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pyAKI—An open source solution to automated acute kidney injury classification
Objective Acute kidney injury (AKI) is a frequent complication in critically ill patients, affecting up to 50% of patients in the intensive care units. The lack of standardized and open-source tools for applying the Kidney Disease Improving Global Outcomes (KDIGO) criteria to time series, requires researchers to implement classification algorithms of their own which is resource intensive and might impact study quality by introducing different interpretations of edge cases. This project introduces pyAKI, an open-source pipeline addressing this gap by providing a comprehensive solution for consistent KDIGO criteria implementation. Materials and methods The pyAKI pipeline was developed and validated using a subset of the Medical Information Mart for Intensive Care (MIMIC)-IV database, a commonly used database in critical care research. We constructed a standardized data model in order to ensure reproducibility. PyAKI implements the Kidney Disease: Improving Global Outcomes (KDIGO) guideline on AKI diagnosis. After implementation of the diagnostic algorithm, using both serum creatinine and urinary output data, pyAKI was tested on a subset of patients and diagnostic accuracy was compared in a comparative analysis against annotations by physicians. Results Validation against expert annotations demonstrated pyAKI’s robust performance in implementing KDIGO criteria. Comparative analysis revealed its ability to surpass the quality of human labels with an accuracy of 1.0 in all categories. Discussion The pyAKI pipeline is the first open-source solution for implementing KDIGO criteria in time series data. It provides a standardized data model and a comprehensive solution for consistent AKI classification in research applications for clinicians and data scientists working with AKI data. The pipeline’s high accuracy make it a valuable tool for clinical research and decision support systems. Conclusion This work introduces pyAKI as an open-source solution for implementing the KDIGO criteria for AKI diagnosis using time series data with high accuracy and performance.
Non-inferiority of minimally invasive rotational cutting in granulomatous mastitis treatment: a comparative trial
CD206+ Trem2+ macrophage accumulation in the murine knee joint after injury is associated with protection against post-traumatic osteoarthritis in MRL/MpJ mice
Post-traumatic osteoarthritis (PTOA) is a painful joint disease characterized by the degradation of bone, cartilage, and other connective tissues in the joint. PTOA is initiated by trauma to joint-stabilizing tissues, such as the anterior cruciate ligament, medial meniscus, or by intra-articular fractures. In humans, ~50% of joint injuries progress to PTOA, while the rest spontaneously resolve. To better understand molecular programs contributing to PTOA development or resolution, we examined injury-induced fluctuations in immune cell populations and transcriptional shifts by single-cell RNA sequencing of synovial joints in PTOA-susceptible C57BL/6J (B6) and PTOA-resistant MRL/MpJ (MRL) mice. We identified significant differences in monocyte and macrophage subpopulations between MRL and B6 joints. A potent myeloid-driven anti-inflammatory response was observed in MRL injured joints that significantly contrasted the pro-inflammatory signaling seen in B6 joints. Multiple CD206+ macrophage populations classically described as M2 were found enriched in MRL injured joints. These CD206+ macrophages also robustly expressed Trem2, a receptor involved in inflammation and myeloid cell activation. These data suggest that the PTOA resistant MRL mouse strain displays an enhanced capacity of clearing debris and apoptotic cells induced by inflammation after injury due to an increase in activated M2 macrophages within the synovial tissue and joint space.
