Hypoxia activates macrophage-NLRP3 inflammasome promoting atherosclerosis via PFKFB3-driven glycolysis.

The onset and progression of atherosclerosis are closely linked to the involvement of macrophages. While the contribution of NLRP3 inflammasome activation to the creation of a local highly inflammatory microenvironment is well recognized, the precise triggers remain unclear. In this study, we aimed to investigate the regulatory mechanism of NLRP3 inflammasome activation in response to hypoxia-induced glycolysis involving PFKFB3 in the development of atherosclerosis. To develop an atherosclerosis model, we selected ApoE knockout mice treated with a high-fat western diet. We then quantified the expression of HIF-1α, PFKFB3, and NLRP3. In addition, we administered the PFKFB3 inhibitor PFK158 during atherosclerosis modeling. The glycolytic activity was subsequently determined through 18F-FDG micro-PET/CT, ex vivo glucose uptake, and ECAR analysis. Furthermore, we employed lipopolysaccharide (LPS) and TNF-α to induce the differentiation of bone marrow-derived macrophages (BMDMs) into M1-like phenotypes under both hypoxic and normoxic conditions. Our histological analyses revealed the accumulation of PFKFB3 in human atherosclerotic plaques, demonstrating colocalization with NLRP3 expression and macrophages. Treatment with PFK158 reduced glycolytic activity and NLRP3 inflammasome activation, thereby mitigating the occurrence of atherosclerosis. Mechanistically, hypoxia promoted glycolytic reprogramming and NLRP3 inflammasome activation in BMDMs. Subsequent blocking of either HIF-1α or PFKFB3 downregulated the NLRP3/Caspase-1/IL-1ß pathway in hypoxic BMDMs. Our study demonstrated that the HIF-1α/PFKFB3/NLRP3 axis serves as a crucial mechanism for macrophage inflammation activation in the emergence of atherosclerosis. The therapeutic potential of PFKFB3 inhibition may represent a promising strategy for atheroprotection.

A deep fuzzy model for diagnosis of COVID-19 from CT images.

From early 2020, a novel coronavirus disease pneumonia has shown a global "pandemic" trend at an extremely fast speed. Due to the magnitude of its harm, it has become a major global public health event. In the face of dramatic increase in the number of patients with COVID-19, the need for quick diagnosis of suspected cases has become particularly critical. Therefore, this paper constructs a fuzzy classifier, which aims to detect infected subjects by observing and analyzing the CT images of suspected patients. Firstly, a deep learning algorithm is used to extract the low-level features of CT images in the COVID-CT dataset. Subsequently, we analyze the extracted feature information with attribute reduction algorithm to obtain features with high recognition. Then, some key features are selected as the input for the fuzzy diagnosis model to the training model. Finally, several images in the dataset are used as the test set to test the trained fuzzy classifier. The obtained accuracy rate is 94.2%, and the F1-score is 93.8%. Experimental results show that, compared with the deep learning diagnosis methods widely used in medical image analysis, the proposed fuzzy model improves the accuracy and efficiency of diagnosis, which consequently helps to curb the spread of COVID-19.

Mining-Induced Time-Series Deformation Investigation Based on SBAS-InSAR Technique: A Case Study of Drilling Water Solution Rock Salt Mine.

Compared to traditional coal mines, the mining-induced dynamic deformation of drilling solution mining activities may result in even more serious damage to surface buildings and infrastructures due to the different exploitation mode. Therefore, long-term dynamic monitoring and analysis of rock salt mines is extremely important for preventing potential geological damages. In this work, the small baseline subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique with Sentinel-1A imagery is utilized to monitor the ground surface deformation of a rock salt mining area. The time-series analysis is carried out to obtain the spatial-temporal characteristics of land subsidence caused by drilling solution mining activities. A typical rock salt mine in Changde, China is selected as the test site. Twenty-four scenes of Sentinel-1A image data acquired from June 2015 to January 2017 are used to obtain the time-series subsidence of the test mine. The temporal-spatial evolution of the derived settlement funnels is revealed. The time-series deformation on typical feature points has been analyzed. Experimental results show that the obtained drilling solution mining-induced subsidence has a spatial characteristic of multiplied peaks along the transversal direction. Temporally, the large-scale surface settlement for the rock salt mine area begins to appear in September 2016, with a time lag of 8 months, and shows an obvious seasonal fluctuation. The maximum cumulative subsidence is detected up to 199 mm. These subsiding characteristics are consistent with the connected groove mining method used in drilling water solution mines. To evaluate the reliability of the results, the SBAS-derived results are compared with the field-leveling measurements. The estimated root mean square error (RMSE) of ±11 mm indicates a high consistency.

