95 ± 0.14 vs. 0.92 ± 0.12 g/cm2, p = 0.002), T score (-0.38 ± 1.17 vs. -0.68 ± 0.98, p = 0.002), and Z score (0.32 ± 1.09 vs. 0.01 ± 0.88, p  less then  0.001). Among the women with breast cancer, no correlations were found of baseline BMD with tumor size or grade, nodal involvement, or breast cancer stage. We concluded that women with newly diagnosed breast cancer tend to have higher BMD than women with similar characteristics but without breast cancer. This implies that BMD might be considered a biomarker for breast cancer risk.Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types. There is currently a need for machine learning models to predict non-clinically evident residual disease using only clinically available health data. Here we developed and tested multiple machine learning models to assess the risk of residual disease post-hysterectomy based on clinical and operative parameters. Data from 3656 hysterectomy patients from the NSQIP dataset over 14 years were used to develop models with a training set of 2925 patients and a validation set of 731 patients. Our models revealed the top postoperative predictors of residual disease were the initial presence of gross abdominal disease on the diaphragm, disease located on the bowel mesentery, located on the bowel serosa, and disease located within the adjacent pelvis prior to resection. There were no statistically significant differences in performances of the top three models. Extreme gradient Boosting, Random Forest, and Logistic Regression models had comparable AUC ROC (0.90) and accuracy metrics (87-88%). Using these models, physicians can identify gynecologic cancer patients post-hysterectomy that may benefit from additional treatment. For patients at high risk for disease recurrence despite adequate surgical intervention, machine learning models may lay the basis for potential prospective trials with prophylactic/adjuvant therapy for non-clinically evident residual disease, particularly in under-resourced settings.Scratch formation on glass surfaces is a ubiquitous phenomenon induced by plastic deformation, often accompanied by radial, lateral or median cracks with consequent chipping and brittle fracture caused during and after the event of dynamic abrasion instigated by shear stress by a harder material. This paper addresses the fundamental aspect of scratch formation on soda-lime-silica (SLS) glass surfaces. A constructive combination of surface-sensitive characterization tools, including field emission scanning electron microscopy (FESEM), laser scanning microscopy (LSM), X-ray photoelectron spectroscopy (XPS), Raman spectroscopy and instrumented indentation technique (IIT), helped to investigate the structural cause of generation of visible scratches on SLS glass surfaces. The experimental results indicate that a silicate network possessing a mechanically weakening structural characteristic in terms of network connectivity confined to the region between 5 and 100 nm below the glass surface is likely to cause a destructive surface scratch eminently visible to the naked eye.The prognosis of heart failure (HF) patients is determined to a decisive extent by comorbidities. The present study investigates the association between a broad spectrum of diseases and the occurrence of HF in a large collective of outpatients. This retrospective case control study assessed the prevalence of 37 cardiac and extracardiac diseases in patients with an initial diagnosis of heart failure (ICD-10 I50) in 1,274 general practices in Germany between January 2005 and December 2019. The study is based on the Disease Analyzer database (IQVIA), which contains drug prescriptions, diagnoses, and basic medical and demographic data. Patients with and without heart failure were matched by sex, age, and index year. Hazard regression models were conducted to evaluate the association between different disease entities and heart failure. The present study included 162,246 patients with heart failure and 162,246 patients without heart failure. Mean age [SD] was 73.7 [12.1] years; 52.6% were women. Out of 37 predefined diagnoses, 36 were more prevalent in HF patients. The highest prevalence was primary hypertension (63.4% in HF patients vs. 53.3% in controls, p  less then  0.001) followed by lipid metabolism disorders (34.6% in HF patients vs. 29.1% in HF patients p  less then  0.001) and diabetes mellitus type II (32.2% in HF patients vs. 25.2% in controls, p  less then  0.001). In the regression analysis, 19 diseases were significantly associated with heart failure. Non-cardiovascular diagnoses strongly associated with HF were obesity (HR = 1.46), chronic bronchitis and COPD (HR = 1.41), gout (HR 1.41), and chronic kidney disease (HR = 1.27). In the present study, we identified a variety of cardiac and extracardiac diseases associated with heart failure. Our data underscore the immense importance of comorbidities, even as early as at the stage of initial diagnosis of heart failure.The COVID-19 pandemic represents an unprecedented global crisis necessitating novel approaches for, amongst others, early detection of emerging variants relating to the evolution and spread of the virus. Recently, the detection of SARS-CoV-2 RNA in wastewater has emerged as a useful tool to monitor the prevalence of the virus in the community. Here, we propose a novel methodology, called lineagespot, for the monitoring of mutations and the detection of SARS-CoV-2 lineages in wastewater samples using next-generation sequencing (NGS). Our proposed method was tested and evaluated using NGS data produced by the sequencing of 14 wastewater samples