Bioengineering最新文献

Most existing methods for magnetic resonance imaging (MRI) reconstruction with deep learning use fully supervised training, which assumes that a fully sampled dataset with a high signal-to-noise ratio (SNR) is available for training. In many circumstances, however, such a dataset is highly impractical or even technically infeasible to acquire. Recently, a number of self-supervised methods for MRI reconstruction have been proposed, which use sub-sampled data only. However, the majority of such methods, such as Self-Supervised Learning via Data Undersampling (SSDU), are susceptible to reconstruction errors arising from noise in the measured data. In response, we propose Robust SSDU, which provably recovers clean images from noisy, sub-sampled training data by simultaneously estimating missing k-space samples and denoising the available samples. Robust SSDU trains the reconstruction network to map from a further noisy and sub-sampled version of the data to the original, singly noisy, and sub-sampled data and applies an additive Noisier2Noise correction term upon inference. We also present a related method, Noiser2Full, that recovers clean images when noisy, fully sampled data are available for training. Both proposed methods are applicable to any network architecture, are straightforward to implement, and have a similar computational cost to standard training. We evaluate our methods on the multi-coil fastMRI brain dataset with novel denoising-specific architecture and find that it performs competitively with a benchmark trained on clean, fully sampled data.

Nannochloris sp. JB17 has been identified as an interesting microalgal species that can tolerate high salinity and high bicarbonate concentrations. In this study, Nannochloris sp. JB17 was long-term adapted to increased bicarbonate concentrations (10-60 g NaHCO₃ per L) in fresh or sea-water-based growing media. This study aimed to evaluate its growth performance and biochemical composition under different cultivation conditions. The highest biomass production (1.24-1.3 g/L) achieved in the study was obtained in fresh water media supplemented with 40 g/L and 60 g/L NaHCO₃, respectively. Total protein content fluctuated at similar levels among the different treatments (32.4-38.5%), displaying good essential amino acids indices of 0.85-1.02, but with low in vitro protein digestibility (15-20%) rates. Total lipids did not show any significant alteration among the different NaHCO₃ concentrations in both fresh and sea water (12.6-13.3%) but at increased sodium strength, a significant increase in unsaturated lipids and in particular a-linolenic acid (C18:3) and linoleic acid (C18:2) was observed. Carbohydrate content also ranged at very similar levels among the cultures (26-30.9%). The main fraction of carbohydrates was in the type of neutral sugars ranging from around 72% to 80% (of total carbohydrates), while uronic acids were in negligible amounts. Moreover, Nannochloris sp. showed that it contained around 8-9% sulfated polysaccharides. Since the microalgae display good growth patterns at high bicarbonate concentrations, they could be a potential species for microalgal-based carbon capture and utilization systems.

Knowledge of the impact of chemicals on the environment is important for assessing the risks that chemicals can generate in ecosystems. With the help of pilot-scale micro-tests, it was possible to evaluate the biological sludge in terms of its chemical and biological composition, information that can be applied on an industrial scale in treatment plants. The important parameters analyzed in the evaluation of the biodegradability of wastewater were pH, chemical composition (NH₄⁺, NO₃^-, NO₂^-, and PO₄^3-), dry substance (DS), inorganic substance (IS), and organic substance (OS), and the biological oxygen demand (BOD)/chemical oxygen consumption (COD) ratio. The examination revealed the presence of free active ciliates Aspidisca polystyla, Lyndonotus setigerum, Vorticella microstoma, fixed by Zooglee, Paramecium sp., Opercularia, Colpoda colpidium, Euplotes, Didinum nasutum, Stentor, and Acineta tuberosa, metazoa Rotifers, filamentous algae, Nostoc and Anabena, and bacteria Bacillus subtilis, Nocardia, and Microccocus luteus. The novelty of this study lies in the fact that we carried out a study to evaluate the population of microorganisms starting from the premise that the probability of biodegradation of substances is directly proportional to the number of microorganisms existing in the environment and their enzymatic equipment.

