Evaluation of reference genes suitable for studying mRNAs and microRNAs expression in thyroid neoplasms.

Analysis of gene expression is a pivotal method at the core of biomarkers studies and cancer research. Currently, RT-qPCR is the most commonly used technique to investigate the expression of certain genes. The accurate and reliable result relies on an effective normalization step using suitable reference genes. The present study was designed to evaluate the eligibility of a set of candidate mRNAs and snoRNA as reference genes in the most common human thyroid neoplasms. We tested the expression levels of eleven mRNA and small RNA housekeeping genes in thyroid samples. The stability of the candidate genes was examined in different thyroid lesions and under different experimental conditions. Results were compared to the reported data in the TCGA database. Our results suggested HPRT1 and ACTB as the best mRNA reference genes, SNORD96A, and SNORD95 as the best miRNA reference genes in thyroid tissues. These genes showed the most stable expression pattern among different thyroid lesions as well as different experimental conditions. The findings in this study highlight the effect of reference genes selection on data interpretation and emphasize the importance of testing for suitable reference genes to be used in specific types of cells and experimental conditions to ensure the validity and accuracy of results.

Whole-liver histogram analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging for predicting progression in patients with cirrhosis.

Background: Liver cirrhosis, as the terminal phase of chronic liver disease fibrosis, is associated with high morbidity and mortality. Traditional methods for assessing liver function, such as clinical scoring systems, offer only a global evaluation and may not accurately reflect regional liver function variations. This study aimed at evaluating the diagnostic potential of whole-liver histogram analysis of gadobenate dimeglumine (Gd-BOPTA)-enhanced magnetic resonance imaging (MRI) for predicting the progression of cirrhosis. Methods: In this retrospective study, 265 consecutive patients with cirrhosis admitted to the Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University from August 2012 to September 2019 were enrolled. After the exclusion criteria were applied, 117 patients (84 males and 33 females) were divided into Child-Pugh A cirrhosis (n=43), Child-Pugh B cirrhosis (n=49), and Child-Pugh C cirrhosis (n=25). After correction for liver signal intensity with the spleen was completed, 19 histogram features of the whole liver were extracted and modeled to evaluate liver function, with the Child-Pugh class being incorporated as a clinical parameter. Receiver operating characteristic (ROC) curves were used to assess the diagnosis capability and determine the optimal cutoffs after a mean follow-up of 42.3±19.1 (range, 8-93) months. The association between significant histogram features and the cumulative incidence of hepatic insufficiency was analyzed with the adjusted Kaplan-Meier curve model. Results: Among 117 patients (12%), 14 developed hepatic insufficiency through a period of follow-up. Five features, including the median (P<0.01), 90th percentile (P<0.01), root mean squared (P<0.01), mean (P<0.01), and 10th percentile (P<0.05), were significantly different between the groups with and without hepatic insufficiency according to the Kruskal-Wallis test; in the ROC curve analysis, the area under the curve (AUC) of these features was 0.723 [95% confidence interval (CI): 0.653-0.793], 0.722 (95% CI: 0.652-0.792), 0.722 (95% CI: 0.652-0.792), 0.721 (95% CI: 0.651-0.791), and 0.674 (95% CI: 0.600-0.748) after correction, respectively (all P values <0.05). Median, 90th percentile, root mean squared, and mean were found to be significant factors in predicting liver insufficiency. The adjusted Kaplan-Meier curves revealed that patients with a feature level less than the cutoff, as compared to those with a level above the cutoff, showed a statistically shorter progression-free survival and higher incidences of hepatic insufficiency for significant features of median (cutoff =26.001; 21.28% versus 5.71%; P=0.02), 90th percentile (cutoff =86.263; 20.41% versus 5.88%; P<0.01), root mean squared (cutoff =1,028.477; 19.15% versus 7.14%; P=0.049), and mean (cutoff =27.484; 19.15% versus 7.14%; P=0.049). Patients with a 10th percentile less than -39.811 also showed a higher cumulative incidence of hepatic insufficiency than did those with a value higher than the cutoff (0.18% versus 7.46%; P=0.22). Conclusions: Whole-liver histogram analysis of Gd-BOPTA-enhanced MRI may serve as a noninvasive analytical method to predict hepatic insufficiency in patients with cirrhosis.

