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Sulfonohydrazide as a potential inhibitor of SARS-CoV-2 infection

Rural labor migration and household waste separation willingness: Evidence from 6849 rural residents in China

Understanding the key determinants of rural household waste separation willingness (HWSW) is indispensable in promoting sustainable development for many developing economies. Few studies have been dedicated to examining the impact of large-scale rural labor migration (RLM) on HWSW in rural China. Based on a nationwide sample including 6849 rural residents, this paper investigates the relationship between RLM and rural residents’ HWSW. The generalized propensity score (GPS) method and the instrumental variable (IV) approach are used to account for potential selection bias and endogenous problems. The results show that RLM inhibits rural residents' HWSW. Specifically, with every 1% increase in RLM, the likelihood of rural residents’ HWSW will decrease by 3.5%. This effect remains significant after a series of robustness checks. Heterogeneity analysis reveals that the negative impact of RLM on HWSW is larger for rural residents who live in Midwest China, with low education and less income. Further mechanism analysis shows that RLM reduces rural residents’ HWSW by decreasing their social capital, undermining their rural community administration, and diminishing their village's collective economic income.

The influence of diet, saliva, and dental history on the oral microbiome in healthy, caries-free Australian adults

The impact of mergers and acquisitions on technological innovation in state-owned enterprises: The moderating role of mixed-ownership

Using data from Chinese A-share listed state-owned enterprises (SOEs) between 2007 and 2019, we examine how mergers and acquisitions (M&As) affect SOE innovation through patent outputs, with a focus on mixed-ownership M&As where SOEs acquire private firms. Our results show that while M&As generally enhance SOE innovation through increased patent applications, mixed-ownership M&As demonstrate significantly stronger positive effects compared to SOE-to-SOE M&As. This enhancement is most notable when control rights are transferred, when acquiring SOEs possess high R&D investment but lower production efficiency, and in regions with less developed markets. The primary mechanism appears to be improved corporate governance through increased private shareholder involvement in strategic decision-making. These findings advance our understanding of how ownership differences influence innovation in M&As while providing practical guidance for SOE reform policies in China and similar emerging economies.

A novel deep learning model based on YOLOv5 optimal method for coal gangue image recognition

Facilitators, barriers, and strategies for the implementation of peer-led tuberculosis active case finding among people who use drugs in Dar es Salaam, Tanzania

Background Globally, tuberculosis (TB) is the leading cause of death from a single infectious agent. In 2023, an estimated 2.7 million cases of TB were undiagnosed or unreported. To address missing cases, the World Health Organization recommends systematic screening for TB. This is synonymous to active case finding (ACF) and involves provider-initiated screening and testing for TB. Despite the high incidence and prevalence of TB among people who use drugs (PWUD), there is a significant gap in data, on their perspectives, regarding the implementation of TB ACF services. This study aimed to explore facilitators and barriers to implementing peer-led TB ACF, as perceived by both, current and potential service users. Methods We conducted in-depth interviews among purposively selected adult PWUD in Dar-es-Salaam region, Tanzania. Study participants included: (1) peer PWUD with prior history of illicit drug use and medication-assisted treatment (MAT) (n = 10), (2) current medication-assisted treatment service users receiving clinic-based daily methadone (n = 8), and (3) community PWUD not on MAT recruited from various community locations (n = 4). All peer PWUD were experienced in TB ACF. Thematic content analysis was utilized with the support of NVivo12. Results Our findings are presented into two categories: individual and structural, with three main themes pertaining to peer-led TB ACF: (1) facilitators (2) facilitators for targeted improvement and optimization and (3) barriers. A critical facilitator was the acceptability of peer PWUD in providing TB ACF services. Key facilitators for targeted improvement and optimization included the TB screening tool, mobile TB diagnostic services, integrated methadone/TB services, and monetary incentives to peer PWUD. Barriers included inadequate adherence to infection prevention and control (IPC) measures when providing TB ACF services resulting in a reluctance to wear face masks due to stigma, misconceptions that prior TB preventive therapy among peers negates their need for continued IPC adherence, high mobility of PWUD and the fear of withdrawal symptoms associated with the use of anti-TB medication. Due to this fear, many PWUD preferred not to take anti-TB, as they were concerned about the potential severity of withdrawal symptoms. Conclusion: Our findings highlight the crucial role of peer-led approaches in enhancing TB ACF among PWUD. Peer acceptance as service providers highlights the potential of community-driven interventions. Strengthening facilitators and addressing challenges is key to optimizing these services. Future research should explore the feasibility of providing peer-supported TB diagnosis and treatment services at friendly drop-in centers. Recommendations: 1. Strengthen mobile diagnostic services by increasing their frequency and coverage, enabling timely diagnosis and treatment. 2. Enhance the TB symptom screening tool by including a symptom-independent test, such as a chest X-ray, as the symptoms of illicit drug use can mask and mimic TB symptoms making diagnosis challenging. 3. Address stigma and misconceptions through peer-led education and awareness campaigns that utilize audio-visual materials tailored to PWUD. This will promote adherence to IPC measures and create a more supportive environment for TB ACF activities. 4. Use anti-TB with minimal interactions with opiates or shorter TB treatment regimens to prevent withdrawal symptoms and improve adherence.

