SCTC: inference of developmental potential from single-cell transcriptional complexity.

Inferring the developmental potential of single cells from scRNA-Seq data and reconstructing the pseudo-temporal path of cell development are fundamental but challenging tasks in single-cell analysis. Although single-cell transcriptional diversity (SCTD) measured by the number of expressed genes per cell has been widely used as a hallmark of developmental potential, it may lead to incorrect estimation of differentiation states in some cases where gene expression does not decrease monotonously during the development process. In this study, we propose a novel metric called single-cell transcriptional complexity (SCTC), which draws on insights from the economic complexity theory and takes into account the sophisticated structure information of scRNA-Seq count matrix. We show that SCTC characterizes developmental potential more accurately than SCTD, especially in the early stages of development where cells typically have lower diversity but higher complexity than those in the later stages. Based on the SCTC, we provide an unsupervised method for accurate, robust, and transferable inference of single-cell pseudotime. Our findings suggest that the complexity emerging from the interplay between cells and genes determines the developmental potential, providing new insights into the understanding of biological development from the perspective of complexity theory.

Emulation and evaluation of tumor cell combined chemotherapy in isotropic/anisotropic collagen fiber microenvironments.

The rapid emergence of anisotropic collagen fibers in the tissue microenvironment is a critical transition point in late-stage breast cancer. Specifically, the fiber orientation facilitates the likelihood of high-speed tumor cell invasion and metastasis, which pose lethal threats to patients. Thus, based on this transition point, one key issue is how to determine and evaluate efficient combination chemotherapy treatments in late-stage cancer. In this study, we designed a collagen microarray chip containing 241 high-throughput microchambers with embedded metastatic breast cancer cell MDA-MB-231-RFP. By utilizing collagen's unique structure and hydromechanical properties, the chip constructed three-dimensional isotropic and anisotropic collagen fiber structures to emulate the tumor cell microenvironment at early and late stages. We injected different chemotherapeutic drugs into its four channels and obtained composite biochemical concentration profiles. Our results demonstrate that anisotropic collagen fibers promote cell proliferation and migration more than isotropic collagen fibers, suggesting that the geometric arrangement of fibers plays an important role in regulating cell behavior. Moreover, the presence of anisotropic collagen fibers may be a potential factor leading to the poor efficacy of combined chemotherapy in late-stage breast cancer. We investigated the efficacy of various chemotherapy drugs using cell proliferation inhibitors paclitaxel and gemcitabine and tumor cell migration inhibitors 7rh and PP2. To ensure the validity of our findings, we followed a systematic approach that involved testing the inhibitory effects of these drugs. According to our results, the drug combinations' effectiveness could be ordered as follows: paclitaxel + gemcitabine > gemcitabine + 7rh > PP2 + paclitaxel > 7rh + PP2. This study shows that the biomimetic chip system not only facilitates the creation of a realistic in vitro model for examining the cell migration mechanism in late-stage breast cancer but also has the potential to function as an effective tool for future chemotherapy assessment and personalized medicine.

Morphological entropy encodes cellular migration strategies on multiple length scales.

Cell migration is crucial for numerous physiological and pathological processes. A cell adapts its morphology, including the overall and nuclear morphology, in response to various cues in complex microenvironments, such as topotaxis and chemotaxis during migration. Thus, the dynamics of cellular morphology can encode migration strategies, from which diverse migration mechanisms can be inferred. However, deciphering the mechanisms behind cell migration encoded in morphology dynamics remains a challenging problem. Here, we present a powerful universal metric, the Cell Morphological Entropy (CME), developed by combining parametric morphological analysis with Shannon entropy. The utility of CME, which accurately quantifies the complex cellular morphology at multiple length scales through the deviation from a perfectly circular shape, is illustrated using a variety of normal and tumor cell lines in different in vitro microenvironments. Our results show how geometric constraints affect the MDA-MB-231 cell nucleus, the emerging interactions of MCF-10A cells migrating on collagen gel, and the critical transition from proliferation to invasion in tumor spheroids. The analysis demonstrates that the CME-based approach provides an effective and physically interpretable tool to measure morphology in real-time across multiple length scales. It provides deeper insight into cell migration and contributes to the understanding of different behavioral modes and collective cell motility in more complex microenvironments.

