Drosophila models used to simulate human ATP1A1 gene mutations that cause Charcot-Marie-Tooth type 2 disease and refractory seizures.

JOURNAL/nrgr/04.03/01300535-202501000-00034/figure1/v/2024-05-14T021156Z/r/image-tiff Certain amino acids changes in the human Na+/K+-ATPase pump, ATPase Na+/K+ transporting subunit alpha 1 (ATP1A1), cause Charcot-Marie-Tooth disease type 2 (CMT2) disease and refractory seizures. To develop in vivo models to study the role of Na+/K+-ATPase in these diseases, we modified the Drosophila gene homolog, Atpα, to mimic the human ATP1A1 gene mutations that cause CMT2. Mutations located within the helical linker region of human ATP1A1 (I592T, A597T, P600T, and D601F) were simultaneously introduced into endogenous DrosophilaAtpα by CRISPR/Cas9-mediated genome editing, generating the AtpαTTTF model. In addition, the same strategy was used to generate the corresponding single point mutations in flies (AtpαI571T, AtpαA576T, AtpαP579T, and AtpαD580F). Moreover, a deletion mutation (Atpαmut) that causes premature termination of translation was generated as a positive control. Of these alleles, we found two that could be maintained as homozygotes (AtpαI571T and AtpαP579T). Three alleles (AtpαA576T, AtpαP579 and AtpαD580F) can form heterozygotes with the Atpαmut allele. We found that the Atpα allele carrying these CMT2-associated mutations showed differential phenotypes in Drosophila. Flies heterozygous for AtpαTTTF mutations have motor performance defects, a reduced lifespan, seizures, and an abnormal neuronal morphology. These Drosophila models will provide a new platform for studying the function and regulation of the sodium-potassium pump.

Differential gene expression and immune cell infiltration in maedi-visna virus-infected lung tissues.

BACKGROUND: Maedi-visna virus (MVV) is a lentivirus that infects monocyte/macrophage lineage cells in sheep, goats, and wild ruminants and causes pneumonia, mastitis, arthritis, and encephalitis. The immune response to MVV infection is complex, and a complete understanding of its infection and pathogenesis is lacking. This study investigated the in vivo transcriptomic patterns of lung tissues in sheep exposed to MVV using the RNA sequencing technology. RESULT: The results indicated that 2,739 genes were significantly differentially expressed, with 1,643 downregulated genes and 1,096 upregulated genes. Many variables that could be unique to MVV infections were discovered. Gene Ontology analysis revealed that a significant proportion of genes was enriched in terms directly related to the immune system and biological responses to viral infections. Kyoto Encyclopedia of Genes and Genomes analysis revealed that the most enriched pathways were related to virus-host cell interactions and inflammatory responses. Numerous immune-related genes, including those encoding several cytokines and interferon regulatory factors, were identified in the protein-protein interaction network of differentially expressed genes (DEGs). The expression of DEGs was evaluated using real-time polymerase chain reaction and western blot analysis. CXCL13, CXCL6, CXCL11, CCR1, CXCL8, CXCL9, CXCL10, TNFSF8, TNFRSF8, IL7R, IFN-γ, CCL2, and MMP9 were upregulated. Immunohistochemical analysis was performed to identify the types of immune cells that infiltrated MVV-infected tissues. B cells, CD4+ and CD8+ T cells, and macrophages were the most prevalent immune cells correlated with MVV infection in the lungs. CONCLUSION: Overall, the findings of this study provide a comprehensive understanding of the in vivo host response to MVV infection and offer new perspectives on the gene regulatory networks that underlie pathogenesis in natural hosts.

Identification of macrophage-related genes correlated with prognosis and immunotherapy efficacy in non-small cell lung cancer.

