Exhaled volatolomics profiling facilitates personalized screening for gastric cancer.

Gastric cancer (GC) is one of the most fatal cancers, characterized by non-specific early symptoms and difficulty in detection. However, there are no valid non-invasive screening tools available for GC. Here we establish a non-invasive method that employs exhaled volatolomics and ensemble learning to detect GC. We developed a comprehensive mass spectrometry-based procedure and determined of a wide range of volatolomics from 314 breath samples. The discovery, identification and verification research screened a biomarker panel to distinguish GC from controls. This panel has achieved 0.90 (0.87-0.94, 95%CI) accuracy, with an area under curve (AUC) of 0.92 (0.89-0.94, 95%CI) in discovery cohort and 0.88 (0.83-0.91, 95%CI) accuracy with an AUC of 0.91 (0.87-0.93, 95%CI) in replication cohort, which outperformed traditional serum markers. Single-cell sequencing and gene set enrichment analysis revealed that these exhaled markers originated from aldehyde oxidation and pyruvate metabolism. Our approach advances the design of exhaled analysis for GC detection and holds promise as a non-invasive method to the clinic.

A data-driven model for power system operating costs based on different types of wind power fluctuations.

The stochastic and intermittent features of wind power as well as the high percentage of wind power grid-connected significantly increase the additional operating costs of the power system. It is difficult to accurately calculate the impact of complex fluctuations in wind power on additional operating costs. To solve the above problems, a power system operating cost model adapted to various wind power fluctuation processes is established. Firstly, based on a two-layer clustering strategy, different types of wind power fluctuations are obtained. Then, a production simulation model of the power system with renewable energy is established. The production simulation model costs include thermal plant operating costs, energy storage system operating costs, positive reserve costs and negative reserve costs. With the optimization objective of minimizing the total operating cost of the power system, realistic and representative system operating parameters and cost samples are obtained for various wind power fluctuations and different wind power grid-connected scenarios. Finally, a data-driven approach based on a deep neural network algorithm is proposed to achieve precise mapping between wind energy fluctuations and the operating costs of power systems and thermal power units, and the operating costs of the power system during the four seasons with different types of wind power fluctuations can be precisely analyzed. The results demonstrate that the method proposed in this paper has high simulation accuracy for the overall simulation operating cost of the power system and the operating cost of thermal power plants. The simulation errors are 4%-18% and 3%-13%, respectively, which verified the effectiveness of the method.

Wind Turbine Condition Monitoring Using the SSA-Optimized Self-Attention BiLSTM Network and Changepoint Detection Algorithm.

Condition-monitoring and anomaly-detection methods used for the assessment of wind turbines are key to reducing operation and maintenance (O&M) cost and improving their reliability. In this study, based on the sparrow search algorithm (SSA), bidirectional long short-term memory networks with a self-attention mechanism (SABiLSTM), and a binary segmentation changepoint detection algorithm (BinSegCPD), a condition-monitoring method (SSA-SABiLSTM-BinSegCPD, SSD) used for wind turbines is proposed. Specifically, the self-attention mechanism, which can mine the nonlinear dynamic characteristics and spatial-temporal features inherent in the SCADA time series, was introduced into a two-layer BiLSTM network to establish a normal-behavior model for wind turbine key components. Then, as a result of the advantages of searching precision and convergence rate methods, the sparrow search algorithm was employed to optimize the constructed SABiLSTM model. Moreover, the BinSegCPD algorithm was applied to the predicted residual sequence to achieve the automatic identification of deterioration conditions for wind turbines. Case studies conducted on multiple wind turbines located in south China showed that the established SSA-SABiLSTM model was superior to other contrast models, achieving a better prediction precision in terms of RMSE, MAE, MAPE, and R2. The MAE, RMSE, and MAPE of SSA-SABiLSTM were 0.2543 °C, 0.3412 °C, and 0.0069, which were 47.23%, 42.19%, and 53.38% lower than those of SABiLSTM, respectively. The R2 of SABiLSTM was 0.9731, which was 4.6% higher than that of SABiLSTM. The proposed SSD method can detect deterioration conditions 47-120 h in advance and trigger fault alarm signals approximately 36 h ahead of the actual failure time.

