Factor analysis of hydrologic services in water-controlled grassland ecosystems by InVEST model and geodetector.

Water conservation is highly important for a successful desert grassland ecosystem, but there was no comprehensive view on how to assess influencing factors in managing and addressing water yield and water conservation in desert steppe. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, which is specifically used for the assessment of ecosystem services, was combined with geographic detectors to identify the priority areas for water conservation function and analyze the driving factors of water conservation in the Tabu River Basin, Inner Mongolia Autonomous Region, China, using different meteorological data sources. (i) The InVEST model has the advantage of modeling water yield and water conservation at spatial scales by fusion downscaling data. High water yield mainly occurs in the southern hilly mountainous areas, low water yield in the northern desert and grassland areas, and between the two in the central agro-pastoral areas; the multi-year average water conservation and water yield based on the InVEST model are 3.3 and 16 mm, respectively. (ii) Water yield and water conservation roughly show a transitional phenomenon of "high in the south and low in the north." The water yield and water conservation per unit area of the Tabu River Basin are relatively large for construction land, unused land, and cropland, relatively small for grassland and forestland, and basically zero for water bodies. Forest land has the strongest water conservation capacity, followed by grassland and farmland, while the order of water yield capacity is the opposite. (iii) Precipitation shows the strongest explanatory power for water yield (q = 0.427), followed by land use types (q = 0.411). The precipitation ∩ actual evapotranspiration has the strongest explanatory power for water yield (q = 0.87). The explanatory power of water yield on water conservation is the strongest (q = 0.752), followed by precipitation (q = 0.4), and the water yield ∩ soil has the greatest explanatory power on water conservation (q = 0.91). These findings are crucial for promoting regional hydrologic services and can provide a water resources management strategy for decision-makers.

Study on Modeling and Evaluating Alfalfa Yield and Optimal Water Use Efficiency in the Agro-Pastoral Ecotone of Northern China.

The agro-pastoral ecotone in northern China is the main production area of agriculture and animal husbandry, in which agricultural development relies entirely on groundwater. Due to the increasing water consumption of groundwater year by year, groundwater resources are becoming increasingly scarce. The substantial water demand and low germination rate in the first year are the main characteristics of alfalfa (Medicago sativa L.) yield in the agro-pastoral ecotone in northern China. Due to unscientific irrigation, water resources are seriously wasted, which restricts the development of local agriculture and animal husbandry. The study constructed the Dssat-Forages-Alfalfa model and used soil water content, leaf area index, and yield data collected with in situ observation experiments in 2022 and 2023 to calibrate and validate the parameters. The study found ARE < 10%, ENRMS < 15%, and R2 ≥ 0.85. The model simulation accuracy was acceptable. The study revealed that the water consumption at the surface soil layer (0-20 cm) was more than 6~12% and 13~31% than that at the 20-40 cm and 40-60 cm soil layers, respectively. The study showed when the irrigation quota was 30 mm, the annual yield of alfalfa (Medicago sativa L.) (7435 kg/ha) was consistent with that of the irrigation quota of 33 mm, and increased by 3.99% to 5.34% and 6.86% to 10.67% compared with that of irrigation quotas of 27 mm and 24 mm, respectively. To ensure the germination rate of alfalfa (Medicago sativa L.), it is recommended to control the initial soil water content at 0.8 θfc~1.0 θfc, with an irrigation quota of 30 mm, which was the best scheme for water-use efficiency and economic yield. The study aimed to provide technological support for the rational utilization of groundwater and the scientific improvement of alfalfa yield in the agro-pastoral ecotone in northern China.

Silencing the CsSnRK2.11 Gene Decreases Drought Tolerance of Cucumis sativus L.

