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Computational thinking (CT) in young children (K to three) has been of much interest among educational researchers due to the applicability of CT to solving problems in daily life and various academic disciplines. This study uses existing data from children's gameplay in a block-based programming game called codeSpark Academy to examine the extent to which we can use children's gameplay behavior to measure their CT and, more generally, their problem-solving skills. The objectives of the study are to operationalize CT and problem-solving constructs using gameplay data, investigate the relationship between CT and problem-solving, and position codeSpark Academy as a valid assessment tool. A total of 72 elementary students (aged 6–9) played codeSpark Academy once a week for six weeks. TechCheck, an externally developed and validated measure of CT, was administered before the first game day and after the last game day. Using fine-grained, moment-to-moment gameplay data, we developed and validated seven game-based indicators (GBIs) of CT using correlational analysis and nonparametric tests and integrated them into a problem-solving framework. Our findings showed that children's gameplay behavior can be used to measure their CT and problem-solving skills. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) remain a leading cause of global mortality and morbidity. Traditional risk prediction models, while foundational, often fail to capture the multifaceted nature of risk factors or leverage the expanding pool of healthcare data. Machine learning (ML) and artificial intelligence (AI) approaches represent a paradigm shift in risk prediction, offering dynamic, scalable solutions that integrate diverse data types. This review examines advancements in AI/ML for CVD risk prediction, analyzing their strengths, limitations, and the challenges associated with their clinical integration. Recommendations for standardization, validation, and future research directions are provided to unlock the potential of these technologies in transforming precision cardiovascular medicine. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a major public health concern around the world. It is a significant risk factor for cardiovascular disease (CVD), and, as it progresses, the risk of cardiovascular events increases. Furthermore, end-stage kidney disease severely affects life expectancy and quality of life. Type 2 diabetes and hypertension are not only primary causes of CKD but also independent risk factors for CVD, which underscores the importance of effective treatment strategies for these conditions. The current therapies, including angiotensin-converting enzyme inhibitors, angiotensin II receptor blockers, and sodium–glucose co-transporter 2 inhibitors, are administered to control hypertension, slow the progression of CKD, and reduce cardiovascular risk. However, their efficacy remains suboptimal in certain instances. Mineralocorticoid receptor (MR), a nuclear receptor found in various tissues, such as the kidney and heart, plays a pivotal role in the progression of CKD. Overactivation of MR triggers inflammation and fibrosis, which exacerbates kidney damage and accelerates disease progression. MR antagonists (MRAs) have substantial beneficial effects in patients with cardiac and renal conditions; however, their use has been constrained because of adverse effects, such as hyperkalemia and kidney dysfunction. Recently, novel non-steroidal MRAs are more efficacious and have superior safety profiles to steroidal MRAs, making them promising potential components of future treatment strategies. Here, we discuss recent findings and the roles of MRAs in the management of hypertension and CKD, with a focus on the evidence obtained from fundamental research and major clinical trials. [ABSTRACT FROM AUTHOR]

Land consolidation is a pivotal element in rural settlement planning globally and specifically in Turkey. Evaluating the efficacy of land consolidation initiatives and incorporating diverse approaches in distinct countries and regions are imperative. This assessment becomes particularly crucial in charting the course for future land consolidation endeavors, wherein an understanding of farmers' knowledge levels and perspectives significantly enhances success rates. This research focuses on evaluating land consolidation studies conducted in 2011 in three settlements—Alhan, Çulhan, and Dereköy—located in the Yenipazar district of Aydın province, Turkey. Data were collected using face-to-face interviews with 296 enterprise owners chosen through the stratified random sampling method across the three villages. The analysis utilized CHAID (Chi-squared Automatic Interaction Detection) analysis, a decision tree model, to comprehensively evaluate the interview results. The findings derived from this study offer crucial insights into the regional farmers' perspectives on land consolidation projects, contributing valuable data to the academic discourse on rural development and settlement planning. It has been revealed that farmers' education level, land holdings, and age have significant effects on their views on land consolidation projects. It has also been determined that the positive perspectives of farmers in the region on land consolidation projects continue to be positive after the project is completed. Moreover, more than half of the farmers who participated in the survey reported that their living standards have changed positively. [ABSTRACT FROM AUTHOR]

The progression of the COVID-19 pandemic has demonstrated significant oscillatory characteristics, underscoring the importance of investigating the impact of driving factors on its evolution. This study included an in-depth analysis of the influence of various driving factors on the pandemic's fluctuations, identifying key elements, to enhance the comprehension of transmission mechanisms and improve scientific precision in formulating mitigation strategies. The experimental outcomes indicate that the Geographically and Temporally Neural Network Weighted Regression (GTNNWR) model achieved commendable accuracy with minimal error in forecasting the number of infected individuals. Leveraging the results from the GTNNWR model, the research meticulously examines the temporal and spatial correlations between the driving factors and the pandemic, delineated the spatiotemporal distribution patterns of each factor's influence, and quantified their significance. This study reveals the substantial impact of vaccines, masks, and social distancing measures across different regions and periods, with their effects on the number of affected individuals being 2 to 10 times more pronounced than other driving factors. These findings contribute to a deeper understanding of the spatiotemporal transmission dynamics and the influence of driving factors in the COVID-19 pandemic, offering critical decision-making support for control and prevention efforts. [ABSTRACT FROM AUTHOR]

Background/Objectives: In heart failure (HF) with reduced ejection fraction (HFrEF), the early diagnosis and proper treatment of comorbidities (CMs) are of fundamental relevance. Our aim was to assess the prevalence of CMs among real-world patients requiring hospitalisation for HFrEF and to investigate the effect of CMs on the implementation of guideline-directed medical therapy (GDMT) and on all-cause mortality (ACM). Methods: The data of a consecutive HFrEF patient cohort hospitalised for HF between 2021 and 2024 were analysed retrospectively. Sixteen CMs (6 CV and 10 non-CV) were considered. Patients were divided into three categories: 0–3 vs. 4–6 vs. ≥7 CMs. GDMT at discharge and ACM were compared among CM categories. The predictors of 1-year ACM were also evaluated. Results: From the 388 patients (male: 76%, age: 61 [50–70] years; NT-proBNP: 5286 [2570–9923] pg/mL; ≥2 cardiovascular–kidney–metabolic disease overlap: 46%), a large proportion received GDMT (RASi: 91%; βB: 85%; MRA: 95%; SGLT2i: 59%; triple therapy [TT: RASi+βB+MRA]: 82%; quadruple therapy [QT: TT + SGLT2i]: 54%) at discharge. Multimorbidity was accompanied with a (p < 0.05) lower application ratio of RASi (96% vs. 92% vs. 85%; 0–3 vs. 4–6 vs. ≥7 CMs) and βB therapy (94% vs. 85% vs. 78%), while MRA (99% vs. 94% vs. 94%) and SGTL2i use (61% vs. 59% vs. 57%) did not differ (p > 0.05). Patients with multimorbidity were less likely to be treated with TT (93% vs. 82% vs. 73%, p = 0.001), while no difference was detected in the implementation of QT (56% vs. 54% vs. 50%, p = 0.685). The 1-year ACM of patients with an increased burden of CMs was higher (9% vs. 13% vs. 25%, p = 0.003). The risk of 1-year ACM was favourably affected by the use of TT/QT and less severe left ventricular systolic dysfunction, while having ≥5 CMs had an unfavourable impact on prognosis. Conclusions: According to our real-world analysis, HFrEF patients with an increased burden of CMs can expect a less favourable outcome. However, modern GDMT can even be applied in this patient population, resulting in a significantly improved prognosis. Thus, clinicians should insist on the early, conscious implementation of a prognosis-modifying drug regime in multimorbid HF patients as well. [ABSTRACT FROM AUTHOR]

Clinical atrial fibrillation (AF) is a well-established major risk factor for stroke and systemic embolism. Pivotal trials have shown that treatment with oral anticoagulation reduces the risk of stroke and systemic embolism in clinical AF with a simultaneous increase in the risk of major bleeding. To help balance the risk of stroke and bleeding in clinical AF, different prediction models including biomarkers and clinical features have been validated. Device-detected AF (DDAF) is also associated with an increased risk of stroke and systemic embolism, but not to the same extent as clinical AF. Two large randomised studies have found significant stroke risk reduction with direct oral anticoagulation in DDAF patients, yet also a significantly increased risk of major bleeding. To date, the question remains how to balance the thromboembolic risk reduction with oral anticoagulation and the increased risk of bleeding in patients with DDAF and to identify the right patients who may benefit from oral anticoagulant treatment. [ABSTRACT FROM AUTHOR]

