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ABSTRACTManagement of urinary tract infection (UTI) in postmenopausal women can be challenging. The recent rise in resistance to most of the available oral antibiotic options together with high recurrence rate in postmenopausal women has further complicated treatment of UTI. As such, intravesical instillations of antibiotics like gentamicin are being investigated as an alternative to oral antibiotic therapies. This study evaluates the efficacy of the candidate intravesical therapeutic VesiX, a solution containing the cationic detergent Cetylpyridinium chloride, against a broad range of uropathogenic bacterial species clinically isolated from postmenopausal women with recurrent UTI (rUTI). We also evaluate the cytotoxicity of VesiX against cultured bladder epithelial cells and find that low concentrations of 0.0063% and 0.0125% provide significant bactericidal effect toward diverse bacterial species including uropathogenic Escherichia coli (UPEC), Klebsiella pneumoniae, Enterococcus faecalis, Pseudomonas aeruginosa, and Proteus mirabilis while minimizing cytotoxic effects against cultured 5637 bladder epithelial cells. Lastly, to begin to evaluate the potential utility of using VesiX in combination therapy with existing intravesical therapies for rUTI, we investigate the combined effects of VesiX and the intravesical antibiotic gentamicin. We find that VesiX and gentamicin are not antagonistic and are able to reduce levels of intracellular UPEC in cultured bladder epithelial cells.IMPORTANCEWhen urinary tract infections (UTIs), which affect over 50% of women, become resistant to available antibiotic therapies dangerous complications like kidney infection and lethal sepsis can occur. New therapeutic paradigms are needed to expand our arsenal against these difficult to manage infections. Our study investigates VesiX, a Cetylpyridinium chloride (CPC)-based therapeutic, as a candidate broad-spectrum antimicrobial agent for use in bladder instillation therapy for antibiotic-resistant UTI. CPC is a cationic surfactant that is FDA-approved for use in mouthwashes and is used as a food additive but has not been extensively evaluated as a UTI therapeutic. Our study is the first to investigate its rapid bactericidal kinetics against diverse uropathogenic bacterial species isolated from postmenopausal women with recurrent UTI and host cytotoxicity. We also report that together with the FDA-approved bladder-instillation agent gentamicin, VesiX was able to significantly reduce intracellular populations of uropathogenic bacteria in cultured bladder epithelial cells.

Parkinson’s disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson’s Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI—the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction–DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset.

This paper presents EPLSimp, an algorithm for map generalization that avoids the creation of topological inconsistencies. EPLSimp is based on Visvalingam-Whyatt's (VW) algorithm on which least "important" points are removed first. Unlike VW's algorithm, when a point is deleted a verification is performed in order to check if this deletion would create topological inconsistencies. This was done by using arbitrary precision rational numbers to completely avoid errors caused by floating-point arithmetic. EPLSimp was carefully implemented to be efficient, although using rational numbers adds an overhead to the computation. This efficiency was achieved by using a uniform grid for indexing the geometric data and parallel computing to speed up the process. As result, simplified models completely free of topologically inconsistent results and round-off errors due to the use of multiple precision rational numbers. In addition, there was a considerable speedup arising from the use of parallel computing.

