Excellent Model Research Articles

New Manifestations of the Strong Nuclear Interactions in Solids

The discovery of global macroscopic, including optical, characteristics with the addition of one neutron made crystals LiH, LiD, Diamond, Graphene an excellent model for studying the strong nuclear interaction. New manifestations of strong interaction in solids are measurements of its dependence on the distance between nucleons, finding a neutron - electron binding energy (106 meV), which is in excellent agreement with the prediction of the theory (106,7 meV). Moreover, the creation of mass in massless Dirac fermions in graphene by strong nuclear interaction has been demonstrated for the first time.

SEMG biceps and triceps effort signals classification using 1D-CNN convolution

In this paper, we present a system for acquiring and classifying surface physiological muscles signals (sEMG) for the biceps and triceps muscles during movement or work, as normal or aggressive effort in order to control and command the aid prostheses to intervene only during aggressive efforts. Thus, the main objective of our work is to developing and improving the performance of the hand prostheses for daily life tasks for elderly persons or for persons who have hand muscle failure. Our contribution consists of detecting and classifying physiological signals of biceps and triceps muscles hand as normal and aggressive efforts, for that we proposed a technique based on a 1D Convolutional Neural Network (CNN-1D) using the Wavelet Scattering Transform as the sEMG feature extraction technique. Our methodology is carried out in two steps: the first step is crucial to build a database for deep learning network for sEMG signals classification based on fifty-five volunteers spanning various ages and genders. The second step achieves the sEMG signals effort classification as normal or aggressive efforts based on the classification network produced based on sEMG signal sequences treated by the WST. The obtained results for the training and validation sets indicate perfect performance of the proposed technique, with an accuracy, precision, sensitivity, and specificity of 100% for the training process, and 99.3%, 98.6%, 100% and 98.7% for the accuracy, precision, sensibility and specificity respectively. It is important to note that while perfect metrics on the training and the test set might suggest excellent model learning.

Pharmacological Perspectives of 2‐alkoxy‐3‐Cyanopyridine Scaffolds: An Up‐to‐Date Review

AbstractThe 2‐alkoxy‐3‐cyanopyridine derivatives’ straightforward synthesis, ease of structural manipulation, and variety of intriguing biological activity continue to captivate medicinal chemists. A substantial body of research has demonstrated that these derivatives exhibit several intriguing biological characteristics, including anticancer, antibacterial, antidiabetic, antioxidant, vasorelaxant, antiviral, and anti‐inflammatory effects. Based on the basic skeleton of 2‐alkoxy‐3‐cyanopyridine, some lead compounds have been prepared with varied pharmaceutical characteristics. These served as excellent models for the development of novel medications since they showed potential in vitro and in vivo action against several cell lines that were both drug‐susceptible and drug‐resistant. To expand the use of 2‐alkoxy‐3‐cyanopyridine in biomedicine, though, a lot more efforts are essential. To combat medication resistance and boost specificity, potential hybrids that unite the 2‐alkoxy‐3‐cyanopyridine moiety with other pharmacophores are indeed required. This review offers a summary of pharmacological screening, including studies of pertinent structure‐activity connections, action mechanisms, and potential therapeutic use.

Predicting atrial fibrillation risk following embolic stroke of unknown source using cardiac magnetic resonance imaging

