Shrinking the know–do gap in psychedelic-assisted therapy
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Photo-assisted technologies for environmental remediation
Industrial processes can lead to air and water pollution, particularly from organic contaminants such as toluene and antibiotics, posing threats to human health. Photo-assisted chemical oxidation technologies leverage light energy to mineralize these contaminants. In this Review, we discuss the mechanisms and efficiencies of photo-assisted advanced oxidation processes for wastewater treatment and photothermal technologies for air purification. The integration of solar energy enhances degradation efficiency and reduces energy consumption, enabling more efficient remediation methods. We evaluate the technological aspects of photo-assisted technologies, such as photo-Fenton, photo-persulfate activation, photo-ozonation and photoelectrochemical oxidation, emphasizing their potential for practical applications. Finally, we discuss the challenges in scaling up photo-assisted technologies for specific environmental remediation needs. Photo-assisted technologies have demonstrated effectiveness in environmental remediation, although large-scale applications remain constrained by high costs. Future potential applications of photo-assisted technologies will require that technology selection be tailored to specific pollution scenarios and engineering processes optimized to minimize costs.
The guided fire from within: intratumoral administration of mRNA-based vaccines to mobilize memory immunity and direct immune responses against pathogen to target solid tumors
We investigated a novel cancer immunotherapy strategy that effectively suppresses tumor growth in multiple solid tumor models and significantly extends the lifespan of tumor-bearing mice by introducing pathogen antigens into tumors via mRNA-lipid nanoparticles. The pre-existing immunity against the pathogen antigen can significantly enhance the efficacy of this approach. In mice previously immunized with BNT162b2, an mRNA-based COVID-19 vaccine encoding the spike protein of the SARS-CoV-2 virus, intratumoral injections of the same vaccine efficiently tagged the tumor cells with mRNA-expressed spike protein. This action rapidly mobilized the pre-existing memory immunity against SARS-CoV-2 to kill the cancer cells displaying the spike protein, while concurrently reprogramming the tumor microenvironment (TME) by attracting immune cells. The partial elimination of tumor cells in a normalized TME further triggered extensive tumor antigen-specific T cell responses through antigen spreading, eventually resulting in potent and systemic tumor-targeting immune responses. Moreover, combining BNT162b2 treatment with anti-PD-L1 therapy yielded a more substantial therapeutic impact, even in “cold tumor” types that are typically less responsive to treatment. Given that the majority of the global population has acquired memory immunity against various pathogens through infection or vaccination, we believe that, in addition to utilizing the widely held immune memory against SARS-CoV-2 via COVID-19 vaccine, mRNA vaccines against other pathogens, such as Hepatitis B Virus (HBV), Common Human Coronaviruses (HCoVs), and the influenza virus, could be rapidly transitioned into clinical use and holds great promise in treating different types of cancer. The extensive selection of pathogen antigens expands therapeutic opportunities and may also overcome potential drug resistance.
International myeloma working group immunotherapy committee recommendation on sequencing immunotherapy for treatment of multiple myeloma
T-cell redirecting therapy (TCRT), specifically chimeric antigen receptor T-cell therapy (CAR T-cells) and bispecific T-cell engagers (TCEs) represent a remarkable advance in the treatment of multiple myeloma (MM). There are several products available around the world and several more in development targeting primarily B-cell maturation antigen (BCMA) and G protein–coupled receptor class C group 5 member D (GRPC5D). The relatively rapid availability of multiple immunotherapies brings the necessity to understand how a certain agent may affect the safety and efficacy of a subsequent immunotherapy so MM physicians and patients can aim at optimal sequential use of these therapies. The International Myeloma Working Group conveyed panel of experts to review patient and disease-related factors affecting efficacy and safety of immunotherapy, summarize existing information on sequencing therapy and provide a series of core recommendations.
High baseline levels of PD-L1 reduce the heterogeneity of immune checkpoint signature and sensitize anti-PD1 therapy in lung and colorectal cancers
Immune checkpoint blockade (ICB) therapy only induces durable responses in a subset of cancer patients. The underlying mechanisms of such selective efficacy remain largely unknown. By analyzing the expression profiles of immune checkpoint molecules in different statuses of murine tumors, we found that tumor progression generally randomly upregulated multiple immune checkpoints, thus increased the Heterogeneity of Immune checkpoint Signature (HIS) and resulted in immunotherapeutic resistance. Interestingly, overexpressing one pivotal immune checkpoint in a tumor hindered the upregulation of a majority of other immune checkpoint genes during tumor progression via suppressing interferon γ, resulting in HIS-low. Indeed, PD-L1 high-expression sensitized baseline large tumors to anti-PD1 therapy without altering the sensitivity of baseline small tumors. In line with these preclinical results, a retrospective analysis of a phase III study involving patients with non-small cell lung cancer (NSCLC) revealed that PD-L1 tumor proportion score (TPS) ≥ 50% more reliably predicted therapeutic response in NSCLC patients with baseline tumor volume (BTV)-large compared to patients with BTV-small. Notably, TPS combined with BTV significantly improved the predictive accuracy. Collectively, the data suggest that HIS reflects the dynamic features of tumor immune evasion and dictates the selective efficacy of ICB in a tumor size-dependent manner, providing a potential novel strategy to improve precision ICB. These findings highlight the application of ICB to earlier stages of cancer patients. The integration of PD-L1 with BTV may immediately improve patient stratification and prediction performance in the clinic.
What large language models know and what people think they know
As artificial intelligence systems, particularly large language models (LLMs), become increasingly integrated into decision-making processes, the ability to trust their outputs is crucial. To earn human trust, LLMs must be well calibrated such that they can accurately assess and communicate the likelihood of their predictions being correct. Whereas recent work has focused on LLMs’ internal confidence, less is understood about how effectively they convey uncertainty to users. Here we explore the calibration gap, which refers to the difference between human confidence in LLM-generated answers and the models’ actual confidence, and the discrimination gap, which reflects how well humans and models can distinguish between correct and incorrect answers. Our experiments with multiple-choice and short-answer questions reveal that users tend to overestimate the accuracy of LLM responses when provided with default explanations. Moreover, longer explanations increased user confidence, even when the extra length did not improve answer accuracy. By adjusting LLM explanations to better reflect the models’ internal confidence, both the calibration gap and the discrimination gap narrowed, significantly improving user perception of LLM accuracy. These findings underscore the importance of accurate uncertainty communication and highlight the effect of explanation length in influencing user trust in artificial-intelligence-assisted decision-making environments.
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