Safety, immunogenicity and protective effectiveness of heterologous boost with a recombinant COVID-19 vaccine (Sf9 cells) in adult recipients of inactivated vaccines.

Vaccines have proven effective in protecting populations against COVID-19, including the recombinant COVID-19 vaccine (Sf9 cells), the first approved recombinant protein vaccine in China. In this positive-controlled trial with 85 adult participants (Sf9 cells group: n = 44; CoronaVac group: n = 41), we evaluated the safety, immunogenicity, and protective effectiveness of a heterologous boost with the Sf9 cells vaccine in adults who had been vaccinated with the inactivated vaccine, and found a post-booster adverse events rate of 20.45% in the Sf9 cells group and 31.71% in the CoronaVac group (p = 0.279), within 28 days after booster injection. Neither group reported any severe adverse events. Following the Sf9 cells vaccine booster, the geometric mean titer (GMT) of binding antibodies to the receptor-binding domain of prototype SARS-CoV-2 on day 28 post-booster was significantly higher than that induced by the CoronaVac vaccine booster (100,683.37 vs. 9,451.69, p < 0.001). In the Sf9 cells group, GMTs of neutralizing antibodies against pseudo SARS-CoV-2 viruses (prototype and diverse variants of concern [VOCs]) increased by 22.23-75.93 folds from baseline to day 28 post-booster, while the CoronaVac group showed increases of only 3.29-10.70 folds. Similarly, neutralizing antibodies against live SARS-CoV-2 viruses (prototype and diverse VOCs) increased by 68.18-192.67 folds on day 14 post-booster compared with the baseline level, significantly greater than the CoronaVac group (19.67-37.67 folds). A more robust Th1 cellular response was observed with the Sf9 cells booster on day 14 post-booster (mean IFN-γ+ spot-forming cells per 2 × 105 peripheral blood mononuclear cells: 26.66 vs. 13.59). Protective effectiveness against symptomatic COVID-19 was approximately twice as high in the Sf9 cells group compared to the CoronaVac group (68.18% vs. 36.59%, p = 0.004). Our study findings support the high protective effectiveness of heterologous boosting with the recombinant COVID-19 vaccine (Sf9 cells) against symptomatic COVID-19 of diverse SARS-CoV-2 variants of concern, while causing no apparent safety concerns.

Visualizing intracellular membrane interactions and cell type-specific differentiation in ferroptosis and apoptosis with Boranil-Carbazole derivative.

Intracellular lipid systems play essential roles in various physiological functions and cell growth processes. However, our understanding of the intricate interactions within this system, especially between mitochondria and lipid droplets, is limited, particularly in the context of cancer cells' altered lipid metabolism. To address this, our study introduces an N-B-O BODIPY-hexylcarbazole derivative, named Cz-Boranil, that sets a new benchmark in visualizing these critical interactions. Cz-Boranil's unique capability lies in its ability to display distinct intracellular distribution patterns in both normal and cancer cells, offering nuanced cell type-specific differentiation. More impressively, this probe tracks the coordinated interactions of lipid droplets and mitochondria during the critical processes of ferroptosis and apoptosis. We believe that the innovative capabilities of Cz-Boranil will revolutionize our understanding of intracellular lipid interactions and prove pivotal in identifying and studying cancerous cells.

Metatranscriptomic analysis revealed Prevotella as a potential biomarker of oropharyngeal microbiomes in SARS-CoV-2 infection.