Sjogren's disease, well-described in people, is rarely identified in veterinary species. In people, Sjogren's disease is one of the most common systemic autoimmune disorders with an incidence of 0.5% in the female population. The hallmark histopathologic finding of primary Sjogren's disease is lymphomononuclear cell infiltrates aggregating as periductal infiltrate in salivary glands. Sjogren's-like disease has been reported in a domestic shorthair cat and golden retriever dog. However, both lacked positive antinuclear antibody (ANA) titers and the dog showed no clinical evidence of dry eye disease. The following case report describes the clinical and immunohistochemical findings suggestive of Sjogren's disease in a 3-year-old spayed female German shepherd cross that was presented for medically refractory absolute dry eye, xerostomia confirmed with oral atropine response tests, and bilateral mandibular salivary gland enlargement. Routine topical lacrostimulants, anti-inflammatories, heterologous serum, ocular lubrication, and oral pilocarpine failed to improve clinical signs or tear production. The ANA titer at 1:160 was interpreted as positive, while the complete blood count and serum biochemistry panels were unremarkable. Head and neck ultrasound revealed bilateral moderately enlarged mandibular salivary glands with a hypoechoic, mottled echotexture consistent with sialoadenitis and regional lymphadenomegaly; thoracic radiography and abdominal ultrasonography were normal. In vivo confocal microscopy and spectral-domain optical coherence tomography of the cornea confirmed lipid keratopathy presumably secondary to corneal desiccation and steroid administration. Salivary gland histopathological and immunohistochemical analyses supported an immune-mediated etiology. Approximately 60% of the salivary section contained inflammatory cells replacing the glandular structures with a focus score of 12. Immunohistochemical markers CD3, CD204, CD79a, and CD20 were evaluated. The inflammatory infiltrate was a mixture of T-cells and macrophages with rare individual immunoreactive B-cells. CD3 and CD4+ T-cells were confirmed using immunohistochemistry and quantitative PCR, respectively. Clinical signs including ocular discharge and mandibular salivary gland enlargement markedly improved following oral immunomodulatory therapy with prednisone (1 mg/kg/d, tapered over 2 months) and long-term leflunomide (2 mg/kg/d). Ocular discomfort improved dramatically decreasing the need for topical lubricants; however, tear production failed to improve likely due to extensive lacrimal gland atrophy. The aim of this report is to increase awareness of Sjogren's disease in dogs and interpret the pathology involved.

Traditionally, cardiac surgery has been performed through a median sternotomy, which allows the surgeon generous access to the heart and surrounding great vessels. As a paradigm shift in the size and location of incisions occurs in cardiac surgery, new methods have been developed to allow the surgeon the same amount of dexterity and accessibility to the heart in confined spaces and in a less invasive manner. Initially, long instruments without pivot points were used, however, more recent robotic telemanipulation systems have been applied that allow for improved dexterity, enabling the surgeon to perform cardiac surgery from a distance not previously possible. In this rapidly evolving field, we review the recent history and clinical results of using robotics in cardiac surgery.

Introduction: The use of discharge β-blockers after cardiac surgery is associated with a long-term mortality benefit. β-blockers have been suggested to be less effective in black cardiovascular patients compared with whites. To date, racial differences in the long-term survival of coronary artery bypass grafting (CABG) patients who receive β-blockers at discharge have not been examined.Methods: A retrospective cohort study was conducted of patients undergoing CABG between 2002 and 2011. Long-term survival was compared in patients who and who were not discharged with β-blockers. Hazard ratios (HR) and 95% confidence intervals (CI) were computed using a Cox regression model. P-for-interaction between race and discharge β-blocker use was computed using a likelihood ratio test.Results: A total of 853 (88%) black (n=970) and 3,038 (88%) white (n=3,460) patients had a history of β-blocker use at discharge (N=4,430). Black patients who received β-blockers survived longer than those not receiving β-blockers and the survival advantage was comparable with white patients (black: adjusted HR=0.33, 95%CI=0.23-0.46; white: adjusted HR=0.48, 95%CI=0.39-0.58; p-for-interaction=0.74). Among patients discharged on β-blockers, we did not observe a long-term survival advantage for white compared with black patients (HR=1.2, 95%CI=0.95-1.5). Conclusions: β-blocker use at discharge was associated with a survival advantage among black patients after CABG and a similar association was observed in white patients.

Robotic cardiac operations evolved from minimally invasive operations and offer similar theoretical benefits, including less pain, shorter length of stay, improved cosmesis, and quicker return to preoperative level of functional activity. The additional benefits offered by robotic surgical systems include improved dexterity and degrees of freedom, tremor-free movements, ambidexterity, and the avoidance of the fulcrum effect that is intrinsic when using long-shaft endoscopic instruments. Also, optics and operative visualization are vastly improved compared with direct vision and traditional videoscopes. Robotic systems have been utilized successfully to perform complex mitral valve repairs, coronary revascularization, atrial fibrillation ablation, intracardiac tumor resections, atrial septal defect closures, and left ventricular lead implantation. The history and evolution of these procedures, as well as the present status and future directions of robotic cardiac surgery, are presented in this review.