The complex collagen network of the native meniscus and the gradient of the density and alignment of this network through the meniscal enthesis is essential for the proper mechanical function of these tissues. This architecture is difficult to recapitulate in tissue-engineered replacement strategies. Prenatally, the organization of the collagen fiber network is established and aggrecan content is minimal. In vitro, fibrochondrocytes (FCCs) produce proteoglycans and associated glycosaminoglycan (GAG) chains early in culture, which can inhibit collagen fiber formation during the maturation of tissue-engineered menisci. Thus, it would be beneficial to both specifically and temporarily block deposition of proteoglycans early in culture. In this study, we transiently inhibited aggrecan production by meniscal fibrochondrocytes using siRNA in collagen gel-based tissue-engineered constructs. We evaluated the effect of siRNA treatment on the formation of collagen fibrils and bulk and microscale tensile properties. Specific inhibition of aggrecan production by fibrochondrocytes via siRNA was successful both in 2D monolayer cell culture and 3D tissue culture. This inhibition during early maturation of these in vitro constructs increased collagen fibril diameter by more than 2-fold. This increase in fibril diameter allowed these tissues to distribute strains more effectively at the local level, particularly at the interface of the bone and soft tissue. These data show that siRNA can be used to modulate the ECM to improve collagen fiber formation and mechanical properties in tissue-engineered constructs, and that a transient decrease in aggrecan promotes the formation of a more robust fiber network.

Accurate segmentation of brain tumors in MRI scans is critical for diagnosis and treatment planning. Traditional segmentation models, such as U-Net, excel in capturing spatial information but often struggle with complex tumor boundaries and subtle variations in image contrast. These limitations can lead to inconsistencies in identifying critical regions, impacting the accuracy of clinical outcomes. To address these challenges, this paper proposes a novel modification to the U-Net architecture by integrating a spatial attention mechanism designed to dynamically focus on relevant regions within MRI scans. This innovation enhances the model's ability to delineate fine tumor boundaries and improves segmentation precision. Our model was evaluated on the Figshare dataset, which includes annotated MRI images of meningioma, glioma, and pituitary tumors. The proposed model achieved a Dice similarity coefficient (DSC) of 0.93, a recall of 0.95, and an AUC of 0.94, outperforming existing approaches such as V-Net, DeepLab V3+, and nnU-Net. These results demonstrate the effectiveness of our model in addressing key challenges like low-contrast boundaries, small tumor regions, and overlapping tumors. Furthermore, the lightweight design of the model ensures its suitability for real-time clinical applications, making it a robust tool for automated tumor segmentation. This study underscores the potential of spatial attention mechanisms to significantly enhance medical imaging models and paves the way for more effective diagnostic tools.

In recent years, image-guided brachytherapy for cervical cancer has become an important treatment method for patients with locally advanced cervical cancer, and multi-modality image registration technology is a key step in this system. However, due to the patient's own movement and other factors, the deformation between the different modalities of images is discontinuous, which brings great difficulties to the registration of pelvic computed tomography (CT/) and magnetic resonance (MR) images. In this paper, we propose a multimodality image registration network based on multistage transformation enhancement features (MTEF) to maintain the continuity of the deformation field. The model uses wavelet transform to extract different components of the image and performs fusion and enhancement processing as the input to the model. The model performs multiple registrations from local to global regions. Then, we propose a novel shared pyramid registration network that can accurately extract features from different modalities, optimizing the predicted deformation field through progressive refinement. In order to improve the registration performance, we also propose a deep learning similarity measurement method combined with bistructural morphology. On the basis of deep learning, bistructural morphology is added to the model to train the pelvic area registration evaluator, and the model can obtain parameters covering large deformation for loss function. The model was verified by the actual clinical data of cervical cancer patients. After a large number of experiments, our proposed model achieved the highest dice similarity coefficient (DSC) metric compared with the state-of-the-art registration methods. The DSC index of the MTEF algorithm is 5.64% higher than that of the TransMorph algorithm. It will effectively integrate multi-modal image information, improve the accuracy of tumor localization, and benefit more cervical cancer patients.

