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Abstract Background The metabolic patterns of human placental-derived mesenchymal stem cell (hP-MSC) treatment for primary sclerosing cholangitis (PSC) remain unclear, and therapeutic effects significantly vary due to individual differences. Therefore, it is crucial to investigate the serological response to hP-MSC transplantation through small molecular metabolites and identify easily detectable markers for efficacy evaluation. Methods Using Mdr2−/− mice as a PSC model and Mdr2+/+ mice as controls, the efficacy of hP-MSC treatment was assessed based on liver pathology, liver enzymes, and inflammatory factors. Serum samples were collected for 12C-/13C-dansylation and DmPA labeling LC–MS analysis to investigate changes in metabolic pathways after hP-MSC treatment. Key metabolites and regulatory enzymes were validated by qRT-PCR and Western blotting. Potential biomarkers of hP-MSC efficacy were identified through correlation analysis and machine learning. Results Collectively, the results of the liver histology, serum liver enzyme levels, and inflammatory factors supported the therapeutic efficacy of hP-MSC treatment. Based on significant differences, 41 differentially expressed metabolites were initially identified; these were enriched in bile acid, lipid, and hydroxyproline metabolism. After treatment, bile acid transport was accelerated, whereas bile acid production was reduced; unsaturated fatty acid synthesis was upregulated overall, with increased FADS2 and elongase expression and enhanced fatty acid β-oxidation; hepatic proline 4-hydroxylase expression was decreased, leading to reduced hydroxyproline production. Correlation analysis of liver enzymes and metabolites, combined with time trends, identified eight potential biomarkers: 2-aminomuconate semialdehyde, l-1-pyrroline-3-hydroxy-5-carboxylic acid, l-isoglutamine, and maleamic acid were more abundant in model mice but decreased after hP-MSC treatment. Conversely, 15-methylpalmitic, eicosenoic, nonadecanoic, and octadecanoic acids were less abundant in model mice but increased after hP-MSC treatment. Conclusions This study revealed metabolic regulatory changes in PSC model mice after hP-MSC treatment and identified eight promising biomarkers, providing preclinical evidence to support therapeutic applications of hP-MSC.

The early and accurate identification of predictive biomarkers for antiviral treatment efficacy remains a significant clinical challenge, particularly in the management of chronic hepatitis B (CHB). This study aimed to assess whether the plasma metabolome could reliably predict the success of antiviral therapy in CHB patients. We conducted a retrospective analysis on 56 treatment-naive CHB patients at the First Affiliated Hospital of Zhejiang University from December 2013 to March 2016. Patients who underwent a 48-week treatment regimen of entecavir (ETV) and interferon-alpha (IFN-α) were randomly assigned to either a discovery cohort (n=29) or a validation cohort (n=27). Based on the outcome of the treatment, patients were classified as HBeAg seroconversion group (High responders, Hrp) or the non-remission group (Low responder, Lrp). Our methodology involved an untargeted analysis of the amine/phenol and carboxylic acid submetabolomes in the CHB patients under treatment, utilizing chemical isotope labeling (CIL) techniques with liquid chromatography-mass spectrometry (LC-MS). Several metabolites were identified as having significant diagnostic potential for distinguishing Hrp from Lrp, with areas under the receiver operating characteristic curve (AUC) exceeding those typical clinical indicators. Notably, four metabolites, namely 2-methyl-3-ketovaleric acid, 2-ketohexanoic acid, 6-oxo-1,4,5,6-tetrahydronicotinic acid, and α-ketoisovaleric acid, demonstrated exceptionally high sensitivity and specificity in both cohorts, nearing 100%. In contrast, the clinical indicators, including HBcAb, log(HBsAg), and HBeAb, demonstrated lower and inconsistent sensitivity and specificity between the discovery and validation cohorts. Using HBcAb as a marker, the sensitivity was 87.5% with 76.9% specificity in the discovery cohort; however, the sensitivity dropped to 46.7% with 91.7% specificity in the validation cohort. Using log(HBsAg), the sensitivity was 84.6% with 69.2% specificity in the discovery cohort, compared to 85.7% sensitivity and 83.3% specificity in the validation cohort. For HBeAb, the separation of Hrp and Lrp had a sensitivity of 87.5% with 69.2% specificity in the discovery cohort, while the validation cohort showed 86.7% sensitivity and 91.7% specificity.

