Metabolic reprogramming-driven homologous recombination and TCA cycle dysregulation contribute to poor prognoses in lung adenocarcinoma.

Increasing evidence has shown that homologous recombination (HR) and metabolic reprogramming are essential for cellular homeostasis. These two processes are independent as well as closely intertwined. Nevertheless, they have rarely been reported in lung adenocarcinoma (LUAD). We analysed the genomic, immune microenvironment and metabolic microenvironment features under different HR activity states. Using cell cycle, EDU and cell invasion assays, we determined the impacts of si-SHFM1 on the LUAD cell cycle, proliferation and invasion. The levels of isocitrate dehydrogenase (IDH) and α-ketoglutarate dehydrogenase (α-KGDH) were determined by ELISA in the NC and si-SHFM1 groups of A549 cells. Finally, cell samples were used to extract metabolites for HPIC-MS/MS to analyse central carbon metabolism. We found that high HR activity was associated with a poor prognosis in LUAD, and HR was an independent prognostic factor for TCGA-LUAD patients. Moreover, LUAD samples with a high HR activity presented low immune infiltration levels, a high degree of genomic instability, a good response status to immune checkpoint blockade therapy and a high degree of drug sensitivity. The si-SHFM1 group presented a significantly higher proportion of cells in the G0/G1 phase, lower levels of DNA replication, and significantly lower levels of cell migration and both TCA enzymes. Our current results indicated that there is a strong correlation between HR and the TCA cycle in LUAD. The TCA cycle can promote SHFM1-mediated HR in LUAD, raising their activities, which can finally result in a poor prognosis and impair immunotherapeutic efficacy.

ESCO2's oncogenic role in human tumors: a pan-cancer analysis and experimental validation.

PURPOSE: Establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) is involved in the mitotic S-phase adhesins acetylation and is responsible for bridging two sister chromatids. However, present ESCO2 cancer research is limited to a few cancers. No systematic pan-cancer analysis has been conducted to investigate its role in diagnosis, prognosis, and effector function. METHODS: We thoroughly examined the ESCO2 carcinogenesis in pan-cancer by combining public databases such as The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), UALCAN and Tumor Immune Single-cell Hub (TISCH). The analysis includes differential expression analysis, survival analysis, cellular effector function, gene mutation, single cell analysis, and tumor immune cell infiltration. Furthermore, we confirmed ESCO2's impacts on clear cell renal cell carcinoma (ccRCC) cells' proliferative and invasive capacities in vitro. RESULTS: In our study, 30 of 33 cancer types exhibited considerably greater levels of ESCO2 expression in tumor tissue using TCGA and GTEx databases, whereas acute myeloid leukemia (LAML) exhibited significantly lower levels. Kaplan-Meier survival analyses in adrenocortical carcinoma (ACC), kidney chromophobe (KICH), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), brain lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), mesothelioma (MESO), and pancreatic adenocarcinoma (PAAD) demonstrated that tumor patients with high ESCO2 expression have short survival periods. However, in thymoma (THYM), colon adenocarcinoma (COAD) and rectum adenocarcinoma (READ), ESCO2 was a favorable prognostic factor. Moreover, ESCO2 expression positively correlates with tumor stage and tumor size in several cancers, including LIHC, KIRC, KIRP and LUAD. Function analysis revealed that ESCO2 participates in mitosis, cell cycle, DNA damage repair, and other processes. CDK1 was identified as a downstream gene regulated by ESCO2. Furthermore, ESCO2 might also be implicated in immune cell infiltration. Finally, ESCO2'S knockdown significantly inhibited the A498 and T24 cells' proliferation, invasion, and migration. CONCLUSIONS: In conclusion, ESCO2 is a possible pan-cancer biomarker and oncogene that can reliably predict the prognosis of cancer patients. ESCO2 was also implicated in the cell cycle and proliferation regulation. In a nutshell, ESCO2 is a therapeutically viable and dependable target.

Machine learning-based techniques for land subsidence simulation in an urban area.

