Search Results (124)

Background: Surgeries represent a mainstay of medical care globally. Patterns of complications are frequently recognized late and place a considerable burden on health care systems. The aim was to develop and test the first deep learning-adjusted CUSUM program (DL-CUSUM) to predict and monitor in-hospital mortality in real time after liver transplantation. Methods: Data from 1066 individuals with 66,092 preoperatively available data point variables from 2004 to 2019 were included. DL-CUSUM is an application to predict in-hospital mortality. The area under the curve for risk adjustment with Model of End-stage Liver Disease (D-MELD), Balance of Risk (BAR) score, and deep learning (DL), as well as the ARL (average run length) and control limit (CL) for an in-control process over 5 years, were calculated. Results: D-MELD AUC was 0.618, BAR AUC was 0.648 and DL model AUC was 0.857. CL with BAR adjustment was 2.3 with an ARL of 326.31. D-MELD reached an ARL of 303.29 with a CL of 2.4. DL prediction resulted in a CL of 1.8 to reach an ARL of 332.67. Conclusions: This work introduces the first use of an automated DL-CUSUM system to monitor postoperative in-hospital mortality after liver transplantation. It allows for the real-time risk-adjusted monitoring of process quality. Full article

Texts recounting the careers of saints were foundational to Christian worship and historical construction in medieval Europe. They were also fluid, living works that evolved over time as individual saints’ stories were revised, adapted, and retold. These texts changed in response to changing contexts in which they were used and understood. This article undertakes a case study to see how the evolution of one urban saint’s legend reflects the history of that saint’s city. Specifically, it analyzes the numerous Latin and vernacular texts produced between the mid-fifth and late twelfth centuries that recount the deeds of Saint Trophimus, first bishop of Arles. It argues that shifts in the saint’s story reflect broad changes in the political, religious, and social life of Arles. It also demonstrates that the number of parties recounting the legend multiplied over time, and that dissonances within the story arose as these groups adapted the tale to their own interests. Full article

Decreased medial cheek fat volume during aging leads to loss of a youthful facial shape. Increasing facial volume by methods such as adipose-derived stem cell (ASC) injection can produce facial rejuvenation. High-intensity focused ultrasound (HIFU) can increase adipogenesis in subcutaneous fat by modulating cilia on ASCs, which is accompanied by increased HSP70 and decreased NF-κB expression. Thus, we evaluated the effect of HIFU on increasing facial adipogenesis in swine (n = 2) via modulation of ASC cilia. Expression of CD166, an ASC marker, differed by subcutaneous adipose tissue location. CD166 expression in the zygomatic arch (ZA) was significantly higher than that in the subcutaneous adipose tissue in the mandible or lateral temporal areas. HIFU was applied only on the right side of the face, which was compared with the left side, where HIFU was not applied, as a control. HIFU produced a significant increase in HSP70 expression, decreased expression of NF-κB and a cilia disassembly factor (AURKA), and increased expression of a cilia increasing factor (ARL13B) and PPARG and CEBPA, which are the main regulators of adipogenesis. All of these changes were most prominent at the ZA. Facial adipose tissue thickness was also increased by HIFU. Adipose tissue volume, evaluated by magnetic resonance imaging, was increased by HIFU, most prominently in the ZA. In conclusion, HIFU increased ASC marker expression, accompanied by increased HSP70 and decreased NF-κB expression. Additionally, changes in cilia disassembly and length and expression of adipogenesis were observed. These results suggest that HIFU could be used to increase facial volume by modulating adipogenesis. Full article

This study investigates the extension of the c-chart control chart to Bayesian methodology, utilizing the gamma distribution to establish control limits. By comparing the performance of the Bayesian approach with that of two existing methods (the traditional frequentist method and the Bayesian with Jeffreys method), we assess its effectiveness in terms of the average run lengths (ARLs) and false alarm rates (FARs). Simulation results indicate that the proposed Bayesian method consistently outperforms the existing techniques, offering larger ARLs and smaller FARs that closely approximate the expected nominal values. While the Bayesian approach excels in most scenarios, challenges may arise with large values of the λ parameter, necessitating adjustments to the hyperparameters of the gamma prior. Specifically, smaller values of the rate parameter are recommended for optimal performance. Overall, our findings suggest that the Bayesian extension of the c-chart provides a promising alternative for enhanced process monitoring and control. Full article

