Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system’s robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.

Lactylation, a recently identified form of protein post‐translational modification (PTM), has emerged as a key player in cancer biology. The Warburg effect, a hallmark of tumour metabolism, underscores the significance of lactylation in cancer progression. By regulating gene transcription and protein function, lactylation facilitates metabolic reprogramming, enabling tumours to adapt to nutrient limitations and sustain rapid growth. Over the past decade, extensive research has revealed the intricate regulatory network underlying lactylation in tumours. Large‐scale sequencing and machine learning have confirmed the widespread occurrence of lactylation sites across the tumour proteome. Targeting lactylation enzymes or metabolic pathways has demonstrated promising anti‐tumour effects, highlighting the therapeutic potential of this modification. This review comprehensively explores the mechanisms of lactylation in cancer cells and the tumour microenvironment. We expound on the application of advanced omics technologies for target identification and data modelling within the lactylation field. Additionally, we summarise existing anti‐lactylation drugs and discuss their clinical implications. By providing a comprehensive overview of recent advancements, this review aims to stimulate innovative research and accelerate the translation of lactylation‐based therapies into clinical practice.Key points: Lactylation significantly influences tumour metabolism and gene regulation, contributing to cancer progression. Advanced sequencing and machine learning reveal widespread lactylation sites in tumours. Targeting lactylation enzymes shows promise in enhancing anti‐tumour drug efficacy and overcoming chemotherapy resistance. This review outlines the clinical implications and future research directions of lactylation in oncology.

Rationale: The role of histone methylation modifications in renal disease, particularly in sepsis-induced acute kidney injury (AKI), remains unclear. This study aims to investigate the potential involvement of the histone methyltransferase zeste homolog 2 (EZH2) in sepsis-induced AKI and its impact on apoptosis and inflammation.

Ferroptosis, a novel form of regulated cell death characterized by disrupted iron metabolism and the accumulation of lipid peroxides, has exhibited enormous potential in the therapy of cancer particularly clear cell renal cell carcinoma (ccRCC). Luteolin (Lut), a natural flavonoid widely existing in various fruits and vegetables, has been proven to exert potent anticancer activity in vitro and in vivo. However, previous studies on the anticancer mechanism of Lut have been shown in apoptosis but not ferroptosis. In the present study, we identified that Lut substantially inhibited the survival of ccRCC in vitro and in vivo, and this phenomenon was accompanied by excessively increased intracellular Fe²⁺ and abnormal depletion of GSH. In addition, Lut induced the imbalance of mitochondrial membrane potential, classical morphological alterations of mitochondrial ferroptosis, generation of ROS, and occurrence of lipid peroxidation in an iron‐dependent manner in ccRCC cells. However, these alterations induced by Lut could be reversed to some extent by the iron ion chelator deferiprone or the ferroptosis inhibitor ferrostatin‐1, indicating that ccRCC cells treated with Lut underwent ferroptosis. Mechanistically, molecular docking further established that Lut probably promoted the heme degradation and accumulation of labile iron pool (LIP) by excessively upregulating the HO‐1 expression, which led to the Fenton reaction, GSH depletion, and lipid peroxidation in ccRCC, whereas blocking this signaling pathway evidently rescued the Lut‐induced cell death of ccRCC by inhibiting ferroptosis. Altogether, the current study shows that the natural compound monomer Lut exerted anticancer efficacy by excessively upregulating HO‐1 expression and activating LIP to trigger ferroptosis in ccRCC and could be a promising and potent drug candidate for ccRCC treatment.

Unilateral ischemia reperfusion injury (UIRI) with longer ischemia time is associated with an increased risk of acute renal injury and chronic kidney disease. Exosomes can transport lipid, protein, mRNA, and miRNA to corresponding target cells and mediate intercellular information exchange. In this study, we aimed to investigate whether exosome-derived miRNA mediates epithelial-mesenchymal cell communication relevant to renal fibrosis after UIRI. The secretion of exosomes increased remarkably in the kidney after UIRI and in rat renal tubular epithelium cells (NRK-52E) after hypoxia treatment. The inhibition of exosome secretion by Rab27a knockout or GW4869 treatment ameliorates renal fibrosis following UIRI in vivo. Purified exosomes from NRK-52E cells after hypoxia treatment could activate rat kidney fibroblasts (NRK-49F). The inhibition of exosome secretion in hypoxic NRK-52E cells through Rab27a knockdown or GW4869 treatment abolished NRK-49F cell activation. Interestingly, exosomal miRNA array analysis revealed that miR-150-5p expression was increased after hypoxia compared with the control group. The inhibition of exosomal miR-150-5p abolished the ability of hypoxic NRK-52E cells to promote NRK-49F cell activation in vitro, injections of miR-150-5p enriched exosomes from hypoxic NRK-52E cells aggravated renal fibrosis following UIRI, and renal fibrosis after UIRI was alleviated by miR-150-5p-deficient exosome in vivo. Furthermore, tubular cell-derived exosomal miR-150-5p could negatively regulate the expression of suppressor of cytokine signaling 1 to activate fibroblast. Thus, our results suggest that the blockade of exosomal miR-150-5p mediated tubular epithelial cell-fibroblast communication may provide a novel therapeutic target to prevents UIRI progression to renal fibrosis.

Background: Mounting evidence indicates that the gut microbiome (GMB) plays an essential role in kidney stone (KS) formation. In this study, we conducted a systematic review and meta-analysis to compare the composition of gut microbiota in kidney stone patients and healthy individuals, and further understand the role of gut microbiota in nephrolithiasis. Results: Six databases were searched to find taxonomy-based comparison studies on the GMB until September 2022. Meta-analyses were performed using RevMan 5.3 to estimate the overall relative abundance of gut microbiota in KS patients and healthy subjects. Eight studies were included with 356 nephrolithiasis patients and 347 healthy subjects. The meta-analysis suggested that KS patients had a higher abundance of Bacteroides (35.11% vs 21.25%, Z = 3.56, P = 0.0004) and Escherichia_Shigella (4.39% vs 1.78%, Z = 3.23, P = 0.001), and a lower abundance of Prevotella_9 (8.41% vs 10.65%, Z = 4.49, P < 0.00001). Qualitative analysis revealed that beta-diversity was different between the two groups (P < 0.05); Ten taxa (Bacteroides, Phascolarctobacterium, Faecalibacterium, Flavobacterium, Akkermansia, Lactobacillus, Escherichia coli, Rhodobacter and Gordonia) helped the detection of kidney stones (P < 0.05); Genes or protein families of the GMB involved in oxalate degradation, glycan synthesis, and energy metabolism were altered in patients (P < 0.05). Conclusions: There is a characteristic gut microbiota dysbiosis in kidney stone patients. Individualized therapies like microbial supplementation, probiotic or synbiotic preparations and adjusted diet patterns based on individual gut microbial characteristics of patients may be more effective in preventing stone formation and recurrence.