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Patient-derived organoids (PDOs) have been proposed as a novel in vitro tumor model that can be applied to tumor research and drug screening. However, current tumor organoid models lack components of the tumor microenvironment, particularly tumor-associated macrophages(TAMs).We collected peripheral blood and tumor samples from 6 patients with extrahepatic cholangiocarcinoma(eCCA). Monocytes were induced into TAMs by cytokine and conditioned medium, and then co-cultured with tumor organoids. Our comprehensive analysis and comparison of histopathology and genomics results confirmed that this co-culture model can better capture intra- and inter-tumor heterogeneity retain the specific mutations of the original tumor. Drug sensitivity data in vitro revealed that gemcitabine and cisplatin are effective drugs for cholangiocarcinoma, but TAMs in the tumor microenvironment promote organoids growth and chemotherapy resistance. In conclusion, our organoid model of cholangiocarcinoma co-cultured with TAMs can not only shorten the model construction cycle, but also preserve the heterogeneity of original tumors to improve the accuracy of drug screening, and can also be applied to the researches of TAMs and tumors.

Abstract Background Cholangiocarcinoma represents a malignant neoplasm originating from the hepatobiliary tree, with a subset of tumors developing inside the liver. Intrahepatic cholangiocarcinomas (ICC) commonly exhibit an asymptomatic presentation, rendering both diagnosis and treatment challenging. Cuproptosis, an emerging regulated cell death pathway induced by copper ions, has garnered attention recently. As cancer cells show altered copper metabolism and comparatively higher copper needs, cuproptosis may play a role in the development of ICC. However, studies investigating this possibility are currently lacking. Methods Single-cell and bulk RNA sequence data were analyzed, and correlations were established between the expression of cuproptosis-related molecules and ICC patient survival. Genes with predicting survival were used to create a CUPT score using Cox and LASSO regression and tumor mutation burden (TMB) analysis. The CIBERSORT software was employed to characterize immune cell infiltration within the tumors. Furthermore, immune infiltration prediction, biological function enrichment, and drug sensitivity analyses were conducted to explore the potential implications of the cuproptosis-related signature. The effects of silencing solute carrier family 39 member 4 gene (SLC39A4) expression using siRNA were investigated using assays measuring cell proliferation, colony formation, and cell migration. Key genes of cuproptosis were detected by western blotting. Results The developed CUPT score divided patients into high and low CUPT score groups. Those with a low score had significantly better prognosis and longer survival. In contrast, high CUPT scores were associated with worse clinical outcomes and significantly higher TMB. Comparisons of the two groups also indicated differences in the immune infiltrate present in the tumors. Finally, we were able to identify 95 drugs potentially affecting the cuproptosis pathway. Some of these might be effective in the treatment of ICC. The in vitro experiments revealed that suppressing the expression of SLC39A4 in ICC cell lines resulted in reduced cell proliferation, colony formation, and cell migration. It also led to an increase in cell death and the upregulation of key genes associated with cuproptosis, namely ferredoxin 1 (FDX1) and dihydrolipoyl transacetylase (DLAT). These findings strongly suggest that this cuproptosis-associated molecule may play a pivotal role in the development and metastasis of ICC. Conclusions Changes in the expression of a cuproptosis-related gene signature can predict the clinical prognosis of ICC with considerable accuracy. This supports the notion that cuproptosis influences the diversity and complexity of the immune microenvironment, mutational landscape, and biological behavior of ICC. Understanding this pathway better may hold promise for the development of innovative strategies in the management of this disease.

ObjectiveThe presence of mental fatigue seriously affects daily life and working conditions. Non-invasive transcranial electrical stimulation has become an increasingly popular tool for relieving mental fatigue. We investigated whether transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) could be used to alleviate the state of mental fatigue in a population of healthy young adults and compared their effects.MethodsWe recruited 10 participants for a blank control, repeated measures study. Each participant received 15 min of anodal tDCS, α-tACS, and blank stimulation. Participants were required to fill in the scale, perform the test task and collect ECG signals in the baseline, fatigue and post-stimulus states. We then assessed participants’ subjective fatigue scale scores, test task accuracy and HRV characteristics of ECG signals separately.ResultsWe found that both anodal tDCS and α-tACS significantly (P < 0.05) reduced subjective fatigue and improved accuracy on the test task compared to the blank group, and the extent of change was greater with tACS. For the HRV features extracted from ECG signals. After tACS intervention, SDNN (t = −3.241, P = 0.002), LF (t = −3.511, P = 0.001), LFn (t = −3.122, P = 0.002), LFn/HFn (−2.928, P = 0.005), TP (t = −2.706, P = 0.008), VLF (t = −3.002, P = 0.004), SD2 (t = −3.594, P = 0.001) and VLI (t = −3.564, P = 0.001) showed a significant increasing trend, and HFn (t = 3.122, P = 0.002), SD1/SD2 (t = 3.158, P = 0.002) and CCM_1 (t = 3.106, P = 0.003) showed a significant decreasing trend. After tDCS intervention, only one feature, TINN, showed a significant upward trend (P < 0.05). The other features showed non-significant changes but roughly the same trend as the tACS group.ConclusionBoth tDCS and α-tACS can be effective in relieving mental fatigue, and α-tACS is more effective than tDCS. This study provides theoretical support for tDCS with α-tACS having a alleviating effect on mental fatigue and the use of ECG as a valid objective assessment tool.