Abstract Background Approximately 25% of embolic strokes of unknown source (ESUS) are attributed to undiagnosed atrial fibrillation (AF).1 The European Stroke Organisation (ESO) recommends implantable loop recorder (ILR) monitoring to identify ESUS patients with AF, permit the initiation of anticoagulation and reduce risk of recurrent stroke.2 However, this approach delays treatment and carries significant cost and resource implications. An alternative approach, where early anticoagulation is initiated in the absence of AF has failed to reduce recurrent stroke in four randomised controlled trials (NAVIGATE ESUS, RE-SPECT ESUS, ATTICUS and ARCADIA).3-6 An urgent need exists for new strategies to identify ESUS patients with undiagnosed AF that are at high risk of future thromboembolism. Purpose The primary objective of this study was to produce a substrate-based predictive model for AF in ESUS patients using atrial cardiac magnetic resonance imaging (CMRi). Method The Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented AF (CARM-AF) Study is a prospective, multi-centre, observational study. All patients underwent CMRi and ILR insertion for AF detection within 3 months of ESUS. The main inclusion criteria were expected survival >12 months, CHA2DS2VASc≥3 and eGFR >30ml/min. Patients were allocated to the study group if AF >30 seconds was detected by ILR during the first year of follow-up and control group if no AF detected (Figure 1). Univariable analysis was used to identify clinical, echocardiographic and CMR parameters associated with AF diagnosis. Parameters with p<0.2 and no co-linearity were used to develop multivariable logistic regression models for AF prediction. Results From September 2020 to September 2022, 102 patients were enrolled and 91 were included in the final analysis. AF was detected in 17 patients during the first year of follow-up (18.6%). In univariable analysis, patients with AF were more likely to be female (p=0.02), but there were no significant differences in age, race, body mass index (BMI) or CHA2DS2VASc score (p=0.06, 0.272, 0.195, 0.211) between groups. Increased left atrial (LA) volume, surface area and reduced LA ejection fraction were associated with increased risk of AF detection at 1 year (p=0.006, 0.044, 0.008). LA fibrosis and sphericity were not associated with AF (p=0.84, 0.98). Logistic regression models for AF risk prediction were developed using patient, echocardiographic and CMR parameters. A combined model of patient and CMR parameters outperformed assessment of patient characteristics alone, achieving an AUC of 0.85 for predicting AF occurrence (p<0.005). Pseudo R2 of 0.30 (McFadden) indicated excellent model fit (Figure 2). Conclusion CARM-AF is the first study to demonstrate feasibility and utility of CMR imaging to predict AF risk following ESUS. A randomised controlled trial initiating anticoagulation using the developed model is recommended.Study DesignModel Comparison (ROC Curves)

Trainee as trainer in paediatric laparoscopic appendicectomy.

Intraoperative teaching is an essential surgical skill, but there is little literature regarding trainees acting as trainers; we characterised these cases in paediatric laparoscopic appendicectomy. This is a retrospective review of casenotes over two years (2015-2017) in a single tertiary paediatric surgical centre in the UK. Operating were: paediatric surgery Core Trainees (CT) (postgraduate year (PGY)3-4), Junior Registrars (JR) (PGY5-6) and Senior Registrars (SR) (PGY7+); collectively described as trainees. A total of 53 (20.7%) of 256 appendicectomies were trainee as trainer (TT) cases; 22 cases (41.5%) were performed by a CT supervised by a Registrar, and 31 (58.4%) by a JR supervised by a SR. Among the cases, 17 (32.1%) were complex, 47 (88.7%) were in working hours (8am-5pm), and 50 (94.3%) took place Monday to Friday. Median (interquartile range) duration of surgery was 65 (52-77) minutes. In the first year, 60 (47%) appendicectomies were performed by JRs. JR 1 was TT in three cases (8.8%) and JR 2 in five cases (19.2%); in all cases, the learner was a CT. Overall, there were 26 (10.6%) negative appendicectomies, 8 (3%) conversions, 19 (7%) readmissions within 30 days of discharge and 3 (1.1%) required reoperation; there was no statistically significant difference in complications between TT and non-TT cases. Laparoscopic appendicectomy is an excellent model for trainees to act as trainer; case selection included simple cases during daylight hours. Our outcomes are comparable with published literature, suggesting that this teaching method is safe for patients.

Modeling Dimensionality of Bilingual Kindergarteners' Language Knowledge in Spanish and English.

