1,2-dithioglycol functionalised carbon nitride quantum dots as a “turn – off” fluorescent sensor for mercury ion detection

A kind of 1,2-dithioglycol (DTG) functionalised carbon nitride quantum dots (DTG-CNQDs) was designed for the first time by modifying DTG on the surface of carbon nitride quantum dots (CNQDs). The as-prepared DTG-CNQDs exhibit strong blue fluorescence under ultraviolet light and have a high quantum yield of 27%. Experiments show that Hg2+ has a good quenching effect on the fluorescence of DTG-CNQDs. In phosphate buffer (PBS, 10 mM, pH 6.0), the fluorescence quenching rate (F0/F) has a good linear relationship with the concentration of Hg2+ in the range of 0.020–0.50 μM with detection limit of 0.63 nM. This fluorescent probe possesses high sensitivity and good selectivity, which can be applied in the rapid detection of Hg2+ in tap and lake water samples.</p

Conjugated Polymer Nanoparticles for Light-Activated Anticancer and Antibacterial Activity with Imaging Capability

A new water-soluble conjugated polymer containing fluorene and boron-dipyrromethene repeat units in the backbones (PBF) that exhibits red emission was synthesized and characterized. Cationic PBF forms uniform nanoparticles with negatively charged disodium salt 3,3′-dithiodipropionic acid (SDPA) in aqueous solution through electrostatic interactions. The nanoparticles display absorption maximum at 550 nm and emission maximum at 590 nm. Upon photoexcitation with white light (400–800 nm) with 90 and 45 mW·cm–2 for bacteria and cancer cells killing respectively, PBF nanoparticles can sensitize the oxygen molecule to readily produce reactive oxygen species (ROS) for rapidly killing neighboring bacteria and cancer cells. Furthermore, PBF nanoparticles concurrently provide optical imaging capability. PBF nanoparticles are therefore a promising multifunctional material for treating cancers and bacteria infections, while concurrently providing optical monitoring capabilities

Additional file 2 of Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches

Additional file 2. The features used in ONN4MST and the selected features used in ONN4MST_FS. There are 44,668 taxa (or features) in total used in ONN4MST, while ONN4MST_FS (ONN4MST based on selected features) has utilized only 1,462 selected features

Additional file 3 of Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches

Additional file 3. Supplementary method about the data representation and other source tracking methods used in this study

Chemical Molecule-Induced Light-Activated System for Anticancer and Antifungal Activities

Except for chemotherapy, surgery, and radiotherapy, photodynamic therapy (PDT) as new therapy modality is already in wide clinic use for the treatment of various diseases. The major bottleneck of this technique is the requirement of outer light source, which always limits effective application of PDT to the lesions in deeper tissue. Here, we first report a new modality for treating cancer and microbial infections, which is activated by chemical molecules instead of outer light irradiation. In this system, in situ bioluminescence of luminol can be absorbed by a cationic oligo­(<i>p</i>-phenylene vinylene) (<b>OPV</b>) that acts as the photosensitizer through bioluminescence resonance energy transfer (BRET) process. The excited <b>OPV</b> sensitizes oxygen molecule in the surroundings to produce reactive oxygen species (ROS) that kill the adjacent cancer cells in vitro and in vivo, and pathogenic microbes. By avoiding the use of light irradiation, this work opens a new therapy modality to tumor and pathogen infections

Additional file 1 of Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches

Additional file 1: Table S1. Samples and data used for model building and testing. Table S2. Biomes and number of samples used in EBI MGnify and this study. Table S3. Evaluation of ONN4MST using the general model built based on the combined dataset. Table S4. Evaluation of ONN4MST using the model trained on the human dataset. Table S5. Evaluation of simple neural network on all five datasets. Table S6. Results of five biome from “Human” using all features by ONN4MST at fifth layer. Table S7. Running time when performing source tracking with one query against different datasets. Table S8. Running time when performing source tracking with different sizes of testing sets on combined dataset. Table S9. Memory utilization when performing source tracking with one query against different datasets. Table S10. Memory utilization when performing source tracking with different sizes of testing sets on combined dataset. Table S11. The prediction results 303 samples from diverse human body sites. Table S12. Average source contributions from mammals (pets) and soil for indoor house environments. Table S13. The prediction results of 148 samples from ceca of bird. Table S14. The prediction results for 203 gut microbiome samples of the Hadza hunter-gatherers of Tanzania. Table S15. The open searching results by using ONN4MST against the combined dataset. Table S16. Databases and software parameters used in this study. Figure S1. The architecture of the ONN model. Figure S2. Overview of ONN4MST for microbial source tracking. Figure S3. ROC curves of ONN4MST on all five datasets. Figure S4. ONN4MST estimations of source contribution to centenarians’ gut microbiome. Figure S5. Source tracking results of a less studied biome. Figure S6. Knowledge discovery of similar samples from ontologically-remote biomes

Step-Economical Syntheses of Functional BODIPY-EDOT π‑Conjugated Materials through Direct C–H Arylation

Palladium-catalyzed direct C–H arylations of 4,4-difluoro-4-bora-3a,4a-diaza-<i>s</i>-indacene (BODIPY) with 3,4-ethylene­dioxythio­phene (EDOT) derivatives at relatively low temperature (60 °C) provide moderate to good yields (47%–72%) of products having potential applications in fluorescent bioimaging and organic optoelectronics

Additional file 4 of Ontology-aware deep learning enables ultrafast and interpretable source tracking among sub-million microbial community samples from hundreds of niches

Additional file 4. Data download links for all five datasets used in this study

DataSheet1_Reliable and Interpretable Mortality Prediction With Strong Foresight in COVID-19 Patients: An International Study From China and Germany.PDF

Cohort-independent robust mortality prediction model in patients with COVID-19 infection is not yet established. To build up a reliable, interpretable mortality prediction model with strong foresight, we have performed an international, bi-institutional study from China (Wuhan cohort, collected from January to March) and Germany (Würzburg cohort, collected from March to September). A Random Forest-based machine learning approach was applied to 1,352 patients from the Wuhan cohort, generating a mortality prediction model based on their clinical features. The results showed that five clinical features at admission, including lymphocyte (%), neutrophil count, C-reactive protein, lactate dehydrogenase, and α-hydroxybutyrate dehydrogenase, could be used for mortality prediction of COVID-19 patients with more than 91% accuracy and 99% AUC. Additionally, the time-series analysis revealed that the predictive model based on these clinical features is very robust over time when patients are in the hospital, indicating the strong association of these five clinical features with the progression of treatment as well. Moreover, for different preexisting diseases, this model also demonstrated high predictive power. Finally, the mortality prediction model has been applied to the independent Würzburg cohort, resulting in high prediction accuracy (with above 90% accuracy and 85% AUC) as well, indicating the robustness of the model in different cohorts. In summary, this study has established the mortality prediction model that allowed early classification of COVID-19 patients, not only at admission but also along the treatment timeline, not only cohort-independent but also highly interpretable. This model represents a valuable tool for triaging and optimizing the resources in COVID-19 patients.</p