Background: Malignant tumours, particularly non-small cell lung cancer (NSCLC), pose a significant threat to human health due to their prevalence and lethality. Treatment methods for NSCLC vary greatly among individuals, making it crucial to identify predictive markers. Moreover, during tumour initiation and progression, tumour cells can release signaling molecules to induce polarization of macrophages towards a more tumour friendly M2 phenotype, which can promote tumour growth, metastasis, and drug resistance. Methods: We employed a comprehensive approach, combining bulk RNA-seq and single-cell sequencing analysis. Results: In our study, we used bulk RNA-seq and single-cell sequencing methods to analyze differential cells in NSCLC and adjacent tissues, searching for relevant marker genes that can predict prognosis and drug efficacy. We scrutinized biological phenomena such as macrophage-related gene methylation, copy number variation, and alternative splicing. Additionally, we utilized a co-culture technique of immune and tumour cells to explore the role of these genes in macrophage polarization. Our findings revealed distinct differences in macrophages between cancerous and adjacent tissues. We identified ANP32A, CCL20, ERAP2, MYD88, TMEM126B, TUBB6, and ZNF655 as macrophage-related genes that correlate with NSCLC patient prognosis and immunotherapy efficacy. Notably, ERAP2, TUBB6, CCL20, and TMEM126B can induce macrophage M0 to M2 polarization, promoting tumour proliferation. Conclusion: These findings significantly contribute to our understanding of the NSCLC tumour immune microenvironment. They pave the way for further research into the potential of these genes as targets for regulating tumour occurrence and development.

Pharmacokinetics and apparent Michaelis constant for metabolite conversion of sorafenib in healthy and hepatocellular carcinoma-bearing rats.

Aim: Investigation of the pharmacokinetics of sorafenib (SRF) in rats with hepatocellular carcinoma (HCC). Methods: A reproducible ultra-HPLC-MS method for simultaneous determination of serum SRF, N-hydroxymethyl sorafenib and N-demethylation sorafenib. Results: Both the maximum serum concentrations (2.5-times) and the area under the serum concentration-time curve from 0 h to infinity (4.5-times) of SRF were observed to be significantly higher, with a greater than 3.0-fold decrease in the clearance rate in the HCC-bearing rats compared with these values in healthy animals. Further study revealed approximately 3.8- and 3.2-times increases in the apparent Michaelis constant for N-hydroxymethyl sorafenib and N-demethylation sorafenib conversions in the HCC-bearing rats. Conclusion: The low efficiency for the SRF conversions was a key contributor to the increased serum concentrations of SRF.

SAA suppresses α-PD-1 induced anti-tumor immunity by driving TH2 polarization in lung adenocarcinoma.

Cancer stem cells (CSCs) are believed to be crucial in the initiation, progression, and recurrence of cancer. CSCs are also known to be more resistant to cancer treatments. However, the interaction between CSCs and the immune microenvironment is complex and not fully understood. In current study we used single cell RNA sequence (scRNA-Seq, public dataset) technology to identify the characteristic of CSCs. We found that the lung adenocarcinoma cancer stem population is highly inflammatory and remodels the tumor microenvironment by secreting inflammatory factors, specifically the acute phase protein serum amyloid A (SAA). Next, we developed an ex-vivo autologous patient-derived organoids (PDOs) and peripheral blood mononuclear cells (PBMCs) co-culture model to evaluate the immune biological impact of SAA. We found that SAA not only promotes chemoresistance by inducing cancer stem transformation, but also restricts anti-tumor immunity and promotes tumor fibrosis by driving type 2 immunity, and α-SAA neutralization antibody could restrict treatment resistant and tumor fibrosis. Mechanically, we found that the malignant phenotype induced by SAA is dependent on P2X7 receptor. Our data indicate that cancer stem cells secreted SAA have significant biological impact to promote treatment resistant and tumor fibrosis by driving cancer stemness transformation and type 2 immunity polarization via P2X7 receptor. Notably, α-SAA neutralization antibody shows therapeutic potential by restricting these malignant phenotypes.

Differences in the pharmacokinetics and steady-state blood concentrations of orally administered lenvatinib in adult and juvenile rats.