A non-metallic PEEK topology optimization reconstruction implant for large mandibular continuity defects, validated using the MANDYBILATOR apparatus
Uncovering NK cell sabotage in gut diseases via single cell transcriptomics
The identification of immune environments and cellular interactions in the colon microenvironment is essential for understanding the mechanisms of chronic inflammatory disease. Despite occurring in the same organ, there is a significant gap in understanding the pathophysiology of ulcerative colitis (UC) and colorectal cancer (CRC). Our study aims to address the distinct immunopathological response of UC and CRC. Using single-cell RNA sequencing datasets, we analyzed the profiles of immune cells in colorectal tissues obtained from healthy donors, UC patients, and CRC patients. The colon tissues from patients and healthy participants were visualized by immunostaining followed by laser confocal microscopy for select targets. Natural killer (NK) cells from UC patients on medication showed reduced cytotoxicity compared to those from healthy individuals. Nonetheless, a UC-specific pathway called the BAG6-NCR3 axis led to higher levels of inflammatory cytokines and increased the cytotoxicity of NCR3+ NK cells, thereby contributing to the persistence of colitis. In the context of colorectal cancer (CRC), both NK cells and CD8+ T cells exhibited significant changes in cytotoxicity and exhaustion. The GALECTIN-9 (LGALS9)-HAVCR2 axis was identified as one of the CRC-specific pathways. Within this pathway, NK cells solely communicated with myeloid cells under CRC conditions. HAVCR2+ NK cells from CRC patients suppressed NK cell-mediated cytotoxicity, indicating a reduction in immune surveillance. Overall, we elucidated the comprehensive UC and CRC immune microenvironments and NK cell-mediated immune responses. Our findings can aid in selecting therapeutic targets that increase the efficacy of immunotherapy.
Evaluating the effect of viral posts on social media engagement
Backbone extraction through statistical edge filtering: A comparative study
The backbone extraction process is pivotal in expediting analysis and enhancing visualization in network applications. This study systematically compares seven influential statistical hypothesis-testing backbone edge filtering methods (Disparity Filter (DF), Polya Urn Filter (PF), Marginal Likelihood Filter (MLF), Noise Corrected (NC), Enhanced Configuration Model Filter (ECM), Global Statistical Significance Filter (GloSS), and Locally Adaptive Network Sparsification Filter (LANS)) across diverse networks. A similarity analysis reveals that backbones extracted with the ECM and DF filters exhibit minimal overlap with backbones derived from their alternatives. Interestingly, ordering the other methods from GloSS to NC, PF, LANS, and MLF, we observe that each method’s output encapsulates the backbone of the previous one. Correlation analysis between edge features (weight, degree, betweenness) and the test significance level reveals that the DF and LANS filters favor high-weighted edges while ECM assigns them lower significance to edges with high degrees. Furthermore, the results suggest a limited influence of the edge betweenness on the filtering process. The backbones global properties analysis (edge fraction, node fraction, weight fraction, weight entropy, reachability, number of components, and transitivity) identifies three typical behavior types for each property. Notably, the LANS filter preserves all nodes and weight entropy. In contrast, DF, PF, ECM, and GloSS significantly reduce network size. The MLF, NC, and ECM filters preserve network connectivity and weight entropy. Distribution analysis highlights the PU filter’s ability to capture the original weight distribution. NC filter closely exhibits a similar capability. NC and MLF filters excel for degree distribution. These insights offer valuable guidance for selecting appropriate backbone extraction methods based on specific properties.
Amino acid modified copper-based metal organic polyhedral with higher peroxidase activity for potassium guaiacol sulfonate detection
Risk assessment and prevention in airport security assurance by integrating LSTM algorithm
The risk assessment and prevention in traditional airport safety assurance usually rely on human experience for analysis, and there are problems such as heavy manual workload, excessive subjectivity, and significant limitations. This article proposed a risk assessment and prevention mechanism for airport security assurance that integrated LSTM algorithm. It analyzed the causes of malfunctioning flights by collecting airport flight safety log datasets. This article extracted features related to risk assessment, such as weather factors, airport facility inspections, and security check results, and conducted qualitative and quantitative analysis on these features to generate a datable risk warning weight table. This article used these data to establish an LSTM model, which trained LSTM to identify potential risks and provide early warning by learning patterns and trends in historical data. It then handed over the new data to the trained LSTM model for risk assessment and prediction, grading and warning of risks. It monitored the airport security situation in real-time based on the results and quickly notified airport security personnel to handle it. The outcome indicates that the standard error of the LSTM algorithm model training is less than 0.18, and the decision coefficients were all greater than 0.9. The predicted data was highly consistent with the actual data. It can be summarized that the algorithmic model has good accuracy and robustness. The LSTM algorithm can play a role in providing early warning, assisting decision-making, optimizing resources, and enhancing real-time monitoring in airport security assurance. It can effectively improve the safety and prevention capabilities of airports, and reduce the losses caused by potential risks.