Lattice Rotation and Deformation Mechanisms under Tensile Loading in a Single-Crystal Superalloy with [001] Misorientation.

This study investigates how deviation angles close to the [001] orientation affect the tensile properties and deformation behavior of a nickel-based single-crystal superalloy at room temperature. The research focuses on samples with deviation angles of 3°, 8°, and 13° from the [001] orientation and examines their strength and ductility. We employed scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), and transmission electron microscopy (TEM) to explore the deformation micro-mechanisms at varying angles. Findings reveal that strength decreases and ductility increases as the deviation angle widens within the [001] vicinity. The study emphasizes that <110> octahedral slip-driven crystal slip and rotation are crucial for understanding tensile deformation. The deformation differences in samples at varying angles are attributed to the differential engagement of mechanisms. Specifically, at lower angles, reduced ductility and increased strength are due to short lattice rotation paths and work hardening causing superlattice stacking faults (SSFs) to slip in two directions on the {111} plane within the γ' phase. As the angles increase, the lattice rotation paths extend, and Shockley partial dislocations (a/6<112>) accumulate in γ channels. This process, involving SSFs moving in a single direction within the γ' phase, results in higher ductility and reduced strength.

Anlotinib plus docetaxel vs. docetaxel alone for advanced non-small-cell lung cancer patients who failed first-line treatment: A multicenter, randomized phase II trial.

OBJECTIVES: Given the modest efficacy of docetaxel in advanced non-small cell lung cancer (NSCLC), this study assesses the therapeutic potential and safety profile of anlotinib in combination with docetaxel compared to docetaxel monotherapy as a second-line therapy for patients with advanced NSCLC. MATERIALS AND METHODS: In this phase II study, patients with advanced NSCLC experiencing failure with first-line platinum-based regimens were randomized in a 1:1 ratio to receive either anlotinib plus docetaxel or docetaxel alone. Primary endpoint was progression-free survival (PFS), with overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety as secondary endpoints. RESULTS: A total of 83 patients were randomized. The combination of anlotinib and docetaxel significantly extended median PFS to 4.4 months compared to 1.6 months for docetaxel alone (hazard ratio [HR] = 0.38, 95 % confidence interval [CI]: 0.23-0.63, P = 0.0002), and also demonstrated superior ORR (32.5 % vs. 9.3 %, P = 0.0089) and DCR (87.5 % vs. 53.5 %, P = 0.0007). Median OS was observed at 12.0 months in the combination group vs. 10.9 months in the monotherapy group (HR = 0.82, 95 % CI: 0.47-1.43, P = 0.4803). For patients previously treated with immunotherapy, the median PFS was notably longer at 7.8 vs. 1.7 months (HR = 0.22, 95 % CI: 0.09-0.51, P = 0.0290). The incidence of grade ≥ 3 treatment-related adverse events, predominantly leukopenia (15.0 % vs. 7.0 %) and neutropenia (10.0 % vs. 5.0 %), was manageable across both groups. CONCLUSION: Anlotinib plus docetaxel offers a viable therapeutic alternative for patients with advanced NSCLC who failed first-line platinum-based treatments.

An entropy-based approach for assessing the directional persistence of cell migration.

Cell migration, which is primarily characterized by directional persistence, is essential for the development of normal tissues and organs, as well as for numerous pathological processes. However, there is a lack of simple and efficient tools to analyze the systematic properties of persistence based on cellular trajectory data. Here, we present a novel approach, the entropy of angular distribution , which combines cellular turning dynamics and Shannon entropy to explore the statistical and time-varying properties of persistence that strongly correlate with cellular migration modes. Our results reveal the changes in the persistence of multiple cell lines that are tightly regulated by both intra- and extracellular cues, including Arpin protein, collagen gel/substrate, and physical constraints. Significantly, some previously unreported distinctive details of persistence have also been captured, helping to elucidate how directional persistence is distributed and evolves in different cell populations. The analysis suggests that the entropy of angular distribution-based approach provides a powerful metric for evaluating directional persistence and enables us to better understand the relationships between cellular behaviors and multiscale cues, which also provides some insights into the migration dynamics of cell populations, such as collective cell invasion.