Hydrolysed tannins (HTs) are polyphenols, which are related to the astringency, flavour, colour, stability, medicinal value and other characteristics of many fruits and vegetables. The biosynthetic mechanism of the majority of HTs remains unknown, and many biosynthetic pathways of HTs are speculative conclusions that have not been confirmed. The fruit of Canarium album L. (Chinese olive), which is notable for its pharmacological and edible properties, is rich in HTs. The fruit has a distinctive bitter and astringent taste when initially consumed, which mellows to a sweet sensation upon chewing. HTs serve as the primary material basis for the formation of the Chinese olive fruit’s astringent quality and pharmacological effects. In this study, the fruit of C. album Changying was utilised as the research material. The objective of this study was to provide a theoretical basis for the quality control of Chinese olive fruit and the application and development of its medicinal value. In addition, the study aimed to identify and screen related MYB transcription factors involved in the synthesis of HTs in the fruit and to clarify the mechanism of MYBs in the process of synthesis and regulation of HTs in Chinese olive fruit. The principal findings were as follows. A total of 83 differentially expressed Chinese olive MYB transcription factors (CaMYBs) were identified, including 54 1R-MYBs (MYB-related), 25 2R-MYBs (R2R3-MYBs), 3 3R-MYBs, and 1 4R-MYB. Through trend analysis and correlation analysis, it was found that CaMYBR04 (Isoform0032534) exhibited a significantly higher expression (FPKM) than the other CaMYBs. The full-length cDNA sequence of CaMYBR04 was cloned and transformed into strawberry. The results demonstrated that CaMYBR04 significantly enhanced the fruit’s hydrolysable tannin content. Consequently, this study elucidated the function of CaMYBR04, a regulator of the Chinese olive fruit hydrolysable tannin synthesis pathway, and established a theoretical foundation for the synthesis and regulation of fruit HTs.

The study of pear dormancy mechanisms is currently a major research area in pear production and has high economic significance for agricultural production. We selected the southern sand pear cultivar Pyrus pyrifolia ‘Huanghua’ as the test material to study the pear dormancy process through microscopic observation of pear flower buds. The endodormancy period is abbreviated as D_bud. Similarly, the endodormancy release initiation period and the ecodormancy period are referred to as DB_bud and G_bud, respectively. Meanwhile, RNA sequencing was used to obtain the gene expression profile of Pyrus pyrifolia ‘Huanghua’ flower buds. The RNA sequencing results indicated that there were 224 differentially expressed genes (DEGs) in endodormancy (D) vs. endodormancy release initiation period (DB), while 975 were identified between endodormancy and ecodormancy (G). Finally, a total of 868 DEGs were found in the DB vs. G comparison. The expression levels of the genes Pbr009498.1 (LAX1-1), Pbr012348.1 (LAX1-2), Pbr021158.1 (GH35), and Pbr031621.1 (LAX2) encoding IAA were significantly higher during the DB_bud than during the D_bud. The expression level of Pbr025864.2 (IAA13) during the D_bud was significantly higher than the DB_bud and G_bud. The Pbr041942.1 (GID1B) gene also showed a significant increase during ecodormancy. Taken together, these results suggest that these genes, annotated as LAX1, GH35, LAX2, IAA13, and GID1C, are involved in endodormancy maintenance and in the transition from endodormancy to ecodormancy in Pyrus pyrifolia ‘Huanghua’.

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition.

Polyphenols play a crucial role in fruit flavor. To elucidate the mechanism of fruit polyphenol metabolism, we constructed a transcriptome atlas through PacBio single-molecule real-time (SMRT) sequencing and Illumina next-generation sequencing (NGS) using Canarium album (Lour.) Raeusch., which is a fantastic fruit rich in polyphenolic compounds. In this work, PacBio full-length transcriptome assembly generated 135,439 isoforms with an average length of all isoforms of 2687.94 bp and an N50 length of 3224 bp. To gain deeper insights into the molecular mechanisms of polyphenol biosynthesis in C. album, we constructed twelve RNA-Seq libraries from four developmental stages of the fruits. We identified a total of 28,658 differentially expressed genes (DEGs). We found that many DEGs were involved in metabolic pathways, biosynthesis of secondary metabolites, biosynthesis of antibiotics, starch and sucrose metabolism, and plant hormone signal transduction. Here, we report the expression profiles of 215 DEGs encoding 27 enzymes involved in the polyphenol biosynthesis pathway in C. album. In addition, 285 differentially expressed transcription factors (TFs) continuously down-regulated in four developmental periods of C. album fruit, which may indicate their potential role in the response to polyphenol metabolism and phenylpropanoid biosynthesis pathways. This report will help us understand polyphenol biosynthesis’s functions and metabolic mechanism in C. album. The transcriptome data provide a valuable resource for genetic and genomics research. They will facilitate future work exploiting C. album and other fruits used as medicine and food.