Simple Summary: Positron emission tomography (PET) is useful for assessing lymphomas, while artificial intelligence (AI) shows potential as a reliable resource for analyzing medical images. In this context, several studies have developed AI models for the automated interpretation of lymphoma PET images. The proposed models achieved promising performance in various medical tasks, including detection, histological subtyping, differential diagnosis, and prognostication. AI techniques for lymphoma PET evaluation are not designed to replace physicians but to assist them in handling large volumes of scans through rapid and accurate calculations, reduce their workload, and provide them with decision support tools that can improve their ability to deliver precise care for better outcomes. Background: Positron emission tomography (PET) is a valuable tool for the assessment of lymphoma, while artificial intelligence (AI) holds promise as a reliable resource for the analysis of medical images. In this context, we systematically reviewed the applications of deep learning (DL) for the interpretation of lymphoma PET images. Methods: We searched PubMed until 11 September 2024 for studies developing DL models for the evaluation of PET images of patients with lymphoma. The risk of bias and applicability concerns were assessed using the prediction model risk of bias assessment tool (PROBAST). The articles included were categorized and presented based on the task performed by the proposed models. Our study was registered with the international prospective register of systematic reviews, PROSPERO, as CRD42024600026. Results: From 71 papers initially retrieved, 21 studies with a total of 9402 participants were ultimately included in our review. The proposed models achieved a promising performance in diverse medical tasks, namely, the detection and histological classification of lesions, the differential diagnosis of lymphoma from other conditions, the quantification of metabolic tumor volume, and the prediction of treatment response and survival with areas under the curve, F1-scores, and R2 values of up to 0.963, 87.49%, and 0.94, respectively. Discussion: The primary limitations of several studies were the small number of participants and the absence of external validation. In conclusion, the interpretation of lymphoma PET images can reliably be aided by DL models, which are not designed to replace physicians but to assist them in managing large volumes of scans through rapid and accurate calculations, alleviate their workload, and provide them with decision support tools for precise care and improved outcomes. [ABSTRACT FROM AUTHOR]

Both chronic kidney disease (CKD) and type 2 diabetes (T2D) are modern epidemics worldwide and have become a severe public health problem. Chronic kidney disease progression in T2D patients is linked to the need for dialysis or kidney transplantation and represents the risk factor predisposing to serious cardiovascular complications. In recent years, important progress has occurred in nephroprotective pharmacotherapy in CKD patients with T2D. In the current position paper, we described a nephroprotective approach in CKD patients with T2D based on the five following pillars: effective antihyperglycemic treatment, SGLT2 inhibitor or semaglutide, antihypertensive therapy, use of RASi (ARB or ACEi), and in selected patients, finerenone, as well as sodium bicarbonate in patients with metabolic acidosis. We thought that the current statement is comprehensive and up-to-date and addresses multiple pathways of nephroprotection in patients with CKD and T2D. [ABSTRACT FROM AUTHOR]

In healthcare, vast amounts of data are increasingly collected through sensors for smart health applications and patient monitoring or diagnosis. However, such medical data often comprise sensitive patient information, posing challenges regarding data privacy, and are resource-intensive to acquire for significant research purposes. In addition, the common case of lack of information due to technical issues, transcript errors, or differences between descriptors considered in different health centers leads to the need for data imputation and partial data generation techniques. This study introduces a novel methodology for partially synthetic tabular data generation, designed to reduce the reliance on sensor measurements and ensure secure data exchange. Using the UMAP (Uniform Manifold Approximation and Projection) visualization algorithm to transform the original, high-dimensional reference data set into a reduced-dimensional space, we generate and validate synthetic values for incomplete data sets. This approach mitigates the need for extensive sensor readings while addressing data privacy concerns by generating realistic synthetic samples. The proposed method is validated on prostate and breast cancer data sets, showing its effectiveness in completing and augmenting incomplete data sets using fully available references. Furthermore, our results demonstrate superior performance in comparison to state-of-the-art imputation techniques. This work makes a dual contribution by not only proposing an innovative method for synthetic data generation, but also studying and establishing a formal framework to understand and solve synthetic data generation and imputation problems in sensor-driven environments. [ABSTRACT FROM AUTHOR]

According to the World Health Organization, chronic illnesses account for over 70% of deaths globally, underscoring the need for effective health risk assessment (HRA). While machine learning (ML) has shown potential in enhancing HRA, no systematic review has explored its application in general health risk assessments. Existing reviews typically focus on specific conditions. This paper reviews published articles that utilize ML for HRA, and it aims to identify the model development methods. A systematic review following Tranfield et al.'s three-stage approach was conducted, and it adhered to the PRISMA protocol. The literature was sourced from five databases, including PubMed. Of the included articles, 42% (11/26) addressed general health risks. Secondary data sources were most common (14/26, 53.85%), while primary data were used in eleven studies, with nine (81.81%) using data from a specific population. Random forest was the most popular algorithm, which was used in nine studies (34.62%). Notably, twelve studies implemented multiple algorithms, while seven studies incorporated model interpretability techniques. Although these studies have shown promise in addressing digital health inequities, more research is needed to include diverse sample populations, particularly from underserved communities, to enhance the generalizability of existing models. Furthermore, model interpretability should be prioritized to ensure transparent, trustworthy, and broadly applicable healthcare solutions. [ABSTRACT FROM AUTHOR]

This review addresses the ongoing global challenge posed by emerging and evolving viral diseases, underscoring the need for innovative vaccine development strategies. It focuses on the modern approaches to creating vaccines based on recombinant proteins produced in different expression systems, including bacteria, yeast, plants, insects, and mammals. This review analyses the advantages, limitations, and applications of these expression systems for producing vaccine antigens, as well as strategies for designing safer, more effective, and potentially 'universal' antigens. The review discusses the development of vaccines for a range of viral diseases, excluding SARS-CoV-2, which has already been extensively studied. The authors present these findings with the aim of contributing to ongoing research and advancing the development of antiviral vaccines. [ABSTRACT FROM AUTHOR]

Simple Summary: In this study we examined whether a specific blood test score, the Scottish Inflammatory Prognostic Score (SIPS), could help predict outcomes in people with advanced lung cancer receiving immunotherapy. Immunotherapy, which uses the body's immune system to fight cancer, is effective for some patients but not all. SIPS is based on two common blood measures: albumin (a protein) and neutrophils (a type of immune cell). Our findings suggest that patients with low SIPS scores, indicating lower inflammation, generally lived longer and had slower cancer progression. SIPS could become a helpful tool for doctors to decide on treatments and guide patients about their prognosis. Using SIPS alongside other health information may improve care by helping select the most suitable therapies for people with advanced lung cancer. Further studies could confirm these findings and support SIPS as a standard measure in clinical care. Background: The Scottish Inflammatory Prognostic Score (SIPS), combining albumin (≥/<35 g/L) and neutrophil count (≤/>7.5 × 109/L), has been identified as a prognostic biomarker for patients with non-small cell lung cancer (NSCLC) undergoing treatment with pembrolizumab monotherapy. We sought to validate this biomarker of systemic inflammation in an external cohort. Methods: Patients treated with first-line pembrolizumab for advanced NSCLC with programmed death-ligand 1 (PD-L1) expression ≥ 50% at an English cancer centre were identified. Pre-treatment clinicopathological characteristics and the SIPS were recorded. The relationship between these and progression-free survival (PFS) and overall survival (OS) was examined. Results: Among 257 patients evaluated, 56% (n = 144) were classified as SIPS 0, 36% (n = 93) as SIPS 1, and 8% (n = 20) as SIPS 2. Factors such as age, performance status (PS) and brain metastases presence were significantly correlated with SIPS categories. Multivariate analysis revealed that both SIPS and PD-L1 status were independently associated with PFS and OS. The combination of SIPS with either PS or PD-L1 expression enhanced the ability to detect patients with the most favourable or poorest survival. Conclusions: Our study confirms the prognostic significance of the SIPS in patients with advanced NSCLC treated with pembrolizumab in the context of high PD-L1 expression. SIPS offers a straightforward, clinically applicable approach to patient stratification, potentially guiding therapeutic decisions and enhancing outcomes in advanced NSCLC. Future research should focus on validating these findings in prospective studies and exploring the integration of SIPS into clinical practice, alongside other prognostic markers, to optimize treatment strategies. [ABSTRACT FROM AUTHOR]