This study used structural equation modeling to investigate the dimensionality of language in Spanish-English bilingual kindergartners. Five theoretical models were compared, including (a) a unidimensional model; (b) a two-dimensional model by language (Spanish, English); (c) a three-dimensional model by domain of language (phonology, semantics, morphosyntax); (d) a second-order model organized first by language (Spanish, English), with each language dimension comprising three domain-specific dimensions (phonology, semantics, morphosyntax); and (e) a six-dimensional model with freely covarying language-specific domains. Participants included 238 Spanish-English bilingual kindergartens, as identified by parent report of current language exposure and direct language measures. All participants completed a battery of phonology, semantics, and morphosyntactic test items in English and in Spanish. The six-dimensional covarying model constrained by domain and language provided the best fit for the data, with six separate factors for phonology, morphosyntax, and semantics in English and Spanish. The excellent model fit is supported by findings of a χ2 to df (degrees of freedom) ratio < 2 with no significance, comparative fit index > .95, standardized root-mean-square residual < .08, and root-mean-square error of approximation values ≤ .05. Results support emergent theories of bilingual language development. Application of the results to the evaluation and intervention of oral language abilities in bilingual children entering the formal education setting are considered. https://doi.org/10.23641/asha.25843846.

#2450 Alternative splicing in podocytes under mechanical stress: a new perspective for the pathogenic effect of glomerular hypertension

Abstract Background and Aims High blood pressure is considered one of the most important risk factors leading to chronic kidney disease. The pathogenesis of hypertension-induced damage to podocytes remains still elusive. Since recent reports have shown that alternative splicing (AS) has a significant impact on disease progression, we investigated alternative splicing of mechanically stretched podocytes, a well-established model for glomerular hypertension, in the Sys_CARE (Systems Medicine Investigation of AS in Cardiac and Renal Diseases) project. Our results provide new insights into the molecular mechanisms that might drive the progression of hypertensive nephropathy. Method Differentiated murine podocytes were exposed to mechanical stretch for 3 days under low and high stretch conditions. Subsequently, mRNA and proteins were analyzed by RNA-Seq and LC-MS/MS, respectively. Enrichment analysis identified transcripts which were classified according to the related biological processes, molecular function, and cellular components. Splicing and transcript expression were evaluated with bioinformatical tools such as MAJIQ, Multivariate Analysis of Transcript Splicing (rMATS), leafcutter, Whippet and IsoformSwitchAnalyzeR. Results Transcriptome analyses of mechanically stretched podocytes by RNA sequencing showed that mechanical stress leads to a high number of differentially expressed genes (1323 with log2 fc ± 1) compared to unstretched podocytes. Detailed gene set enrichment analysis for GO-Terms showed an enrichment among up-regulated transcripts related to mRNA processing and RNA splicing. In contrast, most transcripts with decreased expression upon stretch are associated with cytoskeleton function. To identify AS events, different AS tools were used. We found a wide variety of splice events. The most frequent event was exon skipping (between 63-82% of all AS events), followed by intron retention and alternative 5′ or 3′ splice site. 290 alternatively spliced genes were detected after mechanical stretch by three different splice analysis tools (rMATS, leafcutter, Whippet). Out of these candidates, the IsoformSwitchAnalyzeR identified 17 genes exhibiting significant isoform switches. To prioritize the candidates, we performed a screening that included only those genes that were identified by proteomic analysis and that are expressed in podocytes in vivo or showed an altered expression pattern in glomerular disease. This screening led to the two candidates Shroom3 and Myl6. Shroom3, an actin-binding protein, is crucial for podocyte morphology and function. Genetic variants of Shroom3 have been linked with chronic kidney disease by GWAS. Here we found that two of four Shroom3 isoforms showed significant expression changes due to mechanical stretch. Isoform 2 which showed the highest expression in cultured podocytes, significantly decreased, but the shorter isoform X1 was significantly up-regulated after mechanical stretch. Both changes were verified by qRT-PCR as well as by in situ hybridization in mouse tissue. Myosin light chain 6 (Myl6), a component of the myosin motor protein complex, has two isoforms which are detected in cultured podocytes as well as in vivo. We found an expression switch from isoform 1 to 2 due to mechanical stretch. Our analysis showed that this is accompanied by a change of the amino acid sequence AFVRHILS to ELVRMVLN, sequences that are well conserved in mice, rat, and human. The analysis of the protein structure by FoldX revealed that this change in the amino acid sequence could lead to an impairment of the binding of the motor protein complex to actin and might therefore influence the functionality of the actin cytoskeleton, which is highly important for proper podocyte foot process morphology. Conclusion Mechanical stretch of cultured mouse podocytes, which is an excellent model to simulate hypertensive nephropathy, leads to alterations in isoform expression due to alternative splicing. These changes in the expression of isoforms, such as Shroom 3 and Myl6, have the potential to play a key role in the pathophysiology of hypertension-induced nephropathy.

