyme.

Pathogenic bacterial infections, even at extremely low concentrations, pose significant threats to human health. However, the challenge persists in achieving high-sensitivity bacterial detection, particularly in complex samples. Herein, we present a novel sandwich-type electrochemical sensor utilizing bacteria-imprinted polymer (BIP) coupled with vancomycin-conjugated MnO2 nanozyme (Van@BSA-MnO2) for the ultrasensitive detection of pathogenic bacteria, exemplified by Staphylococcus aureus (S. aureus). The BIP, in situ prepared on the electrode surface, acts as a highly specific capture probe by replicating the surface features of S. aureus. Vancomycin (Van), known for its affinity to bacterial cell walls, is conjugated with a Bovine serum albumin (BSA)-templated MnO2 nanozyme through EDC/NHS chemistry. The resulting Van@BSA-MnO2 complex, serving as a detection probe, provides an efficient catalytic platform for signal amplification. Upon binding with the captured S. aureus, the Van@BSA-MnO2 complex catalyzes a substrate reaction, generating a current signal proportional to the target bacterial concentration. The sensor displays remarkable sensitivity, capable of detecting a single bacterial cell in a phosphate buffer solution. Even in complex milk matrices, it maintains outstanding performance, identifying S. aureus at concentrations as low as 10 CFU mL-1 without requiring intricate sample pretreatment. Moreover, the sensor demonstrates excellent selectivity, particularly in distinguishing target S. aureus from interfering bacteria of the same genus at concentrations 100-fold higher. This innovative method, employing entirely synthetic materials, provides a versatile and low-cost detection platform for Gram-positive bacteria. In comparison to existing nanozyme-based bacterial sensors with biological recognition materials, our assay offers distinct advantages, including enhanced sensitivity, ease of preparation, and cost-effectiveness, thereby holding significant promise for applications in food safety and environmental monitoring.

Deep Learning CNN-GRU Method for GNSS Deformation Monitoring Prediction

Hydraulic structures are the key national infrastructures, whose safety and stability are crucial for socio-economic development. Global Navigation Satellite System (GNSS) technology, as a high-precision deformation monitoring method, is of great significance for the safety and stability of hydraulic structures. However, the GNSS time series exhibits characteristics such as high nonlinearity, spatiotemporal correlation, and noise interference, making it difficult to model for prediction. The Neural Networks (CNN) model has strong feature extraction capabilities and translation invariance. However, it remains sensitive to changes in the scale and position of the target and requires large amounts of data. The Gated Recurrent Units (GRU) model could improve the training effectiveness by introducing gate mechanisms, but its ability to model long-term dependencies is limited. This study proposes a combined model, using CNN to extract spatial features and GRU to capture temporal information, to achieve an accurate prediction. The experiment shows that the proposed CNN-GRU model has a better performance, with an improvement of approximately 45%, demonstrating higher accuracy and reliability in predictions for GNSS deformation monitoring. This provides a new feasible solution for the safety monitoring and early warning of hydraulic structures.

Effect of single and double pulse laser-induced breakdown spectroscopy towards steel alloy in different gaseous media

In recent years, laser-induced breakdown spectroscopy (LIBS) technique showed its promising for material analysis, quality control, forensic science, and environmental safety monitoring. Besides, due to its rapid measurement capability and minimal sample-preparation requirements, it is a widely recognized elemental analysis approach used in a variety of study domains. The current work compares spatially resolved single (SP) and double pulse (DP), especially orthogonal DP, setups for LIBS to examine and enhance the limit of detection (LOD) in duplex stainless steels alloy. Using the Boltzmann plot method and the Stark broadening mechanism, the electron temperature and density of local arc plasma are highlighted due to single pulse and orthogonal pre-pulse double pulse configurations. With delay time, the circumstances of arc plasma's local thermodynamic equilibrium (LTE) of emission spectral lines are investigated. The experimental results show that there are significant increases in the spectral intensities of the Fe lines in double pulse LIBS under Ar-gas compared to those under He-gas. Internal standardization was used to create calibration curves for the spectral lines of Cr, Cu, and Mn in the alloy sample for the both studied media. It demonstrated that argon outperforms helium in determining the elemental composition of steel alloys. Furthermore, LOD for SP-LIBS and DP-LIBS in both media were calculated, which showed a higher detection LOD for DP-LIBS in the presence of Ar-gas compared to that in He-gas.