Background/Objective: Atrial fibrillation (AF) complicates the management of acute ischemic stroke (AIS), necessitating precise predictive models to enhance clinical outcomes. This meta-analysis evaluates the efficacy of machine learning (ML) models in three key areas: stroke prognosis in AF patients, stroke prediction in AF patients, and AF prediction in stroke patients. The study aims to assess the accuracy and variability of ML models in forecasting AIS outcomes and detecting AF in stroke patients, while exploring the clinical benefits and limitations of integrating these models into practice. Methods: We conducted a systematic search of PubMed, Embase, and Cochrane databases up to June 2024, selecting studies that evaluated ML accuracy in stroke prognosis and prediction in AF patients and AF prediction in stroke patients. Data extraction and quality assessment were performed independently by two reviewers, with random-effects modeling applied to estimate pooled accuracy metrics. Results: The meta-analysis included twenty-four studies comprising 7,391,645 patients, categorized into groups for stroke prognosis in AF patients (eight studies), stroke prediction in AF patients (thirteen studies), and AF prediction in stroke patients (three studies). The pooled AUROC was 0.79 for stroke prognosis and 0.68 for stroke prediction in AF, with higher accuracy noted in short-term predictions. The mean AUROC across studies was 0.75, with models such as Extreme Gradient Boosting (XGB) and Random Forest (RF) showing superior performance. For stroke prognosis in AF, the mean AUROC was 0.78, whereas stroke prediction yielded a mean AUROC of 0.73. AF prediction post-stroke had an average AUROC of 0.75. These findings indicate moderate predictive capability of ML models, underscoring the need for further refinement and standardization. The absence of comprehensive sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) metrics limited the ability to conduct full meta-analytic modeling. Conclusions: While ML models demonstrate potential for enhancing stroke prognosis and AF prediction, they have yet to meet the clinical standards required for widespread adoption. Future efforts should focus on refining these models and validating them across diverse populations to improve their clinical utility. [ABSTRACT FROM AUTHOR]

Expanding green areas in cities results in growth in green waste generation. This study presents the findings of an investigation into green waste from selective collection in a large Central European city (Warsaw, Poland), which can be identified as a valuable biomass resource. The research objective was to identify the properties of garden waste from single-family housing to determine valorization opportunities, emphasizing the utilization of GW as a source of energy. The research yielded several findings, including a notable degree of variability in fuel properties, including moisture content (CV = 30%), lower heating value (CV = 14.3%), and ash content (CV = 62.7/56.2%). The moisture content suggests composting, while the fertilizing properties indicate suitability for anaerobic digestion. The instability of the fuel properties, coupled with the elevated levels of chlorine, sulfur, and moisture, constrains the use of garden waste in thermal processes and alternative fuel production. Pyrolysis could be a viable approach for green waste feedstock, offering value-added products depending on the processing conditions and pre-treatment. Nevertheless, implementing a selective collection system is a critical condition for the optimal utilization of bio-waste, facilitating the quality and property control of green and food waste. This is essential for their effective processing, including energy recovery, thereby contributing to the efficient valorization of biomass. [ABSTRACT FROM AUTHOR]

Life Cycle Cost Analysis (LCCA) is a method used to determine the preservation costs required for an object, in this case, a bridge, throughout its service life. This method has been implemented in several advanced countries, including Japan. The LCCA calculation concept in Japan differs from those in other countries, as it involves a detailed segmentation of structural elements of bridges, making the model unique and highly detailed. This model is known as High-Precision Life Cycle Cost Analysis (HP-LCCA) and will be tested for application in Bridge Management Systems (BMS) in Indonesia. Implementing this model requires several adjustments, including aligning perceptions of visual inspection results and adapting the assessment concepts for each element based on research interpretations. Additionally, the difference between non-physical and physical condition assessment models presents a challenge for adopting this calculation model. The research shows a similar pattern and positive correlation between non-physical and physical assessment models. Furthermore, factors influencing LCC calculations include bridge age and the number of segments in structural elements. This research aims to provide optimal LCC calculations and facilitate their application in BMS in Indonesia. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a progressive condition characterized by a continuous decline in renal function, independent of the initial cause of damage or external factors such as infection, inflammation, or toxins. The accurate measurement of renal function, typically assessed using the glomerular filtration rate (GFR), is crucial for managing CKD. The most accepted hypothesis for CKD progression is glomerular damage caused by hyperfiltration. Various factors can accelerate CKD progression, and several biomarkers have been identified to monitor this progression. Numerous studies have explored the risk factors associated with CKD progression, and some of these factors can be modified. Additionally, several drugs are now available that can reduce CKD progression. This review summarizes recent publications and highlights potential future research directions in CKD progression. It discusses the evolution of GFR measurement methods, the mechanisms driving CKD progression, and the latest findings on biomarkers and risk factors. Furthermore, it explores therapeutic strategies, including dietary modifications and pharmacological interventions, to slow CKD progression. Understanding these mechanisms and interventions is crucial for developing effective therapeutic strategies to prevent or slow CKD progression. [ABSTRACT FROM AUTHOR]

Background/Objectives: Pancreatic stone protein (PSP) is an emerging biomarker of sepsis that is secreted from pancreas sensing remote organ damages. We explored the diagnostic and prognostic utilities of PSP in patients with suspected sepsis. Methods: In a total of 285 patients (suspected sepsis, n = 148; sepsis, n = 137), we compared PSP with procalcitonin (PCT) and sequential organ failure assessment (SOFA) score. Sepsis diagnoses were explored using receiver operating characteristic curve analyses with area under the curves (AUCs). Clinical outcomes (in-hospital mortality, 30-day mortality, and kidney replacement therapy [KRT]) were explored using the Kaplan–Meier method and a multivariate analysis with hazard ratio (HR). Results: PCT and PSP were comparable for sepsis diagnosis (AUC = 0.71–0.72, p < 0.001). The sepsis proportion was significantly higher when both biomarkers increased than when either one or both biomarkers did not increase (89.0% vs. 21.3–47.7%, p < 0.001). Each biomarker quartile (Q1–Q4) differed significantly according to their SOFA score (all p < 0.001). Compared with Q1, the Q2–Q4 groups showed worse clinical outcomes (p = 0.002–0.041). Both biomarkers added to the SOFA score showed higher HRs than the SOFA score alone (3.3–9.6 vs. 2.8–4.2, p < 0.001–0.011), with nearly 2.5-fold higher HR (9.6 vs. 4.2) for predicting KRT. Conclusions: Although PCT and PSP did not independently predict clinical outcomes in the multivariate analysis, PSP demonstrated diagnostic and prognostic utilities in patients with suspected sepsis, especially for predicting kidney dysfunction. PSP, alone or in combination with PCT, would be a valuable tool that can be added to clinical assessments. [ABSTRACT FROM AUTHOR]

This systematic review aimed to evaluate prediction models for perioperative blood transfusion in patients undergoing gynecologic surgery. Given the inherent risks associated with blood transfusion and the critical need for accurate prediction, this study identified and assessed models based on their development, validation, and predictive performance. The review included five studies encompassing various surgical procedures and approaches. Predicting factors commonly used across these models included preoperative hematocrit, race, surgical route, and uterine fibroid characteristics. However, the review highlighted significant variability in the definition of perioperative periods, a lack of standardization in transfusion criteria, and a high risk of bias in most models due to methodological issues, such as a low number of events per variable, inappropriate handling of continuous and categorical predictors, inappropriate handling of missing data, improper methods of predictor selection, inappropriate measurement methods for model performance, and inadequate evaluations of model overfitting and optimism in model performance. Despite some models demonstrating good discrimination and calibration, the overall quality and external validation of these models were limited. Consequently, there is a clear need for more robust and externally validated models to improve clinical decision-making and patient outcomes in gynecologic surgery. Future research should focus on refining these models, incorporating rigorous validation, and adhering to standardized reporting practices. [ABSTRACT FROM AUTHOR]

Despite treatment and other interventions, an effective prophylactic HIV vaccine is still an essential goal in the control of HIV. Inducing robust and long-lasting antibody responses is one of the main targets of an HIV vaccine. The delivery of HIV envelope glycoproteins (Env) using nanoparticle (NP) platforms has been shown to elicit better immunogenicity than soluble HIV Env. In this paper, we describe the development of a nanoparticle-based vaccine decorated with HIV Env using the SpyCatcher/SpyTag system. The Env utilised in this study, CAP255, was derived from a transmitted founder virus isolated from a patient who developed broadly neutralising antibodies. Negative stain and cryo-electron microscopy analyses confirmed the assembly and stability of the mi3 into uniform icosahedral NPs surrounded by regularly spaced CAP255 gp140 Env trimers. A three-dimensional reconstruction of CAP255 gp140 SpyTag–SpyCatcher mi3 clearly showed Env trimers projecting from the centre of each of the pentagonal dodecahedral faces of the NP. To our knowledge, this is the first study to report the formation of SpyCatcher pentamers on the dodecahedral faces of mi3 NPs. To investigate the immunogenicity, rabbits were primed with two doses of DNA vaccines expressing the CAP255 gp150 and a mosaic subtype C Gag and boosted with three doses of the NP-developed autologous Tier 2 CAP255 neutralising antibodies (Nabs) and low levels of heterologous CAP256SU NAbs. [ABSTRACT FROM AUTHOR]