The retinal features of Bardet–Biedl syndrome (BBS) are insufficiently characterized in Arab populations. This retrospective study investigated the retinal features and genotypes of BBS in Saudi patients managed at a single tertiary eye care center. Data analysis of the identified 46 individuals from 31 families included visual acuity (VA), systemic manifestations, multimodal retinal imaging, electroretinography (ERG), family pedigrees, and genotypes. Patients were classified to have cone–rod, rod–cone, or generalized photoreceptor dystrophy based on the pattern of macular involvement on the retinal imaging. Results showed that nyctalopia and subnormal VA were the most common symptoms with 76% having VA ≤ 20/200 at the last visit (age: 5–35). Systemic features included obesity 91%, polydactyly 56.5%, and severe cognitive impairment 33%. The predominant retinal phenotype was cone–rod dystrophy 75%, 10% had rod–cone dystrophy and 15% had generalized photoreceptor dystrophy. ERGs were undetectable in 95% of patients. Among the 31 probands, 61% had biallelic variants in BBSome complex genes, 32% in chaperonin complex genes, and 6% had biallelic variants in ARL6; including six previously unreported variants. Interfamilial and intrafamilial variabilities were noted, without a clear genotype–phenotype correlation. Most BBS patients had advanced retinopathy and were legally blind by early adulthood, indicating a narrow therapeutic window for rescue strategies. Full article

Mucopolysaccharidoses (MPS) are a group of diseases caused by mutations in genes encoding lysosomal enzymes that catalyze reactions of glycosaminoglycan (GAG) degradation. As a result, GAGs accumulate in lysosomes, impairing the proper functioning of entire cells and tissues. There are 14 types/subtypes of MPS, which are differentiated by the kind(s) of accumulated GAG(s) and the type of a non-functional lysosomal enzyme. Some of these types (severe forms of MPS types I and II, MPS III, and MPS VII) are characterized by extensive central nervous system disorders. The aim of this work was to identify, using transcriptomic methods, organelle-related genes whose expression levels are changed in neuronopathic types of MPS compared to healthy cells while remaining unchanged in non-neuronopathic types of MPS. The study was conducted with fibroblast lines derived from patients with neuronopathic and non-neuronopathic types of MPS and control (healthy) fibroblasts. Transcriptomic analysis has identified genes related to cellular organelles whose expression is altered. Then, using fluorescence and electron microscopy, we assessed the morphology of selected structures. Our analyses indicated that the genes whose expression is affected in neuronopathic MPS are often associated with the structures or functions of the cell nucleus, endoplasmic reticulum, or Golgi apparatus. Electron microscopic studies confirmed disruptions in the structures of these organelles. Special attention was paid to up-regulated genes, such as PDIA3 and MFGE8, and down-regulated genes, such as ARL6IP6, ABHD5, PDE4DIP, YIPF5, and CLDN11. Of particular interest is also the GM130 (GOLGA2) gene, which encodes golgin A2, which revealed an increased expression in neuronopathic MPS types. We propose to consider the levels of mRNAs of these genes as candidates for biomarkers of neurodegeneration in MPS. These genes may also become potential targets for therapies under development for neurological disorders associated with MPS and candidates for markers of the effectiveness of these therapies. Although fibroblasts rather than nerve cells were used in this study, it is worth noting that potential genetic markers characteristic solely of neurons would be impractical in testing patients, contrary to somatic cells that can be relatively easily obtained from assessed persons. Full article

