Wound healing is critical to the structural and functional restoration of damaged tissue. However, effective wound closure and healing are always great challenges in regenerative engineering. This study provided bioinspired wearable hydrogel composites with drug-releasing hydrogel and nonclose-packed photonic crystals (NPCs) for wound therapy and naked-eye visual early warning of wound dehiscence. Molecular dynamics models and drug-releasing results illustrated the sustained drug release of ibuprofen, and the mechanical properties of the drug-releasing hydrogel were optimized with 1410% tensile strain by introducing fish collagen; their biocompatibility and adhesion were also improved. The structural color of the NPCs blue-shifted from 630 to 500 nm with 15.0% strain, and the original color was customized with poly(methyl methacrylate) (PMMA) concentration and acrylamide content. Compared with the gauze and the traditional hydrogels, the composite provided a moist environment and an effectively closed wound; the debridement and released drug avoided inflammation, and the rat wound was healed 40.5% on the third day and essentially 100% on the 14th day. The work provided a novel strategy for wound healing and naked-eye visual early warning when a wound deforms, which is expected to promote the synergistic development of clinical treatment and visualized early warning.

The emergence of 2-benzylbenzimidazole \"nitazene\" opioids is stirring up the recreational synthetic opioid market. Many nitazene analogues act as potent agonists at the µ‑opioid receptor (MOR), as demonstrated in various in vitro and in vivo studies. Severe intoxication and overdose deaths associated with nitazene analogues are increasingly being reported. Nitazene opioids are classified as a public health threat, stressing the need for close monitoring of new developments on the recreational drug market. This study reports on the detection of N-desethyl etonitazene in a sample handed in by a recreational drug user at a Swiss drug checking service in August 2023. The person bought the sample through an internet source where it was stated to contain isotonitazene. Chemical analyses were conducted to characterize the sample, i.e. nuclear magnetic resonance (NMR), capillary electrophoresis (CE), and high-resolution mass spectrometry (HRMS). The sample was additionally investigated using two different in vitro MOR activation assays. NMR and high-performance liquid chromatography (HPLC) coupled to HRMS confirmed the presence of N-desethyl etonitazene at a high purity and in the absence of isotonitazene and etonitazene. N-Desethyl nitazene analogues have been detected before as metabolites of isotonitazene and etonitazene. However, as first seen with N-desethyl isotonitazene, they are now emerging as standalone drugs. The applied bioassays demonstrated increased efficacy and approximately 6-9-fold higher potency of N-desethyl etonitazene at MOR compared to fentanyl. N-Desethyl etonitazene showed EC50 values of 3.35 nM and 0.500 nM in the β-arrestin 2 recruitment and Aequoscreen® assays, respectively. The opioid activity present in the collected sample was additionally evaluated using the bioassays and showed good overlap with the reference standard, in line with the analytical purity assessment. This demonstrates the potential of these bioassays to provide a rapid opioid activity assessment of authentic samples. The emergence of other N-desethyl nitazene analogues must be considered during forensic and clinical toxicology casework, to avoid misclassification of intake of such analogues as metabolites. Finally, drug checking services enable the close monitoring of market developments and trends and are of great value for early warning and harm reduction purposes.

The frequent occurrence of food safety incidents has aroused public concern about food safety and key contaminants. Foodborne pathogen contamination, pesticide residues, heavy metal residues, and other food safety problems will significantly impact human health. Therefore, developing efficient and sensitive detection method to ensure food safety early warning is paramount. The aptamer-based sensor (aptasensor) is a novel analytical tool with strong targeting, high sensitivity, low cost, etc. It has been extensively utilized in the pharmaceutical industry, biomedicine, environmental engineering, food safety detection, and in other diverse fields. This work reviewed the latest research progress of aptasensors for food analysis and detection, mainly introducing their application in detecting various key food contaminants. Subsequently, the sensing mechanism and performance of aptasensors are discussed. Finally, the review will examine the challenges and opportunities related to aptasensors for detecting major contaminants in food, and advance implementation of aptasensors in food safety and detection.

