Artificial intelligence-aided rapid and accurate identification of clinical fungal infections by single-cell Raman spectroscopy

Integrating artificial intelligence and new diagnostic platforms into routine clinical microbiology laboratory procedures has grown increasingly intriguing, holding promises of reducing turnaround time and cost and maximizing efficiency. At least one billion people are suffering from fungal infections, leading to over 1.6 million mortality every year. Despite the increasing demand for fungal diagnosis, current approaches suffer from manual bias, long cultivation time (from days to months), and low sensitivity (only 50% produce positive fungal cultures). Delayed and inaccurate treatments consequently lead to higher hospital costs, mobility and mortality rates. Here, we developed single-cell Raman spectroscopy and artificial intelligence to achieve rapid identification of infectious fungi. The classification between fungi and bacteria infections was initially achieved with 100% sensitivity and specificity using single-cell Raman spectra (SCRS). Then, we constructed a Raman dataset from clinical fungal isolates obtained from 94 patients, consisting of 115,129 SCRS. By training a classification model with an optimized clinical feedback loop, just 5 cells per patient (acquisition time 2 s per cell) made the most accurate classification. This protocol has achieved 100% accuracies for fungal identification at the species level. This protocol was transformed to assessing clinical samples of urinary tract infection, obtaining the correct diagnosis from raw sample-to-result within 1 h

Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning

In recent years, reinforcement learning and imitation learning have shown great potential for controlling humanoid robots' motion. However, these methods typically create simulation environments and rewards for specific tasks, resulting in the requirements of multiple policies and limited capabilities for tackling complex and unknown tasks. To overcome these issues, we present a novel approach that combines adversarial imitation learning with large language models (LLMs). This innovative method enables the agent to learn reusable skills with a single policy and solve zero-shot tasks under the guidance of LLMs. In particular, we utilize the LLM as a strategic planner for applying previously learned skills to novel tasks through the comprehension of task-specific prompts. This empowers the robot to perform the specified actions in a sequence. To improve our model, we incorporate codebook-based vector quantization, allowing the agent to generate suitable actions in response to unseen textual commands from LLMs. Furthermore, we design general reward functions that consider the distinct motion features of humanoid robots, ensuring the agent imitates the motion data while maintaining goal orientation without additional guiding direction approaches or policies. To the best of our knowledge, this is the first framework that controls humanoid robots using a single learning policy network and LLM as a planner. Extensive experiments demonstrate that our method exhibits efficient and adaptive ability in complicated motion tasks

Discovery and Validation of Nitroxoline as a Novel STAT3 Inhibitor in Drug-resistant Urothelial Bladder Cancer

Repeated cycles of first-line chemotherapy drugs such as doxorubicin (DOX) and cisplatin (CIS) trigger frequent chemoresistance in recurrent urothelial bladder cancer (UBC). Nitroxoline (NTX), an antibiotic to treat urinary tract infections, has been recently repurposed for cancer treatment. Here we aimed to investigate whether NTX suppresses drug-resistant UBC and its molecular mechanism. The drug-resistant cell lines T24/DOX and T24/CIS were established by continual exposure of parental cell line T24 to DOX and CIS, respectively. T24/DOX and T24/CIS cells were resistant to DOX and CIS, respectively, but they were sensitive to NTX time-and dose-dependently. Overexpressions of STAT3 and P-glycoprotein (P-gp) were identified in T24/DOX and T24/CIS, which could be reversed by NTX. Western blot revealed that NTX downregulated p-STAT3, c-Myc, Cyclin D1, CDK4, CDK6, Bcl-xL, Mcl-1, and Survivin, which were further confirmed by Stattic, a selective STAT3 inhibitor. In vivo, NTX exhibited the significant anti-tumor effect in T24/DOX and T24/CIS tumor-bearing mice. These results suggested that NTX-induced P-gp reversal, G0/G1 arrest, and apoptosis in drug-resistant UBC were mediated by inhibition of STAT3 signaling. Our findings repurpose NTX as a novel STAT3 inhibitor to induce P-gp reversal, G0/G1 arrest, and apoptosis in drug-resistant UBC

Nitroxoline inhibits bladder cancer progression by reversing EMT process and enhancing anti-tumor immunity

Nitroxoline is considered to be an effective treatment for the urinary tract infections. Recently, it has been found to be effective against several cancers. However, few studies have examined the anti-tumor activity of nitroxoline in bladder cancer. The purpose of the study was to reveal the possible mechanisms how nitroxoline inhibited bladder cancer progression. In vitro assay, we demonstrated that nitroxoline inhibited bladder cancer cell growth and migration in a concentration-related manner. Western blot analysis demonstrated that nitroxoline downregulated the expressions of epithelial mesenchymal transition (EMT)-related proteins. Furthermore, treatment with nitroxoline in the C3H/He mice bladder cancer subcutaneous model resulted in significant inhibition of tumor growth. Moreover, the percentage of myeloid-derived suppressor cells (MDSC) in peripheral blood cells significantly decreased after treatment of nitroxoline. Taken together, our results suggested that nitroxoline may be used as a potential drug for bladder cancer