Background and Objectives: The assessment of cardiac function in patients with end-stage renal disease (ESRD) is vital due to their high cardiovascular risk. However, contemporary echocardiographic indices and their association with hemodialysis-to-hemodiafiltration transfer are underreported in this population. Materials and Methods: This prospective cohort study enrolled 36 ESRD patients undergoing hemodialysis-to-hemodiafiltration transfer, with baseline and 3-month post-transfer comprehensive echocardiographic assessments. The key parameters included the global work index, global constructed work, global wasted work (GWW), global work efficiency (GWE), and global longitudinal strain (GLS), with secondary measures from conventional echocardiography. The baseline measures were compared to general population reference values and changes pre- to post-transfer were analyzed using the Mann–Whitney U test. Results: Patients exhibited significant deviations from reference ranges in GWW (179.0 vs. 53.0–122.2 mmHg%), GWE (90.0 vs. 53.0–122.2%), and GLS (−16.0 vs. −24.0–(−16.0)%). Post-transfer left ventricular myocardial work and longitudinal strain remained unchanged (p > 0.05), except for increased GWW (179.0, IQR 148.0–217.0 to 233.5, IQR 159.0-315.0 mmHg%, p = 0.037) and improved mid-inferior peak systolic longitudinal strain ((−17.0, IQR −19.0–(−11.0) to −18.7, IQR −20.0–(−18.0)%, p = 0.016). The enrolled patients also showed higher left atrial diameters, left ventricular volumes, and mass, with impaired systolic function in both ventricles compared to reference values. Conclusions: This study highlights baseline impairments in contemporary echocardiographic measures (GWW, GWE, GLS) in ESRD patients versus reference values, but found no association between hemodialysis-to-hemodiafiltration transfer and most myocardial work and strain parameters. [ABSTRACT FROM AUTHOR]

Background: Children who are exposed to HIV in utero but are uninfected (HIV-exposed uninfected or HEU) are at higher risk of neurodevelopmental delays compared to children born to persons without HIV. Magnetic resonance imaging (MRI) studies have revealed differences in grey matter volumes, cerebral perfusion, and white matter changes in these children. However, MRI is costly and not widely available in areas with high HIV prevalence, like Botswana, where more than 15% of children are HEU. To address this, we explored the use of brain ultrasound, conducted by trained study nurses, as a safe, less costly, and accurate alternative method for assessing differences relating to HIV exposure status in the brain structures of neonates. Methods: Brain ultrasounds of newborns in the Following Longitudinal Outcomes to Understand, Report, Intervene and Sustain Health for Infants, Children, Adolescents who are HIV Exposed Uninfected (FLOURISH) observational study—comprising 35 HEU newborns and 24 HIV-unexposed (HU) newborns—were performed by study nurses and evaluated by a pediatric radiologist for quality and structural abnormalities, such as calcifications, cysts, and hemorrhages. Two radiologists measured extra-axial cerebrospinal fluid spaces, ventricles, and the corpus callosum. Results: Ultrasound studies of 59 newborns (59% boys; median gestational age 38.4 weeks) were completed. All studies were of diagnostic quality, with 90.2% rated as being of good or excellent quality. Structural abnormalities were rare (10.2% incidence) and did not differ by HIV exposure group. Corpus callosum length was shorter in HEU infants compared to HU infants (45.7 mm vs. 47.3 mm; p = 0.03). Other ventricular and corpus callosum measurements showed no significant variations. Conclusions: Brain ultrasounds conducted by study nurses are feasible and reveal differences in corpus callosum length between HEU and HU infants. The benefits of easier training, lower cost, and rapid deployment make ultrasound a promising screening tool in resource-limited settings. [ABSTRACT FROM AUTHOR]

Background/Objectives: Chronic kidney disease (CKD) is a major health problem with a rising prevalence due to comorbidities like diabetes and hypertension. The aim of this research was to audit the assessment and therapeutic management of proteinuria in CKD patients at the Cantonal Hospital Baselland (KSBL) in Switzerland and determine associations between patient comorbidities, rehospitalisation, death, and the quality of therapeutic management. Methods: We analysed data from 427 adults with CKD (eGFR < 45 mL/min/1.73 m2) hospitalised on the internal medicine ward in 2022. Results: The mean age was 85 years (range: 79–89), 45.9% were female, and the median eGFR was 32.8 mL/min/1.73 m2 (range: 25–40). Proteinuria assessment was performed in 120 (28.1%) patients (the ProtU group), and a corresponding treatment was prescribed in 59%. The ProtU group had a higher quota of patients with diabetes (44.1% vs. 33%, p = 0.048) and obesity (21.2% vs. 12.5%, p = 0.039) when compared to the group without proteinuria assessment (the Ustix group). Twelve-month survival was not significantly different between the groups (HR: 0.75; 95% CI: 0.488–1.154; p-value = 0.191). However, survival was significantly better in patients who received an antiproteinuric treatment compared to those who did not (HR: 0.30; 95% CI: 0.121–0.0761; p = 0.011). Conclusions: Improvements need to be made in managing CKD at the KSBL in accordance with the guidelines. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) is a global health issue causing a significant health burden. CKD patients develop thrombotic and hemorrhagic complications, and cardiovascular diseases are associated with increased hospitalization and mortality in this population. The hemostatic alterations are multifactorial in these patients; therefore, the results of different studies are varying and controversial. Endothelial and platelet dysfunction, coagulation abnormalities, comorbidities, and hemoincompatibility of the dialysis membranes are major contributors of hypo- and hypercoagulability in CKD patients. Due to the tendency of CKD patients to exhibit a prothrombotic state and bleeding risk, they require personalized clinical assessment to understand the impact of antithrombotic therapy. The evidence of efficacy and safety of antiplatelet and anticoagulant treatments is limited for end-stage renal disease patients due to their exclusion from major randomized clinical trials. Moreover, designing hemocompatible dialyzer membranes could be a suitable approach to reduce platelet activation, coagulopathy, and thrombus formation. This review discusses the molecular mechanisms underlying thrombotic and hemorrhagic risk in patients with CKD, leading to cardiovascular complications in these patients, as well as the evidence and guidance for promising approaches to optimal therapeutic management. [ABSTRACT FROM AUTHOR]

Background/Objectives: The prevalence of atrial fibrillation (AF) has been on the rise over the last 20 years. It is considered to be the most common cardiac arrhythmia and is associated with significant morbidity and mortality. The need for in-hospital management of patients having AF is increasing. Acute decompensation of cardiac rhythm is an indication for hospital admission. In the existing literature, several studies on different pathologies have observed that the risk of death was greater for patients with an increased neutrophil-to-lymphocyte ratio (NLR) and suggested that the NLR can be a useful biomarker to predict in-hospital mortality. This study aims to evaluate the link between the neutrophil-to-lymphocyte ratio at admission and death among the patients admitted to the medical ward for the acute manifestation of AF, and to gain a better understanding of how we can predict in-hospital all-cause death based on the NLR for these patients. Methods: A single-center retrospective study in an academic medical clinic was conducted. We analyzed if the NLR at in-hospital admission can be related to in-hospital mortality among the patients admitted for AF at the Medical Ward of Municipal Emergency University Hospital Timisoara between 2015 and 2016. After identifying a total of 1111 patients, we divided them into two groups: in-hospital death patients and surviving patients. We analyzed the NLR in both groups to determine if it is related to in-hospital mortality or not. One patient was excluded because of missing data. Results: Our analysis showed that patients who died during in-hospital admission had a significantly higher NLR compared to those who survived (p < 0.0001, 95% CI (1.54 to 3.48)). The NLR was found to be an independent predictor of in-hospital death among patients with AF, even for the patients with no raised level of blood leukocytes (p < 0.0001, 95% CI (0.6174 to 3.0440)). Additionally, there was a significant correlation between the NLR and the risk of in-hospital death for patients admitted with decompensated AF (p < 0.0001), with an area under the ROC curve of 0.745. Other factors can increase the risk of death for these patients (such as the personal history of stroke, HAS-BLED score, and age). Conclusions: The NLR is a useful biomarker to predict in-hospital mortality in patients with AF and can predict the risk of death with a sensitivity of 72.8% and a specificity of 70.4%. Further studies are needed to determine the clinical utility of the NLR in risk stratification and management of patients with AF. [ABSTRACT FROM AUTHOR]

