Automated self-optimization, intensification, and scale-up of photocatalysis in flow

GPT-4 as a biomedical simulator

BACKGROUND

Computational simulation of biological processes can be a valuable tool for accelerating biomedical research, but usually requires extensive domain knowledge and manual adaptation. Large language models (LLMs) such as GPT-4 have proven surprisingly successful for a wide range of tasks. This study provides proof-of-concept for the use of GPT-4 as a versatile simulator of biological systems.

METHODS

We introduce SimulateGPT, a proof-of-concept for knowledge-driven simulation across levels of biological organization through structured prompting of GPT-4. We benchmarked our approach against direct GPT-4 inference in blinded qualitative evaluations by domain experts in four scenarios and in two quantitative scenarios with experimental ground truth. The qualitative scenarios included mouse experiments with known outcomes and treatment decision support in sepsis. The quantitative scenarios included prediction of gene essentiality in cancer cells and progression-free survival in cancer patients.

RESULTS

In qualitative experiments, biomedical scientists rated SimulateGPT's predictions favorably over direct GPT-4 inference. In quantitative experiments, SimulateGPT substantially improved classification accuracy for predicting the essentiality of individual genes and increased correlation coefficients and precision in the regression task of predicting progression-free survival.

CONCLUSION

This proof-of-concept study suggests that LLMs may enable a new class of biomedical simulators. Such text-based simulations appear well suited for modeling and understanding complex living systems that are difficult to describe with physics-based first-principles simulations, but for which extensive knowledge is available as written text. Finally, we propose several directions for further development of LLM-based biomedical simulators, including augmentation through web search retrieval, integrated mathematical modeling, and fine-tuning on experimental data.

Synthesis of alcohols: streamlined C1 to Cn hydroxyalkylation through photoredox catalysis

Naturally occurring and readily available α-hydroxy carboxylic acids (AHAs) are utilized as platforms for visible light-mediated oxidative CO2-extrusion furnishing α-hydroxy radicals proved to be versatile C1 to Cn hydroxyalkylating agents. The direct decarboxylative Giese reaction (DDGR) is operationally simple, not requiring activator or sacrificial oxidants, and enables the synthesis of a diverse range of hydroxylated products, introducing connectivity typically precluded from conventional polar domains. Notably, the methodology has been extended to widely used glycolic acid resulting in a highly efficient and unprecedented C1 hydroxyhomologation tactic. The use of flow technology further facilitates scalability and adds green credentials to this synthetic methodology.

Addressing Reproducibility Challenges in High-Throughput Photochemistry

Light-mediated reactions have emerged as an indispensable tool in organic synthesis and drug discovery, enabling novel transformations and providing access to previously unexplored chemical space. Despite their widespread application in both academic and industrial research, the utilization of light as an energy source still encounters challenges regarding reproducibility and data robustness. Herein we present a comprehensive head-to-head comparison of commercially available batch photoreactors, alongside the introduction of the use of batch and flow photoreactors in parallel synthesis. Hence, we aim to establish a reliable and consistent platform for light-mediated reactions in high-throughput mode. Herein, we showcase the identification of several platforms aligning with the rigorous demands for efficient and robust high-throughput experimentation screenings and library synthesis.

Robust Covalent Organic Frameworks for Photosynthesis of H2O2: Advancements, Challenges and Strategies

Since 2020, covalent organic frameworks (COFs) are emerging as robust catalysts for the photosynthesis of hydrogen peroxide (H2O2), benefiting from their distinct advantages. However, the current efficiency of H2O2 production and solar-to-chemical energy conversion efficiency (SCC) remain suboptimal due to various constraints in the reaction mechanism. Therefore, there is an imperative to propose efficiency improvement strategies to accelerate the development of this reaction system. This comprehensive review delineates recent advances, challenges, and strategies in utilizing COFs for photocatalytic H2O2 production. It explores the fundamentals and challenges (e.g., oxygen (O2) mass transfer rate, O2 adsorption capacity, response to sunlight, electron-hole separation efficiency, charge transfer efficiency, selectivity, and H2O2 desorption) associated with this process, as well as the advantages, applications, classification, and preparation strategies of COFs for this purpose. Various strategies to enhance the performance of COFs in H2O2 production are highlighted. The review aims to stimulate further advancements in utilizing COFs for photocatalytic H2O2 production and discusses potential prospects, challenges, and application areas in this field.

