Long-term, 13-year survival after immune cell therapy combined with chemotherapy for extensive-stage small-cell lung cancer: a case report.

While the incidence of small-cell lung cancer is low, it has a poor prognosis. Patients with extensive small-cell lung cancer account for about 70% of all cases of small-cell lung cancer, with a median overall survival duration of 8-13 months and a 5-year overall survival rate of only 1%-5%. Herein, we report small-cell lung cancer diagnosed by bronchoscopic biopsy in an adult male patient in 2011. The patient had a clinical stage of cT2N2M1 and stage IV disease (i.e., extensive small-cell lung cancer). Still, he survived for 13 years through a combination of chemotherapy, radiotherapy, and cytokine-induced killer (CIK) immunocell thera. Comprehensive tumor markers, lymphocyte subsets, and lung CT images were obtained through long-term follow-up. After 12 cycles of chemotherapy (CE/IP regimen) and 5940cgy/33f radiotherapy, we found that the patient was in an immunosuppressive state, so the patient was given CIK cell therapy combined with chemotherapy. After 2 years of immunocell-combined chemotherapy, there were no significant changes in the primary lesion or other adverse events. In the 13 years since the patient's initial diagnosis, we monitored the changes in the patient's indicators such as CEA, NSE, CD4/CD8 ratio, and CD3+CD4+ lymphocytes, suggesting that these may be the factors worth evaluating regarding the patient's immune status and the effectiveness of combination therapy. In this case, CIK cell immunotherapy combined with chemotherapy was applied to control tumor progression. With a good prognosis, we concluded that CIK cell immunotherapy combined with chemotherapy can prolong patient survival in cases of extensive small-cell lung cancer, and the advantages of combined therapy are reflected in improving the body's immune capacity and enhancing the killing effect of immune cells.

Changes in the transformation of nitrogen and phosphorus under different microbial communities in sewage pipes.

Microbial communities living in different environments can affect the transformation of nitrogen and phosphorus in sewage pipes. Two different environments were simulated to investigate the differences in the transformation of nitrogen and phosphorus under different microbial communities in the pipe. Results showed that the concentration of nitrogen and phosphorus changed greatly in the first 25-33 days and the first 21 days, respectively, and then remained stable. The decrease in amino acid nitrogen (AAN) concentration and the increase in ammonia nitrogen (NH4 + -N) concentration in the sediments were evident in the contrast group. The concentrations of total phosphorus (TP), dissolved total phosphorus (DTP), and dissolved reactive phosphorus (DRP) in the overlying water and interstitial water decreased, and that of TP in the sediment increased. Some microorganisms in the sediments of both groups are related to the transformation of nitrogen and phosphorus, such as Clostridium_sensu_stricto_1, Sporacetigenium, Norank_f__Anaerolineaceae, Norank_f__norank_o__PeM15, and Caldisericum. The relative abundance of these microorganisms was remarkably differed between the two groups, which partly caused the difference in nitrogen and phosphorus transformation among overlying water, interstitial water, and sediment in the two environments. PRACTITIONER POINTS: The concentration of N and P changed greatly in the first 20-30 days. AAN and NH4 + -N in sediments had greater concentration variation in contrast group. In two groups, TP, DTP, and DRP of water decreased, and TP of sediment increased. Microbe related to the transformation of N and P differed between the two groups.

Effects of flow rate and wastewater concentration on the transformation of nitrogen in sediment-water system of sewage pipelines.

