Correction: Lonicera japonica Thunb. as a promising antibacterial agent for Bacillus cereus ATCC14579 based on network pharmacology, metabolomics, and in vitro experiments.

[This corrects the article DOI: 10.1039/D3RA00802A.].

A new invertebrate NPY-like polypeptide, ZoaNPY, from the Zoanthus sociatus, as a novel ligand of human NPY Y2 receptor rescues vascular insufficiency via PLC/PKC and Src- FAK-dependent signaling pathways.

Our recent multi-omics studies have revealed rich sources of novel bioactive proteins and polypeptides from marine organisms including cnidarians. In the present study, we initially conducted a transcriptomic analysis to review the composition profile of polypeptides from Zoanthus sociatus. Then, a newly discovered NPY-like polypeptide-ZoaNPY was selected for further in silico structural, binding and virtually pharmacological studies. To evaluate the pro-angiogenic effects of ZoaNPY, we employed an in vitro HUVECs model and an in vivo zebrafish model. Our results indicate that ZoaNPY, at 1-100 pmol, enhances cell survival, migration and tube formation in the endothelial cells. Besides, treatment with ZoaNPY could restore a chemically-induced vascular insufficiency in zebrafish embryos. Western blot results demonstrated the application of ZoaNPY could increase the phosphorylation of proteins related to angiogenesis signaling including PKC, PLC, FAK, Src, Akt, mTOR, MEK, and ERK1/2. Furthermore, through molecular docking and surface plasmon resonance (SPR) verification, ZoaNPY was shown to directly and physically interact with NPY Y2 receptor. In view of this, all evidence showed that the pro-angiogenic effects of ZoaNPY involve the activation of NPY Y2 receptor, thereby activating the Akt/mTOR, PLC/PKC, ERK/MEK and Src- FAK-dependent signaling pathways. Furthermore, in an excision wound model, the treatment with ZoaNPY was shown to accelerate the wound healing process in mice. Our findings provide new insights into the discovery and development of novel pro-angiogenic drugs derived from NPY-like polypeptides in the future.

Non-invasive imaging for predicting labial salivary gland biopsy outcomes in patients with suspected primary Sjögren syndrome.

To identify the value of salivary gland ultrasound (SGUS) combined with magnetic resonance imaging (MRI) and magnetic resonance sialography (MRS) in predicting the results of labial salivary gland biopsy (LSGB) in patients with suspected primary Sjögren syndrome (pSS), and construct a nomogram model to predict LSGB results. A total of 181 patients who were admitted with suspected pSS from December 2018 to April 2023 were examined and divided into a training set (n = 120) and a validation set (n = 61). Baseline data of the two groups were examined, and the value of SGUS, MRI, and MRS in predicting LSGB was analyzed. Multivariate logistic analysis was used to screen for risk factors, and nomogram prediction models were constructed using these results. In the training set, the SGUS, MRI, and MRS scores of patients in the LSGB + group were higher than those in the LSGB - group (all P < 0.001). The positive prediction value (PPV) was 91% for an SGUS score of 3, and 82% for MRI and MRS scores of 2 or more. We developed a nomogram prediction model based on SGUS, MRI, and MRS data, and it had a concordance index (C-index) of 0.94. The Hosmer-Lemeshow test (χ2 = 3.17, P = 0.92) also indicated the nomogram prediction model had good accuracy and calibration for prediction of LSGB results. A nomogram model based on SGUS, MRI, and MRS results can help rheumatologists decide whether LSGB should be performed in patients with suspected pSS.

A nomogram based on contrast-enhanced ultrasound for evaluating the glomerulosclerosis rate in transplanted kidneys.

