Clustered regulatory interspaced short palindromic repeats (CRISPR) has changed biomedical research and provided entirely new models to analyze every aspect of biomedical sciences during the last decade. In the study of cancer, the CRISPR/CRISPR-associated protein (Cas) system opens new avenues into issues that were once unknown in our knowledge of the non-coding genome, tumor heterogeneity, and precision medicines. CRISPR/Cas-based gene-editing technology now allows for the precise and permanent targeting of mutations and provides an opportunity to target small non-coding RNAs such as microRNAs (miRNAs). However, the development of effective and safe cancer gene editing therapy is highly dependent on proper design to be innocuous to normal cells and prevent introducing other abnormalities. This study aims to highlight the cutting-edge approaches in cancer-gene editing therapy based on the CRISPR/Cas technology to target miRNAs in cancer therapy. Furthermore, we highlight the potential challenges in CRISPR/Cas-mediated miRNA gene editing and offer advanced strategies to overcome them.

Globally, despite prostate cancer (PCa) representing second most prevalent malignancy in male, the precise molecular mechanisms implicated in its pathogenesis remain unclear. Consequently, elucidating the key molecular regulators that govern disease progression could substantially contribute to the establishment of novel therapeutic strategies, ultimately advancing the management of PCa.

A total of 49 PCa tissues and 43 adjacent normal tissues were collected from January 2017 to December 2021 at Zhongnan Hospital of Wuhan University. The advanced transcriptomic methodologies were employed to identify differentially expressed mRNAs in PCa. The expression of aspartoacylase (ASPA) in PCa was thoroughly evaluated using quantitative real-time PCR and Western blotting techniques. To elucidate the inhibitory role of ASPA in PCa cell proliferation and metastasis, a comprehensive set of in vitro and in vivo assays were conducted, including orthotopic and tumor-bearing mouse models (n=8 for each group). A combination of experimental approaches, such as Western blotting, luciferase assays, immunoprecipitation assays, mass spectrometry, glutathione S-transferase pull-down experiments, and rescue studies, were employed to investigate the underlying molecular mechanisms of ASPA's action in PCa. The Student's t-test was employed to assess the statistical significance between two distinct groups, while one-way analysis of variance was utilized for comparisons involving more than two groups. A two-sided P<0.05 was deemed to indicate statistical significance.

ASPA was identified as a novel inhibitor of PCa progression. The expression of ASPA was found to be significantly down-regulated in PCa tissue samples, and its decreased expression was independently associated with patients’ prognosis (HR=0.60, 95%CI 0.40–0.92, P=0.018). Our experiments demonstrated that modulation of ASPA activity, either through gain- or loss-of-function, led to the suppression or enhancement of PCa cell proliferation, migration, and invasion, respectively. The inhibitory role of ASPA in PCa was further confirmed using orthotopic and tumor-bearing mouse models. Mechanistically, ASPA was shown to directly interact with the LYN and inhibit the phosphorylation of LYN as well as its downstream targets, JNK1/2 and C-Jun, in both PCa cells and mouse models, in an enzyme-independent manner. Importantly, the inhibition of LYN activation by bafetinib abrogated the promoting effect of ASPA knockdown on PCa progression in both in vitro and in vivo models. Moreover, we observed an inverse relationship between ASPA expression and LYN activity in clinical PCa samples, suggesting a potential regulatory role of ASPA in modulating LYN signaling.

Our findings provide novel insights into the tumor-suppressive function of ASPA in PCa and highlight its potential as a prognostic biomarker and therapeutic target for the management of this malignancy.

Treatment of methicillin-resistant Staphylococcus aureus (MRSA) biofilm infections in implant placement surgery is limited by the lack of antimicrobial activity of titanium (Ti) implants. There is a need to explore more effective approaches for the treatment of MRSA biofilm infections.