In this work, the transformation law of nitrogen in sediment-water system under different flow rates and wastewater concentrations were investigated in a simulated sewage pipeline system. Results showed that the different flow rates and wastewater concentrations in the pipeline caused differences in microbial community in sediments and nitrogen transformation. When the flow rate increased from 0.05 to 0.2 m/s, the scouring effect was enhanced, resulting in higher concentrations of NH4 + -N and NO3 - -N in the overlying water. At 0.2 m/s, the relative abundance of Clostridium_sensu_stricto_1 in sediments was higher, resulting in a greater conversion of amino acid nitrogen (AAN) to NH4 + -N. Meanwhile, many denitrifying bacteria (Trichococcus, Dechloromonas, norank_f__norank_o__Gaiellales, Thiobacillus) had high relative abundance in the sediments, and the denitrification process was common. When the wastewater concentration was high, the nitrification reaction was great in overlying and interstitial water. Moreover, the ammoniation process was great in the sediments, and the variation flux of AAN was large (remarkably reduced). PRACTITIONER POINTS: AAN transformed to NH4 + -N in sediment under different flow rate and concentration. Scouring was enhanced at 0.2 m/s, increasing nitrogen contents in overlying water. Difference in microbial community led to more AAN conversion to NH4 + -N at 0.2 m/s. The ammoniation process was greater in sediment at a high concentration of sewage. NH4 + -N migrated from overlying water to sediment at a high concentration of sewage.

3D printing of MOF-reinforced methacrylated gelatin scaffolds for bone regeneration.

Scaffolds based on gelatin (Gel) play a crucial role in bone tissue engineering. However, the low mechanical properties, rapid biodegradation rate, insufficient osteogenic activity and lacking anti-infective properties limit their applications in bone regeneration. Herein, the incorporation of ibuprofen (IBU)-loaded zeolitic imidazolate framework-8 (ZIF-8) in a methacrylated gelatin (GelMA) matrix was proposed as a simple and effective strategy to develop the IBU-ZIF-8@GelMA scaffolds for enhanced bone regeneration capacity. Results indicated that the IBU-loaded ZIF-8 nanoparticles with tiny particle sizes were uniformly distributed in the GelMA matrix of the IBU-ZIF-8@GelMA scaffolds, and the IBU-loaded ZIF-8 growing in the scaffolds enabled the controlled and sustained releasing of Zn2+ and IBU in pH = 5.5 over a long period for efficient bone repair and long-term anti-inflammatory activity. Furthermore, the doping of the IBU-loaded ZIF-8 nanoparticles efficiently enhanced the compression performance of the GelMA scaffolds. In vitro studies indicated that the prepared scaffolds presented no cytotoxicity to MC3T3-E1 cells and the released Zn2+ during the degradation of the scaffolds promoted MC3T3-E1 cell osteogenic differentiation. Thus, the drug-loaded ZIF-8 modified 3D printed GelMA scaffolds demonstrated great potential in treating bone defects.

Integrating somatic mutation profiles with structural deep clustering network for metabolic stratification in pancreatic cancer: a comprehensive analysis of prognostic and genomic landscapes.

Pancreatic cancer is a globally recognized highly aggressive malignancy, posing a significant threat to human health and characterized by pronounced heterogeneity. In recent years, researchers have uncovered that the development and progression of cancer are often attributed to the accumulation of somatic mutations within cells. However, cancer somatic mutation data exhibit characteristics such as high dimensionality and sparsity, which pose new challenges in utilizing these data effectively. In this study, we propagated the discrete somatic mutation data of pancreatic cancer through a network propagation model based on protein-protein interaction networks. This resulted in smoothed somatic mutation profile data that incorporate protein network information. Based on this smoothed mutation profile data, we obtained the activity levels of different metabolic pathways in pancreatic cancer patients. Subsequently, using the activity levels of various metabolic pathways in cancer patients, we employed a deep clustering algorithm to establish biologically and clinically relevant metabolic subtypes of pancreatic cancer. Our study holds scientific significance in classifying pancreatic cancer based on somatic mutation data and may provide a crucial theoretical basis for the diagnosis and immunotherapy of pancreatic cancer patients.

Structural deep clustering network for stratification of breast cancer patients through integration of somatic mutation profiles.