Deep learning based method for predicting DNA N6-methyladenosine sites.

DNA N6 methyladenine (6mA) plays an important role in many biological processes, and accurately identifying its sites helps one to understand its biological effects more comprehensively. Previous traditional experimental methods are very labor-intensive and traditional machine learning methods also seem to be somewhat insufficient as the database of 6mA methylation groups becomes progressively larger, so we propose a deep learning-based method called multi-scale convolutional model based on global response normalization (CG6mA) to solve the prediction problem of 6mA site. This method is tested with other methods on three different kinds of benchmark datasets, and the results show that our model can get more excellent prediction results.

Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models.

BACKGROUND: Vaccines have revolutionized public health by providing protection against infectious diseases. They stimulate the immune system and generate memory cells to defend against targeted diseases. Clinical trials evaluate vaccine performance, including dosage, administration routes, and potential side effects. CLINICALTRIALS: gov is a valuable repository of clinical trial information, but the vaccine data in them lacks standardization, leading to challenges in automatic concept mapping, vaccine-related knowledge development, evidence-based decision-making, and vaccine surveillance. RESULTS: In this study, we developed a cascaded framework that capitalized on multiple domain knowledge sources, including clinical trials, the Unified Medical Language System (UMLS), and the Vaccine Ontology (VO), to enhance the performance of domain-specific language models for automated mapping of VO from clinical trials. The Vaccine Ontology (VO) is a community-based ontology that was developed to promote vaccine data standardization, integration, and computer-assisted reasoning. Our methodology involved extracting and annotating data from various sources. We then performed pre-training on the PubMedBERT model, leading to the development of CTPubMedBERT. Subsequently, we enhanced CTPubMedBERT by incorporating SAPBERT, which was pretrained using the UMLS, resulting in CTPubMedBERT + SAPBERT. Further refinement was accomplished through fine-tuning using the Vaccine Ontology corpus and vaccine data from clinical trials, yielding the CTPubMedBERT + SAPBERT + VO model. Finally, we utilized a collection of pre-trained models, along with the weighted rule-based ensemble approach, to normalize the vaccine corpus and improve the accuracy of the process. The ranking process in concept normalization involves prioritizing and ordering potential concepts to identify the most suitable match for a given context. We conducted a ranking of the Top 10 concepts, and our experimental results demonstrate that our proposed cascaded framework consistently outperformed existing effective baselines on vaccine mapping, achieving 71.8% on top 1 candidate's accuracy and 90.0% on top 10 candidate's accuracy. CONCLUSION: This study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials.

Factors Influencing the Implementation of Digital Advance Care Planning: Qualitative Interview Study.

BACKGROUND: Palliative care aims to improve the quality of life for people with life-limiting illnesses. Advance care planning conversations that establish a patient's wishes and preferences for care are part of a person-centered approach. Internationally, electronic health record systems are digital interventions used to record and share patients' advance care plans across health care services and settings. They aim to provide tools that support electronic information sharing and care coordination. Within the United Kingdom, Electronic Palliative Care Coordination Systems (EPaCCS) are an example of this. Despite over a decade of policy promoting EPaCCS nationally, there has been limited implementation and consistently low levels of use by health professionals. OBJECTIVE: The aim of this study is to explore the factors that influence the implementation of EPaCCS into routine clinical practice across different care services and settings in 2 major regions of England. METHODS: A qualitative interview study design was used, guided by Normalization Process Theory (NPT). NPT explores factors affecting the implementation of complex interventions and consists of 4 primary components (coherence, cognitive participation, collective action, and reflexive monitoring). Health care and social care practitioners were purposively sampled based on their professional role and work setting. Individual web-based semistructured interviews were conducted. Data were analyzed using thematic framework analysis to explore issues which affected the implementation of EPaCCS across different settings at individual, team, organizational, and technical levels. RESULTS: Participants (N=52) representing a range of professional roles were recruited across 6 care settings (hospice, primary care, care home, hospital, ambulatory, and community). In total, 6 themes were developed which mapped onto the 4 primary components of NPT and represented the multilevel influences affecting implementation. At an individual level, these included (1) EPaCCS providing a clear and distinct way of working and (2) collective contributions and buy-in. At a team and organizational level, these included (3) embedding EPaCCS into everyday practice and (4) championing driving implementation. At a technical level, these included (5) electronic functionality, interoperability, and access. Breakdowns in implementation at different levels led to variations in (6) confidence and trust in EPaCCS in terms of record accuracy and availability of access. CONCLUSIONS: EPaCCS implementation is influenced by individual, organizational, and technical factors. Key challenges include problems with access alongside inconsistent use and engagement across care settings. EPaCCS, in their current format as digital advance care planning systems are not consistently facilitating electronic information sharing and care coordination. A redesign of EPaCCS is likely to be necessary to determine configurations for their optimal implementation across different settings and locations. This includes supporting health care practitioners to document, access, use, and share information across multiple care settings. Lessons learned are relevant to other forms of digital advance care planning approaches being developed internationally.