Chitosan nanoencapsulation of Turbinaria triquetra metabolites in the management of podocyturia in nephrotoxic rats
AbstractCisplatin is a chemotherapeutic drug, which exhibits undesirable side effects. Chitosan nanoparticles are promising for drug delivery. The aim of this study was to determine the effect of the brown alga Turbinaria triquetra ethyl acetate fraction and polysaccharides, either loaded on chitosan nanoparticles or free, against podocyturia and cisplatin nephrotoxicity in rats. Sixty-six male rats were distributed into 11 equal groups: untreated control, chitosan (CSNPs), ethyl acetate fraction (EAE), polysaccharide (PS), EAE loaded on chitosan nanoparticles (EAE-CSNPs), PS loaded on chitosan nanoparticles (PS-CSNPs), Cisplatin or cis-diamminedichloroplatinum(II) (CDDP), CDDP + EAE, CDDP + PS, CDDP + EAE-CSNPs, and CDDP + PS-CSNPs. Serum urea, creatinine, creatinine clearance, renal malondialdehyde, nitric oxide, paraoxonase 1, renal nephrin, and podocin, and their renal mRNA gene expressions, as well as urinary nephrin and podocin were determined. The results indicated that the ethyl acetate fraction and polysaccharides, either free or loaded, efficiently attenuated podocyturia and cisplatin nephrotoxicity compared to the Cis group. However, the improvement was higher in the nephrotoxic groups treated with EAE-CSNPs and PS-CSNPs. The current study revealed that chitosan nanoencapsulation showed ameliorative effects against podocyturia and cisplatin nephrotoxicity in rats compared to free extracts, offering a new therapeutic strategy for attenuating podocyturia and CDDP-induced nephrotoxicity.
Investigation of alpha-glucosidase inhibition activity of Artabotrys sumatranus leaf extract using metabolomics, machine learning and molecular docking analysis
One way to treat diabetes mellitus type II is by using α-glucosidase inhibitor, that will slow down the postprandial glucose intake. Metabolomics analysis of Artabotrys sumatranus leaf extract was used in this research to predict the active compounds as α-glucosidase inhibitors from this extract. Both multivariate statistical analysis and machine learning approaches were used to improve the confidence of the predictions. After performance comparisons with other machine learning methods, random forest was chosen to make predictive model for the activity of the extract samples. Feature importance analysis (using random feature permutation and Shapley score calculation) was used to identify the predicted active compound as the important features that influenced the activity prediction of the extract samples. The combined analysis of multivariate statistical analysis and machine learning predicted 9 active compounds, where 6 of them were identified as mangiferin, neomangiferin, norisocorydine, apigenin-7-O-galactopyranoside, lirioferine, and 15,16-dihydrotanshinone I. The activities of norisocorydine, apigenin-7-O-galactopyranoside, and lirioferine as α-glucosidase inhibitors have not yet reported before. Molecular docking simulation, both to 3A4A (α-glucosidase enzyme from Saccharomyces cerevisiae, usually used in bioassay test) and 3TOP (a part of α-glucosidase enzyme in human gut) showed strong to very strong binding of the identified predicted active compounds to both receptors, with exception of neomangiferin which only showed strong binding to 3TOP receptor. Isolation based on bioassay guided fractionation further verified the metabolomics prediction by succeeding to isolate mangiferin from the extract, which showed strong α-glucosidase activity when subjected to bioassay test. The correlation analysis also showed a possibility of 3 groups in the predicted active compounds, which might be related to the biosynthesis pathway (need further research for verification). Another result from correlation analysis was that in general the α-glucosidase inhibition activity in the extract had strong correlation to antioxidant activity, which was also reflected in the predicted active compounds. Only one predicted compound had very low positive correlation to antioxidant activity.