Drought stress restricts vegetable growth, and abscisic acid plays an important role in its regulation. Sucrose non-fermenting1-related protein kinase 2 (SnRK2) is a key enzyme in regulating ABA signal transduction in plants, and it plays a significant role in response to multiple abiotic stresses. Our previous experiments demonstrated that the SnRK2.11 gene exhibits a significant response to drought stress in cucumbers. To further investigate the function of SnRK2.11 under drought stress, we used VIGS (virus-induced gene silencing) technology to silence this gene and conducted RNA-seq analysis. The SnRK2.11-silencing plants displayed increased sensitivity to drought stress, which led to stunted growth and increased wilting speed. Moreover, various physiological parameters related to photosynthesis, chlorophyll fluorescence, leaf water content, chlorophyll content, and antioxidant enzyme activity were significantly reduced. The intercellular CO2 concentration, non-photochemical burst coefficient, and malondialdehyde and proline content were significantly increased. RNA-seq analysis identified 534 differentially expressed genes (DEGs): 311 were upregulated and 223 were downregulated. GO functional annotation analysis indicated that these DEGs were significantly enriched for molecular functions related to host cells, enzyme activity, and stress responses. KEGG pathway enrichment analysis further revealed that these DEGs were significantly enriched in phytohormone signalling, MAPK signalling, and carotenoid biosynthesis pathways, all of which were associated with abscisic acid. This study used VIGS technology and transcriptome data to investigate the role of CsSnRK2.11 under drought stress, offering valuable insights into the mechanism of the SnRK2 gene in enhancing drought resistance in cucumbers.

Virome Profiling of Chickens with Hepatomegaly Rupture Syndrome Reveals Coinfection of Multiple Viruses.

Liver diseases seriously challenge the health of chickens raised on scaled farms and cause tremendous economic losses to farm owners. The causative agents for liver diseases are still elusive, even though various pathogens, such as the hepatitis E virus, have been reported. In the winter of 2021, a liver disease was observed on a chicken farm in Dalian, China, which increased chicken mortality by up to 18%. We conducted panvirome profiling of the livers, spleens, kidneys, and recta of 20 diseased chickens. The viromic results revealed coinfection of multiple viruses, including pathogenic ones, in these organs. The viruses were highly identical to those detected in other provinces, and the vaccine and field strains of avian encephalomyelitis virus (AEV) and chicken infectious anemia virus (CIAV) cocirculated on the farm. In particular, the liver showed higher abundance of AEV and multiple fowl adenoviruses than other organs. Furthermore, the liver also contracted avian leukemia virus and CIAV. Experimental animals with infected liver samples developed minor to medium lesions of the liver and showed a virus abundance profile for AEV across internal organs similar to that in the original samples. These results suggest that coinfection with multiple pathogenic viruses influences the occurrence and development of infectious liver disease. The results also highlight that strong farm management standards with strict biosafety measures are needed to minimize the risk of pathogenic virus introduction to the farm.

On the Investigation of Frequency Characteristics of a Novel Inductive Debris Sensor.

Lubricants have the ability to reduce frictions, prevent wear, convey metal debris particles and increase the efficiency of heat transfer; therefore, they have been widely used in mechanical systems. To assess the safety and reliability of the machine under operational conditions, the development of inductive debris sensors for the online monitoring of debris particles in lubricants has received more attention from researchers. To achieve a high-precision, high-efficiency sensor for accurate prediction on the degree of wear, the equivalent circuit model of the sensor coil has been established, and its equations discovering the relationship between the induced voltage and excitation frequency have been derived. Furthermore, the influence of excitation frequencies and metal debris on the magnetic flux density has been analyzed throughout the simulations to determine the sensor magnetic field. In order to identify a frequency range suitable for detecting both ferrous and non-ferrous materials with a high level of sensitivity, the analytical analysis and experiments have been conducted to investigate the frequency characteristics of the developed inductive debris sensor prototype and its improved inspection capability. Moreover, the developed inductive debris sensor with the noticeable frequency characteristics has been assessed and its theoretical model has been also validated throughout experimental tests. Results have shown that the detection sensitivity of non-ferrous debris by the developed sensor increases with the excitation frequency in the range of 50 kHz to 250 kHz, while more complex results for the detection of ferrous debris have been observed. The detection sensitivity decreases as the excitation frequency increases from 50 kHz to 300 kHz, and then increases with the excitation frequency from 300 kHz to 370 kHz. This leads to the effective selection of the excitation frequency in the process of inspection. In summary, the investigation into the frequency characteristics of the proposed novel inductive debris sensor has enabled its broad applications and also provided a theoretical basis and valuable insights into the development of inductive debris sensors with improved detection sensitivity.