Herein, an interfacial functionalization strategy is proposed by the integration of mesoporous polydopamine nanoparticles (PDA), nitric oxide (NO) release donor sodium nitroprusside (SNP) and osteogenic growth peptide (OGP) onto Ti implants, denoted as Ti-PDA@SNP-OGP. The physical and chemical properties of Ti-PDA@SNP-OGP were assessed by scanning electron microscopy, X-ray photoelectron spectroscope, water contact angle, photothermal property and NO release behavior. The synergistic antibacterial effect and elimination of the MRSA biofilms were evaluated by 2′,7′-dichlorofluorescein diacetate probe, 1-N-phenylnaphthylamine assay, adenosine triphosphate intensity, O-nitrophenyl-β-D-galactopyranoside hydrolysis activity, bicinchoninic acid leakage. Fluorescence staining, assays for alkaline phosphatase activity, collagen secretion and extracellular matrix mineralization, quantitative real-time reverse transcription-polymerase chain reaction, and enzyme-linked immunosorbent assay (ELISA) were used to evaluate the inflammatory response and osteogenic ability in bone marrow stromal cells (MSCs), RAW264.7 cells and their co-culture system. Giemsa staining, ELISA, micro-CT, hematoxylin and eosin, Masson's trichrome and immunohistochemistry staining were used to evaluate the eradication of MRSA biofilms, inhibition of inflammatory response, and promotion of osseointegration of Ti-PDA@SNP-OGP in vivo.

Ti-PDA@SNP-OGP displayed a synergistic photothermal and NO-dependent antibacterial effect against MRSA following near-infrared light (NIR) irradiation, and effectively eliminated the formed MRSA biofilms by inducing reactive oxygen species (ROS)-mediated oxidative stress, destroying bacterial membrane integrity and causing leakage of intracellular components (P<0.01). In vitro experiments revealed that Ti-PDA@SNP-OGP not only facilitated osteogenic differentiation of MSCs, but also promoted the polarization of pro-inflammatory M1 macrophages to the anti-inflammatory M2-phenotype (P<0.05 or P<0.01). The favorable osteo-immune microenvironment further facilitated osteogenesis of MSCs and the anti-inflammation of RAW264.7 cells via multiple paracrine signaling pathways (P<0.01). In vivo evaluation confirmed the aforementioned results and revealed that Ti-PDA@SNP-OGP induced ameliorative osseointegration in an MRSA-infected femoral defect implantation model (P<0.01).

Ti-PDA@SNP-OGP is a promising multi-functional material for the high-efficient treatment of MRSA infections in implant replacement surgeries.

Nonalcoholic fatty liver disease (NAFLD) is associated with disordered lipid and iron metabolism. Our previous study has substantiated the pivotal role of Caveolin-1 (Cav-1) in protecting hepatocytes and mediating iron metabolism in the liver. This study aimed to explore the specific mechanisms underlying the regulation of iron metabolism by Cav-1 in NAFLD.

Hepatocyte-specific Cav-1 overexpression mice and knockout mice were used in this study. Cav-1- knockdown of RAW264.7 cells and mouse primary hepatocytes were performed to verify the changes in vitro. Moreover, a high-fat diet and palmitic acid plus oleic acid treatment were utilized to construct a NAFLD model in vivo and in vitro, respectively, while a high-iron diet was used to construct an in vivo iron overload model. Besides, iron concentration, the expression of Cav-1 and iron metabolism-related proteins in liver tissue or serum were detected using iron assay kit, Prussian blue staining, Western blotting, immunofluorescence staining, immunohistochemical staining and ELISA. The related indicators of lipid metabolism and oxidative stress were evaluated by the corresponding reagent kit and staining.

Significant disorder of lipid and iron metabolism occurred in NAFLD. The expression of Cav-1 was decreased in NAFLD hepatocytes (P<0.05), accompanied by iron metabolism disorder. Cav-1 enhanced the iron storage capacity of hepatocytes by activating the ferritin light chain/ferritin heavy chain pathway in NAFLD, subsequently alleviating the oxidative stress induced by excess ferrous ions in the liver. Further, CD68⁺CD163⁺ macrophages expressing Cav-1 were found to accelerate iron accumulation in the liver, which was contrary to the effect of Cav-1 in hepatocytes. Positive correlations were also observed between the serum Cav-1 concentration and the serum iron-related protein levels in NAFLD patients and healthy volunteers (P<0.05).