Understanding and mitigating land subsidence (LS) is critical for sustainable urban planning and infrastructure management. We introduce a comprehensive analysis of LS forecasting utilizing two advanced machine learning models: the eXtreme Gradient Boosting Regressor (XGBR) and Long Short-Term Memory (LSTM). Our findings highlight groundwater level (GWL) and building concentration (BC) as pivotal factors influencing LS. Through the use of Taylor diagram, we demonstrate a strong correlation between both XGBR and LSTM models and the subsidence data, affirming their predictive accuracy. Notably, we applied delta-rate (Δr) calculus to simulate a scenario with an 80% reduction in GWL and BC impact, revealing a potential substantial decrease in LS by 2040. This projection emphasizes the effectiveness of strategic urban and environmental policy interventions. The model performances, indicated by coefficients of determination R2 (0.90 for XGBR, 0.84 for LSTM), root-mean-squared error RMSE (0.37 for XGBR, 0.50 for LSTM), and mean-absolute-error MAE (0.34 for XGBR, 0.67 for LSTM), confirm their reliability. This research sets a precedent for incorporating dynamic environmental factors and adapting to real-time data in future studies. Our approach facilitates proactive LS management through data-driven strategies, offering valuable insights for policymakers and laying the foundation for sustainable urban development and resource management practices.

Novel optimized coupled rainfall model simulation based on stepwise decomposition technique.

Precipitation forecasting plays a pivotal role in guiding the effective management of regional water resources and providing crucial warnings for regional droughts and floods. Finding a monthly precipitation simulation model with robust fitting performance is a significant research endeavor in practical precipitation prediction. This paper introduces two modified African vulture optimization algorithms (MAVOA1 and MAVOA2). It provides hyperparameter optimization techniques for the least squares support vector machine (LSSVM), long short-term memory neural network (LSTM), and random forest (RF) models. These techniques are used to construct a monthly precipitation simulation model based on algorithmic optimization coupled with variational mode decomposition for full decomposition. The test results at five typical stations in the North China Plain reveal the following: (1) the LSSVM model demonstrates significantly better performance than the LSTM and RF models. (2) the MAVOA2-LSSVM model has the best-integrated effect: the average test fitting error is RMSE = 17.50 mm/month, MRE = 117.25%, NSE = 0.90, which shows its superiority in practical application and can significantly improve the accuracy of precipitation prediction; MAVOA2 is more suitable for machine learning models with more hyperparameters of its own, which provides a reference for hyperparameter optimization algorithms in the other fields.

Pressure-Gradient Counterwork of Dual-Fuel Driven Nanocarriers in Abnormal Interstitial Fluids for Enhancing Drug Delivery Efficiency.

The abnormal pressure in tumor tissue is a significant limitation on the drug delivery efficiency of tumor therapy. This work reports a gradient-driven nanomotor as drug nanocarrier with the pressure-counterworking function. The dual-fuel nanomotors are formed by co-electrospinning of the photosensitive polymers with calcium peroxide (CaO2 ) and catalase (CAT), followed by ultraviolet (UV) irradiation and bovine serum albumin (BSA) incubation. The UV-responsive cleavage nanomotors can effectively release O2 molecules at the fractures as a driving force to increase the delivery speed and escape the phagocytosis of macrophage system in normal tissues. Furthermore, CAT catalyzes H2 O2 produced by CaO2 and the tumor interstitial fluids to provide stronger power for the nanomotors. Additionally, according to the analysis of directional motions of the nanomotors, the functional relationship between the rotational diffusion coefficient (DR ) and the physiological viscosity is constructed. The dual-fuel nanocarriers enable up to 13.25% of the injected dose (ID)/per gram tissue and significantly improve the penetration in deep tumor. It is of vital importance to design and obtain the adaptive pressure-gradient counterworking nanomotors, which can effectively improve the drug delivery efficiency in vitro and in vivo.

The value of urinary exosomal lncRNA SNHG16 as a diagnostic biomarker for bladder cancer.