Quaternary conducting Cs/GO/PANi hydrogel composites: A smart material for room temperature hydrogen sensing

Hydrogen sensors are essential for hydrogen energy technology development and safety monitoring. However, hydrogen sensing is difficult because it requires effective and fast detection of tiny quantities of hydrogen. In situ oxidative radical polymerization is used in this study to combine polyaniline (PANi) with a chitosan/graphene oxide (Cs/GO) hydrogel, which is very good at conducting electricity, to make it sensor. FTIR, XRD, SEM, and UV were used to evaluate the material's functional groups, crystal structure, morphology, and bandgap. This work shows that Cs/GO/PANi can synthesise a hydrogel-based sensor. Electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and chronoamperometry (CA) were used to evaluate electron transfer mobility, redox behaviour, and hydrogen sensing. Using a hydrogen source with a concentration of 0.5–2.5 M KOH, the hydrogen sensor's response time, recovery sensitivity, and limit of detection were found to be 0.4 s, 0.8 s, 54.3748 μAM, and 3.5520 μM, respectively. Under conventional conditions, the experiment demonstrated significant sensing capabilities at ambient temperature. According to the research, the sensor works so well because of the π-π stacking interaction, the irregular structure, and the way the GO-based hydrogel and PANi work together.

Decentralized Clinical Trials in Early Drug Development—A Framework Proposal

ABSTRACT The COVID-19 pandemic has led to a rethinking of clinical trial design to maintain clinical research activity, with regulatory changes allowing for the wider implementation and development of decentralized design models. Evidence of the feasibility and benefits associated with a remote design comes mainly from observational studies or phase 2 and 3 clinical trials, in which implementation is easier with a better-established safety profile. Early drug development is a slow and expensive process in which accrual and safety are key aspects of success. Applying a decentralized model to phase 1 clinical trials could improve patient accrual by removing geographic barriers, improving patient population diversity, strengthening evidence for rare tumors, and reducing patients’ financial and logistical burdens. However, safety monitoring, data quality, shipment, and administration of the investigational product are challenges to its implementation. Based on published data for decentralized clinical trials, we propose an exploratory framework of solutions to enable the conceptualization of a decentralized model for phase 1 clinical trials.

Precision Oncology Clinical Trials: A Systematic Review of Phase II Clinical Trials with Biomarker-Driven, Adaptive Design.

Novel clinical trial designs are conducted in the precision medicine era. This study aimed to evaluate biomarker-driven, adaptive phase II trials in precision oncology, focusing on infrastructure, efficacy, and safety. We systematically reviewed and analyzed the target studies. EMBASE and PubMed searches from 2015 to 2023 generated 29 eligible trials. Data extraction included infrastructure, biomarker screening methodologies, efficacy, and safety profiles. Government agencies, cancer hospitals, and academic societies with accumulated experiences led investigator-initiated precision oncology clinical trials (IIPOCTs), which later guided sponsor-initiated precision oncology clinical trials (SIPOCTs). Most SIPOCTs were international studies with basket design. IIPOCTs primarily used the central laboratory for biomarker screening, but SIPOCTs used both central and local laboratories. Most of the studies adapted next-generation sequencing and/or immunohistochemistry for biomarker screening. Fifteen studies included an independent central review committee for outcome investigation. Efficacy assessments predominantly featured objective response rate as the primary endpoint, with varying results. Nine eligible studies contributed to the United States Food and Drug Administration's marketing authorization. Safety monitoring was rigorous, but reporting formats lacked uniformity. Health-related quality of life and patient-reported outcomes were described in some protocols but rarely reported. Our results reveal that precision oncology trials with adaptive design rapidly and efficiently evaluate anticancer drugs' efficacy and safety, particularly in specified biomarker-driven cohorts. The evolution from IIPOCT to SIPOCT has facilitated fast regulatory approval, providing valuable insights into the precision oncology landscape.

