Bioengineering最新文献

To elucidate the unidentified roles of a selective peroxisome proliferator-activated receptor α (PPARα) agonist, pemafibrate (Pema), on the pathogenesis of retinal ischemic diseases (RID)s, the pharmacological effects of Pema on the retinal pigment epithelium (RPE), which is involved in the pathogenesis of RID, were compared with the pharmacological effects of the non-fibrate PPARα agonist GW7647 (GW). For this purpose, the human RPE cell line ARPE19 that was untreated (NT) or treated with Pema or GW was subjected to Seahorse cellular metabolic analysis and RNA sequencing analysis. Real-time cellular metabolic function analysis revealed that pharmacological effects of the PPARα agonist actions on essential metabolic functions in RPE cells were substantially different between Pema-treated cells and GW-treated cells. RNA sequencing analysis revealed the following differentially expressed genes (DEGs): (1) NT vs. Pema-treated cells, 37 substantially upregulated and 72 substantially downregulated DEGs; (2) NT vs. GW-treated cells, 32 substantially upregulated and 54 substantially downregulated DEGs; and (3) Pema vs. GW, 67 substantially upregulated and 51 markedly downregulated DEGs. Gene ontology (GO) analysis and ingenuity pathway analysis (IPA) showed several overlaps or differences in biological functions and pathways estimated by the DEGs between NT and Pema-treated cells and between NT and GW-treated cells, presumably due to common PPARα agonist actions or unspecific off-target effects to each. For further estimation, overlaps of DEGs among different pairs of comparisons (NT vs. Pema, NT vs. GW, and Pema vs. GW) were listed up. Angiopoietin-like 4 (ANGPTL4), which has been shown to cause deterioration of RID, was the only DEG identified as a common significantly upregulated DEG in all three pairs of comparisons, suggesting that ANGPTL4 was upregulated by the PPARα agonist action but that its levels were substantially lower in Pema-treated cells than in GW-treated cells. In qPCR analysis, such lower efficacy for upregulation of the mRNA expression of ANGPTL4 by Pema than by GW was confirmed, in addition to substantial upregulation of the mRNA expression of HIF1α by both agonists. However, different Pema and GW-induced effects on mRNA expression of HIF1α (Pema, no change; GW, significantly downregulated) and mRNA expression of ANGPTL4 (Pema, significantly upregulated; GW, significantly downregulated) were observed in HepG2 cells, a human hepatocyte cell line. The results of this study suggest that actions of the PPARα agonists Pema and GW are significantly organ-specific and that lower upregulation of mRNA expression of the DR-worsening factor ANGPTL4 by Pema than by GW in ARPE19 cells may minimize the risk for development of RID.

This pilot study explored how muscle activation influences the pattern recognition of tactile cues delivered using electrical stimulation (ES) during each 10% window interval of the normal walking gait cycle (GC). Three healthy adults participated in the experiment. After identifying the appropriate threshold, ES as the haptic cue was applied to the gastrocnemius lateralis (GL) and biceps brachii (BB) of participants walking on a treadmill. Findings revealed variable recognition patterns across participants, with the BB showing more variability during walking due to its minimal activity compared to the actively engaged GL. Dynamic time warping (DTW) was used to assess the similarity between muscle activation and electro-stimulated haptic perception. The DTW distance between electromyography (EMG) signals and muscle recognition patterns was significantly smaller for the GL (4.87 ± 0.21, mean ± SD) than the BB (8.65 ± 1.36, mean ± SD), showing a 78.6% relative difference, indicating that higher muscle activation was generally associated with more consistent haptic perception. However, individual differences and variations in recognition patterns were observed, suggesting personal variability influenced the perception outcomes. The study underscores the complexity of human neuromuscular responses to artificial sensory stimuli and suggests a potential link between muscle activity and haptic perception.

Liver disease can significantly impact life expectancy, making early diagnosis and therapeutic intervention critical challenges in medical care. Imaging diagnostics play a crucial role in diagnosing and managing liver diseases. Recently, the application of artificial intelligence (AI) in medical imaging analysis has become indispensable in healthcare. AI, trained on vast datasets of medical images, has sometimes demonstrated diagnostic accuracy that surpasses that of human experts. AI-assisted imaging diagnostics are expected to contribute significantly to the standardization of diagnostic quality. Furthermore, AI has the potential to identify image features that are imperceptible to humans, thereby playing an essential role in clinical decision-making. This capability enables physicians to make more accurate diagnoses and develop effective treatment strategies, ultimately improving patient outcomes. Additionally, AI is anticipated to become a powerful tool in personalized medicine. By integrating individual patient imaging data with clinical information, AI can propose optimal plans for treatment, making it an essential component in the provision of the most appropriate care for each patient. Current reports highlight the advantages of AI in managing liver diseases. As AI technology continues to evolve, it is expected to advance personalized diagnostics and treatments and contribute to overall improvements in healthcare quality.