Virus-induced breath biomarkers: A new perspective to study the metabolic responses of COVID-19 vaccinees.

Coronavirus disease 2019 (COVID-19) vaccines can protect people from the infection; however, the action mechanism of vaccine-mediated metabolism remains unclear. Herein, we performed breath tests in COVID-19 vaccinees that revealed metabolic reprogramming induced by protective immune responses. In total, 204 breath samples were obtained from COVID-19 vaccinees and non-vaccinated controls, wherein numerous volatile organic compounds (VOCs) were detected by comprehensive two-dimensional gas chromatography and time-of-flight mass spectrometry system. Subsequently, 12 VOCs were selected as biomarkers to construct a signature panel using alveolar gradients and machine learning-based procedure. The signature panel could distinguish vaccinees from control group with a high prediction performance (AUC, 0.9953; accuracy, 94.42%). The metabolic pathways of these biomarkers indicated that the host-pathogen interactions enhanced enzymatic activity and microbial metabolism in the liver, lung, and gut, potentially constituting the dominant action mechanism of vaccine-driven metabolic regulation. Thus, our findings of this study highlight the potential of measuring exhaled VOCs as rapid, non-invasive biomarkers of viral infections. Furthermore, breathomics appears as an alternative for safety evaluation of biological agents and disease diagnosis.

Experimental and Numerical Study of the Ultimate Flexural Capacity of a Full-Size Damaged Prestressed Concrete Box Girder Strengthened with Bonded Steel Plates.

Using steel plates attached with epoxy resin adhesive to strengthen prestressed reinforced concrete bridges has become a common method to increase bearing capacity in engineering because of the simple technology, low cost and good strengthening effects. The strengthening method of steel plates has been gradually applied to repair damaged bridges in practical engineering. After a cross-line box girder bridge was struck by a vehicle, the steel bars and concrete of a damaged girder were repaired and strengthened by steel plates, and then the ultimate bending bearing capacity was studied through a destructive test. The results of the destructive test were compared with those of an undamaged girder to verify the effect of the repair and strengthening of the damaged girder. The results showed that the actual flexural bearing capacity of the repaired girder strengthened by steel plates was 1.63 times the theoretical bearing capacity, 36.7% more than that of the damaged girder and 95.3% of that of an undamaged girder. The flexural cracking moment of the repaired girder strengthened by steel plates reached 66.3% of that of the undamaged girder. The maximum crack width decreased by 24.6%, and the maximum deflection increased by 2.7%, compared with the undamaged girder when the repaired girder strengthened by steel plates finally failed. Moreover, this method of attaching steel plates can increase the ductility of bridges and reduce the degree of cracking. Additionally, the actual safety factor of the repaired girder was greater than three, and it had a large safety reserve.

Cost-Effectiveness Analysis of Toripalimab Plus Paclitaxel and Cisplatin as First-Line Treatment for Advanced or Metastatic Esophageal Squamous Cell Carcinoma.

INTRODUCTION: First-line treatment with toripalimab plus paclitaxel and cisplatin (TTP) is very effective for patients with advanced or metastatic esophageal squamous cell carcinoma (ESCC) in China, although its effects on economic burden are unknown. The present study aimed to evaluate the cost-effectiveness of TTP from the perspective of the Chinese healthcare system. METHODS: A Markov model was established to evaluate the cost-effectiveness of first-line treatment with TTP for patients with advanced or metastatic ESCC. Survival data were derived from the JUPITER-06 trial. The costs and utilities were gathered from the literature and a local database. The primary outcomes were total costs, quality-adjusted life year (QALY), and incremental cost-effectiveness ratios (ICERs) at a willingness-to-pay (WTP). One-way and probability sensitivity analyses were used to evaluate the robustness of the model. RESULTS: The total cost of TTP was $123,646.43 and gained 1.10 QALYs, while the paclitaxel and cisplatin (TP) chemotherapy group yielded 0.84 QALY at cost of $16,259.65. First-line TTP treatment yielded an incremental cost of $7,386.78 with an additional 0.26 QALY, providing an ICER of $28,348.42/QALY, which was lower than the WTP threshold ($36,257.91) in China. CONCLUSIONS: TTP was likely more cost-effective than TP chemotherapy from the perspective of the Chinese healthcare system. This study may provide evidence required to establish decision-making criteria to support guidance for cost-effective selection of an immunotherapeutic regimen.

