The underlying cause of low response rates to existing immunotherapies is that tumor cells dominate tumor immune escape through surface antigen deficiency and inducing tumor immunosuppressive microenvironment (TIME). Here, we proposed an in situ tumor cell engineering strategy to disrupt tumor immune escape at the root by restoring tumor cell MHC-I/tumor-specific antigen complex (MHC-I/TSA) expression to promote T-cell recognition and by silencing tumor cell CD55 to increase the ICOSL⁺ B-cell proportion and reverse the TIME. A doxorubicin (DOX) and dual-gene plasmid (MAC pDNA, encoding both MHC-I/ASMTNMELM and CD55-shRNA) coloaded drug delivery system (LCPN@ACD) with tumor targeting and charge/size dual-conversion properties was prepared. LCPN@ACD-induced ICD promoted DC maturation and enhanced T-cell activation and infiltration. LCPN@ACD enabled effective expression of MHC-I/TSA on tumor cells, increasing the ability of tumor cell recognition and killing. LCPN@ACD downregulated tumor cell CD55 expression, increased the proportion of ICOSL⁺ B cells and CTLs, and reversed the TIME, thus greatly improving the efficacy of αPD-1 and CAR-T therapies. The application of this in situ tumor cell engineering strategy eliminated the source of tumor immune escape, providing new ideas for solving the challenges of clinical immunotherapy.

Modulating Tankyrases (TNKS), interactions with USP25 to promote TNKS degradation, rather than inhibiting their enzymatic activities, is emerging as an alternative/specific approach to inhibit the Wnt/β-catenin pathway. Here, we identified UAT-B, a novel neoantimycin analog isolated from Streptomyces conglobatus, as a small-molecule inhibitor of TNKS-USP25 protein-protein interaction (PPI) to overcome multi-drug resistance in colorectal cancer (CRC). The disruption of TNKS-USP25 complex formation by UAT-B led to a significant decrease in TNKS levels, triggering cell apoptosis through modulation of the Wnt/β-catenin pathway. Importantly, UAT-B successfully inhibited the CRC cells growth that harbored high TNKS levels, as demonstrated in various in vitro and in vivo studies utilizing cell line-based and patient-derived xenografts, as well as APC^min/+ spontaneous CRC models. Collectively, these findings suggest that targeting the TNKS-USP25 PPI using a small-molecule inhibitor represents a compelling therapeutic strategy for CRC treatment, and UAT-B emerges as a promising candidate for further preclinical and clinical investigations.

Cytopharmaceutical based on macrophages is a breakthrough in the field of targeted drug delivery. However, it remains a challenge to localize and control drug release while retaining macrophage activity and exerting its immunotherapeutic effect. Herein, a localized light-triggered release macrophage cytopharmaceutical (USIP@M) was proposed, which could utilize the tumor targeting and immunotherapy effects of macrophages to reverse the immune suppression of tumor microenvironment (TME). Amphiphilic block copolymers with ultraviolet (UV)-responsive o-nitrobenzyl groups were synthesized and co-loaded with sorafenib (SF), IMD-0354 (IMD), and upconverting nanoparticles (UCNPs), which were then taken up by macrophages, and the targeted delivery of drugs was realized by using the tumor tropism of macrophages. UCNPs converted near-infrared light with strong penetrability and high safety into UV light, which promoted the photoresponsive depolymerization of block copolymers and production of exosomes from USIP@M, accelerated drug efflux and maintained the activity of macrophages. IMD simultaneously polarized carrier macrophages and tumor-associated macrophages to exert the antitumor effect of macrophages, enhance T cell immunity, and alleviate the immunosuppressive state of TME. Synergistically with the chemotherapeutic effect of SF, it could effectively kill tumors. In conclusion, based on the localized light-triggered release strategy, this study constructed a novel macrophage cytopharmaceutical that could localize and control drug release while retaining the activity of macrophages and exerting its immunotherapeutic effect, which could effectively treat solid tumors.