AttnPep: A Self-Attention-Based Deep Learning Method for Peptide Identification in Shotgun Proteomics.

In shotgun proteomics, the proteome search engine analyzes mass spectra obtained by experiments, and then a peptide-spectra match (PSM) is reported for each spectrum. However, most of the PSMs identified are incorrect, and therefore various postprocessing software have been developed for reranking the peptide identifications. Yet these methods suffer from issues such as dependency on distribution, reliance on shallow models, and limited effectiveness. In this work, we propose AttnPep, a deep learning model for rescoring PSM scores that utilizes the Self-Attention module. This module helps the neural network focus on features relevant to the classification of PSMs and ignore irrelevant features. This allows AttnPep to analyze the output of different search engines and improve PSM discrimination accuracy. We considered a PSM to be correct if it achieves a q-value <0.01 and compared AttnPep with existing mainstream software PeptideProphet, Percolator, and proteoTorch. The results indicated that AttnPep found an average increase in correct PSMs of 9.29% relative to the other methods. Additionally, AttnPep was able to better distinguish between correct and incorrect PSMs and found more synthetic peptides in the complex SWATH data set.

A phase Ib/II study of cadonilimab (PD-1/CTLA-4 bispecific antibody) plus anlotinib as first-line treatment in patients with advanced non-small cell lung cancer.

BACKGROUND: Cadonilimab is a bispecific antibody that simultaneously targets programmed cell death receptor-1 and cytotoxic T lymphocyte-associated antigen-4. This study aimed to assess the safety and efficacy of cadonilimab plus anlotinib for the first-line treatment of advanced non-small cell lung cancer (NSCLC) without sensitizing EGFR/ALK/ROS1 mutations. METHODS: Patients received cadonilimab 15 mg/kg and 10 mg/kg every three weeks (Q3W) plus anlotinib at doses of 10 or 12 mg once daily for two weeks on a one-week-off schedule. The primary endpoints included safety and objective response rate (ORR). RESULTS: Sixty-nine treatment-naïve patients received cadonilimab 15 mg/kg Q3W combination (n = 49) and 10 mg/kg Q3W combination (n = 20). Treatment-related adverse events (TRAEs) were reported in 48 (98.0%) and 19 (95.0%) patients, with grade ≥3 TRAEs occurring in 29 (59.2%) and five (25.0%) patients, respectively. TRAEs leading to cadonilimab discontinuation occurred in eight (16.3%) and one (5.0%) patients in the cadonilimab 15 mg/kg Q3W and 10 mg/kg Q3W dosing groups. The confirmed ORRs were 51.0% (25/49) and 60.0% (12/20) accordingly. CONCLUSIONS: Cadonilimab 10 mg/kg Q3W plus anlotinib showed manageable safety and promising efficacy as a first-line chemo-free treatment for advanced NSCLC. GOV IDENTIFIER: NCT04646330.

The emergence and successful elimination of SARS-CoV-2 dominant strains with increasing epidemic potential in Taiwan's 2021 outbreak.

Taiwan's experience with severe acute respiratory syndrome coronavirus (SARS-CoV) in 2003 guided its development of strategies to defend against SARS-CoV-2 in 2020, which enabled the successful control of Coronavirus disease 2019 (COVID-19) cases from 2020 through March 2021. However, in late-April 2021, the imported Alpha variant began to cause COVID-19 outbreaks at an exceptional rate in Taiwan. In this study, we aimed to determine what epidemiological conditions enabled the SARS-CoV-2 Alpha variant strains to become dominant and decline later during a surge in the outbreak. In conjunction with contact-tracing investigations, we used our bioinformatics software, CoVConvert and IniCoV, to analyze whole-genome sequences of 101 Taiwan Alpha strains. Univariate and multivariable regression analyses revealed the epidemiological factors associated with viral dominance. Univariate analysis showed the dominant Alpha strains were preferentially selected in the surge's epicenter (p = 0.0024) through intensive human-to-human contact and maintained their dominance for 1.5 months until the Zero-COVID Policy was implemented. Multivariable regression found that the epidemic periods (p = 0.007) and epicenter (p = 0.001) were two significant factors associated with the dominant virus strains spread in the community. These dominant virus strains emerged at the outbreak's epicenter with frequent human-to-human contact and low vaccination coverage. The Level 3 Restrictions and Zero-COVID policy successfully controlled the outbreak in the community without city lockdowns. Our integrated method can identify the epidemiological conditions for emerging dominant virus with increasing epidemiological potential and support decision makers in rapidly containing outbreaks using public health measures that target fast-spreading virus strains.