Background: A high rate of glomerulosclerosis serves as an important signal of poor response to treatment and a high risk of disease progression or adverse prognosis in transplanted kidneys. We hypothesized that contrast-enhanced ultrasound (CEUS) could serve as a novel imaging biomarker in the early prediction of glomerulosclerosis rate by evaluating renal allograft microcirculation. Methods: A retrospective analysis was performed on 143 transplanted kidney recipients with confirmed pathology, including 100 in the training group and 43 in the validation group. All patients underwent conventional ultrasound (CUS) and CEUS examinations. The patients were divided into two groups: those with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis. The nomograms derived from independent predictors identified by multivariate logistic analysis were assessed using receiver operating characteristic (ROC) curve analysis, 1,000 bootstrap resamples, calibration curves, and decision curve analysis (DCA). Results: The patients with >50% glomerulosclerosis and those with ≤50% glomerulosclerosis showed statistically significant differences in CEUS parameters, including in peak intensity (PI) (25 vs. 30; P<0.001), absolute time to peak (ATTP) (10 vs. 9; P=0.004), and time to peak (TTP) (22 vs. 19.5; P=0.026). Multivariate analysis revealed that PI [odds ratio (OR) =0.852; 95% confidence interval (CI): 0.737-0.986], peak systolic velocity (PSV) of the interlobar artery (OR =0.850; 95% CI: 0.758-0.954), cortical echogenicity (OR =38.429; 95% CI: 3.695-399.641), and time since transplantation (OR =1.017; 95% CI: 1.006-1.028) were independent predictors of whether the glomerulosclerosis rate was >50% and were incorporated into the construction of a nomogram. The area under the curve (AUC) of the nomogram in the training and validation groups was 0.914 (95% CI: 0.840-0.960) and 0.909 (95% CI: 0.781-0.975), respectively, with a bootstrap resampling AUC of 0.877. The calibration curve and DCA confirmed the diagnostic performance of the nomogram model. Conclusions: The nomogram, which combined CUS, CEUS, and clinical indicators, exhibited notable predictive efficacy for the glomerulosclerosis rate in transplanted kidneys, thereby demonstrating the potential to improve clinical decision-making.

Generation of human cerebral organoids with a structured outer subventricular zone.

Outer radial glia (oRG) emerge as cortical progenitor cells that support the development of an enlarged outer subventricular zone (oSVZ) and the expansion of the neocortex. The in vitro generation of oRG is essential to investigate the underlying mechanisms of human neocortical development and expansion. By activating the STAT3 signaling pathway using leukemia inhibitory factor (LIF), which is not expressed in guided cortical organoids, we define a cortical organoid differentiation method from human pluripotent stem cells (hPSCs) that recapitulates the expansion of a progenitor pool into the oSVZ. The oSVZ comprises progenitor cells expressing specific oRG markers such as GFAP, LIFR, and HOPX, closely matching human fetal oRG. Finally, incorporating neural crest-derived LIF-producing cortical pericytes into cortical organoids recapitulates the effects of LIF treatment. These data indicate that increasing the cellular complexity of the organoid microenvironment promotes the emergence of oRG and supports a platform to study oRG in hPSC-derived brain organoids routinely.

Interactive impacts of photoaged micro(nano)plastics and co-occurring chemicals in the environment.

In the environment, sunlight or ultraviolet (UV) radiation is considered to be the primary cause of plastic aging, leading to their fragmentation into particles, including micro(nano)plastics (MNPs). Photoaged MNPs possess diverse interactive properties and ecotoxicological implications substantially different from those of pristine plastic particles. This review aims to highlight the mechanisms and implications of UV-induced photoaging of MNPs, with an emphasis on various UV sources and their interactions with co-occurring organic and inorganic chemicals, as well as the associated ecological and health impacts and factors affecting those interactions. Compared to UV-B, UV-A and UV-C were more widely used in laboratory studies for MNP degradation. Photoaged MNPs act as vectors for the transportation of organic pollutants, organic matter, and inorganic chemicals in the environment. Literature showed that photoaged MNPs exhibit a higher sorption capacity for PPCPs, PAHs, PBDEs, pesticides, humic acid, fulvic acid, heavy metals, and metallic nanoparticles than pristine MNPs, potentially causing significant changes in associated ecological and health impacts. Combined exposure to photoaged MNPs and organic and inorganic pollutants significantly altered mortality rate, decreased growth rate, histological alterations, neurological impairments, reproductive toxicity, induced oxidative stress, thyroid disruption, hepatotoxicity, and genotoxicity in vivo, both in aquatic and terrestrial organisms. Limited studies were reported in vitro and found decreased cellular growth and survival, induced oxidative stress, and compromised the permeability and integrity of the cell membrane. In addition, several environmental factors (temperature, organic matter, ionic strength, time, and pH), MNP properties (polymer types, sizes, surface area, shapes, colour, and concentration), and chemical properties (pollutant type, concentration, and physiochemical properties) can influence the photoaging of MNPs and associated impacts. Lastly, the research gaps and prospects of MNP photoaging and associated implications were also summarized. Future research should focus on the photoaging of MNPs under environmentally relevant conditions, exploiting the polydisperse characteristics of environmental plastics, to make this process more realistic for mitigating plastic pollution.