Impacts of the Chengdu 2021 world university games on NO2 pollution: Implications for urban vehicle electrification promotion.

Emissions of nitrogen oxides (NOx) are a dominant contributor to ambient nitrogen dioxide (NO2) concentrations, but the quantitative relationship between them at an intracity scale remains elusive. The Chengdu 2021 FISU World University Games (July 22 to August 10, 2023) was the first world-class multisport event in China after the COVID-19 pandemic which led to a substantial decline in NOx emissions in Chengdu. This study evaluated the impact of variations in NOx emissions on NO2 concentrations at a fine spatiotemporal scale by leveraging this event-driven experiment. Based on ground-based and satellite observations, we developed a data-driven approach to estimate full-coverage hourly NO2 concentrations at 1 km resolution. Then, a random-forest-based meteorological normalization method was applied to decouple the impact of meteorological conditions on NO2 concentrations for every grid cell, the resulting data were then compared with the timely bottom-up NOx emissions. The SHapley-Additive-exPlanation (SHAP) method was employed to delineate the individual contributions of meteorological factors and various emission sources to the changes in NO2 concentrations. According to the full-coverage meteorologically normalized NO2 concentrations, a decrease in NOx emissions and favorable meteorological conditions accounted for 80 % and 20 % of the NO2 reduction, respectively, across Chengdu city during the control period. Within the strict control zone, a 30 % decrease in the meteorologically normalized NO2 concentrations was observed during the control period. The normalized NO2 concentrations demonstrated a strong correlation with NOx emissions (R = 0.96). Based on the SHAP analysis, traffic emissions accounted for 73 % of the reduction in NO2 concentrations, underscoring the significance of traffic control measures in improving air quality in urban areas. This study provides insights into the relationship between NO2 concentrations and NOx emissions using real-world data, which implies the substantial benefits of vehicle electrification for sustainable urban development.

Gait phase normalization resolves the problem of different phases being compared in gait cycle normalization.

For time-continuous analysis of gait, the problem of variations in cycle durations is resolved by normalizing to the gait cycle, but results depend on the definition of the cycle start. Gait cycle normalization ignores variations in gait phase durations, which results in averaging and comparing data across different phases. We propose gait phase normalization as part of a comprehensive method for independently analyzing magnitude and timing differences. First, gait phases are identified and differences in absolute and/or relative timing of phase durations or any point of interest between conditions or groups are analyzed using standard statistics. Next, time-continuous gait data is normalized to gait phases, and statistical parametric mapping (SPM) is used to assess magnitude differences in gait data. This approach is demonstrated on data recorded from ten young healthy adults walking on a treadmill at five different speeds. Sagittal knee angle was normalized to gait cycle or gait phase using five different gait cycle start events. Walking at different speeds resulted in significant changes in gait phase durations, highlighting a problem ignored by gait cycle normalization. SPM results for knee angle normalized to gait cycle varied from normalization to gait phases. Gait phase normalized SPM results were robust to the definition of the cycle start, in contrast to gait cycle normalized data. The approach of analyzing phase durations and normalizing data to gait phases overcomes previous limitations and enables a comprehensive analysis of magnitude and timing differences in time-continuous gait data and could be readily adapted to other tasks.

Quantitative evaluation of striatal uptake ratios using an adaptive template registration method for 123I-ioflupane dopamine transporter SPECT.