A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment.

Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods: A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results: The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions: This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3.

DNA virome of ticks in the Northeast and Hubei provinces of China reveals diverse single-stranded circular DNA viruses.

BACKGROUND: Ticks are medically important vectors capable of transmitting a variety of pathogens to and between host species. Although the spectrum of tick-borne RNA viruses has been frequently investigated, the diversity of tick-borne DNA viruses remains largely unknown. METHODS: A total of 1571 ticks were collected from forests and infested animals, and the diversity of the viruses they harbored was profiled using a DNA-specific virome method. The viromic data were phylogenetically analyzed and validated by PCR assays. RESULTS: Although diverse and abundant prokaryotic viruses were identified in the collected ticks, only eukaryotic DNA viruses with single-stranded circular genomes covering the anelloviruses and circular replication-associated (Rep) protein-encoding single-stranded (CRESS) DNA viruses were recovered from ticks. Anelloviruses were detected only in two tick pools, but CRESS DNA viruses were prevalent across these ticks except in one pool of Dermacentor spp. ticks. Phylogenetic analyses revealed that these tick-borne CRESS DNA viruses were related to viruses recovered from animal feces, tissues and even environmental samples, suggesting that their presence may be largely explained by environmental factors rather than by tick species and host blood meals. CONCLUSIONS: Based on the results, tick-borne eukaryotic DNA viruses appear to be much less common than eukaryotic RNA viruses. Investigations involving a wider collection area and more diverse tick species are required to further support this speculation.

Elimination of Foreign Sequences in Eukaryotic Viral Reference Genomes Improves the Accuracy of Virome Analysis.

Widespread in public databases, foreign contaminant sequences pose a substantial obstacle in genomic analyses. Such contamination in viral genome databases is also notorious but more complicated and often causes questionable results in various applications, particularly in virome-based virus detection. Here, we conducted comprehensive screening and identification of the foreign sequences hidden in the largest eukaryotic viral genome collections of GenBank and UniProt using a scrutiny pipeline, which enables us to rigorously detect those problematic viral sequences (PVSs) with origins in hosts, vectors, and laboratory components. As a result, a total of 766 nucleotide PVSs and 276 amino acid PVSs with lengths up to 6,605 bp were determined, which were widely distributed in 39 families with many involving highly public health-concerning viruses, such as hepatitis C virus, Crimean-Congo hemorrhagic fever virus, and filovirus. The majority of these PVSs are genomic fragments of hosts including humans and bacteria. However, they cannot simply be regarded as foreign contaminants, since parts of them are results of natural occurrence or artificial engineering of viruses. Nevertheless, they severely disturb such sequence-based analyses as genome annotation, taxonomic assignment, and virome profiling. Therefore, we provide a clean version of the eukaryotic viral reference data set by the removal of these PVSs, which allows more accurate virome analysis with less time consumed than with other comprehensive databases. IMPORTANCE High-throughput sequencing-based viromics highly depends on reference databases, but foreign contamination is widespread in public databases and often leads to confusing and even wrong conclusions in genomic analysis and viromic profiling. To address this issue, we systematically detected and identified the contamination in the largest viral sequence collections of GenBank and UniProt based on a stringent scrutiny pipeline. We found hundreds of PVSs that are related to hosts, vectors, and laboratory components. By the removal of them, the resulting data set greatly improves the accuracy and efficiency of eukaryotic virome profiling. These results refresh our knowledge of the type and origin of PVSs and also have warning implications for viromic analysis. Viromic practitioners should be aware of these problems caused by PVSs and need to realize that a careful review of bioinformatic results is necessary for a reliable conclusion.