Lymphocytes are generally non-adherent. This makes it challenging to fabricate three-dimensional (3D) structures mimicking the three-dimensional lymphoma microenvironment in vivo. This study presents the fabrication of a hemispherical 3D lymphoma model using the on-chip Cell Dome system with a hemispherical cavity (1 mm in diameter and almost 300 µm in height). Both the human brain lymphoma cell line (TK) and human B cell lymphoma cell line (KML-1) proliferated and filled the cavities. Hypoxic regions were observed in the center of the hemispherical structures. CD19 expression did not change in either cell line, while CD20 expression was slightly upregulated in TK cells and downregulated in KML-1 cells cultured in the Cell Dome compared to those cultured in two-dimensional (2D) flasks. In addition, both TK and KML-1 cells in the hemispherical structures exhibited higher resistance to doxorubicin than those in 2D flasks. These results demonstrate the effectiveness of the on-chip Cell Dome for fabricating 3D lymphoma models and provide valuable insights into the study of lymphoma behavior and the development of new drugs for lymphoma treatment.

Hyaluronic acid was proposed to support soft tissue recession surgery and guided tissue regeneration. The molecular mechanisms through which hyaluronic acid modulates the response of connective tissue cells remain elusive. To elucidate the impact of hyaluronic acid on the connective tissue cells, we used bulk RNA sequencing to determine the changes in the genetic signature of gingival fibroblasts exposed to 1.6% cross-linked hyaluronic acid and 0.2% natural hyaluronic acid. Transcriptome-wide changes were modest. Even when implementing a minimum of 1.5 log2 fold-change and a significance threshold of 1.0 -log10, only a dozenth of genes were differentially expressed. Upregulated genes were PLK3, SLC16A6, IL6, HBEGF, DGKE, DUSP4, PTGS2, FOXC2, ATAD2B, NFATC2, and downregulated genes were MMP24 and PLXNA2. RT-PCR analysis supported the impact of hyaluronic acid on increasing the expression of a selected gene panel. The findings from bulk RNA sequencing suggest that gingival fibroblasts experience weak changes in their transcriptome when exposed to hyaluronic acid.

The human skin is a remarkable organ capable of extensive regeneration, especially after severe injuries such as burns and related wounds. The de-epidermized dermis (DED) model has become a valuable in vitro tool for skin regeneration studies, particularly for testing the mechanism of action and the efficacy of clinical cutaneous cell therapies. To further improve the quality and robustness of these applications, our study focused on optimizing and standardizing DED tissue preparation and storage, enhancing its effectiveness for clinical testing. Therefore, we optimized the air-liquid interfacial culture medium composition by simplifying the historical formulation without compromising keratinocyte (therapeutic cell model) viability or proliferation. Furthermore, we investigated the impacts of adding burn wound exudates in the model by focusing on cell behavior for enhanced translational significance. The results revealed notable differences in keratinocyte adhesion and proliferation between burn wound exudates collected at the early stages and late stages of acute patient treatment, providing new information on a possible therapeutic window to apply cell therapies on burn patients. Generally, this study reported a robust method for the preclinical in vitro assessment of keratinocyte-based cutaneous cell therapies using DED models. Overall, the study underscored the importance of using in vitro models with enhanced translational relevance to better predict the clinical effects of cutaneous cell therapies in burn patient populations.

This study aimed to optimize CT esophagography by identifying effective oral contrast dilution ratios and exploring the advantages of dual-energy CT (DECT) over conventional CT for improving image quality. Ex vivo experiments using iodine contrast agents (320-400 mgI/mL) at 21 dilution ratios were scanned at three voltages, with additional dual-energy scans generating various reconstruction images. Image quality was assessed both objectively and subjectively. The study found significant variability in image quality across different dilution ratios. Specific dilution ratios that produced image quality comparable to the control group (a commercial oral contrast agent) and those meeting the standards for clinical diagnosis and high-quality images were identified based on image quality assessments. Recommendations for preparing 100 mL of oral contrast solution were provided, such as for achieving high-quality images at a scanning voltage of 100 kVp: the optimal dilution ratios are 1:6 to 1:19 for 320 mgI/mL, and 1:8 to 1:19 for 350 to 400 mgI/mL. Additionally, beam-hardening artifacts were significantly reduced in DECT images. These findings provide valuable guidance for improving CT esophagography protocols.