Comparative effectiveness of multiple androgen receptor signaling inhibitor medicines with androgen deprivation therapy for metastatic hormone-sensitive prostate cancer: a study in the real world.

Background: The current treatment strategy for metastatic Hormone-Sensitive Prostate Cancer (mHSPC) is the combination of Androgen Receptor Signaling Inhibitors (ARSIs) medicines with androgen deprivation therapy (ADT). However, there is a lack of real-world data comparing the efficacy of different ARSI pharmaceuticals. Therefore, the objective of this study was to compare the effectiveness and safety of bicalutamide, abiraterone, enzalutamide, and apalutamide in combination with ADT for patients with mHSPC. Methods: We retrospectively analyzed 82 patients diagnosed with mHSPC, including 18 patients treated with abiraterone acetate with prednisone, 21 patients with enzalutamide, 20 patients with apalutamide, and 23 patients with bicalutamide. We evaluated PSA progression-free survival (PSA-PFS), imaging progression-free survival (r PFS), castration resistance progression-free survival (CRPC-PFS), and overall survival (OS) using Kaplan-Meier survival analyses. Additionally, we explored relevant factors affecting prognosis through univariate and multivariate Cox risk-proportionality models. PSA response rates at 3, 6, and 12 months, nadir PSA levels (nPSA), and time to nadir (TTN) in different medication subgroups after treatment were documented, and we used one-way ANOVA to determine the effect of these measures on patient prognosis. Results: In comparison with bicalutamide, both enzalutamide and apalutamide have shown significant advantages in delaying disease progression among mHSPC patients. Specifically, enzalutamide has been found to significantly prolong PSA-PFS (HR 2.244; 95% CI 1.366-3.685, p=0.001), rPFS (HR 2.539; 95% CI 1.181-5.461; p= 0.007), CRPC-PFS (HR 2.131; 95% CI 1.295-3.506; p= 0.003), and OS (HR 2.06; 95% CI 1.183-3.585; P=0.005). Similarly, apalutamide has significantly extended PSA-PFS (HR 5.071; 95% CI 1.711-15.032; P= 0.003) and CRPC-PFS (HR 6.724; 95% CI 1.976-22.878; P=0.002) among patients. On the other hand, the use of abiraterone in combination with ADT did not demonstrate a significant advantage in delaying diseases progression when compared with the other three agents in mHSPC patients. There were no significant differences in overall adverse event rates among the four pharmaceuticals in terms of safety. Additionally, the observation of PSA kinetics revealed that enzalutamide, apalutamide, and abiraterone acetate had a significant advantage in achieving deep PSA response (PSA ≤ 0.2 ng/ml) compared with bicalutamide (p=0.007 at 12 months). Enzalutamide and apalutamide exhibited preeminence efficacy, with no substantial difference observed between the two medications. Conclusions: Abiraterone, enzalutamide, and apalutamide were found to significantly reduce and stabilize PSA levels in mHSPC patients more quickly and thoroughly than bicalutamide. Furthermore, enzalutamide and apalutamide were found to significantly prolong survival and delay disease progression in mHSPC patients compared with bicalutamide. It should be noted that abiraterone did not demonstrate a significant advantage in delaying disease compared with enzalutamide and apalutamide. After conducting drug toxicity analyses, it was determined that there were no significant differences among the four drugs.

Deuterium-Depleted Water in Cancer Therapy: A Systematic Review of Clinical and Experimental Trials.

Chemotherapy exhibits numerous side effects in anti-tumour therapy. The clinical experiments indicated that deuterium-depleted water (DDW) monotherapy or in combination with chemotherapy was beneficial in inhibiting cancer development. To further understand the potential mechanism of DDW in cancer therapy, we performed a systematic review. The data from experiments published over the past 15 years were included. PubMed, Cochrane and Web of Science (January 2008 to November 2023) were systemically searched. Fifteen studies qualified for review, including fourteen in vivo and in vitro trials and one interventional trial. The results showed that DDW alone or in combination with chemotherapy effectively inhibited cancer progression in most experiments. The combination treatment enhances the therapeutic effect on cancer compared with chemotherapeutic monotherapy. The inhibitory role of DDW in tumours is through regulating the reactive oxygen species (ROS)-related genes in Kelch-like ECH-associated protein 1 (Keap 1) and Nuclear erythroid 2-related factor 2 (Nrf2) signalling pathways, further controlling ROS production. An abnormal amount of ROS can inhibit the tumour progression. More extensive randomized controlled trials should be conducted to evaluate the accurate effect of DDW in Keap1-Nrf2 signalling pathways.