INTRODUCTION: 123I-FP-CIT (123I-Ioflupane) SPECT shows strong accumulation in the striatum, but morphological standardization is challenging due to low accumulation outside the striatum, particularly in subjects with marked striatal decline. In this study, morphological standardization without MRI was achieved using the adaptive template registration (ATR) method to create a subject-specific optimized template with weighted images of normal-type and egg-shape-type templates. The accuracy of a quantitative method for calculating the ratio with nonspecific accumulation in the occipital lobe was evaluated by placing voxels-of-interest (VOI) on standardized images, particularly targeting the striatum. METHODS: The average images of eight subjects, demonstrating normal-type and egg-shape-type tracer accumulation in 123I-Ioflupane SPECT, were utilized as normal and disease templates, respectively. The study included 300 subjects that underwent both 123I-Ioflupane SPECT and MRI for the diagnosis of suspected Parkinson's disease or for exclusion diagnosis. Morphological standardization of SPECT images using structural MRI (MRI-based method) was considered the standard of truth (SOT). Three morphological standardizations without MRI were conducted. The first involved conventional morphological standardization using a normal template (fixed template method), the second employed the ATR method, with a weighted template, and the third used the split-ATR method, processing the left and right striatum separately to address asymmetrical accumulation. VOIs were set on the striatum, caudate, putamen as regions of specific accumulation, and on the occipital lobe as a reference region for nonspecific accumulation. RESULTS: Results showed significant and robust linearity in the striatal accumulation ratios for all templates when compared with the occipital lobe accumulation ratio when using the MRI-based method. Comparing intra-class correlations for different linearities, the ATR method and split-ATR method demonstrated higher linearity in the striatum, caudate, and putamen. The split-ATR method showed similar improvements, although more linearity than some of the ATR methods; the effectiveness of the Split-ATR method may vary by image quality, and further validation of its effectiveness in diverse asymmetric accumulation cases seemed warranted. CONCLUSION: The use of optimized templates, such as the ATR and split-ATR methods, improved reproducibility in fully automated processing and demonstrated superior linearity compared to that of MRI-based method, in the ratio to the occipital lobe. The ATR method, which enables morphological standardization when using SPECT images only, proved highly reproducible for clinical quantitative analysis of striatal accumulation, facilitating its clinical use.

Identifying high-risk undifferentiated emergency department patients with hyperlactatemia: Predictors of 30-day in-hospital mortality.

BACKGROUND: Hyperlactatemia has been recognized as a significant prognostic indicator in critically ill patients. Nonetheless, there remains a gap in understanding the specific risk factors contributing to increased mortality among undifferentiated emergency department (ED) patients presenting with elevated lactate levels. OBJECTIVES: The objective of the study is to investigate potential risk factors for 30-day in-hospital mortality in ED patients with hyperlactatemia. METHODS: All nontraumatic adult presentations to the ED who had a lactate level of ≥2.5 mmol/L were included. Comorbidities, vital signs, lactate levels, lactate clearance, lactate normalization, and final diagnosis were compared with 30-day in-hospital mortality. RESULTS: A 30-day in-hospital mortality rate of 10.4% was observed in 979 patients. The mortality rate was higher in hypotensive patients (odds ratio [OR] 4.973), in nursing home patients (OR 5.689), and bedridden patients (OR 3.879). The area under the curve for the second lactate level (0.804) was higher than the first lactate level (0.691), and lactate clearance (0.747) for in-hospital mortality. A second lactate level >3.15 mmol/l had a sensitivity of 81.3% in predicting in-hospital mortality. The OR for mortality was 6.679 in patients without lactate normalization. A higher mortality rate was observed in patients with acute renal failure (OR 4.305), septic shock (OR 4.110), and acute coronary syndrome (OR 2.303). CONCLUSIONS: A second lactate measurement more accurately predicts in-hospital mortality than lactate clearance and the first lactate level in ED patients. Nursing home patients, bed-ridden patients, hypotensive patients on initial ED presentation, patients without lactate normalization, and patients with a final diagnosis of acute renal failure, septic shock, and acute coronary syndrome had a higher mortality rate.

Normalization of viral loads in Wastewater-Based Epidemiology using routine parameters: One year monitoring of SARS-CoV-2 in urban and tourist sewersheds.