Lysophosphatidic acids (LPAs) evoke nociception and itch in mice and humans. In this study, we assessed the signaling paths. Hydroxychloroquine was injected intradermally to evoke itch in mice, which evoked an increase of LPAs in the skin and in the thalamus, suggesting that peripheral and central LPA receptors (LPARs) were involved in HCQ-evoked pruriception. To unravel the signaling paths, we assessed the localization of candidate genes and itching behavior in knockout models addressing LPAR5, LPAR2, autotaxin/ENPP2 and the lysophospholipid phosphatases, as well as the plasticity-related genes Prg1/LPPR4 and Prg2/LPPR3. LacZ reporter studies and RNAscope revealed LPAR5 in neurons of the dorsal root ganglia (DRGs) and in skin keratinocytes, LPAR2 in cortical and thalamic neurons, and Prg1 in neuronal structures of the dorsal horn, thalamus and SSC. HCQ-evoked scratching behavior was reduced in sensory neuron-specific Advillin-LPAR5−/− mice (peripheral) but increased in LPAR2−/− and Prg1−/− mice (central), and it was not affected by deficiency of glial autotaxin (GFAP-ENPP2−/−) or Prg2 (PRG2−/−). Heat and mechanical nociception were not affected by any of the genotypes. The behavior suggested that HCQ-mediated itch involves the activation of peripheral LPAR5, which was supported by reduced itch upon treatment with an LPAR5 antagonist and autotaxin inhibitor. Further, HCQ-evoked calcium fluxes were reduced in primary sensory neurons of Advillin-LPAR5−/− mice. The results suggest that LPA-mediated itch is primarily mediated via peripheral LPAR5, suggesting that a topical LPAR5 blocker might suppress "non-histaminergic" itch. [ABSTRACT FROM AUTHOR]

Background and Objectives: This study aims to analyze survival in peritoneal and hemodialysis patients using chest radiography and biochemical parameters, determine common dialysis etiologies and causes of death, reveal prognostic factors, and contribute to clinical practice. Materials and Methods: A retrospective cross-sectional study was conducted with data from 33 peritoneal dialysis and 37 hemodialysis patients collected between October 2018 and February 2020. Survival and mortality were retrospectively tracked over 70 months (October 2018–June 2024). Chest X-ray measurements (cardiothoracic index, pulmonary vascular pedicle width, right pulmonary artery diameter, diaphragmatic height) and biochemical parameters (urea, albumin, creatinine, parathormone, ferritin, hemoglobin, arterial blood gas, potassium) were analyzed for their impact on survival. Statistical analyses included descriptive statistics, chi-square test, Fisher's exact test, Bayesian analysis, McNemar test, Kaplan–Meier survival analysis, Cox regression, Bayesian correlation test, linear regression analysis (scatter plot), and ROC analysis. SPSS 20.0 was used for data analysis, with p < 0.05 considered statistically significant. Results: Hypertension, type 2 diabetes, and urogenital disorders were the main dialysis etiologies. Peritonitis (38.5%) and cardiovascular diseases (47.4%) were the leading causes of death in peritoneal and hemodialysis patients, respectively. Significant chest X-ray differences included pulmonary vascular pedicle width and pulmonary artery diameter in hemodialysis and diaphragm height in peritoneal dialysis. Kaplan–Meier showed no survival difference between methods. Cox regression identified age, intact parathormone levels, iPTH/PVPW ratio, and clinical status as survival and mortality factors. The iPTH/PVPW ratio cut-off for mortality prediction was ≤6.8. Conclusions: Age, intact parathormone levels, pulmonary vascular pedicle width, and clinical status significantly impact survival in dialysis patients. Management of hypertension and diabetes, management and follow-up of urogenital disorders, infection control, patient education, and regular cardiovascular check-ups may improve survival rates. Additionally, the iPTH/PVPW ratio can predict mortality risk. [ABSTRACT FROM AUTHOR]

Background: Depression is the most common mental illness worldwide and generates an enormous health and economic burden. Furthermore, it is known to be associated with an elevated risk of arteriosclerotic cardiovascular diseases (ASCVD), particularly stroke. However, it is not a factor reflected in many ASCVD risk models, including SCORE2. Thus, we analysed the relationship between depression, ASCVD and SCORE2 in our cohort. Methods: We analysed 9350 subjects from the Paracelsus 10,000 cohort, who underwent both a carotid artery ultrasound and completed a Beck Depression Inventory (BDI) screening. Patients were categorised binomially based on the BDI score. Atherosclerotic carotid plaque or absence was dichotomised for logistic regression modelling. Odds ratios and adjusted relative risks were calculated using Stata. Results: Subjects with an elevated BDI (≥14) had higher odds for carotid plaques compared to subjects with normal BDI, especially after adjusting for classical risk factors included in SCORE2 (1.21; 95%CI 1.03–1.43, p = 0.023). The adjusted relative risk for plaques was also increased (1.09; 95%CI 1.01–1.18, p = 0.021). Subgroup analysis showed an increased odds of plaques with increases in depressive symptoms, particularly in women and patients ≤55 yrs. Conclusions: In our cohort, the BDI score is associated with subclinical atherosclerosis beyond classical risk factors. Thus, depression might be an independent risk factor which may improve risk stratification if considered in ASCVD risk prediction models, such as SCORE2. Furthermore, reminding clinicians to take mental health into consideration to identify individuals at increased atherosclerosis risk may provide added opportunities to address measures which can reduce the risk of ASCVD. [ABSTRACT FROM AUTHOR]

Chronic kidney disease (CKD) impacts over 10% of the global population. Adults with CKD face significant morbidity and mortality. As kidney disease progresses, the risk of adverse outcomes increases. Here, we present an overview of strategies to care for adults with advanced CKD (stage 4–5 CKD, not receiving kidney replacement therapy). We aim to guide clinicians through several aspects of CKD care, ranging from recommended laboratory assessments to interdisciplinary support for patients as they plan for kidney replacement therapy (dialysis, transplantation, or conservative management). We incorporate considerations of health equity and person-centered care, empowering clinicians to deliver high-quality care to people with CKD. [ABSTRACT FROM AUTHOR]

Objectives: Our study investigated the inverse relationship between cognitive decline (CD) and the presence of documented atrial fibrillation (AFib), ischemic stroke, heart failure, lower extremity peripheral artery disease, and diabetes mellitus. Methods: We conducted a retrospective cross-sectional study between December 2016 and November 2019. A total of 469 patients were enrolled who underwent cognitive evaluation with three cognitive tests (Montreal Cognitive Assessment—MOCA, Mini-Mental State Examination—MMSE, and General Practitioner Assessment of Cognition—GPCOG). We used the standard cut-off values, and the optimal thresholds were obtained from the receiver operating characteristic curves. Results: The standard cut-off level of the MOCA (<26 points) was associated with the presence of AFib (OR: 1.83, 95% CI: 1.11–3.01) and the optimal cut-off level with <23 points with ischemic stroke (OR: 2.64, 95% CI: 1.47–4.74; p = 0.0011). The optimal cut-off value of the MMSE (<28 points) was associated with the presence of ischemic stroke (OR: 3.07, 95% CI: 1.56–6.07; p = 0.0012), AFib (OR: 1.65, 95% CI: 1.05–2.60; p = 0.0287), and peripheral artery disease (OR: 2.72, 95% CI: 1.38–5.36; p = 0.0039). GPCOG < 8 points were associated with ischemic stroke (OR: 2.18, 95% CI: 1.14–4.14; p = 0.0176) and heart failure (OR: 1.49, 95% CI: 1.01–2.21; p = 0.0430). Conclusions: Our research highlighted the broader utility of cognitive assessment. The MOCA and MMSE scores proved to be associated with documented AFib. Higher cognitive test results than the standard threshold for CD of the MMSE, GPCOG, and lower MOCA scores represented risk factors for the presence of previous ischemic stroke. [ABSTRACT FROM AUTHOR]

Metformin (MTF) is the only biguanide included in the World Health Organization's list of essential medicines; representing a widespread drug in the management of diabetes mellitus. With its accessibility and affordability being one of its biggest assets, it has become the target of interest for many trying to find alternative treatments for varied pathologies. Over time, an increasing body of evidence has shown additional roles of MTF, with unexpected interactions of benefit in other diseases. Metformin (MTF) holds significant promise in mitigating ischemia-reperfusion injury (IRI), particularly in the realm of organ transplantation. As acceptance criteria for organ transplants expand, IRI during the preservation phase remain a major concern within the transplant community, prompting a keen interest in MTF's effects. Emerging evidence suggests that administering MTF during reperfusion may activate the reperfusion injury salvage kinase (RISK) pathway. This pathway is pivotal in alleviating IRI in transplant recipients, potentially leading to improved outcomes such as reduced rates of organ rejection. This review aims to contextualize MTF historically, explore its current uses, pharmacokinetics, and pharmacodynamics, and link these aspects to the pathophysiology of IRI to illuminate its potential future role in transplantation. A comprehensive survey of the current literature highlights MTF's potential to recondition and protect against IRI by attenuating free radical damage, activating AMP-activated protein kinase to preserve cellular energy and promote repair, as well as directly reducing inflammation and enhancing microcirculation. [ABSTRACT FROM AUTHOR]