Research on the development strategy selection of the new energy vehicle industry from the perspective of green credit-Based on the foursquare evolutionary game analysis.

Developing new energy vehicles is vital to promote green development and the harmonious coexistence of humans and nature. It is also the only way to help China move from a significant automobile country to a powerful automobile country. Based on the background of the "recession" of government subsidies and considering the importance of green credit in promoting green and low-carbon transformation, this paper constructs a four-party evolutionary game model that includes government, automotive companies, banks, and consumers to analyze the stability of the strategic choices of various parties in the development process of the new energy vehicle industry. It uses MATLAB simulation tools to analyze the impact of relevant factors on system stability. The research shows that: (1) The government's subsidy mechanism significantly promotes the development of the new energy vehicle industry. Still, there is a subsidy threshold, beyond which the effect will weaken and quickly bring financial pressure. (2) With the gradual decline of government subsidies, the bank's green credit policy has a specific policy complementary effect on the decline of government subsidies. (3) Considering that costs and benefits are the main influencing factors for automotive companies and consumers' strategic choices, the impact of factors such as the punishment of violations, adjustment of subsidy policies, and consumers' environmental awareness must also be paid attention to.

Study on heat transfer law of moving temperature variable gas in thermoacoustic plate stack.

Taking gas and the heat transfer process between gas and plate as the research object, the mathematical model of heat transfer in one working cycle by moving variable temperature air mass under the action of sound field is established, which provides a new idea for understanding thermoacoustic effect. The influence factors in the model are analyzed and it is found that the amplitude of the air mass has a significant influence on the heat transfer, and the heat transfer of the air mass in one working cycle is proportional to the square of the amplitude. In a certain working environment, the thermoacoustic refrigerator has a critical operating frequency, and only when the operating frequency is greater than the critical frequency can refrigeration be realized. The critical operating frequency is independent of the amplitude and increases with the increase of the stack temperature gradient. With the pressure belly point as the reference position, the greater the distance from the reference position, the greater the critical operating frequency. On this basis, the idea of short plate overlapping is put forward and the formation mechanism of temperature difference between two ends of plate overlapping is explained.

Analysis of forming mechanism and influencing factors of thermoacoustic plate end temperature difference.

In order to explore the formation mechanism and influencing factors of the temperature difference between two ends of the plate stack, an expression of the temperature change of the stack with time was established based on the two-dimensional heat conduction equation. Based on this, the finite element model of heat transfer between a single thermoacoustic plate stack and the gas above it is established in Ansys, and the temperature of the plate stack is solved. When the sound field is constant, the variation law of the temperature of the stack with the working time and space is obtained, and the formation mechanism of the temperature difference between the two ends of the plate stack is revealed. From the calculation results, it is found that the net heat transfer between the gas and the plate stack is mainly reflected in the two ends of the plate stack, and the contribution of the air mass in the middle part is mainly the relay heat transfer. In the process of working, part of the sound work is converted into the internal energy of the air mass, which makes the gas temperature on the surface of the plate rise as a whole. The working frequency, stack length and stack thermal conductivity are taken as the influencing factors. When no load is added, the variation of the temperature of the high and low end of the stack with the working time under different working conditions is analyzed. And the theory of series between short plates is put forward to explain the formation mechanism of large temperature difference between the two ends of the plates. In order to further reduce the cooling temperature of thermoacoustic refrigerator, a new research method and exploration direction are proposed.

SCTV-UNet: a COVID-19 CT segmentation network based on attention mechanism.