Background and objectives: Disease severity and prognosis of coronavirus disease 2019 (COVID-19) disease with other viral infections can be affected by the oropharyngeal microbiome. However, limited research had been carried out to uncover how these diseases are differentially affected by the oropharyngeal microbiome of the patient. Here, we aimed to explore the characteristics of the oropharyngeal microbiota of COVID-19 patients and compare them with those of patients with similar symptoms. Methods: COVID-19 was diagnosed in patients through the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by quantitative reverse transcription polymerase chain reaction (RT-qPCR). Characterization of the oropharyngeal microbiome was performed by metatranscriptomic sequencing analyses of oropharyngeal swab specimens from 144 COVID-19 patients, 100 patients infected with other viruses, and 40 healthy volunteers. Results: The oropharyngeal microbiome diversity in patients with SARS-CoV-2 infection was different from that of patients with other infections. Prevotella and Aspergillus could play a role in the differentiation between patients with SARS-CoV-2 infection and patients with other infections. Prevotella could also influence the prognosis of COVID-19 through a mechanism that potentially involved the sphingolipid metabolism regulation pathway. Conclusion: The oropharyngeal microbiome characterization was different between SARS-CoV-2 infection and infections caused by other viruses. Prevotella could act as a biomarker for COVID-19 diagnosis and of host immune response evaluation in SARS-CoV-2 infection. In addition, the cross-talk among Prevotella, SARS-CoV-2, and sphingolipid metabolism pathways could provide a basis for the precise diagnosis, prevention, control, and treatment of COVID-19.

Nanoscopic evaluation on mitochondrial ultrastructures by regulating reactive oxygen species productivity within terpyridyl Zn(II) complexes with different alkyl chain lengths.

Mitochondria targeting complexes are widely utilized as photosensitizers in photodynamic therapy. However, the mechanisms by which they regulate reactive oxygen species (ROS) production at the molecular level and their influence on intracellular mitochondrial signaling and ultrastructures remain rarely studied. Herein, we present two terpyridyl Zn(II) complexes with different side alkyl chain lengths (Zn-2C and Zn-6C) that lead to low and high ROS productivities in vitro, respectively. Both complexes could enter live cells effectively with minimal dark toxicity and accumulate preferably in the mitochondria. We also demonstrated that Zn-6C, with more efficient ROS productivity, could significantly downregulate the caspase signaling pathway but showed no evident influence on mitochondrial membrane proteins. We also highlighted and compared the mitochondrial ultrastructural variations during such a process by stimulated emission depletion (STED) super-resolution nanoscopy.

Skin bacterial richness and diversity in intensive care unit patients with severe pneumonia.

OBJECTIVES: Patients with severe pneumonia admitted to the intensive care unit (ICU) have a high risk of mortality, and the microbiome is likely to affect the outcome of such patients. However, the composition of the skin microbiota of ICU patients with severe pneumonia remains unclear. In this study, on the basis of 16S ribosomal ribonucleic acid sequencing, we explored the difference in skin bacterial richness and diversity between the ICU patient group (PG) with severe pneumonia and the healthy control group (CG). METHODS: The diversity index and taxonomic distribution of skin bacteria were analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) bioinformatics pipeline. Blood, endotracheal aspirate, and bronchoalveolar lavage fluid samples were collected from the same PG subjects for culture. RESULTS: Compared with the CG, the diversity of skin bacteria in the PG decreased significantly. Staphylococcus, Acinetobacter, Stenotrophomonas, Enterococcus, Halomonas, and Brevibacillus were differentially abundant in the PG, and most of these bacteria were also identified in the cultures of upper respiratory tract samples of the same PG. CONCLUSION: We provide evidence that healthcare-associated infection in ICU patients with severe pneumonia is strongly associated with skin microbiota, which necessitates the prevention and control of skin bacterial pathogens for these patients.

Prognostic Value of Albumin-to-Alkaline Phosphatase Ratio for EGFR-Mutated Advanced Non-Small-Cell Lung Cancer Patients Treated with First-Line EGFR-TKIs: A Large Population-Based Study and Literature Review.

