Vascular hyporeactivity and leakage are key pathophysiologic features that produce multi-organ damage upon sepsis. We hypothesized that pericytes, a group of pluripotent cells that maintain vascular integrity and tension, are protective against sepsis via regulating vascular reactivity and permeability.

We conducted a series of in vivo experiments using wild-type (WT), platelet-derived growth factor receptor-β (PDGFR-β)-Cre+mT/mG transgenic mice and Tie2-Cre+Cx43^flox/flox mice to examine the relative contribution of pericytes in sepsis, either induced by cecal ligation and puncture (CLP) or lipopolysaccharide (LPS) challenge. In a separate set of experiments with Sprague–Dawley (SD) rats, pericytes were depleted using CP-673451, a selective PDGFR-β inhibitor, at a dosage of 40 mg/(kg·d) for 7 consecutive days. Cultured pericytes, vascular endothelial cells (VECs) and vascular smooth muscle cells (VSMCs) were used for mechanistic investigations. The effects of pericytes and pericyte-derived microvesicles (PCMVs) and candidate miRNAs on vascular reactivity and barrier function were also examined.

CLP and LPS induced severe injury/loss of pericytes, vascular hyporeactivity and leakage (P<0.05). Transplantation with exogenous pericytes protected vascular reactivity and barrier function via microvessel colonization (P<0.05). Cx43 knockout in either pericytes or VECs reduced pericyte colonization in microvessels (P<0.05). Additionally, PCMVs transferred miR-145 and miR-132 to VSMCs and VECs, respectively, exerting a protective effect on vascular reactivity and barrier function after sepsis (P<0.05). miR-145 primarily improved the contractile response of VSMCs by activating the sphingosine kinase 2 (Sphk2)/sphingosine-1-phosphate receptor (S1PR) 1/phosphorylation of myosin light chain 20 pathway, whereas miR-132 effectively improved the barrier function of VECs by activating the Sphk2/S1PR2/zonula occludens-1 and vascular endothelial-cadherin pathways.

Pericytes are protective against sepsis through regulating vascular reactivity and barrier function. Possible mechanisms include both direct colonization of microvasculature and secretion of PCMVs.

Choosing the appropriate antipsychotic drug (APD) treatment for patients with schizophrenia (SCZ) can be challenging, as the treatment response to APD is highly variable and difficult to predict due to the lack of effective biomarkers. Previous studies have indicated the association between treatment response and genetic and epigenetic factors, but no effective biomarkers have been identified. Hence, further research is imperative to enhance precision medicine in SCZ treatment.

Participants with SCZ were recruited from two randomized trials. The discovery cohort was recruited from the CAPOC trial (n=2307) involved 6 weeks of treatment and equally randomized the participants to the Olanzapine, Risperidone, Quetiapine, Aripiprazole, Ziprasidone, and Haloperidol/Perphenazine (subsequently equally assigned to one or the other) groups. The external validation cohort was recruited from the CAPEC trial (n=1379), which involved 8 weeks of treatment and equally randomized the participants to the Olanzapine, Risperidone, and Aripiprazole groups. Additionally, healthy controls (n=275) from the local community were utilized as a genetic/epigenetic reference. The genetic and epigenetic (DNA methylation) risks of SCZ were assessed using the polygenic risk score (PRS) and polymethylation score, respectively. The study also examined the genetic-epigenetic interactions with treatment response through differential methylation analysis, methylation quantitative trait loci, colocalization, and promoteranchored chromatin interaction. Machine learning was used to develop a prediction model for treatment response, which was evaluated for accuracy and clinical benefit using the area under curve (AUC) for classification, R2 for regression, and decision curve analysis.

Six risk genes for SCZ (LINC01795, DDHD2, SBNO1, KCNG2, SEMA7A, and RUFY1) involved in cortical morphology were identified as having a genetic-epigenetic interaction associated with treatment response. The developed and externally validated prediction model, which incorporated clinical information, PRS, genetic risk score (GRS), and proxy methylation level (proxyDNAm), demonstrated positive benefits for a wide range of patients receiving different APDs, regardless of sex [discovery cohort: AUC=0.874 (95%CI 0.867–0.881), R ²=0.478; external validation cohort: AUC=0.851 (95%CI 0.841–0.861), R ²=0.507].

This study presents a promising precision medicine approach to evaluate treatment response, which has the potential to aid clinicians in making informed decisions about APD treatment for patients with SCZ.

