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<i>Bacillus subtilis</i> Utilizes Decarboxylated <i>S</i>-Adenosylmethionine for the Biosynthesis of Tandem Aminopropylated Microcin C, a Potent Inhibitor of Bacterial Aspartyl-tRNA Synthetase
Spatial mechanisms of quality control during chaperone-mediated assembly of the proteasome
Retraction: Harnessing the power of AI: Advanced deep learning models optimization for accurate SARS-CoV-2 forecasting
Association between clonal hematopoiesis and periodontitis: a two-sample mendelian randomization study
The Role of Protons in CO<sub>2</sub> Reduction on Gold under Acidic Conditions
Ceftazidime-avibactam use selects multidrug-resistance and prevents designing collateral sensitivity-based therapies against Pseudomonas aeruginosa
Retraction: Applying Internet information technology combined with deep learning to tourism collaborative recommendation system
Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification
A Fresh Twist on the Phospha-(Aza)-Wittig Reaction
The regulatory architecture of the primed pluripotent cell state
Gender and medication use in Turkey: Evidence from a general population survey
Gender differences in health behaviors and outcomes were commonly documented by researchers. The focus of this study was the analysis of gender differences in medication use for a general population in Turkey. It also explored a range of factors associated with medication use at the individual level. A nationally representative cross-sectional data set was obtained from the 2019 wave of the Turkish Health Survey. The sample of this study included 17,083 adults residing in different regions of Turkey. Conditional mixed-process regression models were estimated for the whole sample and subsamples by gender. The rates of prescribed and non-prescribed medication use were 40.7% and 30.2%, respectively, in the adult population of Turkey. There were significant gender differences in medication use in the Turkish case. Females were 19.4% more likely to use prescribed medication, and they were 30.8% more likely to use non-prescribed medication compared to males in Turkey. There were negative associations between prescribed and non-prescribed medication use. On average, females were 9.2% less likely to report higher levels of health status, and they were 18.4% more likely to use healthcare services. Individuals with higher levels of self-rated health status were less likely to use prescribed medication. Both prescribed and non-prescribed medication use were positively related to healthcare service use. Complementing the earlier literature, the results of the present study demonstrated that gender-specific designs should be considered by health policies on the use of medications.
Research on water body information extraction and monitoring in high water table mining areas based on Google Earth Engine
Motivation on Intramolecular Through-Space Charge Transfer for the Realization of Thermally Activated Delayed Fluorescence (TADF)–Thermally Stimulated Delayed Phosphorescence (TSDP) in C^C^N Gold(III) Complexes and Their Applications in Organic Light-Emitting Devices
D-orbital Reconstruction Achieves Low Charge Overpotential in Li-oxygen Batteries
Retraction: Ecopolitical discourse: Authoritarianism or democracy? — Evidence from China
Phenotypic variability of plant architecture, easy destemming, and yield for accelerated selection for mechanical harvestability in chile pepper
Synthesis of Electron-Deficient BisAzaCoroneneDiimide-Conjugated Polymers by Light-Locking Dynamic Covalent Bonds
DNA supercoiling-mediated G4/R-loop formation tunes transcription by controlling the access of RNA polymerase
Abstract RNA polymerase (RNAP) is a processive motor that modulates DNA supercoiling and reshapes DNA structures. The feedback loop between the DNA topology and transcription remains elusive. Here, we investigate the impact of potential G-quadruplex forming sequences (PQS) on transcription in response to DNA supercoiling. We find that supercoiled DNA increases transcription frequency 10-fold higher than relaxed DNA, which lead to an abrupt formation of G-quadruplex (G4) and R-loop structures. Moreover, the stable R-loop relieves topological strain, facilitated by G4 formation. The cooperative formation of G4/R-loop effectively alters the DNA topology around the promoter and suppresses transcriptional activity by impeding RNAP loading. These findings highlight negative supercoiling as a built-in spring that triggers a transcriptional burst followed by a rapid suppression upon G4/R-loop formation. This study sheds light on the intricate interplay between DNA topology and structural change in transcriptional regulation, with implications for understanding gene expression dynamics.
Multiplexed cytokine profiling identifies diagnostic signatures for latent tuberculosis and reactivation risk stratification
Active tuberculosis (TB) is caused by Mycobacterium tuberculosis (Mtb) bacteria and is characterized by multiple phases of infection, leading to difficulty in diagnosing and treating infected individuals. Patients with latent tuberculosis infection (LTBI) can reactivate to the active phase of infection following perturbation of the dynamic bacterial and immunological equilibrium, which can potentially lead to further Mtb transmission. However, current diagnostics often lack specificity for LTBI and do not inform on TB reactivation risk. We hypothesized that immune profiling readily available QuantiFERON-TB Gold Plus (QFT) plasma supernatant samples could improve LTBI diagnostics and infer risk of TB reactivation. We applied a whispering gallery mode, silicon photonic microring resonator biosensor platform to simultaneously quantify thirteen host proteins in QFT-stimulated plasma samples. Using machine learning algorithms, the biomarker concentrations were used to classify patients into relevant clinical bins for LTBI diagnosis or TB reactivation risk based on clinical evaluation at the time of sample collection. We report accuracies of over 90% for stratifying LTBI + from LTBI– patients and accuracies reaching over 80% for classifying LTBI + patients as being at high or low risk of reactivation. Our results suggest a strong reliance on a subset of biomarkers from the multiplexed assay, specifically IP-10 for LTBI classification and IL-10 and IL-2 for TB reactivation risk assessment. Taken together, this work introduces a 45-minute, multiplexed biomarker assay into the current TB diagnostic workflow and provides a single method capable of classifying patients by LTBI status and TB reactivation risk, which has the potential to improve diagnostic evaluations, personalize treatment and management plans, and optimize targeted preventive strategies in Mtb infections.