PESSA: A web tool for pathway enrichment score-based survival analysis in cancer.

The activation levels of biologically significant gene sets are emerging tumor molecular markers and play an irreplaceable role in the tumor research field; however, web-based tools for prognostic analyses using it as a tumor molecular marker remain scarce. We developed a web-based tool PESSA for survival analysis using gene set activation levels. All data analyses were implemented via R. Activation levels of The Molecular Signatures Database (MSigDB) gene sets were assessed using the single sample gene set enrichment analysis (ssGSEA) method based on data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), The European Genome-phenome Archive (EGA) and supplementary tables of articles. PESSA was used to perform median and optimal cut-off dichotomous grouping of ssGSEA scores for each dataset, relying on the survival and survminer packages for survival analysis and visualisation. PESSA is an open-access web tool for visualizing the results of tumor prognostic analyses using gene set activation levels. A total of 238 datasets from the GEO, TCGA, EGA, and supplementary tables of articles; covering 51 cancer types and 13 survival outcome types; and 13,434 tumor-related gene sets are obtained from MSigDB for pre-grouping. Users can obtain the results, including Kaplan-Meier analyses based on the median and optimal cut-off values and accompanying visualization plots and the Cox regression analyses of dichotomous and continuous variables, by selecting the gene set markers of interest. PESSA (https://smuonco.shinyapps.io/PESSA/ OR http://robinl-lab.com/PESSA) is a large-scale web-based tumor survival analysis tool covering a large amount of data that creatively uses predefined gene set activation levels as molecular markers of tumors.

Design, synthesis and biological evaluation of new RNF126-based p300/CBP degraders.

Histone acetyltransferase CREB-binding protein (CBP) and its homologous protein p300 are key transcriptional activators that can activate oncogene transcription, which present promising targets for cancer therapy. Here, we designed and synthesized a series of p300/CBP targeted low molecular weight PROTACs by assembling the covalent ligand of RNF126 E3 ubiquitin ligase and the bromodomain ligand of the p300/CBP. The optimal molecule A8 could effectively degrade p300 and CBP through the ubiquitin-proteasome system in time- and concentration-dependent manners, with half-maximal degradation (DC50) concentrations of 208.35/454.35 nM and 82.24/79.45 nM for p300/CBP in MV4-11 and Molm13 cell lines after 72 h of treatment. And the degradation of p300/CBP by A8 is dependent on the ubiquitin-proteasome pathway and its simultaneous interactions with the target proteins and RNF126. A8 exhibits good antiproliferative activity in a series of p300/CBP-dependent cancer cells. It could transcriptionally inhibit the expression of c-Myc, induce cell cycle arrest in the G0/G1 phase and apoptosis in MV4-11 cells. This study thus provided us a new chemotype for the development of drug-like PROTACs targeting p300/CBP, which is expected to be applied in cancer therapy.

ChatGPT's ability to generate realistic experimental images poses a new challenge to academic integrity.

The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT's writing capabilities, recent updates have integrated DALL-E 3's image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT's nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding "invisible watermarks" to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

Enhanced pyroptosis induction with pore-forming gene delivery for osteosarcoma microenvironment reshaping.

Osteosarcoma is the most common malignant bone tumor without efficient management for improving 5-year event-free survival. Immunotherapy is also limited due to its highly immunosuppressive tumor microenvironment (TME). Pore-forming gasdermins (GSDMs)-mediated pyroptosis has gained increasing concern in reshaping TME, however, the expressions and relationships of GSDMs with osteosarcoma remain unclear. Herein, gasdermin E (GSDME) expression is found to be positively correlated with the prognosis and immune infiltration of osteosarcoma patients, and low GSDME expression was observed. A vector termed as LPAD contains abundant hydroxyl groups for hydrating layer formation was then prepared to deliver the GSDME gene to upregulate protein expression in osteosarcoma for efficient TME reshaping via enhanced pyroptosis induction. Atomistic molecular dynamics simulations analysis proved that the hydroxyl groups increased LPAD hydration abilities by enhancing coulombic interaction. The upregulated GSDME expression together with cleaved caspase-3 provided impressive pyroptosis induction. The pyroptosis further initiated proinflammatory cytokines release, increased immune cell infiltration, activated adaptive immune responses and create a favorable immunogenic hot TME. The study not only confirms the role of GSDME in the immune infiltration and prognosis of osteosarcoma, but also provides a promising strategy for the inhibition of osteosarcoma by pore-forming GSDME gene delivery induced enhanced pyroptosis to reshape the TME of osteosarcoma.