PD-1 downregulation enhances CAR-T cell antitumor efficiency by preserving a cell memory phenotype and reducing exhaustion.

BACKGROUND: Despite the encouraging outcome of chimeric antigen receptor T cell (CAR-T) targeting B cell maturation antigen (BCMA) in managing relapsed or refractory multiple myeloma (RRMM) patients, the therapeutic side effects and dysfunctions of CAR-T cells have limited the efficacy and clinical application of this promising approach. METHODS: In this study, we incorporated a short hairpin RNA cassette targeting PD-1 into a BCMA-CAR with an OX-40 costimulatory domain. The transduced PD-1KD BCMA CAR-T cells were evaluated for surface CAR expression, T-cell proliferation, cytotoxicity, cytokine production, and subsets when they were exposed to a single or repetitive antigen stimulation. Safety and efficacy were initially observed in a phase I clinical trial for RRMM patients. RESULTS: Compared with parental BCMA CAR-T cells, PD-1KD BCMA CAR-T cell therapy showed reduced T-cell exhaustion and increased percentage of memory T cells in vitro. Better antitumor activity in vivo was also observed in PD-1KD BCMA CAR-T group. In the phase I clinical trial of the CAR-T cell therapy for seven RRMM patients, safety and efficacy were initially observed in all seven patients, including four patients (4/7, 57.1%) with at least one extramedullary site and four patients (4/7, 57.1%) with high-risk cytogenetics. The overall response rate was 85.7% (6/7). Four patients had a stringent complete response (sCR), one patient had a CR, one patient had a partial response, and one patient had stable disease. Safety profile was also observed in these patients, with an incidence of manageable mild to moderate cytokine release syndrome and without the occurrence of neurological toxicity. CONCLUSIONS: Our study demonstrates a design concept of CAR-T cells independent of antigen specificity and provides an alternative approach for improving the efficacy of CAR-T cell therapy.

Supramolecular salicylic acid combined with niacinamide in chloasma: A randomized controlled trial.

BACKGROUND: No trial of supramolecular salicylic acid (SSA) for chloasma is available yet. OBJECTIVE: The purpose of this study was to assess the efficacy and safety of Bole DA 30% supramolecular salicylic acid (SSA) combined with 10% niacinamide in treating chloasma. METHODS: This multicenter (n=15), randomized, double-blind, parallel placebo-controlled trial randomized the subjects (1:1) to Bole DA 30% SSA or placebo. The primary endpoint was the effective rate after 16 weeks using the modified melasma area severity index (mMASI) [(pretreatment-posttreatment)/pretreatment×100%]. RESULTS: This study randomized 300 subjects (150/group in the full analysis set, 144 and 147 in the per-protocol set). The total mMASI score, overall Griffiths 10 score, left Griffiths 10 score, and right Griffiths 10 score were significantly lower in the Bole DA 30% SSA group than in the placebo group (all P<0.001). One study drug-related AE and one study drug-unrelated adverse events (AE) were reported in the Bole DA 30% SSA group. No AE was reported in the placebo group. CONCLUSION: Bole DA 30% SSA combined with 10% niacinamide is effective and safe for treating chloasma. CLINICAL TRIAL REGISTRATION NUMBER: ChiCTR2200065346.

Graphical models for identifying pore-forming proteins.