In this article, we establish new distribution-free Shewhart-type control charts based on rank sum statistics with signaling multiple scans-type rules. More precisely, two Wilcoxon-type chart statistics are considered in order to formulate the decision rule of the proposed monitoring scheme. In order to enhance the performance of the new nonparametric control charts, multiple scans-type rules are activated, which make the proposed chart more sensitive in detecting possible shifts of the underlying distribution. The appraisal of the proposed monitoring scheme is accomplished with the aid of the corresponding run length distribution under both in- and out-of-control cases. Thereof, exact formulae for the variance of the run length distribution and the average run length (ARL) of the proposed monitoring schemes are derived. A numerical investigation is carried out and depicts that the proposed schemes acquire better performance towards their competitors. Full article

Switchgrass is an important bioenergy crop valued for its biomass yield and abiotic tolerance. Alkali stress is a major abiotic stress that significantly impedes plant growth and yield due to high salinity and pH; however, the response mechanism of switchgrass to alkali stress remains limited. Here, we characterized PvARL1, an ARF-like gene, which was up-regulated in both the shoot and root tissues under alkali stress conditions. Overexpression of PvARL1 not only improved alkali tolerance but also promoted biomass yield with more tiller and higher plant height in switchgrass. Moreover, PvARL1 overexpression lines displayed higher capacities in the maintenance of water content and photosynthetic stability compared with the controls under alkali treatments. A significant reduction in the ratio of electrolyte leakage, MDA content, and reactive oxygen species (ROS) showed that PvARL1 plays a positive role in protecting cell membrane integrity. In addition, PvARL1 also negatively affected the K⁺ efflux or uptake in roots to alleviate ion toxicity under alkali treatments. Overall, our results suggest that PvARL1 functions as a positive regulator in plant growth as well as in the plant response to alkali stress, which could be used to improve switchgrass biomass yield and alkali tolerance genetically. Full article

Primary cilia are hair-like structures found on nearly all mammalian cell types, including cells in the developing and adult brain. A diverse set of receptors and signaling proteins localize within cilia to regulate many physiological and developmental pathways, including the Hedgehog (Hh) pathway. Defects in cilia structure, protein localization, and function lead to genetic disorders called ciliopathies, which present with various clinical features that include several neurodevelopmental phenotypes and hyperphagia-associated obesity. Despite their dysfunction being implicated in several disease states, understanding their roles in central nervous system (CNS) development and signaling has proven challenging. We hypothesize that dynamic changes to ciliary protein composition contribute to this challenge and may reflect unrecognized diversity of CNS cilia. The proteins ARL13B and ADCY3 are established markers of cilia in the brain. ARL13B is a regulatory GTPase important for regulating cilia structure, protein trafficking, and Hh signaling, and ADCY3 is a ciliary adenylyl cyclase. Here, we examine the ciliary localization of ARL13B and ADCY3 in the perinatal and adult mouse brain. We define changes in the proportion of cilia enriched for ARL13B and ADCY3 depending on brain region and age. Furthermore, we identify distinct lengths of cilia within specific brain regions of male and female mice. ARL13B+ cilia become relatively rare with age in many brain regions, including the hypothalamic feeding centers, while ADCY3 becomes a prominent cilia marker in the mature adult brain. It is important to understand the endogenous localization patterns of these proteins throughout development and under different physiological conditions as these common cilia markers may be more dynamic than initially expected. Understanding regional- and developmental-associated cilia protein composition signatures and physiological condition cilia dynamic changes in the CNS may reveal the molecular mechanisms associated with the features commonly observed in ciliopathy models and ciliopathies, like obesity and diabetes. Full article

Staphylococcus aureus can form biofilms on biotic surfaces or implanted materials, leading to biofilm-associated diseases in humans and animals that are refractory to conventional antibiotic treatment. Recent studies indicate that the unique ArlRS regulatory system in S. aureus is a promising target for screening inhibitors that may eradicate formed biofilms, retard virulence and break antimicrobial resistance. In this study, by screening in the library of FDA-approved drugs, tilmicosin was found to inhibit ArlS histidine kinase activity (IC₅₀ = 1.09 μM). By constructing a promoter-fluorescence reporter system, we found that tilmicosin at a concentration of 0.75 μM or 1.5 μM displayed strong inhibition on the expression of the ArlRS regulon genes spx and mgrA in the S. aureus USA300 strain. Microplate assay and confocal laser scanning microscopy showed that tilmicosin at a sub-minimal inhibitory concentration (MIC) had a potent inhibitory effect on biofilms formed by multiple S. aureus strains and a strong biofilm-forming strain of S. epidermidis. In addition, tilmicosin at three-fold of MIC disrupted USA300 mature biofilms and had a strong bactericidal effect on embedded bacteria. Furthermore, in a BioFlux flow biofilm assay, tilmicosin showed potent anti-biofilm activity and synergized with oxacillin against USA300. Full article