BACKGROUND AND OBJECTIVE: Breast cancer is among of the most malignant tumor that occurs in women and is one of the leading causes of death from gynecologic malignancy worldwide. The high degree of heterogeneity that characterizes breast cancer makes it challenging to devise effective therapeutic strategies. Accumulating evidence highlights the crucial role of stratifying breast cancer patients into clinically significant subtypes to achieve better prognoses and treatments. The structural deep clustering network is a graph convolutional network-based clustering algorithm that integrates structural information and has achieved state-of-the-art performance in various applications. METHODS: In this study, we employed structural deep clustering network to integrate somatic mutation profiles for stratifying 2526 breast cancer patients from the Memorial Sloan Kettering Cancer Center into two clinically differentiable subtypes. RESULTS: Breast cancer patients in cluster 1 exhibited better prognosis than breast cancer patients in cluster 2, and the difference between them was statistically significant. The immunogenomic landscape further demonstrated that cluster 1 was associated with remarkable infiltration of the tumor infiltrating lymphocytes. The clustering subtype could be used to evaluate the therapeutic benefit of immunotherapy and chemotherapy in breast cancer patients. Furthermore, our approach effectively classified patients from eight different cancer types, demonstrating its generalizability. CONCLUSIONS: Our study represents a step towards a generic methodology for classifying cancer patients using only somatic mutation data and structural deep clustering network approaches. Employing structural deep clustering network to identify breast cancer subtypes is promising and can inform the development of more accurate and personalized therapies.

Transformation of sulfur in the sediment-water system of the sewage pipeline under different hydraulic retention time.

Transformation of sulfur in sewage pipeline was affected by water flow, and the transformation laws at different locations in the sediment-water system were different. This work studied the changes of sulfur in sediments, sewage, and upper space of the sewage pipeline, analyzed the differences in microbial community under different hydraulic retention time (HRT) and depth, and focused on the transformation law of sulfur. Results showed that sulfate and sulfide concentrations in sewage were higher than those in sediments under anaerobic conditions. Moreover, sulfate and sulfide concentrations in sediments decreased with depth. When HRT decreased from 3 h to 1 h, H2S concentration increased evidently, whereas sulfate concentration decreased in the sewage and sediment, and sulfide concentration increased in sewage and surface sediment. Those differences were related to the relative abundances of the two microbial communities. The relative abundances of sulfate-reducing bacteria (SRB), such as Desulfobacter, Desulfovibrio, and Desulfomicrobium, were higher in surface sediment. Correspondingly, those of Thiobacillus, Bacillus, and other sulfur-oxidizing bacteria (SOB) and Smithella were higher in deep sediment. The decrease of HRT might worsen the mass transfer effect of dissolved oxygen, thereby increasing the production rate of sulfur and causing H2S to easily escape from sewage.

Integrated analysis of ovarian cancer patients from prospective transcription factor activity reveals subtypes of prognostic significance.

Transcription factors are protein molecules that act as regulators of gene expression. Aberrant protein activity of transcription factors can have a significant impact on tumor progression and metastasis in tumor patients. In this study, 868 immune-related transcription factors were identified from the transcription factor activity profile of 1823 ovarian cancer patients. The prognosis-related transcription factors were identified through univariate Cox analysis and random survival tree analysis, and two distinct clustering subtypes were subsequently derived based on these transcription factors. We assessed the clinical significance and genomics landscape of the two clustering subtypes and found statistically significant differences in prognosis, response to immunotherapy, and chemotherapy among ovarian cancer patients with different subtypes. Multi-scale Embedded Gene Co-expression Network Analysis was used to identify differential gene modules between the two clustering subtypes, which allowed us to conduct further analysis of biological pathways that exhibited significant differences between them. Finally, a ceRNA network was constructed to analyze lncRNA-miRNA-mRNA regulatory pairs with differential expression levels between two clustering subtypes. We expected that our study may provide some useful references for stratifying and treating patients with ovarian cancer.

Stratification of ovarian cancer patients from the prospect of drug target-related transcription factor protein activity: the prognostic and genomic landscape analyses.