Roles of Integrin in Cardiovascular Diseases: From Basic Research to Clinical Implications.

Cardiovascular diseases (CVDs) pose a significant global health threat due to their complex pathogenesis and high incidence, imposing a substantial burden on global healthcare systems. Integrins, a group of heterodimers consisting of α and ß subunits that are located on the cell membrane, have emerged as key players in mediating the occurrence and progression of CVDs by regulating the physiological activities of endothelial cells, vascular smooth muscle cells, platelets, fibroblasts, cardiomyocytes, and various immune cells. The crucial role of integrins in the progression of CVDs has valuable implications for targeted therapies. In this context, the development and application of various integrin antibodies and antagonists have been explored for antiplatelet therapy and anti-inflammatory-mediated tissue damage. Additionally, the rise of nanomedicine has enhanced the specificity and bioavailability of precision therapy targeting integrins. Nevertheless, the complexity of the pathogenesis of CVDs presents tremendous challenges for monoclonal targeted treatment. This paper reviews the mechanisms of integrins in the development of atherosclerosis, cardiac fibrosis, hypertension, and arrhythmias, which may pave the way for future innovations in the diagnosis and treatment of CVDs.

Deciphering cell types by integrating scATAC-seq data with genome sequences.

The single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) technology provides insight into gene regulation and epigenetic heterogeneity at single-cell resolution, but cell annotation from scATAC-seq remains challenging due to high dimensionality and extreme sparsity within the data. Existing cell annotation methods mostly focus on the cell peak matrix without fully utilizing the underlying genomic sequence. Here we propose a method, SANGO, for accurate single-cell annotation by integrating genome sequences around the accessibility peaks within scATAC data. The genome sequences of peaks are encoded into low-dimensional embeddings, and then iteratively used to reconstruct the peak statistics of cells through a fully connected network. The learned weights are considered as regulatory modes to represent cells, and utilized to align the query cells and the annotated cells in the reference data through a graph transformer network for cell annotations. SANGO was demonstrated to consistently outperform competing methods on 55 paired scATAC-seq datasets across samples, platforms and tissues. SANGO was also shown to be able to detect unknown tumor cells through attention edge weights learned by the graph transformer. Moreover, from the annotated cells, we found cell-type-specific peaks that provide functional insights/biological signals through expression enrichment analysis, cis-regulatory chromatin interaction analysis and motif enrichment analysis.

MDSC-targeting gold nanoparticles enhance PD-1 tumor immunotherapy by inhibiting NLRP3 inflammasomes.

Myeloid-derived suppressor cells (MDSCs) play a crucial role in the immune escape mechanisms that limit the efficacy of immunotherapeutic strategies. In the tumor microenvironment, NLRP3 inflammasome-driven Interleukin-1ß (IL-1ß) production serves to dampen antitumor immune responses, promoting tumor growth, progression, and immunosuppression. In this study, we revealed that gold nanoparticles (Au NPs) with a size of 30 nm disrupted NLRP3 inflammasome, but not other inflammasomes, in bone marrow-derived macrophages through abrogating NLRP3-NEK7 interactions mediated by reactive oxygen species (ROS). Density functional theory (DFT) calculations provided insights into the mechanism underlying the exceptional ROS scavenging capabilities of Au NPs. Additionally, when coupled with H6, a small peptide targeting MDSCs, Au NPs demonstrated the capacity to effectively reduce IL-1ß levels and diminish the MDSCs population in tumor microenvironment, leading to enhanced T cell activation and increased immunotherapeutic efficacy in mouse tumor models that are sensitive and resistant to PD-1 inhibition. Our findings unraveled a novel approach wherein peptide-modified Au NPs relieved the suppressive impact of the tumor microenvironment by inhibiting MDSCs-mediated IL-1ß release, which is the first time reported the employing a nanostrategy at modulating MDSCs to reverse the immunosuppressive microenvironment and may hold promise as a potential therapeutic agent for cancer immunotherapy.