Heart rhythm complexity analysis in patients with inferior ST-elevation myocardial infarction.

Heart rhythm complexity (HRC), a subtype of heart rate variability (HRV), is an important tool to investigate cardiovascular disease. In this study, we aimed to analyze serial changes in HRV and HRC metrics in patients with inferior ST-elevation myocardial infarction (STEMI) within 1 year postinfarct and explore the association between HRC and postinfarct left ventricular (LV) systolic impairment. We prospectively enrolled 33 inferior STEMI patients and 74 control subjects and analyzed traditional linear HRV and HRC metrics in both groups, including detrended fluctuation analysis (DFA) and multiscale entropy (MSE). We also analyzed follow-up postinfarct echocardiography for 1 year. The STEMI group had significantly lower standard deviation of RR interval (SDNN), and DFAα2 within 7 days postinfarct (acute stage) comparing to control subjects. LF power was consistently higher in STEMI group during follow up. The MSE scale 5 was higher at acute stage comparing to control subjects and had a trend of decrease during 1-year postinfarct. The MSE area under scale 1-5 showed persistently lower than control subjects and progressively decreased during 1-year postinfarct. To predict long-term postinfarct LV systolic impairment, the slope between MSE scale 1 to 5 (slope 1-5) had the best predictive value. MSE slope 1-5 also increased the predictive ability of the linear HRV metrics in both the net reclassification index and integrated discrimination index models. In conclusion, HRC and LV contractility decreased 1 year postinfarct in inferior STEMI patients, and MSE slope 1-5 was a good predictor of postinfarct LV systolic impairment.

Evaluating the Effect of Artificial Liver Support on Acute-on-Chronic Liver Failure Using the Quantitative Difference Algorithm: Retrospective Study.

BACKGROUND: Liver failure, including acute-on-chronic liver failure (ACLF), occurs mainly in young adults and is associated with high mortality and resource costs. The prognosis evaluation is a crucial part of the ACLF treatment process and should run through the entire diagnosis process. As a recently proposed novel algorithm, the quantitative difference (QD) algorithm holds promise for enhancing the prognosis evaluation of ACLF. OBJECTIVE: This study aims to examine whether the QD algorithm exhibits comparable or superior performance compared to the Model for End-Stage Liver Disease (MELD) in the context of prognosis evaluation. METHODS: A total of 27 patients with ACLF were categorized into 2 groups based on their treatment preferences: the conventional treatment (n=12) and the double plasma molecular absorption system (DPMAS) with conventional treatment (n=15) groups. The prognosis evaluation was performed by the MELD and QD scoring systems. RESULTS: A significant reduction was observed in alanine aminotransferase (P=.02), aspartate aminotransferase (P<.001), and conjugated bilirubin (P=.002), both in P values and QD value (Lτ>1.69). A significant decrease in hemoglobin (P=.01), red blood cell count (P=.01), and total bilirubin (P=.02) was observed in the DPMAS group, but this decrease was not observed in QD (Lτ≤1.69). Furthermore, there was a significant association between MELD and QD values (P<.001). Significant differences were observed between groups based on patients' treatment outcomes. Additionally, the QD algorithm can also demonstrate improvements in patient fatigue. DPMAS can reduce alanine aminotransferase, aspartate aminotransferase, and unconjugated bilirubin. CONCLUSIONS: As a dynamic algorithm, the QD scoring system can evaluate the therapeutic effects in patients with ACLF, similar to MELD. Nevertheless, the QD scoring system surpasses the MELD by incorporating a broader range of indicators and considering patient variability.

Machine learning model for the prediction of gram-positive and gram-negative bacterial bloodstream infection based on routine laboratory parameters.

BACKGROUND: Bacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters. METHODS: Data for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model. RESULTS: After feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible. CONCLUSION: The present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

Influence factors of the survival in colorectal cancer patients with second primary malignancy after surgery: A SEER database analysis.