Highlights from the 57th Bürgenstock Conference on Stereochemistry 2024

Herein, we share an overview of the scientific highlights from speakers at the latest edition of the longstanding Bürgenstock Conference.

Photocatalyzed Cascade Hydrogen Atom Transfers for Assembly of Multi-Substituted α-SCF3 and α-SCF2H Cyclopentanones

A photocatalyzed formal (3+2) cycloaddition has been developed to construct original polysubstituted α-SCF3 cyclopentanones in a regio- and diastereoselective manner. This building block approach leverages trifluoromethylthio alkynes and branched/linear aldehydes, as readily available reaction partners, in consecutive hydrogen atom transfers and C-C bond formations. Difluoromethylthio alkynes are also compatible substrates. Furthermore, the potential for telescoped reaction starting from alcohols instead of aldehydes was demonstrated, as well as process automatization and scale-up under continuous microflow conditions. This prompted density functional theory (DFT) calculations to support a radical-mediated cascade process.

A feedback-driven IoT microfluidic, electrophysiology, and imaging platform for brain organoid studies

The analysis of tissue cultures, particularly brain organoids, takes a high degree of coordination, measurement, and monitoring. We have developed an automated research platform enabling independent devices to achieve collaborative objectives for feedback-driven cell culture studies. Unified by an Internet of Things (IoT) architecture, our approach enables continuous, communicative interactions among various sensing and actuation devices, achieving precisely timed control of in vitro biological experiments. The framework integrates microfluidics, electrophysiology, and imaging devices to maintain cerebral cortex organoids and monitor their neuronal activity. The organoids are cultured in custom, 3D-printed chambers attached to commercial microelectrode arrays for electrophysiology monitoring. Periodic feeding is achieved using programmable microfluidic pumps. We developed computer vision fluid volume estimations of aspirated media, achieving high accuracy, and used feedback to rectify deviations in microfluidic perfusion during media feeding/aspiration cycles. We validated the system with a 7-day study of mouse cerebral cortex organoids, comparing manual and automated protocols. The automated experimental samples maintained robust neural activity throughout the experiment, comparable with the control samples. The automated system enabled hourly electrophysiology recordings that revealed dramatic temporal changes in neuron firing rates not observed in once-a-day recordings.

The relevance of sustainable laboratory practices

Scientists are of key importance to the society to advocate awareness of the climate crisis and its underlying scientific evidence and provide solutions for a sustainable future. As much as scientific research has led to great achievements and benefits, traditional laboratory practices come with unintended environmental consequences. Scientists, while providing solutions to climate problems and educating the young innovators of the future, are also part of the problem: excessive energy consumption, (hazardous) waste generation, and resource depletion. Through their own research operations, science, research and laboratories have a significant carbon footprint and contribute to the climate crisis. Climate change requires a rapid response across all sectors of society, modeled by inspiring leaders. A broader scientific community that takes concrete actions would serve as an important step in convincing the general public of similar actions. Over the past years, grassroots movements across the sciences have recognized the overlooked impact of the scientific enterprise, and so-called Green Lab initiatives emerged seeking to address the environmental footprint of research. Driven by the voluntary efforts of researchers and staff, they educate peers, develop sustainability guidelines, write scientific publications and maintain accreditation frameworks. With this perspective we want to advocate for and spark leadership to promote a systemic change in laboratory practices and approach to research. Comprehensive evidence for the environmental impact of laboratories and their root-causes is presented, expanded with data from a current case study of the University of Groningen showcasing annual savings of 398 763 € as well as 477.1 tons of CO2e. This is followed by guidelines for sustainable lab practices and hands-on advice on how to achieve a systemic change at research institutions and industry. How can we expect industry, politics, and society to change, if we as scientists are not changing either? Scientists should lead by example and practice the change they want to see.