CPADS: a web tool for comprehensive pancancer analysis of drug sensitivity.

Drug therapy is vital in cancer treatment. Accurate analysis of drug sensitivity for specific cancers can guide healthcare professionals in prescribing drugs, leading to improved patient survival and quality of life. However, there is a lack of web-based tools that offer comprehensive visualization and analysis of pancancer drug sensitivity. We gathered cancer drug sensitivity data from publicly available databases (GEO, TCGA and GDSC) and developed a web tool called Comprehensive Pancancer Analysis of Drug Sensitivity (CPADS) using Shiny. CPADS currently includes transcriptomic data from over 29 000 samples, encompassing 44 types of cancer, 288 drugs and more than 9000 gene perturbations. It allows easy execution of various analyses related to cancer drug sensitivity. With its large sample size and diverse drug range, CPADS offers a range of analysis methods, such as differential gene expression, gene correlation, pathway analysis, drug analysis and gene perturbation analysis. Additionally, it provides several visualization approaches. CPADS significantly aids physicians and researchers in exploring primary and secondary drug resistance at both gene and pathway levels. The integration of drug resistance and gene perturbation data also presents novel perspectives for identifying pivotal genes influencing drug resistance. Access CPADS at https://smuonco.shinyapps.io/CPADS/ or https://robinl-lab.com/CPADS.

Multimodal ChatGPT-4V for ECG Interpretation: Promise and Limitations.

UNSTRUCTURED: Electrocardiogram (ECG) interpretation is an essential skill in cardiovascular medicine. This study evaluated the capabilities of newly released ChatGPT-4V, a large language model with visual recognition abilities, in interpreting ECG waveforms and answering related multiple-choice questions. A total of 62 ECG-related multiple-choice questions were collected from reputable medical exams. ChatGPT was prompted to answer the questions by analyzing the accompanying ECG images. Requiring at least 1 of 3 responses to be correct, ChatGPT achieved an overall accuracy of 83.87% across all question types. ChatGPT demonstrated significantly lower performance on counting-based questions like calculating QT intervals compared to diagnostic and treatment recommendation questions. The findings indicate that while ChatGPT shows promising potential in ECG interpretation and decision-making, its diagnostic reliability and quantitative analysis abilities need improvement before real clinical use. Further large-scale studies are warranted to fully evaluate ChatGPT's capabilities and track its progress as the model accumulates more medical knowledge through ongoing training. With technological advancements, multimodal AI like ChatGPT may one day play an important role in assisting clinicians with ECG interpretation and cardiovascular care.

Ventricular Netrin-1 deficiency leads to defective pyramidal decussation and mirror movement in mice.

The corticospinal tract (CST) is the principal neural pathway responsible for conducting voluntary movement in the vertebrate nervous system. Netrin-1 is a well-known guidance molecule for midline crossing of commissural axons during embryonic development. Families with inherited Netrin-1 mutations display congenital mirror movements (CMM), which are associated with malformations of pyramidal decussation in most cases. Here, we investigated the role of Netrin-1 in CST formation by generating conditional knockout (CKO) mice using a Gfap-driven Cre line. A large proportion of CST axons spread laterally in the ventral medulla oblongata, failed to decussate and descended in the ipsilateral spinal white matter of Ntn1Gfap CKO mice. Netrin-1 mRNA was expressed in the ventral ventricular zone (VZ) and midline, while Netrin-1 protein was transported by radial glial cells to the ventral medulla, through which CST axons pass. The level of transported Netrin-1 protein was significantly reduced in Ntn1Gfap CKO mice. In addition, Ntn1Gfap CKO mice displayed increased symmetric movements. Our findings indicate that VZ-derived Netrin-1 deletion leads to an abnormal trajectory of the CST in the spinal cord due to the failure of CST midline crossing and provides novel evidence supporting the idea that the Netrin-1 signalling pathway is involved in the pathogenesis of CMM.

The role of ZC3H12D-regulated TLR4-NF-κB pathway in LPS-induced pro-inflammatory microglial activation.