OBJECTIVE: To detect the expression level of urinary exosomal lncRNA SNHG16 in patients with bladder cancer and healthy individuals and explore its clinical application value in the diagnosis of bladder cancer. METHODS: Urine samples were collected from 42 patients with bladder cancer and 42 healthy volunteers who visited Lu'an Hospital of Anhui Medical University and the Second Hospital of Tianjin Medical University from January 2020 to December 2022. The expression levels of lncRNA SNHG16 in urinary exosomes of the two groups were detected by RT‒qPCR, and their correlation with clinical pathological parameters of bladder cancer patients was analysed. An Receiver Operating Characteristic(ROC) curve was drawn to analyse the diagnostic value of urinary exosomal lncRNA SNHG16 for bladder cancer and compared with urinary cytology. RESULTS: The expression of urinary exosomal lncRNA SNHG16 in patients with bladder cancer was significantly higher (P < 0.05), and the expression level had no correlation with the age, sex, pathological T stage, pathological grade, or tumour size of bladder cancer patients (P > 0.05). The Area Under Curve(AUC) of urinary exosomal lncRNA SNHG16 in diagnosing bladder cancer was 0.791, which was superior to that of urinary cytology (AUC = 0.597). CONCLUSION: Urinary exosomal lncRNA SNHG16 with high expression can serve as a potential diagnostic biological marker for bladder cancer.

Characteristics of ecosystem service values in response to strong human activities in water sources of the South-to-North Water Diversion Middle Route in China.

A systematic grasp of the evolution of the spatial and temporal patterns of ecosystem service value (ESV) in the Central Line Project for South-to-North Water Diversion (CLPSNWD) water source area is conducive to deepening the ecological protection and promoting high-quality development of the water source area. In this paper, the dynamically adjusted equivalent factor method is used to reveal the spatial and temporal evolution of ESV in the water source area under strong human activities from 1991 to 2020. The results show that (1) during the 30-year period, urban point expansion increased the construction land area by 63.66 km2, and the degree of fragmentation increased. The water area increased the most, reaching 209.43 km2. (2) The total increase in ESV over the 30-year period was $1434 million, with forests and water accounting for the largest increase, i.e., 98% of the total increase in value. Among the individual service functions, hydrologic regulation generated the most significant service value.

The effects of small-needle-knife therapy on pain and mobility from knee osteoarthritis: a pilot randomized-controlled study.

OBJECTIVE: To investigate the effect of small needle-knife therapy in people with painful knee osteoarthritis. DESIGN: Pilot randomised, controlled trial. SETTING: Rehabilitation hospital. SUBJECTS: In-patients with osteo-arthritis of the knee. INTERVENTIONS: Either 1 to 3 small needle-knife treatments over seven days or oral Celecoxib. All patients stayed in hospital three weeks, receiving the same mobility-focused rehabilitation. MEASURES: Oxford Knee Score (OKS), gait speed and kinematics were recorded at baseline, at three weeks (discharge) and at three-months (OKS only). Withdrawal from the study, and adverse events associated with the small needle knife therapy were recorded. RESULTS: 83 patients were randomized: 44 into the control group, of whom 10 were lost by three weeks and 12 at 3 months; 39 into the experimental group of whom eight were lost at three weeks and three months. The mean (SE) OKS scores at baseline were Control 35.86 (1.05), Exp 38.38 (0.99); at three weeks 26.64 (0.97) and 21.94 (1.23); and at three months 25.83 (0.91) and 20.48 (1.14) The mean (SE) gait speed at baseline was 1.07 (0.03) m/sec (Control) and 0.98 (0.03), and at three weeks was 1.14 (0.03) and 1.12 (0.03) (P < 0.05). Linear mixed model statistical analysis showed that the improvements in the experimental group were statistically significant for total OKS score at discharge and three months. CONCLUSIONS: Small needle-knife therapy added to standard therapy for patients with knee osteoarthritis, was acceptable, safe and reduced pain and improved global function on the Oxford Knee Score. Further research is warranted.

Layer-by-Layer Construction of Antibacterial and Anticoagulant Blood Contacting Materials.

Vascular transplantation is a common treatment for Cardiovascular disease (CVD). However, the mismatch of mechanical, structural, or microenvironmental properties of materials limits the clinical application. Therefore, the functional construction of artificial vessels or other blood contact materials remains an urgent challenge. In this paper, the composite nanofibers of polycaprolactone (PCL) with dopamine and polyethylenimine (PEI) coating are first prepared, which are further self-assembled by anticoagulant hirudin (rH) and antimicrobial peptide (AMP) of HHC36 through layer-by-layer (LBL) method. The results of FTIR and XPS analysis show that hirudin and AMP are successfully loaded on PEI-PDA/PCL nanofibers and the hydrophilicity is improved. They also show good mechanical properties that the ultimate tensile strength and elongation at break are better than natural blood vessels. The antibacterial results show that the antibacterial effect is still 93% against E. coli on the fifth day because of the stable and continuous release of HHC36 and rH. The performance of anticoagulant activity also exhibited the same results, which APTT is even 9.7s longer in the experimental group than the control group on the fifth day. The novel materials would be effectively solve the formation of thrombosis around artificial blood vessel grafts and the treatment of inflammation.