The time-between-events idea is commonly used for monitoring high-quality processes. This study aims to monitor the increase and/or decrease in the process mean rapidly using a one-sided exponentially weighted moving average (EWMA) chart for the detection of upward or downward mean shifts using a truncated gamma distribution. The use of the truncation method helps to enhance and improve the sensitivity of the proposed chart. The performance of the proposed chart with known and estimated parameters is analyzed by using the run length properties, including the average run length (ARL) and standard deviation run length (SDRL), through extensive Monte Carlo simulation. The numerical results show that the proposed scheme is more sensitive than the existing ones. Finally, the chart is implemented in real-world situations to highlight the significance of the proposed chart. Full article

Statistical process control (SPC) is used for monitoring and detecting anomalies in processes in the areas of manufacturing, environmental studies, economics, and healthcare, among others. Herein, we introduce an innovative SPC approach via mathematical modeling and report on its application via simulation studies to examine its suitability for monitoring processes involving correlated data running on advanced control charts. Specifically, an approach for detecting small to moderate shifts in the mean of a process running on a homogenously weighted moving average (HWMA) control chart, which is symmetric about the center line with upper and lower control limits, is of particular interest. A mathematical model for the average run length (ARL) of a moving average process with exogenous variables (MAX) focused only on the zero-state performance of the HWMA control chart is derived based on explicit formulas. The performance of our approach was investigated in terms of the ARL, the standard deviation of the run length (SDRL), and the median run length (MRL). Numerical examples are given to illustrate the efficacy of the proposed method. A detailed comparative analysis of our method for processes on HWMA and cumulative sum (CUSUM) control charts was conducted for process mean shifts in many situations. For several values of the design parameters, the performances of these two control charts are also compared in terms of the expected ARL (EARL), expected SDRL (ESDRL), and expected MRL (EMRL). It was found that the performance of the HWMA control chart was superior to that of the CUSUM control chart for several process mean shift sizes. Finally, the applicability of our method on a HWMA control chart is provided based on a real-world economic process. Full article

A murine osmotic demyelinating syndrome (ODS) model was developed through chronic hyponatremia, induced by desmopressin subcutaneous implants, followed by precipitous sodium restoration. The thalamic ventral posterolateral (VPL) and ventral posteromedial (VPM) relay nuclei were the most demyelinated regions where neuroglial damage could be evidenced without immune response. This report showed that following chronic hyponatremia, 12 h and 48 h time lapses after rebalancing osmolarity, amid the ODS-degraded outskirts, some resilient neuronal cell bodies built up primary cilium and axon hillock regions that extended into axon initial segments (AIS) where ADP-ribosylation factor-like protein 13B (ARL13B)-immunolabeled rod-like shape content was revealed. These AIS-labeled shaft lengths appeared proportional with the distance of neuronal cell bodies away from the ODS damaged epicenter and time lapses after correction of hyponatremia. Fine structure examination verified these neuron abundant transcriptions and translation regions marked by the ARL13B labeling associated with cell neurotubules and their complex cytoskeletal macromolecular architecture. This necessitated energetic transport to organize and restore those AIS away from the damaged ODS core demyelinated zone in the murine model. These labeled structures could substantiate how thalamic neuron resilience occurred as possible steps of a healing course out of ODS. Full article