Objective: The aim of this study was to compare the pharmacokinetics and steady-state serum concentrations of lenvatinib in adult and juvenile rats. Experimental study: An ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) method was developed to quantify lenvatinib in the serum and liver of rats. Six juvenile and six adult rats in each group were orally administered with a single dose of 7.0 mg/kg lenvatinib suspension for pharmacokinetics. Another 12 juvenile and adult rats were subjected to oral gavage with 7.0 mg/kg lenvatinib once daily for 5 days. Biofluild samples were pre-treated by protein precipitation and sorafenib was used as the internal standard for UPLC-MS analysis. The pharmacokinetic parameters were estimated by compartment and statistical model. The mRNA expression of CYP3A2 and SLC22A1 in liver of adult and juvenile rats was measured by real-time fluorescence quantitative PCR (RT-qPCR). Results: The UPLC-MS method met the requirements for quantitative analysis of lenvatinib in serum and liver. The pharmacokinetic results showed that the mean retention time (MRT(0-∞)) was 19.64 ± 7.64 h and 126.38 ± 130.18 h, with AUC(0-∞) values of 3.97 ± 0.73 µg‧mL-1 h and 5.95 ± 2.27 µg mL-1 h in adult and juvenile rats, respectively. When comparing adult rats (0.35 ± 0.15 µg/mL) to juvenile rats, no significant differences were observed in steady-state serum lenvatinib (0.32 ± 0.11 µg/mL), but a noteworthy decrease to one-third of steady-state liver lenvatinib was observed after multiple oral doses of lenvatinib in juvenile rats. Additional findings revealed that the mRNA expression of CYP3A2 and SLC22A1 was notably increased by 6.86 and 14.67 times, respectively, in juvenile rats compared to adult rats. Conclusion: Juvenile rats exhibit lower levels of lenvatinib in the liver's steady-state, potentially due to the disparity in CYP3A2 mRNA expression. These results imply that the dosage of lenvatinib for pediatric patients may need to be augmented in order to attain the desired clinical outcome.

Identification of potential serum biomarkers for congenital heart disease children with pulmonary arterial hypertension by metabonomics.

BACKGROUND: Pulmonary arterial hypertension is a common complication in patients with congenital heart disease. In the absence of early diagnosis and treatment, pediatric patients with PAH has a poor survival rate. Here, we explore serum biomarkers for distinguishing children with pulmonary arterial hypertension associated with congenital heart disease (PAH-CHD) from CHD. METHODS: Samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and 22 metabolites were further quantified by ultra-high-performance liquid chromatography-tandem mass spectroscopy. RESULTS: Serum levels of betaine, choline, S-Adenosyl methionine (SAM), acetylcholine, xanthosine, guanosine, inosine and guanine were significantly altered between CHD and PAH-CHD. Logistic regression analysis showed that combination of serum SAM, guanine and N-terminal pro-brain natriuretic peptide (NT-proBNP), yielded the predictive accuracy of 157 cases was 92.70% with area under the curve of the receiver operating characteristic curve value of 0.9455. CONCLUSION: We demonstrated that a panel of serum SAM, guanine and NT-proBNP is potential serum biomarkers for screening PAH-CHD from CHD.

Metabolomics strategy of Pikang oral liquid in the treatment of psoriasis.

