The annual incidence of acute pancreatitis is approximately 30 per 100,000, with 20

The role of peripheral blood basophil (PBB) and their telomere length (TL) in the pathogenesis and prognosis of hepatocellular carcinoma (HCC) patients has not been fully studied. The correlation between PBB and TL is the risk of HCC. 30 healthy participants and 80 HCC patients from our hospital were included in this study, and their clinical baseline data and clinical characteristics were statistically analyzed. TL and telomerase in PBB were analyzed using quantitative real-time PCR. Univariate and multivariate Cox regression, Kaplan-Meier survival analysis, and natural splines were used. Healthy participants 66.23 ± 11.20 years and 62.71 ± 13.27 HCC patients showed no significant difference in their gender, age, highest education level, and the distribution of PBB counts. There was a significant difference in their TL. A significant correlation between PBB count and recurrence in HCC patients was observed. Significant spline terms (PBB count: p < 0.01; BAS TL: p < 0.001) confirmed nonlinear associations, justifying avoiding dichotomy. Patients with higher PBB counts (>0.125⋅109) having a higher risk of recurrence (HR reaching 182.49 [5.00-6664.20], p<0.01). HCC patients with PBB-TL levels greater than 1.5 have a higher risk of recurrence (HR peaking at 66.62 [8.27-536.51], p<0.001). Sensitivity analysis adjusted spline degrees of freedom (df) to 2, 3, 4. AIC values varied minimally (PBB count: 87.2–89.3; BAS TL: 106.1–110.1), indicating model robustness to df changes. The preliminary results of this study support the use of PBB counting and TL for predicting HCC recurrence, supporting reliable nonlinear modeling.

To accurately predict the remaining useful life (RUL) of rolling bearings under strong noise interference, this paper proposes an RUL prediction method based on adaptive Variational Mode Decomposition (VMD) and a hybrid Temporal Convolutional Network-Gated Recurrent Unit-Self-Attention (TCN-GRU-SA) framework. First, a parameter-optimized VMD algorithm is developed by integrating the Grey Wolf Optimizer (GWO) with VMD to extract effective intrinsic mode components (IMFs) and reconstruct denoised signals, thereby mitigating the impact of strong background noise. Subsequently, time-domain degradation features are extracted from the reconstructed signals to generate more representative feature datasets. These degradation features are then fed into a parallel TCN-GRU-SA prediction model. To enhance the model's generalization capability and RUL prediction performance, the proposed hybrid architecture combines a Temporal Convolutional Network (TCN), which captures local temporal patterns, a Gated Recurrent Unit (GRU) for modeling long-term dependencies, and a Self-Attention (SA) mechanism to prioritize critical degradation-related features. Experimental validation on the PHM2012 rolling bearing accelerated lifetime dataset demonstrates that the proposed method achieves superior noise robustness and prediction accuracy compared to existing approaches. Specifically, it reduces the root mean square error (RMSE) by 18.7 % and improves the coefficient of determination (R2) by 12.3 % under high-noise conditions, confirming its effectiveness in industrial predictive maintenance applications.

To solve the problems with the traditional Variational Mode Decomposition (VMD) method applied in Structural Health Monitoring (SHM), such as parameter sensitivity and high complexity, which can lead to low efficiency and accuracy in data feature extraction. A novel fault diagnosis method is proposed for axle box bearings. Firstly, the Morlet Wavelet combined with a sliding window approach is used to determine the inherent period of the data and define the range of the bearing fault feature data. Secondly, an improved VMD method based on cosine distance is introduced to adaptively determine the optimal number of decomposition modes of fault features. This approach effectively addresses the parameter sensitivity issue and enhances feature extraction efficiency. Thirdly, the Uniform Manifold Approximation and Projection (UMAP) method is used to compress the processed data to achieve online bearing monitoring and reduce data processing scale. Finally, the Convolutional Neural Networks (CNN) method is used to diagnose and classify the fault features to validate the efficiency of the proposed method. The experiment results indicate that the proposed approach yields a significant improvement in bearing fault diagnosis accuracy compared with other methods. Under noise-free conditions, the proposed method achieves an accuracy of approximately 98% across different datasets, which is notably higher than that of other diagnostic methods.

Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality. Circulating tumor cells (CTCs) are widely recognized as the origin of hematogenous tumor metastasis. Cortactin (CTTN) plays a critical role in epithelial-mesenchymal transition, which is a critical step for malignant progression of tumor. This study aims to clarify the correlation between CTCs and recurrence in HCC. A total of 80 clinical patients were participated in this study and received 50 qualified peripheral blood samples. Patient clinical basic information was collected, CTC cells are isolated and counted at baseline to analyze the sensitivity and correlation of CTC counts as a patient recurrence biomarker. In-situ cancer mice of HCC were used to observe the effects of CTTN overexpression and knockdown on CTCs. The sensitivity and specificity of CTCs detection (>4.5/5 mL) were 90

Bearing is the key component to determine the health of machinery, and it is of great significance to monitor its working status in real time and predict its remaining useful life. In recent years, the RUL prediction method based on deep learning has been widely used and achieved good prediction results. Here, a bearing life prediction method based on convolution neural network (CNN), long short term memory (LSTM) and attention mechanism (AM) is proposed. First of all, the time domain and frequency domain features of the original vibration signals of rolling bearings are extracted, and the extracted feature set is normalized as the input of CNN. The main function of CNN is to extract spatial features and reduce the dimension of the data. Then, using LSTM to extract the information that may be ignored by CNN, the feature information extracted by CNN-LSTM is input to the attention mechanism for weighting, and the key information is screened. And then more accurately represent the degradation characteristics of the equipment, and finally get the bearing remaining life. The performance of the model is verified by two sets of public data sets, and the experimental results show that it is compared with the CNN-LSTM method. The root mean square error (RMSE) index based on CNN-LSTM-AM method is reduced by 14.6 % and 13.8 % respectively, and the score index is increased by 2.0 % and 2.4 % respectively. The results show that the proposed method has higher accuracy in bearing RUL prediction.

To solve the problem of large filtering error of the fundamental frequency in the composite Fourier transform profilometry and shorten the measurement time needed in the phase shifting profilometry, a symmetrical transformation method (STM) for measuring the three-dimensional (3D) contour of objects is proposed. Only one composite grating is projected on the object in the STM. The grating is composed of overlapping two orthogonal sinusoidal fringe patterns with a pi/2 phase difference and different frequencies. The two sinusoidal deformed patterns with a pi/2 phase difference are demodulated from the deformed composite pattern reflected on the surface of an object. The other two patterns can be obtained through a symmetric transformation with sub-pixel precision. Based on the captured and calculated fringe patterns, the fast reconstruction of the 3D shape of the objects could be achieved. The STM has broad application prospects in the real-time 3D measurement field due to its single-shot feature.

BACKGROUND:The social motivation hypothesis proposes that the social deficits of autism spectrum disorder (ASD) are related to reward system dysfunction. However, functional connectivity (FC) patterns of the reward network in ASD have not been systematically explored yet.METHODS:The reward network was defined as eight regions of interest (ROIs) per hemisphere, including the nucleus accumbens (NAc), caudate, putamen, anterior cingulate cortex (ACC), ventromedial prefrontal cortex (vmPFC), orbitofrontal cortex (OFC), amygdala, and insula. We computed both the ROI-wise resting-state FC and seed-based whole-brain FC in 298 ASD participants and 348 typically developing (TD) controls from the Autism Brain Imaging Data Exchange I dataset. Two-sample t-tests were applied to obtain the aberrant FCs. Then, the association between aberrant FCs and clinical symptoms was assessed with Pearson's correlation or Spearman's correlation. In addition, Neurosynth Image Decoder was used to generate word clouds verifying the cognitive functions of the aberrant pathways. Furthermore, a three-way multivariate analysis of variance (MANOVA) was conducted to examine the effects of gender, subtype and age on the atypical FCs.RESULTS:For the within network analysis, the left ACC showed weaker FCs with both the right amygdala and left NAc in ASD compared with TD, which were negatively correlated with the Autism Diagnostic Observation Schedule (ADOS) total scores and Social Responsiveness Scale (SRS) total scores respectively. For the whole-brain analysis, weaker FC (i.e., FC between the left vmPFC and left calcarine gyrus, and between the right vmPFC and left precuneus) accompanied by stronger FC (i.e., FC between the left caudate and right insula) were exhibited in ASD relative to TD, which were positively associated with the SRS motivation scores. Additionally, we detected the main effect of age on FC between the left vmPFC and left calcarine gyrus, of subtype on FC between the right vmPFC and left precuneus, of age and age-by-gender interaction on FC between the left caudate and right insula.CONCLUSIONS:Our findings highlight the crucial role of abnormal FC patterns of the reward network in the core social deficits of ASD, which have the potential to reveal new biomarkers for ASD.