In wastewater-based epidemiology, normalization of experimental data is a crucial aspect, as emerged in the recent surveillance of COVID-19. Normalization facilitates the comparison between different areas or periods, and it helps in evaluating the differences due to the fluctuation of the population due to seasonal employment or tourism. Analysis of biomarkers in wastewater (i.e. drugs, beverage and food compounds, microorganisms such as PMMoV or crAssphage, etc.) is complex to perform, and it is not routinely monitored. This study compares the results of alternative normalization approaches applied to SARS-CoV-2 loads in wastewater using population size calculated with conventional hydraulic and/or chemical parameters (i.e. total suspended solids, chemical oxygen demand, nitrogen forms, etc.) commonly used in the routine monitoring of water quality. A total of 12 wastewater treatment plants were monitored, and 1068 samples of influent wastewater were collected in urban areas and in highly touristic areas (summer and/or winter). The results indicated that both census and population estimated with ammonium are effective and reliable parameters with which to normalize SARS-CoV-2 loads in wastewater from urban sewersheds with negligible fluctuating populations. However, this study reveals that, in the case of tourist locations, the population calculated using NH4-N loads can provide a better normalization of the specific viral load per inhabitant.

A Self-Catalytic NO/O2 Gas-Releasing Nanozyme for Radiotherapy Sensitization through Vascular Normalization and Hypoxia Relief.

Radiotherapy (RT), essential for treating various cancers, faces challenges from tumor hypoxia, which induces radioresistance. A tumor-targeted "prosthetic-Arginine" coassembled nanozyme system, engineered to catalytically generate nitric oxide (NO) and oxygen (O2) in the tumor microenvironment (TME), overcoming hypoxia and enhancing radiosensitivity is presented. This system integrates the prosthetic heme of nitric oxide synthase (NOS) and catalase (CAT) with NO-donating Fmoc-protected Arginine and Ru3+ ions, creating HRRu nanozymes that merge NOS and CAT functionalities. Surface modification with human heavy chain ferritin (HFn) improves the targeting ability of nanozymes (HRRu-HFn) to tumor tissues. In the TME, strategic arginine incorporation within the nanozyme allows autonomous O2 and NO release, triggered by endogenous hydrogen peroxide, elevating NO and O2 levels to normalize vasculature and improve blood perfusion, thus mitigating hypoxia. Employing the intrinsic O2-transporting ability of heme, HRRu-HFn nanozymes also deliver O2 directly to the tumor site. Utilizing esophageal squamous cell carcinoma as a tumor model, the studies reveal that the synergistic functions of NO and O2 production, alongside targeted delivery, enable the HRRu-HFn nanozymes to combat tumor hypoxia and potentiate radiotherapy. This HRRu-HFn nanozyme based approach holds the potential to reduce the radiation dose required and minimize side effects associated with conventional radiotherapy.

Estimating visibility and understanding factors influencing its variations at Bangkok airport using machine learning and a game theory-based approach.