Affordability of family foods is associated with Nutritional Status of women with pre-school children in Addis Ababa, Ethiopia
AbstractMalnutrition among women of reproductive age is a critical public health issue in LMICs, where undernutrition coexists with rising overweight and obesity rates. In Ethiopia, particularly among urban women, maternal and child undernutrition remains high despite efforts to combat poverty and food insecurity. This study examined the relationship between food affordability and the nutritional status of 4797 women in Addis Ababa. Using repeated cross-sectional surveys, body mass index was calculated, and women’s nutritional status was categorized. Data on the affordability of 11 healthy family food groups were collected and categorized into terciles. Multinomial logistic regression models analyzed the association between food affordability and nutritional status, adjusting for confounding factors. The prevalence of underweight among women was 7.3%, while 39.1% were overweight or obese. On average, families could afford 6.1 out of the 11 food groups. Women with higher food affordability had increased odds of being overweight (AOR: 1.32; 95% CI 1.09, 1.60) or obese (AOR: 1.50; 95% CI 1.14, 1.97). The findings highlight that two out of five women are either overweight or obese, with obesity more likely in those with greater perceived food affordability. Addressing food choices as food affordability improves is crucial, as better food affordability is associated with an increased risk of becoming overweight and obese.
An overview of healthcare-associated Candida auris outbreaks in Ministry of Health hospitals–Saudi Arabia 2020–2022; Retrospective multicentric study
Background Candida auris (C. auris) is an emerging fungus pathogen associated with nosocomial infections that is seen as a serious global health issue. Aim To describe the epidemiology and features of hospital-acquired Candida auris outbreaks in the Ministry of Health hospitals (MOH). Methods A three-year (2020–2022) retrospective analysis of Candida auris outbreaks in the Saudi MOH hospitals. A total of 45 hospitals were involved, with 511 cases of Candida auris. The data collected from the General Directorate of Infection Prevention and Control (GDIPC) platform comprises all patients’ data for C. auris instances, whether infected or colonized. Results Out of the 511 Candida auris recruited cases, 291 (56.9%) were infected, whereas 220 (43.1%) were colonized. 32.9% of cases were above the age of 65, and 68.9% were male. The majority of cases were admitted in the ICU unit (95.5%). Approximately 18.8% of cases were diabetes, and 18.5% were hypertensive. Central lines were discovered in 37.7% of the cases. Approximately 85.9% of cases had Foley catheters, 68.5% were on ventilators, and 53.8% had a tracheostomy. The crude mortality rate was particularly high (41.5%) among the study cases. It was non-significantly higher among infected individuals (44.7%) than colonized patients (37.3%) at (p value = 0.093). The prevalence of hypertension (p = 0.001), DM (p = 0.003), and peripheral line insertion (p = 0.004) was significantly higher among colonized patients. While the presence of COVID-19 (p = 0.001), and central line insertion (p = 0.001) was significantly higher among infected patients. Conclusion C. auris is a new pathogen that causes hospital outbreaks. Strict adherence to infection prevention and control criteria established by the Centre for Disease Control (CDC) and GDPIC has significantly contained and reduced the spread of these outbreaks. One-month retrospective surveillance before diagnosing the index case and a prospective surveillance strategy for at least three months is highly recommended.
Exploring the effects of perceived social support and psychological distress through mediation and multigroup analyses in work-related quality of life
AbstractWork-related quality of life has emerged as a central focus in the field of occupational health due to its impact on individual well-being and overall quality of life. So, it is crucial to enhance the current theory by conducting a study on the concept across different sectors. Also, the specific mechanisms through which perceived social support influences work-related quality of life remain underexplored. The role of psychological distress as a potential mediator in this relationship has not been investigated. Thus, the study aimed to explore the mediating role of psychological distress in this relationship, while also examining potential gender differences. The study, conducted with 401 teachers through a questionnaire, utilized the AMOS program for data analysis. The findings supported the direct effects among the variables and the mediating role of psychological distress. Additionally, the impact of psychological distress on work-related quality of life was found to be higher for females, but no differences for other paths. The study contributes to the literature by addressing the gap in understanding the mechanisms regarding work-related quality of life. The results highlight the importance of considering psychological distress and gender differences in interventions aimed at improving work-related quality of life.