The Streak-Tube Imaging Lidar (STIL) has been widely used in high-precision measurement systems due to its ability to capture detailed spatial and temporal information. In this paper, we proposed a ranging measurement method that integrates a Time-to-Digital Converter (TDC) with a streak camera in a remote STIL system. In this method, the TDC accurately measures the trigger pulse time, while the streak camera captures high time-resolution images of the laser echo, thereby enhancing both measurement accuracy and range. A corresponding ranging model is developed for this method. To validate the system’s performance, an outdoor experiment covering a distance of up to 6 km was conducted. The results demonstrate that the system achieved a distance measurement accuracy of 0.1 m, highlighting its effectiveness in long-range applications. The experiment further confirms that the combination of STIL and TDC significantly enhances accuracy and range, making it suitable for various long-range, high-precision measurement tasks.

Airborne streak tube imaging LiDAR (STIL) consists of several different data-generating subsystems and introduces system errors each time it is installed on an aircraft. These errors change with each installation, which makes the parametric calibration of the LiDAR meaningless. In this study, we propose a high-precision reconstruction method for point clouds that can be used without calibrating the system parameters. In essence, after each remote sensing measurement, a self-checking process is performed with experimental data to replace the fixed system parameters. In this process, the splicing error of the same region measured under different conditions is used as a criterion to optimize the reconstruction parameters via a particle swarm optimization (PSO) algorithm. For a detection distance of 3000 m, the elevation error of the point cloud reconstruction reaches more than 1 m if the placement parameters are not optimized; after optimization, the elevation error can be controlled within 0.3 m.

Developing functional ductal organoids (FDOs) is essential for liver regenerative medicine. We aimed to construct FDOs with biliary tree networks in rat decellularized liver scaffolds (DLSs) with primary cholangiocytes isolated from mouse bile ducts. The developed FDOs were dynamically characterized by functional assays and metabolomics for bioprocess clarification. FDOs were reconstructed in DLSs retaining native structure and bioactive factors with mouse primary cholangiocytes expressing enriched biomarkers. Morphological assessment showed that biliary tree-like structures gradually formed from day 3 to day 14. The cholangiocytes in FDOs maintained high viability and expressed 11 specific biomarkers. Basal-apical polarity was observed at day 14 with immunostaining for E-cadherin and acetylated α-tubulin. The rhodamine 123 transport assay and active collection of cholyl-lysyl-fluorescein exhibited the specific functions of bile secretion and transportation at day 14 compared to those in monolayer and hydrogel culture systems. The metabolomics analysis with 1075 peak pairs showed that serotonin, as a key molecule of the tryptophan metabolism pathway linked to biliary tree reconstruction, was specifically expressed in FDOs during the whole period of culture. Such FDOs with biliary tree networks and serotonin expression may be applied for disease modeling and drug screening, which paves the way for future clinical therapeutic applications.

Background: Here we report a rare case of citrullinemia type II (CTLN2) accompanied by mental derangement with a deficiency of multidrug resistance 3 (MDR3) in the liver. Case presentation: The clinical data of a 17-year-old girl were collected. Liver puncture was performed, and hepatic expression of MDR3 was determined by immunohistochemistry. Serum amino acids of the patient and her parents wwere determined by a chemical isotope labeling liquid chromatography−mass spectrometry (CIL LC-MS). Genetic mutations of ABCB4 and SLC25A13 were screened by whole-exome sequencing. Immunohistochemical analysis showed a remarkably lower expression of MDR3. Mutation in ABCB4 gene was not found and whole-exome sequencing revealed the SLC25A13 mutation 852–855 del. Elevated serum levels of citrulline, homocitrulline, and homoarginine in the patient and her mother were found. Conclusions: We reported a rare case of CTLN2 combined with MDR3 deficiency, without mutation of ABCB4. The link between MDR3 down-expression and CTLN2 warrants further investigation. Meanwhile, clinicians need to further rule out the possibility of CTLN2 if MDR3 decreases in adolescent patients with mental disorders and abnormal liver function.