Comparative analysis of adverse event profiles of lanreotide and octreotide in somatostatin-responsive endocrine and neoplastic diseases

Integration of bioinformatics and identification of the role of m6A genes in NAFLD

Non-alcoholic fatty liver disease (NAFLD) is prevalent worldwide and seriously affects health. M6A methylation is crucial in its pathogenesis. In this study, a thorough analysis of three gene expression datasets identified nine key differentially expressed genes DEGs associated with m6A methylation in NAFLD that are involved in important biological processes. Subsequently, functional enrichment analysis, weighted gene co-expression network analysis (WGCNA), gene set variation analysis (GSVA) and immune infiltration analysis were conducted to explore the molecular mechanism and gene expression patterns. The LASSO risk model contains a total of 5 m6A-related differentially expressed genes (m6A-RDEGs)(RBM15, IGF2BP2, EIF3B, YTHDC1, WTAP), and the diagnostic model based on these key genes has high accuracy. Among them, YTHDC1 and WTAP are used as prominent biomarkers. In addition, an interaction network between mRNA and miRNA, RNA-binding protein (RBP), transcription factor (TF) and drugs is also constructed. Finally, the animal model of NAFLD was successfully established and validated by RT-qPCR and western blot. This study provides a valuable tool for clinical diagnosis and drives the progress of NAFLD research.

Shipboard observational evidence of supercooled liquid water clouds in the mid-troposphere over the Southern Ocean

Odor classification: Exploring feature performance and imbalanced data learning techniques

This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with molecular fingerprints, mass spectra, and vibrational spectra; however, the influence of the selected representation method on predictive performance is inadequately documented in direct comparative studies. To fill this gap, we assembled a large novel dataset of 2606 molecules with three kinds of features: mass spectra (MS), vibrational spectra (VS) and molecular fingerprint features (FP). We evaluated their performance using four different multi-label classification models. The second objective is to address an inherent challenge in odor classification multi-label datasets (MLD)—the issue of class imbalance by random resampling techniques and an explainable, cost-sensitive multilayer perceptron model (CSMLP). Experimental results suggest significantly better performance of the molecular fingerprint-based features compared with mass and vibrational spectra with the micro-averaged F1 evaluation metric. The proposed resampling techniques and cost-sensitive model outperform the results of previous studies. We also report the predictive performance of multimodal features obtained by fusing the three mentioned features. This comprehensive and systematic study compares the predictive performance for odor classification of different features and utilises a multifaceted approach to deal with data imbalance. Our explainable model sheds further light on features and odour relations. The results hold the potential to guide the development of the electric nose and our dataset will be made publicly available.

Quantum features of gravcats

Wastewater surveillance as a predictive tool for COVID-19: A case study in Chengdu