In view of the Doppler effect caused by rotating blades during dynamic measurement, which reduces the accuracy of tip clearance measurement, a dynamic measurement method of turbine tip clearance based on laser self-mixing interferometric ranging considering the Doppler effect is proposed. Firstly, a self-mixing interference model based on the three-mirror Fabry-P & eacute;rot cavity model is proposed. Then, the relationship between distance, frequency, and rotational speed is constructed which lays the foundation for the dynamic measurement of the blade tip clearance. What's more, the composition of the dynamic self-mixing interferometry system including each experimental measurement parameter is described. Finally, the experimental validation is performed to investigate the relationship between the actual measured distance and frequency after compensating for Doppler shift as well as the accuracy of blade tip clearance measurement. Experimental results demonstrate that as the rotational speed gets faster, greater Doppler frequency shift is generated. Meanwhile, the effect on the accuracy of measurement of blade tip clearance is more obvious. The method considering the influence of Doppler effect effectively improves the reliability and accuracy of the dynamic measurement system of blade tip clearance. The standard deviation of dynamic measurement of blade tip clearance can reach up to 13 mu m.

Aiming at monitoring of gearbox faults, a gear fault feature extraction method based on variational mode decomposition (VMD) and multi -scale discrete entropy (MDE) is proposed in this paper. Firstly, the gear fault signal is decomposed into a series of intrinsic modal function (IMF) by VMD with selected parameters; Secondly, the decomposed IMF are extracted by MDE feature extraction method to form a feature sample set; Finally, the least square support vector machine (LSSVM) is used to classify the data set after feature extraction. The experiment results show that the proposed method owns the higher fault diagnosis accuracy than the traditional multi -scale entropy methods.

Since rolling bearings operate in complex and harsh conditions with high speed and heavy load for a long time, their fault signals have the problems of difficulty in feature extraction and low diagnostic accuracy. Therefore, a rolling bearing fault diagnosis method based on variational mode decomposition (VMD) and hierarchical permutation entropy (HPE) is proposed in this paper. Firstly, the fault signals of rolling bearings are decomposed by variational mode decomposition. Secondly, several node signals are obtained after hierarchical decomposition, and the permutation entropy value of the obtained node signals is calculated as the feature vector. Thirdly, a multi-fault classifier based on Bayes is established to realize the fault diagnosis of rolling bearings. Finally, the method is applied to the data of Bearing Center of Case Western Reserve University, and the experimental results verify the effectiveness of the method.

Pachymic acid (PA), exacted from Polyporaceae, has been known for its biological activities including diuretic, dormitive, anti-oxidant, anti-aging, anti-inflammatory and anticancer properties in several types of diseases. Recently, studies have demonstrated that PA could suppress cell growth and induce cell apoptosis in different kinds of cancer cells. But the underlying mechanisms remain poorly elucidated. In the current study, we investigated the effect of pachymic acid on liver cancer cells and its underlying mechanisms. Our results evidenced that pachymic acid effectively inhibited the cell growth and metastatic potential in HepG2 and Huh7 cells. Mechanistically, we revealed that pachymic acid triggered cell apoptosis by increasing caspase 3 and caspase 9 cleavage, upregulating Bax and cytochrome c expression, while reducing the expression of Bcl2. Besides, pachymic acid could markedly inhibit the cell invasion and migration and cell metastatic potential by mediating epithelial-to-mesenchymal transition (EMT) markers and metastasis-associated genes in HepG2 and Huh7 cells. In addition, we demonstrated that FAK-Src-Jun N-terminal kinase (JNK)-matrix metalloproteinase 2 (MMP2) axis was involved in PA-inhibited liver cell EMT. Together, these results contribute to our deeper understanding of the anti-cancer effects of pachymic acid on liver cancer cells. This study also provided compelling evidence that PA might be a potential therapeutic agent for liver cancer treatment.