Pore-forming toxins (PFTs) are proteins that form lesions in biological membranes. Better understanding of the structure and function of these proteins will be beneficial in a number of biotechnological applications, including the development of new pest control methods in agriculture. When searching for new pore formers, existing sequence homology-based methods fail to discover truly novel proteins with low sequence identity to known proteins. Search methodologies based on protein structures would help us move beyond this limitation. As the number of known structures for PFTs is very limited, it's quite challenging to identify new proteins having similar structures using computational approaches like deep learning. In this article, we therefore propose a sample-efficient graphical model, where a protein structure graph is first constructed according to consensus secondary structures. A semi-Markov conditional random fields model is then developed to perform protein sequence segmentation. We demonstrate that our method is able to distinguish structurally similar proteins even in the absence of sequence similarity (pairwise sequence identity < 0.4)-a feat not achievable by traditional approaches like HMMs. To extract proteins of interest from a genome-wide protein database for further study, we also develop an efficient framework for UniRef50 with 43 million proteins.

Tensorial Properties via the Neuroevolution Potential Framework: Fast Simulation of Infrared and Raman Spectra.

Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning, in particular, the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit the application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared with earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF-), and a prototypical perovskite with strong anharmonicity (BaZrO3). The TNEP approach is implemented in the free and open source software package gpumd, which makes this methodology readily available to the scientific community.

Transferability of Machine Learning Models for Predicting Raman Spectra.

Theoretical prediction of vibrational Raman spectra enables a detailed interpretation of experimental spectra, and the advent of machine learning techniques makes it possible to predict Raman spectra while achieving a good balance between efficiency and accuracy. However, the transferability of machine learning models across different molecules remains poorly understood. This work proposed a new strategy whereby machine learning-based polarizability models were trained on similar but smaller alkane molecules to predict spectra of larger alkanes, avoiding extensive first-principles calculations on certain systems. Results showed that the developed polarizability model for alkanes with a maximum of nine carbon atoms can exhibit high accuracy in the predictions of polarizabilities and Raman spectra for the n-undecane molecule (11 carbon atoms), validating its reasonable extrapolation capability. Additionally, a descriptor space analysis method was further introduced to evaluate the transferability, demonstrating potentials for accurate and efficient Raman predictions of large molecules using limited training data labeled for smaller molecules.

Meta-DHGNN: method for CRS-related cytokines analysis in CAR-T therapy based on meta-learning directed heterogeneous graph neural network.

Chimeric antigen receptor T-cell (CAR-T) immunotherapy, a novel approach for treating blood cancer, is associated with the production of cytokine release syndrome (CRS), which poses significant safety concerns for patients. Currently, there is limited knowledge regarding CRS-related cytokines and the intricate relationship between cytokines and cells. Therefore, it is imperative to explore a reliable and efficient computational method to identify cytokines associated with CRS. In this study, we propose Meta-DHGNN, a directed and heterogeneous graph neural network analysis method based on meta-learning. The proposed method integrates both directed and heterogeneous algorithms, while the meta-learning module effectively addresses the issue of limited data availability. This approach enables comprehensive analysis of the cytokine network and accurate prediction of CRS-related cytokines. Firstly, to tackle the challenge posed by small datasets, a pre-training phase is conducted using the meta-learning module. Consequently, the directed algorithm constructs an adjacency matrix that accurately captures potential relationships in a more realistic manner. Ultimately, the heterogeneous algorithm employs meta-photographs and multi-head attention mechanisms to enhance the realism and accuracy of predicting cytokine information associated with positive labels. Our experimental verification on the dataset demonstrates that Meta-DHGNN achieves favorable outcomes. Furthermore, based on the predicted results, we have explored the multifaceted formation mechanism of CRS in CAR-T therapy from various perspectives and identified several cytokines, such as IFNG (IFN-γ), IFNA1, IFNB1, IFNA13, IFNA2, IFNAR1, IFNAR2, IFNGR1 and IFNGR2 that have been relatively overlooked in previous studies but potentially play pivotal roles. The significance of Meta-DHGNN lies in its ability to analyze directed and heterogeneous networks in biology effectively while also facilitating CRS risk prediction in CAR-T therapy.

Integrated Computational Pipeline for the High-Throughput Discovery of Cell Adhesion Peptides.