The survival rate is significantly reduced in patients with colorectal cancer (CRC) who developing a second primary malignancy (SPM), and however, little has known about the factors that contribute to the mortality of SPMs among CRC survivors. This study aims to explore the influence factors in both the all-cause and cancer-specific mortality of patients with SPMs after CRC surgery. Data of adult CRC patients with SPMs were extracted from the Surveillance, Epidemiology, and End Results (SEER) database in this retrospective cohort study. The associations between potential influence factors and all-cause mortality and cancer-specific mortality were explored using univariate and multivariate Cox proportional hazards analyses. The evaluation indexes were hazard ratios (HRs), and 95% confidence intervals (CIs). We also drew pie charts to respectively reflect the distributions of SPMs sites and time interval in study population. A total of 1202 (56.14%) patients died for all-cause, and 464 (21.67%) died due to CRC. The results showed that after adjusting for covariates, age, sex, marital status, T stage of CRC, second primary cancer site, stage of SPMs, grade of SPMs, TNM stage of SPMs, and time interval were associated with all-cause mortality, while marital status, stage of CRC, T stage of CRC, chemotherapy, second primary cancer site, stage of SPMs, grade of SPMs, TNM stage of SPMs, and time interval were associated with cancer-specific mortality in patients with CRC. In addition, colon (23.5%) was the most common site of SPMs, followed by digestive system (19.0%), and the time interval between CRC and SPMs in most patients was over 5 years (28.4%). Our findings may assist clinicians to identify high-risk patients for SPMs after CRC surgery. Also, the postoperative long-term follow-up and close attention on the key systems where the SPMs may occur are of great necessary in patients with CRC.

Phosphorylation of PPDPF via IL6-JAK2 activates the Wnt/ß-catenin pathway in colorectal cancer.

Inflammation plays an important role in the initiation and progression of colorectal cancer (CRC) and leads to ß-catenin accumulation in colitis-related CRC. However, the mechanism remains largely unknown. Here, pancreatic progenitor cell differentiation and proliferation factor (PPDPF) is found to be upregulated in CRC and significantly correlated with tumor-node-metastasis (TNM) stages and survival time. Knockout of PPDPF in the intestinal epithelium shortens crypts, decreases the number of stem cells, and inhibits the growth of organoids and the occurrence of azoxymethane (AOM)/dextran sodium sulfate (DSS)-induced CRC. Mechanistically, PPDPF is found to interact with Casein kinase 1α (CK1α), thereby disrupting its binding to Axin, disassociating the ß-catenin destruction complex, decreasing the phosphorylation of ß-catenin, and activating the Wnt/ß-catenin pathway. Furthermore, interleukin 6 (IL6)/Janus kinase 2 (JAK2)-mediated inflammatory signals lead to phosphorylation of PPDPF at Tyr16 and Tyr17, stabilizing the protein. In summary, this study demonstrates that PPDPF is a key molecule in CRC carcinogenesis and progression that connects inflammatory signals to the Wnt/ß-catenin signaling pathway, providing a potential novel therapeutic target.

Predicting metabolite-disease associations based on auto-encoder and non-negative matrix factorization.

Metabolism refers to a series of orderly chemical reactions used to maintain life activities in organisms. In healthy individuals, metabolism remains within a normal range. However, specific diseases can lead to abnormalities in the levels of certain metabolites, causing them to either increase or decrease. Detecting these deviations in metabolite levels can aid in diagnosing a disease. Traditional biological experiments often rely on a lot of manpower to do repeated experiments, which is time consuming and labor intensive. To address this issue, we develop a deep learning model based on the auto-encoder and non-negative matrix factorization named as MDA-AENMF to predict the potential associations between metabolites and diseases. We integrate a variety of similarity networks and then acquire the characteristics of both metabolites and diseases through three specific modules. First, we get the disease characteristics from the five-layer auto-encoder module. Later, in the non-negative matrix factorization module, we extract both the metabolite and disease characteristics. Furthermore, the graph attention auto-encoder module helps us obtain metabolite characteristics. After obtaining the features from three modules, these characteristics are merged into a single, comprehensive feature vector for each metabolite-disease pair. Finally, we send the corresponding feature vector and label to the multi-layer perceptron for training. The experiment demonstrates our area under the receiver operating characteristic curve of 0.975 and area under the precision-recall curve of 0.973 in 5-fold cross-validation, which are superior to those of existing state-of-the-art predictive methods. Through case studies, most of the new associations obtained by MDA-AENMF have been verified, further highlighting the reliability of MDA-AENMF in predicting the potential relationships between metabolites and diseases.