This study aimed to investigate the effect of Pikang oral liquid (PK) on psoriasis and analyze its possible mechanism from the perspective of metabolism. A psoriasis-form mouse model established using imiquimod (IMQ) was used to evaluate the anti-psoriatic effects of PK. The serum samples were analyzed by high-resolution nuclear magnetic resonance (1 H NMR)-based metabonomics. Nine amino acids were further quantitatively analyzed by ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC/Q-TOF MS). This study suggested that PK treatment markedly attenuated IMQ-induced psoriasis in a dose-dependent manner. 1 H NMR-based multivariate trajectory analysis revealed that PK had a certain regression effect on eight differential metabolites. The quantitative results showed that PK could significantly regulate the serum levels of alanine, histidine and arginine to healthy control levels. The anti-psoriasis mechanism of PK may be associated with the restoration of the disturbance in the amino acid metabolism, energy metabolism and lipid metabolism and so on. Quantitative results further confirmed that amino acid metabolism play an key role in the pathogenesis of psoriasis. Our investigation provided a holistic view of PK for intervention psoriasis and provided the scientific information in vivo about a clinical value of PK for psoriasis.

Peripheral blood lymphocytes differentiation patterns in responses / outcomes to immune checkpoint blockade therapies in non-small cell lung cancer: a retrospective study.

OBJECTIVES: Programmed Cell Death-1/ Programmed Death-ligand 1 (PD-1 / PD-L1) inhibitor therapies targeting immunocytes induce persistent tumor remission in various cancers. However, the appropriate biomarkers for the therapeutic efficacy of PD-L1 and PD-1 blockade remain elusive. MATERIALS AND METHODS: For a comprehensive analysis of peri-treatment lymphocyte differentiation, in the current study, we enrolled 146 non-small cell lung cancer patients who received α-PD-1 therapies for exploring the peripheral blood lymphocyte differentiation pattern at baseline and post-treatment (dynamic changes) by flow cytometry. RESULTS: At baseline, CD4+ / CD8+ T cell ratio predicts good responses and outcomes, but activated T cell and cytotoxic T cell counts predict poor responses and outcomes. And for dynamic changes, after 6 weeks of immune checkpoint blockade (ICB) treatment, compared with baseline level, the elevation of total T and B cell counts indicate poor responses, and total T and TH cell counts indicate poor prognosis while activated T cell predicts good prognosis. And after 12 weeks, elevated total lymphocyte, cytotoxic T cell counts, and decreased total T cell counts and CD4+ / CD8+ T cell ratio predict good responses / outcomes. Our clinical predicting model shows good performance in predicting ICB treatment responses / outcomes. CONCLUSION: Patients with favorable clinical responses / outcomes have distinctive peripheral blood immunocyte differentiation characteristics, indicating the potential of utilizing the peripheral immunocyte differentiation patterns for predicting ICB responses / outcomes.

[Development and Application of the First Carbon Ion Therapy System in China].

At present, heavy ion is an ideal radiation for cancer treatment, and carbon ion is used in the treatment of many kinds of cancer due to its higher relative biological effect value. In 2019, Wuwei heavy ion center built the first medical heavy ion accelerator-carbon ion radiotherapy system in China, and obtained the registration license from the National Medical Products Administration, and officially received cancer patients in March 2020. This study introduced the development and application of the first carbon ion radiotherapy system in China.

Association between changes in thioredoxin reductase and other peripheral blood biomarkers with response to PD-1 inhibitor-based combination immunotherapy in non-small cell lung cancer: a retrospective study.