Objective     To recognize the different phases of Korotkoff sounds through deep learning technology, so as to improve the accuracy of blood pressure measurement in different populations. Methods     A classification model of the Korotkoff sounds phases was designed, which fused attention mechanism (Attention), residual network (ResNet) and bidirectional long short-term memory (BiLSTM). First, a single Korotkoff sound signal was extracted from the whole Korotkoff sounds signals beat by beat, and each Korotkoff sound signal was converted into a Mel spectrogram. Then, the local feature extraction of Mel spectrogram was processed by using the Attention mechanism and ResNet network, and BiLSTM network was used to deal with the temporal relations between features, and full-connection layer network was applied in reducing the dimension of features. Finally, the classification was completed by SoftMax function. The dataset used in this study was collected from 44 volunteers (24 females, 20 males with an average age of 36 years), and the model performance was verified using 10-fold cross-validation. Results     The classification accuracy of the established model for the 5 types of Korotkoff sounds phases was 93.4%, which was higher than that of other models. Conclusion     This study proves that the deep learning method can accurately classify Korotkoff sounds phases, which lays a strong technical foundation for the subsequent design of automatic blood pressure measurement methods based on the classification of the Korotkoff sounds phases.

Humans ; Perinatology ; Delivery of Health Care

Currently, in precision cardiac surgery, there are still some pressing issues that need to be addressed. For example, cardiopulmonary bypass remains a critical factor in precise surgical treatment, and many core aspects still rely on the experience and subjective judgment of cardiopulmonary bypass specialists and surgeons, lacking precise data feedback. With the increasing elderly population and rising surgical complexity, precise feedback during cardiopulmonary bypass becomes crucial for improving surgical success rates and facilitating high-complexity procedures. Overcoming these key challenges requires not only a solid medical background but also close collaboration among multiple interdisciplinary fields. Establishing a multidisciplinary team encompassing professionals from the medical, information, software, and related industries can provide high-quality solutions to these challenges. This article shows several patents from a collaborative medical and electronic information team, illustrating how to identify unresolved technical issues and find corresponding solutions in the field of precision cardiac surgery while sharing experiences in applying for invention patents.

Chemoimmunotherapy has been approved as standard treatment for triple-negative breast cancer (TNBC), but the clinical outcomes remain unsatisfied. Abnormal epigenetic regulation is associated with acquired drug resistance and T cell exhaustion, which is a critical factor for the poor response to chemoimmunotherapy in TNBC. Herein, macrophage-camouflaged nanoinducers co-loaded with paclitaxel (PTX) and decitabine (DAC) (P/D-mMSNs) were prepared in combination with PD-1 blockade therapy, hoping to improve the efficacy of chemoimmunotherapy through the demethylation of tumor tissue. Camouflage of macrophage vesicle confers P/D-mMSNs with tumor-homing properties. First, DAC can achieve demethylation of tumor tissue and enhance the sensitivity of tumor cells to PTX. Subsequently, PTX induces immunogenic death of tumor cells, promotes phagocytosis of dead cells by dendritic cells, and recruits cytotoxic T cells to infiltrate tumors. Finally, DAC reverses T cell depletion and facilitates immune checkpoint blockade therapy. P/D-mMSNs may be a promising candidate for future drug delivery design and cancer combination therapy in TNBC.

Humans ; Transcranial Magnetic Stimulation ; Pain Management ; Psychotic Disorders/therapy* ; Depressive Disorder, Major ; Treatment Outcome ; Transcranial Direct Current Stimulation

bjective    To use machine learning technology to predict the recurrence of atrial fibrillation (AF) after radiofrequency ablation, and try to find the risk factors affecting postoperative recurrence. Methods    A total of 300 patients with valvular AF who underwent radiofrequency ablation in West China Hospital and its branch (Shangjin Hospital) from January 2017 to January 2021 were enrolled, including 129 males and 171 females with a mean age of 52.56 years. We built 5 machine learning models to predict AF recurrence, combined the 3 best performing models into a voting classifier, and made prediction again. Finally, risk factor analysis was performed using the SHApley Additive exPlanations method. Results    The voting classifier yielded a prediction accuracy rate of 75.0%, a recall rate of 61.0%, and an area under the receiver operating characteristic curve of 0.79. In addition, factors such as left atrial diameter, ejection fraction, and right atrial diameter were found to have an influence on postoperative recurrence. Conclusion    Machine learning-based prediction of recurrence of valvular AF after radiofrequency ablation can provide a certain reference for the clinical diagnosis of AF, and reduce the risk to patients due to ineffective ablation. According to the risk factors found in the study, it can provide patients with more personalized treatment.