3D collagen microchamber arrays for combined chemotherapy effect evaluation on cancer cell numbers and migration.

Breast cancer metastasis involves complex mechanisms, particularly when patients are undergoing chemotherapy. In tissues, tumor cells encounter cell-cell interactions, cell-microenvironment interactions, complex nutrient, and drug gradients. Currently, two-dimensional cell culture systems and animal models are challenging to observe and analyze cell responses to microenvironments with various physical and bio-chemical conditions, and microfluidic technology has been systematically developed to address this dilemma. In this study, we have constructed a combined chemotherapy evaluation chip (CCEC) based on microfluidic technology. The chip possesses 192 diamond-shaped microchambers containing MDA-MB-231-RFP cells, and each microchamber is composed of collagen to mimic breast cancer and its surrounding microenvironment. In addition, by adding medium containing different drugs to the medium channels of CCEC, composite drug (paclitaxel+gemcitabine+7rh and paclitaxel+fluorouracil+PP2) concentration gradients, and single drug (paclitaxel, gemcitabine, 7rh, fluorouracil, PP2) concentration gradients have been established in the five collagen regions, respectively, so that each localized microchamber in the regions has a unique drug microenvironment. In this way, we evaluated the composite and single chemotherapy efficacy on the same chip by statistically analyzing their effects on the numbers and migration of the cell. The quantitative results in CCECs reveal that the inhibition effects on the numbers and migration of MDA-MB-231-RFP cell under the composite drug gradients are more optimal than those of the single drugs. Besides, the cancer cell inhibition effect between the groups composed of two drugs has also been compared, that is the paclitaxel+gemcitabine, paclitaxel+fluorouracil, and paclitaxel+PP2 have better cell numbers and migration inhibition effects than paclitaxel+7rh. The results indicate that the bio-mimetic and high-throughput combined chemotherapy evaluation platform can serve as a more efficient and accurate tool for preclinical drug development and screening.

Rh-endostatin plus camrelizumab and chemotherapy in first-line treatment of advanced non-small cell lung cancer: A multicenter retrospective study.

BACKGROUND: Clinical evidence of immune checkpoint inhibitors combined with antiangiogenic drugs in patients with advanced non-small cell lung cancer (NSCLC) was limited. Recombinant human endostatin (rh-endostatin), an antiangiogenic drug, and camrelizumab, an anti-PD-1 antibody, have been approved for the treatment of advanced NSCLC in China. This study aimed to investigate the efficacy and safety of rh-endostatin plus camrelizumab and chemotherapy in the treatment of advanced NSCLC. METHODS: Eligible patients were enrolled and received camrelizumab (200 mg, day 1) every 3 weeks and continuous intravenous infusion of rh-endostatin (70 mg/day, days 1-3) and cisplatin combined with pemetrexed (for adenocarcinoma) or paclitaxel (for NSCLC other than adenocarcinoma) every 3 weeks. Primary endpoint was progression-free survival (PFS). Secondary endpoints were objective response rate (ORR), disease control rate (DCR), overall survival (OS), and safety profiles. RESULTS: Overall, 27 patients were included, and 25 patients were eligible for efficacy evaluation. For these 25 patients, ORR was 48.15% (13/27) and DCR was 85.19% (23/27). With a median follow-up of 10.37 months, the median PFS was 8.9 (95% CI: 4.23-13.57) months. Median OS was not reached. Overall, 96.3% of patients experienced at least one treatment-related adverse event, and grade 3 TRAEs occurred in 9 (33.3%) patients. No unexpected AEs were observed. CONCLUSION: Rh-endostatin plus camrelizumab and chemotherapy showed favorable efficacy and safety profile in patients with advanced NSCLC, representing a promising treatment regimen for these patients.