Summary: Background: Regulatory T cells (Tregs) can alleviate the development of autoimmune and inflammatory diseases, thereby proposing their role as a new therapeutic strategy. Parasitic helminths have co-evolved with hosts to generate immunological privilege and immune tolerance through inducing Tregs. Thus, constructing a “Tregs-induction”-based discovery pipeline from parasitic helminth is a promising strategy to control autoimmune and inflammatory diseases. Methods: The gel filtration chromatography and reverse-phase high-performance liquid chromatography (RP-HPLC) were used to isolate immunomodulatory components from the egg extracts of Schistosoma japonicum. The extracted peptides were evaluated for their effects on Tregs suppressive functions using flow cytometry, ELISA and T cell suppression assay. Finally, we carried out colitis and psoriasis models to evaluate the function of Tregs induced by helminth-derived peptide in vivo. Findings: Here, based on target-driven discovery strategy, we successfully identified a small 3 kDa peptide (SjDX5-53) from egg extracts of schistosome, which promoted both human and murine Tregs production. SjDX5-53 presented immunosuppressive function by arresting dendritic cells (DCs) at an immature state and augmenting the proportion and suppressive capacity of Tregs. In mouse models, SjDX5-53 protected mice against autoimmune-related colitis and psoriasis through inducing Tregs and inhibiting inflammatory T-helper (Th) 1 and Th17 responses. Interpretation: SjDX5-53 exhibited the promising therapeutic effects in alleviating the phenotype of immune-related colitis and psoriasis. This study displayed a screening and validation pipeline of the inducer of Tregs from helminth eggs, highlighting the discovery of new biologics inspired by co-evolution of hosts and their parasites. Funding: This study was supported by the Natural Science Foundation of China (82272368) and Natural Science Foundation of Jiangsu Province (BK20211586).

Objective: The purpose of this study is to identify the blood pressure variation, which is important in continuous blood pressure monitoring, especially in the case of low blood volume, which is critical for survival.Methods: A pilot study was conducted to identify blood pressure variation with hypovolemia using five Landrace pigs. New multi-dimensional morphological features of Photoplethysmography (PPG) were proposed based on experimental study of hemorrhagic shock in pigs, which were strongly correlated with blood pressure changes. Five machine learning methods were compared to develop the blood pressure variation identification model.Results: Compared with the traditional blood pressure variation identification model with single characteristic based on single period area of PPG, the identification accuracy of mean blood pressure variation based on the proposed multi-feature random forest model in this paper was up to 90%, which was 17% higher than that of the traditional blood pressure variation identification model.Conclusion: By the proposed multi-dimensional features and the identification method, it is more accurate to detect the rapid variation in blood pressure and to adopt corresponding measures.Significance: Rapid and accurate identification of blood pressure variation under low blood volume ultimately has the potential to effectively avoid complications caused by abnormal blood pressure in patients with clinical bleeding trauma.

Big data technology plays an important role in the prevention and control of public health emergencies such as the COVID-19 pandemic. Current studies on model construction, such as SIR infectious disease model, 4R crisis management model, etc., have put forward decision-making suggestions from different perspectives, which also provide a reference basis for the research in this paper. This paper conducts an exploratory study on the construction of a big data prevention and control model for public health emergencies by using the grounded theory, a qualitative research method, with literature, policies, and regulations as research samples, and makes a grounded analysis through three-level coding and saturation test. Main results are as follows: (1) The three elements of data layer, subject layer, and application layer play a prominent role in the digital prevention and control practice of epidemic in China and constitute the basic framework of the “DSA” model. (2) The “DSA” model integrates cross-industry, cross-region, and cross-domain epidemic data into one system framework, effectively solving the disadvantages of fragmentation caused by “information island”. (3) The “DSA” model analyzes the differences in information needs of different subjects during an outbreak and summarizes several collaborative approaches to promote resource sharing and cooperative governance. (4) The “DSA” model analyzes the specific application scenarios of big data technology in different stages of epidemic development, effectively responding to the disconnection between current technological development and realistic needs.

Urban resilience assessment is crucial to guarantee the safe and stable operation of our cities. Looking at the whole process of emergencies from the occurrence, development, causing disaster losses to taking emergency response measures, we combine the danger of urban accident and disaster, the capacity of disaster-bearing carriers, and the emergency management capacity as the influencing factors. Taking Beijing as an example, considering the completeness of the relevant indicators and the difficulty of obtaining data, TOPSIS is used to construct a multi-factor comprehensive assessment model of urban safety resilience; At the same time, the important factors constraining the improvement of safety resilience are further analyzed by using the obstacle degree model. The results show that: among the 16 districts in Beijing, Chaoyang and Haidian districts have the highest level of safety resilience; the level of the degree of disaster tolerance among the multi-factors has the greatest impact on the level of urban safety resilience in Beijing; and the multifactorial comprehensive assessment model of urban resilience is able to reflect the urban resilience distribution, discover the weaknesses that exist in the development of urban safety and provide decision-making aids for the relevant city managers to carry out urban resilience construction.

To improve the diagnosis and classification of Alzheimer’s disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.