Adaptation and Psychometric Properties of an Attitude toward Artificial Intelligence Scale (AIAS-4) among Peruvian Nurses

Background: The integration of Artificial Intelligence (AI) into various aspects of daily life has sparked growing interest in understanding public attitudes toward this technology. Despite advancements in tools to assess these perceptions, there remains a need for culturally adapted instruments, particularly in specific contexts like that of Peruvian nurses. Objective: To evaluate the psychometric properties of the AIAS-4 in a sample of Peruvian nurses. Methods: An instrumental design was employed, recruiting 200 Peruvian nurses. The Attitude toward Artificial Intelligence in Spanish (AIAS-S), a cultural and linguistic adaptation of the AIAS-4, involved data analysis using descriptive statistics, confirmatory factor analysis (CFA), and invariance tests. Results: The Confirmatory Factor Analysis (CFA) confirmed a unidimensional factor structure with an excellent model fit (χ2 = 0.410, df = 1, p = 0.522, CFI = 1.00, TLI = 1.00, RMSEA = 0.00, SRMR = 0.00). The scale demonstrated high internal consistency (α = 0.94, ω = 0.91). Tests of invariance from configural to strict confirmed that the scale is stable across different demographic subgroups. Conclusions: The AIAS-S proved to be a psychometrically solid tool for assessing attitudes toward AI in the context of Peruvian nurses, providing evidence of validity, reliability, and gender invariance. This study highlights the importance of having culturally adapted instruments to explore attitudes toward emerging technologies in specific groups.

Optimization Study of Soil Organic Matter Mapping Model in Complex Terrain Areas: A Case Study of Mingguang City, China

Traditional soil organic matter mapping is mostly polygonal drawing, which is even more difficult to accurately depict in complex terrain areas. The spatial distribution of soil organic matter is closely related to agricultural production, natural resources, environmental governance, and socio-economic development. Efficiently, economically, and accurately obtaining information on changes in soil organic matter in areas with diverse topography is an urgent problem to be solved. Mingguang City has a high research value because of its unique topography and natural landscape. To solve the problem of soil organic matter mapping in this area, this study will construct an excellent organic matter prediction model. Using 173 soil survey samples (123 for training and 50 for testing), the optimal feature variable subsets selected from 31 environmental variables through Pearson correlation, stepwise regression-variance inflation factor, and recursive feature elimination models based on different algorithms were employed. Each selected feature subset was then used to construct organic matter prediction models using multiple advanced machine learning algorithms. By comparing accuracy validation and model performance, the organic matter prediction model suitable for Mingguang City (RFE-RF_SVM) was obtained, that is, the prediction model of organic matter based on support vector machines with the feature variables screened by the feature recursive elimination algorithm of random forest with RMSE = 3.504, VSI = 0.036, and R-squared = 0.730. Furthermore, the analysis focused on assessing the significance of the predictive factors. The mapping results of this study show that the soil organic matter content in the central and northwestern parts of the study area is low, and the reasons for this situation are different. The central part is mainly caused by the change of land use and topography, while the northwestern part is caused by the loose soil structure caused by the parent material. The government can take targeted measures to improve the soil in the areas with poor organic matter.