Tislelizumab augment the efficacy of CD19/22 dual-targeted chimeric antigen receptor T cell in advanced stage relapsed or refractory B-cell non-Hodgkin lymphoma.

Dual-targeted chimeric antigen receptor T (CAR-T) cell is an important strategy to improve the efficacy of CD19 CAR-T cell against refractory or relapsed B cell non-Hodgkin lymphoma (R/R B-NHL). However, durable responses are not achieved in most patients, in part owing CAR-T cell exhaustion caused by PD-1/PD-L1 pathway. We conducted a prospective, single-arm study of dual-targeted CD19/22 CAR-T cell combined with anti-PD-1 antibody, tislelizumab, in R/R B-NHL (NCT04539444). Tislelizumab was administrated on +1 day after patients received infusion of CD19/22 CAR-T cell. Responses, survival and safety were evaluated. From 1 August 2020 to 30 March 2023, 16 patients were enrolled. The median follow-up time is 16.0 (range: 5.0-32.0 months) months. Overall response was achieved in 14 of 16 (87.5%) patients, and the complete response (CR) was achieved in 11 of 16 (68.8%) patients. The 1-year progression-free survival and overall survival rates were 68.8% and 81.3%, respectively. Of the 14 patients responded, 9 patients maintained their response until the end of follow-up. Among the 15 out of 16 (93.8%) patients who had extranodal involvement, 14 (93.3%) patients achieved overall response rate with 11 (73.3%) patients achieving CR. Eight (50%) patients experienced cytokine release syndrome. No neurologic adverse events were reported. Gene Ontology-Biological Process enrichment analysis showed that immune response-related signaling pathways were enriched in CR patients. Our results suggest that CD19/22 CAR-T cell combined with tislelizumab elicit a safe and durable response in R/R B-NHL and may improve the prognosis of those patients.

Effect of an Intensive Food-as-Medicine Program on Health and Health Care Use: A Randomized Clinical Trial.

Importance: Food-as-medicine programs are becoming increasingly common, and rigorous evidence is needed regarding their effects on health. Objective: To test whether an intensive food-as-medicine program for patients with diabetes and food insecurity improves glycemic control and affects health care use. Design, Setting, and Participants: This stratified randomized clinical trial using a wait list design was conducted from April 19, 2019, to September 16, 2022, with patients followed up for 1 year. Patients were randomly assigned to either participate in the program immediately (treatment group) or 6 months later (control group). The trial took place at 2 sites, 1 rural and 1 urban, of a large, integrated health system in the mid-Atlantic region of the US. Eligibility required a diagnosis of type 2 diabetes, a hemoglobin A1c (HbA1c) level of 8% or higher, food insecurity, and residence within the service area of the participating clinics. Intervention: The comprehensive program provided healthy groceries for 10 meals per week for an entire household, plus dietitian consultations, nurse evaluations, health coaching, and diabetes education. The program duration was typically 1 year. Main Outcomes and Measures: The primary outcome was HbA1c level at 6 months. Secondary outcomes included other biometric measures, health care use, and self-reported diet and healthy behaviors, at both 6 months and 12 months. Results: Of 3712 patients assessed for eligibility, 3168 were contacted, 1064 were deemed eligible, 500 consented to participate and were randomized, and 465 (mean [SD] age, 54.6 [11.8] years; 255 [54.8%] female) completed the study. Of those patients, 349 (mean [SD] age, 55.4 [11.2] years; 187 [53.6%] female) had laboratory test results at 6 months after enrollment. Both the treatment (n = 170) and control (n = 179) groups experienced a substantial decline in HbA1c levels at 6 months, resulting in a nonsignificant, between-group adjusted mean difference in HbA1c levels of -0.10 (95% CI, -0.46 to 0.25; P = .57). Access to the program increased preventive health care, including more mean (SD) dietitian visits (2.7 [1.8] vs 0.6 [1.3] visits in the treatment and control groups, respectively), patients with active prescription drug orders for metformin (134 [58.26] vs 119 [50.64]) and glucagon-like peptide 1 medications (114 [49.56] vs 83 [35.32]), and participants reporting an improved diet from 1 year earlier (153 of 164 [93.3%] vs 132 of 171 [77.2%]). Conclusions and Relevance: In this randomized clinical trial, an intensive food-as-medicine program increased engagement with preventive health care but did not improve glycemic control compared with usual care among adult participants. Programs targeted to individuals with elevated biomarkers require a control group to demonstrate effectiveness to account for improvements that occur without the intervention. Additional research is needed to design food-as-medicine programs that improve health. Trial Registration: ClinicalTrials.gov Identifier: NCT03718832.