In this study, six individual machine learning (ML) models and a stacked ensemble model (SEM) were used for daytime visibility estimation at Bangkok airport during the dry season (November-April) for 2017-2022. The individual ML models are random forest, adaptive boosting, gradient boosting, extreme gradient boosting, light gradient boosting machine, and cat boosting. The SEM was developed by the combination of outputs from the individual models. Furthermore, the impact of factors affecting visibility was examined using the Shapley Additive exPlanation (SHAP) method, an interpretable ML technique inspired by the game theory-based approach. The predictor variables include different air pollutants, meteorological variables, and time-related variables. The light gradient boosting machine model is identified as the most effective individual ML model. On an hourly time scale, it showed the best performance across three out of four metrics with the ρ = 0.86, MB = 0, ME = 0.48 km (second lowest), and RMSE = 0.8 km. On a daily time scale, the model performed the best for all evaluation metrics with ρ = 0.92, MB = 0.0 km, ME = 0.3 km, and RMSE = 0.43 km. The SEM outperformed all the individual models across three out of four metrics on an hourly time scale with ρ = 0.88, MB = 0.0 km, (second lowest), and RMSE = 0.75 km. On the daily scale, it performed the best with ρ = 0.93, MB = 0.02 km, ME = 0.27 km, and RMSE = 0.4 km. The seasonal average original (VISorig) and meteorologically normalized visibility (VISnorm) decrease from 2017 to 2021 but increase in 2022. The rate of decrease in VISorig is double than rate of decrease in VISnorm which suggests the effect of meteorology visibility degradation. The SHAP analysis identified relative humidity (RH), PM2.5, PM10, day of the season year (i.e., Julian day) (JD), and O3 as the most important variables affecting visibility. At low RH, visibility is not sensitive to changes in RH. However, beyond a threshold, a negative correlation between RH and visibility is found potentially due to the hygroscopic growth of aerosols. The dependence of the Shapley values of PM2.5 and PM10 on RH and the change in average visibilities under different RH intervals also suggest the effect of hygroscopic growth of aerosol on visibility. A negative relationship has been identified between visibility and both PM2.5 and PM10. Visibility is positively correlated with O3 at lower to moderate concentrations, with diminishing impact at very high concentrations. The JD is strongly negatively related to visibility during winter while weakly associated positively later in summer. Findings from this research suggest the feasibility of employing machine learning techniques for predicting visibility and understanding the factors influencing its fluctuations.

Effectiveness and safety of tenofovir amibufenamide in chronic hepatitis B patients.

This letter to the editor relates to the study entitled "Tenofovir amibufenamide vs tenofovir alafenamide for treating chronic hepatitis B: A real-world study", which was recently published by Peng et al. Hepatitis B virus infection represents a significant health burden worldwide and can lead to cirrhosis and even liver cancer. The antiviral drugs currently used to treat patients with chronic hepatitis B infection still have many side effects, so it is crucial to identify safe and effective drugs to inhibit viral replication.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.

Introduction: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. Methods: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. Results: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Conclusion: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Understanding the challenges of identifying, supporting, and signposting patients with alcohol use disorder in secondary care hospitals, post COVID-19: a qualitative analysis from the North East and North Cumbria, England.

BACKGROUND: Alcohol-related mortality and morbidity increased during the COVID-19 pandemic in England, with people from lower-socioeconomic groups disproportionately affected. The North East and North Cumbria (NENC) region has high levels of deprivation and the highest rates of alcohol-related harm in England. Consequently, there is an urgent need for the implementation of evidence-based preventative approaches such as identifying people at risk of alcohol harm and providing them with appropriate support. Non-alcohol specialist secondary care clinicians could play a key role in delivering these interventions, but current implementation remains limited. In this study we aimed to explore current practices and challenges around identifying, supporting, and signposting patients with Alcohol Use Disorder (AUD) in secondary care hospitals in the NENC through the accounts of staff in the post COVID-19 context. METHODS: Semi-structured qualitative interviews were conducted with 30 non-alcohol specialist staff (10 doctors, 20 nurses) in eight secondary care hospitals across the NENC between June and October 2021. Data were analysed inductively and deductively to identify key codes and themes, with Normalisation Process Theory (NPT) then used to structure the findings. RESULTS: Findings were grouped using the NPT domains 'implementation contexts' and 'implementation mechanisms'. The following implementation contexts were identified as key factors limiting the implementation of alcohol prevention work: poverty which has been exacerbated by COVID-19 and the prioritisation of acute presentations (negotiating capacity); structural stigma (strategic intentions); and relational stigma (reframing organisational logics). Implementation mechanisms identified as barriers were: workforce knowledge and skills (cognitive participation); the perception that other departments and roles were better placed to deliver this preventative work than their own (collective action); and the perceived futility and negative feedback cycle (reflexive monitoring). CONCLUSIONS: COVID-19, has generated additional challenges to identifying, supporting, and signposting patients with AUD in secondary care hospitals in the NENC. Our interpretation suggests that implementation contexts, in particular structural stigma and growing economic disparity, are the greatest barriers to implementation of evidence-based care in this area. Thus, while some implementation mechanisms can be addressed at a local policy and practice level via improved training and support, system-wide action is needed to enable sustained delivery of preventative alcohol work in these settings.

Beyond COVID-19: Wastewater-based epidemiology for multipathogen surveillance and normalization strategies.