Understanding the Influence of Host Radiation on Symbiont Speciation through Parasites of Species Flocks.

(Adaptive) radiations have attracted evolutionary biologists for a long time as ideal model systems to study patterns and processes of often rapid speciation. However, whereas a wealth of (sometimes already genome-scale) data is available for host radiations, very few studies target the patterns of diversification in their symbionts, even though they would be excellent models to study symbiont speciation. Our review summarizes what little is known about general patterns of symbiont diversification in often iconic adaptive host radiations and to what extent these patterns are dependent on the evolutionary trajectories of their hosts. We identify research gaps that need to be addressed in the future and discuss the potential of approaches not yet typically used in these study systems, such as epidemiological disease modeling and new omics technologies, for significantly advancing our understanding of these complex eco-evolutionary relationships.

Activity-dependent dendrite patterning in the postnatal barrel cortex

For neural circuit construction in the brain, coarse neuronal connections are assembled prenatally following genetic programs, being reorganized postnatally by activity-dependent mechanisms to implement area-specific computational functions. Activity-dependent dendrite patterning is a critical component of neural circuit reorganization, whereby individual neurons rearrange and optimize their presynaptic partners. In the rodent primary somatosensory cortex (barrel cortex), driven by thalamocortical inputs, layer 4 (L4) excitatory neurons extensively remodel their basal dendrites at neonatal stages to ensure specific responses of barrels to the corresponding individual whiskers. This feature of barrel cortex L4 neurons makes them an excellent model, significantly contributing to unveiling the activity-dependent nature of dendrite patterning and circuit reorganization. In this review, we summarize recent advances in our understanding of the activity-dependent mechanisms underlying dendrite patterning. Our focus lays on the mechanisms revealed by in vivo time-lapse imaging, and the role of activity-dependent Golgi apparatus polarity regulation in dendrite patterning. We also discuss the type of neuronal activity that could contribute to dendrite patterning and hence connectivity.

O12 Macrophages enhance angiogenesis in an induced pluripotent stem cell-derived skin organoid model

Abstract Introduction and aims Hair-bearing skin organoids (SkOs) are a complex three-dimensional (3D) model that faithfully recapitulates human skin as a multicellular organ with appendage structures. The development of such models provides unique potential for investigating human skin development and translational dermatology. Although this model answers many demands of an investigative tool, one obvious limitation is the absence of immune cells in SkOs. Our aim is to generate coculture systems of SkOs with macrophages to investigate how macrophages influence skin development. Methods Our group, and others, generated single-cell (sc)RNA-Seq and spatial transcriptomic datasets to thoroughly characterize human skin cell populations across the human lifespan. We used these datasets to compare the dynamic tempo of induced pluripotent stem cell (iPSC)-derived SkO (n = 84) differentiation with human prenatal (n = 15) and adult skin. For functional assays, we generated 2D angiogenesis coculture assays with macrophages and endothelial cells (n = 3) and 3D coculture of SkOs with macrophages (n = 3). Macrophages, endothelial cells and SkOs were all derived from the same iPSC line following established protocols. SkO vasculature was analysed after immunowholemount staining of CD31+ endothelial cells. Results Analysis of scRNA-Seq data highlighted a paucity in endothelial cell heterogeneity in SkOs compared with prenatal skin. We hypothesized that this could be explained by the absence of immune cells, as spatial transcriptomics microenvironment analysis of prenatal skin demonstrated that endothelial cells collocate with macrophages. With this in mind, we generated functional assays to investigate interactions of macrophages and endothelial cells in skin development. The 2D and 3D assays revealed that presence of macrophages significantly increased endothelial cell sprouting and network formation. Conclusions Our findings suggest a vital role of macrophages in skin morphogenesis, specifically in relation to the guidance of tip cells during angiogenesis. In addition, we present the SkO as an excellent complex model for investigating skin development, cell–cell interactions, and disease modelling for translational research in regenerative medicine and beyond.