The expression and activity of transcription factors, which directly mediate gene transcription, are strictly regulated to control numerous normal cellular processes. In cancer, transcription factor activity is often dysregulated, resulting in abnormal expression of genes related to tumorigenesis and development. The carcinogenicity of transcription factors can be reduced through targeted therapy. However, most studies on the pathogenic and drug-resistant mechanisms of ovarian cancer have focused on the expression and signaling pathways of individual transcription factors. To improve the prognosis and treatment of patients with ovarian cancer, multiple transcription factors should be evaluated simultaneously to determine the effects of their protein activity on drug therapies. In this study, the transcription factor activity of ovarian cancer samples was inferred from virtual inference of protein activity by enriched regulon algorithm using mRNA expression data. Patients were clustered according to their transcription factor protein activities to investigate the association of transcription factor activities of different subtypes with prognosis and drug sensitivity for filtering subtype-specific drugs. Meanwhile, master regulator analysis was utilized to identify master regulators of differential protein activity between clustering subtypes, thereby identifying transcription factors associated with prognosis and assessing their potential as therapeutic targets. Master regulator risk scores were then constructed for guiding patients' clinical treatment, providing new insights into the treatment of ovarian cancer at the level of transcriptional regulation.

A random forest-based metabolic risk model to assess the prognosis and metabolism-related drug targets in ovarian cancer.

As one of the most common gynecologic malignant tumors, ovarian cancer is usually diagnosed at an advanced and incurable stage because of its early asymptomatic onset. Increasing research into tumor biology has demonstrated that abnormal cellular metabolism precedes tumorigenesis, therefore it has become an area of active research in academia. Cellular metabolism is of great significance in cancer diagnostic and prognostic studies. In this study, we integrated The Cancer Genome Atlas dataset with multiple Gene Expression Omnibus ovarian cancer datasets, identified 17 metabolic pathways with prognostic values using the random forest algorithm, constructed a metabolic risk scoring model based on metabolic pathway enrichment scores, and classified patients with ovarian cancer into two subtypes. Then, we systematically investigated the differences between different subtypes in terms of prognosis, differential gene expression, immune signature enrichment, Hallmark signature enrichment, and somatic mutations. As well, we successfully predicted differences in sensitivity to immunotherapy and chemotherapy drugs in patients with different metabolic risk subtypes. Moreover, we identified 5 drug targets associated with high metabolic risk and low metabolic risk ovarian cancer phenotypes through the weighted correlation network analysis and investigated their roles in the genesis of ovarian cancer. Finally, we developed an XGBoost classifier for predicting metabolic risk types in patients with ovarian cancer, producing a good predictive effect. In light of the above study, the research findings will provide valuable information for prognostic prediction and personalized medical treatment of patients with ovarian cancer.

Transformation trend of nitrogen and phosphorus in the sediment of the sewage pipeline and their distribution along the pipeline.

Microorganisms transform nitrogen and phosphorus in the sediment of sewage pipelines. When the sediment was scoured by water flow, these elements migrate. This work studied the changes in biofilm morphology and microbial community structure, and focused on the differences in the transformation of nitrogen and phosphorus along the pipeline. The results showed that the nitrogen and phosphorus concentrations varied systematically with time and space (the front, middle, and posterior segments of the pipe). With time, amino acid nitrogen (AAN) concentration in the sediment gradually decreased, NH4+-N concentration slowly increased, NO3--N concentration began to increase after 25 days, and TP concentration continued to increase after 9 days. The AAN, NH4+-N, and TP concentrations were highest in the posterior segment of the pipe and lowest in the front segment. However, NO3--N showed two stages: its concentration was highest in the front segment and lowest in the posterior segment during the first 17 days, after which the opposite was observed. Changes in the nitrogen and phosphorus concentrations were related to the microbial communities in the sediments. The abundances of Rhodobacter (0.001

Experiment and calculation of deposition velocity of suspended particles in storm drainage.

Deposition particles can lead to blockage, odor, and corrosion of pipes, and the deposition process of suspended particles is particularly complicated. In order to quantify the deposition process of suspended particles and mastered the critical conditions for the deposition in storm drainage, the process was simulated experimentally, and the deposition states of suspended particles under the different roughness of pipe wall, particle size, and density were analyzed. Two mathematical models of deposition critical velocity and easy deposition velocity were established. Results showed that with the increase of particle size and density, the gravity of particles increased and deposition was more likely to occur. In the rough pipeline, the kinetic energy consumption of water flow increased, the ability to carry particles was weakened, and the deposition rate would increase accordingly. The higher the flow velocity, the lower the deposition rate. The deposition states of particles in the pipeline could be divided into three types according to the deposition rates: "no deposition," "minor deposition," and "bulk deposition." Verification showed that the difference rates between the calculated values and measured values of the deposition critical velocity ranged from - 3.23 to 2.86%, and the difference rates of the easy deposition velocity were - 4.14-4.72%, showing good consistency.