ISA-Net: Improved spatial attention network for PET-CT tumor segmentation.

BACKGROUND AND OBJECTIVE: Achieving accurate and automated tumor segmentation plays an important role in both clinical practice and radiomics research. Segmentation in medicine is now often performed manually by experts, which is a laborious, expensive and error-prone task. Manual annotation relies heavily on the experience and knowledge of these experts. In addition, there is much intra- and interobserver variation. Therefore, it is of great significance to develop a method that can automatically segment tumor target regions. METHODS: In this paper, we propose a deep learning segmentation method based on multimodal positron emission tomography-computed tomography (PET-CT), which combines the high sensitivity of PET and the precise anatomical information of CT. We design an improved spatial attention network(ISA-Net) to increase the accuracy of PET or CT in detecting tumors, which uses multi-scale convolution operation to extract feature information and can highlight the tumor region location information and suppress the non-tumor region location information. In addition, our network uses dual-channel inputs in the coding stage and fuses them in the decoding stage, which can take advantage of the differences and complementarities between PET and CT. RESULTS: We validated the proposed ISA-Net method on two clinical datasets, a soft tissue sarcoma(STS) and a head and neck tumor(HECKTOR) dataset, and compared with other attention methods for tumor segmentation. The DSC score of 0.8378 on STS dataset and 0.8076 on HECKTOR dataset show that ISA-Net method achieves better segmentation performance and has better generalization. CONCLUSIONS: The method proposed in this paper is based on multi-modal medical image tumor segmentation, which can effectively utilize the difference and complementarity of different modes. The method can also be applied to other multi-modal data or single-modal data by proper adjustment.

Improved GAN: Using a transformer module generator approach for material decomposition.

Dual-energy computed tomography (CT) can be used for material decomposition, allowing for the precise quantitative mapping of body substances; this has a wide range of clinical applications, including disease diagnosis, treatment response evaluation and prognosis prediction. However, dual-energy CT has not yet become the mainstream technique in most clinical settings due to its limited accessibility. To fully take advantage of material quantification, researchers have attempted to use deep learning to generate material decomposition maps from conventional single-energy CT images, mainly by synthesizing another single-energy CT image from a conventional single-energy CT image to form a dual-energy CT image first and then generate material decomposition maps. This is not a straightforward process, and it potentially introduces many inaccuracies after multiple steps. In this work, we proposed a generative adversarial network (GAN) framework as the base and improved its generator; this approach combines convolutional neural networks (CNNs) and a transformer module to directly generate material decomposition maps from conventional single-energy CT images. Our model pays attention to both local and global information. Then, we compared our method with 6 competitive deep learning methods on water (calcium) and calcium (water) substrate density image datasets. The average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the water (calcium) substrate density images were 32.7207, 0.9685, 0.0323, and 0.0555, respectively. Furthermore, the average PSNR, SSIM, MAE, and RMSE of the generated and ground truth of the calcium (water) substrate density images were 30.2823, 0.9449, 0.0652, and 0.0715, respectively. Our model achieved better performance and stronger stability than competing approaches.

TG-Net: Combining transformer and GAN for nasopharyngeal carcinoma tumor segmentation based on total-body uEXPLORER PET/CT scanner.