Lipopolysaccharide (LPS) is an important neurotoxin that can cause inflammatory activation of microglia. ZC3H12D is a novel immunomodulator, which plays a remarkable role in neurological pathologies. It has not been characterized whether ZC3H12D is involved in the regulation of microglial activation. The aim of this study was to investigate the role of ZC3H12D in LPS-induced pro-inflammatory microglial activation and its potential mechanism. To elucidate this, we established animal models of inflammatory injury by intraperitoneal injection of LPS (10 mg/kg). The results of the open-field test showed that LPS caused impaired motor function in mice. Meanwhile, LPS caused pro-inflammatory activation of microglia in the mice cerebral cortex and inhibited the expression of ZC3H12D. We also constructed in vitro inflammatory injury models by treating BV-2 microglia with LPS (0.5 µg/mL). The results showed that down-regulated ZC3H12D expression was associated with LPS-induced pro-inflammatory microglial activation, and further intervention of ZC3H12D expression could inhibited LPS-induced pro-inflammatory activation of microglia. In addition, LPS activated the TLR4-NF-κB signaling pathway, and this process can also be reversed by promoting ZC3H12D expression. At the same time, the addition of resveratrol, a nutrient previously proven to inhibit pro-inflammatory microglial activation, can also reverse this process by increasing the expression of ZC3H12D. Summarized, our data elucidated that ZC3H12D in LPS-induced pro-inflammatory activation of brain microglia via restraining the TLR4-NF-κB pathway. This study may provide a valuable clue for potential therapeutic targets for neuroinflammation-related injuries.

Burden of Stroke Attributable to Nonoptimal Temperature in 204 Countries and Territories: A Population-Based Study, 1990-2019.

BACKGROUND AND OBJECTIVES: Stroke attributable to nonoptimal temperature needs more attention with dramatic climate change. The aim of this study was to estimate the global burden and distribution characteristics of the burden. METHODS: In this ecological study, we collected data from the Climate Research Unit Gridded Time Series, the World Bank databases, and the Global Burden of Diseases study to estimate the distribution of burden. We used the joinpoint model, decomposition analysis, age-period-cohort model, panel data analysis, and health inequality analysis to assess the different types of stroke burden attributable to different climatic conditions. RESULTS: The burden of stroke attributable to nonoptimal temperature continued to grow, and aging was a key factor in this increase. In 2019, 521,031 (95% uncertainty interval [UI] 402,433-663,996) deaths and 9,423,649 (95% UI 7,207,660-12,055,172) disability-adjusted life years [DALYs] attributable to stroke due to nonoptimal temperature were recorded globally. Globally, men (age-standardized mortality rate [ASMR] 7.70, 95% UI 5.80-9.73; age-standardized DALY rate [ASDR] 139.69, 95% UI 102.96-178.54 in 2019) had a heavier burden than women (ASMR 5.89, 95% UI 4.50-7.60; ASDR 96.02, 95% UI 72.62-123.85 in 2019). Central Asia (ASMR 18.12, 95% UI 13.40-24.53; ASDR 327.35, 95% UI 240.24-440.61 in 2019) had the heaviest burden at the regional level. In the national level, North Macedonia (ASMR 32.97, 95% UI 20.57-47.44 in 2019) and Mongolia (ASDR 568.54, 95% UI 242.03-1,031.14 in 2019) had the highest ASMR/ASDR, respectively. Low temperature currently contributes to the main burden (deaths 474,002, 95% UI 355,077-606,537; DALYs 8,357,198, 95% UI 6,186,217-10,801,911 attributable to low temperature vs deaths 48,030, 95% UI 5,630-104,370; DALYs 1,089,329, 95% UI 112,690-2,375,345 attributable to high temperature in 2019). However, the burden due to high temperature has increased rapidly, especially among people aged older than 10 years, and was disproportionately concentrated in low sociodemographic index (SDI) regions such as Africa. In addition, the rapid increase in the stroke burden due to high temperature in Central Asia also requires special attention. DISCUSSION: This is the first study to assess the global stroke burden attributed to nonoptimal temperature. The dramatic increase in the burden due to high temperature requires special attention, especially in low-SDI countries.

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.

Wavelength encoding spectral imaging based on the combination of deeply learned filters and an RGB camera.

