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Water scarcity is a particularly severe challenge in arid and desert climates. While a substantial amount of water is present in the form of vapour in the atmosphere, harvesting this water by state-of-the-art dewing technology can be extremely energy intensive and impractical, particularly when the relative humidity (RH) is low (i.e., below ~40% RH). In contrast, atmospheric water generators that utilise sorbents enable capture of vapour at low RH conditions and can be driven by the abundant source of solar-thermal energy with higher efficiency. Here, we demonstrate an air-cooled sorbent-based atmospheric water harvesting device using the metal−organic framework (MOF)-801 [Zr 6 O 4 (OH) 4 (fumarate) 6 ] operating in an exceptionally arid climate (10–40% RH) and sub-zero dew points (Tempe, Arizona, USA) with a  thermal efficiency (solar input to water conversion) of ~14%. We predict that this device delivered over 0.25 L of water per kg of MOF for a single daily cycle. Harvesting water from the atmosphere is an important solution to water scarcity, but doing so in arid climates is highly challenging. Here, the authors develop a metal-organic framework-based water harvesting device that can deliver over 0.25 L of water per kg of adsorbent over a single cycle at relative humidities of 10–40% and at subzero dew points.

The rapid detection of 11-nor-9-carboxy-delta-9-tetrahydrocannabinol (THC-COOH), a primary cannabis metabolite, is critical for forensic and workplace drug testing. However, conventional immunoassays often lack sensitivity and objectivity. We developed a portable lateral flow immunoassay device with a microfluidic cartridge and fluorescent reader for the semi-quantitative detection of THC-COOH in urine. A test-to-reference fluorescence ratio was employed to mitigate matrix effects and ensure objective results. The device was validated for accuracy, repeatability, and stability using spiked urine samples and compared against validated LC-MS/MS results on 100 authentic urine samples (50 positive and 50 negative). At a cutoff of 20 ng/mL, the device achieved 100% sensitivity and specificity, with repeatability and reproducibility CVs of below 15%. The cutoff index (COI) strongly correlated with LC-MS/MS results (R2 = 0.9471). Crucially, this high correlation with hydrolyzed LC-MS/MS data demonstrates that the antibody recognizes both free and glucuronide-conjugated metabolites, validating its reliability without enzymatic pre-treatment. This microfluidic device enables rapid, sensitive on-site THC-COOH detection, featuring automated data management via Wi-Fi connectivity, enhancing its forensic applicability.

Despite advances in precision oncology, developing effective cancer therapeutics remains a significant challenge due to tumor heterogeneity and the limited availability of well-defined drug targets. Recent progress in generative artificial intelligence (AI) offers a promising opportunity to address this challenge by enabling the design of hit-like anti-cancer molecules conditioned on complex genomic features. We present Genotype-to-Drug Diffusion (G2D-Diff), a generative AI approach for creating small molecule-based drug structures tailored to specific cancer genotypes. G2D-Diff demonstrates exceptional performance in generating diverse, drug-like compounds that meet desired efficacy conditions for a given genotype. The model outperforms existing methods in diversity, feasibility, and condition fitness. G2D-Diff learns directly from drug response data distributions, ensuring reliable candidate generation without separate predictors. Its attention mechanism provides insights into potential cancer targets and pathways, enhancing interpretability. In triple-negative breast cancer case studies, G2D-Diff generated plausible hit-like candidates by focusing on relevant pathways. By combining realistic hit-like molecule generation with relevant pathway suggestions for specific genotypes, G2D-Diff represents a significant advance in AI-guided, personalized drug discovery. This approach has the potential to accelerate drug development for challenging cancers by streamlining hit identification. Generative artificial intelligence (AI) can be used to guide cancer drug discovery. Here the authors present a generative AI model that can design drug-like compounds based on specific cancer genotypes.