Nasopharyngeal carcinoma (NPC) is a malignant tumor, and the main treatment is radiotherapy. Accurate delineation of the target tumor is essential for radiotherapy of NPC. NPC tumors are small in size and vary widely in shape and structure, making it a time-consuming and laborious task for even experienced radiologists to manually outline tumors. However, the segmentation performance of current deep learning models is not satisfactory, mainly manifested by poor segmentation boundaries. To solve this problem, this paper proposes a segmentation method for nasopharyngeal carcinoma based on dynamic PET-CT image data, whose input data include CT, PET, and parametric images (Ki images). This method uses a generative adversarial network with a modified UNet integrated with a Transformer as the generator (TG-Net) to achieve automatic segmentation of NPC on combined CT-PET-Ki images. In the coding stage, TG-Net uses moving windows to replace traditional pooling operations to obtain patches of different sizes, which can reduce information loss in the coding process. Moreover, the introduction of Transformer can make the network learn more representative features and improve the discriminant ability of the model, especially for tumor boundaries. Finally, the results of fivefold cross validation with an average Dice similarity coefficient score of 0.9135 show that our method has good segmentation performance. Comparative experiments also show that our network structure is superior to the most advanced methods in the segmentation of NPC. In addition, this work is the first to use Ki images to assist tumor segmentation. We also demonstrated the usefulness of adding Ki images to aid in tumor segmentation.

Comprehensive Evaluation of RNA and DNA Viromic Methods Based on Species Richness and Abundance Analyses Using Marmot Rectal Samples.

Viral metagenomics is the most powerful tool to profile viromic composition for a given sample. Different viromic methods, including amplification-free ones, have been developed, but choosing them for different purposes requires comprehensive benchmarks. Here, we assessed the performance of four routinely used methods, i.e., multiple displacement amplification (MDA), direct metagenomic sequencing (MTG), sequence-independent single-primer amplification (SIA), and metatranscriptomic sequencing (MTT), using marmot rectal samples as the templates spiked with five known viruses of different genome types. The obtained clean data were differently contaminated by host and bacterial genomes, resulting in MDA having the most, with ~72.1%, but MTT had only ~7.5% data, useful for follow-up viromic analysis. MDA showed a broader spectrum with higher efficiency to profile the DNA virome, and MTT captured almost all RNA viruses with extraordinary sensitivity; hence, they are advisable in richness-based viromic studies. MTG was weak in capturing single-stranded DNA viruses, and SIA could detect both RNA and DNA viruses but with high randomness. Due to biases to certain types of viruses, the four methods caused different alterations to species abundance compared to the initial virus composition. SIA and MDA introduced greater stochastic errors to relative abundances of species, genus, and family taxa, whereas the two amplification-free methods were more tolerant toward such errors and thus are recommendable in abundance-based analyses. In addition, genus taxon is a compromising analytic level that ensures technically supported and biologically and/or ecologically meaningful viromic conclusions. IMPORTANCE Viral metagenomics can be roughly divided into species richness-based studies and species abundance-based analyses. Viromic methods with different principles have been developed, but rational selection of these techniques according to different purposes requires comprehensive understanding of their properties. By assessing the four most widely used methods using template samples, we found that multiple displacement amplification (MDA) and metatranscriptomic sequencing (MTT) are advisable for species richness-based viromic studies, as they show excellent efficiency to detect DNA and RNA viruses. Meanwhile, metagenomic sequencing (MTG) and MTT are more compatible with stochastic errors of methods introduced into relative abundance of viromic taxa and hence are rational choices in species abundance-based analyses. This study also highlights that MTG needs to tackle host genome contamination and ameliorate the capacity to detect single-stranded DNA viruses in the future, and the MTT method requires an improvement in bacterial rRNA depletion prior to library preparation.

A novel BiOX photocatalyst for the "green" degradation of polymers used in oilfields.