Cell adhesion peptides (CAPs) often play a critical role in tissue engineering research. However, the discovery of novel CAPs for diverse applications remains a challenging and time-intensive process. This study presents an efficient computational pipeline integrating sequence embeddings, binding predictors, and molecular dynamics simulations to expedite the discovery of new CAPs. A Pro2vec model, trained on vast CAP data sets, was built to identify RGD-similar tripeptide candidates. These candidates were further evaluated for their binding affinity with integrin receptors using the Mutabind2 machine learning model. Additionally, molecular dynamics simulations were applied to model receptor-peptide interactions and calculate their binding free energies, providing a quantitative assessment of the binding strength for further screening. The resulting peptide demonstrated performance comparable to that of RGD in endothelial cell adhesion and spreading experimental assays, validating the efficacy of the integrated computational pipeline.

Mechanistic insights into manganese oxide impacting the oxidation and transport of Cr(III) immobilized by nano-zero valent charged ion particles in water-saturated porous media.

The presence of manganese oxide (MnO2) could influence the stability of green-synthesized nano-zero valent iron (nZVI@GT) associated with trivalent chromium (Cr(III)) after its excess application in the in situ remediation of hexachromium (Cr(VI)) contaminated soil. The research findings revealed that the co-transport of the remaining nZVI@GT with Cr(III) was substantially inhibited by high δ-MnO2 concentrations due to the formation of hetero-aggregates between nZVI@GT and δ-MnO2, resulting in an increased irreversible attachment parameter at second-site in a two-site kinetic attachment model. Simultaneously, the Cr(III) complex immobilized on nZVI@GT could be oxidized leading to high levels of Cr(VI) leaching at high δ-MnO2 concentrations. During this process, Mn(IV) was converted to Mn(III)/Mn(II). Subsequently, leachate containing a partial amount of Cr(VI) preferentially adsorbed onto the nZVI@GT surface, enhancing the dispersion of the nZVI@GT and δ-MnO2 agglomerates. Thereafter, nZVI@GT transportability was enhanced with a decreased second-site attachment parameter and the flow content of dissolved Cr(VI) was increased to double, also increasing the potential risk of Cr(VI) being carried by nZVI@GT to underground water systems. This study provides theoretical support for preserving the long-term stability of nZVI@GT after the in situ remediation of heavy metal-contaminated sites in the presence of δ-MnO2.

BERT-siRNA: siRNA target prediction based on BERT pre-trained interpretable model.

Silencing mRNA through siRNA is vital for RNA interference (RNAi), necessitating accurate computational methods for siRNA selection. Current approaches, relying on machine learning, often face challenges with large data requirements and intricate data preprocessing, leading to reduced accuracy. To address this challenge, we propose a BERT model-based siRNA target gene knockdown efficiency prediction method called BERT-siRNA, which consists of a pre-trained DNA-BERT module and Multilayer Perceptron module. It applies the concept of transfer learning to avoid the limitation of a small sample size and the need for extensive preprocessing processes. By fine-tuning on various siRNA datasets after pretraining on extensive genomic data using DNA-BERT to enhance predictive capabilities. Our model clearly outperforms all existing siRNA prediction models through testing on the independent public siRNA dataset. Furthermore, the model's consistent predictions of high-efficiency siRNA knockdown for SARS-CoV-2, as well as its alignment with experimental results for PDCD1, CD38, and IL6, demonstrate the reliability and stability of the model. In addition, the attention scores for all 19-nt positions in the dataset indicate that the model's attention is predominantly focused on the 5' end of the siRNA. The step-by-step visualization of the hidden layer's classification progressively clarified and explained the effective feature extraction of the MLP layer. The explainability of model by analysis the attention scores and hidden layers is also our main purpose in this work, making it more explainable and reliable for biological researchers.

Deep learning radiomics based prediction of axillary lymph node metastasis in breast cancer.