Intensive vision-guided network for radiology report generation.

Objective.Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processing approaches to tackle this problem are limited in two aspects. First, when extracting image features, most of them neglect multi-view reasoning in vision and model single-view structure of medical images, such as space-view or channel-view. However, clinicians rely on multi-view imaging information for comprehensive judgment in daily clinical diagnosis. Second, when generating reports, they overlook context reasoning with multi-modal information and focus on pure textual optimization utilizing retrieval-based methods. We aim to address these two issues by proposing a model that better simulates clinicians perspectives and generates more accurate reports.Approach.Given the above limitation in feature extraction, we propose a globally-intensive attention (GIA) module in the medical image encoder to simulate and integrate multi-view vision perception. GIA aims to learn three types of vision perception: depth view, space view, and pixel view. On the other hand, to address the above problem in report generation, we explore how to involve multi-modal signals to generate precisely matched reports, i.e. how to integrate previously predicted words with region-aware visual content in next word prediction. Specifically, we design a visual knowledge-guided decoder (VKGD), which can adaptively consider how much the model needs to rely on visual information and previously predicted text to assist next word prediction. Hence, our final intensive vision-guided network framework includes a GIA-guided visual encoder and the VKGD.Main results.Experiments on two commonly-used datasets IU X-RAY and MIMIC-CXR demonstrate the superior ability of our method compared with other state-of-the-art approaches.Significance.Our model explores the potential of simulating clinicians perspectives and automatically generates more accurate reports, which promotes the exploration of medical automation and intelligence.

Small EV-based delivery of CpG ODNs for melanoma postsurgical immunotherapy.

Blocking programmed cell death protein 1 (PD-1) is an effective therapeutic strategy for melanoma. However, patients often develop tumor recurrence postoperatively due to the low response rate to the anti-PD-1 antibody (aPD-1). In this study, we developed an in situ sprayable fibrin gel that contains cytosine-guanine oligodeoxynucleotides (CpG ODNs)-modified ovalbumin (OVA) antigen-expressing bone marrow dendritic cell (DC)-derived small extracellular vesicles (DC-sEVs) and aPD-1. CpG ODNs can activate DCs, which have potent immunostimulatory effects, by stimulating both the maturation and activation of tumor-infiltrating dendritic cells (TIDCs) and DCs in tumor-draining lymph nodes (TDLNs). In addition, DC-sEVs can deliver OVA to the same DCs, leading to the specific expression of tumor antigens by antigen-presenting cells (APCs). In brief, the unique synergistic combination of aPD-1 and colocalized delivery of immune adjuvants and tumor antigens enhances antitumor T-cell immunity, not only in the tumor microenvironment (TME) but also in TDLNs. This effectively attenuates local tumor recurrence and metastasis. Our results suggest that dual activation by CpG ODNs prolongs the survival of mice and decreases the recurrence rate in an incomplete tumor resection model, providing a promising approach to prevent B16-F10-OVA melanoma tumor recurrence and metastasis.

Efficacy and safety of zanubrutinib plus R-CHOP in treatment of non-GCB DLBCL with extranodal involvement.