The wastes from functional polymers (polyanionic cellulose, polyacrylamide, potassium polyacrylamide, and hydroxyethyl cellulose) generated during oil and gas exploration and development are harmful to biodiversity and human health. However, most traditional treatments are inefficient in degradation and cause secondary pollution. In this paper, BiOBr0.5Cl0.5 a 3D flower-like solid solution with in-situ deposition of elementary substance Bi and surface oxygen vacancies was synthesized by the hydrolysis and the redox methods. The chemical compositions, the morphologies, and the UV-visible absorption properties of Bi/BiOBr0.5Cl0.5 were characterized. Moreover, the photocatalytic activity of Bi/BiOBr0.5Cl0.5 and the kinetic behavior of the RhB photocatalytic degradation were investigated. The photocatalytic degradation of RhB followed a pseudo-first-order kinetic reaction, and Bi/BiOBr0.5Cl0.5-0.3 demonstrated the highest photocatalytic activity: The RhB degradation efficiency of Bi/BiOBr0.5Cl0.5-0.3 was 85%, and the COD removal rate of the functional polymers conducted by Bi/BiOBr0.5Cl0.5-0.3 was greater than 80%. The exciton photocatalytic processes of Bi/BiOBr0.5Cl0.5 was found through the electron spin resonance (ESR) and the active-species trapping analyses of the photocatalytic degradations of RhB by Bi/BiOBr0.5Cl0.5. In summary, in this paper, the synthesis methods of Bi/BiOBr0.5Cl0.5 photocatalyst and the photocatalytic activity of the Bi/BiOBr0.5Cl0.5 on the degradations of polymers used in oilfields were reported, addressing the shortcomings of the existing treatments for polymer waste fluids that are incorporated into the oil and gas exploration and development process.

Cadmium Toxicity on Chondrocytes and the Palliative Effects of 1α, 25-Dihydroxy Vitamin D3 in White Leghorns Chicken's Embryo.

Cadmium (Cd) can causes osteoporosis and joint swelling. However, the mechanism of Cd toxicity in chondrocytes and how to alleviate Cd poisoning to chondrocytes are still unclear. Herein, we evaluated the toxicity of Cd to chicken chondrocytes, and whether vitamin D can relieve the toxicity of Cd to chondrocytes. Primary chondrocytes were collected from knee-joint cartilage of 15-day-old chicken embryos. They were treated with (0, 1, 2, and 4) µM Cd alone, 10-8 M 1α,25-(OH)2D3 alone, or 2 µM Cd combined with 10-8 M 1α,25-(OH)2D3. We found that Cd significantly inhibited Sox9 and ACAN mRNA expression, which are markers for chondrocyte differentiation, downregulated the mitochondrial membrane potential, upregulated the Bax/B-cell lymphoma 2 ratio. Furthermore, Cd significantly promoted matrix metalloproteinase (MMP)-9 expression, thus accelerating the degradation of extracellular matrix. And Cd also inhibited the expression of main macromolecular protein of extracellular matrix, Collagen type IIα1 (COL2A1) and acid mucopolysaccharide. However, 1α,25-(OH)2D3 pretreatment significantly alleviated the toxicity effects of Cd on the differentiation, apoptosis and extracellular matrix gene expression in primary chondrocytes. Conclusively, Cd exposure could inhibited chicken embryo chondrocytes differentiation, extracellular matrix gene expression, and induced chondrocyte apoptosis. However, these toxic effects of Cd are alleviated by the pretreatment of chondrocytes with 1α,25-(OH)2D3.

Role of microRNA and long non-coding RNA in Marek's disease tumorigenesis in chicken.

Marek's disease virus (MDV), the causative agent of Marek's disease (MD), results in highly infectious phymatosis, lymphatic tissue hyperplasia, and neoplasia. MD is associated with high morbidity and mortality rate. Non-coding RNAs (ncRNAs) entails long non-coding RNA (lncRNA) and microRNA (miRNA). Numerous studies have reported that specific miRNAs and lncRNAs participate in multiple cellular processes, such as proliferation, migration, and tumor cell invasion. Specialized miRNAs and lncRNAs militate a similar role in MD tumor oncogenesis. Despite its growing popularity, only a few reviews are available on ncRNA in MDV tumor oncogenes. Herein, we summarized the role of the miRNAs and lncRNAs in MD tumorigenesis. Altogether, we brought forth the research issues, such as MD prevention, screening, regulatory network formation, novel miRNAs, and lncRNAs analysis in MD that needs to be explored further. This review provides a theoretical platform for the further analysis of miRNAs and lncRNAs functions and the prevention and control of MD and malignancies in domestic animals.