Background: Resistance inevitably develops in epidermal growth factor receptor (EGFR)-mutated advanced non-small-cell lung cancer (NSCLC) patients after treatment of EGFR tyrosine kinase inhibitors (EGFR-TKIs). The albumin-to-alkaline phosphatase ratio (AAPR), a novel index, has been reported to be associated with survival in various cancers. In this study, we explored the prognostic value of AAPR in EGFR-mutated advanced NSCLC patients treated with first-line EGFR-TKIs. Methods: The clinical and pretreatment laboratory data were retrospectively extracted from hospital medical system. The Log-rank and Kaplan-Meier analyses were adopted to detect differences in survival between groups. Univariate and multivariate Cox's proportional hazard regression models were applied to assess the prognostic value of AAPR for progression-free survival (PFS) and overall survival (OS). Results: Totally, 598 EGFR-mutated NSCLC patients with stage IIIB-IV were enrolled into this study. The median age of all patients was 60 years, and 56.9% were women. About 97% patients had common EGFR gene mutations of deletions in exon 19 (19 del) or a point mutation in exon 21 (L858R). Using receiver operating characteristic (ROC) curve analysis and the Youden index, the optimal cut-off value of pretreatment AAPR was 0.47. Patients with high AAPR achieved longer median PFS and OS than patients with low AAPR (14.0 months vs 10.4 months, P<0.01; 58.2 months vs 36.7 months, P<0.001, respectively). The multivariate analysis by Cox's proportional hazards regression model demonstrated that AAPR was an independent prognostic factor for both PFS (HR: 0.813, 95% CI: 0.673-0.984, P=0.033) and OS (HR: 0.629, 95% CI: 0.476-0.830, P=0.001). Conclusion: Pretreatment AAPR, measured as part of routine blood biochemical test, may be a reliable prognostic indicator in EGFR-mutated advanced NSCLC patients treated with first-line first-generation EGFR-TKIs.

Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study.

BACKGROUND: Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFR mutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFR genotype and prognosis with EGFR-TKI treatment. METHODS: We included 18 232 patients with lung cancer with CT imaging and EGFR gene sequencing from nine cohorts in China and the USA, including a prospective cohort in an Asian population (n=891) and The Cancer Imaging Archive cohort in a White population. These cohorts were divided into thick CT group and thin CT group. The FAIS was built for predicting EGFR genotype and progression-free survival of patients receiving EGFR-TKIs, and it was evaluated by area under the curve (AUC) and Kaplan-Meier analysis. We further built two tumour-based deep learning models as comparison with the FAIS, and we explored the value of combining FAIS and clinical factors (the FAIS-C model). Additionally, we included 891 patients with 56-panel next-generation sequencing and 87 patients with RNA sequencing data to explore the biological mechanisms of FAIS. FINDINGS: FAIS achieved AUCs ranging from 0·748 to 0·813 in the six retrospective and prospective testing cohorts, outperforming the commonly used tumour-based deep learning model. Genotype predicted by the FAIS-C model was significantly associated with prognosis to EGFR-TKIs treatment (log-rank p<0·05), an important complement to gene sequencing. Moreover, we found 29 prognostic deep learning features in FAIS that were able to identify patients with an EGFR mutation at high risk of TKI resistance. These features showed strong associations with multiple genotypes (p<0·05, t test or Wilcoxon test) and gene pathways linked to drug resistance and cancer progression mechanisms. INTERPRETATION: FAIS provides a non-invasive method to detect EGFR genotype and identify patients with an EGFR mutation at high risk of TKI resistance. The superior performance of FAIS over tumour-based deep learning methods suggests that genotype and prognostic information could be obtained from the whole lung instead of only tumour tissues. FUNDING: National Natural Science Foundation of China.

Development of a deep learning-based method to diagnose pulmonary ground-glass nodules by sequential computed tomography imaging.