Research on the Derated Power Data Identification Method of a Wind Turbine Based on a Multi-Gaussian-Discrete Joint Probability Model.

This paper focuses on how to identify normal, derated power and abnormal data in operation data, which is key to intelligent operation and maintenance applications such as wind turbine condition diagnosis and performance evaluation. Existing identification methods can distinguish normal data from the original data, but usually remove power curtailment data as outliers. A multi-Gaussian-discrete probability distribution model was used to characterize the joint probability distribution of wind speed and power from wind turbine SCADA data, taking the derated power of the wind turbine as a hidden random variable. The maximum expectation algorithm (EM), an iterative algorithm derived from model parameters estimation, was applied to achieve the maximum likelihood estimation of the proposed probability model. According to the posterior probability of the wind-power scatter points, the normal, derated power and abnormal data in the wind turbine SCADA data were identified. The validity of the proposed method was verified by three wind turbine operational data sets with different distribution characteristics. The results are that the proposed method has a degree of universality with regard to derated power operational data with different distribution characteristics, and in particular, it is able to identify the operating data with clustered distribution effectively.

Experimental-Numerical Study on the Flexural Ultimate Capacity of Prestressed Concrete Box Girders Subjected to Collision.

Precise evaluation for flexural ultimate capacity of bridges which are subjected to the collision of over-height trucks is essential for making decisions on corresponding maintenance, strengthening or replacement. When the span of a cross-line continuous bridge with a double-box girder was hit by an overly high vehicle, the concrete floor of one girder was severely damaged, and part of the prestressed strands and reinforcements in the girder were broken. After the double-box girder was removed and separated into two single box girders, the ultimate flexural capacity of both box girders was studied by destructive tests, and a comparison was made between the damaged and undamaged girders. Moreover, finite element analysis was conducted to simulate the failure process. The results show that the flexural bearing capacity of the damaged box girder decreased by 33%, but it was still 1.07 times greater than the design bearing capacity, which basically meets the design requirements. Also, the damaged box girder showed a desirable serviceable limit state for three-axle vehicles and five-axle vehicles, but showed an undesirable serviceable limit state for six-axle vehicles. This study shows that repairing or strengthening the damaged span may be better than demolishing and rebuilding the whole superstructure bridge.

Icariin regulates miR-23a-3p-mediated osteogenic differentiation of BMSCs via BMP-2/Smad5/Runx2 and WNT/ß-catenin pathways in osteonecrosis of the femoral head.

Icariin is commonly used for the clinical treatment of osteonecrosis of the femoral head (ONFH). miR-23a-3p plays a vital role in regulating the osteogenic differentiation of bone marrow-derived mesenchymal stem cells (BMSCs). The present study aimed to investigate the roles of icariin and miR-23a-3p in the osteogenic differentiation of BMSCs and an ONFH model. BMSCs were isolated and cultured in vitro using icariin-containing serum at various concentrations, and BMSCs were also transfected with a miR-23a inhibitor. The alkaline phosphatase (ALP) activity and cell viability as well as BMP-2/Smad5/Runx2 and WNT/ß-catenin pathway-related mRNA and protein expression were measured in BMSCs. Additionally, a dual-luciferase reporter assay and pathway inhibitors were used to verify the relationship of icariin treatment/miR-23a and the above pathways. An ONFH rat model was established in vivo, and a 28-day gavage treatment and lentivirus transfection of miR-23a-3p inhibitor were performed. Then, bone biochemical markers (ELISA kits) in serum, femoral head (HE staining and Digital Radiography, DR) and the above pathway-related proteins were detected. Our results revealed that icariin treatment/miR-23a knockdown promoted BMSC viability and osteogenic differentiation as well as increased the mRNA and protein expression of BMP-2, BMP-4, Runx2, p-Smad5, Wnt1 and ß-catenin in BMSCs and ONFH model rats. In addition, icariin treatment/miR-23a knockdown increased bone biochemical markers (ACP-5, BAP, NTXI, CTXI and OC) and improved ONFH in ONFH model rats. In addition, a dual-luciferase reporter assay verified that Runx2 was a direct target of miR-23a-3p. These data indicated that icariin promotes BMSC viability and osteogenic differentiation as well as improves ONFH by decreasing miR-23a-3p levels and regulating the BMP-2/Smad5/Runx2 and WNT/ß-catenin pathways.