Wastewater-based surveillance has emerged as an important method for monitoring the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). This study investigated the presence of SARS-CoV-2 in wastewater in Zambia. We conducted a longitudinal study in the Copperbelt and Eastern provinces of Zambia from October 2023 to December 2023 during which 155 wastewater samples were collected. The samples were subjected to three different concentration methods, namely bag-mediated filtration, skimmed milk flocculation, and polythene glycol-based concentration assays. Molecular detection of SARS-CoV-2 nucleic acid was conducted using real-time Polymerase Chain Reaction (PCR). Whole genome sequencing was conducted using Illumina COVIDSEQ assay. Of the 155 wastewater samples, 62 (40%) tested positive for SARS-CoV-2. Of these, 13 sequences of sufficient length to determine SARS-CoV-2 lineages were obtained and 2 sequences were phylogenetically analyzed. Various Omicron subvariants were detected in wastewater including BA.5, XBB.1.45, BA.2.86, and JN.1. Some of these subvariants have been detected in clinical cases in Zambia. Interestingly, phylogenetic analysis positioned a sequence from the Copperbelt Province in the B.1.1.529 clade, suggesting that earlier Omicron variants detected in late 2021 could still be circulating and may not have been wholly replaced by newer subvariants. This study stresses the need for integrating wastewater surveillance of SARS-CoV-2 into mainstream strategies for monitoring SARS-CoV-2 circulation in Zambia.

This article offers a thorough review of current early warning systems (EWS) and advocates for establishing a unified research network for EWS in infectious diseases between China and Australia. We propose that future research should focus on improving infectious disease surveillance by integrating data from both countries to enhance predictive models and intervention strategies. The article highlights the need for standardized data formats and terminologies, improved surveillance capabilities, and the development of robust spatiotemporal predictive models. It concludes by examining the potential benefits and challenges of this collaborative approach and its implications for global infectious disease surveillance. This is particularly relevant to the ongoing project, early warning systems for Infectious Diseases between China and Australia (NetEWAC), which aims to use seasonal influenza as a case study to analyze influenza trends, peak activities, and potential inter-hemispheric transmission patterns. The project seeks to integrate data from both hemispheres to improve outbreak predictions and develop a spatiotemporal predictive modeling system for seasonal influenza transmission based on socio-environmental factors.

To clarify the complex relationship between the factors causing safety accidents in metallurgical enterprises and predict the risk of accidents in enterprises, a correlation analysis model of the factors causing safety accidents in metallurgical enterprises based on grey Decision-Making Trial and Evaluation Laboratory/Interpretative Structural Modeling (DEMATEL/ISM) was established, and a Bayesian network early warning model was constructed on this basis. The relationship and action path of accident-causing factors in metallurgical enterprises were clarified. The factors were hierarchically divided and a multi-layer hierarchical structure model was established to obtain the neighboring cause, transitional cause, and essential cause of the accident. The results showed that the employee violation rate, the hazardous substances reserves, the toxic gas and dust pollution control compliance rate, the pass rate for equipment maintenance, and the qualification rate of special equipment were the neighboring causes of the accident. The perfection of the safety production management system was the essential cause. The Bayesian network early warning model was applied to the Fuxin Jiuxing Titanium work site. The expected risk probability of an accident was 17.9%, which was in a comparatively safe state (State2). The results obtained by the Bayesian model are consistent with those obtained by AHP and fuzzy comprehensive evaluation method, which proved the accuracy of the early warning model. The Bayesian model can give the risk probability value of the accident and the risk probability value of the accident cause factors at the same time, and include the causal relationship and conditional correlation relationship among the indicator variables in the reasoning process, which can provide targeted technical support for the construction of the emergency system of risk classification management and control.

BACKGROUND: Yearly bronchiolitis and influenza-like illness epidemics in France often involve high morbidity and mortality, which severely impacts healthcare. Epidemics are declared by the French National Institute of Public Health based on syndromic surveillance of primary care and emergency departments (ED), using statistics-based alarms. Although the effective reproduction number (Rt) is used to monitor the dynamics of epidemics, it has never been used as an early warning tool for bronchiolitis or influenza-like illness epidemics in France.We assessed whether Rt is useful for detecting seasonal epidemics by comparing it to the tool currently used (MASS) by epidemiologists to declare epidemic phases.
METHODS: We used anonymized ED syndromic data from the Île-de-France region in France from 2010 to 2022. We estimated Rt and compared the indication of accelerated transmission (Rt >1) to the MASS epidemic alarm time points. We computed the difference between those two time points, time to epidemic peak, and the daily cases documented at first indication and peak.
RESULTS: Rt provided alarms for influenza-like illness and bronchiolitis epidemics that were, respectively, 6 days (IQR[4;8]) and 64 days (IQR[52;80]) - in median - earlier than the alarms provided by MASS.
CONCLUSIONS: Rt detected earlier signals of bronchiolitis and influenza-like illness epidemics. Using this early-warning indicator in combination with others to declare an annual epidemic could provide opportunities to improve healthcare system readiness.