Wastewater-based epidemiology (WBE) is a critical tool for monitoring community health. Although much attention has focused on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a causative agent of coronavirus disease 2019 (COVID-19), other pathogens also pose significant health risks. This study quantified the presence of SARS-CoV-2, influenza A virus (Inf-A), and noroviruses of genogroups I (NoV-GI) and II (NoV-GII) in wastewater samples collected weekly (n = 170) from July 2023 to February 2024 from five wastewater treatment plants (WWTPs) in Yamanashi Prefecture, Japan, by quantitative PCR. Inf-A RNA exhibited localized prevalence with positive ratios of 59 %-82 % in different WWTPs, suggesting regional outbreaks within specific areas. NoV-GI (94 %, 160/170) and NoV-GII (100 %, 170/170) RNA were highly prevalent, with NoV-GII (6.1 ± 0.8 log10 copies/L) consistently exceeding NoV-GI (5.4 ± 0.7 log10 copies/L) RNA concentrations. SARS-CoV-2 RNA was detected in 100 % of the samples, with mean concentrations of 5.3 ± 0.5 log10 copies/L in WWTP E and 5.8 ± 0.4 log10 copies/L each in other WWTPs. Seasonal variability was evident, with higher concentrations of all pathogenic viruses during winter. Non-normalized and normalized virus concentrations by fecal indicator bacteria (Escherichia coli and total coliforms), an indicator virus (pepper mild mottle virus (PMMoV)), and turbidity revealed significant positive associations with the reported disease cases. Inf-A and NoV-GI + GII RNA concentrations showed strong correlations with influenza and acute gastroenteritis cases, particularly when normalized to E. coli (Spearman's ρ = 0.70-0.81) and total coliforms (ρ = 0.70-0.81), respectively. For SARS-CoV-2, non-normalized concentrations showed a correlation of 0.61, decreasing to 0.31 when normalized to PMMoV, suggesting that PMMoV is unsuitable. Turbidity normalization also yielded suboptimal results. This study underscored the importance of selecting suitable normalization parameters tailored to specific pathogens for accurate disease trend monitoring using WBE, demonstrating its utility beyond COVID-19 surveillance.

Amelioration of breast cancer therapies through normalization of tumor vessels and microenvironment: paradigm shift to improve drug perfusion and nanocarrier permeation.

Breast cancer (BC) is the most commonly diagnosed cancer among women. Chemo-, immune- and photothermal therapies are employed to manage BC. However, the tumor microenvironment (TME) prevents free drugs and nanocarriers (NCs) from entering the tumor premises. Formulation scientists rely on enhanced permeation and retention (EPR) to extravasate NCs in the TME. However, recent research has demonstrated the inconsistent nature of EPR among different patients and tumor types. In addition, angiogenesis, high intra-tumor fluid pressure, desmoplasia, and high cell and extracellular matrix density resist the accumulation of NCs in the TME. In this review, we discuss TME normalization as an approach to improve the penetration of drugs and NCSs in the tumor premises. Strategies such as normalization of tumor vessels, reversal of hypoxia, alleviation of high intra-tumor pressure, and infiltration of lymphocytes for the reversal of therapy failure have been discussed in this manuscript. Strategies to promote the infiltration of anticancer immune cells in the TME after vascular normalization have been discussed. Studies strategizing time points to administer TME-normalizing agents are highlighted. Mechanistic pathways controlling the angiogenesis and normalization processes are discussed along with the studies. This review will provide greater tumor-targeting insights to the formulation scientists.

Factors influencing general practice nurse's implementation of culturally responsive care, using normalization process theory: A cross-sectional study.