BACKGROUND: Early identification of the malignant propensity of pulmonary ground-glass nodules (GGNs) can relieve the pressure from tracking lesions and personalized treatment adaptation. The purpose of this study was to develop a deep learning-based method using sequential computed tomography (CT) imaging for diagnosing pulmonary GGNs. METHODS: This diagnostic study retrospectively enrolled 762 patients with GGNs from West China Hospital of Sichuan University between July 2009 and March 2019. All patients underwent surgical resection and at least two consecutive time-point CT scans. We developed a deep learning-based method to identify GGNs using sequential CT imaging on a training set consisting of 1524 CT sections from 508 patients and then evaluated 256 patients in the testing set. Afterwards, an observer study was conducted to compare the diagnostic performance between the deep learning model and two trained radiologists in the testing set. We further performed stratified analysis to further relieve the impact of histological types, nodule size, time interval between two CTs, and the component of GGNs. Receiver operating characteristic (ROC) analysis was used to assess the performance of all models. RESULTS: The deep learning model that used integrated DL-features from initial and follow-up CT images yielded the best diagnostic performance, with an area under the curve of 0.841. The observer study showed that the accuracies for the deep learning model, junior radiologist, and senior radiologist were 77.17%, 66.89%, and 77.03%, respectively. Stratified analyses showed that the deep learning model and radiologists exhibited higher performance in the subgroup of nodule sizes larger than 10 mm. With a longer time interval between two CTs, the deep learning model yielded higher diagnostic accuracy, but no general rules were yielded for radiologists. Different densities of components did not affect the performance of the deep learning model. In contrast, the radiologists were affected by the nodule component. CONCLUSIONS: Deep learning can achieve diagnostic performance on par with or better than radiologists in identifying pulmonary GGNs.

A Rationale for Drug Design Provided by Co-Crystal Structure of IC261 in Complex with Tubulin.

Microtubules composed of α/ß tubulin heterodimers are an essential part of the cytoskeleton of eukaryotic cells and are widely regarded as targets for cancer chemotherapy. IC261, which is discovered as an ATP-competitive inhibitor of serine/threonine-specific casein kinase 1 (CK1), has shown its inhibitory activity on microtubule polymerization in recent studies. However, the structural information of the interaction between tubulin and IC261 is still unclear. Here, we provided a high-resolution (2.85 Å) crystal structure of tubulin and IC261 complex, revealed the intermolecular interaction between tubulin and IC261, and analyzed the structure-activity relationship (SAR). Subsequently, the structure of tubulin-IC261 complex was compared with tubulin-colchicine complex to further elucidate the novelty of IC261. Furthermore, eight optimal candidate compounds of new IC261-based microtubule inhibitors were obtained through molecular docking studies. In conclusion, the co-crystal structure of tubulin-IC261 complex paves a way for the design and development of microtubule inhibitor drugs.

Structural insights into targeting of the colchicine binding site by ELR510444 and parbendazole to achieve rational drug design.

Microtubules consisting of α- and ß-tubulin heterodimers have proven to be an efficient drug target for cancer therapy. A broad range of agents, including ELR510444 and parbendazole, can bind to tubulin and interfere with microtubule assembly. ELR510444 and parbendazole are colchicine binding site inhibitors with antiproliferative activities. However, the lack of structural information on the tubulin-ELR510444/parbendazole complex has hindered the design and development of more potent drugs with similar scaffolds. Therefore, we report the crystal structures of tubulin complexed with ELR510444 at a resolution of 3.1 Å and with parbendazole at 2.4 Å. The structure of these complexes revealed the intermolecular interactions between the two colchicine binding site inhibitors and tubulin, thus providing a rationale for the development of novel benzsulfamide and benzimidazole derivatives targeting the colchicine binding site.

Immunotherapy in non-small cell lung cancer: rationale, recent advances and future perspectives.

Lung cancer, with non-small cell lung cancer (NSCLC) being the major type, is the second most common malignancy and the leading cause of cancer-related death globally. Immunotherapy, represented by immune checkpoint inhibitors (ICIs), has been one of the greatest advances in recent years for the treatment of solid tumors including NSCLC. However, not all NSCLC patients experience an effective response to immunotherapy with the established selection criteria of programmed death ligand 1 (PD-L1) and tumor mutational burden (TMB). Furthermore, a considerable proportion of patients experience unconventional responses, including pseudoprogression or hyperprogressive disease (HPD), immune-related toxicities, and primary or acquired resistance during the immunotherapy process. To better understand the immune response in NSCLC and provide reference for clinical decision-making, we herein review the rationale and recent advances in using immunotherapy to treat NSCLC. Moreover, we discuss the current challenges and future strategies of this approach to improve its efficacy and safety in treating NSCLC.