Background: Immunotherapy deeply changed the treatment paradigm of advanced non-small cell lung cancer (NSCLC) in the past years. However, the objective response rate (ORR) after immunotherapy is about 20-30% of NSCLC patients. Therefore, identification of predictive biomarkers is crucial for selecting patients with NSCLC who would most benefit from programmed cell death receptor protein 1 (PD-1) inhibitor-based immunotherapy. Methods: We retrospectively collected medical records and thioredoxin reductase (TrxR) data from 90 patients with a NSCLC who received PD-1 inhibitor-based combination therapy. Serum biomarkers were also measured at 6- and 12-week post-treatment and compared with their baseline values. Associations between changes in serum biomarkers, clinical characteristics and treatment efficacy were evaluated using univariate tests. The patients who were still alive were followed up remotely by phone or email to assess survival. The association between serum biomarkers and TrxR with overall survival (OS) and progression-free survival (PFS) were assessed by univariate and multivariate Cox proportional hazard regression. Nomogram prediction models were constructed using factors associated with PFS and OS, respectively. Results: The median follow-up time among the 90 patients was 19.7 (range, 13.6 to 25.8) months. Median PFS and OS were 13.6 [95% confidence interval (CI): 13.5 to 13.7] and 19.7 (95% CI: 13.6 to 25.8) months, respectively. Patients with decreased carcinoembryonic antigen (CEA), albumin (Alb), and TrxR values at 6- and 12-week post-treatment compared to baseline had statistically significantly improved disease remission rates (P<0.05). Patients with decreased white blood cell (WBC), neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR) at week 6, and decreased Alb, CEA, and lymphocyte-to-monocyte ratio (LMR) at week 12 had statistically significantly increased ORRs (P<0.05). According to the univariate and multivariate Cox regression analyses, we included adenocarcinoma, Eastern Cooperative Oncology Group performance status (ECOG PS), and CEA change at week 6 post-treatment as predictors for PFS, and adenocarcinoma, change in absolute lymphocyte count (ALC), and TrxR at week 6 as predictors for OS in the nomogram models. Each nomogram was also validated internally using a bootstrap method with 1,000 resamples. Conclusions: Change in TrxR at 6 weeks post-treatment in combination with other clinical and hematological biomarkers could be used as a predictor for treatment outcome and prognosis in NSCLC patients after PD-1 inhibitor-based combination immunotherapy.

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO.

BACKGROUND: Lung cancer is among the major diseases threatening human health. Although the immune response plays an important role in tumor development, its exact mechanisms are unclear. MATERIALS AND METHODS: Here, we used CIBERSORT and ESTIMATE algorithms to determine the proportion of tumor-infiltrating immune cells (TICs) as well as the number of immune and mesenchymal components from the data of 474 lung cancer patients from the Gene Expression Omnibus database. And we used data from The Cancer Genome Atlas database (TCGA) for validation. RESULTS: We observed that immune, stromal, and assessment scores were only somewhat related to survival with no statistically significant differences. Further investigations revealed these scores to be associated with different pathology types. GO and KEGG analyses of differentially expressed genes revealed that they were strongly associated with immunity in lung cancer. In order to determine whether the signaling pathways identified by GO and KEGG signaling pathway enrichment analyses were up- or down-regulated, we performed a gene set enrichment analysis using the entire matrix of differentially expressed genes. We found that signaling pathways involved in hallmark allograft rejection, hallmark apical junction, hallmark interferon gamma response, the hallmark P53 pathway, and the hallmark TNF-α signaling via NF-ĸB were up-regulated in the high-ESTIMATE-score group. CIBERSORT analysis for the proportion of TICs revealed that different immune cells were positively correlated with the ESTIMATE score. Cox regression analysis of the differentially expressed genes revealed that CPA3, C15orf48, FCGR1B, and GNG4 were associated with patient prognosis. A prognostic model was constructed wherein patients with high-risk scores had a worse prognosis (p < 0.001 using the log-rank test). The Area Under Curve (AUC)value for the risk model in predicting the survival was 0.666. The validation set C index was 0.631 (95% CI: 0.580-0.652). The AUC for the risk formula in the validation set was 0.560 that confirmed predictivity of the signature. CONCLUSION: We found that immune-related gene expression models could predict patient prognosis. Moreover, high- and low-ESTIMATE-score groups had different types of immune cell infiltration.

Elevated Serum Uric Acid is Associated With Poor Survival in Advanced HCC Patients and Febuxostat Improves Prognosis in HCC Rats.