This study aimed to develop and validate a deep learning radiomics nomogram (DLRN) for the preoperative evaluation of axillary lymph node (ALN) metastasis status in patients with a newly diagnosed unifocal breast cancer. A total of 883 eligible patients with breast cancer who underwent preoperative breast and axillary ultrasound were retrospectively enrolled between April 1, 2016, and June 30, 2022. The training cohort comprised 621 patients from Hospital I; the external validation cohorts comprised 112, 87, and 63 patients from Hospitals II, III, and IV, respectively. A DLR signature was created based on the deep learning and handcrafted features, and the DLRN was then developed based on the signature and four independent clinical parameters. The DLRN exhibited good performance, yielding areas under the receiver operating characteristic curve (AUC) of 0.914, 0.929, and 0.952 in the three external validation cohorts, respectively. Decision curve and calibration curve analyses demonstrated the favorable clinical value and calibration of the nomogram. In addition, the DLRN outperformed five experienced radiologists in all cohorts. This has the potential to guide appropriate management of the axilla in patients with breast cancer, including avoiding overtreatment.

Higher percentage of CD34+ stem cells and elevated efficacy in androgenetic alopecia treatment observed in CGF prepared from 640 nm laser-pretreated blood: A preliminary study.

BACKGROUND: Concentrated growth factor (CGF) injection has proven effective in treating androgenetic alopecia (AGA). The primary mechanism of CGF in treating AGA is thought to be the CD34+ stem cells and platelets-associated growth factors being injected into the scalp. CGF efficacy in treating AGA may rely on the activation level of these stem cells and platelets. The 640 nm laser is a United States Food and Drug Administration approved AGA treatment that activates follicle stem cells. Therefore, we hypothesize that pretreating CGF with a 640 nm laser may further activate CD34+ stem cells and platelets, thereby improving the efficacy of CGF in treating AGA. OBJECTIVE: This study aims to investigate whether 640 nm laser pretreated CGF (640CGF) has a greater effect in treating AGA than 640 nm laser non-pretreated CGF (N640CGF) and evaluate whether 640 nm laser pretreatment changed CD34+ cell percentage. METHODS: This study enrolled 10 patients (8 male, 2 female) with AGA aged 18-60 years who received CGF injections. The 640CGF group was pretreated with a 640 nm laser at an energy density of 4 J/cm2 , with a 30 cm irradiation distance for 30 min. Half of the scalp was treated with 640CGF, whereas the other half was treated with N640CGF. The injection was prepared by a doctor who did not know which blood tube had been pretreated. The treatment efficacy was evaluated using a trichoscope 1 month after injection. RESULTS: All 10 (100%) patients participated in the follow-up visit, and a higher quantity of new hairs was observed on the side injected with 640CGF than N640CGF (p = 0.019). Additionally, fewer malnourished hairs were observed on the 640CGF pretreated side (p = 0.015). No serious adverse events were reported. CONCLUSIONS: A higher percentage of CD34+ stem cells and improved efficacy in AGA treatment could be observed with CGF prepared from 640 nm laser-pretreated blood.

Knowledge, attitudes and practices among medical workers toward outpatient diabetes information platform.