This study aims to employ a causal network model based on transfer entropy for the early warning of systemic risk in commodity markets. We analyzed the dynamic causal relationships of prices for 25 commodities related to China (including futures and spot prices of energy, industrial metals, precious metals, and agricultural products), validating the effect of the causal network structure among commodity markets on systemic risk. Our research results identified commodities and categories playing significant roles, revealing that industry and precious metal markets possess stronger market information transmission capabilities, with price fluctuations impacting a broader range and with greater force on other commodity markets. Under the influence of different types of crisis events, such as economic crises and the Russia-Ukraine conflict, the causal network structure among commodity markets exhibited distinct characteristics. The results of the effect of external shocks to the causal network structure of commodity markets on the entropy of systemic risk suggest that network structure indicators can warn of systemic risk. This article can assist investors and policymakers in managing systemic risk to avoid unexpected losses.

BACKGROUND: The escalating global prevalence of obesity has necessitated the exploration of novel diagnostic approaches. Recent scientific inquiries have indicated potential alterations in voice characteristics associated with obesity, suggesting the feasibility of using voice as a noninvasive biomarker for obesity detection.
OBJECTIVE: This study aims to use deep neural networks to predict obesity status through the analysis of short audio recordings, investigating the relationship between vocal characteristics and obesity.
METHODS: A pilot study was conducted with 696 participants, using self-reported BMI to classify individuals into obesity and nonobesity groups. Audio recordings of participants reading a short script were transformed into spectrograms and analyzed using an adapted YOLOv8 model (Ultralytics). The model performance was evaluated using accuracy, recall, precision, and F1-scores.
RESULTS: The adapted YOLOv8 model demonstrated a global accuracy of 0.70 and a macro F1-score of 0.65. It was more effective in identifying nonobesity (F1-score of 0.77) than obesity (F1-score of 0.53). This moderate level of accuracy highlights the potential and challenges in using vocal biomarkers for obesity detection.
CONCLUSIONS: While the study shows promise in the field of voice-based medical diagnostics for obesity, it faces limitations such as reliance on self-reported BMI data and a small, homogenous sample size. These factors, coupled with variability in recording quality, necessitate further research with more robust methodologies and diverse samples to enhance the validity of this novel approach. The findings lay a foundational step for future investigations in using voice as a noninvasive biomarker for obesity detection.

UNASSIGNED: Animal health surveillance systems in Kenya have undergone significant changes and faced various challenges throughout the years.
UNASSIGNED: In this article, we present a comprehensive overview of the Kenya animal health surveillance system (1944 to 2024), based on a review of archived documents, a scoping literature review, and an examination of past surveillance assessments and evaluation reports.
UNASSIGNED: The review of archived documents revealed key historical events that have shaped the surveillance system. These include the establishment of the Directorate of Veterinary Services in 1895, advancements in livestock farming, the implementation of mandatory disease control interventions in 1944, the growth of veterinary services from a section to a ministry in 1954, the disruption caused by the Mau Mau insurrection from 1952 to 1954, which led to the temporary halt of agriculture in certain regions until 1955, the transition of veterinary clinical services from public to private, and the progressive privatization plan for veterinary services starting in 1976. Additionally, we highlight the development of electronic surveillance from 2003 to 2024. The scoping literature review, assessments and evaluation reports uncovered several strengths and weaknesses of the surveillance system. Among the strengths are a robust legislative framework, the adoption of technology in surveillance practices, the existence of a formal intersectoral coordination platform, the implementation of syndromic, sentinel, and community-based surveillance methods, and the presence of a feedback mechanism. On the other hand, the system\'s weaknesses include the inadequate implementation of strategies and enforcement of laws, the lack of standard case definitions for priority diseases, underutilization of laboratory services, the absence of formal mechanisms for data sharing across sectors, insufficient resources for surveillance and response, limited integration of surveillance and laboratory systems, inadequate involvement of private actors and communities in disease surveillance, and the absence of a direct supervisory role between the national and county veterinary services.
UNASSIGNED: To establish an effective early warning system, we propose the integration of surveillance systems and the establishment of formal data sharing mechanisms. Furthermore, we recommend enhancing technological advancements and adopting artificial intelligence in surveillance practices, as well as implementing risk-based surveillance to optimize the allocation of surveillance resources.