Abstract Sleep apnea syndrome (SAS) is a disorder in which respiratory airflow frequently stops during sleep. Alterations in electroencephalogram (EEG) signal are one of the physiological changes that occur during apnea, and can be used to diagnose and monitor sleep apnea events. Herein, we proposed a method to automatically distinguish sleep apnea events using characteristics of EEG signals in order to categorize obstructive sleep apnea (OSA) events, central sleep apnea (CSA) events and normal breathing events. Through the use of an Infinite Impulse Response Butterworth Band pass filter, we divided the EEG signals of C3-A2 and C4-A1 into five sub-bands. Next, we extracted sample entropy and variance of each sub-band. The neighbor composition analysis (NCA) method was utilized for feature selection, and the results are used as input coefficients for classification using random forest, K-nearest neighbor, and support vector machine classifiers. After a 10-fold cross-validation, we found that the average accuracy rate was 88.99%. Specifically, the accuracy of each category, including OSA, CSA and normal breathing were 80.43%, 84.85%, and 95.24%, respectively. The proposed method has great potential in the automatic classification of patients' respiratory events during clinical examinations, and provides a novel idea for the development of an automatic classification system for sleep apnea and normal events without the need for expert intervention.

Hypopnea refers to the state in which insufficient alveolar ventilation during night sleep decreases the respiratory airflow by more than 50% of the airflow. However, sleep apnea is a more serious respiratory event, such as complete cessation of respiratory airflow for 10 seconds. The occurrence of hypopnea is a precursor to the occurrence of apnea events and the two are closely connected. In this paper, we propose a method based on the combination of discrete wavelet transform and approximate entropy of EEG signals to detect sleep apnea and hypopnea events. For this purpose, first, data preprocessing is performed on the EEG record data set obtained from Tianjin Chest Hospital, and then infinite impulse response (IIR) Butterworth bandpass filter is used to decompose the data into delta, theta, alpha, beta and gamma. Second, descriptive features are extracted based on sub-bands discrete wavelet transform such as the approximate entropy of high-frequency coefficients. Third, the features are filtered based on Support Vector Machine (SVM) recursive elimination. Finally, several machine learning algorithms including SVM, K-Nearest Neighbor (KNN) and Random forest (RF) are employed to identify the occurrence of sleep hypopnea-apnea events. The highest accuracy rate reached 94.33%, the sensitivity reached 93.10%, and the specificity reached 95.07%. The obtained results validate that the proposed method is an effective and practical diagnostic method to detect the occurrence of hypopnea-apnea events.

Good sleep quality is essential to our life and work. The two major challenges in evaluating sleep quality are the scoring of sleep stages and sleep apnea detection. In addition to sleep apnea, hypopnea is also a respiratory event that occurs at an equal or even higher frequency compared to that of apnea. However, the two phenomena are not only different in terms of physical manifestation, there are also significant differences in the methods and means of clinical treatment. Therefore, in this study we propose an effective algorithm to distinguish between hypopnea and apnea events. We consider two aspects of feature extraction. On the one hand, from the original time-domain waveform of electrocardiogram signals, we extract the time difference sequence between the appearance of the R-R wave in two adjacent QRS waves (RR intervals) and explore the way they change when apnea and hypopnea event occur. On the other hand, considering that the degree of sequence disorder caused by hypopnea and apnea events is different, multi-scale entropy and complexity are chosen as the features that describe this change. The results obtained using the proposed algorithm show that the classification between hypopnea events and any type of apnea events all reach an accuracy rate greater than 90%. For obstructive apnea events, which occur most frequently among the three different types of apneas, the classification accuracy, sensitivity, specificity and F1-score were 91.78%, 92.21%, 91.30%, 92.20%, respectively. The classification results between central apnea events and hypopnea events reached an accuracy of 93.18%, sensitivity of 95.00%, specificity of 91.67% and F1-score of 92.68%. At the same time, the algorithm achieved an accuracy of 92.59%, sensitivity of 90.38%, specificity of 94.64% and F1-score of 92.16% when classifying mixed apnea and hypopnea events. In the classification of normal breathing, hypopnea events and apnea events, the total accuracy rate achieved was 91.92%, with that for normal breathing reaching 93.62%, for hypopnea events reaching 90.39%, and 91.91% for apnea events, with a Kappa coefficient of 0.99. The algorithm proposed in this study constitutes an efficient and convenient approach for apnea and hypopnea event detection and classification.

Abstract Background Hepatic granuloma formation and fibrosis as the consequence of tissue entrapped eggs produced by female schistosomes characterize the pathology of Schistosoma japonicum infection. It has been proposed that fucoidan, a sulfated polysaccharide existing naturally in brown seaweed Fucus vesiculosus, plays a diversified role to perform immunomodulatory activities. However, whether fucoidan functions in the host hepatic pathology is unknown and identifying the potential mechanism that is responsible for hepatic improvement is still necessary. Methods We evaluated the hepatic pathology from S. japonicum-infected mice after treatment with fucoidan. qRT-PCR and immunofluorescence were used to detect the pro- or anti-inflammatory factors and the phosphorylated p65 in the livers. In addition, flow cytometry was also performed to investigate the T cell subsets in the S. japonicum-infected mice after treatment with fucoidan, and functional molecules relatively specific to Treg cells were detected in vitro. Furthermore, macrophages were treated with fucoidan in vitro and to detect the inflammatory cytokines. Results Treatment with fucoidan significantly reduced the hepatic granuloma size and fibrosis response during S. japonicum infection. The attenuated phospho-p65 protein levels and the mRNA levels of pro-inflammatory cytokines (IL-6, IL-12 and TNF-α) were observed in the livers from fucoidan-treated S. japonicum-infected mice; however, the mRNA levels of anti-inflammatory cytokines (IL-4 and IL-13) were increased. In addition, the infiltration of Treg cells was significantly enhanced both in the livers and spleens from fucoidan-treated S. japonicum-infected mice. Consistent with this, the mRNA levels of IL-10 and TGF-β were dramatically increased in the livers from S. japonicum-infected mice after fucoidan treatment. Furthermore, in vitro stimulated splenocytes with fucoidan resulted in increasing Treg cells in splenocytes as well as the functional expression of CC chemokine receptor type 4 (CCR4) and CXC chemokine receptor type 5 (CXCR5) in Treg cells. Additionally, fucoidan promoted the mRNA levels of IL-4 and IL-13 in macrophages. Conclusions These findings suggest an important role of natural fucoidan in reducing hepatic pathology in the progress of S. japonicum infection with a stronger Treg response, which may reveal a new potential therapeutic strategy for hepatic disease caused by parasitic chronic infection.