The extensive use of plastics has led to a significant environmental threat due to the generation of waste plastic, which has shown significant challenges during recycling. The catalytic hydrocracking route, however, is viewed as a key strategy to manage this fossil-fuel-derived waste into plastic-derived fuels with lower carbon emissions. Despite numerous efforts to identify an effective bi-functional catalyst, especially metal-loaded zeolites, the high-performing zeolite for hydrocracking plastics has yet to be synthesized. This is due to the microporous nature of zeolite, which results in the diffusional limitations of bulkier polymer molecules entering the structure and reducing the overall cracking of plastic and catalyst cycle time. These constraints can be overcome by developing hierarchical zeolites that feature shorter diffusion paths and larger pore sizes, facilitating the movement of bulky polymer molecules. However, if the hierarchical modification process of zeolites is not controlled, it can lead to the synthesis of hierarchical zeolites with compromised functionality or structural integrity, resulting in reduced conversion for the hydrocracking of plastics. Therefore, we provide an overview of various methods for synthesizing hierarchical zeolites, emphasizing significant advancements over the past two decades in developing innovative strategies to introduce additional pore systems. However, the objective of this review is to study the various synthesis approaches based on their effectiveness while developing a clear link between the optimized preparation methods and the structure-activity relationship of the resulting hierarchical zeolites used for the hydrocracking of plastics. [ABSTRACT FROM AUTHOR]

According to the World Health Organization (WHO), major depressive disorder (MDD) is the fourth leading cause of disability worldwide and the second most common disease after cardiovascular events. Approximately 280 million people live with MDD, with incidence varying by age and gender (female to male ratio of approximately 2:1). Although a variety of antidepressants are available for the different forms of MDD, there is still a high degree of individual variability in response and tolerability. Given the complexity and clinical heterogeneity of these disorders, a shift from "canonical treatment" to personalized medicine with improved patient stratification is needed. OPADE is a non-profit study that researches biomarkers in MDD to tailor personalized drug treatments, integrating genetics, epigenetics, microbiome, immune response, and clinical data for analysis. A total of 350 patients between 14 and 50 years will be recruited in 6 Countries (Italy, Colombia, Spain, The Netherlands, Turkey) for 24 months. Real-time electroencephalogram (EEG) and patient cognitive assessment will be correlated with biological sample analysis. A patient empowerment tool will be deployed to ensure patient commitment and to translate patient stories into data. The resulting data will be used to train the artificial intelligence/machine learning (AI/ML) predictive tool. [ABSTRACT FROM AUTHOR]

Simple Summary: A 90-day mortality predictive model for curative gastric cancer resection based on the Spanish EURECCA Esophagogastric Cancer database was externally validated using the GASTRODATA registry. The externally validated model showed a modestly worse performance compared to the original model, nevertheless maintaining its discriminating ability in clinical practice. Background: Radical gastrectomy remains the main treatment for gastric cancer, despite its high mortality. A clinical predictive model of 90-day mortality (90DM) risk after gastric cancer surgery based on the Spanish EURECCA registry database was developed using a matching learning algorithm. We performed an external validation of this model based on data from an international multicenter cohort of patients. Methods: A cohort of patients from the European GASTRODATA database was selected. Demographic, clinical, and treatment variables in the original and validation cohorts were compared. The performance of the model was evaluated using the area under the curve (AUC) for a random forest model. Results: The validation cohort included 2546 patients from 24 European hospitals. The advanced clinical T- and N-category, neoadjuvant therapy, open procedures, total gastrectomy rates, and mean volume of the centers were significantly higher in the validation cohort. The 90DM rate was also higher in the validation cohort (5.6%) vs. the original cohort (3.7%). The AUC in the validation model was 0.716. Conclusion: The externally validated model for predicting the 90DM risk in gastric cancer patients undergoing gastrectomy with curative intent continues to be as useful as the original model in clinical practice. [ABSTRACT FROM AUTHOR]

This paper presents a fully automated experimental setup tailored for evaluating the effectiveness of augmented and virtual reality technologies in healthcare settings for regulatory purposes, with a focus on the characterization of depth sensors. The setup is constructed as a modular benchtop platform that enables quantitative analysis of depth cameras essential for extended reality technologies in a controlled environment. We detail a design concept and considerations for an experimental configuration aimed at simulating realistic scenarios for head-mounted displays. The system includes an observation platform equipped with a three-degree-of-freedom motorized system and a test object stage. To accurately replicate real-world scenarios, we utilized an array of sensors, including commonly available range-sensing cameras and commercial augmented reality headsets, notably the Intel RealSense L515 LiDAR camera, integrated into the motion control system. The paper elaborates on the system architecture and the automated data collection process. We discuss several evaluation studies performed with this setup, examining factors such as spatial resolution, Z-accuracy, and pixel-to-pixel correlation. These studies provide valuable insights into the precision and reliability of these technologies in simulated healthcare environments. [ABSTRACT FROM AUTHOR]

This article develops a multi-perspective view on motivations and methods for tobamovirus purification through the ages and presents a novel, efficient, easy-to-use approach that can be well-adapted to different species of native and functionalized virions. We survey the various driving forces prompting researchers to enrich tobamoviruses, from the search for the causative agents of mosaic diseases in plants to their increasing recognition as versatile nanocarriers in biomedical and engineering applications. The best practices and rarely applied options for the serial processing steps required for successful isolation of tobamoviruses are then reviewed. Adaptations for distinct particle species, pitfalls, and 'forgotten' or underrepresented technologies are considered as well. The article is topped off with our own development of a method for virion preparation, rooted in historical protocols. It combines selective re-solubilization of polyethylene glycol (PEG) virion raw precipitates with density step gradient centrifugation in biocompatible iodixanol formulations, yielding ready-to-use particle suspensions. This newly established protocol and some considerations for perhaps worthwhile further developments could serve as putative stepping stones towards preparation procedures appropriate for routine practical uses of these multivalent soft-matter nanorods. [ABSTRACT FROM AUTHOR]

The misdiagnosis of headache disorders is a serious issue, and AI-based headache model diagnoses with external validation are scarce. We previously developed an artificial intelligence (AI)-based headache diagnosis model using a database of 4000 patients' questionnaires in a headache-specializing clinic and herein performed external validation prospectively. The validation cohort of 59 headache patients was prospectively collected from August 2023 to February 2024 at our or collaborating multicenter institutions. The ground truth was specialists' diagnoses based on the initial questionnaire and at least a one-month headache diary after the initial consultation. The diagnostic performance of the AI model was evaluated. The mean age was 42.55 ± 12.74 years, and 51/59 (86.67%) of the patients were female. No missing values were reported. Of the 59 patients, 56 (89.83%) had migraines or medication-overuse headaches, and 3 (5.08%) had tension-type headaches. No one had trigeminal autonomic cephalalgias or other headaches. The models' overall accuracy and kappa for the ground truth were 94.92% and 0.65 (95%CI 0.21–1.00), respectively. The sensitivity, specificity, precision, and F values for migraines were 98.21%, 66.67%, 98.21%, and 98.21%, respectively. There was disagreement between the AI diagnosis and the ground truth by headache specialists in two patients. This is the first external validation of the AI headache diagnosis model. Further data collection and external validation are required to strengthen and improve its performance in real-world settings. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, a considerable proportion of patients developed a severe condition that included respiratory failure, shock, or multiple organ dysfunction. Acute Kidney Injury (AKI) has been recognized as a possible cause of severe COVID-19 development. Given this, this study investigates the occurrence and consequences of AKI in Mexican patients to contribute to better knowledge and management of this problem. Methods: Using a retrospective observational cohort methodology, we investigated 313 cases from a cohort of 1019 patients diagnosed with COVID-19 at the IMSS Zacatecas General Hospital of Zone No. 1 in 2020. The prevalence of AKI was determined using the AKIN criteria based on serum creatinine levels and a detailed review of demographic characteristics, medical history, comorbidities, and clinical development. Results: The data showed a 25.30% prevalence of AKI among patients infected with severe COVID-19. Remarkably, these patients with AKI exhibited an advanced age (>65 years), arterial hypertension, a higher number of white blood cells during admission and the hospital stay, and elevated levels of C-reactive protein, serum creatinine, and blood urea nitrogen (BUN). Clinically, patients with AKI had signs of prostration, pneumonia, and the requirement for ventilatory assistance when compared to those without AKI. Finally, those diagnosed with AKI and COVID-19 had a 74% death rate. Relative risk analyses indicated that age (>65 years), arterial hypertension, high creatinine levels, endotracheal intubation, and pneumonia are associated with the development of AKI. On the other hand, among the protective factors against AKI, high hemoglobin levels and the consumption of statins during COVID-19 were found. Conclusions: The findings of this study underscore the significance of promptly identifying and effectively managing AKI to potentially alleviate the negative consequences of this complication within the Mexican population during COVID-19. [ABSTRACT FROM AUTHOR]