Objective This study was conducted to enhance conventional epidemiological surveillance by implementing city-wide wastewater monitoring of SARS-CoV-2 RNA. The research aimed to develop a quantitative model for estimating infection rates and to compare these predictions with clinical case data. Furthermore, this wastewater surveillance was utilized as an early warning system for potential COVID-19 outbreaks during a large international event, the Chengdu 2023 FISU Games. Methods This study employed wastewater based epidemiology (WBE), utilizing samples collected twice a week from nine wastewater treatment plants that serve 66.1% of Chengdu’s residents, totaling 15.2 million people. The samples were collected between January 18, 2023, and June 15, 2023, and were tested for SARS-CoV-2 RNA. A model employed back-calculation of SARS-CoV-2 infections by integrating wastewater viral load measurements with human fecal and urinary shedding rates, as well as population size estimates derived from NH4-N concentrations, utilizing Monte Carlo simulations to quantify uncertainty. The model’s predictions compared with the number of registered cases identified by the Nucleic Acid Testing Platform of Chengdu during the same period. Additionally, we conducted sampling from two manholes in the wastewater pipeline, which encompassed all residents of the Chengdu 2023 FISU World University Games village, and tested for SARS-CoV-2 RNA. We also gathered data on COVID-19 cases from the symptom monitoring system between July 20 and August 11. Results From the third week to the twenty-fourth week of 2023, the weekly median concentration of SARS-CoV-2 RNA fluctuated, starting at 16.94 copies/ml in the third week, decreasing to 1.62 copies/ml by the fifteenth week, then gradually rising to a peak of 41.27 copies/ml in the twentieth week, before ultimately declining to 8.74 copies/ml by the twenty-fourth week. During this period, the number of weekly new cases exhibited a similar trend, and the results indicated a significant correlation between the viral concentration and the number of weekly new cases (spearman’s r = 0.93, P < 0.001). The quantitative wastewater surveillance model estimated that approximately 2,258,245 individuals (P5-P95: 847,869 - 3,928,127) potentially contracted COVID-19 during the epidemic wave from March 4th to June 15th, which is roughly 33 times the number of registered cases (68,190 cases) reported on the Nucleic Acid Testing Platform. Furthermore, the infection rates of SARS-CoV-2, as estimated by the model, ranged from 0.012% (P5-P95: 0.004% - 0.020%) at the lowest baseline to 3.27% (P5-P95: 1.23% - 5.69%) at the peak of the epidemic, with 15.1% (P5-P95: 5.65% - 26.2%) of individuals infected during the epidemic wave between March 4th and June 15th. Additionally, we did not observe any COVID-19 outbreaks or cluster infections at the Chengdu 2023 FISU World University Games village, and there was no significant difference in the concentrations of SARS-CoV-2 in athletes before and after check-in at the village. Conclusions This study demonstrates the effectiveness of wastewater surveillance as a long-term sentinel approach for monitoring SARS-CoV-2 and providing early warnings for COVID-19 outbreaks during large international events. This method significantly enhances traditional epidemiological surveillance. The quantitative wastewater surveillance model offers a reliable means of estimating the number of infected individuals, which can be instrumental in informing policy decisions.

Functional group characteristics of coal treated with clean biomass surfactant via FTIR spectroscopy

The antiangiogenic peptide VIAN-c4551 inhibits lung melanoma metastasis in mice by reducing pulmonary vascular permeability

Introduction Cancer cells drive the increase in vascular permeability mediating tumor cell extravasation and metastatic seeding. VIAN-c4551, an antiangiogenic peptide analog of vasoinhibin, inhibits the growth and vascularization of melanoma tumors in mice. Because VIAN-c4551 is a potent inhibitor of vascular permeability, we evaluated whether its antitumor action extended to a reduction in metastasis generation. Methods Circulating levels of vascular endothelial growth factor (VEGF), lung vascular permeability, melanoma cell extravasation, and melanoma pulmonary nodules were assessed in C57BL/6J mice intravenously inoculated with murine melanoma B16-F10 cells after acute treatment with VIAN-c4551. VEGF levels, transendothelial electrical resistance, and transendothelial migration in cocultures of B16-F10 cells and endothelial cell monolayers supported the findings. Results B16-F10 cells increased circulating VEGF levels and elevated lung vascular permeability 2 hours after inoculation. VIAN-c4551 prevented enhanced vascular permeability and reduced melanoma cell extravasation after 2 hours and the number and size of macroscopic and microscopic melanoma tumors in lungs after 17 days. In vitro, VIAN-c4551 suppressed the B16-F10 cell-induced and VEGF mediated increase in endothelial cell monolayer permeability and the transendothelial migration of B16-F10 cells. No detrimental effect of VIAN-c4551 was observed on hematological, biochemical, and histological parameters after its intravenous administration in mice for 14 days. Conclusions These findings support the inhibition of distant vascular permeability for the prevention of tumor metastasis and unveil the anti-vascular permeability factor VIAN-c4551 as a potential and safe therapeutic drug able to prevent metastasis generation by lowering the extravasation of melanoma cells.