Owing to the special working systems of streak tube imaging lidar (STIL), the time and space dimensions are coupled together on the streak images. This coupling can cause measurement errors in 3D point clouds and can make measurement results more complicated to calibrate than other kinds of lidars. This paper presents a method to generate a time calibration array and an angle calibration array to separate the offset of the streak into time dimension and space dimension. The time and space information of the signal at any position on the streak image can be indexed through these two arrays. A validation experiment on aircraft was carried out, and the range error of the 3D point cloud was improved from 0.41 m to 0.27 m using the proposed calibration method. Thus, using the proposed calibration method can improve the accuracy of the point cloud produced by STIL.

Currently approved therapeutical strategies for inflammatory bowel diseases (IBD) suffer from variable efficacy and association with risk of serious side effects. Therefore, efforts have been made in searching for alternative therapeutics strategies utilizing gut microbiota manipulation. In this study, we show that the probiotic strain Ligilactobacillus salivarius Li01 (Li01) and the phytochemical prebiotic resveratrol (RSV) have synergistic effect in ameliorating colitis in mice. Oral coadministration of Li01 (109 CFU/d) and RSV (1.5 g/kg/d) promoted restoration of various inflammatory injuries and gut microbiota composition, exhibiting a favorable anti-inflammatory effect in DSS-induced colitis mice. The combination treatment was associated with reductions in the levels of proinflammatory cytokines IL-1β and IL-6 and increases in the levels of the anti-inflammatory cytokine IL-17A in mouse serum. Moreover, the combination treatment was found to alter the composition and metabolism of the gut microbiota, especially influencing the production of short chain fatty acids and anti-inflammatory related molecules. The mechanism underlying the improved anti-inflammatory effect from the RSV and Li01 combination treatment was found to be associated with the environmental sensor mammalian aryl hydrocarbon receptor (AHR) and tryptophan metabolism pathway. Administration of RSV in combination with Li01 in different mouse model led to enhanced conversion of RSV into metabolites, including dihydroresveratrol (DHR), resveratrol-sulfate, and resveratrol-glucuronide. DHR was found to be the dominant metabolite of RSV in conventional and colitis mice. An increased DHR/RSV ratio was confirmed to activate AHR and contribute to an enhanced anti-inflammatory effect. DHR is considered as a potential AHR ligand. The DHR/RSV ratio also affected the serotonin pathway by controlling the expression of Tph1, SERT, and 5-HT7R leading to amelioration of colitis in mice. Our data suggest that treatment with a combination of Li01 and RSV has potential as a therapeutic strategy for IBD; further investigation of this combination in clinical settings is warranted.

In this paper, a ground target extraction system for a novel LiDAR, airborne streak tube imaging LiDAR (ASTIL), is proposed. This system depends on only a single echo and a single data source, and can achieve fast ground target extraction. This system consists of two modules: Autofocus SSD (Single Shot MultiBox Detector) and post-processing. The Autofocus SSD proposed in this paper is used for object detection in the ASTIL echo signal, and its prediction speed exceeds that of the original SSD by a factor of three. In the post-processing module, we describe in detail how the echoes are processed into point clouds. The system was tested on a test set, and it can be seen from a visual perspective that satisfactory results were obtained for the extraction of buildings and trees. The system mAPIoU=0.5 is 0.812, and the FPS is greater than 34. The results prove that this ASTIL processing system can achieve fast ground target extraction based on a single echo and a single data source.