AIMS: To explore levers and barriers to providing culturally responsive care for general practice nurses (GPNs) using normalization process theory. DESIGN: A self-administered online cross-sectional survey. METHODS: A participatory co-designed adapted version of the normalization of complex interventions measure (NoMAD) validated tool was distributed to a convenience sample of GPNs between December 2022 and February 2023. The sample comprised of GPNs working in general practice services in Ireland (n = 122). Data were analysed using descriptive and analytical statistics (Pearson correlations) and principles of content analysis. This study was conducted and reported in line with the Consensus-Based Checklist for Reporting of Survey Studies (CROSS). RESULTS: GPNs in this study indicated their familiarity with, acknowledged the importance of and were committed to, providing culturally responsive care. However, implementing culturally responsive care in daily practice was problematic due to insufficient education and training, scarcity of resources and supports and a lack of organizational leadership. Subsequently, GPNs experience difficulties adapting everyday practices to respond appropriately to the care needs of culturally and linguistically diverse (CaLD) patients. CONCLUSION: This analysis highlights the necessity of exploring the intricacies of factors that influence capabilities and capacity for providing culturally responsive care. Despite demonstrating awareness of the importance of providing nursing care that responds to the needs of CaLD patients, GPNs do not have full confidence or capacity to integrate culturally responsive care into their daily work practices. IMPACT: Using normalization process theory, this study elucidates for the first time how GPNs in Ireland make sense of, legitimize, enact and sustain culturally responsive care as a routine way of working. It illuminates the multitude of micro-level (individual), meso-level (organizational) and macro-level (structural) factors that require attention for normalizing culturally responsive care in general practice services. PATIENT OR PUBLIC CONTRIBUTION: The study question was identified in a participatory research prioritization for Irish research about migrant health that involved migrants in the process.

Therapeutic Anti-Tumor Efficacy of DC-Based Vaccines Targeting TME-Associated Antigens Is Improved When Combined with a Chemokine-Modulating Regimen and/or Anti-PD-L1.

We previously reported that dendritic cell (DC)-based vaccines targeting antigens expressed by tumor-associated vascular endothelial cells (VECs) and pericytes effectively control tumor growth in translational mouse tumor models. In the current report, we examined whether the therapeutic benefits of such tumor blood vessel antigen (TBVA)-targeted vaccines could be improved by the cotargeting of tumor antigens in the s.c. B16 melanoma model. We also evaluated whether combination vaccines incorporating anti-PD-L1 checkpoint blockade and/or a chemokine-modulating (CKM; IFNα + TLR3-L [rintatolimod] + Celecoxib) regimen would improve T cell infiltration/functionality in tumors yielding enhanced treatment benefits. We report that DC-peptide or DC-tumor lysate vaccines coordinately targeting melanoma antigens and TBVAs were effective in slowing B16 growth in vivo and extending survival, with superior outcomes observed for DC-peptide-based vaccines. Peptide-based vaccines that selectively target either melanoma antigens or TBVAs elicited a CD8+ T cell repertoire recognizing both tumor cells and tumor-associated VECs and pericytes in vitro, consistent with a treatment-induced epitope spreading mechanism. Notably, combination vaccines including anti-PD-L1 + CKM yielded superior therapeutic effects on tumor growth and animal survival, in association with the potentiation of polyfunctional CD8+ T cell reactivity against both tumor cells and tumor-associated vascular cells and a pro-inflammatory TME.

Quantifying the pollution changes and meteorological dependence of airborne trace elements coupling source apportionment and machine learning.

Airborne trace elements (TEs) present in atmospheric fine particulate matter (PM2.5) exert notable threats to human health and ecosystems. To explore the impact of meteorological conditions on shaping the pollution characteristics of TEs and the associated health risks, we quantified the variations in pollution characteristics and health risks of TEs due to meteorological impacts using weather normalization and health risk assessment models, and analyzed the source-specific contributions and potential sources of primary TEs affecting health risks using source apportionment approaches at four sites in Shandong Province from September to December 2021. Our results indicated that TEs experience dual effects from meteorological conditions, with a tendency towards higher TE concentrations and related health risks during polluted period, while the opposite occurred during clean period. The total non-carcinogenic and carcinogenic risks of TEs during polluted period increased approximately by factors of 0.53-1.74 and 0.44-1.92, respectively. Selenium (Se), manganese (Mn), and lead (Pb) were found to be the most meteorologically influenced TEs, while chromium (Cr) and manganese (Mn) were identified as the dominant TEs posing health risks. Enhanced emissions of multiple sources for Cr and Mn were found during polluted period. Depending on specific wind speeds, industrialized and urbanized centers, as well as nearby road dusts, could be key sources for TEs. This study suggested that attentions should be paid to not only the TEs from primary emissions but also the meteorology impact on TEs especially during pollution episodes to reduce health risks in the future.