Enhanced Interfacial Electronic Transfer of BiVO4 Coupled with 2D g‐C3N4 for Visible‐light Photocatalytic Performance

A BiVO4/2D g‐C3N4 direct dual semiconductor photocatalytic system has been fabricated via electrostatic self‐assembly method of BiVO4 microparticle and g‐C3N4 nanosheet. According to experimental measurements and first‐principle calculations, the formation of built‐in electric field and the opposite band bending around the interface region in BiVO4/2D g‐C3N4 as well as the intimate contact between BiVO4 and 2D g‐C3N4 will lead to high separation efficiency of charge carriers. More importantly, the intensity of bulid‐in electric field is greatly enhanced due to the ultrathin nanosheet structure of 2D g‐C3N4. As a result, BiVO4/2D g‐C3N4 exhibits excellent photocatalytic performance with the 93.0% Rhodamine B (RhB) removal after 40 min visible light irradiation, and the photocatalytic reaction rate is about 22.7 and 10.3 times as high as that of BiVO4 and 2D g‐C3N4, respectively. In addition, BiVO4/2D g‐C3N4 also displays enhanced photocatalytic performance in the degradation of tetracycline (TC). It is expected that this work may provide insights into the understanding the significant role of built‐in electric field in heterostructure and fabricating highly efficient direct dual semiconductor systems

The Cell Cycle Checkpoint Gene, RAD17 rs1045051, Is Associated with Prostate Cancer Risk

Human RAD17, as an agonist of checkpoint signaling, plays an essential role in mediating DNA damage. This hospital-based case-control study aimed to explore the association between RAD17 rs1045051, a missense sin-gle nucleotide polymorphism (SNP), and prostate cancer risk. Subjects were 358 prostate cancer patients and 314 cancer-free urology patients undergoing treatment at the Zhujiang Hospital of Southern Medical University in China. RAD17 gene polymorphism rs1045051 was evaluated by the SNaPshot method. Compared with the RAD17 gene polymorphism rs1045051 AA genotype, there was a higher risk of prostate cancer for the CC gen-otype (adjusted odds ratio [AOR] = 1.731, 95% confidence interval [95%CI] = 1.031−2.908, p = 0.038). Compared with the A allele, the C allele was significantly associated with the disease status (AOR = 1.302, 95%CI = 1.037−1.634, p = 0.023). All these findings indicate that in the SNP rs1045051, both the CC genotype and C allele may have a substantial influence on the prostate cancer risk

Repurposing of posaconazole as a hedgehog/SMO signaling inhibitor for embryonal rhabdomyosarcoma therapy

Posaconazole (POS) is a novel antifungal agent, which has been repurposed as an anti-tumor drug for its potential inhibition of Hedgehog signaling pathway. Hedgehog pathway is reported to be abnormally activated in embryonal rhabdomyosarcoma (ERMS), this study aimed to reveal whether POS could inhibit Hedgehog signaling pathway in ERMS. Following POS treatment, XTT viability assay was used to determine the cell proliferation of ERMS cell lines. Protein changes related to Hedgehog signaling, cell cycle and autophagy were detected by Western blot. The cell cycle distribution was analyzed by flow cytometry. Moreover, a subcutaneous tumor mouse model of ERMS was established to assess the anti-tumor effect of POS. POS was found to inhibit tumor progression by inducing G0/G1 arrest and autophagy of RD, RMS-YM, and KYM-1 cells dose-dependently. Western blot demonstrated that POS downregulated the expressions of SMO, Gli1, c-Myc, CDK4, and CDK6, while upregulated the expressions of autophagy-related proteins. Immunofluorescence microscopy revealed a significant increase of LC3B puncta in POS-treated ERMS cells. Furthermore, POS treatment led to a significant inhibition of tumor growth in mice bearing ERMS. Our findings could provide a theoretical basis and have important clinical implications in developing POS as a promising agent against ERMS by targeting Hedgehog pathway

MULTI: Multimodal Understanding Leaderboard with Text and Images

Rapid progress in multimodal large language models (MLLMs) highlights the need to introduce challenging yet realistic benchmarks to the academic community, while existing benchmarks primarily focus on understanding simple natural images and short context. In this paper, we present MULTI as a cutting-edge benchmark for evaluating MLLMs on understanding complex tables and images, and reasoning with long context. MULTI provides multimodal inputs and requires responses that are either precise or open-ended, reflecting real-life examination styles. MULTI includes over 18,000 questions and challenges MLLMs with a variety of tasks, ranging from formula derivation to image detail analysis and cross-modality reasoning. We also introduce MULTI-Elite, a 500-question selected hard subset, and MULTI-Extend, with more than 4,500 external knowledge context pieces. Our evaluation indicates significant potential for MLLM advancement, with GPT-4V achieving a 63.7% accuracy rate on MULTI, in contrast to other MLLMs scoring between 28.5% and 55.3%. MULTI serves not only as a robust evaluation platform but also paves the way for the development of expert-level AI.Comment: 16 pages, 9 figures, 10 tables. Details and access are available at: https://OpenDFM.github.io/MULTI-Benchmark