Objective: Serum uric acid is associated with tumor progression and hepatocarcinogenesis. Here, we aimed to determine whether serum uric acid is related to the survival time of patients with hepatocellular carcinoma (HCC) and whether the inhibition of uric acid production affects the progression and survival of rats with HCC. Methods: The follow-up data of 288 patients with advanced HCC were analyzed. Ten purine metabolites in serum and liver samples of diethylnitrosamine (DEN)-induced HCC rats were quantitatively determined by an established UPLC-MS/MS method. On this basis, febuxostat, a specific inhibitor of xanthine oxidase (XOD), was used to interfere with HCC rats. Results: The serum uric acid level of HCC patients was significantly negatively correlated with survival days (r = -0.155). The median survival time was 133.5 days in the high uric acid group (>360 µmol/L, n = 80) and 176.0 days in the normal serum uric acid group (<360 µmol/L, n = 208, p = 0.0013). The levels of hypoxanthine, guanine, and uric acid; XOD activity; and xanthine dehydrogenase mRNA expression in the serum or liver samples of HCC rats were significantly upregulated compared with those in the control group. After febuxostat intervention in DEN-induced HCC rats, the number of atypical cells and inflammatory cells decreased significantly; the serum alpha fetoprotein level and Fisher's ratio tended to return to normal; the median survival time increased from 36 to 96 days (p = 0.08). In addition, serum malondialdehyde, superoxide dismutase, and glutathione activity nearly returned to the level of the healthy control group. Conclusion: The elevation of serum uric acid implies a risk of poor survival in advanced HCC patients and Febuxostat can reduce the generation of reactive oxygen species, thereby playing a role in delaying the progression of liver cancer.

High-Salt Diet Impairs the Neurons Plasticity and the Neurotransmitters-Related Biological Processes.

Salt, commonly known as sodium chloride, is an important ingredient that the body requires in relatively minute quantities. However, consuming too much salt can lead to high blood pressure, heart disease and even disruption of circadian rhythms. The biological process of the circadian rhythm was first studied in Drosophila melanogaster and is well understood. Their locomotor activity gradually increases before the light is switched on and off, a phenomenon called anticipation. In a previous study, we showed that a high-salt diet (HSD) impairs morning anticipation behavior in Drosophila. Here, we found that HSD did not significantly disrupt clock gene oscillation in the heads of flies, nor did it disrupt PERIOD protein oscillation in clock neurons or peripheral tissues. Remarkably, we found that HSD impairs neuronal plasticity in the axonal projections of circadian pacemaker neurons. Interestingly, we showed that increased excitability in PDF neurons mimics HSD, which causes morning anticipation impairment. Moreover, we found that HSD significantly disrupts neurotransmitter-related biological processes in the brain. Taken together, our data show that an HSD affects the multiple functions of neurons and impairs physiological behaviors.

[Peripheral Blood Inflammation Indicators as Predictive Indicators in 
Immunotherapy of Advanced Non-small Cell Lung Cancer].

BACKGROUND: Lung cancer is the leading cause of cancer-related death, of which non-small cell lung cancer (NSCLC) is the most common type. Immune checkpoint inhibitors (ICIs) have now become one of the main treatments for advanced NSCLC. This paper retrospectively investigated the effect of peripheral blood inflammatory indexes on the efficacy of immunotherapy and survival of patients with advanced non-small cell lung cancer, in order to find strategies to guide immunotherapy in NSCLC. METHODS: Patients with advanced non-small cell lung cancer who were hospitalized in The Affiliated Cancer Hospital of Nanjing Medical University from October 2018 to August 2019 were selected to receive anti-PD-1 (pembrolizumab, sintilimab or toripalimab) monotherapy or combination regimens. And were followed up until 10 December 2020, and the efficacy was evaluated according to RECIST1.1 criteria. Progression-free survival (PFS) and overall survival (OS) were followed up for survival analysis. A clinical prediction model was constructed to analyze the predictive value of neutrophil-to-lymphocyte ratio (NLR) based on NLR data at three different time points: before treatment, 6 weeks after treatment and 12 weeks after treatment (0w, 6w and 12w), and the accuracy of the model was verified. RESULTS: 173 patients were finally included, all of whom received the above treatment regimen, were followed up for a median of 19.7 months. The objective response rate (ORR) was 27.7% (48/173), the disease control rate (DCR) was 89.6% (155/173), the median PFS was 8.3 months (7.491-9.109) and the median OS was 15.5 months (14.087-16.913). The chi-square test and logistic multi-factor analysis showed that NLR6w was associated with ORR and NLR12w was associated with ORR and DCR. Further Cox regression analysis showed that NLR6w and NLR12w affected PFS and NLR0w, NLR6w and NLR12w were associated with OS. CONCLUSIONS: In patients with advanced non-small cell lung cancer, NLR values at different time points are valid predictors of response to immunotherapy, and NLR <3 is often associated with a good prognosis.