Abstract Background Severe fever with thrombocytopenia syndrome (SFTS) is a severe viral disease caused by SFTSV. It is important to estimate the rate of missed SFTS diagnosis and to further understand the actual incidence in high endemic areas in China. Methods This study was conducted in two high SFTS endemic provinces in 2015. Patients hospitalized in 2014 or within 1 year before investigation were selected after considering their clinical manifestations, specifically, fever, platelet, and white blood cell. During retrospective investigation, sera were collected to detect SFTSV antibodies to assess SFTSV infection. To further understand SFTSV infection, acute phase sera were detected; SFTSV infection rate among a healthy population was also investigated to determine the basic infection level. Results In total, 246 hospitalized cases were included, including 83 cases (33.7%) with fever, thrombocytopenia and leukopenia, 38 cases (15.4%) with fever and thrombocytopenia but without leukopenia, and 125 cases (50.8%) without fever but with thrombocytopenia and leukopenia. In total, 13 patients (5.3%) were SFTSV IgM antibody-positive, 48 (19.5%) were IgG-positive. Of the 13 IgM-positive cases, 11 (84.6%) were IgG-positive (9 with titres ≥1:400). Seropositive rates of antibodies were high (8.4% for IgM and 30.1% for IgG) in patients with fever, thrombocytopenia and leukopenia. Furthermore, among IgG-positive cases in this group, 76% (19/25) of patients’ IgG antibody titres were ≥1:400. Additionally, 28 of 246 cases were initially diagnosed with suspected SFTS and were then excluded, and 218 patients were never diagnosed with SFTS; the seropositive rates of IgM and IgG in these two groups were 25% and 67.9% and 2.8% and 13.3%, respectively. These rates were 64.3% and 21.4% in 14 sera collected during acute phase of the 28 cases mentioned above. Seropositive rate of SFTSV IgG was only 1.3% in the patient-matched healthy group, and no IgM antibody was detected. A preliminary estimate of 8.3% of SFTS cases were missed in SFTS high endemic provinces. Conclusions The actual SFTS incidence was underestimated. Effective measures such as adding a new SFTS case category - “SFTS clinical diagnosis cases” or using serological detection methods during acute phase should be considered to avoid missed diagnoses.

Early detection and effective interventions for liver cirrhosis (LC) remain an urgent unmet clinical need. Inspired from intestinal disorders in LC patients, we investigated the associations between gut microbiome and disease progression based on a raw metagenomic dataset of 47 healthy controls, 49 compensated, and 46 decompensated LC patients from our previous study, and a metabolomic dataset of urine samples from the same controls/patients using ultra-performance liquid chromatography/mass spectrophotometry system. It was found that the combination and relative abundance of gut microbiome, the inter-microbiome regulatory networks, and the microbiome-host correlation patterns varied during disease progression. The significant reduction of bacteria involved in fermentation of plant cell wall polysaccharides and resistant starch (such as Alistipes sp. HG5, Clostridium thermocellum) contributed to the reduced supply of energy sources, the disorganized self-feeding and cross-feeding networks and the thriving of some opportunistic pathogens in genus Veillonella. The marked decrease of butyrate-producing bacteria and increase of Ruminococcus gnavus implicated in degradation of elements from the mucus layer provided an explanation for the impaired intestinal barrier function and systematic inflammation in LC patients. Our results pave the way for further developments in early detection and intervention of LC targeting on gut microbiome.

Sensor-based human activity recognition (HAR) has attracted enormous interests due to its wide applications in the Internet of Things (IoT), smart homes and healthcare. In this paper, a low-resolution infrared array sensor-based HAR approach is proposed using the deep learning framework. The device-free sensing system leverages the infrared array sensor of 8×8 pixels to collect the infrared signals, which can ensure users’ privacy and effectively reduce the deployment cost of the network. To reduce the influence of temperature variations, a combination of the J-filter noise reduction method and the Butterworth filter is performed to preprocess the infrared signals. Long short-term memory (LSTM), a representative recurrent neural network, is utilized to automatically extract characteristics from the infrared signal and build the recognition model. In addition, the real-time HAR interface is designed by embedding the LSTM model. Experimental results show that the typical daily activities can be classified with the recognition accuracy of 98.287%. The proposed approach yields a better result compared to the existing machine learning methods, and it provides a low-cost yet promising solution for privacy-preserving scenarios.

Multiphoton microscopy is a well-established technique for biomedical applications, but real-time multidepth multimodal multiphoton microscopy using non-imaging detection has barely been discussed. We demonstrate a novel label-free imaging system capable of generating multimodal multiphoton signals at different focal planes simultaneously. Two spatially overlapped and temporally interlaced beams are obtained by applying cost-effective electro-optic modulator (EOM)-based fast-switching light paths. The switching beams have different divergence properties, enabling imaging at different depths into samples. The EOM is synchronized to the pixel clock from the microscope, achieving pixel-to-pixel focus-switching. The capability of the imaging system is demonstrated by performing real-time multidepth two-photon fluorescence (TPF) and second-harmonic generation (SHG) imaging of freshly excised mouse lung lobes. TPF and SHG images are acquired at two wavelength ranges. One is between 415 and 455 nm, and the other is between 495 and 635 nm. The microenvironment of pulmonary alveoli is depicted by the distributions of both elastin fibers visualized by TPF and collagen fibers illustrated by SHG. Macrophages residing inside apparent alveolar lumens are also identified by TPF, which shows that the imaging system is capable of localizing biological objects in three dimensions and has the potential of monitoring in vivo cellular dynamics in the axial direction.