Correction: Will R&D make investors more tolerant? Analysis based on the performance forecast of Chinese listed companies
Study on mechanical properties of sandstone damaged by blasting load under triaxial cyclic loading and unloading
Monocyte-cancer cell fusion is mediated by phosphatidylserine—CD36 receptor interaction and induced by ionizing radiation
Emerging evidence suggests that fusion of cancer cells with leucocytes, such as macrophages, plays a significant role in cancer metastasis and results in tumor hybrid cells that acquire resistance to chemo- and radiation therapy. However, the precise mechanisms behind the leukocyte-cancer cell fusion remain unclear. The present in vitro study explores the presence of fusion between the monocyte cell line (THP-1) and the breast cancer cell line (MCF-7) in relation to the expression of CD36 and phosphatidylserine with and without treatment of these cells with ionizing radiation. The study reveals that spontaneous THP-1/MCF-7 cell fusion increases significantly from 2.8% to 6% after irradiation. The interaction between CD36 and phosphatidylserine plays a pivotal role in THP-1/MCF-7 cell fusion, as inhibiting this interaction using anti-CD36 antibodies significantly reduces cell fusion. While irradiation leads to a dose-dependent escalation in phosphatidylserine expression in MCF-7 cells, it does not impact the expression of CD36 in either THP-1 or MCF-7 cells. To the best of our knowledge, this is the first study to demonstrate the involvement of the CD36-phosphatidylserine interaction in the fusion between monocytes and cancer cells, shedding light on a novel explanatory mechanism for the roles of CD36 and phosphatidylserine in tumor progression.
Design and synthesis of antiproliferative 2-oxoindolin-3-ylidenes incorporating urea function with potential VEGFR-2 inhibitory properties
AbstractTargeted therapy is preferable over other therapeutics due to its limitation of drawbacks and better pharmaceutical outcomes. VEGF and its receptors have been observed to be hyper-activated in many cancer types and are considered promising targets for assigning anticancer agents. The current study is directed towards synthesis of novel antiproliferative 2-oxoindolin-3-ylidenes incorporating urea function with VEGFR-2 properties. The targeted agents were obtained through a two-step reaction. Addition of the appropriate 1-(acetylphenyl)-3-phenylurea 9a,b to the corresponding isatin 10a–f in ethanol containing a quantitative amount of Et2NH followed by acidic dehydration (AcOH/HCl) afforded the targeted agents 12a–j. Promising antiproliferation properties (MTT assay) were observed for most of the synthesized agents against HCT116 (colon), MCF7 (breast) and PaCa2 (pancreatic) cancer cell lines relative to sunitinib. VEGFR-2 inhibitory properties are consistent with the antiproliferation properties exhibited against the tested cell lines. Compound 12b (R = 4-NHCONHPh, R′ = H; % inhibition = 87.2) is the most promising/potent anti-VEGFR-2 agent synthesized with activity close to that of sunitinib (% inhibition = 89.4) at 10 μM. Molecular docking studies (PDB: 3WZE and 3AGD) support the antiproliferation effects against cancer cell lines tested with VEGFR-2 inhibitory properties. The results are consistent with collaboration of the pharmacophores considered (2-oxoindolyl heterocycle and urea) in improving the bio-properties.
Unravelling the complexity of the relationship between social support sources and loneliness: A mixed-methods study with older adults
Loneliness is an increasingly significant social and public health issue in contemporary societies. The available evidence suggests that social support is one of the key psychosocial processes for the reduction and prevention of loneliness. This study investigated the role played by sources of social support in the experience of social and emotional loneliness, identifying seven sources of support split between family (spouse/partner, children, grandchildren, siblings) and non-family (friends, neighbours). The study population comprised people aged 65 years and over living in Spain, with a partner (without cohabiting children), alone or in a nursing home. A mixed-methods approach was used, combining data from a survey involving 887 participants (quantitative phase) and data from semi-structured interviews with 30 older adults (qualitative phase). The relationship between the various sources and loneliness was analysed using structural equation modelling (SEM) for the survey data and thematic analysis for the qualitative information. The results from both phases of the study suggest different association dynamics between sources of social support and the social and emotional dimensions of loneliness. Lower levels of emotional loneliness were related to support from the following sources: spouse, children, grandchildren, siblings and friends. Lower levels of social loneliness were related to support from the following sources: spouse, grandchildren, siblings and friends. In contrast, greater levels of emotional loneliness were related to support from neighbours and greater levels of social loneliness were related to support from children. The findings of this study contribute to a better understanding of the association between social support and loneliness and suggest that interventions aimed at reducing loneliness could be more effectively targeted by considering the specific effects of support derived from different sources.