A biomimetic mesh for treating female stress urinary incontinence.

For patients with medium to severe incontinence, sub-urethral support surgery has a high cure rate, but using synthetic meshes leads to some complications such as mesh erosion/exposure and thigh pain. Autologous or acellular extracellular matrix grafts present few complications but have a high recurrence rate. Regensling™ is a new sling product made of a synthetic material with a biomimetic structure, aiming to provide long-term mechanical support with a lower complication rate. To assess the safety and effectiveness of Regensling™, both in vitro and in vivo experiments were performed. The mesh was implanted in the subcutaneous, intramuscular and sub-urethral regions of rabbits. At 4, 12, and 26 weeks post-implantation, the animals were executed and the implants were studied for their mechanical and biocompatible properties. Compared to the control material, the Regensling™ was covered by a thin layer of fibrous tissue with good compliance, and had a milder inflammatory response. During the period of the experiment, Regensling™ showed stable strength with an increasing trend over time.

Nanofibrous biomimetic mesh can be used for pelvic reconstructive surgery: A randomized study.

BACKGROUND: Implantation of nonabsorbable polypropylene (PP) mesh in the vagina is the main surgical treatment for pelvic organ prolapse (POP); however, clinical outcomes remain controversial and far from satisfactory. In particular, reducing the exposure or erosion of vaginal implants to obtain improved functional reconstruction is challenging. There is an urgent need for the development of new materials and/or products for POP treatment. A nanofibrous biomimetic mesh was recently developed to address this issue. OBJECTIVE: In this study, the basic properties of the newly developed mesh, including structural characteristics, mechanical properties, biological response of human umbilical cord mesenchymal stem cells in vitro, and tissue regeneration and biocompatibility in vivo, were evaluated and compared with those of Gynemesh™PS. METHODS: Scanning electron microscopy and uniaxial tensile methods were used to evaluate microstructure and mechanical properties, respectively. Mesenchymal stem cell growth on the meshes was observed by fluorescence microscopy to visualize the expression of enhanced red fluorescent protein. Twenty-four mature female Sprague Dawley rats were randomly assigned to two groups: group 1 (nanofibrous biomimetic mesh, Medprin, Germany, n=12) and group 2 (Gynemesh(TM)PS, Ethicon, USA; n=12). The posterior vaginal wall was incised from the introitus, and the mesh was then implanted. Three implants of each type were tested for 1, 4, 8 and 12 weeks. Connective tissue organization, inflammation, vascularization, and regenerated tissue were histologically assessed. RESULTS: The nanofibrous biomimetic mesh is a relatively heavy material and exhibited lower porosity than Gynemesh(TM)PS. The new mesh was stiffer than Gynemesh(TM)PS (p<0.001) but supported human umbilical cord mesenchymal stem cell attachment. Erosion of the grafts did not occur in any animal. The nanofibrous biomimetic mesh was encapsulated by a thicker layer of connective tissue and was associated with significantly greater inflammatory scores compared with Gynemesh(TM)PS. At 12 weeks, the vascularization of the new mesh was greater than that of Gynemesh(TM)PS (p<0.05). No significant difference in the thickness of the smooth muscle layer following implantation was observed between the two groups (p>0.05). CONCLUSIONS: The nanofibrous biomimetic mesh is a candidate for reinforcing pelvic reconstruction. The mesh could be improved by decreasing its weight and stiffness and increasing its porosity. This mesh could serve as a carrier for stem cells in future regenerative medicine and tissue engineering research.