Organic-inorganic hybrid perovskite solar cells (PSCs) have been extensively studied because of their outstanding performance: a power conversion efficiency exceeding 22% has been achieved. The most commonly used PSCs consist of CH 3 NH 3 PbI 3 (MAPbI 3 ) with a hole-selective contact, such as 2,2′,7,7′-tetrakis( N , N -di- p -methoxyphenylamine)-9,9-spiro-bifluorene (spiro-OMeTAD), for collecting holes. From the perspective of long-term operation of solar cells, the cell performance and constituent layers (MAPbI 3 , spiro-OMeTAD, etc.) may be influenced by external conditions like temperature, light, etc. Herein, we report the effects of temperature on spiro-OMeTAD and the interface between MAPbI 3 and spiro-OMeTAD in a solar cell. It was confirmed that, at high temperatures (85 °C), I − and CH 3 NH 3 + (MA + ) diffused into the spiro-OMeTAD layer in the form of CH 3 NH 3 I (MAI). The diffused I − ions prevented oxidation of spiro-OMeTAD, thereby degrading the electrical properties of spiro-OMeTAD. Since ion diffusion can occur during outdoor operation, the structural design of PSCs must be considered to achieve long-term stability.

The evaluation of potential drug toxicity is a crucial step in early drug development. in vivo toxicity assessment represents a key challenge that must be addressed before advancing to clinical trials. However, traditional in vivo experiments primarily rely on animal models, raising concerns regarding cost, time efficiency, and ethical considerations. To address these challenges, various computational approaches have been developed to support in vivo toxicity evaluations, though these methods often demonstrate limited generalizability due to data scarcity. In this study, we propose MT-Tox, a knowledge transfer-based multi-task learning model specifically designed for in vivo toxicity prediction that overcomes data scarcity. Our model implements a sequential knowledge transfer strategy across three stages: general chemical knowledge pretraining, in vitro toxicological auxiliary training, and in vivo toxicity fine-tuning. This hierarchical approach significantly improves model performance by systematically leveraging information from both chemical structure and toxicity data sources. MT-Tox outperforms baseline models across three in vivo toxicity endpoints: carcinogenicity, drug-induced liver injury (DILI), and genotoxicity. Through ablation studies and attention analyses, we demonstrate that each knowledge transfer technique makes meaningful contributions to the prediction process. Finally, we demonstrate the real-world application of our model as a prediction tool for early-stage drug discovery through comprehensive DrugBank database screening. Scientific contribution: We propose a knowledge transfer framework that integrates chemical and in vitro toxicological information to enhance in vivo toxicity prediction in low-data regimes. Our model provides dual-level interpretability across chemical and biological domains through attention mechanism. Moreover, we demonstrate our model’s applicability by screening the DrugBank database, simulating practical toxicity screening scenarios in drug development.

Background This study aimed to assess the effect of antibiotics on the clinical outcomes of patients with solid cancers undergoing treatment with immune checkpoint inhibitors (ICIs). Methods The medical records of 234 patients treated with ICIs for any type of solid cancer between February 2012 and May 2018 at the Seoul St. Mary’s Hospital were retrospectively reviewed. The data of patients who received antibiotics within 60 days before the initiation of ICI treatment were analyzed. The patients’ responses to ICI treatment and their survival were evaluated. Results Non-small-cell lung carcinoma was the most common type of cancer. About half of the patients were treated with nivolumab (51.9%), and cephalosporin (35.2%) was the most commonly used class of antibiotics. The total objective response rate was 21%. Antibiotics use was associated with a decreased objective response (odds ratio 0.466, 95% confidence interval [CI] 0.225–0.968, p  = 0.040). The antibiotics group exhibited shorter progression-free survival (PFS) and overall survival (OS) than the no antibiotics group (median PFS: 2 months vs. 4 months, p  < 0.001; median OS: 5 months vs. 17 months, p  < 0.001). In the multivariate analysis, antibiotics use was a significant predictor of patient survival (PFS: hazard ratio [HR] 1.715, 95% CI 1.264–2.326, p  = 0.001; OS: HR 1.785, 95% CI 1.265–2.519, p  = 0.001). Conclusions The use of antibiotics may affect the clinical outcomes of patients with solid cancers treated with ICIs. Careful prescription of antibiotics is warranted in candidates who are scheduled for ICI treatment. Trial registration Not applicable (retrospective study).