Muscle stem cells (MuSCs) are essential for skeletal muscle regeneration, influenced by a complex interplay of mechanical, biochemical, and molecular cues. Properties of the extracellular matrix (ECM) such as stiffness and alignment guide stem cell fate through mechanosensitive pathways, where forces like shear stress translate into biochemical signals, affecting cell behavior. Aging introduces senescence which disrupts the MuSC niche, leading to reduced regenerative capacity via epigenetic alterations and metabolic shifts. Transplantation further challenges MuSC viability, often resulting in fibrosis driven by dysregulated fibro-adipogenic progenitors (FAPs). Addressing these issues, scaffold designs integrated with pharmacotherapy emulate ECM environments, providing cues that enhance graft functionality and endurance. These scaffolds facilitate the synergy between mechanotransduction and intracellular signaling, optimizing MuSC proliferation and differentiation. Innovations utilizing human pluripotent stem cell-derived myogenic progenitors and exosome-mediated delivery exploit bioactive properties for targeted repair. Additionally, 3D-printed and electrospun scaffolds with adjustable biomechanical traits tackle scalability in treating volumetric muscle loss. Advanced techniques like single-cell RNA sequencing and high-resolution imaging unravel muscle repair mechanisms, offering precise mapping of cellular interactions. Collectively, this interdisciplinary approach fortifies tissue graft durability and MuSC maintenance, propelling therapeutic strategies for muscle injuries and degenerative diseases.

Introduction: Mesenchymal stem cells (MSCs) have been introduced as a promising treatment for diabetic wounds. The effects of stem cell therapy are thought to be caused by bioactive molecules secreted by stem cells. Stem cell-based gene therapies can target bioactive molecules. Therefore, treatment using conditioned medium (CM) derived from genetically engineered stem cells has been proposed as an alternative option for diabetic ulcer care.

Methods: MSCs derived from human umbilical cords were obtained and engineered to overexpress the angiogenin-1 gene (MSCs^Ang1) through plasmid transfection. This study extracted conditioned medium from MSCs (MSC-CM) or MSCs^Ang1(MSC^Ang1-CM) for wound treatment applications. Via in vitro experiments, the proangiogenic effects of MSC^Ang1-CM were assessed via the migration and tube formation of human umbilical vein endothelial cells (HUVECs). Furthermore, the efficacy of MSC^Ang1-CM in promoting wound healing, re-epithelialization, hair follicle, and angiogenesis was evaluated via a diabetic mouse skin defect model.

Results: In vitro assays demonstrated that MSC^Ang1-CM significantly enhanced HUVECs' functions, including migration and tube formation. In vivo assays revealed that MSC^Ang1-CM exhibited notable advancements in healing speed, re-epithelialization, hair follicle, and angiogenesis.

Conclusion: These results indicate that MSC^Ang1-CM can promote wound healing in diabetic mice and make the vascular structure in regenerated tissues more stable without inducing tissue fibrosis, providing a new therapeutic strategy for treating diabetic skin wounds. This provides a valuable theoretical basis for further research on regenerative medicine and cell therapy.

The body contour market has grown rapidly in recent years, due to persistent requests for noninvasive treatments for localized fat adiposities, cellulite, and skin laxity. A variety of different methods are now available to improve body shaping. This review aims to provide an exhaustive compendium of the main recommendations for the optimal use of an innovative device delivering microwaves (MWs) for unwanted fat and cellulite reduction (Onda Coolwaves, DEKA, Florence, Italy), resulting from the experiences of the most expert international users. The availability of this new technology has led to an increasing number of treated patients and clinical studies. However, what is still missing, to the best of our knowledge, is an evaluation of the long-term efficacy and safety of this method. Based on the most recent data available, this compendium focuses on the ideal parameters, patient selection, and treatment methodology for providing safe and effective treatment protocols. Future research findings may suggest changes to the conclusions or recommendations in this report.

Background: Sagittal alignment in the lumbar spine is essential for spinal stability and functionality, with significant implications in surgical planning for spinal deformity correction. However, standardized lumbar partitioning, particularly identifying a critical sagittal alignment zone, remains underdefined. This study aims to establish a reliable lumbar partition to guide surgical decisions and optimize clinical outcomes.