Chinese Clinical Trial Registry (https://www.chictr.org.cn/), 18 Aug 2009 retrospectively registered: CAPOC-ChiCTR-RNC-09000521 (https://www.chictr.org.cn/showproj.aspx?proj=9014), CAPEC-ChiCTRRNC-09000522 (https://www.chictr.org.cn/showproj.aspx?proj=9013).

Triple negative breast cancer (TNBC), the most aggressive subtype of breast cancer, is characterized by a high incidence of brain metastasis (BrM) and a poor prognosis. As the most lethal form of breast cancer, BrM remains a major clinical challenge due to its rising incidence and lack of effective treatment strategies. Recent evidence suggested a potential role of lipid metabolic reprogramming in breast cancer brain metastasis (BCBrM), but the underlying mechanisms are far from being fully elucidated.

Through analysis of BCBrM transcriptome data from mice and patients, and immunohistochemical validation on patient tissues, we identified and verified the specific down-regulation of retinoic acid receptor responder 2 (RARRES2), a multifunctional adipokine and chemokine, in BrM of TNBC. We investigated the effect of aberrant RARRES2 expression of BrM in both in vitro and in vivo studies. Key signaling pathway components were evaluated using multi-omics approaches. Lipidomics were performed to elucidate the regulation of lipid metabolic reprogramming of RARRES2.

We found that downregulation of RARRES2 is specifically associated with BCBrM, and that RARRES2 deficiency promoted BCBrM through lipid metabolic reprogramming. Mechanistically, reduced expression of RARRES2 in brain metastatic potential TNBC cells resulted in increased levels of glycerophospholipid and decreased levels of triacylglycerols by regulating phosphatase and tensin homologue (PTEN)-mammalian target of rapamycin (mTOR)-sterol regulatory element-binding protein 1 (SREBP1) signaling pathway to facilitate the survival of breast cancer cells in the unique brain microenvironment.

Our work uncovers an essential role of RARRES2 in linking lipid metabolic reprogramming and the development of BrM. RARRES2-dependent metabolic functions may serve as potential biomarkers or therapeutic targets for BCBrM.

Biomimetic materials have emerged as attractive and competitive alternatives for tissue engineering (TE) and regenerative medicine. In contrast to conventional biomaterials or synthetic materials, biomimetic scaffolds based on natural biomaterial can offer cells a broad spectrum of biochemical and biophysical cues that mimic the in vivo extracellular matrix (ECM). Additionally, such materials have mechanical adaptability, micro-structure interconnectivity, and inherent bioactivity, making them ideal for the design of living implants for specific applications in TE and regenerative medicine. This paper provides an overview for recent progress of biomimetic natural biomaterials (BNBMs), including advances in their preparation, functionality, potential applications and future challenges. We highlight recent advances in the fabrication of BNBMs and outline general strategies for functionalizing and tailoring the BNBMs with various biological and physicochemical characteristics of native ECM. Moreover, we offer an overview of recent key advances in the functionalization and applications of versatile BNBMs for TE applications. Finally, we conclude by offering our perspective on open challenges and future developments in this rapidly-evolving field.

Heart injury such as myocardial infarction leads to cardiomyocyte loss, fibrotic tissue deposition, and scar formation. These changes reduce cardiac contractility, resulting in heart failure, which causes a huge public health burden. Military personnel, compared with civilians, is exposed to more stress, a risk factor for heart diseases, making cardiovascular health management and treatment innovation an important topic for military medicine. So far, medical intervention can slow down cardiovascular disease progression, but not yet induce heart regeneration. In the past decades, studies have focused on mechanisms underlying the regenerative capability of the heart and applicable approaches to reverse heart injury. Insights have emerged from studies in animal models and early clinical trials. Clinical interventions show the potential to reduce scar formation and enhance cardiomyocyte proliferation that counteracts the pathogenesis of heart disease. In this review, we discuss the signaling events controlling the regeneration of heart tissue and summarize current therapeutic approaches to promote heart regeneration after injury.

Immune checkpoint blockade (ICB) therapy for cancer has achieved great success both in clinical results and on the market. At the same time, success drives more attention from scientists to improve it. However, only a small portion of patients are responsive to this therapy, and it comes with a unique spectrum of side effects termed immune-related adverse events (irAEs). The use of nanotechnology could improve ICBs’ delivery to the tumor, assist them in penetrating deeper into tumor tissues and alleviate their irAEs. Liposomal nanomedicine has been investigated and used for decades, and is well-recognized as the most successful nano-drug delivery system. The successful combination of ICB with liposomal nanomedicine could help improve the efficacy of ICB therapy. In this review, we highlighted recent studies using liposomal nanomedicine (including new emerging exosomes and their inspired nano-vesicles) in associating ICB therapy.

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.