The global outbreak of COVID-19 has become an important research topic in healthcare since 2019. RT-PCR is the main method for detecting COVID-19, but the long detection time is a problem. Therefore, the pathological study of COVID-19 with CT image is an important supplement to RT-RCT. The current TVLoss-based segmentation promotes the connectivity of diseased areas. However, normal pixels between some adjacent diseased areas are wrongly identified as diseased pixels. In addition, the proportion of diseased pixels in CT images is small, and the traditional BCE-based U-shaped network only focuses on the whole CT without diseased pixels, which leads to blurry border and low contrast in the predicted result. In this way, this paper proposes a SCTV-UNet to solve these problems. By combining spatial and channel attentions on the encoder, more visual layer information are obtained to recognize the normal pixels between adjacent diseased areas. By using the composite function DTVLoss that focuses on the pixels in the diseased area, the problem of blurry boundary and low contrast caused by the use of BCE in traditional U-shaped networks is solved. The experiment shows that the segmentation effect of the proposed SCTV-UNet has significantly improved by comparing with the SOTA COVID-19 segmentation networks, and can play an important role in the detection and research of clinical COVID-19.

A Deep Learning Model for Diagnosing COVID-19 and Pneumonia through X-ray.

BACKGROUND: The new global pandemic caused by the 2019 novel coronavirus (COVID-19), novel coronavirus pneumonia, has spread rapidly around the world, causing enormous damage to daily life, public health security, and the global economy. Early detection and treatment of COVID-19 infected patients are critical to prevent the further spread of the epidemic. However, existing detection methods are unable to rapidly detect COVID-19 patients, so infected individuals are not detected in a timely manner, which complicates the prevention and control of COVID-19 to some extent. Therefore, it is crucial to develop a rapid and practical COVID-19 detection method. In this work, we explored the application of deep learning in COVID-19 detection to develop a rapid COVID-19 detection method. METHODS: Existing studies have shown that novel coronavirus pneumonia has significant radiographic performance. In this study, we analyze and select the features of chest radiographs. We propose a chest X-Ray (CXR) classification method based on the selected features and investigate the application of transfer learning in detecting pneumonia and COVID-19. Furthermore, we combine the proposed CXR classification method based on selected features with transfer learning and ensemble learning and propose an ensemble deep learning model based on transfer learning called COVID-ensemble to diagnose pneumonia and COVID-19 using chest x-ray images. The model aims to provide an accurate diagnosis for binary classification (no finding/pneumonia) and multivariate classification (COVID-19/No findings/ Pneumonia). RESULTS: Our proposed CXR classification method based on selection features can significantly improve the CXR classification accuracy of the CNN model. Using this method, DarkNet19 improved its binary and triple classification accuracies by 3.5% and 5.78%, respectively. In addition, the COVIDensemble achieved 91.5% accuracy in the binary classification task and 91.11% in the multi-category classification task. The experimental results demonstrate that the COVID-ensemble can quickly and accurately detect COVID-19 and pneumonia automatically through X-ray images and that the performance of this model is superior to that of several existing methods. CONCLUSION: Our proposed COVID-ensemble can not only overcome the limitations of the conventional COVID-19 detection method RT-PCR and provide convenient and fast COVID-19 detection but also automatically detect pneumonia, thereby reducing the pressure on the medical staff. Using deep learning models to automatically diagnose COVID-19 and pneumonia from X-ray images can serve as a fast and efficient screening method for COVID-19 and pneumonia.

Thermo-/pH-Dual-Sensitive PEG/PAMAM Nanogel: Reaction Dynamics and Plugging Application of CO2 Channeling.

Smart hydrogels, owing to their exceptional viscoelastic and deformable capacity in response to environmental stimulation involving temperature and pH, have been successfully applied in oilfields for purposes such as water and/or gas shutoff treatments. However, the CO2 breakthrough problem in low permeability reservoirs has not been well solved. In this work, a rheological method-based Avrami dynamics model and Dickinson dynamics model were employed to investigate the dynamic gelation process of thermo-/pH-dual-sensitive PEG/PAMAM nanogels to further our understanding of the microstructure of their gelation and pertinence plugging application. Plugging experiments were performed by alternating injections of CO2 and hydrogel solution in a slug type on three fractured low permeability cores with a backpressure of 13 MPa. The nanogels presented a secondary growth pattern from three to one dimension from micrometer to nanometer size with a morphological transformation from a sphere to an irregular ellipsoid or disk shape. The phase transition temperature was 50 °C, and the phase transition pH was 10. If both or either were below these values, the hydrogel swelled; otherwise, it shrank. Plugging results show that the plugging efficiency was higher than 99%. The maximum breakthrough pressure was 19.93 MPa, and the corresponding residual pressure remained 17.64 MPa for a 10 mD core, exhibiting great plugging performance and high residual resistance after being broken through by CO2.