Elimination of certain honeybee venom activities by adipokinetic hormone

The impact of signal variability on COVID-19 epidemic growth rate estimation from wastewater surveillance data

Testing samples of wastewater for markers of infectious disease became a widespread method of surveillance during the COVID-19 pandemic. While these data generally correlate well with other indicators of national prevalence, samples that cover localised regions tend to be highly variable over short time scales. Here we introduce a procedure for estimating the real-time growth rate of pathogen prevalence using time series data from wastewater sampling. The number of copies of a target gene found in a sample is modelled as time-dependent random variable whose distribution is estimated using maximum likelihood. The output depends on a hyperparameter that controls the sensitivity to variability in the underlying data. We apply this procedure to data reporting the number of copies of the N1 gene of SARS-CoV-2 collected at water treatment works across Scotland between February 2021 and February 2023. The real-time growth rate of the SARS-CoV-2 prevalence is estimated at all 121 wastewater sampling sites covering a diverse range of locations and population sizes. We find that the sensitivity of the fitting procedure to natural variability determines its reliability in detecting the early stages of an epidemic wave. Applying the same procedure to hospital admissions data, we find that changes in the growth rate are detected an average of 2 days earlier in wastewater than in hospital admissions. In conclusion, this paper provides a robust method to generate reliable estimates of epidemic growth from highly variable data. Applying this method to samples collected at wastewater treatment works provides highly responsive situational awareness to inform public health.

Publisher Correction: Advancing non-destructive sex determination on human dental enamel using Raman spectroscopy

Prevalence of SARS-CoV-2 infection and immunity in a New York county in 2022 reveals frequent asymptomatic or undiagnosed infections

Accurate and timely surveillance of SARS-CoV-2 prevalence and immunity is critical to local and national COVID-19 pandemic responses. Representative surveillance surveys reveal more accurate estimates of COVID-19 infection than other measures based on reported test results. Our main research objectives were (i) to provide local health department officials with prevalence estimates calculated from a representative sample to better inform their decision-making efforts in response to the COVID-19 pandemic and (ii) to identify characteristics associated with COVID-19 infections among high-risk groups. Three municipalities were sampled at one timepoint (February, April, or October 2022) using a 2-stage cluster sampling design. Participants provided anterior nares swabs, which were tested for SARS-CoV-2 with a RT-PCR and for nucleocapsid protein and receptor binding domain antibodies by multiplex Luminex assay. Participants completed a survey on socio-demographics, SARS-CoV-2 prevention behaviors and attitudes, and vaccination and infection history. A total of 233 individuals from 221 households provided anterior nares swabs, and 215 samples were linked to survey data. After adjusting for study design, the household prevalence of PCR-positive tests was less than 5%, but approximately half of the population had antibodies from a prior infection and most (81% to 92%) had antibodies from either infection or vaccination. Discrepancies between self-reported positive test and vaccination status and antibody results suggested a high prevalence of asymptomatic infection and waning antibody titers. County-level infection prevalences, estimated from the county test reporting system, were 16.6% in February, 19.1% in April, and 23.8% in October, substantially lower than the prevalence of individuals with antibodies from infection in the surveys, also supporting a high prevalence of asymptomatic or unconfirmed infections. The overall small sample size precluded an analysis of characteristics associated with active or past infection. In conclusion, surveillance surveys can provide timely data on infection status and immunity to support public health responses.

An integrated approach using morphological, biochemical, and RAPD markers to assess the genetic diversity of Olive (Olea Europaea L.) cultivars in India

Artificial intelligence for contextual well-being: Protocol for an exploratory sequential mixed methods study with medical students as a social microcosm

Introduction AI-powered conversational agents have proven effective in alleviating psychological distress, however, concerns about autonomy and authentic psychological development remain, especially in youth during critical stages of identity and resilience formation. Despite the increasing use of AI technologies, there is a significant gap in well-being literacy within educational systems. This gap leaves young adults ill-prepared to navigate the complexities of real world challenges, contributing to rising rates of anxiety, stress, and depression. Furthermore, the lack of AI literacy can exacerbate psychological distress, negatively impacting academic performance and overall well-being. As young adults actively engage with AI, efforts should focus not on resisting technological progress but on fostering their development as users who are capable, aware, and ethical in addressing their contextual well-being needs. This study aims to extend the understanding of the factors influencing well-being and determine how to harness artificial intelligence for contextual well-being from a human-centred perspective. Methods and analysis The research is an exploratory sequential mixed methods Study, combining semi-structured interviews and an electronic Delphi study (eDelphi) to gather insights for consensus building. The study adopts a pragmatism paradigm with a foresight approach, ideal for addressing the dynamic, evolving intersection of AI and well-being. Interpretative phenomenological analysis, reflective thematic analysis and descriptive statistical analysis will be used accordingly. Medical students (aged 18–30) were selected as a social microcosm study cohort representing youth. Ethics and dissemination This study was approved by the University College Dublin Human Research Ethics Committee (HREC); Reference: LS-C-24-375-Xie-Cullen.The outcomes of the study will be communicated through publications in peer-reviewed journals, presentations at academic conferences.