BACKGROUND:Inattention is a key characteristic of attention deficit hyperactivity disorder (ADHD). Specific brain abnormalities associated with this symptom form a discernible pattern related with ADHD in children (i.e., ADHD related pattern) in our earlier research. The developmental processes of segregation and integration may be crucial to ADHD. However, how brains reconfigure these processes of the ADHD related pattern in different subtypes of ADHD and across sexes remain unclear. METHODS:Nested-spectral partition method was applied to identify effects of subtype and sex on segregation and integration of the ADHD related pattern, using 145 ADHD patients and 135 typically developing controls (TDC) aged 7-14. Relationships between the measures and inattention symptoms were also investigated. RESULTS:Children with ADHD exhibited lower segregation of the ADHD related pattern (p = 1.17 × 10-8) than TDCs. Only the main effect of subtype was significant (p = 1.14 × 10-5). Both ADHD-C (p = 2.16 × 10-6) and ADHD-I (p = 2.87 × 10-6) patients had lower segregation components relative to the TDC. Moreover, segregation components were negatively correlated with inattention scores. CONCLUSIONS:This study identified impaired segregation in the ADHD related pattern of children with ADHD and found shared neural bases among different subtypes and sexes.

Different from the mainstream nonlinear multivariate statistical process monitoring approaches, which usually implement offline feature extraction for a given dataset sampled from the normal operating condition, the proposed method analyzes the inconsistency inherited in each specific online monitored sample of current interest in a timely manner so that a novel monitoring statistic called locality constrained index (LCI) can be simultaneously calculated. Through taking advantage of an explicit nonlinear mapping (ENM), the sampled data is first transformed into a higher-dimensional space so as to explicitly reflect the inherited nonlinearity between measured variables. With the involvement of the online monitored sample in designing the corresponding objective function, the calculation of LCI is then targeted to point out the deviation within the neighbourhood. According to the comparisons with the counterparts, the proposed ENM-LCI-based method demonstrated its salient superiority and consistent effectiveness in nonlinear process monitoring.

A dual-frequency digital Moiré measurement method(DFDM)is proposed for the three-dimensional(3D)shape measurement of an object.The high-and low-frequency fringes are modulated separately along orthogonal direction using different carrier frequencies before being projected onto the measured object.After collecting and demodulating the composite fringe,the digital π phase shift is used to remove the DC component of the demodulated fringes,resulting in high-precision Moiré fringes for calculating the wrapped phase.The unwrapping of the high-frequency wrapped phase is guided by the low-frequency phase to further realistically reconstruct the surface of the measured object.When compared with existing single-shot digital Moiré profilometry,DFDM effectively removes the DC component of the fringe and calculates the phase more accurately.

Hypoxia-inducible factor (HIF) plays a crucial role in regulating the hypoxia-inducible state of nucleus pulposus cells in the intervertebral disc. In addition, the oxygen-dependent conversion of HIF-1α in nucleus pulposus cells is controlled by the protein proline 4-hydroxylase domain (PHD) family. To explore whether HIF-1α can be regulated by modulating PHD homologs to inhibit nucleus pulposus degeneration, PHD2-shRNAs were designed and a PHD2 interference vector was constructed. The expression of HIF-1α and PHD2 genes in the nucleus pulposus cells in the experimental group was detected by RT-PCR, and the expression of HIF-1α, MMP-2, Aggrecan, and Col II proteins in the P0-P3 cells in the experimental group and the control group was detected by Western blotting. The apoptosis of P0-P3 nucleus pulposus cells was detected by flow cytometry. After lentivirus infection, the interference efficiency of the PHD2 gene decreased with cell passage. The apoptosis of P1-P3 cells in the experimental group was significantly lower than that in the control group or degeneration group. Compared to the control group, the expression of HIF-1α, Aggrecan, and Col II proteins increased significantly, and the expression of MMP-2 protein decreased significantly. In conclusion, interference with PHD2 can upregulate the expression of HIF-1α, accelerate anabolism, reduce catabolism, inhibit apoptosis of nucleus pulposus cells, and then these can inhibit degeneration of nucleus pulposus cells. Our results can provide an effective therapeutic target in intervertebral discs during intervertebral disc degeneration, and this may have important clinical significance.

Discography is an important method for diagnosing discogenic low back pain (LBP) and replicating the effects of pain. However, its development is not smooth due to its safety and reliability, which have not been completely confirmed. Beginning with the clinicians using discography, there remains constant controversy. With the continuous progress of related research on discography, clinicians and scholars' understanding of discography is constantly improving. This article reviews the background, clinical application, and safety of discography.