BACKGROUND: This study aimed to assess the knowledge, attitudes and practices among medical workers toward outpatient diabetes information platform. METHODS: This web-based cross-sectional study was conducted between May 2023 and June 2023 at the First Hospital of Zhangjiakou, China. A self-designed questionnaire was developed to collect demographic information of medical workers, and assess their knowledge, attitudes and practices toward outpatient diabetes information platform. RESULTS: A total of 685 questionnaires were collected. Among the participants, 603 (88.03%) were female, 432 (63.07%) work in a tertiary hospital, 548 (80.00%) have a bachelor degree, 270 (39.42%) of them work in the department of internal medicine and 315 (45.99%) of them received previous training on outpatient diabetes information platform. The mean knowledge, attitudes and practices scores were 4.32 ± 1.27 (possible range: 0-6), 56.76 ± 5.72 (possible range: 14-70), and 32.22 ± 8.42 (possible range: 9-45), respectively. 350 (51.09%) of them have sufficient knowledge, 168 (24.53%) have positive attitudes and 395 (57.66%) have active practices. Pearson correlation analysis showed that knowledge was positively correlated with attitudes (r = 0.397, P < 0.001), and attitudes were positively correlated with practices (r = 0.306, P < 0.001). Multivariate analysis showed that primary hospital (OR = 0.32, 95% CI: 0.14-0.71, P = 0.005), secondary hospital (OR = 0.48, 95% CI: 0.32-0.72, P < 0.001), doctor (OR = 2.44, 95% CI: 1.39-4.28, P = 0.002) were independently associated with sufficient knowledge. Knowledge (OR = 1.49, 95% CI: 1.29-1.73, P < 0.001), community hospital staff (OR = 0.21, 95% CI: 0.05-0.88, P = 0.032) were independently associated with positive attitudes. Attitudes (OR = 1.13, 95% CI: 1.09-1.17, P < 0.001), junior college (OR = 1.72, 95% CI: 1.07-2.77, P = 0.026) were independently associated with active practices. The structural equation model demonstrated that knowledge had a direct effect on attitudes (path coefficient = 0.521, P < 0.001), and attitudes had a direct effect on practices (path coefficient = 0.542, P < 0.001). Moreover, the type of hospital had a direct effect on knowledge (path coefficient = 0.085, P < 0.001). Additionally, previous training on the outpatient diabetes platform had direct effects on attitudes (path coefficient = 0.191, P < 0.001) and practices (path coefficient = 0.184, P < 0.001). CONCLUSION: These findings revealed that medical workers have insufficient knowledge, positive attitudes and inactive practices toward the outpatient diabetes information platform. Comprehensive training programs are needed to improve medical staff's practices in this area.

Regional delivery of mesothelin-targeted chimeric antigen receptor T-cell effectively and safely targets colorectal cancer liver metastases in mice.

Background: Liver metastasis is the major cause of colorectal cancer related death. Mesothelin (MSLN)-targeted chimeric antigen receptor (CAR) T-cell therapy has been illustrated effective and safe through regional delivery of breast cancer, ovarian cancer and malignant mesothelioma tumors. Herein, we investigated the safety, efficacy, and immune microenvironment of regional delivery of MSLN (CAR) T-cell in the treatment of colorectal carcinoma liver metastases (CRLM). Methods: Second-generation MSLN CAR T-cells were administered by portal vein (PV) or caudal vein (CV, systemic administration) delivery in an orthotopic MSLN+ CRLM nonobese diabetic (NOD)/severe combined immunodeficient (SCID)/γc-/- (NSG) mouse model. A total of 20 mice were randomly divided into control group, non-transduced T cell (NT)-CV group, NT-PV group, MSLN CAR T-cell CV (MSLN-CV) group, and MSLN CAR T-cell PV (MSLN-PV) group, with each group containing four mice to examine the safety and efficacy. The bioluminescence intensity (BLI) of tumor burden, tumor tissue macroscopic and microscopic observation were used to evaluate treatment efficacy. The safety was examined by body weight, survival time, and vital organ damage of mice. CAR T-cell infiltration and cytokine concentration were analyzed by flow cytometry, and immunostaining. The change of immune microenvironment between regional delivery and systemic delivery was investigated on an immune reconstructed CRLM patient-derived xenograft (PDX) model. Additionally, T cell subsets and immunosuppressive markers were examined. Results: PV administration of 1×107/100 µL MSLN CAR T-cells in 20 NSG mice was well tolerated, and no overt toxicity was observed. The tumor burden in the PV group was obviously alleviated. The BLI was (0.73±0.52)×109 in PV group and (1.97±0.11)×109 in CV group (P<0.05), CD8+ granzyme B (GB)+ T cell percentage (MSLN-CV 4.42%±0.47% vs. MSLN-PV 13.5%±4.67%, P<0.01) and cytokine concentration were obviously increased in the MSLN-PV group. In the immune reconstituted CRLM PDX model, intratumor (IT) delivery of MSLN CAR T-cells exhibited much more infiltration of CD4+ and CD8+ T cells accompanied with elevated expression levels of PD-1, LAG-3, and TIM-3. Conclusions: Regional delivery of MSLN-targeted CAR T-cell therapy has encouraging results in the orthotopic CRLM NSG mouse model and PDX model, and converts the tumor microenvironment from cold to hot. This study may provide a new therapeutic approach for CRLM. Further clinical study is needed.