The effect of irrigating solutions on the hydration of tricalcium silicate cements: an in vitro study

Background: Calcium silicate cements are hydraulic cements, routinely used for perforation repairs. During such repairs, these cements are invariably exposed to irrigating solutions. Aim: This study aimed to understand the effect of irrigating solutions on the hydration of calcium silicate cements.  Materials and Methods: Sixty extracted teeth were taken and horizontal sections of 2mm were obtained. These samples were randomly divided into two groups viz. Biodentine and BioMTA Plus groups later these cements were condensed into the canal spaces and allowed to set until their setting time. These samples were further subdivided and allowed to encounter three irrigating solutions viz. Normal saline, 17% EDTA, and 2% Chlorhexidine for 5 minutes. These were allowed to mature in an incubator for seven days and subjected to Scanning Electron Microscopy and Energy Dispersive X-ray analysis. Results: The SEM analysis of the Biodentine/control group displayed a petal-like appearance, with a Ca/Si ratio of 2. Whereas, the Biodentine/Normal saline, Biodentine/17% EDTA and Biodentine/2% Chlorhexidine group displayed crumbled paper-like appearance. The Ca/Si ratios for the Biodentine/Normal saline, Biodentine/17% EDTA and Biodentine/2% Chlorhexidine were 2.72, 1.6, and 4.21, respectively. In the BioMTA Plus group, all the SEM analyses displayed round crystalline structures in all groups. The Ca/Si ratio of BioMTA Plus/Control, BioMTA Plus/17% EDTA and BioMTA Plus/2% Chlorhexidine were 25.5, 17.42, 24.1, and 39.4, respectively. Conclusion: The study concluded that the irrigating solutions did not affect the hydration mechanism of Biodentine and BioMTA Plus despite the variations in the Ca/Si ratios and surface morphology

Hierarchical graphene oxide-Ni3S2 quantum dots nanocomposites modified glassy carbon electrode for electrochemical detection of dopamine and tyrosine

A facile synthetic strategy is demonstrated to generate nickel sulfide quantum dots (Ni3S2). The thus formed Ni3S2 quantum dots are assembled onto exfoliated graphene oxide sheets hydrothermally to form nickel sulfide-graphene oxide nanocomposite material (GO-Ni3S2). The microscopic and spectroscopic characterization of the GO-Ni3S2 nanocomposites revealed the shape, size, crystalline phases, and oxidation states (of elements) of the hybrid material. The GO-Ni3S2 nanocomposites are then coated onto the glassy carbon electrode by drop casting to form GO-Ni3S2@GCE. The modified electrode is then used to detect dopamine and tyrosine simultaneously. The effect of scan rate, analyte concentrations, pH, and interfering agents on the peak current are studied to establish a plausible mechanism for oxidizing dopamine and tyrosine at GO-Ni3S2@GCE. The GO-Ni3S2@GCE is stable for 3 weeks and ten cycles of washing with minimal loss in the peak current in each cycle. Dopamine with a concentration as low as 12 nM can be detected using the GO-Ni3S2@GCE system

Prediction of outcomes in acute exacerbation of COPD with DECAF score and BAP 65 score in a rural population

Background: Prognostic research in exacerbations of chronic obstructive pulmonary disease (COPD) requiring hospitalization has been limited and there appears to be little common ground between predictors of mortality in stable disease and during AECOPD. Furthermore, none of the prognostic tools developed in stable disease have been tested on hospitalised patients, and most require clinical measurements not routinely available at hospital admission. This study intends to test dyspnoea, eosinopenia, consolidation, acidemia, and atrial fibrillation (DECAF) and biological assessment profile (BAP) 65 Scores on Indian patients in a tertiary care set up and validate the same to be used as a routine and effective score in predicting the outcome in AECOPD.  Methods: Hospital based prospective observational study was carried out in 100 patients with AECOPD who was present to general medicine. DECAF and BAP-65 Scores were calculated. Data was analyzed using SPSS 22 version software.Results: In our study both DECAF score and BAP‑65 score performed equally well for prediction of need for Mechanical Ventilation. The AUROC for need for Mechanical Ventilation was 0.77 (95% CI=0.67–0.84) for DECAF score and 0.77 (95% CI=0.67–0.85) for BAP‑65 score. The AUROC for prediction of mortality for DECAF score was 0.83 (95% confidence interval [CI]=0.74–0.89) and for BAP‑65 score was 0.79 (95% CI=0.69–0.86).Conclusions: DECAF and BAP-65 are good and also equal in predicting mortality as well as need for mechanical ventilation. Both scores can be easily applicable in AECOPD patients, so that death during hospitalization for AECOPD and need for mechanical ventilation can be minimized.