JACLNet:Application of adaptive code length network in JavaScript malicious code detection.

Currently, JavaScript malicious code detection methods are becoming more and more effective. Still, the existing methods based on deep learning are poor at detecting too long or too short JavaScript code. Based on this, this paper proposes an adaptive code length deep learning network JACLNet, composed of convolutional block RDCNet, BiLSTM and Transfrom, to capture the association features of the variable distance between codes. Firstly, an abstract syntax tree recombination algorithm is designed to provide rich syntax information for feature extraction. Secondly, a deep residual convolution block network (RDCNet) is designed to capture short-distance association features between codes. Finally, this paper proposes a JACLNet network for JavaScript malicious code detection. To verify that the model presented in this paper can effectively detect variable JavaScript code, we divide the datasets used in this paper into long text dataset DB_Long; short text dataset DB_Short, original dataset DB_Or and enhanced dataset DB_Re. In DB_Long, our method's F1 - score is 98.87%, higher than that of JSContana by 2.52%. In DB_Short, our method's F1-score is 97.32%, higher than that of JSContana by 7.79%. To verify that the abstract syntax tree recombination algorithm proposed in this paper can provide rich syntax information for subsequent models, we conduct comparative experiments on DB_Or and DB_Re. In DPCNN+BiLSTM, F1-score with abstract syntax tree recombination increased by 1.72%, and in JSContana, F1-score with abstract syntax tree recombination increased by 1.50%. F1-score with abstract syntax tree recombination in JACNet improved by 1.00% otherwise unused.

Deep learning-based transcription factor activity for stratification of breast cancer patients.

Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activities can be used to characterize genomic aberrations in cancer cell. In this study, the activity profile of transcription factors inferred by VIPER algorithm. The autoencoder model was applied for compressing the transcription factor activity profile for obtaining more useful transformed features for stratifying patients into two different breast cancer subtypes. The deep learning-based subtypes exhibited superior prognostic value and yielded better risk-stratification than the transcription factor activity-based method. Importantly, according to transformed features, a deep neural network was constructed to predict the subtypes, and achieved the accuracy of 94.98% and area under the ROC curve of 0.9663, respectively. The proposed subtypes were found to be significantly associated with immune infiltration, tumor immunogenicity and so on. Furthermore, the ceRNA network was constructed for the breast cancer subtypes. Besides, 11 master regulators were found to be associated with patients in cluster 1. Given the robustness performance of our deep learning model over multiple breast cancer cohorts, we expected this model may be useful in the area of prognosis prediction and lead some possibility for personalized medicine in breast cancer patients.

Metal-organic framework mixed-matrix membrane-based extraction combined HPLC for determination of bisphenol A in milk and milk packaging.

The residues of bisphenol A (BPA) in milk packaging may transfer to milk, adversely affecting the human endocrine system. Consequently, to analyse or monitor BPA, it is imperative to develop rapid and effective approaches to BPA extraction from milk and milk packing as BPA is usually present in trace levels. Herein, we developed a rapid, simple, and low-cost dispersive-membrane-solid-phase-extraction (DME) for BPA with MIL-101(Cr) mixed-matrix-membrane (MMM). The MMM had large surface area (1322.09 m2/g) and pore volume (0.65 cm3/g), possessed great extraction efficiency of BPA, and kept more than 90% extraction efficiency after 20 times of reuse. Using the developed MIL-101(Cr)-MMM-based DME coupled with HPLC-fluorescence detector, we received an adequate linearity in the range of 0.1 âˆ¼ 50 µg/L BPA and a limit of detection as low as 16 ng/L under optimized conditions. The recoveries of BPA in milk and milk bottles were from 74.2% to 110.6%, with RSDs less than 9.4%.