Abstract Fecal microbiota transplantation (FMT) by manual preparation has been applied to treat diseases for thousands of years. However, this method still endures safety risks and challenges the psychological endurance and acceptance of doctors, patients and donors. Population evidence showed the washed microbiota preparation with microfiltration based on an automatic purification system followed by repeated centrifugation plus suspension for three times significantly reduced FMT-related adverse events. This washing preparation makes delivering a precise dose of the enriched microbiota feasible, instead of using the weight of stool. Intraperitoneal injection in mice with the fecal microbiota supernatant obtained after repeated centrifugation plus suspension for three times induced less toxic reaction than that by the first centrifugation following the microfiltration. The toxic reactions that include death, the change in the level of peripheral white blood cells, and the proliferation of germinal center in secondary lymphoid follicles in spleen were noted. The metagenomic next-generation sequencing (NGS) indicated the increasing types and amount of viruses could be washed out during the washing process. Metabolomics analysis indicated metabolites with pro-inflammatory effects in the fecal microbiota supernatant such as leukotriene B4, corticosterone, and prostaglandin G2 could be removed by repeated washing. Near-infrared absorption spectroscopy could be served as a rapid detection method to control the quality of the washing-process. In conclusion, this study for the first time provides evidence linking clinical findings and animal experiments to support that washed microbiota transplantation (WMT) is safer, more precise and more quality-controllable than the crude FMT by manual.

Magnetic detection electrical impedance tomography (MDEIT) is a novel imaging technique that aims to reconstruct the conductivity distribution with electrical current injection and the external magnetic flux density measurement by magnetic sensors. Aiming at improving the resolution and accuracy of MDEIT and providing an efficient imaging method for breast cancer diagnosis, a new algorithm based on stacked auto-encoder (SAE) neural network is proposed. Both numerical simulation and phantom experiments are done to verify its feasibility. In the numerical simulation, an amount of sample data with different conductivity distribution are calculated. Then a neural network model is established and trained by training these samples. Finally, the conductivity distribution of an imaging target with the anomaly location can be reconstructed by the network model. The reconstruction result of the SAE algorithm is compared with the reconstruction results of the traditional sensitivity matrix (SM) algorithm and the back propagation (BP) neural network algorithm. Under the noise of 30dB, the relative errors of BP algorithm, SM algorithm and SAE algorithm are 137.19%, 24.90% and 15.28% respectively. Result shows by the SAE algorithm, the location of anomalies is reconstructed more accurately, the conductivity value is more closely to the real one and the anti-noise performance is more robust. At last, a breast phantom experiment by self-made platforms is completed to verify the application feasibility of the new algorithm. The relative reconstruction error of conductivity by proposed SAE algorithm can be reduced to 14.56%. The results show that by SAE algorithm, MDEIT can be a promising approach in clinical diagnosis of breast cancer, and it also provide more potential application prospect for the extensive application of MDEIT.

As a noninvasive and quantitative method, fluorescence molecular tomography (FMT) has many potential applications in biomedical field. It has the power to resolve in three-dimension (3D), the molecular processes in small animal in-vivo in both theory and practice. This paper proposes to solve the problem of reconstruction error and speed by using stacked auto-encoders (SAE). A finite element method (FEM) solution to the Laplace transformed time-domain coupled diffusion equations is employed as the forward model. The reconstruction model is formulated under the framework of SAE. Numerical simulation experiments were conducted to compare the reconstruction results of SAE and algebraic reconstruction technique (ART). We demonstrated that the proposed reconstruction algorithm can retrieve the positions and shapes of the targets more accurately than ART. This advantage of SAE is especially reflected in the reconstruction for small targets with a radius of 2 mm and 3 mm.

This paper proposes an improved region-based active contour model for segmenting magnetic resonance imaging (MRI) images of brain tuberculosis by combining a global energy fitting term and a local energy fitting term. First, a global energy fitting term is utilized to extract global image information, which guides the evolving curve globally and approximates the image intensity inside and outside the contour. Second, a local energy fitting term is proposed to describe the intensity inhomogeneity based on the local intensity variance and the adaptive image difference. Third, an improved Fuzzy C-Means (FCM) clustering method is applied to pre-segment the MRI images to automatically track the approximate location of brain tuberculosis and provide the initial contour for the hybrid model segmentation. By combining the global and local weighting functions, a hybrid region-based model is defined. Experiments demonstrate that the proposed model provides initialization of the contours automatically and offers superior segmentation performance for brain tuberculosis MRI medical images with intensity inhomogeneity.