A novel optimization rainfall coupling model based on stepwise decomposition technique.

Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).

Machine learning assisted microfluidics dual fluorescence flow cytometry for detecting bladder tumor cells based on morphological characteristic parameters.

BACKGROUND: Bladder cancer (BC) is the most common malignant tumor and has become a major public health problem, leading the causes of death worldwide. The detection of BC cells is of great significance for clinical diagnosis and disease treatment. Urinary cytology based liquid biopsy remains high specificity for early diagnosis of BC, however, it still requires microscopy examination which heavily relies on manual operations. It is imperative to investigate the potential of automated and indiscriminate cell differentiation technology to enhance the sensitivity and efficiency of urine cytology. RESULTS: Here, we developed a machine learning algorithm empowered dual-fluorescence flow cytometry platform (µ-FCM) for urinary cytology analysis. A phenotype characteristic parameter (CP) which correlated with the size of the cell and nucleus was defined to achieve the differentiation of the BC cells and uroepithelial cells with high throughput and high accuracy. Based on CP analysis, SV-HUC-1 cells were almost differentiated from EJ cells and effectively reduced the overlap with 5637 cells. To further differentiate SV-HUC-1 cells and 5637 cells, support vector machine (SVM) machine learning algorithm was optimized to assist data analysis with the highest accuracies of 84.7 % for cell differentiation including the specificity of 91.0 % and the sensitivity of 75.0 %. Furthermore, the false positive rate (FPR) compensation enabled the detection rates of rare BC cells predicted by the well-trained SVM model were close to the true proportions with the recognition error in 0.4 % for the tumor cells. SIGNIFICANCE: As a proof of concept, the developed µ-FCM system successfully demonstrates the capacity to identify the distribution of exfoliated cells in real urine samples. This system underscores the significance of integrating AI with microfluidics to perform high-throughput phenotyping of exfoliated cells, offering a pathway toward scalable, efficient, and automatic microfluidic systems in the fields of both biosensing and in vitro diagnosis of BC.

The multifaceted roles of COL4A4 in lung adenocarcinoma: An integrated bioinformatics and experimental study.

BACKGROUND: Abnormal expression of collagen IV subunits has been reported in cancers, but the significance is not clear. No study has reported the significance of COL4A4 in lung adenocarcinoma (LUAD). METHODS: COL4A4 expression data, single-cell sequencing data and clinical data were downloaded from public databases. A range of bioinformatics and experimental methods were adopted to analyze the association of COL4A4 expression with clinical parameters, tumor microenvironment (TME), drug resistance and immunotherapy response, and to investigate the roles and underlying mechanism of COL4A4 in LUAD. RESULTS: COL4A4 is differentially expressed in most of cancers analyzed, being associated with prognosis, tumor stemness, immune checkpoint gene expression and TME parameters. In LUAD, COL4A4 expression is down-regulated and associated with various TME parameters, response to immunotherapy and drug resistance. LUAD patients with lower COL4A4 have worse prognosis. Knockdown of COL4A4 significantly inhibited the expression of cell-cycle associated genes, and the expression and activation of signaling pathways including JAK/STAT3, p38, and ERK pathways, and induced quiescence in LUAD cells. Besides, it significantly induced the expression of a range of bioactive molecule genes that have been shown to have critical roles in TME remodeling and immune regulation. CONCLUSIONS: COL4A4 is implicated in the pathogenesis of cancers including LUAD. Its function may be multifaceted. It can modulate the activity of LUAD cells, TME remodeling and tumor stemness, thus affecting the pathological process of LUAD. COL4A4 may be a prognostic molecular marker and a potential therapeutic target.

Mechanobiology studies of bladder tumor cells using laterally squeezing microfluidic flow cytometry.