Background: Chronic obstructive pulmonary disease (COPD) is a known predictor of decreased long-term survival after coronary artery bypass grafting (CABG). Differences in survival by race have not been examined. Methods: A retrospective cohort study was conducted of CABG patients between 2002 and 2011. Long-term survival was compared in patients with and without COPD and stratified by race. Hazard ratios (HR) and 95% confidence intervals (95%CI) were computed using a Cox regression model. Results: A total of 984 (20%) patients had COPD (black n=182; white n=802) at the time of CABG (N=4,801). The median follow-up for study participants was 4.4 years. White but not black race was observed to be a statistically significant predictor of decreased survival among COPD patients (no COPD: HR=1.0; white COPD: adjusted HR=1.5, 95%CI=1.3-1.7; black COPD: adjusted HR=1.2, 0.90-1.7). Conclusion: Contrary to the expected increased risk of mortality among black COPD patients in the general population, a similar survival disadvantage was not observed in our CABG population.

Traditionally, cardiac surgery has been performed through a median sternotomy, which allows the surgeon generous access to the heart and surrounding great vessels. As a paradigm shift in the size and location of incisions occurs in cardiac surgery, new methods have been developed to allow the surgeon the same amount of dexterity and accessibility to the heart in confined spaces and in a less invasive manner. Initially, long instruments without pivot points were used, however, more recent robotic telemanipulation systems have been applied that allow for improved dexterity, enabling the surgeon to perform cardiac surgery from a distance not previously possible. In this rapidly evolving field, we review the recent history and clinical results of using robotics in cardiac surgery.

Breast cancer remains a major public health concern, and early detection is crucial for improving survival rates. Metabolomics offers the potential to develop non-invasive screening and diagnostic tools based on metabolic biomarkers. However, the inherent complexity of metabolomic datasets and the high dimensionality of biomarkers complicates the identification of diagnostically relevant features, with multiple studies demonstrating limited consensus on the specific metabolites involved. Unlike previous studies that rely on singular feature selection techniques such as Partial Least Square (PLS) or LASSO regression, this research combines supervised and unsupervised machine learning methods with random sampling strategies, offering a more robust and interpretable approach to feature selection. This study aimed to identify a parsimonious and robust set of biomarkers for breast cancer diagnosis using metabolomics data. Plasma samples from 185 breast cancer patients and 53 controls (from the Cooperative Human Tissue Network, USA) were analyzed. This study also overcomes the common issue of dataset imbalance by using propensity score matching (PSM), which ensures reliable comparisons between cancer and control groups. We employed Univariate Naïve Bayes, L2-regularized Support Vector Classifier (SVC), Principal Component Analysis (PCA), and feature engineering techniques to refine and select the most informative features. Our best-performing feature set comprised 11 biomarkers, including 9 metabolites (SM(OH) C22:2, SM C18:0, C0, C3OH, C14:2OH, C16:2OH, LysoPC a C18:1, PC aa C36:0 and Asparagine), a metabolite ratio (Kynurenine-to-Tryptophan), and 1 demographic variable (Age), achieving an area under the ROC curve (AUC) of 98%. These results demonstrate the potential for a robust, cost-effective, and non-invasive breast cancer screening and diagnostic tool, offering significant clinical value for early detection and personalized patient management. [ABSTRACT FROM AUTHOR]