The Predictive Value of Pre-operative N-Terminal Pro-B-Type Natriuretic Peptide in the Risk of Acute Kidney Injury After Non-cardiac Surgery.

Objective: To evaluate the association between N-terminal pro-B-type natriuretic peptide (NT-proBNP) and risk of post-operative acute kidney injury (PO-AKI). Methods: The electronic medical records and laboratory results were obtained from 3,949 adult patients (≥18 years) undergoing non-cardiac surgery performed between 1 October 2012 to 1 October 2019 at the Third Xiangya Hospital, Central South University, China. Collected data were analyzed retrospectively. Results: In all, 5.3% (209 of 3,949) of patients developed PO-AKI. Pre-operative NT-proBNP was an independent predictor of PO-AKI. After adjustment for significant variables, OR for AKI of highest and lowest NT-proBNP quintiles was 1.96 (95% CI, 1.04-3.68, P = 0.008), OR per 1-unit increment in natural log transformed NT-proBNP was 1.20 (95% CI, 1.09-1.32, P < 0.001). Compared with clinical variables alone, the addition of NT-proBNP modestly improved the discrimination [change in area under the curve(AUC) from 0.82 to 0.83, ΔAUC=0.01, P = 0.024] and the reclassification (continuous net reclassification improvement 0.15, 95% CI, 0.01-0.29, P = 0.034, improved integrated discrimination 0.01, 95% CI, 0.002-0.02, P = 0.017) of AKI and non-AKI cases. Conclusions: Results from our retrospective cohort study showed that the addition of pre-operative NT-proBNP concentrations could better predict post-operative AKI in a cohort of non-cardiac surgery patients and achieve higher net benefit in decision curve analysis.

SRAS-net: Low-resolution chromosome image classification based on deep learning.

Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super-resolution network, Self-Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS-net. The method first inputs the low-resolution chromosome images into the super-resolution network to generate high-resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state-of-the-art methods.

Corrigendum: The predictive value of pre-operative N-terminal pro-B-type natriuretic peptide in the risk of acute kidney injury after non-cardiac surgery.

[This corrects the article DOI: 10.3389/fmed.2022.898513.].

Multiclassification of Endoscopic Colonoscopy Images Based on Deep Transfer Learning.

With the continuous improvement of human living standards, dietary habits are constantly changing, which brings various bowel problems. Among them, the morbidity and mortality rates of colorectal cancer have maintained a significant upward trend. In recent years, the application of deep learning in the medical field has become increasingly spread aboard and deep. In a colonoscopy, Artificial Intelligence based on deep learning is mainly used to assist in the detection of colorectal polyps and the classification of colorectal lesions. But when it comes to classification, it can lead to confusion between polyps and other diseases. In order to accurately diagnose various diseases in the intestines and improve the classification accuracy of polyps, this work proposes a multiclassification method for medical colonoscopy images based on deep learning, which mainly classifies the four conditions of polyps, inflammation, tumor, and normal. In view of the relatively small number of data sets, the network firstly trained by transfer learning on ImageNet was used as the pretraining model, and the prior knowledge learned from the source domain learning task was applied to the classification task about intestinal illnesses. Then, we fine-tune the model to make it more suitable for the task of intestinal classification by our data sets. Finally, the model is applied to the multiclassification of medical colonoscopy images. Experimental results show that the method in this work can significantly improve the recognition rate of polyps while ensuring the classification accuracy of other categories, so as to assist the doctor in the diagnosis of surgical resection.

[Spermatic vein and artery ligation for varicocele].

OBJECTIVE: To assess the influence of spermatic vein and artery ligation on testicular hemodynamics, spermatogenesis and testis volume in varicocele patients. METHODS: Eighty-eight varicocele patients were randomly divided into a spermatic vein and artery ligation (n = 46) and a spermatic vein and artery preservation group (n = 42). The testicular hemodynamic parameters, testis volume and results of semen analyses were obtained before and 6 months after the surgery and compared between the two groups. RESULTS: There were no significant differences in peak systolic velocity (V(max)), end diastolic velocity (V(min)), mean enveloped velocity (V(mean)) and V(min) of the capsular artery (CA) either between the ligation and preservation groups (P > 0.05) or between pre- and post-operation (P > 0.05). Sperm density, vitality and motility were significantly improved after surgery (P < 0.01), with no significant differences between the two groups (P > 0.05). No significant difference was found in the testis volume between the two groups before and after the operation (P > 0.05). CONCLUSION: Spermatic vein and artery ligation in varicocelectomy does not affect the testicular hemodynamics, spermatogenesis and testis volume of varicocele patients. Both the spermatic vein and artery should be ligated when necessary.