A novel electrochemical sensor for the detection of metronidazole residues in food samples

The widespread use and misuse of antibiotics in pharmaceuticals and animal farming has resulted in their accumulation in food sources and the environment, posing significant threats to human health, the environment, and the global economy. In this study, we have developed a hypersensitive, and ultra-selective electrochemical sensor, the first of its kind, by integrating a thermally annealed gold-silver alloy nanoporous matrix (TA-Au-Ag-ANpM) with reduced graphene oxide (r-GO) and poly(glycine) at the surface of a glassy carbon electrode (GCE). This sensor aims to detect life-threatening metronidazole (MTZ) residues in food samples. TA-Au-Ag-ANpM/r-GO/poly(glycine)/GCE was thoroughly characterized using a range of analytical techniques, including UV–Vis, FT-IR, XRD, SEM, and EDX. Furthermore, its electrochemical properties were investigated by cyclic voltammetry (CV), square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS). The sensor exhibited outstanding performance, with a broad linear range of 2.0 pM–410 μM. The limits of detection (LOD) and quantification (LOQ) were determined to be 0.0312 pM and 0.104 pM, respectively. The TA-Au-Ag-ANpM/r-GO/poly(glycine)/GCE exhibited exceptional reproducibility, repeatability, stability, and resistance to interferences. Moreover, the sensor demonstrated outstanding performance in detecting MTZ residues in milk powder, pork, and chicken meat samples, achieving very good recoveries (96.9%–101.4%) with a relative standard deviation (RSD) below 5%. This performance highlights the potential for practical applications in food safety and quality monitoring. Therefore, the developed sensor contributes to the advancement of electrochemical sensing technology and its application in ensuring food safety and integrity by combating antibiotic residues.

A Wireless Surveillance and Safety Monitoring System for Mine Workers Using ZigBee

n the mining industry, ZigBee technology is used in wireless surveillance and safety monitoring systems specifically designed for mine workers. By continuously monitoring the ambient conditions and vital indicators of miners in real-time, the technology seeks to improve their safety and security. In order to detect dangerous situations in the mine unit, the proposed system consists of a variety of sensors that are integrated together. ZigBee-enabled base stations are used for data collecting and transmission. By means of Zigbee, the system enables smooth communication between the base stations and sensors, guaranteeing prompt alerts and reactions to possible safety hazards. Through the mining industry, where worker safety is of utmost significance, this research advances wireless monitoring systems in dangerous environments. [1]. The bell in an emergency signals a quick reaction from the base station. This research represents a significant advancement in wireless sensor networking, improving mine worker safety. Our goal is to improve the lives of mine workers facing dangerous circumstances by providing a rapid means of rescue through wireless communication.

Analyzing DNA barcoding and identifying toxins caused by neurotoxic mushroom poisoning using liquid chromatography tandem mass spectrometry

Background: Neurotoxic mushroom poisoning often exhibits rapid symptom onset, typically attributed to compounds such as Ibotenic acid, which affect the central nervous system. This study addresses a new case of mushroom-related food poisoning in southern Thailand. Objective: The objectives are to determine the presence of ibotenic acid in cases of mushroom-related food poisoning utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) and to identify toxic Amanita species implicated in these cases. Materials and methods: Remnant mushroom samples obtained from three clinically reported cases were used. Nucleotide similarity was compared against the rRNA/ ITS databases using NCBI BLAST search. Phylogenetic analyses were conducted using maximum likelihood (ML) and FastTree approaches. LC-MS/MS was employed to separate of Ibotenic acid, determine its molecular weight and perform precursor ion fragmentation. Results: Analysis of the rRNA/ITS databases revealed a high nucleotide similarity between suspected mushroom samples and Amanita digitosa. Detailed phylogenetic analysis confirmed that mushroom samples from the three poisoning cases clustered with A. digitosa. LC-MS/MS analysis showed the presence of ibotenic acid, with precursor ion (m/z 159) and product ion (m/z 113.1) as the major toxic substances. Clinically, patients poisoned by ibotenic acid-containing mushrooms exhibited a short latent period with symptoms of nausea, vomiting, vertigo, delirium, confusion, and fatigue. Conclusion: The genus Amanita comprises both edible and inedible species that produce several lethal toxins. The report of ibotenic acid in A. digitosa is a novel finding, valuable for food safety monitoring and healthcare decision-maker. This is especially notable due to the accuracy and rapidity of the analytical process.