Many freshwater mussels (order Unionida) have an unusual system of doubly uniparental inheritance (DUI) of mitochondrial (mt) DNA. In species with DUI, males possess a female-transmitted (F-type) mt genome and a male-transmitted (M-type) mt genome. These genomes contain non-canonical open reading frame (orf) genes, referred to as f-orf and m-orf, present in F and M mt genomes, respectively. These genes have been implicated in sexual development in Unionida. When gonochoric species become hermaphroditic, which has happened several times in Unionida, they lose their M-type mt genome and f-orf genes evolve dramatically. Resulting F-ORF proteins are highly divergent in terms of primary nucleotide sequence, inferred amino acids, and hydrophobic properties; these genes (and proteins) are referred to as hermaphroditic orfs or h-orfs (and H-ORFs). We investigated patterns of hydrophobicity divergence for H-ORF proteins in hermaphrodites versus F-ORF proteins in closely related gonochoric species against cytochrome c oxidase subunit 1 (cox1) divergences. This approach was used to assess whether cryptic hermaphrodites can be detected. Although we did not detect evidence for the recent transition of any populations of eastern floaters (Pyganodon cataracta (Say, 1817)) to hermaphroditism, our analyses demonstrate that molecular signatures in mtDNA can be used to detect hermaphroditism in freshwater mussels. Key words: mitochondrial DNA, doubly uniparental inheritance, hermaphroditism, freshwater mussels, hydrophobicity profiles, eastern floater, Pyganodon cataracta. Des nombreuses mulettes (ordre des unionidés) présentent un système inhabituel de double hérédité uniparentale (DHU) de l'ADN mitochondriale (mt). Chez les espèces présentant une DHU, les mâles possèdent un génome mt transmis par la mère (type F) et un génome mt transmis par le père (type M). Ces génomes contiennent des gènes à cadres de lecture ouverts (open reading frame ou orf) non canoniques, appelés f-orf et m-orf, présents dans les génomes mt M et F, respectivement. Ces gènes interviendraient dans le développement sexuel chez les unionidés. Quand des espèces gonochoriques deviennent hermaphrodites, un phénomène qui s'est produit plusieurs fois chez les unionidés, elles perdent leur génome mt de type M et les gènes f-orf évoluent de manière dramatique. Les protéines F-ORF qui en résultent sont très divergentes sur le plan de la séquence primaire de nucléotides, des aminoacides inférés et des propriétés hydrophobes; ces gènes (et protéines) sont appelés orf hermaphrodites ou h-orf (et H-ORF). Nous avons comparé les motifs de divergence en ce qui concerne l'hydrophobicité pour les protéines H-ORF chez des hermaphrodites et les protéines F-ORF chez des espèces gonochoriques étroitement apparentées par rapport à des divergences reliées à la sous-unité 1 de la cytochrome c oxydase (cox1). Cette approche a été utilisée pour évaluer la possibilité de détecter des hermaphrodites cryptiques. Bien que des indices d'une transition récente à l'hermaphroditisme n'aient été décelés dans aucune des populations d'anodontes de l'Est (Pyganodon cataracta (Say, 1817)), nos analyses démontrent que les signatures moléculaires dans l'ADNmt peuvent être utilisées pour détecter l'hermaphroditisme chez les mulettes. [Traduit par la Rédaction] Mots-clés : ADN mitochondrial, double hérédité uniparentale, hermaphroditisme, mulettes, profils d'hydrophobicité, anodonte de l'Est, Pyganodon cataracta., Introduction Gonochoric species of freshwater mussels, together with several other lineages of bivalves, exhibit an unusual system of mitochondrial (mt) DNA transmission referred to as 'doubly uniparental inheritance' or DUI [...]

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. The exact cause of PD is still unknown, and the diagnosis of PD does not include a specific objective test with certainty. Although deep learning has made great progress in medical neuroimaging analysis, these methods are very susceptible to biases present in neuroimaging datasets. An innovative decorrelated deep learning technique is introduced to mitigate class bias and scanner bias while simultaneously focusing on finding distinguishing characteristics in resting-state functional MRI (rs-fMRI) data, which assists in recognizing PD with good accuracy. The decorrelation function reduces the nonlinear correlation between features and bias in order to learn bias-invariant features. The publicly available Parkinson's Progression Markers Initiative (PPMI) dataset, referred to as a single-scanner imbalanced dataset in this study, was used to validate our method. The imbalanced dataset problem affects the performance of the deep learning framework by overfitting to the majority class. To resolve this problem, we propose a new decorrelated convolutional neural network (DcCNN) framework by applying decorrelation-based optimization to convolutional neural networks (CNNs). An analysis of evaluation metrics comparisons shows that integrating the decorrelation function boosts the performance of PD recognition by removing class bias. Specifically, our DcCNN models perform significantly better than existing traditional approaches to tackle the imbalance problem. Finally, the same framework can be extended to create scanner-invariant features without significantly impacting the performance of a model. The obtained dataset is a multiscanner dataset, which leads to scanner bias due to the differences in acquisition protocols and scanners. The multiscanner dataset is a combination of two publicly available datasets, namely, PPMI and FTLDNI—the frontotemporal lobar degeneration neuroimaging initiative (NIFD) dataset. The results of t-distributed stochastic neighbor embedding (t-SNE) and scanner classification accuracy of our proposed feature extraction–DcCNN (FE-DcCNN) model validated the effective removal of scanner bias. Our method achieves an average accuracy of 77.80% on a multiscanner dataset for differentiating PD from a healthy control, which is superior to the DcCNN model trained on a single-scanner imbalanced dataset. [ABSTRACT FROM AUTHOR]