Several prognosis prediction models have been developed for breast cancer (BC) patients with curative surgery, but there is still an unmet need to precisely determine BC prognosis for individual BC patients in real time. This is a retrospectively collected data analysis from adjuvant BC registry at Samsung Medical Center between January 2000 and December 2016. The initial data set contained 325 clinical data elements baseline characteristics with demographics, clinical and pathologic information, and follow-up clinical information including laboratory and imaging data during surveillance. Weibull Time To Event Recurrent Neural Network (WTTE-RNN) by Martinsson was implemented for machine learning. We searched for the optimal window size as time-stamped inputs. To develop the prediction model, data from 13,117 patients were split into training (60%), validation (20%), and test (20%) sets. The median follow-up duration was 4.7 years and the median number of visits was 8.4. We identified 32 features related to BC recurrence and considered them in further analyses. Performance at a point of statistics was calculated using Harrell's C-index and area under the curve (AUC) at each 2-, 5-, and 7-year points. After 200 training epochs with a batch size of 100, the C-index reached 0.92 for the training data set and 0.89 for the validation and test data sets. The AUC values were 0.90 at 2-year point, 0.91 at 5-year point, and 0.91 at 7-year point. The deep learning-based final model outperformed three other machine learning-based models. In terms of pathologic characteristics, the median absolute error (MAE) and weighted mean absolute error (wMAE) showed great results of as little as 3.5%. This BC prognosis model to determine the probability of BC recurrence in real time was developed using information from the time of BC diagnosis and the follow-up period in RNN machine learning model.Musculoskeletal diseases are a group of clinical conditions affecting the body's movement and remain a common source of pain affecting the quality of life. The aetio-pathological reasons for pain associated with musculoskeletal diseases can be varied and complex. Conventional medicine can treat or modify pain due to musculoskeletal diseases; however, these may be associated with some side effects and at times may not be able to relieve pain completely. These treatment modalities also have ceiling effects like doses of analgesics, the number of nerve blocks, etc. Complementary and Alternative Medicine (CAM) provides a supplementary, unconventional modality to alleviate discomfort and disability associated with these mostly chronic conditions to manage activities of daily living. These modalities have been variedly combined with conventional management for symptom control and thus improve day-to-day activities.  https://www.selleckchem.com/Androgen-Receptor.html  We assess the role of commonly used CAM modalities in the management of pain arising from Musculoskeletal diseases.Toxicity related failures in drug discovery and clinical development have motivated scientists and regulators to develop a wide range of in-vitro, in-silico tools coupled with data science methods. Older drug discovery rules are being constantly modified to churn out any hidden predictive value. Nonetheless, the dose-response concepts remain central to all these methods. Over the last 2 decades medicinal chemists, and pharmacologists have observed that different physicochemical, and pharmacological properties capture trends in toxic responses. We propose that these observations should be viewed in a comprehensive property-response framework where dose is only a factor that modifies the inherent toxicity potential. We then introduce the recently proposed "Drug Toxicity Index (DTI)" and briefly summarize its applications. A webserver is available to calculate DTI values (https//all-tool-kit.github.io/Web-Tool.html).In this study, we prepared chitosan (CS)-coated iron oxide (Fe3O4) nanocomposites (NCs) by employing the aqueous leaf extract of Brassica oleracea L. and evaluated its antimicrobial potential. The characterization of hybrid CS-Fe3O4 NCs was performed using Fourier-transform infrared spectroscopy (FTIR) analysis to evaluate the chemical bonding of chitosan to nanoparticles (NPs). X-ray photoelectron spectroscopy (XPS) studies revealed the presence of oxidation state elements Fe 2p, O 1s, N 1s, and C 1s, and the zeta potential analysis was found to have well-colloidal stability (+ 76.9 mV) of NCs. Transmission electron microscopy (TEM) analysis determined that CS-Fe3O4 NCs were spherical with an average particle size of 27 nm. The X-ray diffractometer (XRD) spectrum ascertained the crystallinity of the hybrid NCs and the vibrating sample magnetometer (VSM) inferred the ferromagnetic behavior of the synthesized NCs. Furthermore, the significant antibacterial efficacy of NPs was demonstrated against foodborne bacterial pathogens, such as Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), and the highest zone of inhibition was observed to be 11.5 mm and 13.5 mm in CS-Fe3O4 NCs, respectively. In comparison with Fe3O4 NPs, synergistic impacts of CS-Fe3O4 NCs displayed great antibacterial potential as exhibited by a clearly enlarged zone. Thus, CS-Fe3O4 NCs could be used as efficacious antimicrobial agents in food packaging and food preservation fields.An economic, eco-friendly and efficient synthesis route for Zinc oxide (ZnO) nanoparticles (NPs) using fungus Phanerochaete chrysosporium has been explored along with the single-step impregnation of these nanoparticles on cellulose fibers. The transmission electron microscopy confirmed 50 nm as an average size of ZnO NPs and showed the presence of hexagonal phases. ZnO NPs-cellulose composite was fabricated by amending sugarcane bagasse-extracted cellulose in the reaction mixture during the nanoparticle synthesis. The composite was characterized using Fourier transform infrared, X-ray diffraction patterns, Scanning electron microscopy, and Energy dispersive spectroscopy, thermogravimetric analysis, and also evaluated for its antimicrobial potential. The analyses revealed that well-dispersed hexagonal wurtzite ZnO NPs were present on the surface of the cellulose fibers. ZnO NPs-cellulose demonstrated antibacterial activity against Staphylococcus aureus and Escherichia coli, and antifungal activity against Aspergillus niger , Geotrichum candidum, and Phanerochaete chrysosporium.