The deformability and uptake capability of cells are critical indicators of their biomechanical properties and functional behaviors, particularly in tumor heterogeneity and cancer research. Here, we introduce a microfluidic flow cytometry platform integrated with a laterally adjustable squeezing structure for the characterization of bladder tumor cells (including 5637 and EJ cell lines) and uroepithelial cells (SV-HUC-1 cell line). The deformability of these cell types under varying channel width conditions was clearly assessed using this platform. The results demonstrated that tumor cells exhibited higher deformability compared to uroepithelial cells, with the EJ cell line exhibiting the greatest difference. Furthermore, the relationship between the malignancy, deformability, and uptake capability of bladder cells was explored through co-cultivation experiments with 2 µm particles. As the malignancy increased, the cells became more deformable and exhibited stronger phagocytic capability with particles. Subsequently, the heterogeneity of tumor cells was investigated by analyzing the deformability of phagocytic and non-phagocytic subpopulations within EJ cells. The developed microfluidic platform offers a promising high-throughput method to assess the biomechanical and phagocytic characteristics of cells, providing valuable insights into tumor cell biology, and potentially improving clinical status of urinary cytology examinations for bladder cancer.

Efficacy and Safety of Ultrasound-Guided Acupotomy Versus Celecoxib in Patients with Thoracodorsal Myofascial Pain Syndrome: A Randomized Controlled Trial.

Objective: To evaluate the efficacy and safety of ultrasound-guided acupotomy (UgA) for the treatment of thoracodorsal myofascial pain syndrome (TDMPS) and monitor its mid-term efficacy at 3 months after treatment. Methods: A 3-week, evaluator-blinded randomized clinical trial was conducted among 100 patients with TDMPS (visual analogue scale [VAS] score > 3) in the outpatient clinic of the Department of Orthopaedics of the Second Affiliated Hospital of Anhui University of Traditional Chinese Medicine, with a 3-month follow-up starting after completion of treatment. These patients were randomly assigned to receive UgA (n = 50) or oral celecoxib (n = 50). Recruitment was conducted between January 2021 and July 2022. The primary outcome was the VAS score, and the secondary outcomes included the Oswestry Disability Index (ODI), Pain Anxiety Symptoms Scale (PASS), and TNF-α and IL-1ß levels. Outcome data were collected at baseline, week 3 (post-treatment) and week 15 (follow-up). Results: Compared with that in the celecoxib group, the pain in the UgA group was alleviated more strongly, with an adjusted mean group difference of -0.69 (95% CI,-1.07 to -0.31 after multiple imputation) at week 3 and -1.96 (95% CI,-2.33 to -1.59 after multiple imputation) at week 15 (p < 0.001 for overall group × time interaction). Both groups exhibited improvements in the ODI and PASS scores at weeks 3 and 15, but these improvements were significantly greater in the UgA group (p < 0.05). At week 3, the TNF-α and IL-1 levels were significantly lower in both groups, but celecoxib was more effective (p < 0.05). Results from analyses with multilevel multiple imputation for missingness were similar. Conclusion: UgA led to greater and safer alleviation of pain, dysfunction, and pain anxiety in patients treated with TDMPS than did celecoxib and had a durable 3-month efficacy but was inferior to celecoxib in reducing the level of inflammatory factors. These findings may prompt clinicians to recommend UgA as an alternative and supplementary therapy for pain management in patients with TDMPS.

The coupling of anammox with microalgae-bacteria symbiosis: Nitrogen removal performance and microbial community.

