工程技术最新文献

Our group has developed a new nitinol endoluminal self-expandable device for microvascular anastomosis. It attaches to each vessel ending with opposite directed microspikes and reaches complete expansion at body temperature, using the nitinol shape memory capacity. The main purpose of this first in vivo trial is to evaluate the mechanical viability of the device and its immediate and early functionality. A recuperation study with seven New Zealand White rabbits was designed. A 1.96 mm outer diameter prototype of the new device was placed on the right femoral artery of each rabbit. Each anastomosis was reassessed on the seventh postoperative day to reevaluate the device function. The average anastomosis time with the new device was 18 min and 45 seg (±0.3 seg). It could be easily placed in all the cases with an average of 1.14 (1) complementary stitches needed to achieve a sealed anastomosis. Patency test was positive for all the cases on the immediate assessment. On the 1 week revision surgery, patency test was negative for the seven rabbits due to blood clot formation inside the device. The new device that we have developed is simple to use and shows correct immediate functionality. On the early assessment, the presence of a foreign body in the endoluminal space caused blood clot formation. We speculate that a heparin eluting version of the device could avoid thrombosis formation. We consider that the results obtained can be valuable for other endoluminal sutureless devices.

Dental caries is considered as the most common and multifactorial disease worldwide, caused by a variety of oral microorganisms like Streptococcus spp., Veillonella spp., Actinomyces spp., Bifidobacterium spp., and Lactobacillus fermentum, which colonize food debris in oral cavities. Of them, Streptococcus mutans is the predominant bacterium and can induce progressive tooth destruction, especially during dentition. The superior characteristics of S. mutans, such as the presence of the cell surface protein P1 and exopolysaccharide-synthesizing enzymes, acid tolerance, biofilm-forming ability mediated by brpA gene, and multidrug resistance, render it a highly virulent pathogen in the etiology of dental caries. Given its significant role in dental caries, extensive research has been conducted over the past few decades, focusing on the development of specific antimicrobial treatments, and other innovative therapeutic approaches. To gain deeper insights into the genetic diversity and epidemiological patterns of S. mutans, various genotypic methods have been developed and successfully employed. By combining the insights gained from genetic studies of S. mutans with the suitable control measures against the biofilm, we can develop innovative and effective strategies for preventing and treating dental caries.

The muscle spindle is an essential proprioceptor, significantly involved in sensing limb position and movement. Although biological spindle models exist for years, the gold-standard for motor control in biomechanics are still sensors built of homogenized spindle output models due to their simpler combination with neuro-musculoskeletal models. Aiming to improve biomechanical simulations, this work establishes a more physiological model of the muscle spindle, aligned to the advantage of easy integration into large-scale musculoskeletal models. We implemented four variations of a spindle model in Matlab/Simulink®: the Mileusnic et al. (2006) model, Mileusnic model without mass, our enhanced Hill-type model, and our enhanced Hill-type model with parallel damping element (PDE). Different stretches in the intrafusal fibers were simulated in all model variations following the spindle afferent recorded in previous experiments in feline soleus muscle. Additionally, the enhanced Hill-type models had their parameters extensively optimized to match the experimental conditions, and the resulting model was validated against data from rats' triceps surae muscle. As result, the Mileusnic models present a better overall performance generating the afferent firings compared to the common data evaluated. However, the enhanced Hill-type model with PDE exhibits a more stable performance than the original Mileusnic model, at the same time that presents a well-tuned Hill-type model as muscle spindle fibers, and also accounts for real sarcomere force-length and force-velocity aspects. Finally, our activation dynamics is similar to the one applied to Hill-type model for extrafusal fibers, making our proposed model more easily integrated in multi-body simulations.