These findings confirm that Cav-1 is an essential target protein that regulates iron and lipid metabolic homeostasis. It is a pivotal molecule for predicting and protecting against the development of NAFLD.

The present study aimed to explore the potential of artificial intelligence (AI) methodology based on magnetic resonance (MR) images to aid in the management of prostate cancer (PCa). To this end, we reviewed and summarized the studies comparing the diagnostic and predictive performance for PCa between AI and common clinical assessment methods based on MR images and/or clinical characteristics, thereby investigating whether AI methods are generally superior to common clinical assessment methods for the diagnosis and prediction fields of PCa. First, we found that, in the included studies of the present study, AI methods were generally equal to or better than the clinical assessment methods for the risk assessment of PCa, such as risk stratification of prostate lesions and the prediction of therapeutic outcomes or PCa progression. In particular, for the diagnosis of clinically significant PCa, the AI methods achieved a higher summary receiver operator characteristic curve (SROC-AUC) than that of the clinical assessment methods (0.87 vs. 0.82). For the prediction of adverse pathology, the AI methods also achieved a higher SROC-AUC than that of the clinical assessment methods (0.86 vs. 0.75). Second, as revealed by the radiomics quality score (RQS), the studies included in the present study presented a relatively high total average RQS of 15.2 (11.0–20.0). Further, the scores of the individual RQS elements implied that the AI models in these studies were constructed with relatively perfect and standard radiomics processes, but the exact generalizability and clinical practicality of the AI models should be further validated using higher levels of evidence, such as prospective studies and open-testing datasets.

Spinal cord injury (SCI) is a devastating and disabling medical condition generally caused by a traumatic event (primary injury). This initial trauma is accompanied by a set of biological mechanisms directed to ameliorate neural damage but also exacerbate initial damage (secondary injury). The alterations that occur in the spinal cord have not only local but also systemic consequences and virtually all organs and tissues of the body incur important changes after SCI, explaining the progression and detrimental consequences related to this condition. Psychoneuroimmunoendocrinology (PNIE) is a growing area of research aiming to integrate and explore the interactions among the different systems that compose the human organism, considering the mind and the body as a whole. The initial traumatic event and the consequent neurological disruption trigger immune, endocrine, and multisystem dysfunction, which in turn affect the patient's psyche and well-being. In the present review, we will explore the most important local and systemic consequences of SCI from a PNIE perspective, defining the changes occurring in each system and how all these mechanisms are interconnected. Finally, potential clinical approaches derived from this knowledge will also be collectively presented with the aim to develop integrative therapies to maximize the clinical management of these patients.

Rheumatoid arthritis (RA) is a systemic autoimmune disease that is primarily manifested as synovitis and polyarticular opacity and typically leads to serious joint damage and irreversible disability, thus adversely affecting locomotion ability and life quality. Consequently, good prognosis heavily relies on the early diagnosis and effective therapeutic monitoring of RA. Activatable fluorescent probes play vital roles in the detection and imaging of biomarkers for disease diagnosis and in vivo imaging. Herein, we review the fluorescent probes developed for the detection and imaging of RA biomarkers, namely reactive oxygen/nitrogen species (hypochlorous acid, peroxynitrite, hydroxyl radical, nitroxyl), pH, and cysteine, and address the related challenges and prospects to inspire the design of novel fluorescent probes and the improvement of their performance in RA studies.

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’ anatomy. However, the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians. Moreover, some potentially useful quantitative information in medical images, especially that which is not visible to the naked eye, is often ignored during clinical practice. In contrast, radiomics performs high-throughput feature extraction from medical images, which enables quantitative analysis of medical images and prediction of various clinical endpoints. Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis, demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine. However, radiomics remains in a developmental phase as numerous technical challenges have yet to be solved, especially in feature engineering and statistical modeling. In this review, we introduce the current utility of radiomics by summarizing research on its application in the diagnosis, prognosis, and prediction of treatment responses in patients with cancer. We focus on machine learning approaches, for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling. Furthermore, we introduce the stability, reproducibility, and interpretability of features, and the generalizability and interpretability of models. Finally, we offer possible solutions to current challenges in radiomics research.