Renal cell carcinoma (RCC) is a malignancy of the kidney originating from the tubular epithelium. Inactivation of the von Hippel–Lindau tumor-suppressor gene (VHL) is found in most clear cell renal cell carcinomas (ccRCCs). The VHL–HIF–VEGF/VEGFR pathway, which involves the von Hippel–Lindau tumor suppressor protein (VHL), hypoxia-inducible factor (HIF), vascular endothelial growth factor (VEGF), and its receptor (VEGFR), is a well-studied therapeutic target for metastatic ccRCC. Therefore, over the past decade, anti-angiogenic agents targeting VEGFR have served as the standard treatment for metastatic RCC. Recently, based on the immunomodulatory effect of anti-VEGFR therapy, anti-angiogenic agents and immune checkpoint inhibitor combination strategies have also emerged as therapeutic strategies. These advances were made possible by the improved understanding of the VHL–HIF pathway. In this review, we summarize the historical evolution of ccRCC treatments, with a focus on the involvement of the VHL–HIF pathway.

Background Glioblastoma (GBM) is a complex disease with extensive molecular and transcriptional heterogeneity. GBM can be subcategorized into four distinct subtypes; tumors that shift towards the mesenchymal phenotype upon recurrence are generally associated with treatment resistance, unfavorable prognosis, and the infiltration of pro-tumorigenic macrophages. Results We explore the transcriptional regulatory networks of mesenchymal-associated tumor-associated macrophages (MA-TAMs), which drive the malignant phenotypic state of GBM, and identify macrophage receptor with collagenous structure (MARCO) as the most highly differentially expressed gene. MARCO high TAMs induce a phenotypic shift towards mesenchymal cellular state of glioma stem cells, promoting both invasive and proliferative activities, as well as therapeutic resistance to irradiation. MARCO high TAMs also significantly accelerate tumor engraftment and growth in vivo. Moreover, both MA-TAM master regulators and their target genes are significantly correlated with poor clinical outcomes and are often associated with genomic aberrations in neurofibromin 1 (NF1) and phosphoinositide 3-kinases/mammalian target of rapamycin/Akt pathway (PI3K-mTOR-AKT)-related genes. We further demonstrate the origination of MA-TAMs from peripheral blood, as well as their potential association with tumor-induced polarization states and immunosuppressive environments. Conclusions Collectively, our study characterizes the global transcriptional profile of TAMs driving mesenchymal GBM pathogenesis, providing potential therapeutic targets for improving the effectiveness of GBM immunotherapy.

Background Intratumoral heterogeneity hampers the success of marker-based anticancer treatment because the targeted therapy may eliminate a specific subpopulation of tumor cells while leaving others unharmed. Accordingly, a rational strategy minimizing survival of the drug-resistant subpopulation is essential to achieve long-term therapeutic efficacy. Results Using single-cell RNA sequencing (RNA-seq), we examine the intratumoral heterogeneity of a pair of primary renal cell carcinoma and its lung metastasis. Activation of drug target pathways demonstrates considerable variability between the primary and metastatic sites, as well as among individual cancer cells within each site. Based on the prediction of multiple drug target pathway activation, we derive a combinatorial regimen co-targeting two mutually exclusive pathways for the metastatic cancer cells. This combinatorial strategy shows significant increase in the treatment efficacy over monotherapy in the experimental validation using patient-derived xenograft platforms in vitro and in vivo . Conclusions Our findings demonstrate the investigational application of single-cell RNA-seq in the design of an anticancer regimen. The approach may overcome intratumoral heterogeneity which hampers the success of precision medicine.

Addressing the global disparity in cancer care necessitates the development of rapid and affordable nucleic acid (NA) testing technologies. This need is particularly critical for cervical cancer, where molecular detection of human papillomavirus (HPV) has emerged as an accurate screening method. However, implementing this transition in low- and middle-income countries has been challenging due to the high costs and centralized facilities required for current NA tests. Here, we present CreDiT (CRISPR Enhanced Digital Testing) for on-site NA detection. The CreDiT platform integrates i) a one-pot CRISPR strategy that simultaneously amplifies both target NAs and analytical signals and ii) a robust fluorescent detection based on digital communication (encoding/decoding) technology. These features enable a rapid assay (<35 minutes) in a single streamlined workflow. We demonstrate the sensitive detection of cell-derived HPV DNA targets down to single copies and accurate identification of HPV types in clinical cervical brushing specimens ( n  = 121). Developing rapid and affordable HPV tests is imperative for effective cervical cancer screening in resource-limited regions. Here, the authors demonstrate an advanced HPV diagnostic system that integrates CRISPR technology with digital signal processing.