A Study on Working Capital Management in Automotive chassis Manufacturing Pvt Ltd

The effective management of working capital is crucial for all companies. Key elements of working capital management include days sales outstanding (DSO), days inventory outstanding (DIO), days payable outstanding (DPO), and cash conversion cycle (CCC). A study conducted on 332 Czech companies, 20 of which were certified by the EFQM (European Foundation for Quality Management) Model, investigated how these components of working capital management impact a company's profitability. Two regression models were utilized for testing - pooled regression and maximum likelihood estimation (MLE). The results indicated that all components of working capital management have a negative effect on profitability. Interestingly, the interaction between EFQM certified companies and working capital components had a positive impact on profitability, suggesting a beneficial relationship between these components and profitability for certified firms. However, it was found that receiving a quality certificate/award from the EFQM Excellence Model actually decreased a company's profitability. Keywords Working capital, Firm profitability CCC, EFQM, Czech firms

Privacy-Preserving Breast Cancer Prediction Based on Logistic Regression

Abstract With the increasing strain on today’s healthcare resources, there is a growing demand for pre-diagnosis testing. In response, researchers have suggested diverse machine learning models for disease prediction, among which logistic regression stands out as one of the most effective models. Its objective is to enhance the accuracy and efficiency of pre-diagnosis testing, thereby alleviating the burden on healthcare resources. However, when multiple medical institutions collaborate to train models, the untrusted cloud server may pose a risk of private data leakage, enabling participants to steal data from one another. Existing privacy-preserving methods often suffer from drawbacks such as high communication costs, long training times and lack of security proofs. Therefore, it is imperative to jointly train an excellent model collaboratively and uphold data privacy. In this paper, we develop a highly optimized two-party logistic regression algorithm based on CKKS scheme. The algorithm optimizes ciphertext operations by employing ciphertext segmentation and minimizing the multiplication depth, resulting in time savings. Furthermore, it utilizes least squares to approximate sigmoid functions within specific intervals that cannot be handled by homomorphic encryption. Finally, the proposed algorithm is evaluated on a breast cancer dataset, and simulation experiments demonstrate that the model’s prediction accuracy, after machine learning training, exceeds 96% for two-sided encrypted data.

Genome reduction and horizontal gene transfer in the evolution of Endomicrobia-rise and fall of an intracellular symbiosis with termite gut flagellates.

Bacterial endosymbionts of eukaryotic hosts typically experience massive genome reduction, but the underlying evolutionary processes are often obscured by the lack of free-living relatives. Endomicrobia, a family-level lineage of host-associated bacteria in the phylum Elusimicrobiota that comprises both free-living representatives and endosymbionts of termite gut flagellates, are an excellent model to study evolution of intracellular symbionts. We reconstructed 67 metagenome-assembled genomes (MAGs) of Endomicrobiaceae among more than 1,700 MAGs from the gut microbiota of a wide range of termites. Phylogenomic analysis confirmed a sister position of representatives from termites and ruminants, and allowed to propose eight new genera in the radiation of Endomicrobiaceae. Comparative genome analysis documented progressive genome erosion in the new genus Endomicrobiellum, which comprises all flagellate endosymbionts characterized to date. Massive gene losses were accompanied by the acquisition of new functions by horizontal gene transfer, which led to a shift from a glucose-based energy metabolism to one based on sugar phosphates. The breakdown of glycolysis and many anabolic pathways for amino acids and cofactors in several subgroups was compensated by the independent acquisition of new uptake systems, including an ATP/ADP antiporter, from other gut microbiota. The putative donors are mostly flagellate endosymbionts from other bacterial phyla, including several, hitherto unknown lineages of uncultured Alphaproteobacteria, documenting the importance of horizontal gene transfer in the convergent evolution of these intracellular symbioses. The loss of almost all biosynthetic capacities in some lineages of Endomicrobiellum suggests that their originally mutualistic relationship with flagellates is on its decline.IMPORTANCEUnicellular eukaryotes are frequently colonized by bacterial and archaeal symbionts. A prominent example are the cellulolytic gut flagellates of termites, which harbor diverse but host-specific bacterial symbionts that occur exclusively in termite guts. One of these lineages, the so-called Endomicrobia, comprises both free-living and endosymbiotic representatives, which offers the unique opportunity to study the evolutionary processes underpinning the transition from a free-living to an intracellular lifestyle. Our results revealed a progressive gene loss in energy metabolism and biosynthetic pathways, compensated by the acquisition of new functions via horizontal gene transfer from other gut bacteria, and suggest the eventual breakdown of an initially mutualistic symbiosis. Evidence for convergent evolution of unrelated endosymbionts reflects adaptations to the intracellular environment of termite gut flagellates.