from the municipality of Thessaloniki, Greece, covering a 6-month period. The results showed the presence of SARS-CoV-2 variants in wastewater data. lineagespot was able to record the evolution and rapid domination of the Alpha variant (B.1.1.7) in the community, and allowed the correlation between the mutations evident through our approach and the mutations observed in patients from the same area and time periods. lineagespot is an open-source tool, implemented in R, and is freely available on GitHub and registered on bio.tools.The effect of a hydrolyzed protein diet on the fecal microbiota has not been studied in feline chronic enteropathy (CE). Our study aimed to (1) compare the fecal microbiota of cats with CE to control cats with no gastrointestinal signs and (2) determine the effect of a hydrolyzed protein diet on the fecal microbiota of cats with CE and whether this differs between dietary responders and non-responders. The fecal microbiome of cats with CE (n = 36) showed decreased α-diversity in terms of genus richness (P = 0.04) and increased β-diversity in terms of Bray-Curtis Dissimilarity (P  less then  0.001) compared to control cats (n = 14). Clostridium was the only genera significantly over-represented in cats with CE compared to control cats (adjusted P  less then  0.1). After 6-weeks of feeding the diet, fifteen cats were classified as responders and 18 as non-responders, based on clinical signs. At the genus level, α-diversity was increased in non-responders versus responders at diagnosis, but decreased after dietae gastrointestinal signs in some cats. However, responders metabolized the diet in a similar manner, reflected by sustained β-diversity, while the microbiome of non-responders became increasingly dissimilar compared to diagnosis at the family level. Therefore, the microbiome may not be as tightly regulated in cats with CE that are non-responders and therefore, these cats would require additional therapy for remission of clinical signs.Inflammatory states are associated with anemia of chronic disease and acute infection. Hepcidin, a regulator of iron metabolism, is involved in iron pathophysiology during inflammation. We investigated biochemical characteristics in children with anemia from different causes. Four patient groups (n = 38; mean age 12.44 ± 4.35 years) were studied (1) inflammatory bowel disease (IBD, 10 patients); (2) iron deficiency anemia (IDA, 12); (3) celiac disease (CD, 8); (4) acute infection (AI, 8). Laboratory measurements were evaluated at diagnosis blood count, serum iron, transferrin, ferritin, vitamin B12, folic acid, CRP, erythropoietin, hepcidin and soluble transferrin receptor (sTfR). IDA patients had the lowest Hgb (6.9 ± 1.7 g/dL), MCV (63.2 ± 7.2 fL), iron (16.8 ± 13.5 µg/dL), ferritin (4.5 ± 4.5 ng/mL) and hepcidin (3.1 ± 0.8 ng/mL) values, and the highest transferrin and sTfR values. AI patients had the highest ferritin (156.2 ± 124.5 ng/mL), CRP (144.6 ± 94 mg/L) and hepcidin (74.67 ± 12.3 ng/ml) values. Overall, hepcidin levels correlated with CRP and with ferritin (r = 0.83 and 0.85, respectively). Elucidating specific etiology-related biochemical profiles in pediatric patients with anemia from different causes using a combination of laboratory biomarkers, including hepcidin, can help physicians treat the anemia.Majority of the pollination related studies are based on the diurnal pollinators, and the nocturnal pollinators received less scientific attention. We reveal the significance of settling moths in pollination of angiosperm families in Himalayan ecosystem of North-East India. The refined and novel method of pollen extraction from the proboscides provides a more robust assessment of the pollen carrying capacity. The study is based on one of the largest data sets (140 pollen transporter moth species (PTMS)), with interpretation based on seasonal as well as altitudinal data. In the present study about 65% moths (91 species) carried sufficient quantities of pollen grains to be considered as potential pollinators (PPMS). Teliphasa sp. (Crambidae) and Cuculia sp. (Noctuidae) are found to carry the highest quantity of pollen. We found pollen grains of 21 plant families and the abundant pollen are from Betulaceae, Fabaceae, Rosaceae and Ericaceae. Species composition of PTMS and PPMS in pre-monsoon, monsoon, and post-monsoon revealed the dominance of Geometridae. Maximum diversity of PTMS and PPMS is found from 2000 to 2500 m altitude. The nocturnal pollen transfer network matrices exhibited high degree of selectivity (H2’ = 0.86).The continuing reports of plastic pollution in various ecosystems highlight the threat posed by the ever-increasing consumption of synthetic polymers. Therefore, Pseudomonas capeferrum TDA1, a strain recently isolated from a plastic dump site, was examined further regarding its ability to degrade polyurethane (PU) compounds. The previously reported degradation pathway for 2,4-toluene diamine, a precursor and degradation intermediate of PU, could be confirmed by RNA-seq in this organism. In addition, different cell fractions of cells grown on a PU oligomer were tested for extracellular hydrolytic activity using a standard assay. Strikingly, purified outer membrane vesicles (OMV) of P. capeferrum TDA1 grown on a PU oligomer showed higher esterase activity than cell pellets. Hydrolases in the OMV fraction possibly involved in extracellular PU degradation were identified by mass spectrometry. On this basis, we propose a model for extracellular degradation of polyester-based PUs by P. capeferrum TDA1 involving the role of OMVs in synthetic polymer degradation.