BACKGROUND:The incidence of blunt abdominal injury has significantly increased, and the liver is one of the most commonly damaged organs. In this study, we explored and established a nomogram model for patients with liver ruptures undergoing surgical treatment.METHODS:A retrospective analysis was conducted for 66 adult patients with liver rupture, who were admitted to our hospital from January 2011 to October 2018. These patients were classified into two groups, according to whether the patient had surgery: surgery group (41 cases) and non-surgical group (25 cases). The following data were collected from these two groups of patients: gender, age, injury mechanism, liver damage, laboratory test results, and hospitalization. Multivariate logistic regression analysis was performed to screen the risk factors of patients who require surgical treatment, establish a predictive model based on the selected indicators, and draw the nomogram. Receiver operating characteristic curves and the calibration curve were used to evaluate the predictive value of the model.RESULTS:Compared to the non-surgical group, the body temperature decreased, the heart rate increased, the injury severity score grade increased, the blood urea nitrogen, blood uric acid, creatinine (Cr), arterial partial pressure of oxygen, alkali excess, blood lactic acid and creatine kinase isoenzymes MB (CK-MB) increased, and the HCO- and Glasgow Coma Scale (GCS) coma scores decreased for patients in the surgical group (all, p<0.05). The logistic regression analysis revealed that Cr, arterial partial pressure of oxygen, HCO3-, CK-MB, and the Glasgow coma score were the influencing factors for surgical intervention for liver rupture. The nomo-gram model constructed based on these five indicators had a good degree of discrimination (area under the curve = 0.971, 95% CI: 0.896-0.997) and accuracy.CONCLUSION:A nomogram model established based on Cr, arterial partial pressure of oxygen, HCO3-, CK-MB, the GCS, and other parameters can accurately predict the surgical treatment of patients with liver rupture.

To achieve high accuracy navigation for deep space probes, the △differential one-way ranging (△DOR) measurement relies on the corrections from the time delay difference (TDD) estimation of reference weak Quasar signal, which is guided by the forecasting data compensation. However, disturbed by the interference, the forecasting data sometimes is unavailable, which directly leads to invalid compensation and Quasar TDD estimation failure. In this article, an intelligent 2-D chart method with autodetection is proposed for △DOR measurement without forecasting data support. Consisted of 2-D correlation chart generation, two stages autodetection and rough estimations iteration refining, the proposed scheme realizes the autodetection and TDD blind estimations of weak Quasar signal. The validation experiment is established with developed radio science software defined receiver systems, which are installed in ground stations for space △DOR navigation and positioning. Results show that the presented 2-D correlation chart extends the system weak Quasar signal detection ability with signal SNR can reach –23 dB. The overall accuracy of designed two stages autodetection for Quasar correlation spectral line search is 85.17%. The proposed scheme improves the utilization rate of Quasar data collected by the system by 24.01%. And the average P-F curve fitting residue error which reflects the TDD estimation accuracy is 0.1931/rad. It proves an equivalent precision to accurate forecasting data processing, which meets the △DOR measurement accuracy requirements.

Ball grid array (BGA) packaging is a high-density surface mount technology with the advantages of small size, good heat dissipation, and electrical properties, and is widely applied in the production of large-scale integrated circuits. With the rapid development of IC integration, devices assembled using BGA technology generally have greater complexity. However, BGA defects can seriously affect device performance and bring difficulties to product quality inspection. More importantly, in the process of BGA defect inspection, the high complexity of the device brings unprecedented challenges to the precise location of defects, which means that corresponding inspection methods should be improved. To this end, this paper proposes an automatic detection method for BGA defects based on x-ray imaging. First, x-ray imaging technology is utilized to achieve non-destructive detection of the BGA area inside the device and generate image data. On this basis, a set of algorithms including threshold separation, detection filling, and closing operation is designed to complete automatic detection of BGA defects. Furthermore, to objectively evaluate the effectiveness and performance of the proposed method, we conduct a series of comparative experiments using simulated and real data, and generate visual outputs. Through these experiments and analyses, we confirm that the proposed method plays an active and effective role and has robust performance in BGA defect detection. In particular, our method shows the expected performance in precisely finding BGA edge defects and subtle defects.