The efficiency of migration and profile control with emulsion systems in class III reservoirs.

In order to reveal the migration law and profile control efficiency of the emulsion system in the core of class III reservoir (the permeability less than 100 mD), the influence of the size and distribution of the droplets, the effect of the migration law and the adjustment of the interlayer contradiction to class III reservoir are studied in this paper. By measuring the droplet distribution in the emulsion system, we found that the average droplet diameter decreases with the increase in water cut. But, the droplet distribution is the most uniform when the water cut is 50%, which is the transition point of the emulsion. Through the core seepage test, the pressure is measured when the emulsion system is flowing through the core. It can be seen that the emulsion flowing is related to the resistance coefficient, the viscosity of the chemical agent and the core permeability; that is, the greater the resistance coefficient, the greater the viscosity of the chemical agent, the smaller the core permeability is and the higher the level of the emulsion is. The matching chart between different emulsion systems and layers with different permeability of class III reservoir was established. The effect of profile control with different permeability contrasts was predicted according to the matching chart. The emulsification has a positive effect on the enhancement of recovery when the permeability contrast is small, but a negative effect when the permeability contrast is larger. The study of the emulsion migration and profile control efficiency in class III reservoir are of great significance for understanding the emulsification in class III reservoirs.

Adaptive internal model guidance law for weaving maneuvering target.

For the terminal phase guidance problem of the missile intercepting weaving maneuvering target, an adaptive internal model guidance laws in the three-dimensional (3-D) engagement space is proposed in this paper. The guidance law adopts the disturbance rejection theory by treating the target weaving maneuvering accelerations as external disturbance, which comprises of nominal part and adaptive part. The nominal part based on feedback linearization method ensures the whole guidance system stable and the adaptive part based on internal model principle is used to recover the disturbance signals on-line to reject the target maneuver asymptotically. The algorithm guarantees the whole guidance system with satisfying performance both in transient and steady state on the effect of target maneuver on guidance system. The stability analyses and theory proof are provided in this paper. At last, numerical simulations are carried out to illustrate the effectiveness of the proposed guidance law.

Five-year follow-up results of a randomized controlled trial comparing bipolar plasmakinetic and monopolar transurethral resection of the prostate.

PURPOSE: To report the 5-year follow-up results of a randomized controlled trial comparing bipolar transurethral resection of the prostate (TURP) with standard monopolar TURP for the treatment of benign prostatic obstruction (BPO). MATERIALS AND METHODS: A total of 220 patients were randomized to bipolar plasmakinetic TURP (PK-TURP) or monopolar TURP (M-TURP). Catheterization time was the primary endpoint of this study. Secondary outcomes included operation time, hospital stay, as well as decline in postoperative serum sodium and hemoglobin levels. All patients were assessed preoperatively and followed-up at 1, 6, 12, 24, 36, 48, and 60 months postoperatively. Parameters assessed included quality of life, transrectal ultrasound, serum prostate-specific antigen level, postvoid residual urine volume, maximum urinary flow rates (Q(max)), and International Prostate Symptom Score. Patient baseline characteristics, perioperative data including complications, and postoperative outcomes were compared. Complication occurrence was graded according to the modified Clavien classification system. RESULTS: PK-TURP was significantly superior to M-TURP in terms of operation time, intraoperative irrigation volume, resected tissue weight, decreases in hemoglobin and sodium, postoperative irrigation volume and time, catheterization time, and hospital stay. At 5 years postoperatively, efficacy was comparable between arms. No differences were detected in safety outcomes except that the clot retention rate was significantly greater after M-TURP. CONCLUSION: Our results indicate that PK-TURP is equally as effective in the treatment of BPO, but has a more favorable safety profile in comparison to M-TURP. The clinical efficacy of PK-TURP is long-lasting and comparable with M-TURP.