Multisource information fusion model for deformation safety monitoring of earth and rock dams based on deep graph feature fusion

Constructing a long-term deformation monitoring model for earth–rock dams that integrates multisource monitoring information is highly important for enhancing the safety state evaluation and monitoring effectiveness of such dams. In this paper, we propose a new health monitoring model named the deformation–seepage–water level multimeasurement point health monitoring (DSW-MPHM) model for earth–rock dams based on deep graph feature fusion. This model fuses coupled seepage, deformation, and water level features from different monitoring sites of the dam body, base, and shoulder. To achieve this goal, we first establish a new module to fuse spatial and temporal features using graph convolutional networks and long short-term memory. Seepage features and water level features are then extracted using graph attention mechanisms. Subsequently, we employ the feature fusion technique, which incorporates principal component analysis and gated fusers, to construct the DSW-MPHM model, which effectively fuses information from multiple sources. This novel approach successfully addresses the issues of information redundancy and the limited reliability of monitoring models. To verify the validity of the model, it is applied to an endoscopic deformation monitoring program of a panel rockfill dam with a height of 185.5 m. The results demonstrate the superior stability and effectiveness of the proposed method compared to those of 10 baseline prediction models. Additionally, the characterization of the seepage and water level features extracted from the model is verified for its reasonableness. Thus, our proposed model is well suited for practical engineering applications.

Enabling near-real-time safety glove detection through edge computing and transfer learning: comparative analysis of edge and cloud computing-based methods

PurposeComputer vision and deep learning (DL) methods have been investigated for personal protective equipment (PPE) monitoring and detection for construction workers’ safety. However, it is still challenging to implement automated safety monitoring methods in near real time or in a time-efficient manner in real construction practices. Therefore, this study developed a novel solution to enhance the time efficiency to achieve near-real-time safety glove detection and meanwhile preserve data privacy.Design/methodology/approachThe developed method comprises two primary components: (1) transfer learning methods to detect safety gloves and (2) edge computing to improve time efficiency and data privacy. To compare the developed edge computing-based method with the currently widely used cloud computing-based methods, a comprehensive comparative analysis was conducted from both the implementation and theory perspectives, providing insights into the developed approach’s performance.FindingsThree DL models achieved mean average precision (mAP) scores ranging from 74.92% to 84.31% for safety glove detection. The other two methods by combining object detection and classification achieved mAP as 89.91% for hand detection and 100% for glove classification. From both implementation and theory perspectives, the edge computing-based method detected gloves faster than the cloud computing-based method. The edge computing-based method achieved a detection latency of 36%–68% shorter than the cloud computing-based method in the implementation perspective. The findings highlight edge computing’s potential for near-real-time detection with improved data privacy.Originality/valueThis study implemented and evaluated DL-based safety monitoring methods on different computing infrastructures to investigate their time efficiency. This study contributes to existing knowledge by demonstrating how edge computing can be used with DL models (without sacrificing their performance) to improve PPE-glove monitoring in a time-efficient manner as well as maintain data privacy.

Metal-organic framework/MXene nanohybrid composites as an emerging electrochemical sensing platform for food safety and biomedical monitoring: From synthesis to application

Metal-organic frameworks (MOFs) are created through the self-assembly process of metal ions or clusters with organic linkers. These materials have attracted important interest owing to their advantageous features, comprising abundant pore structures, ultrahigh porosity, and well-exposed active sites. However, some traditional MOFs have limitations in terms of low stability and conductivity. Despite significant endeavors to enhance the stability of MOF materials, limited advancement has prompted researchers to concentrate on the development of hybrid materials. MXenes, a group of 2D transition-metal compounds comprising nitrides, carbides, and carbonitrides, are recognized for their diverse composition and their ability to form various buildings with rich surface chemistry. The hybridization of MOFs with functional MXene layered could be advantageous if the host structure offers suitable interactions to enhance and stabilize the desired features. Current research has concentrated on incorporating MXenes and MOFs to generate nanohybrid materials with boosted electrochemical features, thus broadening the scope for new applications. This review explores potential design approaches for MXene@MOF nanohybrids, the tunable characteristics of the resulting hybrids, and their use in electrochemical sensing platforms for food safety and biomedical monitoring. Finally, the authors discuss the challenges and future opportunities of MOF@MXene-derived nanocomposites.