Objective: The objective of this study was to investigate the role of the high-frequency cochlear dysfunction in the cognitive-ear link.Methods: Seventy-four presbycusis patients (PC group) and seventy-one age-, sex-, and education-level matched normal hearing controls (NH group) were recruited in this study. Participants underwent a battery of cognitive tests estimated by Montreal Cognitive Assessment (MoCA), Stroop Color-Word Interference Test (Stroop), Symbol Digit Modalities Test (SDMT), Auditory Verbal Learning Test (AVLT), and Trail-Making Test (TMT-A and B), as well as auditory tests including distortion product otoacoustic emission (DPOAE), pure tone (PT) thresholds, and speech reception thresholds (SRT). Data were analyzed using the factor analysis, partial correlation analysis, multiple linear regression models, and mediation models.Results: Distortion product otoacoustic emission detection amplitudes and PT thresholds performed worse gradually from low to high frequencies in both the NH and PC groups. High-frequency DPOAE (H-DPOAE) was significantly correlated with cognitive domains in the PC group (AVLT: r = 0.30, p = 0.04; SDMT: r = 0.36, p = 0.01; Stroop: r = –0.32, p = 0.03; TMT-A: r = –0.40, p = 0.005; TMT-B: r = –0.34, p = 0.02). Multiple linear regression models showed that H-DPOAE predicted cognitive impairment effectively for aspects of memory (R2 = 0.27, 95% CI, 0.03 to 1.55), attention (R2 = 0.32, 95% CI, –6.18 to –0.40), processing speed (R2 = 0.37, 95% CI, 0.20 to 1.64), and executive function (TMT-A: R2 = 0.34, 95% CI, –5.52 to 1.03; TMT-B: R2 = 0.29, 95% CI, –11.30 to –1.12). H-DPOAE directly affected cognition and fully mediated the relationship between pure tone average (PTA)/SRT and cognitive test scores, excluding MoCA.Conclusion: This study has demonstrated that the high-frequency cochlear amplifier dysfunction has a direct predictive effect on the cognitive decline and makes a large contribution to the cognitive-ear link.

ObjectiveThis study aimed to evaluate the performance of the deep convolutional neural network (DCNN) to discriminate between benign, borderline, and malignant serous ovarian tumors (SOTs) on ultrasound(US) images.Material and MethodsThis retrospective study included 279 pathology-confirmed SOTs US images from 265 patients from March 2013 to December 2016. Two- and three-class classification task based on US images were proposed to classify benign, borderline, and malignant SOTs using a DCNN. The 2-class classification task was divided into two subtasks: benign vs. borderline & malignant (task A), borderline vs. malignant (task B). Five DCNN architectures, namely VGG16, GoogLeNet, ResNet34, MobileNet, and DenseNet, were trained and model performance before and after transfer learning was tested. Model performance was analyzed using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).ResultsThe best overall performance was for the ResNet34 model, which also achieved the better performance after transfer learning. When classifying benign and non-benign tumors, the AUC was 0.96, the sensitivity was 0.91, and the specificity was 0.91. When predicting malignancy and borderline tumors, the AUC was 0.91, the sensitivity was 0.98, and the specificity was 0.74. The model had an overall accuracy of 0.75 for in directly classifying the three categories of benign, malignant and borderline SOTs, and a sensitivity of 0.89 for malignant, which was better than the overall diagnostic accuracy of 0.67 and sensitivity of 0.75 for malignant of the senior ultrasonographer.ConclusionDCNN model analysis of US images can provide complementary clinical diagnostic information and is thus a promising technique for effective differentiation of benign, borderline, and malignant SOTs.

Background and AimsThe heat shock protein (Hsp) 90α is induced by stress and regulates inflammation through multiple pathways. Elevated serum Hsp90α had been found in nonalcoholic steatohepatitis (NASH). Geranylgeranylacetone (GGA, also called teprenone) is a terpenoid derivative. It was reported to induce Hsp and alleviate insulin resistance. We aimed to evaluate the Hsp90α as a biomarker in predicting metabolic-associated fatty liver disease (MAFLD) and define the therapeutic effects of geranylgeranylacetone for the disease.MethodsA clinical study was conducted to analyze the elements associated with Hsp90α, and a predictive model of MAFLD was developed based on Hsp90α. The histopathological correlation between Hsp90α and MAFLD was investigated through a diet-induced mouse model. Furthermore, GGA was applied to the mouse model.ResultsSerum Hsp90α was increased in patients with MAFLD. A positive linear relationship was found between age, glycosylated hemoglobin (HbA1c), MAFLD, and serum Hsp90α. Meanwhile, a negative linear relationship with body mass index (BMI) was found. A model using Hsp90α, BMI, HbA1c, and ALT was established for predicting MAFLD. The area under the receiver operating characteristic (ROC) curves was 0.94 (95% CI 0.909–0.971, p = 0.000). The sensitivity was 84.1%, and the specificity was 93.1%. In vitro experiments, GGA induced Hsp90α in steatosis cells. In the mice model, Hsp90α decreased in the GGA treatment group. Hepatic steatosis, inflammation, insulin resistance, and glucose intolerance were improved in the GGA-treated group. Serum Hsp90α was positively correlated with steatohepatitis activity according to hepatic histopathology.ConclusionsSerum Hsp90α was elevated in MAFLD, and a positive correlation between serum Hsp90α and the grade of activity of steatohepatitis was observed. The model using BMI, HbA1c, and alanine aminotransferase (ALT) had a good value to predict MAFLD. The findings also revealed the effectiveness of GGA in the treatment of MAFLD.