The partial nitrification-anammox process for ammonia nitrogen wastewater treatment requires mechanical aeration to provide oxygen, which is not conducive to energy saving. The microalgae-bacteria symbiotic system (MaBS) has the advantages of low carbon and energy saving in wastewater biological nitrogen removal. Therefore, this study combined the MaBS with an anammox process to provide oxygen, through the photosynthesis of microalgae instead of mechanical aeration. We investigated the nitrogen removal efficiency and long-term operation of a co-culture system comprising microalgae, nitrifying bacteria (NB), denitrifying bacteria (DnB), and anaerobic ammonium-oxidation bacteria (AnAOB) in a sequencing batch reactor without mechanical aeration. The experiment was divided into three steps: firstly, cultivating NB; then, adding three kinds of microalgae which were Chlorella sp., Anabaena sp., and Navicula sp. to the bioreactor to construct a microalgae-bacteria symbiotic system; finally, adding anammox sludge to construct the anammox and microalgae-bacteria symbiosis (Anammox-MaBS) system. The results demonstrated that nitrification, denitrification, and anammox processes were coupled successfully, and the maximum TN removal efficiency of the stable Anammox-MaBS system was 99.51 % when the concentration of the influent NH4+-N was 100 mg/L. The addition of microalgae in ammonia wastewater promoted the enrichment of DnB and AnAOB, which were Denitratisoma, Haliangium, unclassified_Rhodocyclaceae, and Candidatus_Brocadia. Furthermore, the unique biofilm structure could effectively alleviate the photoinhibition of light-sensitive bacteria, which may be the reason for the long-term adaptation of Candidatus_Brocadia to light conditions. This research can provide a low-cost solution to bacterial photoinhibition in the coexistence system of microalgae and bacteria without mechanical aeration, offering theoretical support for low-carbon and energy-efficient treatment of wastewater.

Multi-Chambered Core/Shell Supraparticles for Real-Time, Full-Time Diagnosis and Treatment Integration of Tumors.

To a certain extent, theranostic nanoplatforms promote tumor treatment efficiency. However, timely monitoring of the critical stages and signal sustainability of the entire process is challenging. In this study, multi-chambered core/shell magnetic nanoparticles (MC-MNPs) as drug and imaging agent multi-loaded nanocarriers with a synergistic release function are reported. Supraparticles with stable chambers are formed by the supercooling self-assembly of several core/shell magnetic nanoparticles composed of amphiphilic copolymers as the core and hydrophilic magnetic iron oxide nanoparticles as the shell. Desalinized doxorubicin and coumarin 6 are stored in different cavities of nanocarriers, and chitosan is used as an outer encapsulation layer. Based on their construction properties, MC-MNPs can exhibit gradient-degraded and steady-released controllability in the tumor environment. Furthermore, real-time accumulation situations and full-time diagnostic signals of nanocarriers are thoroughly demonstrated using fluorescence imaging and T2-weighted magnetic resonance imaging before and after magnetic hyperthermia in targeted tumors under an alternating magnetic field. Thus, MC-MNPs as theranostic nanocarriers exhibit great potential for the timely monitoring and full-time guidance of tumor treatment.

Study of LY9 as a potential biomarker for prognosis and prediction of immunotherapy efficacy in lung adenocarcinoma.

Background: Lymphocyte antigen 9 (LY9) participates in the development of several tumors and diseases but has not been reported yet in lung adenocarcinoma (LUAD). Methods: First, we analyzed the expression and prognostic value of LY9 in pan-cancer, including LUAD. Additionally, we conducted a correlation analysis of LY9 expression in LUAD with immune cell infiltration using the TIMER database and the CIBERSORT algorithm, and with immune checkpoints using the GEPIA database. Also, we constructed a potential ceRNA network for LY9. Furthermore, we explored LY9-related pathways by Gene Set Enrichment Analysis (GSEA). Finally, validation of differential expression at the mRNA level was obtained from the GEO database. We collected LUAD tissues for Quantitative Real-time PCR (qRT-PCR) to verify the expression of LY9, CD8, and CD4 and calculated the correlation between them. We also conducted immunohistochemistry (IHC) to verify the protein expression of LY9. Results: Results showed that LY9 was highly expressed in various tumors, including LUAD. Besides, patients with high LY9 expression presented longer overall survival (OS) and more multiple lymphocyte infiltrations. The expression of LY9 in LUAD strongly and positively correlates with multiple immune cell infiltration and immune checkpoints. The functional enrichment analysis indicated that LY9 was involved in multiple immune-related pathways and non-small cell lung cancer. Moreover, a ceRNA regulatory network of LINC00943-hsa-miR-141-3p-LY9 might be involved. Finally, GSE68465 dataset confirmed differential expression of LY9 mRNA levels in LUAD and the qRT-PCR results verified LY9 had a strong and positive correlation with CD4 and CD8 T cells. Unfortunately, IHC did not detect the expression of LY9 protein level in tumor tissues and WB experiments validated the protein expression of LY9 in the OCI-AML-2 cell line. Conclusions: Therefore, we hypothesized that LY9 could serve as a potential, novel prognostic biomarker for LUAD and could predict immunotherapy efficacy at the mRNA level.