Background: Quality-of-life metrics are increasingly important for oncological patients alongside traditional endpoints like mortality and disease progression. Statistical tools such as Win Ratio, Win Odds, and Net Benefit prioritize clinically significant outcomes using composite endpoints. In randomized trials, Win Statistics provide fair comparisons between treatment and control groups. However, their use in observational studies is complicated by confounding variables. Propensity score (PS) matching mitigates confounding variables but may reduce the sample size, affecting the power of win statistics analyses. Alternatively, PS matching can stratify samples, preserving the sample size. This study aims to assess the long-term impact of these methods on decision making, particularly in colorectal cancer patients. Methods: A motivating example involves a cohort of patients from the ReSARCh observational study (2016–2021) with locally advanced adenocarcinoma of the rectum, situated up to 12 cm from the anal verge. These patients underwent either a watch-and-wait approach (WW) or trans-anal local excision (LE). Win statistics compared the effects of WW and LE on a composite outcome (overall survival, recurrence, presence of ostomy, and rectum excision). For matched win statistics, we used robust inference techniques proposed by Matsouaka et al. (2022), and for stratified win statistics, we applied the method proposed by Dong et al. (2018). A simulation study assessed the coverage probability of matched and stratified win statistics in balanced and unbalanced groups, calculating how often the confidence intervals included the true values of WR, NB, and WO across 1000 simulations. Results: The results suggest a better efficacy of the LE approach when considering efficacy outcomes alone (WR: 0.47 (0.01 to 1.14); NB: −0.16 (−0.34 to 0.02); and WO: 0.73 (0.5 to 1.05)). However, when QoL outcomes are included in the analyses, the estimates are closer to 1 (WR: 0.87 (0.06 to 2.06); WO: 0.93 (0.61 to 1.4)) and to 0 (NB: −0.04 (−0.25 to 0.17)), indicating a negative impact of the treatment effect of LE regarding the presence of ostomy and the excision of the rectum. Moreover, based on the simulation study, our findings underscore the superior performance of matched compared to stratified win statistics in terms of coverage probability (matched WR: 97% vs. stratified WR: 33.3% in a high-imbalance setting; matched WR: 98% vs. stratified WR: 34.4% in a medium-imbalance setting; and matched WR: 99.2% vs. stratified WR: 37.4% in a low-imbalance setting). Conclusions: In conclusion, our study sheds light on the interpretation of the results of win statistics in terms of statistical significance, providing insights into the application of pairwise comparison in observational settings, promoting its use to improve outcomes for cancer patients. [ABSTRACT FROM AUTHOR]

Background: Psoriasis is a common, T-cell-mediated inflammatory and immune-mediated skin disease. Numerous studies confirmed that patients with psoriasis have a significant frequency of cardiovascular (CV) risk factors and CV diseases (CVDs). Risk stratification is helpful in light of the elevated risk of CVD in psoriasis patients. SCORE2 and SCORE2-OP, a new algorithm derived, calibrated and validated to predict the 10-year risk of first-onset CVD in European populations, enhances the identification of individuals at higher risk of developing CVD across Europe. Objective: Using the SCORE2 and SCORE2-OP scoring systems, the current study objective was to evaluate CV risk in Slovak psoriasis patients and the relationship between CV risk and psoriasis features in a real-world setting. Results: A case–control study was conducted involving 115 outpatients with plaque psoriasis and 66 age- and gender-matched controls with skin conditions other than psoriasis. Patients with psoriasis had significantly higher mean SCORE2 values. In the age group up to 50 years, more psoriasis patients were classified as moderate risk than controls (33.8% vs. 13.6%, p = 0.010); the high-risk category was dominated by psoriasis patients. Analysing the relationship between CV risk and selected variables, we determined, using linear regression, the dependence of the SCORE2 risk score on gender in the age group up to 50 years, on age in both age groups, on waist circumference (WC) in the category up to 50 years and on the duration and severity of psoriasis in both age groups using linear regression. For individuals older than 70, we estimated the SCORE2-OP risk score, with the average risk score being 19.5 ± 4.95. We did not observe controls with a high risk score. Psoriasis patients were more likely to be smokers and had significantly higher mean values for body mass index (BMI), WC, total cholesterol (TC), low-density lipoprotein (LDL) and systolic blood pressure (BP). Conclusions: Because CV risk factors and psoriasis are strongly related, the importance of CV risk stratification is growing, and initiating preventive lifestyle changes or therapeutic interventions in patients with psoriasis is warranted. [ABSTRACT FROM AUTHOR]

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans. [ABSTRACT FROM AUTHOR]

Simple Summary: Non-small cell lung cancer (NSCLC) is a common diagnosis from which many patients die. Blood tests reflecting systemic inflammation are routinely collected in the NSCLC clinic and may provide information on a patient's likelihood of a future event. Despite this, these "prognostic biomarkers" are not routinely used in clinical practice. In this narrative review we describe the key biomarkers of systemic inflammation and their prognostic significance in NSCLC. We highlight several challenges that limit their clinical application, including the need to; define an optimal inflammatory biomarker, consider NSCLC as a collection of different diseases, and explore outcomes with respect to how they may change clinical practice. We discuss how these challenges may be overcome through collaboration and the standardisation of recording and reporting of inflammatory biomarker studies. Further, we highlight the potential of modern electronic patient records and advanced data-analyses techniques in this area of research. Non-small cell lung cancer (NSCLC) is a common malignancy and is associated with poor survival outcomes. Biomarkers of systemic inflammation derived from blood tests collected as part of routine clinical care offer prognostic information for patients with NSCLC that may assist clinical decision making. They are an attractive tool, as they are inexpensive, easily measured, and reproducible in a variety of healthcare settings. Despite the wealth of evidence available to support them, these inflammatory biomarkers are not yet routinely used in clinical practice. In this narrative review, the key inflammatory indices reported in the literature and their prognostic significance in NSCLC are described. Key challenges limiting their clinical application are highlighted, including the need to define the optimal biomarker of systemic inflammation, a lack of understanding of the systemic inflammatory landscape of NSCLC as a heterogenous disease, and the lack of clinical relevance in reported outcomes. These challenges may be overcome with standardised recording and reporting of inflammatory biomarkers, clinicopathological factors, and survival outcomes. This will require a collaborative approach, to which this field of research lends itself. This work may be aided by the rise of data-driven research, including the potential to utilise modern electronic patient records and advanced data-analysis techniques. [ABSTRACT FROM AUTHOR]

Background: There are algorithms to predict the risk of SARS-CoV-2-related complications. Given the spread of anti-COVID vaccination, which sensibly modified the burden of risk of the infection, these tools need to be re-calibrated. Therefore, we updated our vulnerability index, namely, the Health Search (HS)-CoVulnerabiltyIndex (VI)d (HS-CoVId), to predict the risk of SARS-CoV-2-related hospitalization/death in the primary care setting. Methods: We formed a cohort of individuals aged ≥15 years and diagnosed with COVID-19 between 1 January and 31 December 2021 in the HSD. The date of COVID-19 diagnosis was the study index date. These patients were eligible if they had received an anti-COVID vaccine at least 15 days before the index date. Patients were followed up from the index date until one of the following events, whichever came first: COVID-19-related hospitalization/death (event date), end of registration with their GPs, and end of the study period (31 December 2022). To calculate the incidence rate of COVID-19-related hospitalization/death, a patient-specific score was derived through linear combination of the coefficients stemming from a multivariate Cox regression model. Its prediction performance was evaluated by obtaining explained variation, discrimination, and calibration measures. Results: We identified 2192 patients who had received an anti-COVID vaccine from 1 January to 31 December 2021. With this cohort, we re-calibrated the HS-CoVId by calculating optimism-corrected pseudo-R2, AUC, and calibration slope. The final model reported a good predictive performance by explaining 58% (95% CI: 48–71%) of variation in the occurrence of hospitalizations/deaths, the AUC was 83 (95% CI: 77–93%), and the calibration slope did not reject the equivalence hypothesis (p-value = 0.904). Conclusions: Two versions of HS-CoVId need to be differentially adopted to assess the risk of COVID-19-related complications among vaccinated and unvaccinated subjects. Therefore, this functionality should be operationalized in related patient- and population-based informatic tools intended for general practitioners. [ABSTRACT FROM AUTHOR]