Magnetic induction tomography (MIT) is a non-invasive and non-contact imaging method that uses an excitation coil to generate time-varying magnetic fields in space and reconstruct the internal conductivity distribution based on the phase difference. In this study, a new MIT reconstruction algorithm was proposed for non-contact measurement and monitoring of the location of the anomaly in the biomedical object of interest. To reconstruct the distribution of electrical characteristics inside the biological tissue, this technique uses a stacked auto-encoder (SAE) neural network composed of a multi-layer automatic encoder. The location and reconstruction accuracy of the anomaly based on SAE and back-projection were compared, and a hemorrhagic stroke was simulated to verify the practicability of the proposed algorithm. The results showed that the relative error of reconstruction based on the SAE network algorithm reached 0.29%, which improved anomaly reconstruction accuracy and reduced the prediction time to 0.02 s. At the same time, the network was used for the reconstruction of hemorrhagic stroke in different locations, amounts, and shapes. Accordingly, the SAE neural network reconstruction algorithm proposed in this study, which can autonomously learn the non-linear relationship between input and output, can solve the defects of the traditional reconstruction algorithm, such as serious artifacts and complex calculations.

The diffuse optical tomography (DOT) technique which uses the traditional linear iterative algorithm has the problems of slow calculation speed and low reconstruction imaging accuracy in the inverse problem reconstruction, which limits its clinical application and development. This paper proposes an inverse-problem solving technology based on a stacked auto-encoder (SAE) network to improve the reconstruction accuracy of anomaly position, size and absorption coefficient in tissues. The reconstruction accuracy of anomaly position, size and absorption coefficient obtained by the traditional algebraic reconstruction technique (ART) method and the SAE based method are experimentally compared. The experimental results show that the SAE based method achieves the prediction accuracy of anomaly position of 96.25%, thus improving the accuracy and shortening the reconstruction time compared with the traditional ART method. Accordingly, the proposed method provides a better solution to the problem of the inaccurate reconstruction of the position and size of the rapid DOT based positioning of anomalies.

In this paper, the signal-to-noise ratios (SNR) of two image channels were enhanced with the fast digital lock-in algorithm. In order to simultaneously improve the quality of white and fluorescence images obtained by fluorescence endoscope, and improve the SNR to achieve a better image processing effect, two sources of white light and near-infrared light of a fluorescence endoscope were modulated, then the acquired images were demodulated into white and fluorescence images. A fluorescent endoscope experimental platform was setup to acquire endoscopic images of a target dyed by indocyanine green. The experimental results showed that the SNR of white and fluorescent images without the lock-in algorithm were 36.56 dB and 33.47 dB, respectively. However, with the lock-in algorithm, the SNR of white and fluorescent images were 39.54 dB and 35.70 dB, respectively. The SNR of white and fluorescent images was increased by 8.2% and 6.7%, respectively, by appling the digital lock-in algorithm. Therefore, this novel fluorescence endoscope based on the fast digital lock-in algorithm can rapidly and simultaneously obtain two-channel images of white light and fluorescence, effectively enhance the SNR of white and fluorescent images, and improve the imaging quality.

Brain-controlled wheelchair (BCW) has the potential to improve the quality of life for people with motor disabilities. A lot of training is necessary for users to learn and improve BCW control ability and the performances of BCW control are crucial for patients in daily use. In consideration of safety and efficiency, an indoor simulated training environment is built up in this paper to improve the performance of BCW control. The indoor simulated environment mainly realizes BCW implementation, simulated training scenario setup, path planning and recommendation, simulated operation, and scoring. And the BCW is based on steady-state visual evoked potentials (SSVEP) and the filter bank canonical correlation analysis (FBCCA) is used to analyze the electroencephalography (EEG). Five tasks include individual accuracy, simple linear path, obstacles avoidance, comprehensive steering scenarios, and evaluation task are designed, 10 healthy subjects were recruited and carried out the 7-days training experiment to assess the performance of the training environment. Scoring and command-consuming are conducted to evaluate the improvement before and after training. The results indicate that the average accuracy is 93.55% and improves from 91.05% in the first stage to 96.05% in the second stage (p = 0.001). Meanwhile, the average score increases from 79.88 in the first session to 96.66 in the last session and tend to be stable (p < 0.001). The average number of commands and collisions to complete the tasks decreases significantly with or without the approximate shortest path (p < 0.001). These results show that the performance of subjects in BCW control achieves improvement and verify the feasibility and effectiveness of the proposed simulated training environment.

Abstract In recent years, oil–water separation has been widely researched to reduce the influences of industrial wastewater and offshore oil spills. A filter membrane with special wettability can achieve the separation because of its opposite wettability for water phase and oil phase. In the field of filter membrane with special wettability, porous metal filter membranes have been much investigated because of the associated high efficiency, portability, high plasticity, high thermal stability, and low cost. This article provides an overview of the research progress of the porous metal filter membrane fabrication and discusses the future developments in this field.