Corrigendum: Association of Dynamic Changes in Peripheral Blood Indexes With Response to PD-1 Inhibitor-Based Combination Therapy and Survival Among Patients With Advanced Non-Small Cell Lung Cancer.

[This corrects the article DOI: 10.3389/fimmu.2021.672271.].

Association of Dynamic Changes in Peripheral Blood Indexes With Response to PD-1 Inhibitor-Based Combination Therapy and Survival Among Patients With Advanced Non-Small Cell Lung Cancer.

Background: PD-1 inhibitors have been routinely used in the treatment of advanced non-small cell lung cancer (NSCLC), and have demonstrated to significantly improve survivorship when combining with other conventional therapies, such as chemotherapy and anti-angiogenesis therapy. PD-L1 is the most commonly used biomarker to select benefiting groups, while not all patients with high PD-L1 expression benefit from immunotherapy. Therefore, identifying other prognostic and predictive biomarkers, including peripheral blood indexes, is essential. Methods: We retrospectively collected medical records and hematological data of 151 patients with advanced NSCLC treated with PD-1 inhibitor-based combination therapy in our hospital. The peripheral blood indexes of interest were NLR, PLR, PAR, Hb, LDH, CEA, and NSE. The association between peripheral blood indexes and treatment responses or survival outcomes was examined by multivariable logistic regression and Cox regression, respectively. Results: The decreased CEA at week 6 (OR = 4.209, 95%CI: 1.287-13.758) or 12 (OR = 7.267, 95%CI: 1.508-35.006) post-treatment was related to a higher disease control rate. The decrease or NLR at week 6 (OR = 3.081, 95%CI: 1.464-6.483) or 12 (OR = 3.304, 95%CI: 1.560-7.001) post-treatment, or CEA at week 12 post-treatment (OR = 2.469, 95%CI: 1.134-5.375), was associated with a higher objective response rate. Patients whose NLR (HR = 0.610, 95%CI: 0.411-0.907) or CEA (HR = 0.477, 95%CI: 0.320-0.710) decreased at week 6 post-treatment tended to have longer progression-free survival, and similar results were found in those with decreased NLR (HR = 0.587, 95%CI: 0.388-0.886) or CEA (HR = 0.406, 95%CI: 0.270-0.609) at week 12 post-treatment. Patients whose CEA (HR = 0.543, 95%CI: 0.339-0.871) or NSE (HR = 0.619, 95%CI: 0.386-0.994) decreased after 6 weeks post-treatment appeared to have longer overall survival, and the same was found for those whoseCEA (HR = 0.620, 95%CI: 0.390-0.986) or NSE (HR = 0.578, 95%CI: 0.353-0.947) was decreased at 12 weeks after treatment. Conclusion: Post-treatment NLR, CEA and NSE changes are suggestive indicators for the prognosis of NSCLC patients after immunotherapy.

A model for phenotyping crop fractional vegetation cover using imagery from unmanned aerial vehicles.