The development of an organizational excellence architecture model to support the implementation of business excellence

PurposeThe purpose of this study is to address a critical gap in the existing literature on business excellence implementation. While various studies have examined different aspects of business excellence, there is still a lack of comprehensive research on the optimal organizational excellence architecture (OEA) for an award-winning business excellence journey. The absence of a unified framework has led to inconsistent practices across organizations. The aim of this research based on data collected from 50 organizations across 17 countries is to address this gap and present a refined OEA model.Design/methodology/approachThis study employed quantitative and qualitative data collection methods. Member organizations from the Global Excellence Model (GEM) Council participated through an online survey administered via the Qualtrics software platform. Quantitative data were analyzed using the SPSS and Microsoft Excel software tools, while content analysis techniques were applied to the qualitative data to gain detailed insights.FindingsThe findings refine and extend the OEA model. These contributions lead to a refined OEA definition and open avenues for future research, enriching the current understanding of BE implementation.Originality/valueThis study introduces a novel concept of OEA to the literature on business excellence implementation. The refined OEA model provides a fresh perspective on the critical components necessary for an award-winning business excellence journey. This research offers significant value to both practitioners and academics by presenting a unified approach to BE implementation, effectively addressing the previously identified gap in BE research.

Pathogenic mechanisms and etiologic aspects of Mycobacterium avium subspecies paratuberculosis as an infectious cause of cutaneous melanoma

AbstractInfectious etiologies have previously been proposed as causes of both melanoma and non‐melanoma skin cancer. This exploratory overview explains and presents the evidence for the hypothesis that a microorganism excreted in infected ruminant animal feces, Mycobacterium avium subspecies paratuberculosis (MAP), is the cause of some cases of cutaneous melanoma (CM). Occupational, residential, and recreational contact with MAP‐contaminated feces, soil, sand, and natural bodies of water may confer a higher rate of CM. Included in our hypothesis are possible reasons for the differing rates and locations of CM in persons with white versus nonwhite skin, why CM develops underneath nails and in vulvar skin, why canine melanoma is an excellent model for human melanoma, and why the Bacille Calmette‐Guérin (BCG) vaccine has demonstrated efficacy in the prevention and treatment of CM. The pathogenic mechanisms and etiologic aspects of MAP, as a transmittable agent underlying CM risk, are carefully deliberated in this paper. Imbalances in gut and skin bacteria, genetic risk factors, and vaccine prevention/therapy are also discussed, while acknowledging that the evidence for a causal association between MAP exposure and CM remains circumstantial.

Acceptability and Feasibility of Survivorship Group Medical Visits for Breast Cancer Survivors in a Safety Net Hospital.