Key technologies in empty bottle inspection systems are studied in this paper to solve detecting error and poor adaptability problems. Those technologies have two different approaches: the ones in the first group locate and track bottle mouth, bottle bottom and walls while the other group technologies involve defect detecting. Such vision inspection systems are required to perform with high accuracy and adaptability under high speed and mechanical vibration working conditions. This study proposes distinctive algorithms for bottle locating, tracking and defect detecting based on inspection requirements and images of bottle mouth, bottom and walls. On the premise of satisfying the inspecting requirements, the simplicity, ease of implementation and universality of the algorithms are considered to improve the detection speed and the adaptability of the system to different kinds of bottles. Experiments showed that the system proposed in this paper can improve the detection accuracy and speed.

Corpus Callosum (CC) is an important white-matter structure in the human brain. Magnetic resonance imaging (MRI) is a non-invasive medical imaging technique that provides high resolution images for the structures. Segmentation is an important step in medical image analysis. This paper proposes a fully automated technique for segmentation of CC on the midsagittal slice of T1-weighted brain MR images. The proposed technique consists of three modules. First it clusters all homogenous regions in the image with an adaptive mean shift (AMS) technique. The automatic CC contour initialization (ACI) is achieved using the region analysis, template matching and location analysis, thus identify the CC region. Finally, the boundary of recognized CC region is used as the initial contour in the Geometric Active Contour (GAC) model, and is evolved to obtain the final segmentation result of CC. Experimental results demonstrate that the proposed AMS-ACI technique is able to provide accurate initial CC contour, and the proposed AMS-ACI-GAC technique overcomes the problem of user-guided initialization in existing GAC techniques, and provides a reliable and accurate performance in CC segmentation.

In this paper, bovine serum albumin (BSA)-imprinted calcium alginate (CaAlg) hydrogel membrane was prepared using BSA as a template, sodium alginate (NaAlg) as a functional monomer, and CaCl2 as a cross-linker. The thickness of the CaAlg membrane was controlled by a glass rod enlaced with brass wires (the diameter was 0.1, 0.2, 0.3, 0.4, and 0.5 mm). The swelling properties of the CaAlg membranes prepared with different contents of NaAlg were researched. Circular dichroism indicated that the conformation of BSA did not change during the preparing and eluting process. The thinner the CaAlg hydrogel membrane was, the larger the adsorption capacity and the higher the imprinting efficiency of the CaAlg. The maximum adsorption capacity of molecularly imprinted polymer (MIP) and non-imprinted CaAlg hydrogel membrane (NIP) was 38.6 mg·g−1 and 9.2 mg·g−1, respectively, with an imprinting efficiency of 4.2. The MIP was loaded on the electrode to monitor the selective adsorption of BSA by voltammetry curve.

Stress-induced DNA duplex destabilization (SIDD) analysis exploits the known structural and energetic properties of DNA to predict sites that are susceptible to strand separation under negative superhelical stress. When this approach was used to calculate the SIDD profile of the entire Escherichia coli K12 genome, it was found that strongly destabilized sites occur preferentially in intergenic regions that are either known or inferred to contain promoters, but rarely occur in coding regions. Here, we investigate whether the genes grouped in different functional categories have characteristic SIDD properties in their upstream flanks. We report that strong SIDD sites in the E. coli K12 genome are statistically significantly overrepresented in the upstream regions of genes encoding transcriptional regulators. In particular, the upstream regions of genes that directly respond to physiological and environmental stimuli are more destabilized than are those regions of genes that are not involved in these responses. Moreover, if a pathway is controlled by a transcriptional regulator whose gene has a destabilized 5' flank, then the genes (operons) in that pathway also usually contain strongly destabilized SIDD sites in their 5' flanks. We observe this statistically significant association of SIDD sites with upstream regions of genes functioning in transcription in 38 of 43 genomes of free-living bacteria, but in only four of 18 genomes of endosymbionts or obligate parasitic bacteria. These results suggest that strong SIDD sites 5' to participating genes may be involved in transcriptional responses to environmental changes, which are known to transiently alter superhelicity. We propose that these SIDD sites are active and necessary participants in superhelically mediated regulatory mechanisms governing changes in the global pattern of gene expression in prokaryotes in response to physiological or environmental changes.

Magnetic induction tomography (MIT) is an emerging imaging technology holding significant promise in the field of cerebral hemorrhage monitoring. The commonly employed imaging method in MIT is time-difference imaging. However, this approach relies on magnetic field signals preceding cerebral hemorrhage, which are often challenging to obtain. Multiple bioelectrical impedance information with different frequencies is added to this study on the basis of single-frequency information, and the collected signals with different frequencies are identified to obtain the magnetic field signal generated by single-layer heterogeneous tissue. The Stacked Autoencoder (SAE) neural network algorithm is used to reconstruct the images of head multi-layer tissues. Both numerical simulation and phantom experiments are carried out. The results indicate that the relative error of the multi-frequency SAE reconstruction is only 7. 82°/o, outperforming traditional algorithms. Moreover, under a noise level of 40 dB, the anti-interference capability of the MIT algorithm based on frequency identification and SAE is superior to traditional algorithms. This research explores a novel approach for the dynamic monitoring of cerebral hemorrhage and demonstrates the potential advantages of MIT in non-invasive monitoring. [ABSTRACT FROM AUTHOR]