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Identification and inhibition of PIN1-NRF2 protein–protein interactions through computational and biophysical approaches
Abstract NRF2 is a transcription factor responsible for coordinating the expression of over a thousand cytoprotective genes. Although NRF2 is constitutively expressed, its stability is modulated by the redox-sensitive protein KEAP1 and other conditional binding partner regulators. The new era of NRF2 research has highlighted the cooperation between NRF2 and PIN1 in modifying its cytoprotective effect. Despite numerous studies, the understanding of the PIN1-NRF2 interaction remains limited. Herein, we described the binding interaction of PIN1 and three different 14-mer long phospho-peptides mimicking NRF2 protein using computer-based, biophysical, and biochemical approaches. According to our computational analyses, the residues positioned in the WW domain of PIN1 (Ser16, Arg17, Ser18, Tyr23, Ser32, Gln33, and Trp34) were found to be crucial for PIN1-NRF2 interactions. Biophysical FP assays were used to verify the computational prediction. The data demonstrated that Pintide, a peptide predominantly interacting with the PIN1 WW-domain, led to a significant reduction in the binding affinity of the NRF2 mimicking peptides. Moreover, we evaluated the impact of known PIN1 inhibitors (juglone, KPT-6566, and EGCG) on the PIN1-NRF2 interaction. Among the inhibitors, KPT-6566 showed the most potent inhibitory effect on PIN1-NRF2 interaction within an IC50 range of 0.3–1.4 µM. Furthermore, our mass spectrometry analyses showed that KPT-6566 appeared to covalently modify PIN1 via conjugate addition, rather than disulfide exchange of the sulfonyl-acetate moiety. Altogether, such inhibitors would also be highly valuable molecular probes for further investigation of PIN1 regulation of NRF2 in the cellular context and potentially pave the way for drug molecules that specifically inhibit the cytoprotective effects of NRF2 in cancer.
Pre-frailty is associated with higher risk of gastroesophageal reflux disease: a large prospective cohort study
Enhancing solubility and dissolution of felodipine using self-nanoemulsifying drug systems through in vitro evaluation
Low cost and compact six switch seven level grid tied transformerless PV inverter
Experimental and theoretical investigations on wavelength-specific probe for divalent metal ion detection
Mitigating spatial hallucination in large language models for path planning via prompt engineering
Abstract Spatial reasoning in Large Language Models (LLMs) serves as a foundation for embodied intelligence. However, even in simple maze environments, LLMs often struggle to plan correct paths due to hallucination issues. To address this, we propose S2ERS , an LLM-based technique that integrates entity and relation extraction with the on-policy reinforcement learning algorithm Sarsa for optimal path planning. We introduce three key improvements: (1) To tackle the hallucination of spatial, we extract a graph structure of entities and relations from the text-based maze description, aiding LLMs in accurately comprehending spatial relationships. (2) To prevent LLMs from getting trapped in dead ends due to context inconsistency hallucination by long-term reasoning, we insert the state-action value function Q into the prompts, guiding the LLM’s path planning. (3) To reduce the token consumption of LLMs, we utilize multi-step reasoning, dynamically inserting local Q-tables into the prompt to assist the LLM in outputting multiple steps of actions at once. Our comprehensive experimental evaluation, conducted using closed-source LLMs ChatGPT 3.5, ERNIE-Bot 4.0 and open-source LLM ChatGLM-6B, demonstrates that S2ERS significantly mitigates the spatial hallucination issues in LLMs, and improves the success rate and optimal rate by approximately 29% and 19%, respectively, in comparison to the SOTA CoT methods.
Alternate encoder and dual decoder CNN-Transformer networks for medical image segmentation
A vibration compensation approach for shipborne atomic gravimeter based on particle swarm optimization
Quality by design based ecofriendly HPLC analytical method for simultaneous quantification of erastin and lenalidomide in mesoporous silica nanoparticles
Abstract The aims of this work to optimize and validate a RP-HPLC method to quantify erastin (ERT) and lenalidomide (LND) in mesoporous silica nanoparticles (MSNs). The Design of Experiments (DoE) strategy optimized the RP-HPLC method. The independent variables were buffer ratio, buffer pH, flow rate and injection volume. The dependent variables were retention time (Rt), Peak area, and resolution between the peaks of the analytes. The optimized conditions were: buffer ratio 68% and methanol 32%, flow rate 0.8 mL/min, buffer pH 5.8, and injection volume 10 µL. The ICH Q2(R1) recommendations were followed in the validation of the optimized RP-HPLC method. The method demonstrated linearity of more than 0.99 for both ERT and LND. The LOD and LOQ were 0.75 and 1.62 ng/mL for ERT; for LND 31.25 and 50 ng/mL. The specificity of the established RP-HPLC method was unaffected by the MSNs matrix. The drugs-loaded MSNs were analyzed using the suggested RP-HPLC technique. The % entrapment efficiency of ERT and LND was found to be 72.65 and 79.50%, and drug loading of ERT and LND was found to be 14 and 17% in MSNs, respectively. The optimized RP-HPLC method was used to check the in-vitro drug release of the ERT and LND from the ERT-LND@MSNs. Surface properties of synthesized MSNs was checked through particle and SEM analysis. The developed analytical method was eco-friendly according to AGREE analysis and GAPI analysis.
Design, synthesis, and optimization of a novel ternary photocatalyst for degradation of cephalexin antibiotic in aqueous solutions
A theory and data-driven method for rapid bottom hole pressure calculation in UGS
Multi-objective optimization of flexible positioning platform considering displacement frequency and dynamic stiffness responses
Deep learning-based classification of hemiplegia and diplegia in cerebral palsy using postural control analysis
Sex differences in the risk of incident systemic sclerosis: a nationwide population-based study with subgroup analyses
Author Correction: Comparative analysis of amino acid sequence level in plant GATA transcription factors
Improved glycemic outcomes in people with type 2 diabetes using smart blood glucose monitoring integrated with popular digital health therapeutics
Abstract The increasing prevalence of metabolic syndrome and type 2 diabetes places a burden on healthcare systems, necessitating cost-effective, engaging and accessible interventions to address the underlying behavioral and lifestyle drivers. Our study evaluated combining Bluetooth connected OneTouch blood glucose meters (BGM) and the OneTouch Reveal mobile app with one of four digital therapeutic apps. Each group was independent, with people with type 2 diabetes (PwT2D) themselves choosing their therapeutic intervention, to better reflect real-world use. Our 3-month decentralized study screened 912 subjects, with 612 returning mail-in A1cs, providing 191 subjects (Noom = 68, Fitbit = 31, Cecelia Health = 47, Welldoc = 45) who met all inclusion criteria, including entry A1c 7.5 to 12.0%. The primary endpoint of A1c change showed improvement in the overall group by − 0.77% (95% CI − 0.98 to − 0.56, n = 141) after 3-months, Noom − 1.03% (CI − 1.4 to − 0.61, n = 49), Fitbit − 0.56% (CI − 1.0 to − 0.11, n = 24), Cecelia Health − 0.76% (CI − 1.2 to − 0.36, n = 36), Welldoc − 0.55% (CI − 0.94 to − 0.17, n = 32). In terms of secondary endpoints, more than half (56%) of these PwT2D lowered A1c by ≥ 0.5% and more than a third (36%) lowered A1c by ≥ 1.0%, with similar improvements across each of the four independent groups. Our real-world approach shows the potential for connected BGMs and widely accessible digital therapeutics to contribute to improvements in glycemic outcomes.