Introduction: Treatment with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) shows poor response rates in non-germinal center B cell-like (non-GCB) diffuse large B-cell lymphoma (DLBCL) patients with multiple extranodal involvement. This study aims to evaluate anti-tumor activity and safety of zanubrutinib with R-CHOP (ZR-CHOP) in treatment naïve non-GCB DLBCL with extranodal involvement. Methods: In this single-arm, phase 2, prospective, single-center study, patients with newly diagnosed non-GCB DLBCL with extranodal involvement enrolled between October 2020 to March 2022 received ZR-CHOP for 6 cycles followed by 2 cycles of maintenance treatment with rituximab and zanubrutinib. The primary endpoint included progression-free survival (PFS) in the intent-to-treat (ITT) population whereas the secondary endpoints included overall response rate (ORR), complete response (CR), and duration of response. Further, next-generation sequencing (NGS) was used for detection of different oncogenic mutations closely related to DLBCL pathogenesis. Results: From October 2020 to March 2022, 26 patients were enrolled, and 23 of them were evaluated for efficacy after receiving 3 cycles of ZR-CHOP treatment. 1-year PFS and OS were 80.8% and 88.5% respectively while expected PFS and OS for 2-years are 74.0% and 88.5% respectively with median follow-up of 16.7 months and ORR was 91.3% (CR: 82.61%; PR: 8.70%). Oncogenic mutations closely related to DLBCL pathogenesis were assessed in 20 patients using NGS. B-cell receptor and NF-κB pathway gene mutations were detected in 10 patients, which occurred in MYD88 (7/19), CD79B (4/19), CARD11 (5/19), and TNFAIP3 (2/19). Hematological adverse events (AEs) ≥ grade 3 included neutropenia (50%), thrombocytopenia (23.1%), and anemia (7.7%) whereas non-hematological AEs ≥ grade 3 included pulmonary infection (19.2%). Conclusion: ZR-CHOP is safe and effective for treating treatment naïve non-GCB DLBCL patients with extranodal involvement. Clinical Trial Registration: Clinicaltrials.gov, NCT04835870.

Heat-driven molecule gatekeepers in MOF membrane for record-high H2 selectivity.

Hydrogen/carbon dioxide (H2/CO2) separation for sustainable energy is in desperate need of reliable membranes at high temperatures. Molecular sieve membranes take their nanopores to differentiate sizes between H2 and CO2 but have compromised at a marked loss of selectivity at high temperatures owing to diffusion activation of CO2. We used molecule gatekeepers that were locked in the cavities of the metal-organic framework membrane to meet this challenge. Ab initio calculations and in situ characterizations demonstrate that the molecule gatekeepers make a notable move at high temperatures to dynamically reshape the sieving apertures as being extremely tight for CO2 and restitute with cool conditions. The H2/CO2 selectivity was improved by an order of magnitude at 513 kelvin (K) relative to that at the ambient temperature.

Identifying B-cell epitopes using AlphaFold2 predicted structures and pretrained language model.

MOTIVATION: Identifying the B-cell epitopes is an essential step for guiding rational vaccine development and immunotherapies. Since experimental approaches are expensive and time-consuming, many computational methods have been designed to assist B-cell epitope prediction. However, existing sequence-based methods have limited performance since they only use contextual features of the sequential neighbors while neglecting structural information. RESULTS: Based on the recent breakthrough of AlphaFold2 in protein structure prediction, we propose GraphBepi, a novel graph-based model for accurate B-cell epitope prediction. For one protein, the predicted structure from AlphaFold2 is used to construct the protein graph, where the nodes/residues are encoded by ESM-2 learning representations. The graph is input into the edge-enhanced deep graph neural network (EGNN) to capture the spatial information in the predicted 3D structures. In parallel, a bidirectional long short-term memory neural networks (BiLSTM) are employed to capture long-range dependencies in the sequence. The learned low-dimensional representations by EGNN and BiLSTM are then combined into a multilayer perceptron for predicting B-cell epitopes. Through comprehensive tests on the curated epitope dataset, GraphBepi was shown to outperform the state-of-the-art methods by more than 5.5% and 44.0% in terms of AUC and AUPR, respectively. A web server is freely available at http://bio-web1.nscc-gz.cn/app/graphbepi. AVAILABILITY AND IMPLEMENTATION: The datasets, pre-computed features, source codes, and the trained model are available at https://github.com/biomed-AI/GraphBepi.

Acceptance and associated factors of HIV testing among college students in China: A systematic review and meta-analysis.