Measles, a member of the Paramyxoviridae family and the Morbillivirus genus, is an infectious disease caused by the measles virus that is extremely contagious and can be prevented through vaccination. When a person with the measles coughs or sneezes, the virus is disseminated by respiratory droplets. Normally, the appearance of measles symptoms takes 10-14 d following viral exposure. Conjunctivitis, a high temperature, a cough, a runny nose, and a distinctive rash are some of the symptoms. Despite the measles vaccination being available, it is still widespread worldwide. To eradicate measles, the Reproduction Number (i.e. $R_0<1$) must remain less than unity. This study examines a SEIVR compartmental model in the caputo sense using a double dose of vaccine to simulate the measles outbreak. The reproduction number $R_0$ and model properties are both thoroughly examined. Both the local and global stabilities of the proposed model are determined for $R_0$ less and greater than 1. To achieve the model's global stability, the Lyapunov function is used while the existence and uniqueness of the proposed model are demonstrated In addition to the calculated and fitted biological parameters, the forward sensitivity indices for $R_0$ are also obtained. Simulations of the proposed fractional order (FO) caputo model are performed in order to analyse their graphical representations and the significance of FO derivatives to illustrate how our theoretical findings have an impact. The graphical results show that the measles outbreak is reduced by increasing vaccine dosage rates.

The multivariable tumor-growth dynamic model has been widely used to describe the inhibition of tumor-cells proliferation under the simultaneous infusion of multiple chemotherapeutic drugs. In this article, a nonlinear optimal (H-infinity) control method is developed for the multi-variable tumor-growth model. First, differential flatness properties are proven for the associated state-space description. Next, the state-space description undergoes approximate linearization with the use of first-order Taylor series expansion and through the computation of the associated Jacobian matrices. The linearization process takes place at each sampling instant around a time-varying operating point which is defined by the present value of the system's state vector and by the last sampled value of the control inputs vector. For the approximately linearized model of the system a stabilizing H-infinity feedback controller is designed. To compute the controller's gains an algebraic Riccati equation has to be repetitively solved at each time-step of the control algorithm. The global stability properties of the control scheme are proven through Lyapunov analysis. Finally, the performance of the nonlinear optimal control method is compared against a flatness-based control approach.

Breast cancer poses a significant health threat worldwide. Contrastive learning has emerged as an effective method to extract critical lesion features from mammograms, thereby offering a potent tool for breast cancer screening and analysis. A crucial aspect of contrastive learning is negative sampling, where the selection of hard negative samples is essential for driving representations to retain detailed lesion information. In large-scale contrastive learning applied to natural images, it is often assumed that extracted features can sufficiently capture semantic content, and that each mini-batch inherently includes ideal hard negative samples. However, the unique characteristics of breast lumps challenge these assumptions when dealing with mammographic data. In response, we introduce ManiNeg, a novel approach that leverages manifestations as proxies to select hard negative samples. As a condensed representation of a physician's domain knowledge, manifestations represent observable symptoms or signs of a disease and can provide a robust basis for choosing hard negative samples. This approach benefits from its invariance to model optimization, facilitating efficient sampling. We tested ManiNeg on the task of distinguishing between benign and malignant breast lumps. Our results demonstrate that ManiNeg not only improves representation in both unimodal and multimodal contexts but also offers benefits that extend to datasets beyond the initial pretraining phase. To support ManiNeg and future research endeavors, we have developed the MVKL mammographic dataset. This dataset includes multi-view mammograms, corresponding reports, meticulously annotated manifestations, and pathologically confirmed benign-malignant outcomes for each case. The MVKL dataset and our codes are publicly available at https://github.com/wxwxwwxxx/ManiNeg to foster further research within the community.

An efficient novel approach is introduced to predict the effectiveness of chemotherapy treatment in lung cancer by monitoring the serum N-glycome of patients combined with artificial intelligence-based data analysis. The study involved thirty-three lung cancer patients undergoing chemotherapy treatments. Serum samples were taken before and after the treatment. The N-linked oligosaccharides were enzymatically released, fluorophore-labeled, and analyzed by capillary electrophoresis with laser-induced fluorescence detection. The resulting electropherograms were thoroughly processed and evaluated by artificial intelligence-based classifiers, i.e., utilizing a machine learning algorithm to categorize the data into two (binary) classes. The classifier analysis method revealed a strong association between the structural changes in the N-glycans and the outcomes of the chemotherapy treatments (ROC >0.9). This novel combination of bioanalytical and AI methods provided a precise and rapid tool for predicting the effectiveness of chemotherapy.