Introduction. Green fluorescent protein (GFP) is widely used as a reporter gene in regenerative medicine research to label and track stem cells. Here, we examined whether expressing GFP gene may impact the metabolism of human placental mesenchymal stem cells (hPMSCs). Methods. The GFP gene was transduced into hPMSCs using lentiviral-based infection to establish GFP+hPMSCs. A sensitive 13C/12C-dansyl labeling LC-MS method targeting the amine/phenol submetabolome was used for in-depth cell metabolome profiling. Results. A total of 1151 peak pairs or metabolites were detected from 12 LC-MS runs. Principal component analysis and partial least squares discriminant analysis showed poor separation, and the volcano plots demonstrated that most of the metabolites were not significantly changed when hPMSCs were tagged with GFP. Overall, 739 metabolites were positively or putatively identified. Only 11 metabolites showed significant changes. Metabolic pathway analyses indicated that three of the identified metabolites were involved in nine pathways. However, these metabolites are unlikely to have a large impact on the metabolic pathways due to their nonessential roles and limited hits in pathway analysis. Conclusion. This study indicated that the expression of ectopic GFP reporter gene did not significantly alter the metabolomics pathways covered by the amine/phenol submetabolome.

Recent years,the rapid development of solar photovoltaic has become a new hope to save the environment pollution and resource shortage in the electric power era.Countries have introduced relevant policies to support the development of photovoltaic power generation industry actively.Independent household solar photovoltaic system and Large-scale photovoltaic grid-connected system have been used successfully.However,development of countries’ photovoltaic inductry is uneven due to synchronized technology and resources.This paper describes the status quo of PV industry in major PV application countries.

3,3'-Diindolylmethane (DIM), one of the active products derived from Brassica plants, is a promising antitumor agent. The present study indicated that DIM significantly induced apoptosis in U937 human leukemia cells in dose- and time-dependent manners. These events were also noted in other human leukemia cells (Jurkat and HL-60) and primary human leukemia cells (AML) but not in normal bone marrow mononuclear cells. We also found that DIM-induced lethality is associated with caspases activation, myeloid cell leukemia-1 (Mcl-1) down-regulation, p21(cip1/waf1) up-regulation, and Akt inactivation accompanied by c-jun NH2-terminal kinase (JNK) activation. Enforced activation of Akt by a constitutively active Akt construct prevented DIM-mediated caspase activation, Mcl-1 down-regulation, JNK activation, and apoptosis. Conversely, DIM lethality was potentiated by the PI3K inhibitor LY294002. Interruption of the JNK pathway by pharmacologic or genetic approaches attenuated DIM-induced caspases activation, Mcl-1 down-regulation, and apoptosis. Lastly, DIM inhibits tumor growth of mouse U937 xenograft, which was related to induction of apoptosis and inactivation of Akt, as well as activation of JNK. Collectively, these findings suggest that DIM induces apoptosis in human leukemia cell lines and primary human leukemia cells, and exhibits antileukemic activity in vivo through Akt inactivation and JNK activation.

We apply the Gaussian-oscillator model of the infrared dielectric function of ZnS materials prepared by hot pressing (HP) and chemical vapor deposition (CVD). Chemical factor analysis of the 700 to 1300 cm-1 spectral range shows 3 oscillators in the HP material and 4 in that prepared by CVD. The origin of these oscillators is identified. In both materials, high-order transverse-optical (TO) and longitudinal-acoustic (LA) vibration modes are excited. In HP-ZnS, the four oscillators are related to the 3TO, 1TO+2LA, 3LA, and 4LA modes. Only the first three are found in the CVD material. These results clarify the physical mechanisms of lattice absorption of ZnS in this spectral range. [ABSTRACT FROM AUTHOR]