BACKGROUND: Although HIV testing is helpful for early detection and treatment of HIV, its utilization rate is low among college students in China. Understanding the acceptance and associated factors of HIV testing is the key to improve the detection rate. The purpose of the systematic review was to examine the acceptance and associated factors of HIV testing (including HIV self-testing and HIV counseling and testing services) among college students in China. METHODS: This systematic review was reported following PRISMA guidelines 2020. Electronic sources such as PubMed, Embase, Web of Science, CNKI, CBM, Wanfang Database and VIP Database were searched for relevant studies published before September 2022. The tool by Agency for Healthcare Research and Quality (AHRQ) was used to assess quality for cross-sectional studies. The random-effects and fixed-effect model were employed to estimate the pooled proportions and associated factor of HIV testing acceptance. The Cochrane's Q statistic and I2 test were used to examine heterogeneity. All the quantitative meta analyses were conducted using STATA version 12 software. RESULTS: A total of 21 eligible studies with 100, 821 participants were included in the systematic review. The pooled acceptance rate of HIV testing was 68% (95% CI = 60, 76), and varies between regions in China. Male, heterosexual and urban college students had higher HIV testing acceptance. Gender, medical specialty, sexual education, sexual behavior, HIV/AIDS knowledge, perception HIV risk, and previous HIV testing were the factors associated with HIV testing acceptance. CONCLUSION: The review revealed that most of the college students intend to accept HIV detection, and the proportion of acceptance influenced by different factors. Therefore, the government and universities should implement targeted measures, improve HIV testing services, and promote HIV testing behavior. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42022367976.

PSLT: A Light-Weight Vision Transformer With Ladder Self-Attention and Progressive Shift.

Vision Transformer (ViT) has shown great potential for various visual tasks due to its ability to model long-range dependency. However, ViT requires a large amount of computing resource to compute the global self-attention. In this work, we propose a ladder self-attention block with multiple branches and a progressive shift mechanism to develop a light-weight transformer backbone that requires less computing resources (e.g., a relatively small number of parameters and FLOPs), termed Progressive Shift Ladder Transformer (PSLT). First, the ladder self-attention block reduces the computational cost by modelling local self-attention in each branch. In the meanwhile, the progressive shift mechanism is proposed to enlarge the receptive field in the ladder self-attention block by modelling diverse local self-attention for each branch and interacting among these branches. Second, the input feature of the ladder self-attention block is split equally along the channel dimension for each branch, which considerably reduces the computational cost in the ladder self-attention block (with nearly [Formula: see text] the amount of parameters and FLOPs), and the outputs of these branches are then collaborated by a pixel-adaptive fusion. Therefore, the ladder self-attention block with a relatively small number of parameters and FLOPs is capable of modelling long-range interactions. Based on the ladder self-attention block, PSLT performs well on several vision tasks, including image classification, objection detection and person re-identification. On the ImageNet-1 k dataset, PSLT achieves a top-1 accuracy of 79.9% with 9.2 M parameters and 1.9 G FLOPs, which is comparable to several existing models with more than 20 M parameters and 4 G FLOPs. Code is available at https://isee-ai.cn/wugaojie/PSLT.html.

Identifying spatial domain by adapting transcriptomics with histology through contrastive learning.

Recent advances in spatial transcriptomics have enabled measurements of gene expression at cell/spot resolution meanwhile retaining both the spatial information and the histology images of the tissues. Accurately identifying the spatial domains of spots is a vital step for various downstream tasks in spatial transcriptomics analysis. To remove noises in gene expression, several methods have been developed to combine histopathological images for data analysis of spatial transcriptomics. However, these methods either use the image only for the spatial relations for spots, or individually learn the embeddings of the gene expression and image without fully coupling the information. Here, we propose a novel method ConGI to accurately exploit spatial domains by adapting gene expression with histopathological images through contrastive learning. Specifically, we designed three contrastive loss functions within and between two modalities (the gene expression and image data) to learn the common representations. The learned representations are then used to cluster the spatial domains on both tumor and normal spatial transcriptomics datasets. ConGI was shown to outperform existing methods for the spatial domain identification. In addition, the learned representations have also been shown powerful for various downstream tasks, including trajectory inference, clustering, and visualization.

Cell-Based Drug Delivery Systems Participate in the Cancer Immunity Cycle for Improved Cancer Immunotherapy.