Background and Objectives: Several RCTs have reported significant reductions in depression symptoms with the Mediterranean diet (MedDiet), but observational studies have reported inconsistent findings. Moreover, studies have rarely investigated the mediating role of socioeconomic status (SES), including objective material status, in adherence to the MedDiet and its impact on depressive symptoms in the same population. Therefore, this cross-sectional study investigated the relationship between adherence to the MedDiet, socioeconomic factors, and depression severity. Materials and Methods: A cross-sectional online survey was conducted between June and December 2022 across Saudia Arabia. The snowball sampling technique was used to recruit participants aged ≥18 years. Mediterranean diet adherence screener (MEDAS) and Patient Health Questionnaire-9 (PHQ-9) were used to assess adherence to the MedDiet and depression severity. An SES index, validated in the Saudi Arabian context, was used to assess SES. The data were analyzed using the Chi-square and Pearson's correlation tests. Results: Only 21% of our study population (n = 467) was MedDiet adherent. Adherence was significantly associated with education (p = 0.014) but not employment status among traditional SES indicators. Similarly, only television ownership (p = 0.009) was associated with MedDiet adherence among the 20 objective material possessions investigated. Nonetheless, the MedDiet-adherent group had a significantly lower PHQ-9 score than the non-adherent group (6.16 ± 0.68 vs. 8.35 ± 0.31, p = 0.002). A moderate but significantly negative correlation between MEDAS and PHQ-9 scores (r = −0.16, p = 0.001) was noted. Conclusions: MedDiet adherence was associated with lower depression severity scores. In addition to education and television ownership, adherence was not associated with any objective indicators of SES. [ABSTRACT FROM AUTHOR]

The development of HIV prophylactic vaccines is facing an impasse, since all phase IIb/III clinical trials were halted in 2023 without demonstrating efficacy. Thus, the field is in need of developing novel immunogens and vaccination strategies that induce broadly neutralising antibodies together with potent Fc-dependent effector functions, as well as protective cross-reactive CD4+ and CD8+ T cell responses. Nucleic acid vaccines, particularly mRNA vaccines, have been one of the major groundbreaking advances in the current decade. Nucleic acid vaccines may help recalibrate the HIV vaccine field towards the use of delivery systems that allow the proper expression of immunogens as a sole antigen (i.e., membrane-bound trimeric envelope glycoproteins) or even to be displayed in a multiantigen platform that will be synthesised by the host. In this review, we will summarise how the multiple HIV vaccine strategies pursued in the last 40 years of HIV research have driven current vaccine development, which are the most relevant immunogens identified so far to induce balanced adaptive immune responses, and how they can benefit from the acceptance of nucleic acid vaccines in the market by reducing the limitations of previous delivery systems. The incorporation of nucleic acid vaccines into the current heterogeneous repertoire of vaccine platforms may represent an invaluable opportunity to reignite the fight against HIV. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive assessment of indoor particulate matter (PM) concentrations, focusing on PM1, PM2.5, and PM10 in five primary health centers (PHCs): Arakale (Urban), the Federal College of Agriculture (FECA) (Urban), Iju (semi-urban), Oba-Ile (semi-urban), and Owode (Rural) within Akure local government areas in Nigeria. The primary novelty of this research lies in its detailed exploration of the toxicity potential of these PM fractions, providing valuable insights into the local air quality and associated health risks. This study assesses the health implications by calculating the toxicity potential (TP), relative risk (RR), excess risk (ER), and attributable fractions (Afs) for cardiopulmonary and lung cancer mortality. The results reveal varying RR values for all-cause mortality, with Arakale showing a slight elevation (RR: 1.061), indicating potential health risks. ER values for cardiopulmonary mortality range from 14.728 ± 7.25 to 19.04 ± 0.38, emphasizing the substantial excess risk associated with long-term PM2.5 exposure. The study also uncovers Afs for cardiopulmonary mortality ranging between 11.03 ± 0.31 and 19.22 ± 0.04, underscoring the significant contribution of PM to cardiovascular and respiratory health risks. Similarly, Afs for lung cancer mortality range from 10.03 ± 0.012 to 17.13 ± 0.10, highlighting the substantial association between PM2.5 exposure and lung cancer risk. These findings underscore the urgent need for targeted air quality management strategies and public health interventions in the studied locations to mitigate the heightened health risks associated with particulate matter pollution. The calculated RR, ER, and AF values offer crucial insights into the complex relationship between PM exposure and adverse health outcomes, providing a foundation for informed decision-making and future research endeavors. [ABSTRACT FROM AUTHOR]

Background: Apixaban has been increasingly utilized for various FDA-approved indications, including stroke prevention and venous thromboembolism (VTE) treatment in patients with end stage kidney disease (ESKD) on hemodialysis. However, the safety and efficacy of its use in this population is not well established. Hence, the purpose of this study is to evaluate the safety and effectiveness of apixaban by examining outcomes in this population. Methods: This was a retrospective observational study that involved adults with ESKD who were on hemodialysis and prescribed apixaban from our hospital's outpatient pharmacy between 1 May 2015, and 31 March 2022. Demographics, apixaban indications, dose appropriateness, concomitant antiplatelet use, and comorbidities data were collected. Bleeding and thromboembolic events were also collected. Results: Sixty-six patients fulfilled the inclusion criteria, 50% of them males. Median age was 71 (63.5–82) years, and the median BMI 28.2 (59.5–86.25) kg/m2. The median follow-up time was 5 (1.9–12.3) months. Concomitant antiplatelet use (39.4%) and high medication adherence (84.8%) were observed. During follow-up, major bleeding events occurred in 15.2% of cases, with minor bleeding being more common (36.4%), and VTE and stroke events occurred in 4.5% of cases; appropriate dosing was prevalent (62.1%), and there was an overall all-cause mortality rate of 34.8%. Most patients received a 2.5 mg BID apixaban dose (56.1%), including both NVAF and VTE groups. Notably, the multivariate logistic regression analysis indicated that weight, and daily dose were insignificant predictors of bleeding events (p = 0.104, 0.591), however, the BMI was the main independent risk factor for bleeding in this population [OR = 0.9, 95% CI: 0.8–0.99; p = 0.023]. Conclusions: Our analysis of apixaban-treated ESKD patients highlights that the risk of bleeding is significant, and BMI was the main independent risk factor. A larger prospective study is needed to confirm our findings. [ABSTRACT FROM AUTHOR]

Aims: Overlapping asymmetric data sets are where a large cohort of observations have a small amount of information recorded, and within this group there exists a smaller cohort which have extensive further information available. Missing imputation is unwise if cohort size differs substantially; therefore, we aim to develop a way of modelling the smaller cohort whilst considering the larger. Methods: Through considering traditionally once penalized P-Spline approximations, we create a second penalty term through observing discrepancies in the marginal value of covariates that exist in both cohorts. Our now twice penalized P-Spline is designed to firstly prevent over/under-fitting of the smaller cohort and secondly to consider the larger cohort. Results: Through a series of data simulations, penalty parameter tunings, and model adaptations, our twice penalized model offers up to a 58% and 46% improvement in model fit upon a continuous and binary response, respectively, against existing B-Spline and once penalized P-Spline methods. Applying our model to an individual's risk of developing steatohepatitis, we report an over 65% improvement over existing methods. Conclusions: We propose a twice penalized P-Spline method which can vastly improve the model fit of overlapping asymmetric data sets upon a common predictive endpoint, without the need for missing data imputation. [ABSTRACT FROM AUTHOR]

Meteorological conditions and air quality are important environmental factors in the occurrence and development of cardiovascular diseases (CVDs) such as hypertension. The aim of this study was to take Haikou City, located on the tropical edge, as the research area and to analyze the exposure–response relationship and lag effect between its meteorological conditions, air quality, and the number of hypertensive patients. Using the data from the hypertension outpatient department of Hainan Provincial People's Hospital from 2016 to 2018, together with meteorological data and air quality data, a distributed lag nonlinear model based on the nested generalized addition model of meteorological element base variables was established. The results showed that the impact of temperature on the risk of hypertension was mainly due to the cold effect, which was associated with high risk, with a lag of 1–10 days. When the temperature dropped to 10 °C, the cumulative effect on the risk of hypertension of relative risk (RR) reached its highest value on the day the low temperature occurred (RR was 2.30 and the 95% confidence interval was 1.723~3.061), passing the test with a significance level of 0.05. This result indicated that efforts should be made to strengthen the prevention of hypertension under low-temperature conditions and the prediction and early warning of disease risks. The impact of the air-quality effect (the environmental Air Quality Index was selected as an indicator) on the risk of hypertension was mainly characterized by a low air-quality effect, with a lag effect of 0–8 days. When the risk reached approximately 124, the RR was highest (RR was 1.63 and the 95% confidence interval was 1.104~2.408), passing the test with a significance level of 0.05. The research results can provide technical support for conducting medical meteorological forecasting, early warning, and services for hypertension. A joint work and research mechanism among multiple departments such as meteorology and medical health should be established to improve the level of medical and health care, optimize the allocation of social resources, and develop targeted prevention and control strategies to reduce the health and economic burden of hypertension. [ABSTRACT FROM AUTHOR]