Prediction of suspended sediment concentration in the lower Yellow River in China based on the coupled CEEMD-NAR model.

The scientific and accurate prediction of suspended sediment concentrations is of great importance for river management in the lower reaches of the Yellow River and for the scheduling of water conservancy projects in the upper and middle reaches. In order to solve the influence of the non-linear and non-smooth characteristics of the suspended sediment concentration series in the lower Yellow River on the prediction results and improve the prediction accuracy, this paper proposes a coupled model based on Complementary Ensemble Empirical Mode Decomposition (CEEMD) and non-linear autoregressive (NAR) model. Take the predicted suspended sediment concentrations in the lower reaches of the Yellow River at the Huayuankou hydrographic station as an example. The accuracy and stability of the coupled CEEMD-NAR model were verified through the Gaocun and Lijin hydrological stations. The CEEMD-NAR model predicted suspended sediment concentrations with a Nash-Sutcliffe efficiency (NSE) factor of 0.93. The three statistical evaluation indicators of the CEEMD-NAR model, mean absolute error (MAE), mean relative error (MRE), and root mean square error (RMSE) were 2.12 kg/m3, 1.07, and 3.75 kg/m3 respectively. In contrast to the NAR, EMD-NAR, and EEMD-NAR models, the coupled CEEMD-NAR model has good stability and high prediction accuracy and can be used in non-linear, non-smooth suspended sediment concentration long series prediction.

Functionalization of PCL-based nanofibers loaded with hirudin as blood contact materials.

Blood-contacting materials with good mechanical property, excellent anticoagulant function and promoting effect on endothelialization are in great demand for clinical application such as vascular grafts in treating cardiovascular diseases. In this study, electrospinning nanofiber scaffolds of polycaprolactone (PCL) were functionalized by oxidative self-polymerization of dopamine (PDA) on the surface followed by the modification of anticoagulant recombinant hirudin (rH) molecules. The morphology, structure, mechanical property, degradation behavior, cellular compatibility and blood compatibility of the multifunctional PCL/PDA/rH nanofiber scaffolds were evaluated. The diameter of the nanofibers was between 270-1030 nm. The ultimate tensile strength of the scaffolds was around 4 MPa and the elastic modulus increased with the amount of rH. The degradation tests in vitro indicated that the nanofiber scaffolds began to crack on the 7th day, but still maintained the nanoscale architecture within a month. The cumulative release of rH from the nanofiber scaffold was up to 95.9 % at 30th day. The functionalized scaffolds promoted the adhesion and proliferation of endothelial cells, while resisting platelet adhesion and enhancing anticoagulation effects. The hemolysis ratios of all scaffolds were <2 %. The nanofiber scaffolds are promising candidates for vascular tissue engineering.

Deep learning precipitation prediction models combined with feature analysis.

Precise rainfall forecasting modeling assumes a pivotal role in water resource planning and management. Conducting a comprehensive analysis of the rainfall time series and making appropriate adjustments to the forecast model settings based on the characterization results of the rainfall series significantly enhance the accuracy of the forecast model. This paper employed the Mann-Kendall and sliding T mutation tests to identify the mutational components in rainfall between 1961 and 2013 at four meteorological stations located in Henan Province. Wavelet analysis was utilized to determine the periodicity of the precipitation series. The model parameters were adjusted based on the mutation and periodicity findings, and appropriate training and test sets were selected accordingly. Rainfall simulation in Henan Province, China, was conducted using a combination of complementary ensemble empirical mode decomposition (CEEMD) and bi-directional long short-term memory (BiLSTM) networks. The integrated approach aimed at predicting rainfall in the region. The findings of this study demonstrate that the CEEMD-BiLSTM model, coupled with feature analysis, yielded favorable results in terms of prediction accuracy. The model achieved a mean MAE (mean absolute error) of 131.210, a mean MRE (mean relative error) of 0.637, a mean RMSE (root mean square error) of 187.776, and an NSE (Nash-Sutcliffe efficiency) above 0.910. The data processed according to the feature analysis results and then predicted; Zhengzhou, Anyang, Lushi, and Xinyang stations improved by 39.548%, 14.478%, 11.548%, and 19.037% respectively compared with the original prediction model.