Immunotherapy aims to activate the cancer patient's immune system for cancer therapy. The whole process of the immune system against cancer referred to as the "cancer immunity cycle", gives insight into how drugs can be designed to affect every step of the anticancer immune response. Cancer immunotherapy such as immune checkpoint inhibitor (ICI) therapy, cancer vaccines, as well as small molecule modulators has been applied to fight various cancers. However, the effect of immunotherapy in clinical applications is still unsatisfactory due to the limited response rate and immune-related adverse events. Mounting evidence suggests that cell-based drug delivery systems (DDSs) with low immunogenicity, superior targeting, and prolonged circulation have great potential to improve the efficacy of cancer immunotherapy. Therefore, with the rapid development of cell-based DDSs, understanding their important roles in various stages of the cancer immunity cycle guides the better design of cell-based cancer immunotherapy. Herein, an overview of how cell-based DDSs participate in cancer immunotherapy at various stages is presented and an outlook on possible challenges of clinical translation and application in future development.

VSTH: a user-friendly web server for structure-based virtual screening on Tianhe-2.

SUMMARY: VSTH is a user-friendly web server with the complete workflow for virtual screening. By self-customized visualization software, users can interactively prepare protein files, set docking sites as well as view binding conformers in a target protein in a few clicks. We provide serval purchasable ligand libraries for selection. And, we integrate six open-source docking programs as computing engine, or as conformational sampling tools for DLIGAND2. Users can select various docking methods simultaneously and personalize computing parameters. After docking processing, user can filter docking conformations by ranked scores, or cluster-based molecular similarity to find highly populated clusters of low-energy conformations. AVAILABILITY AND IMPLEMENTATION: The VSTH web server is free and open to all users at https://matgen.nscc-gz.cn/VirtualScreening.html. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Spatio-temporally differentiated impacts of temperature inversion on surface PM2.5 in eastern China.

Temperature inversion (TI) is one of the meteorological conditions that significantly affect regional air quality. Knowledge gap regarding the impacts of TI on surface PM2.5 in different topographies still existed. In the present study, the occurrence frequency, temperature lapse rate (TLR), depth, and the diurnal variations of TI, surface-based TI (SBTI), elevated TI (ElTI), and multiple layers of TIs (MultiTI) and their impacts on near-surface PM2.5 concentrations over eastern China that covers a range of topographies and climates, are systematically investigated based on global reanalysis ERA5 and the nationwide monitoring PM2.5 dataset from 2014 to 2020. TIs occurred mostly in the early morning. Different types of TIs present distinctive seasonal and spatial patterns. The majority of SBTIs and ElTIs occurred during nighttime in northern China and daytime in southern China, respectively, as the result of their formation mechanisms. SBTIs usually had larger TLR while ElTIs had deeper depth. SBTIs showed strong enhancement effects on PM2.5 concentration over the study domain while ElTIs showed more obvious impacts on northern nocturnal PM2.5. The peak time of PM2.5 was found around 18:00-22:00 LST, and TLR and depth of TIs are thought to be more relevant to PM2.5 peak concentration due to their coincident peak times. The strength of TIs is therefore more crucial in regulating PM2.5 than its occurrence frequency. Based on statistical analysis, our study provided a large picture of the generic spatiotemporal patterns of TIs and illustrated the impacts of different TIs on surface PM2.5 pollution on a diurnal basis. For a deeper understanding of the formation of PM2.5 pollution, more attention needs to be paid to the nocturnal PM2.5 not only at surface level but also at higher levels in the presence of TIs.

Quantitative evaluation of explainable graph neural networks for molecular property prediction.

Graph neural networks (GNNs) have received increasing attention because of their expressive power on topological data, but they are still criticized for their lack of interpretability. To interpret GNN models, explainable artificial intelligence (XAI) methods have been developed. However, these methods are limited to qualitative analyses without quantitative assessments from the real-world datasets due to a lack of ground truths. In this study, we have established five XAI-specific molecular property benchmarks, including two synthetic and three experimental datasets. Through the datasets, we quantitatively assessed six XAI methods on four GNN models and made comparisons with seven medicinal chemists of different experience levels. The results demonstrated that XAI methods could deliver reliable and informative answers for medicinal chemists in identifying the key substructures. Moreover, the identified substructures were shown to complement existing classical fingerprints to improve molecular property predictions, and the improvements increased with the growth of training data.