Hyperspectral imaging is a critical tool for gathering spatial-spectral information in various scientific research fields. As a result of improvements in spectral reconstruction algorithms, significant progress has been made in reconstructing hyperspectral images from commonly acquired RGB images. However, due to the limited input, reconstructing spectral information from RGB images is ill-posed. Furthermore, conventional camera color filter arrays (CFA) are designed for human perception and are not optimal for spectral reconstruction. To increase the diversity of wavelength encoding, we propose to place broadband encoding filters in front of the RGB camera. In this condition, the spectral sensitivity of the imaging system is determined by the filters and the camera itself. To achieve an optimal encoding scheme, we use an end-to-end optimization framework to automatically design the filters' transmittance functions and optimize the weights of the spectral reconstruction network. Simulation experiments show that our proposed spectral reconstruction network has excellent spectral mapping capabilities. Additionally, our novel joint wavelength encoding imaging framework is superior to traditional RGB imaging systems. We develop the deeply learned filter and conduct actual shooting experiments. The spectral reconstruction results have an attractive spatial resolution and spectral accuracy.

Development and validation of a radiosensitivity model to evaluate radiotherapy benefits in pan-cancer.

Radiotherapy, one of the most fundamental cancer treatments, is confronted with the dilemma of treatment failure due to radioresistance. To predict the radiosensitivity and improve tumor treatment efficiency in pan-cancer, we developed a model called Radiation Intrinsic Sensitivity Evaluation (RISE). The RISE model was built using cell line-based mRNA sequencing data from five tumor types with varying radiation sensitivity. Through four cell-derived datasets, two public tissue-derived cohorts, and one local cohort of 42 nasopharyngeal carcinoma patients, we demonstrated that RISE could effectively predict the level of radiation sensitivity (area under the ROC curve [AUC] from 0.666 to 1 across different datasets). After the verification by the colony formation assay and flow cytometric analysis of apoptosis, our four well-established radioresistant cell models successfully proved higher RISE values in radioresistant cells by RT-qPCR experiments. We also explored the prognostic value of RISE in five independent TCGA cohorts consisting of 1137 patients who received radiation therapy and found that RISE was an independent adverse prognostic factor (pooled multivariate Cox regression hazard ratio [HR]: 1.84, 95% CI 1.39-2.42; p < 0.01). RISE showed a promising ability to evaluate the radiotherapy benefit while predicting the prognosis of cancer patients, enabling clinicians to make individualized radiotherapy strategies in the future and improve the success rate of radiotherapy.

Effects of arsenic exposure on blood trace element levels in rats and sex differences.

Arsenic (As) is a widespread environmental metalloid and human carcinogen, and its exposure is associated with a wide range of toxic effects, leading to serious health hazards. As poisoning is a complex systemic multi-organ and multi-system damage disease. In this study, a rat model of As poisoning was established to investigate the levels of trace elements in the blood of rats and sex differences in the effect of As on every trace elements in rat blood. Twenty 6-week-old SD (Sprague Dawley) rats were randomly divided into the control group and the As-exposed group. After 3 months, the contents of 19 elements including As in the blood were detected in these two groups by inductively coupled plasma mass spectrometry (ICP-MS). As levels in the blood of As-exposed rats were significantly higher than those in the control group, with increased levels of Rb, Sr, Cs and Ce, and decreased levels of Pd. As showed a significant positive correlation with Rb. There were significant sex differences in blood Se, Pd, Eu, Dy, Ho, and Au levels in the As-exposed group. The results showed that As exposure can lead to an increase of As content in blood and an imbalance of some elements. There were sex differences in the concentration and the correlation between elements of some elements. Elemental imbalances may affect the toxic effects of As and play a synergistic or antagonistic role in As toxicity.

A hyper-knowledge graph system for research on AI ethics cases.

Current studies on the artificial intelligence (AI) ethics focus either on very broad guidelines or on a very special domain. Therefore, the research outcome can hardly be converted into actionable measures or transferred to other domains. Potential correlations between various cases of AI ethics at different granularity levels are unexplored. To overcome these deficiencies, the authors designed a case-oriented ontological model (COOM) and a hyper-knowledge graph system (HKGS) for the research of collected AI ethics cases. COOM describes criteria for modelling cases by attributes from three perspectives: event attributes, relational attributes, and positional attributes on the value chain. Based on it, HKGS stores the correlation between cases as knowledge and allows advanced visual analysis. The correlations between cases and their dynamic changes on value chain can be observed and explored. In HKGS's implementation part, one of the collected ethics cases is used as an example to demonstrate how to generate a hyper-knowledge graph and to visually analyze it. The authors also anticipated how different practitioners of AI ethics, can achieve the desired outputs from HKGS in their diverse scenarios.