Oral cancer poses a significant global health challenge, with increasing incidence rates and substantial morbidity and mortality. This study aimed to investigate the antiproliferative effects of cirsiliol in human oral cancer cells. Results from the MTT cell viability assay showed that cirsiliol significantly (p < 0.05) inhibited the growth of all oral cancer cell lines tested, with the IC₅₀ values ranging from 12 to 25 μM. The lowest IC₅₀ of 12 μM was observed against SCC-1 and SCC-25 cell lines, while the IC₅₀ for normal hTRET-OME cells was 75 μM, approximately 6 times higher than against the oral cancer cells. Further molecular analysis revealed that cirsiliol disrupted cellular morphology in SCC-1 and SCC-25 cells with minor effects on the normal hTRET-OME cells. Annexin V/PI staining indicated that the percentage of SCC-1 and SCC-25 apoptotic cells increased significantly from 4.70 and 5.27% in controls to 31.4 and 35.28% at 24 μM cirsiliol, respectively. This effect correlated with an upregulation of Bax, downregulation of Bcl-2, and increased p53 expression. Nonetheless, the apoptotic effects of cirsiliol were considerably lower in normal hTRET-OME cells. Western blotting together with molecular docking analysis suggested that cirsiliol may inhibit the expression of topoisomerase I. Additionally, wound healing and transwell assays demonstrated that cirsiliol significantly (p < 0.05) suppressed the migration and invasion of SCC-1 and SCC-25 cells. In conclusion, these findings indicate that cirsiliol induces apoptosis in oral cancer cells through the inhibition of topoisomerase I.

Although KI24RGDS peptide hydrogel that acts as a cell adhesion has been reported to repair tissue in meniscus injury, its effect on tendon injuries remains unknown. The purpose of this study was to clarify the effect of KI24RGDS for tendon repair based on histological and biomechanical evaluation. After introducing defects (length: 10 mm; width: 3 mm) at the centers of rabbits' patellar tendons, and the KI24RGDS group was implanted with KI24RGDS and observed for 8 weeks. KI24RGDS implantation resulted in limited tendon elongation and better histological scores with uniformed collagen fiber orientation and early vascularization. The failure load of the patellar tendon was higher in the KI24RGDS group than that in the defect group (p < 0.05) and no significant difference with the control group (intact patellar tendon) at 8 weeks postoperatively. In conclusion, KI24RGDS administration might have therapeutic potential for tendon injuries by accelerating collagen remodeling.

In this work, we are comparing biomimetic niosomal nanoparticles (BNNs) with biomimetic liposomal nanoparticles (BLNs) and studying their drug carrier properties. A-BNNs and A-BLNs are prepared by lipid hydration method and characterized using DLS for size and zeta potential analysis, surface morphology by SEM, structural details by TEM, crystallinity and phase change by XRD, thermodynamic properties by DSC, TGA and DTGA, drug carrier properties by entrapment efficiency, drug release studies by open-end tube method and its mechanistic assessment by fitting with various models such as zero order, first order, Higuchi and Korsmeyer-Peppas models. The A-BNNs had an average size of 157.0 ± 3.58 nm and A-BLNs had an average size of 173 ± 1.24 nm. The A-BNNs had an average zeta potential of -29.0 ± 1.11 mV and A-BLNs had an average zeta potential of -46.5 ± 1.11 mV. The A-BNNs have an average entrapment efficiency of 94 ± 0.4% and A-BLNs have an average entrapment efficiency of 98 ± 0.14%. The BNNs have an average drug release of 78.12 ± 1.57% and A-BLNs have an average release of 98.41 ± 1.87% over 24 hours. Our results show that the vesicular size dependence influences the resulting nanoparticle drug carrier properties. This is a robust demonstration of the phenomena at the nanoscale that the precursor vesicular system size dependency will be reflected in bulk-engineered nanoparticle properties. These novel nanoparticles are potential candidates for development as an injection to suppress clots in stroke and myocardial infarction.