Fractional vegetation cover (FVC) is the key trait of interest for characterizing crop growth status in crop breeding and precision management. Accurate quantification of FVC among different breeding lines, cultivars, and growth environments is challenging, especially because of the large spatiotemporal variability in complex field conditions. This study presents an ensemble modeling strategy for phenotyping crop FVC from unmanned aerial vehicle (UAV)-based multispectral images by coupling the PROSAIL model with a gap probability model (PROSAIL-GP). Seven field experiments for four main crops were conducted, and canopy images were acquired using a UAV platform equipped with RGB and multispectral cameras. The PROSAIL-GP model successfully retrieved FVC in oilseed rape (Brassica napus L.) with coefficient of determination, root mean square error (RMSE), and relative RMSE (rRMSE) of 0.79, 0.09, and 18%, respectively. The robustness of the proposed method was further examined in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and cotton (Gossypium hirsutum L.), and a high accuracy of FVC retrieval was obtained, with rRMSEs of 12%, 6%, and 6%, respectively. Our findings suggest that the proposed method can efficiently retrieve crop FVC from UAV images at a high spatiotemporal domain, which should be a promising tool for precision crop breeding.

Combination of Tertiary Lymphoid Structure and Neutrophil-to-Lymphocyte Ratio Predicts Survival in Patients With Hepatocellular Carcinoma.

Background: Hepatocellular carcinoma (HCC) is the most common pathological type of primary liver cancer. The lack of prognosis indicators is one of the challenges in HCC. In this study, we investigated the combination of tertiary lymphoid structure (TLS) and several systemic inflammation parameters as a prognosis indicator for HCC. Materials and Methods: We retrospectively recruited 126 postoperative patients with primary HCC. The paraffin section was collected for TLS density assessment. In addition, we collected the systemic inflammation parameters from peripheral blood samples. We evaluated the prognostic values of those parameters on overall survival (OS) using Kaplan-Meier curves, univariate and multivariate Cox regression. Last, we plotted a nomogram to predict the survival of HCC patients. Results: We first found TLS density was positively correlated with HCC patients' survival (HR=0.16, 95% CI: 0.06 - 0.39, p < 0.0001), but the power of TLS density for survival prediction was found to be limited (AUC=0.776, 95% CI:0.772 - 0.806). Thus, we further introduced several systemic inflammation parameters for survival analysis, we found neutrophil-to-lymphocyte ratio (NLR) was positively associated with OS in univariate Cox regression analysis. However, the combination of TLS density and NLR better predicts patient's survival (AUC=0.800, 95% CI: 0.698-0.902, p < 0.001) compared with using any single indicator alone. Last, we incorporated TLS density, NLR, and other parameters into the nomogram to provide a reproducible approach for survival prediction in HCC clinical practice. Conclusion: The combination of TLS density and NLR was shown to be a good predictor of HCC patient survival. It also provides a novel direction for the evaluation of immunotherapies in HCC.

Discrimination of Chrysanthemum Varieties Using Hyperspectral Imaging Combined with a Deep Convolutional Neural Network.

Rapid and accurate discrimination of Chrysanthemum varieties is very important for producers, consumers and market regulators. The feasibility of using hyperspectral imaging combined with deep convolutional neural network (DCNN) algorithm to identify Chrysanthemum varieties was studied in this paper. Hyperspectral images in the spectral range of 874⁻1734 nm were collected for 11,038 samples of seven varieties. Principal component analysis (PCA) was introduced for qualitative analysis. Score images of the first five PCs were used to explore the differences between different varieties. Second derivative (2nd derivative) method was employed to select optimal wavelengths. Support vector machine (SVM), logistic regression (LR), and DCNN were used to construct discriminant models using full wavelengths and optimal wavelengths. The results showed that all models based on full wavelengths achieved better performance than those based on optimal wavelengths. DCNN based on full wavelengths obtained the best results with an accuracy close to 100% on both training set and testing set. This optimal model was utilized to visualize the classification results. The overall results indicated that hyperspectral imaging combined with DCNN was a very powerful tool for rapid and accurate discrimination of Chrysanthemum varieties. The proposed method exhibited important potential for developing an online Chrysanthemum evaluation system.