Floral-like enhancement might reflect an active liver tuberculous lesion to avoid systemic hematogenous dissemination by surgery: A case report.

After primary dissemination of Mycobacterium tuberculosis bacillus infection that is localized in liver, disease progression and changes to immune function in the body occur. Various forms of tuberculosis, including granuloma, caseous necrosis, liquefactive necrosis, fibrosis, and calcification, occur that could be presented at different stages, and imaging examination shows findings that are consistent with these stages. Not all liver tuberculosis patients are suitable for liver resection, and preoperative imaging examination and pathological immunohistochemical results could be used to determine whether tuberculosis was active, avoid unnecessary liver resection, and prevent the postoperative spread of tuberculosis. Here, we reported a case of miliary tuberculosis, pelvic tuberculosis, and tuberculous abscess of the thigh muscle in a 51-year-old man after liver lesion resection. The liver lesion was confirmed to be tuberculosis by surgical pathology, which is rare and has not been previously reported. The purpose of this case report is to remind radiologists of the importance of the floral-like enhancement and to estimate whether liver tuberculosis is active. This will help to guide clinicians to determine the timing of surgery, avoid unnecessary liver resection, and avoid hematogenous transmission.

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Study on antibacterial mechanism of copper-bearing austenitic antibacterial stainless steel by atomic force microscopy.

A study was made on the antibacterial mechanism of copper-bearing austenitic antibacterial stainless steel by a series of methods such as atomic force microscopy (AFM) observation, force-distance curves and inductively coupled plasma mass spectrometer test. It was observed by AFM that the structure of the outer cell membrane responsible for the cell permeability was substantially changed for the bacteria after contacting with the antibacterial stainless steel, showing that cell walls were seriously damaged and a lot of contents in the cells leaked. It was also found that the adhesion force of bacteria to antibacterial stainless steel was considerably greater than that to the contrast steel, indicating that the electrostatic forces by Cu(2+ )being an important factor for killing bacteria.

[Anatomic observation of the prostate for clinical application of local drug injection through the rectum for chronic prostatitis].

OBJECTIVE: To provide anatomic evidences for local drug injection through the rectum for treating chronic prostatitis and observe the therapeutic effect of this treatment. METHODS: Anatomic observation of the median sagittal plane of 16 male pelvic specimens was conducted, with special attention to the structures between the prostate and the rectum. The distance from the posterior wall of the prostate to the anterior wall of the rectum, the scope that allowed the entry of the puncture needle, the appropriate puncture depth, and the distance from the anus to the safe were carefully measured. Analysis of the clinical record was performed in 51 chronic prostatitis cases treated with local injection of prednisolone, antibiotic and lidocaine through the rectum. RESULTS: Only some fat tissues and venous plexus were found between the prostate and the anterior wall of the rectum, and the distance from posterior wall of the prostate to the anterior wall of the rectum averaged 5.773+/-0.710 mm. The scope for possible puncture was 15.408 8+/-1.438 2 mm with a depth of 15.703 1+/-0.944 1 mm. The maximum and minimum distances from the anus to the puncture were 47.594+/-2.432 mm and 35.781+/-1.850 mm, respectively. Among the 51 cases investigated, the total cure rate was 84.31%, and improvement was achieved in 13.73% of the case, with only one case (2%) failed to respond to the treatment. CONCLUSION: Local drug injection through the rectum can be ideal for the treatment of prostatitis for its safety and effectiveness.