Isolation and evolutionary analysis of Australasian topotype of bluetongue virus serotype 4 from India

Bluetongue (BT) is a Culicoides-borne disease caused by several serotypes of bluetongue virus (BTV). Similar to other insect-borne viral diseases, distribution of BT is limited to distribution of Culicoides species competent to transmit BTV. In the tropics, vector activity is almost year long, and hence, the disease is endemic, with the circulation of several serotypes of BTV, whereas in temperate areas, seasonal incursions of a limited number of serotypes of BTV from neighbouring tropical areas are observed. Although BTV is endemic in all the three major tropical regions (parts of Africa, America and Asia) of the world, the distribution of serotypes is not alike. Apart from serological diversity, geography-based diversity of BTV genome has been observed, and this is the basis for proposal of topotypes. However, evolution of these topotypes is not well understood. In this study, we report the isolation and characterization of several BTV-4 isolates from India. These isolates are distinct from BTV-4 isolates from other geographical regions. Analysis of available BTV seg-2 sequences indicated that the Australasian BTV-4 diverged from African viruses around 3,500 years ago, whereas the American viruses diverged relatively recently (1,684 CE). Unlike Australasia and America, BTV-4 strains of the Mediterranean area evolved through several independent incursions. We speculate that independent evolution of BTV in different geographical areas over long periods of time might have led to the diversity observed in the current virus population

Human protein reference database—2006 update

Human Protein Reference Database (HPRD) () was developed to serve as a comprehensive collection of protein features, post-translational modifications (PTMs) and protein–protein interactions. Since the original report, this database has increased to >20 000 proteins entries and has become the largest database for literature-derived protein–protein interactions (>30 000) and PTMs (>8000) for human proteins. We have also introduced several new features in HPRD including: (i) protein isoforms, (ii) enhanced search options, (iii) linking of pathway annotations and (iv) integration of a novel browser, GenProt Viewer (), developed by us that allows integration of genomic and proteomic information. With the continued support and active participation by the biomedical community, we expect HPRD to become a unique source of curated information for the human proteome and spur biomedical discoveries based on integration of genomic, transcriptomic and proteomic data

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Recent advances in the development of high efficiency quantum dot sensitized solar cells (QDSSCs): A review

Quantum dots play an important role in third-generation photovoltaics. The key focus on quantum dots is due to their cost effect, capacity to work in diffused light, ease of fabrication, light weight, and flexibility which pique curiosity to further research. The incorporation of quantum dots into photovoltaics results in theoretically high thermodynamic conversion efficiencies of up to 40%, but in practise, the efficiencies are lower than those of dye-sensitized solar cells. Recent developments of different components like photoanode, quantum dot sensitizer, electrolyte, and counter electrode were discussed in detail. It was observed that by changing the synthesis methods, the adhesion properties might vary, which leads to enhancing the photovoltaic properties such as power conversion efficiency (PCE), open circuit voltage (Voc), short circuit current (Jsc), and fill factor (FF). The first report on the efficiency of Quantum Dot Sensitized Solar Cells (QDSSCs) was 0.12%. As of today, the efficiency is reported as 18.1 %, and further, the researchers are working to improve the efficiency of QDSSCs