Providing cost-effective, comprehensive survivorship care remains a significant challenge. Breast cancer survivors (BCS) who have limited income and are from marginalized racial and ethnic groups experience a worse quality of life and report higher distress. Thus, innovative care models are required to address the needs of BCSin low resource settings. Group medical visits (GMV), utilized in chronic disease management, are an excellent model for education and building skills. This single-arm intervention study was conducted at a public hospital in California. GMVs consisted of five 2-h weekly sessions focused on survivorship care planning, side effects of treatment and prevention, emotional health, sexual health, physical activity, and diet. The patient navigators recruited three consecutive GMV groups of six English-speaking BCS (N = 17). A multidisciplinary team delivered GMVs, and a patient navigator facilitated all the sessions. We used attendance rates, pre- and post-surveys, and debriefing interviews to assess the feasibility and acceptability of the intervention. We enrolled 18 BCS. One participant dropped out before the intervention started, 17 BCS consistently attended and actively participated in the GMV, and 76% (13) attended all planned sessions. Participants rated GMVs in the post-survey and shared their support for GMVs in debriefing interviews. The BCS who completed the post-survey reported that GMVs increased their awareness, confidence, and knowledge of survivorship care. GMVs were explicitly designed to address unmet needs for services necessary for survivorship care but not readily available in safety net settings. Our pilot data suggest that patient-navigator-facilitated GMVs are a feasible and acceptable model for integrating survivorship care in public hospitals.

Operational Reliability Analysis of Turbine Blisk Using an Enhanced Moving Neural Network Framework

As one of the key components of an aeroengine, turbine blisk endures complex coupling loads under a harsh operational environment so that the reliability of turbine blisk directly influences the safe operation of aeroengine. It is urgent to precisely perform the reliability estimation of a complex blisk structure. To address this issue, an enhanced Moving Neural Network Framework (MNNF) is proposed by integrating compact support region theory, improve sooty tern optimization algorithm (ISTOA), and Bayesian regularization strategy into artificial neural network. The compact support region theory is applied to select the efficient samples for modeling from the training samples set, the ISTOA is to determine the optimal compact support region, and Bayesian regularization thought is utilized to improve the generalization ability of neural network model. The operational reliability assessment of aeroengine blisk is performed with the consideration of transient loads to verify the proposed MNNF method. It is shown that the reliability degree of turbine blisk stain is 0.9984 when the allowable value is 5.2862 × 10−3 m. In line with the comparison of methods, the developed MNNF approach has 0.99738 in root means square error, 3.1634 × 10−4 m in goodness of fit, 0.423 s in modeling time, 99.99% in simulation precision, and 0.496 s in simulation time under 10,000 simulations, which are superior to all other methods (i.e., 99.96%, 99.91%, 99.93%, 99.97%, and 99.97% in simulation precision and 16.27%, 4.82%, 30.07%, 39.87%, and 23.59% in simulation efficiency, for the response surface method (RSM), Kriging, support vector machine (SVM), back propagation-artificial neural network (BP-NN), and BP-NN based on particle swarm optimization (BP-PSO) methods, respectively). It is demonstrated that the MNNF method holds excellent modeling and simulation performances. The efforts of this study provide promising tools and insights into the reliability design of complex structures, and enrich and develop reliability theory.

The Brilliance, Attitude, Leadership and Materials (BALM) framework of clinical excellence: an adoptable model for sub-Saharan Africa

Clinical excellence is the ability to provide rigorous, evidence-based, and rewarding care to the right patients. The BALM (brilliance, attitude, leadership, and materials) framework is a novel model for adjudicating clinical excellence. There are a few models and frameworks for evaluating clinical excellence, but each has its flaws and does not consider the peculiarities of economically-challenged regions such as sub-Saharan Africa. Coincidentally, such low-resource areas have an urgent need for a framework to assess clinical excellence because of their low health-related indices. The five-step model of professional excellence is a well-recognised tool and was the model upon which the BALM framework was built. So, the BALM framework determines to what extent, using the five-step stages of novice, advanced beginner, competent, proficient, and expert, a clinician has achieved in each of the four critical domains, namely brilliance, attitude, leadership, and materials. Therefore, a clinician, who strives for excellence, aims to be an “expert” in each of the domains of the BALM framework. The framework is concise, practical, easy to use, and multidimensional, although it still needs to be applied widely to assess its reproducibility.