The purpose of this work is to substantiate the approach to assessing the state of organic waste generation by households of a given community, which is based on passive production observations and intellectual analysis of statistical data, which ensures consideration of the factors and features of organic waste generation, as well as the development of qualitative models for forecasting their receipt. To achieve the goal, the following tasks were solved: the analysis of the state of organic waste generation by households in the EU countries was performed; an approach to assessing the state of organic waste generation by households of a given community is proposed; based on the use of the proposed approach, and models for assessing the state of organic waste generation of households in a given community were substantiated. The hypothesis of the study is to substantiate and use an approach to assessing the generation of organic waste by households in individual communities, based on the method of association learning and search for association rules, which will identify factors that have a significant impact on the volume of organic waste generated by households, the consideration of which will improve the accuracy of forecasting models and improve the quality of management of the processes of collection and processing of this waste in communities. The research methodology used allows for the use of data mining, probability theory, mathematical statistics, machine learning technology, and the Associative Rule Learning (ARL) method. Based on the use of a reasonable algorithm, they identify key trends and relationships between the factors of organic waste generation in communities in different countries, which is the basis for creating accurate models for predicting the volume of collection and processing of this waste in communities. The study found that the largest number of households produced organic waste per capita in the range of 0.14–0.25 kg/person. At the same time, most households have from two to four residents and are located on the adjoining territory from 350 m² to 680 m². Based on the method of learning associative rules, it was found that there are no close correlations between individual factors that determine the daily volume of organic waste generation by households per capita. The highest correlation coefficient between the type of housing and the income level of household residents is 0.13. The number of residents and the occupied area of the adjacent territory have the greatest impact on the daily volume of organic waste generated by households per capita. The substantiated associative rules of relationships, as well as the diagrams of relationships between factors, have helped to identify those factors that have the greatest impact on the volume of organic waste generation. They are the basis for creating accurate models for predicting the volume of collection and planning the processing of this waste in communities. Based on the proposed approach, Python 3.9 software was developed. It makes it possible to quickly carry out calculations and perform a quantitative assessment of the state of organic waste generation by households of a given community according to the specified rules of association between the volumes of organic waste generation and their factors. The results of the study are the basis for the further development of models for accurate forecasting of the collection and planning of the processing of organic waste from households in communities. Full article

Aim: Method: This research presents a model combining machine learning (ML) techniques and eXplainable artificial intelligence (XAI) to predict breast cancer (BC) metastasis and reveal important genomic biomarkers in metastasis patients. Method: A total of 98 primary BC samples was analyzed, comprising 34 samples from patients who developed distant metastases within a 5-year follow-up period and 44 samples from patients who remained disease-free for at least 5 years after diagnosis. Genomic data were then subjected to biostatistical analysis, followed by the application of the elastic net feature selection method. This technique identified a restricted number of genomic biomarkers associated with BC metastasis. A light gradient boosting machine (LightGBM), categorical boosting (CatBoost), Extreme Gradient Boosting (XGBoost), Gradient Boosting Trees (GBT), and Ada boosting (AdaBoost) algorithms were utilized for prediction. To assess the models’ predictive abilities, the accuracy, F1 score, precision, recall, area under the ROC curve (AUC), and Brier score were calculated as performance evaluation metrics. To promote interpretability and overcome the “black box” problem of ML models, a SHapley Additive exPlanations (SHAP) method was employed. Results: The LightGBM model outperformed other models, yielding remarkable accuracy of 96% and an AUC of 99.3%. In addition to biostatistical evaluation, in XAI-based SHAP results, increased expression levels of TSPYL5, ATP5E, CA9, NUP210, SLC37A1, ARIH1, PSMD7, UBQLN1, PRAME, and UBE2T (p ≤ 0.05) were found to be associated with an increased incidence of BC metastasis. Finally, decreased levels of expression of CACTIN, TGFB3, SCUBE2, ARL4D, OR1F1, ALDH4A1, PHF1, and CROCC (p ≤ 0.05) genes were also determined to increase the risk of metastasis in BC. Conclusion: The findings of this study may prevent disease progression and metastases and potentially improve clinical outcomes by recommending customized treatment approaches for BC patients. Full article