The efficacy and safety of immune checkpoint inhibitors combined with chemotherapy or anti-angiogenic therapy as a second-line or later treatment option for advanced non-small cell lung cancer: a retrospective comparative cohort study.

Background: Although immune checkpoint inhibitor (ICI) monotherapy remains the standard of second-line treatment for patients with advanced non-small cell lung cancer (NSCLC) , the objective response rate (ORR) is low. There is an urgent need to increase the response population of second-line immunotherapy, and ICI combination therapy may be a possible option. However, the evidence is insufficient. Methods: We retrospectively collected the medical records of patients who received ICI monotherapy or ICI combination therapy as a second-line or later treatment option. We further analysed baseline clinical characteristics, evaluated treatment efficacy, assessed treatment-related adverse events (AEs) and followed up survival. The outcome variables assessed in the study were ORR, disease control rate (DCR), progression-free survival (PFS), overall survival (OS) and AEs. Results: A total of 145 patients were ultimately enrolled in this study, including the ICI monotherapy group (n=63) and ICI combination therapy group (n=82). The ICI combination therapy group was further divided into the ICI/chemotherapy group (n=57) and ICI/anti-angiogenic therapy group (n=25). The baseline was comparable among the three subgroups. The ICI combination therapy groups showed a higher ORR (29.3% vs. 11.1%, P=0.008) and DCR (85.4% vs. 61.9%, P=0.001) and a longer PFS (6.77 vs. 3.47 months, P<0.001) and OS (18.60 vs. 8.47 months, P<0.001) than the ICI monotherapy group. The ICI/chemotherapy group showed a significantly higher ORR (31.6% vs. 11.1%, P=0.006) and DCR (84.2% vs. 61.9%, P=0.006) and a longer PFS (6.37 vs. 3.47 months, P<0.001) and OS (18.60 vs. 8.47 months, P<0.001) than the ICI monotherapy group. The ICI/anti-angiogenic therapy group showed a significantly higher DCR (88.0% vs. 61.9%, P=0.021) and a longer PFS (8.17 vs. 3.47 months, P<0.001) and OS (19.20 vs. 8.47 months, P=0.005) than the ICI monotherapy group. Neither of the combined ICI therapy groups showed a significant increase in the incidence of AEs compared to the ICI monotherapy group. Conclusions: ICI combined with chemotherapy or anti-angiogenic therapy as second-line or later treatment demonstrated superiority over ICI monotherapy in advanced NSCLC patients without prior immunotherapy. These results provide a potentially superior treatment strategy and require verification in prospective clinical trials.

MtNF-YC6 and MtNF-YC11 are involved in regulating the transcriptional program of arbuscular mycorrhizal symbiosis.

Arbuscular mycorrhizal fungi are obligate symbionts that transfer mineral nutrients to host plants through arbuscules, a fungal structure specialized for exchange for photosynthetic products. MtNF-YC6 and MtNF-YC11, which encode the C subunits of nuclear factor Y (NF-Y) family in Medicago truncatula are induced specifically by arbuscular mycorrhizal symbiosis (AMS). A previous study showed that MtNF-YC6 and MtNF-YC11 are activated in cortical cells of mycorrhizal roots, but the gene functions were unknown. Herein, we identified both MtNF-YB17 and MtNF-YB12 as the interacting partners of MtNF-YC6 and MtNF-YC11 in yeast and plants. MtNF-YB17 was highly induced by AMS and activated in cortical cells only in mycorrhizal roots but MtNF-YB12 was not affected. The formation of B/C heterodimers led the protein complexes to transfer from the cytoplasm to the nucleus. Silencing MtNF-YC6 and C11 by RNA interference (RNAi) resulted in decreased colonization efficiency and arbuscule richness. Coincidently, genes associated with arbuscule development and degeneration in RNAi roots were also downregulated. In silico analysis showed CCAAT-binding motifs in the promoter regions of downregulated genes, further supporting the involvement of NF-Y complexes in transcriptional regulation of symbiosis. Taken together, this study identifies MtNF-YC6- or MtNF-YC11-containing protein complexes as novel transcriptional regulators of symbiotic program and provides a list of potential downstream target genes. These data will help to further dissect the AMS regulatory network.