Integrated multi-cohorts for characterizing the immunogenomic landscape and predicting drug response in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is the breast cancer subtype with the highest fatality rate, and it seriously threatens women's health. Recent studies found that the level of immune cell infiltration in TNBC was associated with tumor progression and prognosis. However, due to practical constraints, most of these TNBC immune infiltration studies only used a small number of patient samples and a few immune cell types. Therefore, it is necessary to integrate more TNBC patient samples and immune cell types to comprehensively study immune infiltration in TNBC to contribute to the prognosis and treatment of patients. In this study, 12 TNBC datasets were integrated and an extensive collection of 182 gene sets with immune-related signatures were included to comprehensively investigate tumor immune microenvironment of TNBC. A single sample gene set enrichment analysis was performed to calculate the infiltration score of each immune-related signature in each patient, and an immune-related risk scoring model for TNBC was constructed to accurately assess patient prognosis. Significant differences were found in immunogenomic landscape between different immune risk subtypes. In addition, the immunotherapy response and chemotherapy drug sensitivity of patients with different immune risk subtypes were also analyzed. The results showed that there were significant differences in these characteristics. Finally, a prediction model for immune risk subtypes of TNBC patients was constructed to accurately predict patients with unknown subtypes. Based on the aforementioned findings, we believed that the immune-related risk score constructed in this study can assist in providing personalized medicine to TNBC patients.

Migration and transformation of phosphorus in sediment-water system in storm and sewage sewers.

During rainfall, phosphorus in drainage pipe sediments is easily washed and released. This study investigates the migration of phosphorus between sediments and water in storm and sewage sewers, the microbial community structure in sediments, and phosphorus transformation under biological action. Results showed that when the initial concentration of phosphorus in stormwater (water column) in storm sewer was high (1-2 mg/L), the total phosphorus (TP) level decreased in the water column but increased in the sediments, showing a trend of phosphorus migration from the water column to the sediments. Moreover, under high concentration (2 mg/L), the TP level decreased by 83.19% in the water column within 210 min, which was greater than 64.9% of the medium-concentration stormwater (1 mg/L). In sewage sewer, when the initial concentration of phosphorus in sewage was about 2 mg/L, phosphorus would migrate from the sediments and interstitial water to the water column because of the high concentration of phosphorus in the sediments. In addition, the variation in phosphorus was caused not only by concentration gradient but also by microbial communities. Phosphate accumulating organisms, such as Alphaproteobacteria, Gammaproteobacteria, and Actinobacteria, existed in the storm and sewage sewers, which could ingest dissolved reactive phosphorus in the water column and interstitial water and convert it into phosphorus in organisms. In storm sewers, Acidimicrobiia transferred phosphorus from the water column and interstitial water to the sediments through biochemical reactions and physical adsorption. In sewage sewers, organic acids secreted by Clostridia, Bacteroidia, and Bacilli could dissolve some insoluble phosphorus in sediments and then transfer them to interstitial water.

Prognostic and predictive value of a metabolic risk score model in breast cancer: an immunogenomic landscape analysis.

Breast cancer is a kind of malignant tumor that occurs in breast tissue, which is the most common cancer in women. Cellular metabolism is a critical determinant of the viability and function of cancer cells in tumor microenvironment. In this study, based on the gene expression profile of metabolism-related genes, the prognostic value of 20 metabolic pathways in patients with breast cancer was identified. A universal risk stratification signature that relies on 20 metabolic pathways was established and validated in training cohort, two testing cohorts and The Cancer Genome Atlas pan cancer cohort. Then, the relationship between metabolic risk score subtype, prognosis, immune infiltration level, cancer genotypes and their impact on therapeutic benefit were characterized. Results demonstrated that the patients with the low metabolic risk score subtype displayed good prognosis, high level of immune infiltration and exhibited a favorable response to neoadjuvant chemotherapy and immunotherapy. Taken together, the work presented in this study may deepen the understanding of metabolic hallmarks of breast cancer, and may provide some valuable information for personalized therapies in patients with breast cancer.