Methods: A systematic review of four major biomedical databases yielded 32 studies, of which 4 met the inclusion criteria. Studies on asymptomatic adults with segmental lordosis data stratified by pelvic incidence were analyzed. Lumbar lordosis values were converted to percentages, allowing for cross-study comparison. Sensitivity analysis and bias assessment were performed to ensure methodological rigor.

Results: The findings identified the L3-L5 interval, especially around the L4 vertebra, as a critical biomechanical zone across various populations and pelvic incidence groups. Individuals with higher pelvic incidence had concentrated lordosis in lower segments, while those with lower pelvic incidence had greater lordosis in upper segments, underscoring the L3-L5 region's stability as a surgical reference.

Conclusions: The L3-L5 interval serves as a key partition zone for sagittal alignment, providing a stable reference for lumbar spine fusion. These findings offer a foundational clinical reference, potentially improving alignment outcomes and reducing postoperative complications.

Background: Orthotic treatment is a well-acknowledged conservative treatment for moderate adolescent idiopathic scoliosis (AIS). The efficacy of this treatment is significantly determined by the forces applied to the bodies of patients. However, there is uncertainty regarding the optimal force levels that should be applied to the patient's torso by spinal orthosis. This study aims to identify reference values for the controlling forces in AIS management.

Methods: A comprehensive literature search was performed in five databases (PubMed, Scopus, Cochrane Library, CINAHL, and Web of Science). Only studies written in English and covering the force/pressure measurements of spinal orthosis for the treatment of AIS were included, without publication date restrictions. The methodological index for non-randomized studies (MINORS) was employed for the methodological quality assessment, and force measurements were standardized to pressure in kilopascals (kPa) for comparison.

Results: From the initial 10,452 records, 10 studies were admitted for the final analysis. All the included studies reported the interface pressure between the thoracic (T) pad and patient's trunk, and seven studies evaluated the pressure from the thoracolumbar/lumbar (TL/L) pad. These studies used different pressure sensors or transducers with the range from 5.6 to 82.5 kPa for the T pads, and 4.8 to 85.1 kPa for the TL/L pads. Four studies reported strap tensions of 26.8 to 60.4 N. Higher strap tension was correlated with increased interface pressure (r = 0.84).

Conclusion: The mean strap tension was 42.5 N, the median interface pressure of the T pads was 8.75 kPa, and the median pressure of TL/L regions was 7.11 kPa without the outliers. The findings provide a baseline value for designing adjustable straps and strategically distributing pressure in orthoses.

Although diabetic polyneuropathy (DPN) has a very high prevalence among people with diabetes, gait analysis using cyclograms is very limited, and cyclogram research, in general, is limited to standard measures available in software packages. In this study, cyclograms (movements of the centre of pressure, COP, on and between the plantar surfaces) of diabetics and healthy individuals recorded with a smart insole were compared in terms of geometry and balance index, BI. The latter was calculated as the summed product of standard deviations of cyclogram markers, i.e., start/end points, turning points, and intersection points of the COP. The geometry was assessed by the positions of, and distances between, these points, and the distance ratios (14 parameters in total). The BI of healthy and diabetic individuals differed significantly. Of the fifteen parameters (including the BI), three were suitable as classifiers to predict DPN, namely two distances and their ratio, with false negatives ranging from 1.8 to 12.5%, and false positives ranging from 2.9 to 7.1%. The standard metric of the cyclogram provided by the software packages failed as a classifier. While the BI captures both DPN-related balance and other balance disorders, the changing geometry of the cyclogram in diabetics appears to be DPN-specific.

Cardiovascular diseases are some of the underlying reasons contributing to the relentless rise in mortality rates across the globe. In this regard, there is a genuine need to integrate advanced technologies into the medical realm to detect such diseases accurately. Moreover, numerous academic studies have been published using AI-based methodologies because of their enhanced accuracy in detecting heart conditions. This research extensively delineates the different heart conditions, e.g., coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, and myocardial infarction, and their underlying reasons and symptoms and subsequently introduces AI-based detection methodologies for precisely classifying such diseases. The review shows that the incorporation of artificial intelligence in detecting heart diseases exhibits enhanced accuracies along with a plethora of other benefits, like improved diagnostic accuracy, early detection and prevention, reduction in diagnostic errors, faster diagnosis, personalized treatment schedules, optimized monitoring and predictive analysis, improved efficiency, and scalability. Furthermore, the review also indicates the conspicuous disparities between the results generated by previous algorithms and the latest ones, paving the way for medical researchers to ascertain the accuracy of these results through comparative analysis with the practical conditions of patients. In conclusion, AI in heart disease detection holds paramount significance and transformative potential to greatly enhance patient outcomes, mitigate healthcare expenditure, and amplify the speed of diagnosis.